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14th Annual IEEE International Systems Conference, SYSCON 2020

作者： Shaikh, Salahuddin Changan, Liu Malik, Maaz Rasheed Dept. School of Control and Computer Engineering North China Electric Power University Beijing China Dept. Information and Communication Engineering Guilin University of Electronics Technology Guilin China

ISBN: (纸本)9781728153650

Software Engineers are battling always with defects from the earliest starting point of the software history. The defect has significant commitment and assumes an important job in the hour of freeing software. Software defect-prone might be recognized before the software announcement or after the announcement. Although, the desire for designers is to give defect free software to their customers. Hence, recognizing the software defect-prone before announcement makes a decent preferred position for the designers. Here, the difficult assignment is to discover the defect-prone region or defective module of software. On the off chance that defective modules are distinguished before the arrival of software, at that point it could be effectively counteracted and fixed by the designer. In most recent two decades scientists have concentrated on software deformity prophecy issue by applying a few statistical and machine learning systems. The software defect-prone information experiences the class-imbalance issue due to the slanted conveyance of defective and non-defective software modules. Generally, machine learning calculations consider equivalent circulation of information tests in each class and expect the misclassification cost of each class is similarly significant. We have used SMOTE with ONE-R by using its MinBucketsize number n=1, 2, 3, 4, 5 & 6 for over-fit the data and resultant we got that minbucketsize n= 1 & 2 are highly good for base accuracy and efficiency for defect-prone datasets model. We have used datasets model in three different ways and we observed that use training datasets is very suitable for our experiments as compare to other two ways. We also observed that al evaluation measures results are highly good at use of training datasets model. © 2020 IEEE

关键词： Classification (of information)

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Polytopic input constraints in learning-based optimal control using neural networks

arXiv

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arXiv 2021年

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Group Dept. of Electrical Engineering and Computer Science University of Kassel Germany

This work considers artificial feed-forward neural networks as parametric approximators in optimal control of discrete-time systems. Two different approaches are introduced to take polytopic input constraints into account. The first approach determines (sub-)optimal inputs by the application of gradient methods. Closed-form expressions for the gradient of general neural networks with respect to their inputs are derived. The approach allows to consider state-dependent input constraints, as well as to ensure the satisfaction of state constraints by exploiting recursive reachable set computations. The second approach makes use of neural networks with softmax output units to map states into parameters, which determine (sub-)optimal inputs by a convex combination of the vertices of the input constraint set. The application of both approaches in model predictive control is discussed, and results obtained for a numerical example are used for illustration. Copyright © 2021, The Authors. All rights reserved.

关键词： Discrete time control systems

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Pinching Antennas: Principles, Applications and Challenges

arXiv

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arXiv 2025年

作者： Yang, Zheng Wang, Ning Sun, Yanshi Ding, Zhiguo Schober, Robert Karagiannidis, George K. Wong, Vincent W.S. Dobre, Octavia A. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou350117 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Khalifa University Abu Dhabi United Arab Emirates University of Manchester ManchesterM1 9BB United Kingdom Germany Group Electrical & Computer Engineering Dept. Aristotle University of Thessaloniki Thessaloniki54 124 Greece Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering Memorial University St. John’sNL Canada

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects

The Goldilocks Framework: Towards Selecting the Optimal Appr...

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AI engineering - Software engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Rimman Dzhusupova Jan Bosch Helena Holmströ m Olsson Dept. of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies’ industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.

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FDP-GAMLP: New Financial Distress Prediction Technique based on Genetic Algorithm and Multi-Layer Perceptron 19

FDP-GAMLP: New Financial Distress Prediction Technique based...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Khedr, Ahmed M. Bannany, Magdi El Saif, Abdul-Wahid A. University of Sharjah Computer Science Dept. Sharjah27272 United Arab Emirates College of Business Administration Umm Al Quwain University Umm Al Quwain356 United Arab Emirates Ain Shams University Faculty of Business Cairo11566 Egypt King Fahd University of Petroleum & Minerals Control and Instrumentation Engineering Department Dhahran Saudi Arabia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Developing a reliable financial distress prediction model has been a long-standing research area. Recently, machine learning algorithms have been increasingly popular in the area. In this paper, a hybrid approach of Genetic Algorithm (GA) and Multi-Layer Perceptron (MLP) for Financial Distress Prediction (FDP) (FDP-GAMLP) is proposed. FDP-GAMLP emphasizes on GA-based tuning of the four major hyper-parameters, namely network width, network dept., network optimizer, and dense layer activation function, which can influence on whether the algorithm explodes or converges. An improved GA is utilized to optimize the MLP model's hyper-parameters for better prediction. The prediction performance is evaluated using real data set with samples of companies from countries in Middle East region. The resampling technique using k-fold evaluation metrics is adopted to get unbiased and most accurate results. The simulation results show that FDP-GAMLP outperforms the classical machine learning models in terms of predictive accuracy. © 2022 IEEE.

关键词： Genetic algorithms

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Automated Optical Inspection for Printed Circuit Board Assembly Manufacturing with Transfer Learning and Synthetic Data Generation

Automated Optical Inspection for Printed Circuit Board Assem...

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Mediterranean Conference on control and Automation (MED)

作者： Syed Saad Saif Kerem Aras Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) of the University of Rome "La Sapienza" Roma Italy Token Financial Technologies Istanbul Turkey Technical University of Berlin Berlin Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Automated Optical Inspection (AOI) is among the most common and effective quality checks employed in production lines. This paper details the design of a Deep Learning solution that was developed for addressing a specific quality control in a Printed Circuit Board Assembly (PCBA) manufacturing process. The developed Deep Neural Network exploits transfer learning and a synthetic data generation process to be trained even if the quantity of the data samples available is low. The overall AOI system was designed to be deployed on low-cost hardware with limited computing capabilities to ease its deployment in industrial settings.

关键词： Deep learning Transfer learning Printed circuits Neural networks Optical computing Quality control Production

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Challenges in developing and deploying AI in the engineering, procurement and construction industry

Challenges in developing and deploying AI in the engineering...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Rimma Dzhusupova Jan Bosch Helena Holmströ m Olsson Dept. of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

AI in the engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-dept. studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

关键词： Industries Procurement Companies Manuals Petrochemicals Software Safety

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Modeling the Productivity of a Sugar Factory using Machine Learning Methods

Modeling the Productivity of a Sugar Factory using Machine L...

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International computer Sciences and Information Technologies (CSIT)

作者： Nataliia Lutska Lidiia Vlasenko Nataliia Zaiets Vitaliy Lysenko dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350334326

Relevant today is the digitalization of industrial enterprises, which is an evolutionary continuation of the introduction of MES/MOM. At the same time, an important role is played by the construction of mathematical models for various purposes, which are the main component of the Digital Twin development. Digital Twin provides a new philosophy for conducting enterprise production processes based on these models, which is aimed at improving the efficiency of both individual sectors and the enterprise as a whole. To increase the profit of the enterprise, it is necessary to ensure the planned productivity of technological processes, the final indicator of which is the yield of sugar. Taking into account that a significant number of material flows operate at the enterprise, only production flows with which the operator-technologist works are singled out in the work. The selected flows, based on expert assessments, were assigned weighted values of the impact on the overall performance of the sugar production. This made it possible to build a mathematical model that predicts the yield of sugar from input flows. The structure and parameters of this model are determined by machine learning methods. This is a feed-forward neural network MLP 7-23-23-1, which provides forecast accuracy with an error of less than 1%. The model can be used to model, correct, and predict the process conditions of an enterprise, which will increase the overall productivity of the sugar factory.

关键词： Productivity Biological system modeling Machine learning Predictive models Mathematical models Sugar industry Production facilities

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Direct data-driven LPV control of nonlinear systems: An experimental result

arXiv

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arXiv 2022年

作者： Verhoek, Chris Abbas, Hossam S. Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck Lübeck23558 Germany Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points. © 2022, CC BY-NC-SA.

关键词： Nonlinear systems

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Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

arXiv

引用

arXiv 2022年

作者： van Haren, Max Poot, Maurice Kostić, Dragan van Es, Robin Portegies, Jim Oomen, Tom Control Systems Technology Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands ASM Pacific Technology Centre of Competency Beuningen Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder. © 2022, CC BY.

关键词： Gaussian distribution

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