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A Constructive Heuristic for the Uniform Capacitated Vertex k-center Problem

ACM Journal of Experimental Algorithmics 2023年第2期28卷 1-26页

作者： Cornejo-Acosta, José Alejandro García-Díaz, Jesús Pérez-Sansalvador, Julio César Ríos-Mercado, Roger Z. Pomares-Hernández, Saúl Eduardo Instituto Nacional de Astrofísica Óptica y Electrónica Computer Science Department Puebla72840 Mexico Tecnológico Nacional de México/ITS de Purísima Del Rincón Computer Engineering Department Guanajuato36425 Mexico Consejo Nacional de Humanidades Ciencias y Tecnologías Mexico City03940 Mexico Universidad Autónoma de Nuevo León Graduate Program in Systems Engineering San Nicolás de los GarzaNL 66455 Mexico LAAS-CNRS Université de Toulouse France INSA ToulouseF31400 France

The uniform capacitated vertex k-center problem is an -hard combinatorial optimization problem that models real situations where k centers can only attend a maximum number of customers, and the travel time or distance from the customers to their assigned center has to be minimized. This article introduces a polynomial-time constructive heuristic algorithm that exploits the relationship between this problem and the minimum capacitated dominating set problem. The proposed heuristic is based on the one-hop farthest-first heuristic that has proven effective for the uncapacitated version of the problem. We carried out different empirical evaluations of the proposed heuristic, including an analysis of the effect of a parallel implementation of the algorithm, which significantly improved the running time for relatively large instances. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Travel time

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Optimizing One-Stage Object Detection for URLLC: A Two-Stage Pipeline with Oculi SPU

Optimizing One-Stage Object Detection for URLLC: A Two-Stage...

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Information Technology (ACIT)

作者： Adam Bou Moghlebey Abdallah Moubayed MohammadNoor Injadat Electrical & Computer Engineering Department Lebanese American University Byblos Lebanon Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Pedestrian-related accidents account for an estimated 20-25% of the approximately 1.19 million road fatalities occurring annually, highlighting the urgent need for enhanced detection systems in surveillance and smart transportation to mitigate such incidents. The escalating demand for Ultra-Reliable Low-Latency Communications (URLLC) in modern surveillance and transportation systems has become a solution to that problem but highlighted significant challenges in reducing inference times of machine learning models, particularly on constrained hardware. The maximum permissible latency for most vehicular applications ranges from 6 to 20 milliseconds based on the application. When tested on an 8GB RAM setup using the NTU, LLVIP, and VIRAT datasets, YOLOv8n achieved a minimum inference time of approximately 200 milliseconds per frame, making it unsuitable for URLLC applications despite 8GB RAM being an adequate hardware setup for edge devices. This research introduces a new two-stage pipeline that integrates Oculi’s Sensing and Processing Unit (SPU) simulation with the YOLOv8n one-stage object detection model to enhance pedestrian detection, thereby addressing these challenges. By leveraging Regions of Interest (ROIs) generated by Oculi’s SPU, our approach optimizes the input data for the YOLO model, substantially reducing computational overhead and thus making it suitable for URLLC applications. This integration can lead to a reduction in inference time by up to 93% in the tested frames when being compared to YOLOv8n pre-trained model.

关键词： YOLO Pedestrians Computational modeling Surveillance Pipelines Random access memory Ultra reliable low latency communication Hardware Smart transportation Sensors

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A New Optimal Centralized Demand Side Management for a Campus Smart Microgrid

A New Optimal Centralized Demand Side Management for a Campu...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Mohamed A. Hassan Abdelfattah A. Eladl Bishoy E. Sedhom Mohammed A. Saeed Interdisciplinary Research Center for Renewable Energy and Power Systems (IRCREPS) Research Institute King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department Faculty of Engineering Mansoura University El-Mansoura Egypt

Based on demand response and energy theft detection, a novel optimal demand side management (DSM) is proposed for a smart microgrid (SMG). A campus SMG containing photovoltaic (PV), wind turbine, diesel station, smart meters, inverters, batteries, sensors, domestic loads, and central} {DSM unit is built in MATLAB. The SMG elements are communicated using Wi-Fi protocol and internet of things (IoT) technology. The proposed DSM is optimally designed using Grey Wolf Optimization (GWO) to minimize the customer’s electricity bill, maximize the user comfort, and obtain the optimal load patterns in the hypothetical campus SMGs. It inspires consumers to alter their daily consumption patterns over a variable tariff according to real-time pricing (RTP), time of use (ToU), and critical peak pricing (CPP) methods. The results proved the proposed technique effectiveness for reducing the user’s electricity bill and conserving utility resources through energy theft detection. Finally, the proposed GWO technique is compared with firefly algorithm (FFA) and bacteria foraging algorithm (BFA) to verify its superiority.

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An adaptable robust optimization model for a dual-channel closed-loop supply chain considering cost and demand uncertainty

An adaptable robust optimization model for a dual-channel cl...

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作者： Kaoud, Essam Abdel-Aal, Mohammad A.M. Sakaguchi, Tatsuhiko Uchiyama, Naoki Department of Mechanical Engineering Toyohashi University of Technology 1-1 Hibarigaoka Tempaku-cho Aichi Toyohashi441-8580 Japan Mechanical Design and Production Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Industrial and Systems Engineering Department King Fahd University of Petroleum and Minerals P.O. Box 5063 Dhahran31261 Saudi Arabia Interdisciplinary Research Center of Smart Mobility and Logistics King Fahd University of Petroleum and Minerals P.O. Box 5067 Dhahran31261 Saudi Arabia Department of Informatics Faculty of Engineering Kindai University Higashi-Hiroshima739-2116 Japan

Most real-life optimization problems are subjected to uncertainty, and the robust optimization approach is one of the efficient techniques to deal with uncertain optimization problems. Supply chain optimization problems are highly sensitive to data perturbations mostly due to inappropriate estimation of the problems’ parameters and the highly dynamic environment. In this study, we propose an adaptable robust optimization model for the dual-channel closed-loop supply chain (CLSC) and present two counterpart models;the first model is a mixed integer linear programming (MILP) model based on the adjustable box uncertainty set, while the second robust model is a mixed integer nonlinear programming (MINLP) model based on the adjustable ellipsoidal uncertainty set. We provide a novel approach for considering multiple uncertainty sets in the objective function, that provide flexibility and control risk based on the preferences of the decision-makers. This study aims at minimizing the total cost of the dual-channel CLSC network considering uncertain purchasing, transportation, fixed, and processes costs, in addition to uncertain customer demand. Intensive computational experiments are conducted on the two robust models using GAMS software. Robust solutions are obtained and sensitivity analysis is conducted on both models considering 10% perturbation of the uncertain parameters around their nominal values as well as probability guarantee for not violating the constraints. © 2022 The Japan Society of Mechanical Engineers.

关键词： Supply chains

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Guest editorial: Latin American perspectives on sustainability in higher education

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International Journal of Sustainability in Higher Education 2023年第2期24卷 233-234页

作者： Izabela Simon Rampasso Rosley Anholon Osvaldo Luiz Gonçalves Quelhas Luis Velazquez Claudia Mac-Lean Departamento de Ingeniería Industrial Universidad Católica del Norte Antofagasta Chile School of Mechanical Engineering State University of Campinas Campinas Brazil Master Program in Management Systems and Doctoral Program in Sustainable Management Systems Federal Fluminense University Niterói Brazil Department of Industrial Engineering Division of Engineering University of Sonora Hermosillo Mexico GAIA Antarctic Research Center Universidad de Magallanes Punta Arenas Chile

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Resonant Terahertz Bolometric Detectors Using Plasmonic Response in Graphene-Channel FETs with b-P/h-BN Gate Barrier Layers

Resonant Terahertz Bolometric Detectors Using Plasmonic Resp...

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IEEE International Conference on Microwaves, Communications, Antennas and Electronic systems, COMCAS

作者： Maxim Ryzhii Victor Ryzhii Taiichi Otsuji Chao Tang Vladimir Mitin Michael S. Shur Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan Research Institute of Electrical Communication Tohoku University Sendai Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai Japan Department of Electrical Engineering University at Buffalo SUNY Buffalo New York USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York USA

ISBN: (数字)9798350348187

ISBN: (纸本)9798350348194

We propose and model the terahertz bolometric detectors based on the graphene-channel field-effect transistors (GC-FETs) with the black-Phosphorus/hexagonal-Boron Nitride (b-P/h-BN) barrier layers. The GC is encapsulated in h-BN with a narrow b-P window (collector window) in the gate layer. The proposed device structure enables decreased electron scattering due to the elevated quality of the interfaces and promotes a more pronounced plasmonic response of the two-dimensional electron system in the GC. This improves the THz detector performance at the plasmonic resonances. Narrowing the collector window diminishes the Peltier effect and improves the detector characteristics.

关键词： Microwave antennas Performance evaluation Scattering Detectors Terahertz radiation Logic gates Plasmons

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Towards High Generalization Performance on Electrocardiogram Classification

Towards High Generalization Performance on Electrocardiogram...

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2021 Computing in Cardiology, CinC 2021

作者： Han, Hyeongrok Park, Seongjae Min, Seonwoo Choi, Hyun-Soo Kim, Eunji Kim, Hyunki Park, Sangha Kim, Jinkook Park, Junsang An, Junho Lee, Kwanglo Jeong, Wonsun Chon, Sangil Ha, Kwonwoo Han, Myungkyu Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of HUINNO Co. Ltd Seoul Korea Republic of LG AI Research Seoul Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of Department of Biological Sciences Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665479165

Recently, many electrocardiogram (ECG) classification algorithms using deep learning have been proposed. The characteristics of ECG vary from dataset to dataset for various reasons (i.e., hospital, race, etc.). Therefore, it is important for a model to have high generalization performance consistently over all datasets. In this paper, as part of the PhysioNet / Computing in Cardiology Challenge 2021, we present a model developed to classify cardiac abnormalities from 12 lead and reduced-lead ECGs. In particular, to upgrade our previous model for improving generalization performance, we newly adopt constant-weighted cross-entropy loss, additional features, Mixup augmentation, and squeeze/excitation block, OneCycle learning rate scheduler, which are selected via evaluation of generalization performance using leave-one-dataset-out cross-validation setting. With the present model, our DSAILSNU team has received challenge scores of 0.55, 0.58, 0.58, 0.57 and 0.57 (ranked 2nd, 1st, 1st, 2nd, 2nd out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set, respectively. The present model achieves higher generalization performance over all versions of the hidden test set than the model submitted last year. © 2021 Creative Commons.

关键词： Deep learning

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Complexity Analysis based on Parietal Fuzzy Entropy to Facilitate ADHD Diagnosis in Young Children 46

Complexity Analysis based on Parietal Fuzzy Entropy to Facil...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Huang, I-Wen Jheng, Yu-Ci Chen, I-Chun Ko, Li-Wei National Yang Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300040 Taiwan National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu300040 Taiwan National Yang Ming Chiao Tung University International Program in Interdisciplinary Neuroscience College of Biological Science and Technology Hsinchu300040 Taiwan Ton-Yen General Hospital Department of Physical Medicine and Rehabilitation Hsinchu302048 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung807378 Taiwan

ISBN: (纸本)9798350371499

Attention deficit hyperactivity disorder (ADHD) is the most common condition affecting the development of neurons in children. Therefore, early and accurate diagnosis of ADHD in young children is of paramount importance. In this study, the 8-channel wireless wearable EEG measurement device was employed to record EEG data from 30 children diagnosed with ADHD and 30 typical development (TD) young children aged 4-7 years. The data was collected both during rest and while the children performed a Kiddie Continuous Performance Test (K-CPT). We extract relative power spectral density (PSD) unaffected by factors like skull resistance and thickness. Additionally, a range of complex entropy values based on the time domain were extracted. These included sample entropy (SaEn), permutation entropy (PeEn), singular value decomposition entropy (SvdEn), and fuzzy entropy (FuEn). We compare the performance of k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and XGBoost, and utilized the sequential forward selection (SFS) feature selection method in the wrapper approach. Through this process, the study identified the most effective EEG data segments and feature subsets. The findings indicated that using a combination of resting and K-CPT EEG data yielded greater discriminability. Notably, the study found that extracting beta power from the right occipital lobe along with fuzzy entropy from the parietal lobe resulted in optimal accuracy of 90% in distinguishing between children with ADHD and TD children. These outcomes highlight the potential of relative PSD and complexity metrics to support the clinical diagnosis of early ADHD. Furthermore, these metrics may contain unique neurobiomarkers that could be valuable for devising early intervention strategies. © 2024 IEEE.

关键词： Diseases

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On the Enhanced Surveillance Methods for High-quality Processes

On the Enhanced Surveillance Methods for High-quality Proces...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Mahmood, Tahir King Fahd University of Petroleum and Minerals College of Computing and Mathematics Industrial and Systems Engineering Department Dhahran3126 Saudi Arabia King Fahd University of Petroleum and Minerals Interdisciplinary Research Centre for Smart Mobility and Logistics Dhahran31261 Saudi Arabia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Nowadays, processes are equipped with advanced tools;therefore, they produce near zero-defect items and are termed as high-quality processes. The high-quality data often follows the Zero-Inflated Poisson or Negative Binomial (ZIP or ZINB) distributions. In literature, most surveillance methods are designed to monitor ZIP and ZINB distributed quality characteristics. However, some covariates are also available along with the count-based quality characteristics of a process. Therefore, this study is intended to propose moving average (MA) and double MA (DMA) based surveillance methods designed on the ZIP and ZINB residuals (i.e., Pearson). A simulation-based study is carried out to evaluate the performance of proposed methods and their comparative results with an existing method using run-length properties. The findings reveal that the proposed MA and DMA methods outperformed the existing Shewhart chart. Moreover, a real-life example is presented, which supports the simulated results. © 2022 IEEE.

关键词： Statistical process control

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INTERPRETABLE LANGUAGE MODELING VIA INDUCTION-HEAD NGRAM MODELS

arXiv

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arXiv 2024年

作者： Kim, Eunji Mantena, Sriya Yang, Weiwei Singh, Chandan Yoon, Sungroh Gao, Jianfeng Microsoft Research United States Department of Electrical and Computer Engineering Seoul National University Korea Republic of Stanford University United States Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Recent large language models (LLMs) have excelled across a wide range of tasks, but their use in high-stakes and compute-limited settings has intensified the demand for interpretability and efficiency. We address this need by proposing Induction-head ngram models (Induction-Gram), a method that builds an efficient, interpretable LM by bolstering modern ngram models with a hand-engineered "induction head". This induction head uses a custom neural similarity metric to efficiently search the model’s input context for potential next-word completions. This process enables Induction-Gram to provide ngram-level grounding for each generated token. Moreover, experiments show that this simple method significantly improves next-word prediction over baseline interpretable models (up to 26%p) and can be used to speed up LLM inference for large models through speculative decoding. We further study Induction-Gram in a natural-language neuroscience setting, where the goal is to predict the next fMRI response in a sequence. It again provides a significant improvement over interpretable models (20% relative increase in the correlation of predicted fMRI responses), potentially enabling deeper scientific investigation of language selectivity in the brain. The code is available at https://***/ejkim47/induction-gram. © 2024, CC BY-NC-SA.

关键词： Digital elevation model

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