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arXiv 2025年

作者： Rozada, Sergio Paternain, Santiago Bazerque, Juan Andrés Marques, Antonio G. Dept. of Signal Theory and Communications King Juan Carlos University Madrid Spain Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Dept. of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States

In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order tensor structure. Recognizing that close-related tasks may require similar actions, our proposed method imposes a low-rank condition on this aggregated Q-tensor. The rationale behind this approach to multi-task learning is that the low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar, but inferring this information from a reduced amount of data simultaneously with the stochastic optimization of the Q-tensor. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments, first in a benchmark environment formed by a collection of inverted pendulums, and then into a practical scenario involving multiple wireless communication devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi-task learning

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A 0.77-pJ/bit 40-Gb/s/pin Single-Ended Hybrid DAC-Based Transmitter for Memory Interfaces

A 0.77-pJ/bit 40-Gb/s/pin Single-Ended Hybrid DAC-Based Tran...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Sanghyuk Seo Yong-un Jeong Jeakwang Yun Jaewook Kim Suhwan Kim Dept. of Electrical and Computer Engineering Seoul National University Seoul Korea Dept. of Semiconductor Systems Engineering Sejong University Seoul Korea Dept. of DRAM Design SK Hynix Icheon Korea

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

This paper presents a single-ended hybrid digital-to-analog converter (DAC)-based transmitter (TX) using four-level pulse-amplitude modulation (PAM-4) for memory interfaces. The proposed hybrid DAC composed of voltage mode (VM) driver unit segments and current mode (CM) driver unit segments reduces the total number of unit segments without increasing complexity in impedance matching. Therefore, the hybrid DAC significantly reduces area and the associated power consumption. Digital signal processing (DSP)-based feed-forward equalization (FFE) provides precise and flexible equalization. The proposed linearity enhancement technique compensates for additional non-linearity caused by short-length devices in the CM driver segments. The proposed TX operating at 40Gb/s/pin consumes 30.9mW with 1.0/0.9/0.15V supply voltages, achieving 0.77pJ/bit energy efficiency and a level separation mismatch ratio (RLM) of 0.989. The TX occupies an active area of 0.023mm 2 .

关键词： Power demand Transmitters Digital-analog conversion Impedance matching Memory management Linearity Voltage

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SIMHEURISTIC AND LEARNHEURISTIC FOR SOLVING STOCHASTIC AND/OR DYNAMIC PORTFOLIO OPTIMIZATION PROBLEMS 11

SIMHEURISTIC AND LEARNHEURISTIC FOR SOLVING STOCHASTIC AND/O...

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11th Operational Research Society Simulation Workshop, SW 2023

作者： Li, Yuda Polat, Onur Juan, Angel A. Calvet, Laura Kizys, Renatas Dept. of Computer Science Universitat Oberta de Catalunya Barcelona Spain Dept. of Applied Statistics and Operations Research Universitat Politcnica de Valncia Alcoy Spain Dept. of Public Finance Bilecik Şeyh Edebali University Bilecik Turkey Dept. of Telecomm. and Systems Engineering Autonomous University of Barcelona Sabadell Spain Dept. of Banking and Finance Southampton Business School University of Southampton Southampton United Kingdom

ISBN: (纸本)9781713870951

Constructing portfolio by proper asset selection to maximize return and minimize risk has been considered an essential task for investment activities. Rich portfolio optimizations with realistic constraints are NP-hard problems and are commonly solved using metaheuristics. However, financial markets are characterized by their high volatility and uncertainty, and metaheuristics do not fully account for these random and/or dynamic components, which renders them unrealistic in the presence of heightened uncertainty and dynamism in financial markets. Therefore, this paper proposes a simulationoptimization approach specifically, a simheuristic algorithm to deal with the stochastic version of the problem and a learnheuristic algorithm for solving the dynamic version of the problem. Computational experiments are performed on a benchmark instance to illustrate the advantages of the proposed methodologies and analyze how the solutions change in response to a different degree of stochasticity, dynamism, and minimum required return. © SW *** rights reserved

关键词： Financial markets

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Attack Tree Refinements Analysis and Verification by applying Coloured Petri Nets 48

Attack Tree Refinements Analysis and Verification by applyin...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Pasandideh, Shabnam Pereira, Pedro Gomes, Luis Nova School of Science and Technology-Nova University Lisbon Center of Technology and Systems-UNINOVA Dept. Electrical and Computer Engineering Caparica Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Recently, Graphical Security Models (GrSMs) became widely used for security analysis. The basic formalism called Attack Tree (AT) has been augmented with new attributes covering defence, response, and countermeasure aspects to support security modelling and analysis in vulnerable systems. Although the models have strength in visualising and analysing small attack-defence scenarios, these suffer from lack of scalability when increasing nodes and adaptability with other refinement models to show the dynamic nature and state of systems in interest. In this work, Coloured Petri net (CPN) is used to fulfil the mentioned shortcomings in GrSMs (specifically Tree-based models). It is applied for evaluating each component's interactions, the impact of threats as well as defence systems to mitigate those threats. For that end and pointing out the CPN adaptability with GrSMs, a set of mapping rules are proposed allowing translation of ATs extension into CPN and their analysis. The quantitative analysis aspect is addressed in this work by introducing computing transition. We validate our proposed approach by applying it in an example of SCADA systems cyber-security analysis. © 2022 IEEE.

关键词： Cybersecurity

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

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International Conference on Industrial and Information systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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Decentralized Drug Transaction Tracking: A Novel Hybrid Blockchain-IPFS Platform

Decentralized Drug Transaction Tracking: A Novel Hybrid Bloc...

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Public Safety Technology (WFPST), IEEE World Forum on

作者： Mouhamadou Mouctar Gueye Wael Jaafar Fatoumata Baldé Dept. Computer Science Université Alioune Diop Bambey Sénégal Dept. Software and IT Engineering École de Technologie Superieure (ÉTS) Montreal Canada

ISBN: (数字)9798350329148

ISBN: (纸本)9798350329155

Counterfeit drugs are a serious global problem that poses threats to public health. It occurs when falsified or counterfeit drugs are intentionally manufactured and introduced into the pharmaceutical supply chain, masquerading as genuine products. This is due to the complexity of the supply chain that involves independent entities. As a result, a drug transits through several stages before reaching the patient. Current drug management systems are centralized, thus creating data transparency and authenticity risks within the supply chain. To bypass this issue, we propose in this paper a novel hybrid and distributed drug transaction tracking platform that leverages a combination of the Interplanetary File System (IPFS) protocol and Ethereum blockchain. Relying on IPFS guarantees the integrity of drug traceability data, while the blockchain ensures the authenticity of this data. We developed our platform and, through experiments, we prove that our method is more efficient, in terms of process costs, than the conventional blockchain-based drug tracking system.

关键词： Drugs Costs Supply chains Smart contracts Public security Blockchains InterPlanetary File System

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Parameter fault estimation in distributed heating/cooling systems 7

Parameter fault estimation in distributed heating/cooling sy...

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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Kurniawan, Wijaya Hangos, Katalin M. Marton, Lorinc Faculty of Computer Science University of Brawijaya Malang Indonesia Dept. of Electrical Engineering and Information Systems University of Pannonia Veszprem Hungary Institute for Computer Science and Control Budapest Hungary Dept. of Electrical Engineering Sapientia Hungarian University of Transylvania Targu Mures Romania

ISBN: (纸本)9781450397117

In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to the heat transfer between the system and the external environment. First, based on the bi-linear dynamic state space model of the system in the presence of a fault, structural observability analysis using Signed Directed Graph (SDG) has been performed to investigate the sensor placement problem. Then, a nonlinear observer with a parameter adaptation algorithm was proposed for fault estimation. The simulation results show that it can successfully detect and estimate the fault. Fault localization along the length of the element has also been attempted, but it has been found that the localization cannot be performed using practically changeable input variables. Frequency domain analysis is presented to discuss this phenomenon. © 2022 ACM.

关键词： Parameter estimation

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Navigating Green Transportation in Urban Agglomerations: A Refined Approach for Carbon Emissions Assessment 24

Navigating Green Transportation in Urban Agglomerations: A R...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chen, Boyou Wang, Yuanqing Abolarin, Emmanuel Zhang, Shuangyan Liu, Xihao Guo, Hanlin Ren, Baolong Dept. of Built Environment Bartlett School of Planning Univ. College London London United Kingdom Dept. of Transportation Engineering College of Transportation Engineering Chang’an Univ. Xi’an China Dept. of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science Univ. of Michigan–Dearborn Dearborn United States School of Civil and Transportation Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China Sino-French Engineer School Beihang Univ. Beijing China Dept. of Instrument Science and Technology College of Mechanical Engineering Nanjing Univ. of Science and Technology Nanjing China Sino-Danish College Univ. of Chinese Academy of Sciences Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Rapid urbanization has expanded urban agglomerations, escalating carbon emissions from increased transportation needs. These agglomerations have distinct multi-modal transportation systems. Analyzing their carbon footprints and eco-friendly transportation evolution is essential for sustainability. However, research in this domain is limited. This research refines emissions estimation methods, introducing models tailored for urban agglomerations. An evaluation framework for green transportation detailing a computational method for future policies is proposed. The research highlights primary transportation structures and energy consumption. Using a "bottom-up" approach, carbon estimation models for inter- and intra-city transportation are drafted. Assessment indicators are redefined by merging the DPSIR model with urban metrics, forming five main modules with 19 indicators. Using the Regression Random Forest method and focusing on the Yangtze River Delta in China, the link between annual emissions and these indicators is examined, providing a theoretical analysis approach for general cases. © ASCE.

关键词： Carbon sequestration

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Invertible Prediction Model for Si3N4 Wet Etching Using DHF

Invertible Prediction Model for Si3N4 Wet Etching Using DHF

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Koki Shibata Takashi Ota Koji Ando Naoko Misawa Chihiro Matsui Ken Takeuchi Dept. of Electrical Engineering and Information Systems The University of Tokyo Japan Application Software Engineering Operations SCREEN Semiconductor Solutions Co. Ltd. Japan

ISBN: (数字)9798331504168

ISBN: (纸本)9798331504175

Optimizing semiconductor fabrication is challenging due to numerous recipe parameters. This study explores various nozzle operations and wafer rotation speeds to enable selective Si 3 N 4 etching using dilute hydrogen fluoride (DHF). By using measured etching rate (ER), the invertible model dependent on the nozzle scanning period is constructed, enabling determination of etching recipes for the desired ER results. The model is sufficiently accurate for complex scanning patterns, providing a practical tool for process optimization.

关键词： Semiconductor device modeling Semiconductor device measurement Wet etching Computational modeling Predictive models Semiconductor device manufacture Throughput Silicon Data models Optimization

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A Web Based Four-Tier Architecture using Reduced Feature Based Neural Network Approach for Prediction of Student Performance 2

A Web Based Four-Tier Architecture using Reduced Feature Bas...

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2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021

作者： Hossen, Anwar Alamgir, Rakib Bin Alam, Arman Ul Siddika, Fatema Hossain, Shah Fahad Arman, Md. Shohel Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415767

Enhancing student's performance is a significant part of developing quality education in any educational institute. It is very difficult to get promising student performance without student categorization according to their academic performance as there are different standardized students. In this paper, our aim is to determine the performance of the students. For this purpose, a survey has been conducted on students in our university in order to collect data and to analyze and predict the student category based on their performance. Apart from this, another purpose of this study is to examine the effect of the reduced features on the classification model using state-of-art machine learning algorithms. Here, we propose a workflow of web-based four-tier architecture for the student performance prediction that will define the student's category in order to help them exactly pinpoint their learning capabilities. Hence, we used multiple supervised learning-based machine learning techniques for the prediction of student performance. Each of the student category categorized by considering on the top features. The analysis results indicate that we got the highest performance that is 88.00% by using the Artificial Neural Network (ANN) among the classifiers by showing its superiority to the existing model. © 2021 IEEE.

关键词： Students

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