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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

IEEE Transactions on Automation science and engineering 2024年第3期21卷 4829-4850页

作者： Liu, Anbang Luh, Peter B. Sun, Kailai Bragin, Mikhail A. Yan, Bing Department of Automation and BNRist Beijing100084 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tsinghua University Chaired Professors Group Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States

Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view of the increasing demand for customized products, problem sizes are growing. A promising direction is to take advantage of Machine Learning (ML). Direct learning to predict solutions for job-shop scheduling, however, suffers from major difficulties when problem scales are large. In this paper, a Deep Neural Network (DNN) is synergistically integrated within the decomposition and coordination framework of Surrogate Lagrangian Relaxation (SLR) to predict good-enough solutions for subproblems. Since a subproblem is associated with a single part, learning difficulties caused by large scales are overcome. Nevertheless, the learning still presents challenges. Because of the high-variety nature of parts, the DNN is desired to be able to generalize to solve all possible parts. To this end, our idea is to establish 'surrogate' part subproblems that are easier to learn, develop a DNN based on Pointer Network to learn to predict their solutions, and calculate the solutions of the original part subproblems based on the predictions. Moreover, a masking mechanism is developed such that all the predictions are feasible. Numerical results demonstrate that good-enough subproblem solutions are predicted in many iterations, and high-quality solutions of the overall problem are obtained in a computationally efficient manner. The performance of the method is further improved through continuous learning. Note to Practitioners - Scheduling is important for the planning and operation of job shops, and high-quality schedules need to be obtained quickly at the beginning of each shift. To take advantage of ML, in this paper, a DNN is integrated within our recent decomposition and coordination approach to learn to predict 'good-enough' solutions to part subproblems. To be able t

关键词： Deep neural networks

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Integrated preparation and manipulation of high‑dimensional flying structured photons

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eLight 2024年第1期4卷 185-192页

作者： Haoqi Zhao Yichi Zhang Zihe Gao Jieun Yim Shuang Wu Natalia MLitchinitser Li Ge Liang Feng Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Materials Science and Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering and Department of Physics Duke UniversityDurhamNC 27708USA Department of Engineering Science and Physics College of Staten IslandCUNYStaten IslandNY 10314USA The Graduate Center CUNYNew YorkNY 10016USA

The hope for a futuristic global quantum internet that provides robust and high-capacity quantum information transfer lies largely on qudits,the fundamental quantum information carriers prepared in high-dimensional superposition ***,preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks,which require precise and simultaneous maneuver of 2(N-1)parameters across multiple degrees of ***,using an integrated approach,we explore the synergy from two degrees of freedom of light,spatial mode and polarization,to generate,encode,and manipulate flying structured photons and their formed qudits in a four-dimensional Hilbert space with high quantum fidelity,intrinsically enabling enhanced noise resilience and higher quantum data *** four eigen spin–orbit modes of our qudits possess identical spatial–temporal characteristics in terms of intensity distribution and group velocity,thereby preserving long-haul coherence within the entirety of the quantum data transmission *** leveraging the bi-photon entanglement,which is well preserved in the integrated manipulation process,we present versatile spin–orbit cluster states in an extensive dimensional Hilbert *** cluster states hold the promise for quantum error correction which can further bolster the channel robustness in long-range quantum communication.

关键词： cluster thereby preparing

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Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing

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SN computer science 2024年第4期5卷 363页

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Amar, Oumkeltoum Chekroun, Mohamed Hasidi, Oussama Lachihab, Oussama Computer Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakech Morocco SEIA Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco R & amp D and Engineering Center Reminex Managem Group Marrakech Morocco Computer and Systems Engineering Laboratory (L2IS) Cadi Ayyad University Marrakech Morocco

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Data augmentation Industrial data analysis Machine learning Mineral processing

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PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid Clouds

arXiv

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arXiv 2024年

作者： Wang, Yuandou Kanwal, Neel Engan, Kjersti Rong, Chunming Grosso, Paola Zhao, Zhiming Multiscale Networked Systems University of Amsterdam Netherlands Department of Electrical Engineering and Computer Science University of Stavanger Norway

Running deep neural networks for large medical images is a resource-hungry and time-consuming task with centralized computing. Outsourcing such medical image processing tasks to hybrid clouds has benefits, such as a significant reduction of execution time and monetary cost. However, due to privacy concerns, it is still challenging to process sensitive medical images over clouds, which would hinder their deployment in many real-world applications. To overcome this, we first formulate the overall optimization objectives of the privacy-preserving distributed system model, i.e., minimizing the amount of information about the private data learned by the adversaries throughout the process, reducing the maximum execution time and cost under the user budget constraint. We propose a novel privacy-preserving and cost-effective method called PriCE to solve this multi-objective optimization problem. We performed extensive simulation experiments for artifact detection tasks on medical images using an ensemble of five deep convolutional neural network inferences as the workflow task. Experimental results show that PriCE successfully splits a wide range of input gigapixel medical images with graph-coloring-based strategies, yielding desired output utility and lowering the privacy risk, makespan, and monetary cost under user’s budget. © 2024, CC BY-NC-ND.

关键词： Multiobjective optimization

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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC systems

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 34-48页

作者： Hammadi, Ahmed Al Bariah, Lina Muhaidat, Sami Al-Qutayri, Mahmoud Sofotasios, Paschalis C. Debbah, Merouane Technology Innovation Institute Abu Dhabi United Arab Emirates Khalifa University Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaK1S 5B6 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere33014 Finland Khalifa University KU 6G Center Abu Dhabi United Arab Emirates

Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of both the overall SE and Jain's Fair Index, achieved at a fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance. © 2023 The Authors.

关键词： Spectrum efficiency

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A Multi-Lane Platooning Paradigm with ETSI DCC

A Multi-Lane Platooning Paradigm with ETSI DCC

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IEEE Vehicular Networking Conference (VNC)

作者： Huy Nguyen Suryansh Sharma R Venkatesha Prasad Falko Dressler Networked Systems Group TU Delft The Netherlands School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

Smart autonomous vehicles can cooperatively drive as platoons offering benefits like enhanced safety, traffic efficiency, and fuel conservation. While traditionally platoons have followed a single-lane, train-like structure they face challenges when scaling that include communication range limitations and lane-change difficulties. In this article, we propose a new paradigm of multi-lane platoons that spreads platoons across multiple lanes. We explore the characteristics of multi-lane platoons particularly focusing on communication parameters. Additionally, we propose a cross-layer mechanism to seamlessly integrate this concept within the existing communication standard, ETSI. Our work significantly enhances platoon communication performance in mixed traffic scenarios and we propose optimizations to improve its effectiveness.

关键词： Wireless communication Cross layer design Roads Focusing Regulation Safety Proposals

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

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computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the *** a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer ***, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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On tuning of ADRC with competing design indices:a quantitative study

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Control Theory and Technology 2023年第1期21卷 16-33页

作者： Yutao Zhao Yi Huang Zhiqiang Gao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Electrical Engineering and Computer Science Center for Advanced Control TechnologiesCleveland State UniversityClevelandOH 44115USA

Controller optimization has mostly been done by minimizing a certain single cost *** practice,however,engineers must contend with multiple and conflicting considerations,denoted as design indices(DIs)in this *** to account for such complexity and nuances is detrimental to the applications of any advanced control *** paper addresses this challenge heads on,in the context of active disturbance rejection controller(ADRC)and with four competing DIs:stability margins,tracking,disturbance rejection,and noise *** this end,the lower bound for the bandwidth of the extended state observer is first established for guaranteed closed-loop ***,one by one,the mathematical formula is meticulously derived,connecting each DI to the set of controller *** our best knowledge,this has not been done in the context of *** formulas allow engineers to see quantitatively how the change of each tuning parameter would impact all of the DIs,thus making the guesswork *** example is given to show how such analytical methods can help engineers quickly determine controller parameters in a practical scenario.

关键词： Uncertain systems Active disturbance rejection control(ADRC) Design indices(DIs) Extended state observer(ESO)

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Deep Convolutional Neural Network based Wheat Leaf Disease Detection in Precision Agriculture 5

Deep Convolutional Neural Network based Wheat Leaf Disease D...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Dey, Sreyashi Naidu, Roshan Dey, Om Subrato Balasundaram, A. Shaik, Ayesha Vellore Institute of Technology Chennai School of Computer Science and Engineering Chennai India Vellore Institute of Technology Chennai Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798331504403

Wheat is one of the fundamental crops that sustains billions of people globally. Unfortunately its production faces major challenges from numerous plant based diseases which can drastically decrease yields as well as the economic returns. There exist a variety of frameworks utilising computer vision and deep learning techniques which can be invaluable for numerous tasks aiding the stakeholders in agriculture and crop management by identifying diseased sections of *** methods to detect the disease are prone to human errors and such limitations underscore the need for automated and reliable disease detection systems. Also AI is likely to predict a future outbreak of any such disease by analysing the historical and geographical data. In this paper, a novel technique have been proposed whose novelty lies in the result that has been achieved using the proposed algorithm which when compared to two other deep learning models that are ResNet50 and VGG16 achieved a higher accuracy. The deep learning approach for wheat leaf disease detection implemented in this paper is performed using a convolutional neural network (CNN) architecture implemented in Python, OpenCV, NumPy, and TensorFlow alongside implementing some data augmentation leveraging the power of deep learning to extract complex features from wheat leaf images enabling accurate classification between healthy and diseased leaves. The aforementioned algorithm has been trained using a default Crop Disease Dataset extracted from a Github repository which has been able to achieve an accuracy of 88.53% with one full cycle of 30 epochs recording its final epoch with 91.04% accuracy and the final K Fold procedure resulting in 91.42% accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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