https://ir.lib.ncu.edu.tw/handle/987654321/85 Search the Channel s https://ir.lib.ncu.edu.tw/simple-search https://ir.lib.ncu.edu.tw/handle/987654321/98282 title: 企業跨廠區資訊服務之高可用性研究─以 C 公司為例;High Availability Architecture for Cross-Site Enterprise Information Services: A Case Study of Company C abstract: 隨著數位科技快速發展，資訊服務的持續可用性已成為企業營運成功的關鍵因素之一。高可用性（High Availability, HA）架構可確保資訊系統在面臨硬體故障、軟體錯誤或災難事件時，仍能穩定運作，進而降低業務中斷風險與系統停擺所帶來的經濟損失。隨企業全球化布局日益擴大，對跨地域資訊服務不中斷的需求愈加迫切，如何縮短系統異常後的恢復時間，亦成為資訊化策略的重要挑戰。本研究目的在提升企業資訊系統之可用性與營運持續性，設計並建構一套支援跨廠區部署之超融合基礎架構（Hyper-Converged Infrastructure, HCI），整合 VMware vSphere 虛擬化平台與 StorMagic SvSAN 軟體定義儲存（Software-Defined Storage, SDS）技術。透過導入第三方仲裁節點與跨廠區資料同步機制，有效預防雙節點架構中常見之裂腦（Split-Brain）問題，並確保系統資料之一致性與即時性。研究中於實際企業場域建置此高可用性架構，並透過模擬主機故障、網路中斷及裂腦發生等情境，驗證系統於異常發生時能在 99 至 123 秒內自動完成容錯切換與服務恢復。實驗結果顯示，所建構之架構具備完善的故障偵測、自動接管與儲存一致性控制機制，能有效降低營運中斷風險，並提升企業資訊系統之韌性與穩定性。論文除詳述說明其架構設計理念與實作流程，亦透過多項異常情境測試，針對其適用條件進行實證分析，驗證其於實務環境中之可行性與應用效益。;With the rapid advancement of digital technology, the continuous availability of information services has become a critical factor for enterprise success. High Availability (HA) architectures ensure systems remain operational during hardware failures, software errors, or disasters, reducing risks of downtime and business loss. As global operations expand, enterprises face increasing demand for uninterrupted cross-site services and rapid recovery from failures. This study aims to enhance system availability and continuity by designing a cross-site Hyper-Converged Infrastructure (HCI) integrating VMware vSphere and StorMagic SvSAN Software-Defined Storage (SDS). A third-party witness node and real-time data synchronization are introduced to prevent split-brain issues and ensure data consistency. The architecture was deployed in a real enterprise environment and validated through simulated host failures, network disconnections, and split-brain scenarios. Results showed that automated failover and service recovery completed within 99 to 123 seconds. The proposed solution provides robust fault detection, automated takeover, and consistent storage control, significantly improving system resilience and stability. This research confirms the architecture’s practical feasibility and its value in supporting continuous enterprise operations. <br> https://ir.lib.ncu.edu.tw/handle/987654321/98279 title: When Words and Scores Diverge: A Machine Learning Study on Wine Review Inconsistencies abstract: 在當代的數位消費環境中，線上評論已成為影響購買決策的重要依據，特別是對於如葡萄酒般的體驗型商品。然而，文本評論與其所附的評分間常出現不一致的現象，導致消費者誤判產品品質，並進一步削弱平台的公信力與信任度。針對此一問題，本研究以 Vivino 平台上之葡萄酒評論作為研究對象，建構出一套用於偵測評論內容與評分是否一致的二元分類模型。 本研究首先透過爬蟲收集逾 72 萬筆用戶評論資料，並以評論內容與星等差異為依據，自行標註一致性標籤。接著，分別使用 TF-IDF 與 SBERT 進行文本向量化處理，並搭配隨機森林、類神經網路、XGBoost演算法 與 LightGBM 模型等多種分類模型進行訓練與測試。在資料處理上，本研究設計多種篩選條件 (如最小按讚數、文件頻率閾值)，並透過 SMOTE 處理類別不平衡問題，以提升模型效能。 實驗結果顯示，LightGBM 在多數條件下表現最為穩定，且 TF-IDF 與 SBERT 各有優勢：SBERT 在語意處理上具有優勢，TF-IDF 則在高文件頻率下具備較佳分類效果。本研究不僅提出一套可應用於評論平台的自動偵測機制，亦呼應數位時代下使用者生成內容的可信度與透明性議題，對於平台設計者與消費者皆具實務與學術參考價值。;In today’s digital consumption environment, online reviews play a critical role in shap-ing consumer decisions, particularly for experiential products like wine. However, inconsist-encies between textual reviews and their accompanying scores are frequently observed, po-tentially misleading users and undermining trust in review platforms. This study investigates this issue by constructing a binary classification model to detect alignment between review text and numerical ratings, using data collected from the Vivino platform. Over 720,000 user reviews were collected via web scraping and manually labeled for consistency based on discrepancies between text content and star ratings. Textual features were extracted using both Term Frequency-Inverse Document Frequency (TF-IDF) and Sen-tence-BERT (SBERT), and four classification algorithms were applied: Random Forest (RF), Neural Network (NN), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). To enhance model performance, the study incorporated various filter-ing criteria, for instance, minimum like counts and document frequency (DF) thresholds, also used SMOTE to address class imbalance. Experimental results indicate that LightGBM consistently outperformed other models across multiple data conditions. While SBERT offered superior semantic representation, TF-IDF exhibited stronger performance in high-document-frequency settings. This research not only proposes an effective automated framework for detecting review-score inconsistencies but also contributes to broader discussions on the credibility and transparency of user-generated content in digital platforms. <br> https://ir.lib.ncu.edu.tw/handle/987654321/98277 title: 時間序列模型預測公共自行車短期需求變化比較研究;Comparison of Time Series Models for Public Bicycle Short-Term Demand Prediction abstract: 隨著永續發展與淨零排放成為全球關注議題，公共自行車系統在推動綠色運輸與智慧城市建設中展現了潛在的價值。透過各區域站點所蒐集之即時資料，結合天氣與時間等外部資訊，可以有效預測需求變化量並有系統地調整營運資源。本研究以臺中市 YouBike 2.0 系統為研究對象，比較不同時間序列模型於短期需求預測之表現。 雖然近年來已有部分研究應用機器學習方法預測公共運輸需求，但鮮少有研究系統性比較多種時間序列模型在不同場域與預測區間下之表現，導致營運單位做出即時調度決策時缺乏足夠的參考依據。本研究實驗利用 ARIMA、XGBoost 與 LSTM 三種預測模型，並加入時間與天氣特徵進行預測分析，從站點層級評估模型在短期預測任務下的準確性，並透過消融實驗分析特徵組合對預測準確度之影響。實驗結果顯示，XGBoost 在大多數情境中具備最佳整體預測表現，特別是在高使用量站點與較長預測時間下，R² 可達 0.53 以上。LSTM 模型於部分站點亦表現穩定，惟對資料完整性與特徵變動較為敏感，在較長時間預測下具有較佳準確性。ARIMA 模型整體表現不佳，無法有效處理非線性與高波動性資料。此外，加入過多時間與天氣特徵未能進一步提升預測效能，反而在僅使用單一天氣特徵時表現更佳。空間場域分析結果顯示，學區型與商圈型站點因使用模式較為規律，預測效果較佳。 本研究建立一套具擴展性與實務應用價值的預測流程，提供模型選擇與特徵設計之建議，期能作為未來智慧交通管理與綠色運輸調度政策的重要參考。;This study explores short-term demand forecasting for public bicycle systems using the YouBike 2.0 system in Taichung. Although machine learning has been applied to transport demand prediction, few studies have systematically compared statistical, machine learning, and deep learning models across different station types and forecast horizons. This research develops ARIMA, XGBoost, and LSTM models, integrating temporal and weather features, and evaluates their performance through feature combination and ablation experiments. Results show that XGBoost consistently outperforms other models, achieving R2 values above 0.53 at high-demand stations and longer intervals. LSTM demonstrates stable performance at select stations but is more sensitive to data quality and feature dimensions. ARIMA performs poorly overall due to its limited adaptability to nonlinear and volatile demand. Moreover, including too many time and weather features does not improve performance. On the contrary, excessive features reduce accuracy, while using a single weather variable (e.g., rainfall or temperature) yields better results. Spatial analysis also reveals that stations in school and commercial zones, where usage patterns are more regular, yield higher predictive accuracy. This study offers a scalable forecasting framework and practical insights for model selection, feature engineering, and real-time dynamic deployment in public bicycle systems. <br> https://ir.lib.ncu.edu.tw/handle/987654321/98273 title: 人格特質與組織公平對留任意願之影響： 以工作滿意度為中介變項;The Influence of Personality Traits and Organizational Justice on Retention Intention: The Mediating Role of Job Satisfaction abstract: 現代職場中，影響員工工作表現與留任意願的因素日趨多元，尤其在人際互動與組織環境方面的感受已成為重要關鍵。人格特質與組織公平作為影響員工行為與態度的重要因素，日益受到企業與學術界重視。 本研究主要關注探討人格特質與組織公平對員工留任意願的影響性，並進一步探究工作滿意度所扮演之中介角色。人格特質為個體在情緒、行為與認知上的穩定傾向，能有效預測其工作適應能力與整體績效，尤以大五人格模型中的嚴謹性、親和性與神經性與工作表現關聯最為密切。組織公平則關乎員工對資源分配、決策程序及人際互動的公平感受，對提升工作滿意度與投入具有關鍵影響力。 本研究對象以具正職身份之在職員工為主，透過電子問卷進行資料蒐集，問卷內容涵蓋大五人格、組織公平、工作滿意度與留任意願等構面，並採用便利抽樣與滾雪球抽樣方式發放。資料分析包含敘述統計、皮爾森相關、多元迴歸及中介效果檢定，後者採用 PROCESS Macro 工具。結果顯示，個體之留任意願程度與人格特質及組織公平皆有影響，且工作滿意度在其中具中介效果。研究結果亦期能提供企業於人才適配與留任管理之參考依據，促進組織穩定與永續發展。 ;This study explores how personality traits and organizational justice impact employees’ intention to stay, with a focus on how job satisfaction influences these connections. Traits such as conscientiousness, agreeableness, and neuroticism are considered strong indicators of workplace behavior. At the same time, how employees perceive equity in how resources are allocated, decisions are made, and they are treated by others significantly influences their attitudes toward the organization. This study gathered responses from full-time employees using an online survey, which assessed individual personality characteristics, perceptions of fairness within the work environment, levels of job satisfaction, and employees’ intentions to remain with their organization. Data were gathered through convenience and snowball sampling. The analysis involved descriptive statistics, Pearson correlation, multiple regression, and mediation testing using the PROCESS Macro. The findings reveal that employees’ intention to stay is closely linked to their personality traits and how fairly they perceive their organization operates. Furthermore, job satisfaction functions as a key element that bridges these associations. These insights offer practical value for organizations seeking to enhance employee alignment, foster stronger workplace commitment, and develop effective retention strategies to ensure long-term organizational stability. <br>