A classification technique incorporating a novel feature derivation method is proposed for predicting failure of a system or device with multivariate time series sensor data. We treat the multivariate time series sensor data as images for both visualization and computation. Failure follows various patterns which are closely related to the root causes. Different predefined transformations are applied on the original sensors data to better characterize the failure patterns. In addition to feature derivation, ensemble method is used to further improve the performance. In addition, a general algorithm architecture of deep neural network is proposed to handle multiple types of data with less manual feature engineering. We apply the proposed method on the early predict failure of computer disk drive in order to improve storage systems availability and avoid data loss. The classification accuracy is largely improved with the enriched features, named smart features.
Data61, CSIRO（Data61联邦科学与工业研究组织）；Western Water（西部水务公司）；FEIT, University of Technology Sydney（悉尼科技大学工程与信息技术学院）
Data61 and Western Water worked collaboratively to apply engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the region west of Melbourne, where on average 400 water main failures occur per year. To achieve this objective, we constructed a detailed picture and understanding of the behaviour of the water pipe network by 1) discovering the underlying drivers of water main breaks, and 2) developing a Machine Learning system to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors…
HERO: History-Enriched Rollout Training for Long-Horizon Autoregressive Neural Operators
School of Computer Science and Engineering, UESTC（电子科技大学计算机科学与工程学院）；Electronics and Information Convergence Engineering, KHU（明知大学电子信息融合工程系）；Department of Mathematical Sciences, UOL（利物浦大学数学科学系）；School of Computer Science and Technology, XDU（西安电子科技大学计算机科学与技术学院）；School of Mechanical and Electrical Engineering, UESTC（电子科技大学机械与电气工程学院）；College of Computer and Information Engineering, XAUAT（西安建筑科技大学计算机与信息工程学院）
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition
Advanced Driver-Assistance Systems (ADAS) have been thriving and widely deployed in recent years. In general, these systems receive sensor data, compute driving decisions, and output control signals to the vehicles. To smooth out the uncertainties brought by sensor outputs, they usually leverage multi-sensor fusion (MSF) to fuse the sensor outputs and produce a more reliable understanding of the surroundings. However, MSF cannot completely eliminate the uncertainties since it lacks the knowledge about which sensor provides the most accurate data and how to optimally integrate the data provided…
Livermore Software Technology, an ANSYS company（利弗莫尔软件技术公司（ANSYS旗下公司））
A fundamental issue in multiscale materials modeling and design is the consideration of traction-separation behavior at the interface. By enriching the deep material network (DMN) with cohesive layers, the paper presents a novel data-driven material model which enables accurate and efficient prediction of multiscale responses for heterogeneous materials with interfacial effect. In the newly invoked cohesive building block, the fitting parameters have physical meanings related to the length scale and orientation of the cohesive layer. It is shown that the enriched material network can be effect…
Plasticity Loss in Deep Reinforcement Learning: A Survey
Faculty of Computer Science, University of Vienna（维也纳大学计算机科学系）；Doctoral School Computer Science, University of Vienna（维也纳大学计算机科学博士学院）；Independent Researcher（独立研究员）；Microsoft AI（微软人工智能）；Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry（生物化学研究所机器学习与系统生物学部门）
