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The stretchable carbon black-based strain fiber with a remarkable linearity in a wide sensing range

International Journal of Smart and Nano Materials 2022年第3期13卷 529-541页

作者： Hao Wang Yang Yue Wenze Zou Yang Pan Xiaogang Guo Institute of Advanced Structure Technology Beijing Institute of TechnologyBeijingHebeiChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Xuteli School Beijing Institute of TechnologyBeijingHebeiChina

Ascribed to its wide sensing range,high sensitivity,and low stiff-ness to match target objects with complex 3D shapes,the stretch-able strain sensor has shown its promising applications in various fields,ranging from healthcare,bodynet,and intelligent traffic system,to the robotic *** paper presents a low-cost and straightforward fabrication technology for the stretchable strain fiber with the combined attributes of a wide sensing range,excep-tional linearity,and high *** hybrid composite consist-ing of carbon black and silicone is utilized as the functional material to respond to the external mechanical deformation due to the piezoresistive *** address the remarkable hysteresis of the CB-silicone composites,the latex tubes with excellent mechanical robustness and a considerable accessible tensile strain are intro-duced as the outer supporting *** injecting the conductive CB-silicone composite into these tubes,the stretchable strain fibers are successfully ***,the stretchable strain sensor exhibits linearity(R^(2)=0.9854)in a wide sensing range(0-400%)and remarkable durability even after the 2500 cycles under 100%***,the potential of this stretchable strain fiber as the wearable strain sensor and the realtime feedback is demonstrated by detecting the body motion and the expansion devices.

关键词： Soft and stretchable strain sensor high linearity wide sensing range wearable electronics

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Enhancing EV Charging Demand Forecasting for Highway Rest Area Stations: Integrating Day Type, Traffic Volume, and Weather Conditions

Enhancing EV Charging Demand Forecasting for Highway Rest Ar...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Lee, Yeaeun Kim, Byeongchang Kim, Dongju Hwang, Euiseok Choi, Jaehyuk Kim, Hyunsup Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju Korea Republic of Gwangju Institute of Science and Technology Ai Graduate School Gwangju Korea Republic of Hyundai Motors Company Korea Republic of

ISBN: (纸本)9798350362480

With global EV sales projected to reach 3.5 million units by 2023 and public charging stations increasing by 40%, effective management and optimization of charging infrastructure have become critical. While current research primarily focuses on models that address either users or charging stations, these approaches often overlook external factors such as weather and traffic conditions, which significantly impact driving patterns and energy consumption. The integration of these external variables into forecasting models is thus critical for enhancing prediction accuracy and infrastructure optimization. In this study, we conducted an in-depth analysis of EV charging patterns at highway rest areas. This study employed deep learning models, GRU, and LSTM architectures, trained using various data combinations. The experiments revealed that the inclusion of traffic data notably improves forecasting precision. In particular, the LSTM model demonstrated a 10.5% reduction in mean absolute percentage error(MAPE), decreasing the standard deviation from 4.95 to 3.87 when external factors were included. © 2024 IEEE.

关键词： deep learning demand forecasting Electric vehicle charging station external variables

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Integrating All-optical Switching and Entangled Photon Source Placement for Entanglement Routing 31

Integrating All-optical Switching and Entangled Photon Sourc...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zhu, Qiucheng Zhao, Yangming Xu, Hongli Huang, Liusheng Qiao, Chunming School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China University at Buffalo Department of Computer Science and Engineering United States

ISBN: (纸本)9798350399738

Entanglement routing plays a vital role in quantum networks. Previous works on entanglement routing did not note that all-optical switching capacity can be used to improve the network throughput or ignored that the placement of Entangled Photon Sources (EPS), another type of precious (and costly) quantum source, which also limits the network throughput. In this paper, we propose OptEPS to jointly optimize all-optical switching and EPS placement in entanglement routing to maximize the quantum network throughput. The main challenge of OptEPS lies in two folds: i). an entanglement may fail to be created;ii). the joint optimization problem suffers from a large time complexity. To overcome these challenges, we first formulate the joint optimization problem as a link-path based model and prune out some candidate paths in order to reduce the time complexity. Then, efficient algorithms are proposed to derive a near-optimal solution in a timely manner. Extensive simulations show that compared with the solutions ignoring EPS placement or without introducing all-optical switching, OptEPS will increase the network throughput by 1557% and 109%, respectively. © 2023 IEEE.

关键词： Photons

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Production Scheduling Optimization Method Based on Improved Particle Swarm Optimization Algorithm 4

Production Scheduling Optimization Method Based on Improved ...

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4th International Conference on Mechatronics technology and Intelligent Manufacturing, ICMTIM 2023

作者： Wang, Chuchu Zang, Haijuan Jiangsu Institute of Technology School of Mechanical Engineering Changzhou China Jiangsu Institute of Technology School of Computer Science Changzhou China

ISBN: (纸本)9798350314434

In the environment of smart manufacturing, smart and agile production scheduling is an important measure for manufacturing companies to meet the new trend. To solve complex production scheduling problems consisting of different optimization objectives and constraints, an optimization model is constructed, an improved particle swarm optimization algorithm is proposed, and a particle selection based on the tournament selection method and a new speed update mechanism is designed. The practicality of the algorithm was verified with production data, confirming that the improved algorithm improved order completion time by 24.6% over traditional scheduling methods. © 2023 IEEE.

关键词： Production control

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Enhancing IoT-Enabled Healthcare with Genetic-based Encryption and Authentication for Secure and Efficient wireless Data Transmission 7

Enhancing IoT-Enabled Healthcare with Genetic-based Encrypti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Padhy, Satyanarayan Vijaya Lakshmi, T.R. Ahmad, Shahnawaz Sophia, S. Senthilkumar, G. Natrayan, L. Centurion University of Technology And Management Department of Electronics And Communication Engineering Odisha India Mahatma Gandhi Institute of Technology Department of Electronics And Communication Engineering Telangana Hyderabad India Bennett University School of Computer Science Engineering And Technology Uttar Pradesh Greater Noida India Karunya Institute of Technology And Sciences Department of Computer Science And Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

The Internet of Things' incorporation with healthcare systems has enabled new prospects in patient monitoring and healthcare management. This research work offers a new Internet of Things (IoT) enabled healthcare system that integrates modern sensor technology with genetically-based authentication, and encryption methods. The proposed model is designed to improve healthcare sector wireless data transfer security, and efficiency in order to allow seamless remote monitoring, and timely intervention. The proposed system offers a comprehensive real-time health monitoring protocol through integration of pressure, temperature, and electrocardiogram (ECG) sensors. Genetically-based encryption and authentication schemes are employed to address the pressing requirement for robust security in healthcare systems, and data security. Performance tests including, doctor visualization time, sensor data transmission rate, and authentication time show an overall efficiency of 98.45% proving the effectiveness of the system over a multitude of parameters. This research work presents a complete solution to real-time monitoring, and data security in secure and efficient IoT enabled healthcare systems, and makes an important addition to this field. As demonstrated through the findings, such technology can potentially revolutionize remote patient monitoring and healthcare delivery. © 2024 IEEE.

关键词： Internet of things

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Prohibited Object Detection using YOLOv7 on X-Ray Images of Airport Luggages (OPIXray) 14

Prohibited Object Detection using YOLOv7 on X-Ray Images of ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Deena, S. Jayaprakash, L.S. Sudharson, S. Amrita Vishwa Vidyapeetham Computer Science and Engineering Amrita School of Computing Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai600127 India

ISBN: (纸本)9798350335095

Ensuring airport security and reducing risks for passengers safe travels is crucial. Conventional security inspection methods involve manual inspection of passengers luggage using X-ray scanners to identify any prohibited items. However, this manual process is prone to human error and can be time-consuming. To address these challenges, there is a need for automated security inspection systems that can enhance efficiency and accuracy. Deep learning techniques, particularly in the field of computer vision, offer potential solutions for autonomous security inspection. One such model is YOLO (You Only Look Once), a real-time object detection system capable of identifying multiple objects in images or video streams. This makes YOLO an ideal choice for airport security applications that require both speed and accuracy. This paper proposes a method for the automatic detection of prohibited objects in X-ray images, aiming to identify potential threats such as weapons, explosives, and other prohibited items in real-time. The proposed system was evaluated by conducting extensive tests on the publicly available benchmark dataset known as OPIXray. This dataset consists of labeled X-ray images that contain prohibited objects. The key component of our method involves the utilization of the YOLOv7 network, which was trained on a subset of the OPIXray dataset. The study demonstrates the effectiveness of this approach in detecting and recognizing prohibited items in OPIXray image datasets. The proposed methodology achieved a precision of 0.73, recall of 0.76, mAP@0.5 of 0.491, and mAP@0.5:0.9 of 0.397 on the OPIXray dataset. The experiments demonstrate the model's high accuracy and speed, thereby indicating its suitability for real-world airport security applications. Comparisons with other methods show that our model outperforms in terms of accuracy and speed. This paper presents a high-accuracy method for real-time airport security applications utilizing the YOLOv7 network. © 2023

关键词： Airport security

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Data-driven strategies for digital native market segmentation using clustering

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 178-191页

作者： Uddin, Md Ashraf Talukder, Md. Alamin Ahmed, Md. Redwan Khraisat, Ansam Alazab, Ammar Islam, Md. Manowarul Aryal, Sunil Jibon, Ferdaus Anam School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Artificial Intelligence and Optimization Torrens University Australia

The rapid growth of internet users and social networking sites presents significant challenges for entrepreneurs and marketers. Understanding the evolving behavioral and psychological patterns across consumer demographics is crucial for adapting business models effectively. Particularly, the emergence of new firms targeting adolescents and future generations underscores the importance of comprehending online consumer behavior and communication dynamics. To tackle these challenges, we introduce a Machine Learning-based Digital Native Market Segmentation designed to cater specifically to the interests of digital natives. Leveraging an open-access prototype dataset from social networking sites (SNS), our study employs a variety of clustering techniques, including Kmeans, MiniBatch Kmeans, AGNES, and Fuzzy C-means, to uncover hidden interests of teenage consumers from SNS data. Through rigorous evaluation of these clustering approaches by default parameters, we identify the optimal number of clusters and group consumers with similar tastes effectively. Our findings provide actionable insights into business impact and critical patterns driving future marketing growth. In our experiment, we systematically evaluate various clustering techniques, and notably, the Kmeans cluster outperforms others, demonstrating strong segmentation ability in the digital market. Specifically, it achieves silhouette scores of 63.90% and 58.06% for 2 and 3 clusters, respectively, highlighting its effectiveness in segmenting the digital market. © 2024 The Authors

关键词： Cluster analysis

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Endogenous Security of FQn Networks: Adaptive System-Level Fault Self-Diagnosis

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IEEE Transactions on Reliability 2024年第3期73卷 1659-1668页

作者： Lin, Yuhang Lin, Limei Huang, Yanze Xu, Li Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Endogenous security has the ability to discover, eliminate, and solve internal security problems and hidden dangers within the network, and is a superior technology to ensure future network security. The t/k-diagnosis strategy, as a strong and adaptive self-diagnosis strategy, is an important part for ensuring endogenous security. Moreover, the folded hypercube (FQn) as a data transmission network (e.g., optical network) topology offers new potential for the construction of large-scale, high data throughput, and low-latency systems, such as computing network, human-cyber-physical systems, and smart grid. However, there are few studies on endogenous security based on FQn networks. Therefore, this article designs an adaptive system-level fault self-diagnosis strategy, namely Fast t/k-Diagnosis Under Maeng-Malek Model (Ftk-DIAG-MM*) to diagnosis the faulty vertices in FQn network under the Maeng-Malek model (MM∗ mod). Then, we provide a proof of the algorithm correctness theoretically by the fault tolerance of FQn network. It is derived by theoretical derivation that the t/k-diagnosability inherent to the FQn network is (n+1)(k+1)-k(k+3)/2. The simulation experiments demonstrate that the designed Ftk-DIAG-MM∗ strategy can correctly diagnose all vertices within the range allowed by the diagnosability, and still has a great performance when it exceeds the range allowed by the diagnosability. It greatly enhances the fault diagnosis capability of FQn network in the circumstance of misdiagnosing a few vertices, which provides an important theoretical basis for the reliability and endogenous security of FQn networks. © 1963-2012 IEEE.

关键词： Topology

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Modulated advancements in semiconductor-based nanomaterials for environmental solutions

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Nanotechnology for Environmental engineering 2024年第4期9卷 525-537页

作者： Tirmare, Aarti Hemant Gowda V, Dankan Dhabarde, Rupali J Tirmare, Hemant Appa Kale, Satish Bapuso Suryawanshi, Varsha Amol Kumar N, Anil Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth’s college of Engineering Maharashtra Kolhapur416013 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka560064 India Department of Computer Science and Technology Department of Technology Shivaji University Kolhapur Maharashtra Kolhapur416008 India Department of Computer Science and Technology Department of Technology Shivaji University Kolhapur Maharashtra Kolhapur416004 India Department of Mechanical Engineering Department of Technology Shivaji University Kolhapur Maharashtra Kolhapur416004 India Maharashtra Kolhapur India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India

This research explores significant advancements in semiconductor-based nanomaterials and their crucial applications in addressing environmental challenges. Highlighted for their expansive surface area, chemical stability, and adaptable electrical and optical properties, these nanomaterials present innovative solutions for pressing environmental issues. The paper details novel synthesis methods enhancing these materials’ effectiveness in environmental applications, including pollution remediation, sustainable energy production, and the development of environmental monitoring tools. Key findings include the discovery of advanced techniques for fabricating nanoscale semiconductor components, improving the efficiency of photocatalytic degradation of pollutants, energy conversion, and sensor sensitivity. Additionally, this study investigates the integration of semiconductor nanomaterials with biocompatible polymers and metals, aiming to enhance their operational stability and longevity in environmental applications. The outcomes contribute to the advancement of nanotechnology in environmental conservation, offering insights into the development of eco-friendly materials and innovative strategies for environmental protection. This work underscores the potential of semiconductor-based nanomaterials to revolutionize the field of environmental solutions, presenting a promising avenue for future research and application in sustainable technologies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Nanostructured materials

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RealDriftGenerator: A Novel Approach to Generate Concept Drift in Real World Scenario

RealDriftGenerator: A Novel Approach to Generate Concept Dri...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Borong Huang, Chao Zhu, Xiaohui Jin, Nanlin School of Advance Technology Xi'an Jiaotong-Liverpool University Department of Computer Science and Software Engineering SuZhou China University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9781665410205

Concept drift refers to the probability distribution of data generation changes over time in a data stream environment. In recent years, there has been an increasing interest in drift detection models. However, due to the lack of labeled concept drift datasets, most researchers tend to using synthetic drift data generators for model training. These generators only have relatively simple feature distributions, which fail to capture the complexity found in real-world scenarios. This paper introduces a real scenario concept drift label generator (RealDriftGenerator). This generator aims to preserve the complexity and temporal correlation of real-world scenario while generating concept drifts with user defined drift positions and drift widths. The validation result show that the temporal correlation coefficients of RealDriftGenerator is significantly higher than benchmark drift generators. Additionally, the ability of RealDriftGenerator to capture the complexity in real-world scenarios is 20% higher than benchmark drift generators(measured by model performance). The source code of RealDriftGenerator has been published on https://***/sniperrifle71/realDriftGenerator. © 2024 IEEE.

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