生活中到處充滿著不確定性，例如吃飯排隊的等候時間、機台生產的良率、民調統計數字的分析等等。工業系也開設許多相關的課程，例如品質管制、資料分析、智慧製造、實驗設計、機器學習、人工智慧等等，以解決工商業的問題。不確定性和雜訊，也是 (電機、軟工、機械、化工、土木、生醫、材料) 工程系統中常見的問題。
許多人修這門課的時候，很痛苦 (註 1)。除了了解其應用外， 有許多的網路資源：
Steven L. Brunton and J. Nathan Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, 2nd edition, Cambridge University Press (pdf, amazing videos, connection with dynamical systems in engineering)
Practical Engineering is all about infrastructure and the human-made world around us. It is hosted, written, and produced by civil engineer, Grady Hillhouse. We have new videos posted every first and third Tuesday, so please subscribe for updates.
Autopilot Safety
In 2021, we recorded 0.22 crashes for every million miles driven in which drivers were using Autopilot technology (Autosteer and active safety features). For drivers who were not using Autopilot technology (no Autosteer and active safety features), we recorded 0.77 crashes for every million miles driven. By comparison, NHTSA’s most recent data shows that in the United States there are 1.81 automobile crashes for every million miles driven.
Berk Öztürk, Global and Robust Optimization for Engineering Design, Ph.D. Thesis, MIT, 2022. (thesis, code, talk)
There is a need to adapt and improve conceptual design methods through better optimization, in order to address the challenge of designing future engineered systems. Aerospace design problems are tightly-coupled optimization problems, and require all-at-once solution methods for design consensus and global optimality. Although the literature on design optimization has been growing, it has generally focused on the use of gradient-based and heuristic methods, which are limited to local and low-dimensional optimization respectively. There are significant benefits to leveraging structured mathematical optimization instead. Mathematical optimization provides guarantees of solution quality, and is fast, scalable, and compatible with using physics-based models in design. More importantly perhaps, there has been a wave of research in optimization and machine learning that provides new opportunities to improve the engineering design process. This thesis capitalizes on two such opportunities.
Tava Lennon Olsen, Brian Tomlin (2020) Industry 4.0: Opportunities and Challenges for Operations Management. Manufacturing & Service Operations Management, 22(1):113-122. (pdf)
Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which include both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.
Robert N. Charette, How Software Is Eating the Car, IEEE Spectrum, 07 Jun 2021.
Ten years ago, only premium cars contained 100 microprocessor-based electronic control units (ECUs) networked throughout the body of a car, executing 100 million lines of code or more. Today, high-end cars like the BMW 7-series with advanced technology like advanced driver-assist systems (ADAS) may contain 150 ECUs or more, while pick-up trucks like Ford’s F-150 top 150 million lines of code. Even low-end vehicles are quickly approaching 100 ECUs and 100 million of lines of code as more features that were once considered luxury options, such as adaptive cruise control and automatic emergency braking, are becoming standard....
Today we’re joined by Pieter Abbeel, a Professor at UC Berkeley, co-Director of the Berkeley AI Research Lab (BAIR), as well as Co-founder and Chief Scientist at Covariant.In our conversation with Pieter, we cover a ton of ground, starting with the specific goals and tasks of his work at Covariant, the shift in needs for industrial AI application and robots, if his experience solving real-world problems has changed his opinion on end to end deep learning, and the scope for the three problem domains of the models he’s building.We also explore his recent work at the intersection of unsupervised and reinforcement learning, goal-directed RL, his recent paper “Pretrained Transformers as Universal Computation Engines” and where that research thread is headed, and of course, his new podcast Robot Brains, which you can find on all streaming platforms today!The complete show notes for this episode can be found at twimlai.com/go/476.
陳泳翰，智能工廠來了！：一場水五金與手工具的創新實驗紀錄，天下雜誌，2021
歷時兩年半的「水五金與手工具產業智動化計畫」，簡稱「水手計畫」，同時也象徵著水手們乘風破浪的勇敢無懼精神。來自上銀科技的精密機械工程師，攜手四間傳統工廠：隴鈦銅器、勝泰衛材、銳泰精密、伯鑫⼯具，讓機器人實現產線智慧化、自動化的願景，不僅減輕第一線的人力負擔，也改變了黑手工廠形象，吸引更多年輕世代投身其中。
Michael A. Cusumano, Boeing's 737 MAX: A Failure of Management, Not Just Technology, Communications of the ACM, January 2021, Vol. 64, No. 1, Pages 22-25.
The congressional report had extensive access to company email and documents as well as detailed media coverage. These sources all describe the same decisions along with gradual but fundamental changes in Boeing's strategy and culture.
Jay Lee, Industrial AI: Applications with Sustainable Performance, Springer, 2020.
This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.
翁芊儒，組織、人才和技術5年布局， 中鋼如何用AI煉成智慧鋼廠，iThome，2021-03-04
這一場變革，莫約從5年前開始推展。「我們展開數位轉型，是為了提升鋼鐵生產效率、降低成本、縮短交期，來提升產品競爭力。」中鋼技術部門代理副總經理鄭際昭，一句話點出轉型任務最重要的目的。...
Debby Wu, Gabrielle Coppola, and Keith Naughton, A Year of Poor Planning Led to Carmakers’ Massive Chip Shortage, Bloomberg, 2021/1/19.
Near-sighted planning, supply-chain complexities and a tradition of keeping inventories low caused the semiconductor shortage that is now forcing carmakers to idle production lines and straining their relationship with chip manufacturers.
Kyle Wiggers, Microsoft launches Project Bonsai, an AI development platform for industrial systems, Venture Beat, May 19, 2020.
Microsoft announced the public preview of Project Bonsai, a platform for building autonomous industrial control systems, during its Build 2020 online conference. The company also debuted an experimental platform called Project Moab that’s designed to familiarize engineers and developers with Bonsai’s functionality.
Project Bonsai is a “machine teaching” service that combines machine learning, calibration, and optimization to bring autonomy to the control systems at the heart of robotic arms, bulldozer blades, forklifts, underground drills, rescue vehicles, wind and solar farms, and more. Control systems form a core component of machinery across sectors like manufacturing, chemical processing, construction, energy, and mining, helping manage everything from electrical substations and HVAC installations to fleets of factory floor robots. But developing AI and machine learning algorithms atop them — algorithms that could tackle processes previously too challenging to automate — requires expertise....
Brett Hansard, Researchers develop machine-learning optimizer to slash product design costs, Argonne National Laboratory, NOVEMBER 16, 2020.
Speed up the product design optimization process:
It employs a novel machine learning technique that helps users focus on how to most efficiently target computational resources. (Machine learning is an application of artificial intelligence that allows systems to automatically learn and improve from experience.)
"ActivO runs the simulations in a very smart way and quickly identifies the parts of the design space we should focus on," explained Pal. "A process that used to take two to three months to give you the optimum design can now be completed within about a week."