The use of distributed optimization in machine learning can be motivated either by the resulting preservation of privacy or the increase in computational efficiency. On the one hand, training data might be stored acro...
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The use of distributed optimization in machine learning can be motivated either by the resulting preservation of privacy or the increase in computational efficiency. On the one hand, training data might be stored across multiple devices. Training a global model within a network where each node only has access to its confidential data requires the use of distributed algorithms. Even if the data is not confidential, sharing it might be prohibitive due to bandwidth limitations. On the other hand, the ever-increasing amount of available data leads to large-scale machine learning problems. By splitting the training process across multiple nodes its efficiency can be significantly increased. This paper aims to demonstrate how dual decomposition can be applied for distributed training of K-means clustering problems. After an overview of distributed and federated machine learning, the mixed-integer quadratically constrained programming-based formulation of the K-means clustering training problem is presented. The training can be performed in a distributed manner by splitting the data across different nodes and linking these nodes through consensus constraints. Finally, the performance of the subgradient method, the bundle trust method, and the quasi-Newton dual ascent algorithm are evaluated on a set of benchmark problems. The main benefit stemming from the formulation of the clustering problem as a mixed-integer program and from the use of dual decomposition within a federated learning framework, apart from the preservation of privacy, is the computation of a lower bound of the objective of the overall clustering problem. In this way, the worst-case distance of any found solution to the global optimum can be easily assessed. While the mixed-integer programming-based formulation of the clustering problems suffers from weak integer relaxations, the presented approach can potentially be used to enable an efficient solution in the future, both in a central and distributed sett
The high concentration of fluoride (F-) ions in contaminated water has become an urgent challenge for public health and environmental safety. In this contribution, we adopted asymmetric flow electrode capacitive deion...
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The purpose of this review is three-fold. First, sketch the directions that research and industrial applications of ''intelligent systems'' have taken in several areas of processengineering. Second, i...
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The purpose of this review is three-fold. First, sketch the directions that research and industrial applications of ''intelligent systems'' have taken in several areas of processengineering. Second, identify the emerging trends in each area, as well as the common threads that cut across several domains of inquiry. Third, stipulate research and development themes of significant importance for the future evolution of ''intelligent systems'' in processengineering. The paper covers the following seven areas: diagnosis of process operations;monitoring and analysis of process trends;intelligent control;heuristics and logic in planning and scheduling of process operations;modeling languages, simulation, and reasoning;intelligence in scientific computing;knowledge-based engineering design. Certain trends seem to be common and will (in all likelihood) characterize the nature of the future deployment of ''intelligent systems''. These trends are: (1) Specialization to narrowly defined classes of problems. (2) Integration of multiple knowledge representations, so that all of relevant knowledge is captured and utilized. (3) Integration of processing methodologies, which tends to blur the past sharp distinctions between AI-based techniques and those from operations research, systems and control theory, probability and statistics. (4) Rapidly expanding range of industrial applications with significant increase in the scope of engineering tasks and size of problems.
Multifaceted (multiview, multicontext, multilevel) modeling of processing systems is a central requirement for further automation of processengineering tasks. This paper describes how the formal framework of ***., es...
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Multifaceted (multiview, multicontext, multilevel) modeling of processing systems is a central requirement for further automation of processengineering tasks. This paper describes how the formal framework of ***., established in Part I, is being used to construct multifaceted models of processing systems. More specifically, it shows how ***. (a) can access multiple views of a model; (b) generate contextual models; and (c) establish consistency among multiple, concurrent models developed at different levels of abstraction. The procedures carrying out these operations are described and a series of illustrations is used to clarify the various concepts.
The proposed paper will present a standardized solution for Industrial IoT, to employ event-based sampling for detecting equipment faults and preventing product impact in large-scale manufacturing. This is important b...
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ISBN:
(数字)9798331531850
ISBN:
(纸本)9798331531867
The proposed paper will present a standardized solution for Industrial IoT, to employ event-based sampling for detecting equipment faults and preventing product impact in large-scale manufacturing. This is important because IoT sensors can generate dense datasets with wide distributions, attributed to varying operating conditions in the fab; sensor measurements summarized without the appropriate context makes it difficult to correlate IoT data with fab processes. To improve our detection rate and minimize false alarms, it is critical to intelligently sample this data using contextual events before applying tighter control. We implemented a standardized solution for event-based sampling which is easily extensible to any Industrial IoT scenario, since it was designed and optimized using industry best practices and evaluated against real fab use-cases. It significantly reduced server CPU consumption and prevented message loss / connection timeouts with other manufacturing systems. Improving our signal-to-noise-ratio (SNR) based on context consequentially helped with tighter process-control, enhanced fab operations, and transformed our detection from reactive to predictive / preventive.
The scale up of hydrogen production to large scale production systems demands a structured approach to control these systems. With suitable control principles, optimization for operation can be achieved whilst maintai...
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Large-scale water electrolysis systems are built from a multitude of stack units. These stack units have to be controlled during operation. The question arises how processcontrol of the system can be designed in that...
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The application of artificial intelligence techniques to the operation of water and wastewater treatment plants in recent years is reviewed. The expert system approach is the most prevalent, but difficulties in acquir...
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The application of artificial intelligence techniques to the operation of water and wastewater treatment plants in recent years is reviewed. The expert system approach is the most prevalent, but difficulties in acquiring and representing knowledge of the complex phenomena in these plants have led to the search for additional approaches. Fuzzy logic and statistical processcontrol are used for formulating expert rules from plant historical operating data, but artificial neural networks, which can learn from examples, are believed to be a better solution for this task and for many additional problems encountered in the operation of the plants. Basic concepts of neural network organization and training are given as well as recent advances in learning speed improvement that have paved the way for easy application of this technique in large industrial plants. Current and future utilization of neural networks in areas of water and wastewater plant modelling, expert rule extraction, fault detection and diagnosis, plant and instrument monitoring, dynamic forecasting, and robust control are discussed. Examples are given from the application of neural networks to the operation of the Shafdan wastewater treatment plant in Israel. Some limitations of the neural network approach, together with ways of overcoming these limitations, are described. The overall conclusion is that we will soon see neural network techniques applied to achieve better plant operation.
Environmentally sustainable and economically viable process and energy systems are imperative to a successful energy transition. Often, design configurations are derived from a global perspective, in which the individ...
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作者:
Shang, JunLi, YuzheChen, TongwenTongji University
Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University
State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta
Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada
This paper investigates stealthy attacks on sampled-data controlsystems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels...
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