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Towards a real-time control of robotic ultrasound using haptic force feedback

Current Directions in Biomedical engineering 2022年第1期8卷 81-84页

作者： Schreiter, J. Semshchikov, V. Hanses, M. Elkmann, N. Hansen, C. University of Magdeburg Faculty of Computer Science & Research Campus Stimulate Magdeburg Germany University of Magdeburg Faculty of Electrical Engineering and Information Technology Magdeburg Germany Fraunhofer Institute for Factory Operation and Automation Iff Magdeburg Germany

Ultrasound is a widely used imaging technique and is appreciated for its non-invasiveness, absence of radiation, widespread availability, and compact equipment. Ergonomic difficulties in manual handling of the probe could be enhanced by a robotic controlled ultrasound. The paper addresses the development of such a system which enables remote operation of a ultrasound probe and includes haptic force feedback as well as video conferencing components for visual feedback. The development process followed a user-centered approach by investigating needs of potential end-users. Preliminary results demonstrated the functionality of the developed system for generating medical image data under laboratory conditions. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Probes

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Research on the Structural Design and Basin Characteristics of Pilot Electromagnetic Valves for New Energy Wastewater Treatment Systems 5

Research on the Structural Design and Basin Characteristics ...

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5th IEEE International Conference on Advanced Electrical and Energy Systems, AEES 2024

作者： Wang, Na Dong, Yueyang Zhen, Xiaofei Jiao, Ruonan Fu, Haoran Chen, Yuying Lanzhou Jiaotong University School of Mechatronic Engineering Lanzhou China Lanzhou Jiaotong University School of New Energy and Power Engineering Lanzhou China The University of Adelaide Faculty of Engineering Computer and Mathematical Sciences Adelaide Australia Shandong University of Science and Technology Academy of Electrical and Automation Engineering Qingdao China

ISBN: (纸本)9798331517724

Pilot-operated solenoid valves have the advantage of fast response times and can meet high pressure and large diameter requirements through their structural characteristics, making them widely used in various fields. The pilot-operated solenoid valve discussed in this article is applied in a new energy system a sewage treatment system based on solar photovoltaic and solar thermal technology. The pilot-operated solenoid valve designed in this paper controls the flow rate and velocity of the sewage. This article uses Fluent to calculate the flow coefficient of pilot-type solenoid valves at various openings, exploring the flow characteristics of these valves. Based on computational fluid dynamics, simulations of the cavitation flow field were performed for pilot-type solenoid valve models at different openings, resulting in pressure contour plots and liquid velocity vector diagrams within the pilot valve. The study investigated the effects of the valve opening and the pressure difference at the inlet and outlet on the cavitation conditions within the valve. This provides a foundation for subsequent analysis of the vibration characteristics and structural optimization of solenoid pilot valves. The results indicate that negative pressure occurs within the solenoid pilot valve at all openings, leading to cavitation phenomena. When the opening is greater than or equal to 0.5, significant negative pressure appears near the valve port of the valve sleeve, with more pronounced cavitation phenomena, which can easily lead to cavitation bubbles and deformation. © 2024 IEEE.

关键词： Solenoid valves

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An Embedded System for Non-Invasive Glucose Monitoring 6

An Embedded System for Non-Invasive Glucose Monitoring

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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Pham, Quoc-Hung Nguyen, Thanh-Nhan Ba, Anh-Quan Huynh Ngo, Hoang-Huy Vo, Huy-Hoang Tran, Nhut-Thanh University of Information Technology Faculty of Computer Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Can Tho University Faculty of Automation Engineering Can Tho Viet Nam

ISBN: (纸本)9798350327410

Diabetes is a persistent condition characterized by the body's inability to properly utilize insulin. Therefore, noninvasive methods have been proposed to mitigate the pain and risk of infection due to non-required blood extraction compared to the methods for glucose measurement. The invasive methods are done by finger-pricking blood samples from the body. However, this method increases the risk of blood-related infections and pains. Hence, non-invasive Glucose in the blood is an essential method to measure glucose levels. This study successfully implemented a portable embedded system device based on a multi-spectral sensor and Raspberry Pi 4 microprocessor for noninvasive glucose measurement. This study used machine learning, including Multiple Linear Regression and Support Vector Machine, to predict the glucose level with over 90% accuracy. Moreover, the Clarke error grid analysis was used to analyze the accuracy results of this study. It also compared with invasive measurements and previous studies. © 2023 IEEE.

关键词： Support vector machines

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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A Study to Compare YOLO Family Models for Metal Surface Anomaly Detection

A Study to Compare YOLO Family Models for Metal Surface Anom...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Koc, Abdusselam Albayrak, Ozlem Kurt, Yasar Unal, Perin Ted University Faculty of Computer Engineering Ankara Turkey Research and Development Teknopar Industrial Automation Ostim Technical University Graduation School of Engineering and Sciences Ankara Turkey Ostim Technical University Graduation School of Engineering and Sciences Turkey

ISBN: (纸本)9798350324457

Anomaly detection is an important process that needs to be automated. Metal is an essential material that is heavily used in manufacturing of many different products. Automated detection of anomalies on metal surfaces is a topic that has been widely studied both by the researchers in the academia and by the practitioners. In the recent years, many studies have been conducted that apply deep learning for anomaly detection on metal surfaces. In this study, using NEU-DET data set, we have applied YOLO family models (YOLOv4, YOLOv5, YOLOv6, YOLOv7 and YOLOv8) in order to classify defects of images of steel. The results presented that with respect to mAP50 YOLOv8 has outperformed the other models. In the short term, we aim to utilize real images taken from home appliances products and perform a replication study. As for the long-term research we aim to develop and test our model based on real data set belonging to a Horizon Europe project. YOLOv8 has presented the best performance. © 2023 IEEE.

关键词： Anomaly Detection computer Vision Deep Learning

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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A single-stream adaptive scene layout modeling method for scene recognition

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Neural Computing and Applications 2024年第22期36卷 13703-13714页

作者： Wang, Qun Zhu, Feng Lin, Zhiyuan Wang, Jianyu Li, Xiang Zhao, Pengfei Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Robot Science and Engineering Northeastern University Shenyang110016 China Faculty of Computer Science and Engineering Northeastern University Shenyang110016 China

Scene recognition has been the foundation of research in computer vision fields. Because scene images typically are composed of specific regions distributed in some layout, so modeling layouts of various scenes is a key clue for scene recognition. Existing methods usually require an additional stream to detect regions for subsequent modeling, which accumulate errors and may miss important information. Meanwhile, they use manual features to model relations between regions, which weakens the representation ability of layouts. In this paper, we propose a single-stream adaptive scene layout modeling approach based on a layout modeling module (LMM), which constructs layouts without additional detection streams and adaptively captures the relations to take advantage of graph attention network. LMM is directly concatenated to a convolutional neural network, where each pixel of the activation maps of the last convolutional layer is defined as a region that is the initial input node of the LMM. LMM first models the layout of each region, and then uses all regions with layout information to model the entire scene. Layout relations are encoded as edges, which are automatically analyzed according to region co-occurrence and relative position. Our work can be understood as optimizing features of the activation maps from a scene layout modeling perspective for scene recognition. Experimental results on MIT67, SUN397, and Places365 show that our single-stream model achieves competitive performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems

arXiv

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arXiv 2024年

作者： Kůdela, Jakub Dobrovský, Ladislav Institute of Automation and Computer Science Faculty of Mechanical Engineering Brno University of Technology Czech Republic

Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely studied methods for their capability to solve expensive real-world optimization problems. However, the development of new methods and benchmarking with other techniques still relies almost exclusively on artificially created problems. In this paper, we use two real-world computational fluid dynamics problems to compare the performance of eleven state-of-the-art single-objective SAEAs. We analyze the performance by investigating the quality and robustness of the obtained solutions and the convergence properties of the selected methods. Our findings suggest that the more recently published methods, as well as the techniques that utilize differential evolution as one of their optimization mechanisms, perform significantly better than the other considered methods. © 2024, CC BY.

关键词： Evolutionary algorithms

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Self-adaptive Bat Algorithm With Genetic Operations

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1284-1294页

作者： Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou H.Vincent Poor School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electrical Engineering Princeton UniversityPrinceton NJ 08544 USA IEEE

Swarm intelligence in a bat algorithm(BA)provides social *** operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization *** integration provides an opportunity for improved search ***,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions *** from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated ***,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and *** by these exemplars,SBAGO improves both BA’s efficiency and global search *** evaluate this approach by using 29 widely-adopted problems from four test *** is also evaluated by a real-life optimization problem in mobile edge computing *** results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

关键词： Bat algorithm(BA) genetic algorithm(GA) hybrid algorithm learning mechanism meta-heuristic optimization algorithms

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Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Ajian Tan, Zichang Liang, Yanyan Wan, Jun Institute of Automation Chinese Academy of Sciences Mais Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350302493

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack Pattern Generation (Adv-APG) module and a Supervised learning-based Attack Pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets. © 2023 IEEE.

关键词： Face recognition

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