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Design of a large-range rotary microgripper with freeform geometries using a genetic algorithm

Microsystems & Nanoengineering 2022年第1期8卷 59-72页

作者： Chen Wang Yuan Wang Weidong Fang Xiaoxiao Song Aojie Quan Michiel Gidts Hemin Zhang Huafeng Liu Jian Bai Sina Sadeghpour Michael Kraft College of Optical Science and Engineering Zhejiang UniversityHangzhouChina Department of Electrical Engineering and Computer Science University of LiegeLiegeBelgium ESAT-MNS University of LeuvenLeuvenBelgium PGMF and School of Physics Huazhong University of Science and TechnologyWuhanChina

This paper describes a novel electrostatically actuated microgripper with freeform geometries designed by a genetic *** new semiautomated design methodology is capable of designing near-optimal MEMS devices that are robust to fabrication *** use of freeform geometries designed by a genetic algorithm significantly improves the performance of the *** experiment shows that the designed microgripper has a large displacement(91.5μm)with a low actuation voltage(47.5 V),which agrees well with the *** microgripper has a large actuation displacement and can handle micro-objects with a size from 10 to 100μm.A grasping experiment on human hair with a diameter of 77μm was performed to prove the functionality of the *** result confirmed the superior performance of the new design methodology enabling freeform *** design method can also be extended to the design of many other MEMS devices.

关键词： handle algorithm. algorithm

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Using Artificial Intelligence Techniques in Law Enforcement: A Survey 25

Using Artificial Intelligence Techniques in Law Enforcement:...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Shaikh, Ala'a Alalfi, Ashraf Hasan Al-Nsour, Esam Y. A. Afaneh, Suha Samara, Ghassan Alqawasmi, Khaled Al-Milli, Nabeel Khouj, Mohammed Salem, Amer Abu Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Integration of Artificial Intelligence (AI) with law enforcement has improved humane safety and crime prevention. It offers numerous applications, including predictive policing, facial recognition systems, natural language processing, and algorithmic decision-making tools, which can improve crime prevention and resolution. However, the employment of AI creates ethical problems concerning privacy, bias, and accountability. Addressing these challenges is vital to guarantee AI applications conform with legal and ethical standards, encouraging public trust. This survey paper reviews some of the tools and techniques of AI used in law enforcement (Criminal Identification, Virtual Crime Scene Reconstruction, Automated License Plate Recognition (ALPR), Natural Language Processing (NLP), Gunshot Detection Systems, Data-driven Policing Strategies), and explains the challenges, and mentions some opinions regarding this use. © 2024 IEEE.

关键词： c Artificial Intelligence law enforcement

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Hybrid Local Cache Model for Enhancing Performance and Memory Footprint of Mobile Heap Allocator 12

Hybrid Local Cache Model for Enhancing Performance and Memor...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Lee, Kyung-Seok Lee, Teahyung Eom, Young Ik Sungkyunkwan University Department of Digital Media and Communications Engineering Suwon Korea Republic of Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University College of Computing and Informatics Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350340181

The representative mobile heap allocator that has been used in Android system, called SCUDO, supports two local cache models: the shared model and the exclusive model. However, neither the shared model nor the exclusive model cannot satisfy both memory usage and performance requirements simultaneously since mobile applications have diverse memory allocation/free patterns and the number of threads varies from single to several tens. This paper suggests a hybrid local cache model that aims to adaptively meet memory usage and performance goals. Our evaluation results show that our hybrid local cache model outperforms the shared model by up to 22% on allocation time, while preserving the memory usage. © 2023 IEEE.

关键词： Android (operating system)

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Enhancing WSN Performance with DRL-CaCC: An Adaptive Cache-Aware Congestion Control Mechanism 15

Enhancing WSN Performance with DRL-CaCC: An Adaptive Cache-A...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Alipio, Melchizedek Bures, Miroslav De La Salle University Department of Electronics and Computer Engineering 2401 Taft Avenue Manila Malate1004 Philippines Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science Karlovo namesti 13 Prague 212135 Czech Republic

ISBN: (纸本)9798350364637

Traditionally, rule-based is the predominant approach in Wireless Sensor Networks (WSN) communication protocols for managing caching and congestion control that relies on explicitly defined, unchanging models. A growing trend has recently been toward incorporating adaptive methods that leverage machine learning (ML) and deep learning (DL) during network congestion conditions. However, the design of an adaptive cache-aware congestion control mechanism in WSN using Deep Reinforcement Learning (DRL) is yet to be explored. Hence, this work developed a DRL-based adaptive cache-aware congestion control mechanism called DRL-CaCC to alleviate WSN during congestion scenarios. DRL-CaCC employs interme-diate caching parameters as its state space and adaptively moves the congestion window as its action space. It aims to find the optimal congestion window movement to avoid further network congestion while ensuring maximum cache utilization. Results show that DRL-CaCC achieved an average improvement gain from 10% to 40% compared to two other cache-based congestion control mechanisms in terms of cache utilization, throughput, and end-to-end delay metrics. © 2024 IEEE.

关键词： Deep reinforcement learning

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Studying Physical Properties of Gas-To-Liquid Compared with Mineral Oil under Accelerated Thermal Aging 25

Studying Physical Properties of Gas-To-Liquid Compared with ...

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25th International Middle East Power System Conference, MEPCON 2024

作者： Badawi, Mohamed Ibrahim, Shimaa A. Mansour, Diaa-Eldin A. El-Faraskoury, Adel Ward, Sayed A. Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Egypt Egypt-Japan University of Science and Technology School of Electronics Communications and Computer Engineering Electrical Power Engineering Department Alexandria Egypt Tanta University Faculty of Engineering Electrical Power and Machines Engineering Department Tanta Egypt Egyptian Electricity Holding Company Extra High Voltage Research Centre Egypt Delta University for Science and Technology Faculty of Engineering Department of Electrical Engineering Egypt

ISBN: (纸本)9798350379648

Mineral oil (MO) has long been utilized in high-voltage power transformers, as a cooling and dielectric medium;because of it is availability as it is extracted from fossil oil. Recently, Gas-To-Liquid (GTL) has a significant increase in use due to its superior performance, its purity and chemical consistency over conventional mineral oils. This paper presents examination of the temperature effects on mineral (Diala S2) and gas to liquid (Diala S4) oil physical properties. Fresh oil and aged oil samples, of the two oil types, have been tested in this paper. Accelerated thermal aging test have been conducted in laboratory to simulate the heat stress experienced by transformers during operation. Three aged oil samples, of the two types of oil, have been prepared in laboratory (3, 6, and 10 days). Density and viscosity measurements were performed on these fresh and aged oil samples. The results concluded that during the initial years, chemical changes cause a minor drop in the density of the mineral oil. Conversely, GTL oil has superior qualities since it stays more stable over time. Over time, the density of both oils exhibits comparable stability, however GTL oil is favored over mineral oil due to its consistency and stability. Diala S4 and Diala S2 oils have higher viscosity, suggesting superior cooling performance before aging. However, Diala S2 dropped to 10.649 mm2/s after three years, indicating its long-term consequences. Diala S4 oil had higher flow efficiency, enhancing transformer cooling and insulating performance within thermal aging. This is due to GTL oil, derived from natural gas, has higher resistance to chemical deterioration, allowing it to sustain its viscosity stability over extended periods. These results emphasize the importance of choosing oils, especially GTL oil type based on their apparent and physical stability over time. © 2024 IEEE.

关键词： Viscosity

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction 3

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

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

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. © 2025 IEEE.

关键词： Smart contract

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Intelligent Farming in Rural Areas: CBGRU-Based Smart Agriculture and Precision Solutions 4

Intelligent Farming in Rural Areas: CBGRU-Based Smart Agricu...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Laxmi, M. Tamilselvan, V. Ramesh, C. Renganathan, K. Sukumar, P. Kaliappan, S. Geetanjali College of Engineering and Technology Department of Electrical and Electronics Engineering Telangana India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore India M.Kumarasamy College of Engineering Department of Mechanical Engineering Karur India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Department of Computer Science and Engineering Erode India Lovely Professional University Division of Research and Development Phagwara Jalandhar India

ISBN: (纸本)9798350352931

Climate-Smart Agriculture (CSA) aims to reduce food insecurity, boost rural economies, and protect the environment. CSA programs sometimes ignore small-scale farmers' demands, who face higher challenges. This research introduces Vulnerable-Smart Agriculture (VSA) to address small-scale farmers' needs and create a sustainable farming framework. The recommended method involves preprocessing, feature selection, and model training. Data cleansing, normalization, and correlation analysis are preprocessing steps. MRMR and CFS are used to select features. The CBGRU architecture trains the model. The CBGRU model outperforms CNN and BiGRU with an average accuracy of 93.60%. High accuracy shows that the VSA strategy meets small-scale farmers' needs through data-driven insights. VSA can cover the CSA gap by including small-scale farmers, according to this research. Strong preprocessing, feature selection, and CBGRU-based model training can boost agricultural production, sustainability, and rural lives. © 2024 IEEE.

关键词： Smart agriculture

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A Survey on Machine Learning Approaches in Water Analysis 13th

A Survey on Machine Learning Approaches in Water Analysis

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Tsimpidi, Ilektra Sartjärvi, Rosa Juntunen, Petri Nikolakopoulos, George Robotics and AI Team Department of Computer Science Space and Electrical Engineering Luleå University of Technology Luleå Sweden Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031632266

The aim of this article is to present a survey on Machine Learning approaches for performing water analysis as in general integrating Artificial Intelligence in water analysis has a transformative potential for optimizing and sustaining water resources. Recent trends in water treatment and desalination applications effectively address pollution and scarcity challenges, while by combining AI with technologies like data analytics, water management complexities are simplified, ensuring sustainability and cost-effectiveness through predictive data assessment. This survey presents examples of the ML’s significance in water treatment and in water quality analysis, such as for enhancing accuracy in water quality index (WQI) assessments and examples of exploring and predicting pollutants in water processes, and aiding in the early detection and categorization of contaminants. Furthermore, this survey will reveal the immense potential in leveraging ML algorithms to enhance water analysis accuracy, speed, and efficiency, paving the way for reduced chemical usage and improved understanding of microbiological processes. As it will be presented, ML approaches offer valuable tools across various stages of water analysis, from identifying critical indicators to accurately measuring and predicting water parameters. The survey concludes by emphasizing the potential of AI and particularly ML to revolutionize water resource management, offering precision, efficiency, and foresight in addressing challenges of water scarcity and sustainable resource utilization. © IFIP International Federation for Information Processing 2024.

关键词： Machine learning

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Characterization of All Optimal Finite-length Codes of Size Four for Binary Symmetric Channels

Characterization of All Optimal Finite-length Codes of Size ...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Dong, Yanyan Yang, Shenghao National University of Singapore Department of Electrical and Computer Engineering Singapore The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen China

ISBN: (纸本)9781665475549

The search for optimal finite-length binary block codes is a long-standing open problem for memoryless binary symmetric channels (BSCs) with the maximum likelihood decoding. A recent work studied the optimal codes among all binary codes of size four, including both linear codes and nonlinear codes, and showed the existence of optimal codes in the set composed of linear codes and Class-I codes for any given blocklength. Furthermore, for blocklength up to 300, it has been shown that there exists a linear code that is optimal among all the codes of size four. However, it is unknown whether there are optimal codes outside the set of linear codes and Class-I codes for a general blocklength. In this paper, we derive a subset of nonlinear codes called Class-II codes and justify that the set composed of linear, Class-I and Class-II codes and their equivalent codes includes all the optimal codes of size four when the blocklength is not equal to 3. For blocklength 3, we verify that there are nonlinear codes (not equivalent to linear, Class-I or Class-II codes) that are optimal. For the blocklength from 2 to 300 and not equal 3, our computer evaluations show that no nonlinear code is optimal except for the ones that are equivalent to linear codes. Moreover, we characterize all the best codes among all linear codes of size four for any given blocklength. © 2023 IEEE.

关键词： Optimal systems

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Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach 36th

Enhancing Biomedical Semantic Annotations through a Knowledg...

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36th International Florida Artificial Intelligence Research Society Conference, FLAIRS-36 2023

作者： Abbas, Asim Khalid, Mutahira Chalarca, Sebastian Keshtkar, Fazel Bukhari, Syed Ahmad Chan Division of Computer Science Mathematics and Science Collins College of Professional Studies St. John’s University Utopia Parkway QueensNY8000 United States School of Electrical Engineering and Computer Science NUST IslamabadH-12 Pakistan

An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines’ efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing envi-ronment are interlinked and play critical roles. Authorial queries can be posted on the ’’Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowl-edge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, Fl-score, and accuracy assessment matrices. Our results showed an ac-ceptable level of accuracy at approximately 90%. The source code for ’Semantically" is freely available at: https://***/bukharilab/Semantically. © 2023 by the authors. All rights reserved.

关键词： Knowledge graph

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