<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Zhiverse</title><link>https://yao-chih.netlify.app/en/</link><description>Josh's website, record my life.</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>yaochihh@gmail.com (Josh)</managingEditor><webMaster>yaochihh@gmail.com (Josh)</webMaster><copyright>Zhiverse</copyright><lastBuildDate>Tue, 08 Sep 2026 10:26:29 +0800</lastBuildDate><atom:link href="https://yao-chih.netlify.app/en/index.xml" rel="self" type="application/rss+xml"/><item><title>Customize Hugo LoveIt Theme Pagination: Previous/Next Controls and Page Jump</title><link>https://yao-chih.netlify.app/en/customize-hugo-loveit-theme-pagination-previous-next-controls-and-page-jump/</link><pubDate>Sun, 06 Sep 2026 09:35:05 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/customize-hugo-loveit-theme-pagination-previous-next-controls-and-page-jump/</guid><description><![CDATA[<p>Hugo LoveIt comes with a built-in Pagination feature that allows users to navigate between different pages using page numbers on the homepage. This prevents all posts from being displayed on a single page and can also help improve the website&rsquo;s loading speed.</p>
<p>The original pagination navigation is primarily based on page numbers, which is generally sufficient for everyday browsing. However, as the website&rsquo;s content gradually grows and the number of pages increases, it becomes impractical to display every page number in the navigation bar. Relying solely on page numbers can therefore make navigation somewhat inconvenient.</p>
<p>For this reason, I decided to make a small modification to LoveIt&rsquo;s pagination navigation. While keeping the original page-number navigation, I added <strong>Previous Page</strong>, <strong>Next Page</strong>, and <strong>page number jump</strong> functionality, making pagination more convenient and complete.</p>]]></description></item><item><title>[Test] Website Network Connection Speed Test</title><link>https://yao-chih.netlify.app/en/website-network-connection-speed-test/</link><pubDate>Fri, 21 Aug 2026 10:00:26 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/website-network-connection-speed-test/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://josh-test-lab.github.io/posts/Website%20Network%20Connection%20Speed%20Test/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>Cover image was generated by ChatGPT.</p>
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            <i class="icon fas fa-lightbulb fa-fw" aria-hidden="true"></i>Tip<i class="details-icon fas fa-angle-right fa-fw" aria-hidden="true"></i>
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        <div class="details-content">
            <div class="admonition-content">This speed test is powered by Netlify Functions. Test results may be affected by server load, network routing, CDN nodes, and current network conditions. This test is intended only to evaluate the network connection quality between this website and the user&rsquo;s device. It is not intended to measure the user&rsquo;s actual Internet bandwidth, ISP-provided plan speed, or connection speed to other websites. Results are for reference only and may differ from the actual network speed.</div>
        </div>
    </div>]]></description></item><item><title>[Note] Git Command Notes</title><link>https://yao-chih.netlify.app/en/git-command-notes/</link><pubDate>Fri, 07 Aug 2026 09:28:37 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/git-command-notes/</guid><description></description></item><item><title>Spatiotemporal Prediction of Unknown Areas Based on Structured State Space Diffusion and Resolution Adaptive Fixed Rank Kriging</title><link>https://yao-chih.netlify.app/en/spatiotemporal-prediction-of-unknown-areas-based-on-structured-state-space-diffusion-and-resolution-adaptive-fixed-rank-kriging/</link><pubDate>Tue, 28 Jul 2026 00:00:00 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/spatiotemporal-prediction-of-unknown-areas-based-on-structured-state-space-diffusion-and-resolution-adaptive-fixed-rank-kriging/</guid><description><![CDATA[<div class="details admonition info open">
        <div class="details-summary admonition-title">
            <i class="icon fas fa-info-circle fa-fw" aria-hidden="true"></i>Abstract<i class="details-icon fas fa-angle-right fa-fw" aria-hidden="true"></i>
        </div>
        <div class="details-content">
            <div class="admonition-content"><p>With the rapid advancement of sensing and remote sensing technologies, data collected in transportation, meteorology, and environmental monitoring have become increasingly characterized by high-frequency observations and cross-regional spatiotemporal dependencies. However, missing observations, irregular sampling, and prediction at unobserved locations remain major challenges for spatiotemporal forecasting. To address these issues, this study proposes a spatiotemporal forecasting framework, $\text{SSSD}^{\text{S4+MRTS}}$ + AFRK, which integrates Structured State Space Diffusion Model with S4 Layers ($\text{SSSD}^{\text{S4}}$) and Multi-Resolution Thin-Plate Spline (MRTS) basis functions to jointly model long-range temporal dependencies and spatial structural patterns. MRTS is incorporated into the S4 layers as additional spatial conditioning information, enabling $\text{SSSD}^{\text{S4+MRTS}}$ to learn spatiotemporal dynamics conditioned on observed locations. During inference, the framework further integrates MRTS-based Resolution Adaptive Fixed Rank Kriging (AFRK), which preserves the spatial consistency of the predicted field while enabling interpolation at unobserved locations. Experiments conducted on the Weather2K, MERRA-2, and the dataset from The Second Competition on Spatial Statistics for Large Datasets are used to compare the proposed framework with Temporal Fusion Transformers (TFT), Vector Autoregression (VAR), Stochastic Variational Gaussian Process (SVGP), and Spatio-temporal DeepKriging (STDK). The results demonstrate that $\text{SSSD}^{\text{S4+MRTS}}$ generally improves Mean Squared Prediction Error (MSPE) under most experimental settings, with particularly notable gains in future forecasting at unobserved locations. Overall, the findings indicate that integrating temporal dynamic modeling with spatial statistical methods effectively enhances spatiotemporal forecasting performance under missing-data and spatial extrapolation scenarios.</p>
<p><strong>Keywords</strong>: Structured State Space Diffusion Model, Multi-Resolution Thin-Plate Spline Basis Functions, Resolution Adaptive Fixed Rank Kriging, spatiotemporal forecasting, spatial interpolation, missing data imputation</p>
</div>
        </div>
    </div>]]></description></item><item><title>CNN Image Recognition Experiment</title><link>https://yao-chih.netlify.app/en/cnn-image-recognition-experiment/</link><pubDate>Fri, 17 Jul 2026 11:03:26 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/cnn-image-recognition-experiment/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://Josh-test-lab.github.io/posts/CNN%20Image%20Recognition%20Experiment/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image was generated by ChatGPT.</p>
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            <i class="icon fas fa-lightbulb fa-fw" aria-hidden="true"></i>Tip<i class="details-icon fas fa-angle-right fa-fw" aria-hidden="true"></i>
        </div>
        <div class="details-content">
            <div class="admonition-content">This project was written in August 2023. The following content primarily documents my initial experience learning and experimenting with deep learning at that time. As such, some explanations and viewpoints have been preserved to reflect my understanding during that period.</div>
        </div>
    </div>
<p>The year 2023 marked the rapid rise of generative artificial intelligence. From large language models to a wide range of AI-powered applications, artificial intelligence gradually became part of everyday life, making <strong>deep learning</strong> one of the most prominent and widely discussed technologies of the year.</p>
<p>For me, 2023 also marked the beginning of my journey into the world of deep learning, as I built and experimented with my first deep learning models.</p>]]></description></item><item><title>[Thought] Historical Earthquake Locations Around Taiwan</title><link>https://yao-chih.netlify.app/en/historical-earthquake-locations-around-taiwan/</link><pubDate>Wed, 15 Jul 2026 11:03:01 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/historical-earthquake-locations-around-taiwan/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://Josh-test-lab.github.io/posts/Historical%20Earthquake%20Locations%20Around%20Taiwan/cover%20image.webp" referrerpolicy="no-referrer">
            </div><div class="details admonition tip open">
        <div class="details-summary admonition-title">
            <i class="icon fas fa-lightbulb fa-fw" aria-hidden="true"></i>Tip<i class="details-icon fas fa-angle-right fa-fw" aria-hidden="true"></i>
        </div>
        <div class="details-content">
            <div class="admonition-content">This project was written in January 2026.</div>
        </div>
    </div>]]></description></item><item><title>Historical Typhoon Data Download for Taiwan</title><link>https://yao-chih.netlify.app/en/historical-typhoon-data-download-for-taiwan/</link><pubDate>Mon, 13 Jul 2026 11:03:14 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/historical-typhoon-data-download-for-taiwan/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://Josh-test-lab.github.io/posts/Historical%20Typhoon%20Data%20Download%20for%20Taiwan/cover%20image.jpg" referrerpolicy="no-referrer">
            </div><p>The cover image is a downloaded typhoon satellite image. It is a satellite water vapor cloud image produced by the Central Weather Administration (CWA) using CCU/SSL. The image was captured at 07:10 on July 11, 2026, and was obtained from the Weather Image Archive of the Department of Atmospheric Sciences at Chinese Culture University (Atmospheric Science Research and Application Databank).</p>
<p>This is a small program that was originally written to download typhoon satellite images and related data. Its initial purpose was to collect image data for image recognition research. Since the program was developed at an earlier stage, it still contains several relatively immature implementation approaches. Therefore, this article serves only as a record and reference for the data download process, and does not provide an in-depth discussion of the program architecture or optimization methods. The programming language used in this project is Python.</p>]]></description></item><item><title>Vibe Coding Collaboration Project - Map of work and school closures in Taiwan</title><link>https://yao-chih.netlify.app/en/vibe-coding-collaboration-project/</link><pubDate>Sun, 12 Jul 2026 21:24:28 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/vibe-coding-collaboration-project/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://josh-test-lab.github.io/posts/Vibe%20Coding%20Collaboration%20Project/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image was generated by Google Gemini.</p>
<p>In 2026, with the rapid development of AI, Vibe Coding has become a commonly used approach for assisting software development.</p>]]></description></item><item><title>Map of Work and School Closures in Taiwan</title><link>https://yao-chih.netlify.app/en/map-of-work-and-school-closures-in-taiwan/</link><pubDate>Sat, 11 Jul 2026 14:39:16 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/map-of-work-and-school-closures-in-taiwan/</guid><description></description></item><item><title>Python Mathematical Computation Performance Benchmark</title><link>https://yao-chih.netlify.app/en/python-mathematical-computation-performance-benchmark/</link><pubDate>Fri, 10 Jul 2026 11:03:37 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/python-mathematical-computation-performance-benchmark/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://josh-test-lab.github.io/posts/Python%20Mathematical%20Computation%20Performance%20Benchmark/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image was generated by Google Gemini.</p>
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        <div class="details-content">
            <div class="admonition-content">This project was written in October 2024.</div>
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<p>While taking the graduate course <em>Simulation Study</em>, the instructor mentioned that the random numbers used in modern computers and programming are mostly not truly random in the strict sense, but rather <strong>pseudo-random numbers</strong>. Pseudo-random numbers are generated through mathematical algorithms that compute the next value based on the previous state. Therefore, as long as the algorithm, initial value (Seed), and current state are known, the entire random number sequence can theoretically be reproduced. This raises an interesting question: if every pseudo-random number can be calculated from the previous value, where does the very first number come from?</p>]]></description></item><item><title>Learning Control Charts</title><link>https://yao-chih.netlify.app/en/learning-control-charts/</link><pubDate>Mon, 06 Jul 2026 12:47:58 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/learning-control-charts/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Learning%20Control%20Charts/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image was generated by ChatGPT.</p>
<p>While attending <a href="/en/the-35th-south-taiwan-statistics-conference" rel="">The 35th South Taiwan Statistics Conference</a>, I first learned about control charts from an experienced professor who had spent many years working in manufacturing environments. Intrigued by the concept, I decided to spend some time organizing my notes and gaining a foundational understanding of what control charts are.</p>]]></description></item><item><title>[Forbidden City Project] Building the Forbidden City in Minecraft</title><link>https://yao-chih.netlify.app/en/forbidden-city-project/</link><pubDate>Fri, 03 Jul 2026 15:23:59 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/forbidden-city-project/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Forbidden%20City%20Project/cover%20image.png" referrerpolicy="no-referrer">
            </div><p>The Forbidden City, now home to the Palace Museum in Beijing, served as the imperial palace of the Ming and Qing dynasties. It is the largest and best-preserved ancient palace complex in the world, consisting of nearly 10,000 rooms and representing the pinnacle of traditional Chinese imperial architecture.</p>]]></description></item><item><title>Jotting #9 - A Special Feature of National Dong Hwa University</title><link>https://yao-chih.netlify.app/en/1150425-jotting/</link><pubDate>Sat, 25 Apr 2026 14:23:19 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/1150425-jotting/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150425%20Jotting/cover%20image.jpeg" referrerpolicy="no-referrer">
            &lt;/div></description></item><item><title>[Thought] The 35th South Taiwan Statistics Conference</title><link>https://yao-chih.netlify.app/en/the-35th-south-taiwan-statistics-conference/</link><pubDate>Wed, 15 Apr 2026 10:00:58 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/the-35th-south-taiwan-statistics-conference/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/The%2035th%20South%20Taiwan%20Statistics%20Conference/cover%20image.webp" referrerpolicy="no-referrer">
            </div><!-- filpclock 的 css -->
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<div class="flip-wrapper" id="35th-stsc" data-target-time="2026-06-25T09:30:00&#43;08:00" data-format="year,month,day,hour,minute,second">
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document.addEventListener("DOMContentLoaded", function () {
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</div>]]></description></item><item><title>[Thoughts] 2024 Joint Conference of University Administrators for Teacher Education</title><link>https://yao-chih.netlify.app/en/2024-joint-conference-of-university-administrators-for-teacher-education/</link><pubDate>Mon, 12 Jan 2026 17:32:43 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/2024-joint-conference-of-university-administrators-for-teacher-education/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/2024%20Joint%20Conference%20of%20University%20Administrators%20for%20Teacher%20Education/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover photo was taken at the 2024 Joint Conference of University Administrators for Teacher Education, held on November 1, 2024.</p>
<div class="details admonition tip open">
        <div class="details-summary admonition-title">
            <i class="icon fas fa-lightbulb fa-fw" aria-hidden="true"></i>Tip<i class="details-icon fas fa-angle-right fa-fw" aria-hidden="true"></i>
        </div>
        <div class="details-content">
            <div class="admonition-content">Written on November 1, 2024</div>
        </div>
    </div>
<p>The Joint Conference of University Administrators for Teacher Education is an important annual event for Taiwan&rsquo;s teacher education system, where administrators from teacher-training universities across the country come together to discuss education policies, share experiences, exchange insights on policy implementation, and engage in face-to-face dialogue with officials from the Ministry of Education. The conference functions as a convergence point, gathering perspectives, expectations, and questions from all sides, gradually shaping the future direction of education. For me, it represents one of the most essential spaces for dialogue in the education sector, not only to review the current situation, but also to explore future strategies and confirm policy directions.</p>]]></description></item><item><title>Time Complexity</title><link>https://yao-chih.netlify.app/en/time-complexity/</link><pubDate>Thu, 01 Jan 2026 11:36:20 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/time-complexity/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/time%20complexity/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>Time complexity is used to describe how the computation time of an algorithm grows as the input size increases during its execution. Because the processing capabilities, hardware architecture, and system environment differ across computer devices, comparing algorithms solely based on actual execution time often fails to yield representative and portable conclusions. Therefore, time complexity is usually measured by the &ldquo;number of basic operations executed,&rdquo; and asymptotic complexity is employed to analyze the efficiency of algorithms, establishing a hardware-independent and universally applicable performance evaluation method.</p>]]></description></item><item><title>[Thoughts] 2024 Hualien County Cat Cup Scratch Animation and Interactive Game Competition</title><link>https://yao-chih.netlify.app/en/2024-hualien-county-cat-cup-scratch-animation-and-interactive-game-competition/</link><pubDate>Tue, 30 Dec 2025 10:21:47 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/2024-hualien-county-cat-cup-scratch-animation-and-interactive-game-competition/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/2024%20Hualien%20County%20Cat%20Cup%20Scratch%20Animation%20and%20Interactive%20Game%20Competition/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image was generated by ChatGPT.</p>
<p>Programming has been incorporated into Taiwan&rsquo;s 12-Year Basic Education Curriculum as an essential skill for future talents. Through coding, students can not only develop systems thinking, logical reasoning, and problem-solving abilities, but also cultivate aesthetics, digital literacy, and innovative adaptability during the creative process. The Hualien County Information Education Competition is designed with these goals in mind, aiming to stimulate students&rsquo; computational thinking and problem-solving potential by requiring them to design animations and games using the intuitive Scratch programming language.</p>]]></description></item><item><title>[Thoughts] 2023 Hualien County Dreams Take Flight - 10th Exhibition for Young Inventors</title><link>https://yao-chih.netlify.app/en/2023-hualien-county-dreams-take-flight-10th-exhibition-for-young-inventors/</link><pubDate>Mon, 29 Dec 2025 16:26:13 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/2023-hualien-county-dreams-take-flight-10th-exhibition-for-young-inventors/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/2023%20Hualien%20County%20Dreams%20Take%20Flight%20-%2010th%20Exhibition%20for%20Young%20Inventors/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover photo was taken on the way to the national competition with another team from our school on January 26, 2024.</p>
<p>Seeing my wholehearted dedication and enthusiasm for scientific inquiry in the <a href="/en/pacific-cup-national-student-research-and-local-mission-based-action-research-competition-hualien-county-2023" rel="">Student Research Competition</a>, the school invited me to join the guidance team for this year&rsquo;s Youth Invention Exhibition. It was my first time serving as a mentor, and I felt a mix of excitement and nervousness. With a careful and attentive mindset, I led the students through brainstorming sessions, encouraging them to identify problems in daily life and design innovative solutions. From the initial conception to practical implementation, every step required careful thought and effort, but also brought a great sense of accomplishment.</p>]]></description></item><item><title>[Thoughts] 2023 Hualien County Pacific Cup National Student Research &amp; Community Action Research Competition</title><link>https://yao-chih.netlify.app/en/2023-hualien-county-pacific-cup-national-student-research-and-community-action-research-competition/</link><pubDate>Sun, 28 Dec 2025 10:44:15 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/2023-hualien-county-pacific-cup-national-student-research-and-community-action-research-competition/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/2023%20Hualien%20County%20Pacific%20Cup%20National%20Student%20Research%20and%20Community%20Action%20Research%20Competition/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover photo shows unprocessed Taiwan jade materials taken during an interview at the <a href="http://www.taiwanjade.com.tw/" target="_blank" rel="noopener noreffer ">Rufeng Jade Workshop</a> on September 7, 2023. The photograph was taken by the author.</p>
<p>&ldquo;Positive thinking will let you do everything better than negative thinking will, because attitude is contagious.&rdquo; This quote by American author Zig Ziglar has always reminded me that the way we engage often determines the final outcome. I also hope that my own positive attitude can bring constructive influence to the educational setting.</p>]]></description></item><item><title>Parking model for the parking lot in Zhixue station</title><link>https://yao-chih.netlify.app/en/parking-model-for-the-parking-lot-in-zhixue-station/</link><pubDate>Sat, 27 Dec 2025 13:56:08 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/parking-model-for-the-parking-lot-in-zhixue-station/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/parking-lot-simulation/refs/heads/main/images/%e8%a1%9b%e6%98%9f%e5%9c%b0%e5%9c%96.png" referrerpolicy="no-referrer">
            </div><p>The cover image shows Zhixue Station and the surrounding parking facilities of Taiwan Railway Co., Ltd., captured from Google Maps on December 29, 2024.</p>]]></description></item><item><title>Jotting #8 - School Sports Day</title><link>https://yao-chih.netlify.app/en/1141225-jotting/</link><pubDate>Fri, 26 Dec 2025 17:34:53 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/1141225-jotting/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1141225%20Jotting/IMG_7417.webp" referrerpolicy="no-referrer">
            </div><p>The cover photo was taken by the author on December 25, 2025.</p>
<p>The Sports Day is the most ceremonial event in the school year. It is not only a gathering of track and field events, team competitions, and fun activities, but also a unifying ceremony for all students and staff. Shoufeng Junior High School holds its Sports Day biennially, combining it with the school anniversary and hosting a &ldquo;Joint Sports Day with Surrounding Villages,&rdquo; inviting local community members to participate, giving the event a unique community flavor.</p>]]></description></item><item><title>Logistics Management Research Analysis</title><link>https://yao-chih.netlify.app/en/logistics-management-research-analysis/</link><pubDate>Wed, 24 Dec 2025 12:00:00 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/logistics-management-research-analysis/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Logistics%20Management%20Research%20Analysis/cover%20image.webp" referrerpolicy="no-referrer">
            </div>This project was commissioned by the East Center for Transportation Research & Development. It conducts an analysis of the current utilization of public transportation services in Hualien County and Taitung County to ascertain existing supply-demand characteristics and usage patterns. The findings will serve as a basis for reviewing and enhancing the current public transportation system, with the aim of improving service performance in non-metropolitan areas, strengthening overall regional transportation accessibility, and promoting mobility and transportation equity for all population groups.]]></description></item><item><title>The Analysis of Characters in Dream of the Red Chamber</title><link>https://yao-chih.netlify.app/en/the-analysis-of-characters-in-dream-of-the-red-chamber/</link><pubDate>Tue, 23 Dec 2025 10:28:34 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/the-analysis-of-characters-in-dream-of-the-red-chamber/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/The%20Analysis%20of%20Characters%20in%20Dream%20of%20the%20Red%20Chamber/cover%20image.webp" referrerpolicy="no-referrer">
            &lt;/div>Classical Chinese novels are an indispensable and important part of Chinese literature, with many masterpieces throughout history worthy of in-depth exploration. Among them, Dream of the Red Chamber is regarded as one of the representative works of classical Chinese fiction and possesses a high degree of scholarly interest. The fates of the characters and the numerous events in the novel are intricately and closely interconnected, requiring thorough study to fully understand their true significance.</description></item><item><title>Jotting #7 - Download Adobe Acrobat Reader Offline Installer</title><link>https://yao-chih.netlify.app/en/download-adobe-acrobat-reader-offline-installer/</link><pubDate>Sat, 06 Dec 2025 22:47:23 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/download-adobe-acrobat-reader-offline-installer/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/d808c4a41d5666e57fc29f8dc7d3ca6a004cc8a8/posts/Download%20Adobe%20Acrobat%20Reader%20Offline%20Installer/cover%20image.svg" referrerpolicy="no-referrer">
            &lt;/div>Recently, when reinstalling my operating system, I needed a PDF viewer. Following my usual habit, I chose to install Adobe Acrobat Reader, only to discover that I could not find an offline installer for it. I realized this when I downloaded the Adobe Acrobat Reader installer and noticed that the file was only 1.55 MB, I thought that it was far smaller than the usual installer size. During installation, I found out it was an online installer, which is very inconvenient for users who prefer offline installation.</description></item><item><title>Global Spatiotemporal Data: Exploring and Downloading Resources from NASA GES DISC</title><link>https://yao-chih.netlify.app/en/global-spatiotemporal-data-exploring-and-downloading-resources-from-nasa-ges-disc/</link><pubDate>Sat, 29 Nov 2025 21:53:36 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/global-spatiotemporal-data-exploring-and-downloading-resources-from-nasa-ges-disc/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Global%20Spatiotemporal%20Data%20Exploring%20and%20Downloading%20Resources%20from%20NASA%20GES%20DISC/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>GES DISC is a scientific data center under NASA that preserves Earth science–related data and provides free access to all users, making it an excellent source for obtaining spatiotemporal data.</description></item><item><title>Setting Up a Local LaTeX Environment: Build Your Own Overleaf with TeX Live and VS Code</title><link>https://yao-chih.netlify.app/en/setting-up-a-local-latex-environment-build-your-own-overleaf-with-tex-live-and-vs-code/</link><pubDate>Mon, 03 Nov 2025 18:54:57 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/setting-up-a-local-latex-environment-build-your-own-overleaf-with-tex-live-and-vs-code/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Setting%20Up%20a%20Local%20LaTeX%20Environment%20Build%20Your%20Own%20Overleaf%20with%20TeX%20Live%20and%20VS%20Code/cover%20image.webp" referrerpolicy="no-referrer">
            &lt;/div>Have you ever been frustrated by Overleaf's compilation timeouts? Or encountered network issues that prevented you from compiling online? Maybe you're a beginner learning to typeset with LaTeX? This article will guide you step by step through installing the TeX Live distribution on your own computer and using VS Code as your LaTeX editor.</description></item><item><title>Notes on Creating Python Modules</title><link>https://yao-chih.netlify.app/en/notes-on-creating-python-modules/</link><pubDate>Mon, 13 Oct 2025 19:40:04 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/notes-on-creating-python-modules/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Notes%20on%20Creating%20Python%20Modules/cover%20image.webp" referrerpolicy="no-referrer">
            &lt;/div>Recently, I've been learning how to package Python code for quick sharing and easy installation. After exploring various distribution methods, I realized that creating a Python module is probably one of the most convenient options, mainly because it saves me from having to reinstall everything repeatedly.</description></item><item><title>Proof of the Variance of the Sum of Two Random Variables</title><link>https://yao-chih.netlify.app/en/proof-of-the-variance-of-the-sum-of-two-random-variables/</link><pubDate>Sat, 04 Oct 2025 12:06:14 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/proof-of-the-variance-of-the-sum-of-two-random-variables/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Proof%20of%20the%20Variance%20of%20the%20Sum%20of%20Two%20Random%20Variables/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>This is a classic proof in mathematical statistics. That is, <strong>whether the variance of the sum of two random variables equals the sum of their variances</strong>.</p>]]></description></item><item><title>Jotting #6 - From Post-Quake Fire to Demolition</title><link>https://yao-chih.netlify.app/en/1140904-jotting/</link><pubDate>Thu, 04 Sep 2025 22:05:18 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/1140904-jotting/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1140904%20jotting/cover%20image.webp" referrerpolicy="no-referrer">
            &lt;/div>From the fire to demolition — a jotting on Building D of the College of Science and Engineering.</description></item><item><title>From Gradient Descent to Stochastic Gradient Descent</title><link>https://yao-chih.netlify.app/en/from-gradient-descent-to-stochastic-gradient-descent/</link><pubDate>Sat, 30 Aug 2025 10:54:32 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/from-gradient-descent-to-stochastic-gradient-descent/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/From%20Gradient%20Descent%20to%20Stochastic%20Gradient%20Descent/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>Cover image was generated by ChatGPT.</p>
<p>Gradient descent is an iterative method used in most modern models to help find optimal parameters. However, in today&rsquo;s era of big data, the massive amount of data results in significant time costs for gradient descent. One key goal is to compute parameters quickly without sacrificing accuracy.</p>]]></description></item><item><title>Sampling Methods</title><link>https://yao-chih.netlify.app/en/sampling-methods/</link><pubDate>Tue, 12 Aug 2025 17:17:44 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/sampling-methods/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Sampling%20Methods/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>Cover image generated by ChatGPT.</p>
<p>Why do we need sampling? What are the benefits for data analysis? When building models, we sometimes find that the amount of data required for training is too large, which prolongs the training time. The purpose of sampling is to select a representative finite sample from a large population and perform statistical analysis on it, thereby inferring the characteristics or properties of the overall population.</p>
<p>From this, we can see that a sample is a subset of the population. <strong>Sampling methods</strong> refer to how we reasonably select a subset of the population as a sample. Since the sample size is smaller than the population, we hope that the <strong>sample statistics</strong> calculated from the selected sample approximate the <strong>population statistics</strong>, minimizing the bias introduced by sampling.</p>]]></description></item><item><title>Logistic Regression</title><link>https://yao-chih.netlify.app/en/logistic-regression/</link><pubDate>Wed, 23 Jul 2025 10:55:24 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/logistic-regression/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Logistic%20Regression/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image was generated by ChatGPT.</p>
<h2 id="introduction">Introduction</h2>
<p><strong>Logistic regression</strong> is a type of binary classification model derived from <strong>linear regression</strong>. Unlike linear regression, which fits a regression line that passes through the data points by minimizing the sum of squared distances, known as the <strong>least squares method</strong>. The goal of logistic regression is to find a decision boundary that clearly separates the data into two distinct classes.</p>]]></description></item><item><title>Methods for Selecting Models</title><link>https://yao-chih.netlify.app/en/methods-for-selecting-models/</link><pubDate>Sun, 20 Jul 2025 11:55:51 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/methods-for-selecting-models/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Methods%20for%20Selecting%20Models/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>How can we evaluate and compare multiple models, and select the best one for statistical analysis or machine learning? Simple models may fail to fit the data well, while overly complex models may suffer from "overfitting", reducing their predictive performance on future data.</description></item><item><title>Bellman-Ford Algorithm</title><link>https://yao-chih.netlify.app/en/bellman-ford-algorithm/</link><pubDate>Thu, 17 Jul 2025 15:20:54 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/bellman-ford-algorithm/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Bellman-Ford%20Algorithm/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>To address the limitation in Dijkstra's Algorithm, which cannot handle paths with **negative** edge weights, the Bellman-Ford Algorithm was developed. The Bellman-Ford Algorithm performs $V - 1$ relaxation operations for $V$ nodes, repeatedly relaxing all edges to continuously improve and find the shortest paths.</description></item><item><title>Jotting #5</title><link>https://yao-chih.netlify.app/en/1140716-jotting/</link><pubDate>Thu, 17 Jul 2025 13:26:33 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/1140716-jotting/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1140716%20jotting/0.jpg" referrerpolicy="no-referrer">
            &lt;/div></description></item><item><title>Image Compression Tool</title><link>https://yao-chih.netlify.app/en/image-compression-tool/</link><pubDate>Tue, 15 Jul 2025 20:23:26 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/image-compression-tool/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Image%20compression%20tool/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>在平時建立網站頁面時，我們會發現有時候有些頁面需要讀取一段時間才能出現，而有些頁面卻沒有。經過比較不同網頁後得出一個結論，網頁內的圖片如果大小過大，會造成網頁讀取速度較慢，因此萌生出要找到一款能夠壓縮目前網站目錄下所有圖片工具。但找來找去，網路上都沒有找到合適的應用程式，最後決定自己製作一款能夠壓縮所有圖片的程式。</description></item><item><title>K-Means Clustering</title><link>https://yao-chih.netlify.app/en/k-means-clustering/</link><pubDate>Fri, 11 Jul 2025 14:49:59 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/k-means-clustering/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/k-means%20clustering/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>K-means clustering, often referred to in Chinese as the "k-means algorithm", is a method used to partition $n$ data points into $k$ clusters. Each point is assigned to the cluster whose center (centroid) is closest to it.</description></item><item><title>Methods of Normalization</title><link>https://yao-chih.netlify.app/en/methods-of-normalization/</link><pubDate>Wed, 09 Jul 2025 14:36:01 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/methods-of-normalization/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Methods%20of%20Normalization/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div></description></item><item><title>A* Algorithm</title><link>https://yao-chih.netlify.app/en/a-star-algorithm/</link><pubDate>Tue, 08 Jul 2025 14:06:30 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/a-star-algorithm/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/A-Star%20Algorithm/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div></description></item><item><title>About Greedy Algorithm</title><link>https://yao-chih.netlify.app/en/greedy-algorithm/</link><pubDate>Mon, 07 Jul 2025 15:38:46 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/greedy-algorithm/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Greedy%20Algorithm/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>The **Greedy Algorithm** is an approach that solves problems by **choosing the option that appears most favorable at each step**. It makes simple and intuitive decisions based on the **locally optimal choice** among all available options at the moment, with the expectation that these local choices will accumulate into a globally optimal solution, continuing this process until a termination condition is met.</description></item><item><title>Dijkstra's Algorithm</title><link>https://yao-chih.netlify.app/en/dijkstras-algorithm/</link><pubDate>Wed, 02 Jul 2025 17:12:30 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/dijkstras-algorithm/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Dijkstra%27s%20Algorithm/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>Dijkstra's Algorithm is a method for finding the shortest path between nodes in a graph. It was originally designed to find the shortest path between two points, but was later extended to find the shortest paths from a fixed point to all other points, forming what is called a "shortest path tree".</description></item><item><title>Add Related Posts to Articles in the LoveIt Theme</title><link>https://yao-chih.netlify.app/en/add-related-posts-to-articles-in-the-loveit-theme/</link><pubDate>Sat, 28 Jun 2025 19:23:02 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/add-related-posts-to-articles-in-the-loveit-theme/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Add%20Related%20Posts%20to%20Articles%20in%20the%20LoveIt%20Theme/cover%20image.en.png" referrerpolicy="no-referrer">
            </div><h2 id="introduction">Introduction</h2>
<p>Recently, while reading articles, I noticed that it&rsquo;s quite troublesome to find similar posts after finishing one. While researching, I also saw that many websites have sections like &ldquo;Related Posts&rdquo; or &ldquo;You May Also Like,&rdquo; which inspired me to create a &ldquo;Related Posts&rdquo; section on my personal site.</p>
<p>Since it&rsquo;s about related posts, we need a way to determine which ones are similar, right? But re-tagging all posts or going through the trouble of using a classification model seems exhausting (or just plain silly?). Then I realized my articles already use <a href="/en/tags" rel="">tags</a> and <a href="/en/categories" rel="">categories</a>. So I thought: &ldquo;Why not make use of what&rsquo;s already there?&rdquo; That&rsquo;s why I decided to use existing metadata for classification.</p>
<p>Below is a guide on how to add a &ldquo;Related Posts&rdquo; section to each article.</p>]]></description></item><item><title>Common Distance Measures</title><link>https://yao-chih.netlify.app/en/common-distance-measures/</link><pubDate>Fri, 20 Jun 2025 11:09:08 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/common-distance-measures/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Common%20Distance%20Measures/cover%20image.png" referrerpolicy="no-referrer">
            </div><p>Cover image generated by ChatGPT.</p>
<h2 id="introduction">Introduction</h2>
<p>How to calculate distance is an important aspect of modern statistical analysis. Choosing different distance metrics may lead to different results in statistical analysis methods. The following introduces commonly used distance metrics.</p>]]></description></item><item><title>[Thought] The 34th South Taiwan Statistics Conference</title><link>https://yao-chih.netlify.app/en/the-34th-south-taiwan-statistics-conference/</link><pubDate>Sun, 08 Jun 2025 18:06:17 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/the-34th-south-taiwan-statistics-conference/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/The%2034th%20South%20Taiwan%20Statistics%20Conference/cover%20image.jpg" referrerpolicy="no-referrer">
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  const pastTitle = titleDiv.dataset.past?.trim();
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    const temp = new Date(past ? target : now);
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    timeParts.year = ref.getFullYear() - temp.getFullYear();
    timeParts.month = ref.getMonth() - temp.getMonth();
    timeParts.day = ref.getDate() - temp.getDate();
    timeParts.hour = ref.getHours() - temp.getHours();
    timeParts.minute = ref.getMinutes() - temp.getMinutes();
    timeParts.second = ref.getSeconds() - temp.getSeconds();

    
    if (timeParts.second < 0) { timeParts.second += 60; timeParts.minute--; }
    if (timeParts.minute < 0) { timeParts.minute += 60; timeParts.hour--; }
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      const daysInPrevMonth = new Date(ref.getFullYear(), ref.getMonth(), 0).getDate();
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    wrapper.classList.add(clockId);
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      const div = document.createElement("div");
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      div.innerHTML = `
        <div class="flip-number" id="${clockId}-flip-${unit}">00</div>
        <div class="flip-label">${timeLabels[unit]}</div>
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  function updateClock() {
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        el.style.transform = "rotateX(180deg)";
        setTimeout(() => {
          el.textContent = pad(val);
          el.style.transform = "rotateX(0deg)";
        }, 150);
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  createClockDisplay();
  updateClock();
  setInterval(updateClock, 1000);
});
</script>

</div>]]></description></item><item><title>Rectangular, Polar, and Spherical Coordinates</title><link>https://yao-chih.netlify.app/en/rectangular-polar-and-spherical-coordinates/</link><pubDate>Sun, 08 Jun 2025 14:57:34 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/rectangular-polar-and-spherical-coordinates/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/Rectangular%20Polar%20and%20Spherical%20Coordinates/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>Common coordinate systems include the rectangular coordinate system, the polar coordinate system, and the spherical coordinate system. Each has its own applications, advantages, and limitations.</description></item><item><title>Useful Websites</title><link>https://yao-chih.netlify.app/en/useful-websites/</link><pubDate>Sun, 08 Jun 2025 12:22:59 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/useful-websites/</guid><description></description></item><item><title>Anscombe's Quartet</title><link>https://yao-chih.netlify.app/en/anscombes-quartet/</link><pubDate>Mon, 02 Jun 2025 21:40:50 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/anscombes-quartet/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/Anscombe%27s%20quartet/cover%20image.png" referrerpolicy="no-referrer">
            </div><p>The cover image about Anscombe&rsquo;s Quartet was generated by ChatGPT, and it used the following prompt: &ldquo;The digital design highlights the title &lsquo;Anscombe&rsquo;s Quartet&rsquo; in bold, white sans-serif letters, centered against a dynamic abstract backdrop. The image is split into four colorful quadrants, each showcasing unique textures and patterns—ranging from painterly hues and curved lines to scattered circles and dots.&rdquo;</p>
<h2 id="introduction">Introduction</h2>
<p>While listening to a presentation today, I happened to hear a term——Anscombe&rsquo;s Quartet, a term I had never encountered before, yet it has significant implications in both statistics and data visualization.</p>]]></description></item><item><title>Introduction to Perceptron</title><link>https://yao-chih.netlify.app/en/introduction-to-perceptron/</link><pubDate>Tue, 20 May 2025 22:30:24 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/introduction-to-perceptron/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/Introduction%20to%20Perceptron/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>The Perceptron is one of the earliest models of artificial neural networks and laid the foundation for modern machine learning, often regarded as the 'ancestor' of neural networks. It performs simple binary classification tasks and mathematically simulates the logic of how neurons operate. Even today, it is frequently used as a teaching example for beginners in machine learning.</description></item><item><title>Multi-dimensional Scaling</title><link>https://yao-chih.netlify.app/en/multi-dimensional-scaling/</link><pubDate>Thu, 15 May 2025 15:58:34 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/multi-dimensional-scaling/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/Multi-Dimensional%20Scaling/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>Multi-dimensional Scaling (MDS) is a dimensionality reduction technique that calculates pairwise distances between objects in a dataset and represents them in a lower-dimensional space while preserving the original relative distance structure. MDS can be seen as a method for visualizing data to intuitively observe the relative relationships between data points.</description></item><item><title>Definition of Dataset</title><link>https://yao-chih.netlify.app/en/definition-of-dataset/</link><pubDate>Fri, 09 May 2025 23:30:30 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/definition-of-dataset/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/Definition%20of%20Dataset/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>In this era of big data, our lives are filled with various forms of information. If we treat each piece of data as an observation and collect them together, we can roughly obtain a dataset composed of many individual data points. These datasets can be either structured or unstructured. By organizing and filtering the data to extract useful information and eliminate irrelevant parts, we can create a dataset suitable for analysis, model training, or academic research. A dataset may include different data types such as numbers, text, images, audio, or video, depending on its intended application and the methods used to collect and organize it.</description></item><item><title>Methods of Cross-Validation</title><link>https://yao-chih.netlify.app/en/methods-of-cross-validation/</link><pubDate>Wed, 30 Apr 2025 10:42:50 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/methods-of-cross-validation/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Methods%20of%20Cross-Validation/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>Cross-validation (CV) is a commonly used model validation technique in machine learning, employed to assess a model’s generalization ability on unseen data. By splitting the dataset into training, validation, and test sets, and repeatedly conducting training and evaluation, cross-validation can effectively test a model’s performance even when the data is limited. It helps evaluate generalization ability and is useful in reducing issues like overfitting and bias. Cross-validation plays a crucial role in controlling overfitting, selecting the best model, and tuning hyperparameters. In particular, it helps predict how a model will perform on new data, making it an indispensable part of modern machine learning workflows.</description></item><item><title>Dataset Exploration and Analysis #2</title><link>https://yao-chih.netlify.app/en/dataset-exploration-and-analysis-ep.2/</link><pubDate>Mon, 28 Apr 2025 19:51:50 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/dataset-exploration-and-analysis-ep.2/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/Dataset%20Exploration%20and%20Analysis%20EP.2/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div></description></item><item><title>Dinosaur Game</title><link>https://yao-chih.netlify.app/en/dinosaur-game/</link><pubDate>Sat, 26 Apr 2025 20:42:30 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/dinosaur-game/</guid><description>&lt;p>The classic Chrome offline mini-game, developed by Google&amp;rsquo;s Chrome User Experience team in 2014.&lt;/p></description></item><item><title>Solitaire</title><link>https://yao-chih.netlify.app/en/solitaire/</link><pubDate>Sat, 26 Apr 2025 17:25:30 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/solitaire/</guid><description></description></item><item><title>[NCHC Exploration] #2 Getting Started with TWCC</title><link>https://yao-chih.netlify.app/en/nchc-exploration-ep.2-getting-started-with-twcc/</link><pubDate>Fri, 11 Apr 2025 15:53:25 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/nchc-exploration-ep.2-getting-started-with-twcc/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/NCHC%20Exploration%20EP.2%20Getting%20Started%20with%20TWCC/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>The Taiwan Computing Cloud (TWCC) is a platform under the National Center for High-Performance Computing (NCHC), offering AI technology development and cloud computing services. TWCC features rapid deployment, efficient orchestration, intelligent computing, and centralized data integration. By leveraging advanced container and GPU technologies, it enables the swift creation of secure and flexible computing environments. These capabilities support large-scale parallel processing and efficient data management, accelerating development workflows and meeting diverse application demands.</description></item><item><title>[NCHC Exploration] #1 Account Creation</title><link>https://yao-chih.netlify.app/en/nchc-exploration-ep.1-account-creation/</link><pubDate>Thu, 10 Apr 2025 12:49:44 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/nchc-exploration-ep.1-account-creation/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/NCHC%20Exploration%20EP.1%20Account%20Creation/cover%20image.jpg" referrerpolicy="no-referrer">
            &lt;/div>The National Center for High-performance Computing (NCHC), part of the National Applied Research Laboratories (NARLabs), is a research institution dedicated to technologies such as high-performance computing, storage, networking, and platform integration. As big data and AI become increasingly prevalent, having a solid environment for model training has become crucial. As Taiwan’s leading national lab in large-scale computing platforms and academic research network infrastructure, NCHC not only excels in cybersecurity and data encryption but is also actively developing advanced HPC techniques, quantum computing, and big data analytics. Training and deploying models on NCHC's systems ensures not only fast computation but also stability and security, making it a top choice for researchers.</description></item><item><title>Lottery Wheel</title><link>https://yao-chih.netlify.app/en/lottery-wheel/</link><pubDate>Mon, 07 Apr 2025 23:28:00 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/lottery-wheel/</guid><description></description></item><item><title>Lottery Wheel - Beautified Version</title><link>https://yao-chih.netlify.app/en/lottery-wheel-beauty/</link><pubDate>Mon, 07 Apr 2025 23:28:00 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/lottery-wheel-beauty/</guid><description></description></item><item><title>Catch the Cat</title><link>https://yao-chih.netlify.app/en/catch-the-cat/</link><pubDate>Thu, 03 Apr 2025 23:17:16 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/catch-the-cat/</guid><description></description></item><item><title>Introduction to Machine Learning</title><link>https://yao-chih.netlify.app/en/introduction-to-machine-learning/</link><pubDate>Wed, 02 Apr 2025 15:16:47 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/introduction-to-machine-learning/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Introduction%20to%20Machine%20Learning/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed step by step. In traditional data analysis and programming, developers often need to manually write specific rules for each analytical task. In contrast, machine learning relies on data and uses trained models to automatically identify rules or discover patterns. With the rise of the digital age, massive amounts of information and data are generated and collected every day. Regardless of whether these datasets are inherently valuable or contain subtle, hidden insights, machine learning allows us to uncover and leverage this information. ML technologies are now widely applied across various industries, including finance, healthcare, manufacturing, and autonomous driving.</description></item><item><title>[Thoughts] Junior high school students' career exploration - Chemical Engineering Group</title><link>https://yao-chih.netlify.app/en/junior-high-school-students-career-exploration-chemical-engineering-group/</link><pubDate>Sat, 29 Mar 2025 22:07:10 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/junior-high-school-students-career-exploration-chemical-engineering-group/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Junior%20high%20school%20students%27%20career%20exploration%20-%20Chemical%20Engineering%20Group/IMG_6535.jpg" referrerpolicy="no-referrer">
            </div><p>Career exploration is an opportunity for junior high school students who are still feeling uncertain about their future to discover their interests.</p>
<p>By participating in activities from various career fields, students can develop their interests while also gaining an understanding of different industries, their job responsibilities, operations, and underlying principles. They also learn through hands-on experiences.</p>
<p>This time, the chemical engineering career exploration event was organized by National Hualien Industrial Vocational Senior High School, which held a weekend workshop on making photoengraved stamps. Through this activity, students not only learned how textbook knowledge applies to real life but also used their imagination to create their own unique stamps, experiencing the joy of hands-on work and creative expression.</p>]]></description></item><item><title>Added sorting buttons by Publish or Update date on the home page</title><link>https://yao-chih.netlify.app/en/added-sorting-buttons-by-publish-or-update-date-on-the-home-page/</link><pubDate>Sat, 29 Mar 2025 21:44:06 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/added-sorting-buttons-by-publish-or-update-date-on-the-home-page/</guid><description>&lt;h2 id="introduction">Introduction&lt;/h2>
&lt;p>Sometimes, when browsing a website, you just want to see the most recently updated articles. However, since this site uses Hugo, which is a static site generator, the article information on the homepage is fixed at the moment the site is generated, making dynamic switching impossible. In this case, we can generate a page similar to the homepage and use buttons to switch between different views, achieving a dynamic effect to implement article sorting functionality.&lt;/p></description></item><item><title>Prove $\frac{64}{16} = 4$</title><link>https://yao-chih.netlify.app/en/64-divided-by-16/</link><pubDate>Sat, 29 Mar 2025 00:06:43 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/64-divided-by-16/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/64%20divided%20by%2016/6416.png" referrerpolicy="no-referrer">
            &lt;/div>Weird proof $\frac{64}{16} = 4$ .</description></item><item><title>Principal Component Analysis (PCA)</title><link>https://yao-chih.netlify.app/en/principal-component-analysis/</link><pubDate>Tue, 25 Mar 2025 15:09:49 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/principal-component-analysis/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Principal%20Component%20Analysis/cover%20image.webp" referrerpolicy="no-referrer">
            &lt;/div>Principal Component Analysis (PCA) is a commonly used dimension reduction method. It transforms an originally linearly dependent dataset into a set of linearly independent new variables through an orthogonal transformation. This process minimizes the loss of information when projecting the dataset onto these new variables. These new variables are ranked based on their variance, with the one having the highest variance called the first principal component, the second highest called the second principal component, and so on. PCA is widely used to map high-dimensional data into lower-dimensional space while preserving as much of the original data’s key features and information as possible.</description></item><item><title>Understanding the Confusion Matrix</title><link>https://yao-chih.netlify.app/en/understanding-the-confusion-matrix/</link><pubDate>Fri, 21 Mar 2025 18:04:29 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/understanding-the-confusion-matrix/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Understanding%20the%20Confusion%20Matrix/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>In machine learning, we can build various models, ranging from simple linear regression to complex neural network models. These models can be applied to different fields, such as image recognition, language generation, and financial forecasting. Each model has its advantages and applicable scenarios. For example, linear regression is suitable for fitting and analyzing datasets with target variables, while deep learning can handle complex nonlinear datasets. However, how do we assess the quality of a model? Simply relying on accuracy may not be sufficient to evaluate model performance, especially in cases with imbalanced class distributions. Therefore, we use **Confusion Matrix** to analyze the performance of classification models.</description></item><item><title>Dataset Exploration and Analysis</title><link>https://yao-chih.netlify.app/en/dataset-exploration-and-analysis/</link><pubDate>Tue, 18 Mar 2025 12:58:27 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/dataset-exploration-and-analysis/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Dataset%20Exploration%20and%20Analysis/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image is generated by ChatGPT, depicting data analysis. The prompt used is &ldquo;A modern data analysis concept illustration featuring a futuristic workspace. The image includes multiple data charts, graphs, and dashboards displayed on transparent holographic screens. A diverse team of analysts and data scientists collaborate, analyzing trends and insights on large monitors. The scene has a sleek, high-tech atmosphere with glowing blue and purple hues, reflecting a professional and cutting-edge environment.&rdquo; 。</p>
<h2 id="introduction">Introduction</h2>
<p>The dataset used in this analysis is the <strong>Swiss bank notes</strong> dataset, which is utilized by banks to distinguish between genuine and counterfeit old Swiss Francs. This study downloads the <code>bank2.dat</code> dataset from <a href="https://github.com/QuantLet/MVA/tree/master/QID-1530-MVAscabank56" target="_blank" rel="noopener noreffer ">https://github.com/QuantLet/MVA/tree/master/QID-1530-MVAscabank56</a> and conducts the analysis using the <a href="https://www.r-project.org/" target="_blank" rel="noopener noreffer ">R</a> programming language.</p>]]></description></item><item><title>Hugo LoveIt theme categories page modification</title><link>https://yao-chih.netlify.app/en/hugo-loveit-theme-categories-page-modification/</link><pubDate>Mon, 17 Mar 2025 23:31:49 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/hugo-loveit-theme-categories-page-modification/</guid><description>&lt;h2 id="introduction">Introduction&lt;/h2>
&lt;p>The default categories page in the LoveIt theme can be quite dull, with every category represented by a folder icon. This article documents how to modify the categories page in the LoveIt theme to allow custom images for each category.&lt;/p></description></item><item><title>Jotting #4</title><link>https://yao-chih.netlify.app/en/1140315-jotting/</link><pubDate>Sun, 16 Mar 2025 13:07:00 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/1140315-jotting/</guid><description>&lt;p>Yesterday (the 15th), I participated in the department’s softball game. Despite the bad weather, everyone’s enthusiasm remained unstoppable. Rain or shine, we kept going! Fighting!&lt;/p></description></item><item><title>Locking articles in Hugo</title><link>https://yao-chih.netlify.app/en/locking-articles-in-hugo/</link><pubDate>Fri, 14 Mar 2025 22:40:01 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/locking-articles-in-hugo/</guid><description><![CDATA[<h2 id="introduction">Introduction</h2>
<p>Sometimes, a specific article may have flaws or contain information that we don&rsquo;t want everyone to see, but we still want the article to be accessible to a select group of people. This is where article passwords come in handy!</p>]]></description></item><item><title>Jotting #3</title><link>https://yao-chih.netlify.app/en/1140313-jotting/</link><pubDate>Thu, 13 Mar 2025 15:57:49 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/1140313-jotting/</guid><description>I received a message today about helping an education intern collect the teacher certificate. Let’s give a warm welcome to this fresh blood in education, full of fresh ideas, as they join the big family of the education field!</description></item><item><title>Jotting #2</title><link>https://yao-chih.netlify.app/en/1140309-jotting/</link><pubDate>Sun, 09 Mar 2025 17:30:34 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/1140309-jotting/</guid><description>&lt;p>Today, while taking the train, I happened to see a Juguang Express train being coupled. Although this is not unusual, I still wanted to capture the moment.&lt;/p></description></item><item><title>Jotting #1</title><link>https://yao-chih.netlify.app/en/1140307-jotting/</link><pubDate>Fri, 07 Mar 2025 12:40:13 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/1140307-jotting/</guid><description></description></item><item><title>Modify Hugo's 404.html page</title><link>https://yao-chih.netlify.app/en/modify-hugos-404.html-page/</link><pubDate>Wed, 05 Mar 2025 20:41:44 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/modify-hugos-404.html-page/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Modify%20Hugo%27s%20404.html%20page/404%20page.en.png" referrerpolicy="no-referrer">
            &lt;/div></description></item><item><title>Book Recommendations</title><link>https://yao-chih.netlify.app/en/book-recommendations/</link><pubDate>Tue, 04 Mar 2025 14:58:42 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/book-recommendations/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/book%20recommendations/cover%20image.webp" referrerpolicy="no-referrer">
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