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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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Meta-Heuristics for Solving Curve Fitting Problems in Optical-Diffraction Based Image Depth Reconstruction 3

Meta-Heuristics for Solving Curve Fitting Problems in Optica...

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3rd IEEE International Conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Zhu, Qianyao Gao, Kaizhou Liu, Yongjun Macau University of Science and Technology Institute of Systems Engineering China Changshu Institute of Technology Faculty of Computer Science and Engineering Suzhou China

ISBN: (纸本)9798350337952

This paper investigates use meta-heuristic to solve curve fitting problems in Optical-Diffraction Based Image Depth Reconstruction. We aim to accurately establish a relationship curve between object distance and diffraction blur kernel through discrete data points. A mathematical model is first developed to formulate the concerned problems. Then, four meta-heuristic algorithms, particle swarm optimization, artificial bee colony, genetic algorithm and differential evolution, are employed and improved according to the problem's feature. Finally, experiments are executed on a data set. A comparison of the similarity of the least squares method verifies the feasibility of meta-heuristics for solving curve fitting problems. Among the four meta-heuristics, differential evolution algorithm has the best competitiveness. © 2023 IEEE.

关键词： Image reconstruction

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

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Computers, Materials & Continua 2023年第1期74卷 2097-2113页

作者： Hamayun Khan Irfan Ud Din Arshad Ali Mohammad Husain Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor *** performance measurement of computational systems is changing with the advancement in *** to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded *** operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the *** this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task *** migration avoids peak temperature values in the multicore *** utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature *** technique switches the core bymigrating such task to a core that has less temperature and is in a low power *** proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature *** effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and *** simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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Conception of the C19H2RM-2 prototype based on an embedded camera module for driver state monitoring 3

Conception of the C19H2RM-2 prototype based on an embedded c...

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3rd International Conference on Embedded systems and Artificial Intelligence, ESAI 2024

作者： Boussaki, Hoda El Latif, Rachid Saddik, Amine LISTI ENSA Laboratory of Systems Engineering and Information Technology Agadir Morocco Faculty of Applied Sciences Ait Melloul Morocco

ISBN: (纸本)9798331522056

This paper presents the C16H2RM-2 prototype, designed using AutoCAD and representing a cutting-edge solution for monitoring driver health metrics, including heart rate, breathing rate, and signs of fatigue. The protoptype was based on a first version C16H2RM that uses mainly the raspberry pi 4 board. The system integrates photoplethysmography (PPG) sensors, which are strategically positioned to accurately measure physiological parameters. The AutoCAD-designed enclosure, crafted from durable and transparent plexiglass, houses the Jetson Nano plateform, the fan, the LED and the uninterruptible power supply module (UPS). The prototype enhances both the accuracy of data collection and the ease of use, aiming to improve driver safety and reduce fatigue-related incidents on the road. © 2024 IEEE.

关键词： Uninterruptible power systems

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

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Computers, Materials & Continua 2023年第1期74卷 965-981页

作者： Hamayun Khan Irfan Ud din Arshad Ali Sami Alshmrany Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor *** the advancement of technology,the performance management of central processing unit(CPU)is *** densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip *** energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life *** this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power *** selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)*** in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more *** proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple *** performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for *** are running and accurately scheduled to make an energy-efficient

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Optimizing Energy Efficiency in Smart Buildings Through Intelligent HVAC Control systems 8

Optimizing Energy Efficiency in Smart Buildings Through Inte...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Cengiz, Korhan Biruni University Department of Electrical-Electronics Engineering Istanbul Turkey University of Jordan Faculty of Information Technology and Systems Jordan

ISBN: (纸本)9798350354423

This work explores the optimization of HVAC systems in smart buildings through the integration of advanced methodologies, including machine learning algorithms. Specif-ically, we investigate the application of neural networks, and genetic algorithms to enhance energy efficiency and user comfort. Through a comprehensive experimental evaluation, we validated of our proposed approach over existing optimization strategies, as evidenced by improvements in predictive accuracy, user satisfaction, and energy consumption reduction. Our findings highlight the efficacy of leveraging advanced optimization techniques in addressing the complex challenges of HVAC control in modern building infrastructures. © 2024 IEEE.

关键词： Smart homes

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Assessment of Water Volume Allocation in Network Water Supply Tanks Using Hulbert Method 18th

Assessment of Water Volume Allocation in Network Water Suppl...

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18th International Conference Current Issues of Civil and Environmental engineering Lviv - Košice – Rzeszów, CEE 2023

作者： Rak, Janusz R. Boryczko, Krzysztof Stręk, Magdalena The Faculty of Civil and Environmental Engineering and Architecture Department of Water Supply and Sewage Systems Rzeszow University of Technology Rzeszow Poland

ISBN: (纸本)9783031449543

The collective water supply system (CWSS) belongs to the critical infrastructure. This requires the appropriate level of safety of the water supply system functioning. Diversification of the number of water intakes and allocation of water volume related to the number of network tanks in the distribution system is important to achieve this. Water in water network tanks is stored when the amount of water supplied by the high pressure pumping station exceeds the demand for water, and it is used when there is an increased demand for water in the supplied area. This paper presents a method to evaluate water allocation in network water tanks according to the Stuart H. Hulbert index. The calculation of allocation ratios was presented for 11 selected cities in south-eastern Poland. A scale to assess the degree of water volume allocation in network water tanks were developed. On this basis, an assessment of the degree of allocation of water volume in network water tanks was made in the examined cities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Water tanks

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