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7th International Conference on Optimization and Applications, ICOA 2021

作者： Lamjahdi, Aicha Bouloiz, Hafida Gallab, Maryam Ibn Zohr University Laboratory of Systems Engineering and Decision-making Support National School of Applied Sciences Agadir Morocco ENSMR MIS - LISTD Laboratory Department of Computer Science Rabat Morocco

ISBN: (纸本)9781665441032

The mining industry is the source of the production of many consumer goods and equipment. Therefore, the companies that control this activity play a significant role in the global economy. However, it is an important source of pollution of water, air, soil and ecosystems by metals. It exploits fossil and non-renewable resources, requiring large amounts of energy and sometimes water. Thus, implementing a sustainable approach through an overall performance integration in manufacturing practices, is become a necessity in this sector. Hence, this research presents a collection of key performance indicators for overall performance evaluation and management in mining industry. The proposed indicators were established from literature on the basis of the sustainability triple bottom line of economic, social, and environmental factors. A survey is conducted then, in a Moroccan mining industry to validate the adaptability of the chosen indicators with the industry activities and practices. Future work will further develop an evaluation tool that will help managers to assess the overall performance in mining industry and to take the appropriate actions to increase its competitiveness. © 2021 IEEE.

关键词： Decision making

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High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

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Journal of Bionic engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

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Improvement artificial bee colony meta-heuristic for solving manufacturing flow-shop scheduling problem under blocking constraint

Improvement artificial bee colony meta-heuristic for solving...

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International Conference on Intelligent systems: Theories and Applications (SITA)

作者： Omar Nejjarou Said Aqil Mohamed Lahby Laboratory of Applied Mathematics and Computer Sciences Higher Normal School Hassan II University of Casablanca Morocco Laboratory Artificial Intelligence and Complex Systems Engineering National Higher School Of Arts And Crafts Hassan II University of Casablanca Morocco

Manufacturing flow-shop scheduling problem is a highly attractive optimization challenge in operations research. When there is limited space in a workshop or capacity queue of machine is null, a new problem called manufacturing blocking flow-shop scheduling problem (MBFSP) is investigated. This optimization problem is solved by considering that the job must be blocked during scheduling when the next machine is not available. The address this challenge effectively, we present two novel algorithms: the Iterative Local Search (ILS) with neighborhood solutions, and the Artificial Bee Colony (ABC) algorithm inspired by nature’s meta-heuristics. These cutting-edge approaches are designed to address the problem from distinct and innovative perspectives, with a primary focus on optimizing the overall quality of the solution. By embracing these advanced techniques, we can effectively expand the scope of potential outcomes, opening up new avenues for achieving superior results. The target is to decrease the total weighted flow time (TWFT) , which represents the duration of completing all tasks. We provide a mathematical statement of the MBFSP optimization case and propose two solution meta-heuristics. In order to comprehensively assess the effectiveness of the proposed algorithms, we conducted a rigorous comparative study. This study involved an extensive set of instances, carefully curated to encompass a wide range of scenarios with varying numbers of jobs and machines. By testing the algorithms across diverse instances, we aimed to obtain a holistic understanding of their performance. Simulation results show that the ABC algorithm overcame the ILS algorithm, with a hit ratio 88% over the set of test instances and a minimum mean relative deviation in percent of 0.0154%.

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： Pradeep Kumar S CH Hussaian Basha C. Arun Prasath G. Amirthayogam Syed Ismail Abdul Lathif R. Leelavathi Computer Science Design Department Rajalakshmi Engineering College Thandalam Chennai Department of Electrical and Electronics Engineering SR University Warangal Department of ECE Mahendra Engineering College Namakkal Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods.

关键词： Training Costs Supply chain management Supply chains Smart contracts Key performance indicator Blockchains Stakeholders Internet of Things Optimization

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VQACL: A Novel Visual Question Answering Continual Learning Setting

VQACL: A Novel Visual Question Answering Continual Learning ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xi Zhang Feifei Zhang Changsheng Xu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Engineering Tianjin University of Technology Peng Cheng Laboratory

Research on continual learning has recently led to a variety of work in unimodal community, however little attention has been paid to multimodal tasks like visual question answering (VQA). In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. The former devotes to simulating the ever-changing multimodal datastream in real world and the latter aims at measuring models' generalizability for cognitive reasoning. Based on our VQACL, we perform in-depth evaluations of five well-established continual learning methods, and observe that they suffer from catastrophic forgetting and have weak generalizability. To address above issues, we propose a novel representation learning method, which leverages a sample-specific and a sample-invariant feature to learn representations that are both discriminative and generalizable for VQA. Furthermore, by respectively extracting such representation for visual and textual input, our method can explicitly disentangle the skill and concept. Extensive experimental results illustrate that our method significantly outperforms existing models, demonstrating the effectiveness and compositionality of the proposed approach. The code is available at https://***/zhangxi1997/VQACL.

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RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

arXiv

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arXiv 2025年

作者： Shi, Jianrui Zhao, Yong Cui, Zeyang Shen, Xiaoming Zeng, Minhang Liu, Xiaojie Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computing and Data Science The University of Hong Kong Hong Kong Pengcheng Laboratory Shenzhen China

Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural network (DNN)-based detection models, particularly when processing high-resolution video. Conventional strategies, such as input down-sampling and network up-scaling, often compromise detection accuracy for faster performance or lead to higher inference latency. To address these issues, this paper introduces RE-POSE, a Reinforcement Learning (RL)Driven Partitioning and Edge Offloading framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments. Our approach features an RL-Based Dynamic Clustering Algorithm (RL-DCA) that partitions video frames into non-uniform blocks based on object distribution and the computational characteristics of DNNs. Furthermore, a parallel edge offloading scheme is implemented to distribute these blocks across multiple edge servers for concurrent processing. Experimental evaluations show that RE-POSE significantly enhances detection accuracy and reduces inference latency, surpassing existing methods. © 2025, CC BY.

关键词： Computation offloading

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Label-free impedimetric immunosensor for point-of-care detection of COVID-19 antibodies

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Microsystems & Nanoengineering 2023年第1期9卷 161-176页

作者： Lian C.T.Shoute Gaser N.Abdelrasoul Yuhao Ma Pedro A.Duarte Cole Edwards Ran Zhuo Jie Zeng Yiwei Feng Carmen L.Charlton Jamil N.Kanji Shawn Babiuk Jie Chen Department of Electrical and Computer Engineering University of AlbertaEdmontonABT6G 2V4Canada Public Health Laboratory Alberta Precision LaboratoriesEdmontonABCanada Department of Laboratory Medicine and Pathology University of AlbertaEdmontonABT6G 2B7Canada Li Ka Shing Institute for Virology University of AlbertaEdmontonABCanada Division of Infectious Diseases Department of MedicineCumming School of MedicineUniversity of CalgaryCalgaryABCanada Department of Pathology&Laboratory Medicine Cumming School of MedicineUniversity of CalgaryCalgaryABCanada National Centre for Foreign Animal Disease Canadian Food Inspection AgencyWinnipegMBCanada Department of Immunology University of ManitobaWinnipegMBCanada Department of Biomedical Engineering University of AlbertaEdmontonABT6G 2R3Canada

The COVID-19 pandemic has posed enormous challenges for existing diagnostic tools to detect and monitor ***,there is a need to develop point-of-care(POC)devices to perform fast,accurate,and accessible diagnostic methods to detect infections and monitor immune *** most amenable to miniaturization and suitable for POC applications are biosensors based on electrochemical *** have developed an impedimetric immunosensor based on an interdigitated microelectrode array(IMA)to detect and monitor SARS-CoV-2 antibodies in human *** chemistry was applied to functionalize and covalently immobilize the spike protein(S-protein)of SARS-CoV-2 on the surface of the IMA to serve as the recognition layer and specifically bind anti-spike *** bound to the S-proteins in the recognition layer result in an increase in capacitance and a consequent change in the impedance of the *** impedimetric immunosensor is label-free and uses non-Faradaic impedance with low nonperturbing AC voltage for *** sensitivity of a capacitive immunosensor can be enhanced by simply tuning the ionic strength of the sample *** device exhibits an LOD of 0.4 BAU/ml,as determined from the standard curve using WHO IS for anti-SARS-CoV-2 immunoglobulins;this LOD is similar to the corresponding LODs reported for all validated and established commercial assays,which range from 0.41 to 4.81 BAU/*** proof-of-concept biosensor has been demonstrated to detect anti-spike antibodies in sera from patients infected with COVID-19 within 1 h.

关键词： antibodies impedance free

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Optimizing Transformer-based Models for Radiation Estimation at High Energy Accelerator Facility

Optimizing Transformer-based Models for Radiation Estimation...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Jackie Wei Hongfang Zhang Adam Stavola Bence Budavari Chiman Kwan Hongyi Wu Jiang Li Piedmont Hills High School San Jose CA Department of Electrical and Computer Engineering Old Dominion University Norfolk VA Thomas Jefferson National Accelerator Facility Newport News VA Applied Research LLC Rockville MD Force Projection Sector Johs Hopkins University Applied Physics Laboratory Laurel MD Department of Electrical & Computer Engineering University of Arizona Tuscon AZ

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

At high energy accelerator facilities, it is important to control radiation exposure to ensure the safety of the staff and public. In this paper, we optimized various Transformer-based deep learning models to efficiently estimate radiation exposure at the Thomas Jefferson National Accelerator Facility (JLab). We explored the effects of utilizing various model embedding types and training with either channel dependence or independence. The models were trained and optimized on radiation, energy, current, and beam loss accounting data collected from sensors at JLab. Our results demonstrated that the Transformer-based model utilizing temporal embedding and channel dependence achieved the best performance with an average R 2 score of 0.771. To the best of our knowledge, this work represents the first attempt to explore transformer deep learning models for radiation estimation at JLab and showed potential to further improve radiation estimation performance.

关键词： Deep learning Training Estimation Focusing Channel estimation Transformers Mobile communication Data models Sensors Safety

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An Adaptive Service Function Chains Mapping With Multi-Task Deep Reinforcement Learning

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IEEE Transactions on Network Science and engineering 2025年

作者： Wei, Wenting Wang, Qingyi Gu, Huaxi Zheng, Danyang Zhang, Ning Wu, Celimuge Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Southwest Jiaotong University School of Computing and Artificial Intelligence China University of Windsor Department of Electrical and Computer Engineering Canada University of Electro-Communications Meta-Networking Research Center Japan

Network function virtualization (NFV) facilitates different virtual network functions (VNF) to be dynamically chained in sequence to offer new services in a flexible, scalable, and cost-effective manner. Recent years have witnessed the increasing diverse service demands from the ever-increasing new applications, which has posed significant challenges to the efficient and sequential execution of VNFs to achieve specific objectives, especially under conditions of shared resources. To address these challenges, substantial efforts have been dedicated to enhancing resource utilization and minimizing the costs associated with service function chains (SFCs), while maintaining high quality of service. However, an overemphasis on cost reduction can sometimes result in network congestion, which ultimately degrades both network performance and service quality. Given the time-varying and unpredictable characteristics of SFCs, it is essential to leverage their temporal features, along with those of network states, for adaptive SFC mapping. In this paper, we introduce an adaptive online SFC mapping algorithm to reduce operational costs and alleviate network congestion. This is achieved through the adaptive allocation of VNFs and the control of traffic routing between them. Our approach incorporates multi-task deep reinforcement learning to manage the coexistence of multiple SFC requests with varying resource requirements. Specifically, we integrate a long short-term memory (LSTM) layer into our model to capture the temporal dynamics of network states and resource demands, thereby enabling more effective long-term planning. To address the issue of reward sparsity, we implement a hierarchical reward mechanism and reward shaping techniques. Experimental results demonstrate that our algorithm achieves near-optimal performance in optimizing service delay, bandwidth consumption, and network congestion, while also ensuring a high acceptance rate for user requests. © 2013 IEEE.

关键词： Network function virtualization

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Mitigating aberrations in metalenses: physical principles, design rules and fundamental limitations

Mitigating aberrations in metalenses: physical principles, d...

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Latin America Optics and Photonics Conference, LAOP 2022

作者： Martins, Augusto Li, Juntao Borges, Ben-Hur V. Krauss, Thomas F. Martins, Emiliano R. São Carlos School of Engineering Department of Electrical and Computer Engineering University of São Paulo São Carlos13566-590 Brazil State Key Laboratory of Optoelectronic Materials and Technologies School of Physics Sun Yat-Sen University Guangzhou510275 China School of Physics Engineering and Technology University of York YorkYO10 5DD United Kingdom

A double surface is required to mitigate aberrations of metalenses. Doublets are still not well understood, and designs rely on numerical optimization. We unveil the physical principles and limitations of doublets, le... 详细信息

ISBN: (纸本)9781957171135

关键词： Optimization

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