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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74715
標題: | 職業衛生設計: 建立人工智慧應用之先期研究 Occupational Health by Design: A Development Towards Applications of Artificial Intelligence |
作者: | Shao-Zu Huang 黃紹祖 |
指導教授: | 吳焜裕(Kuen-Yuh Wu) |
關鍵字: | 職業衛生設計,人工智慧,StoffenmanagerR,群聚分析,穩健決策, Occupational Health by Design,Artificial Intelligence,StoffenmanagerR,Cluster analysis,Robust decision-making, |
出版年 : | 2020 |
學位: | 博士 |
摘要: | 隨著大數據和人工智慧(AI)技術的進步,暴露評估在作業環境設計中推估潛在風險的應用也隨之發展,以確保在設計階段就考量勞工之職業健康。為因應作業場所在設計及分析其暴露情境時所牽涉的各種參數與變項,整合複雜的資訊組合提供了將作業場所設計流程自動化的動力。自動化作業場所設計可被視為自動規劃(Automated planning, AI Planning)的問題,根據先定義之設計目標,依照暴露濃度預測結果回推最適合的設計參數。但實施此系統在現今仍面臨許多挑戰,如 1) 採用黑箱模型進行高風險決策時,可能導致不公平偏見的擔憂; 2)收集適用於學習演算法模型的高品質暴露數據; 以及 3)決策時如何考量互相牴觸之目標以及未來情境的不確定性。因此,本研究目的為探討目前建立AI作業場所設計系統的當前挑戰,並提出縮小其差距的方法。本研究分為三個部分:1)使用Stoffenmanager®和貝氏統計模型進行設計階段風險評估的案例研究,提出現有工具的機會和限制; 2)利用群聚分析法,描述且量化暴露情境間的相似程度,以增加可使用的數據的樣本數,以改善暴露預測; 3)以穩健決策框架,利用學習演算法搜索最佳決策策略的模擬研究。根據研究結果,本研究提出了一個自動化作業場所設計系統的架構,以建議如何結合數據導向的AI技術與傳統的知識導向模型以改善設計決策過程。 Advances in big data and artificial intelligence (AI) technology are encouraging improvements in applications that interpret existing occupational exposure data for predicting potential exposures in new workplaces, to ensure the protection of workers early in the design stage. As workplace designs and their resulting exposure scenarios are correlated with a large variety of features, the amount of details and potential permutations of combinations can easily surpass the human capacity for conducting a comprehensive analysis that carefully considers each feature and their implication of the risk outcome. This provides a strong incentive for an automated, if not computer-assisted decision support system for making occupational health by design assessments. An automated workplace design process can be viewed as a problem of AI planning, in which the planning system must synthesize the best design strategy given a description of the workplace scenario and the desired objectives. However, several challenges arise for the implementation of such system, namely 1) the concern of using black-box models for high-stake decisions that may lead to unfair biases; 2) challenges in obtaining high-quality data for model training; and 3) challenges in optimizing decisions under competing objectives and deep uncertainties of future states. The objective of this study was therefore to identify current challenges of achieving an AI-based automated workplace design system and propose methods for reducing the gap. Specifically, the study was organized into three parts: 1) a case study of design stage risk assessment using Stoffenmanager® and Bayesian modeling to identify opportunities and shortcomings of current tools; 2) a cluster analysis method for characterizing similar exposure scenarios to increase the size of usable data for improving exposure estimates; 3) a simulation study using robust decision-making framework to search for optimal decision strategies using unsupervised learning techniques, namely many-objective evolution and rule inductions algorithms. Based on the results, an automated workplace design system was proposed to recommend how data-driven AI techniques can be applied along with traditional knowledge-driven models to improve the decision process. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74715 |
DOI: | 10.6342/NTU202000025 |
全文授權: | 有償授權 |
顯示於系所單位: | 環境與職業健康科學研究所 |
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