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A Legged Small Celestial Body Landing Mechanism:Landing Simulation and Experimental Test

Space(science & technology) 2023年第1期3卷 453-463页

作者： Zhijun Zhao Shuang Wu Jingdong Zhao Yaobing Wang Chuncheng Zhao Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System EngineeringBeijing 100094China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150006China Department of Aerospace Science and Technology Polytechnic University of MilanMilan 20133Italy

Landing mechanism tends to rebound and turn over,and the stability time is long when landing on the small celestial *** landing performance in different conditions is necessary to be evaluated to guide the ***,landing performance evaluation is realized by *** factors affecting the landing performance including cardan element damping,foot anchors,retro-rocket thrust,landing slope angle,and landing attitude are analyzed.A microgravity platform is built to test the landing mechanism,and the consistency between the simulation and the experiment is *** the basis of simulation and experiment,some landing suggestions are proposed to improve the landing performance.

关键词： thrust mechanism landing

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Dynamic Scheduling of Operators in an Unbalanced Assembly Line Based on Weighted Fuzzy Petri Nets Decision

Dynamic Scheduling of Operators in an Unbalanced Assembly Li...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Tang, Delian Wang, Junfeng Tang, Xia School of Mechanical Science and Engineering Huazhong University of Science and Technology Department of Industrial and Manufacturing System Engineering Wuhan430074 China

ISBN: (纸本)9798350323153

The production tasks of aerospace enterprises are increasing and the order demands are variable. The production flexibility, production capacity and efficiency of aerospace products are gradually enhanced. Usually, because of unbalanced process time of works, the operators in aerospace products assembly line are not belonged to a fixed workstation. For dynamic scheduling of limited operators in shift-based assembly lines of this paper, the knowledge of scheduling decisions for multiple dynamic changes in the assembly line is analyzed, and a decision model based on a weighted fuzzy Petri net is proposed, which can reasonably allocate the limited operators to different assembly stations in order to improve the assembly efficiency. A case study is given to shown the effect of the operators dynamic scheduling strategy on the throughput improvement of the assembly line. © 2023 IEEE.

关键词： assembly line fuzzy Petri net Operator scheduling unbalanced process time

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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Removing/Minimising Gaussian noise from real time CCTV footages using GAN 1

Removing/Minimising Gaussian noise from real time CCTV foota...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Prashanth, B.S. Puneeth, B.H. Manoj Kumar, M.V. Sreenivasa, B.R. Suraj, Adyapadi Hegde, Akshay Prashant Pranava Aithal, K.S. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Computer Science and Design Karnataka Davanagere577004 India

ISBN: (纸本)9798350376517

This paper describes a system that uses generative adversarial networks (GANs) to eliminate gaussian noise from CCTV of footages. Unwanted noise like gaussian noise frequently degrades the quality of images and makes them more difficult to interpret. In our approach, a discriminator network is used to direct the training of a generator network, whose job it is to produce denoised images from noisy inputs. This framework includes operations like picture enhancement, noise reduction, and evaluation with metrics like the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The resultant denoised images show enhanced visual quality and have potential uses in image analysis and computer vision. © 2024 IEEE.

关键词： Generative adversarial networks

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The posterior selection method for hyperparameters in regularized least squares method

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Control Theory and technology 2024年第2期22卷 184-194页

作者： Yanxin Zhang Jing Chen Yawen Mao Quanmin Zhu School of Science Jiangnan UniversityWuxi214122JiangsuChina The Science and Technology on Near-Surface Detection Laboratory Wuxi214122JiangsuChina Department of Engineering Design and Mathematics University of the West of EnglandBristolBS161QYUK

The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.

关键词： Regularization method Hyperparameter system identification Least squares algorithm

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Direct Synthesis of Layer-Tunable and Transfer-Free Graphene on Device-Compatible Substrates Using Ion Implantation Toward Versatile Applications

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Energy & Environmental Materials 2024年第5期7卷 408-418页

作者： Bingkun Wang Jun Jiang Kevin Baldwin Huijuan Wu Li Zheng Mingming Gong Xuehai Ju Gang Wang Caichao Ye Yongqiang Wang Department of Microelectronic Science and Engineering School of Physical Science and TechnologyNingbo UniversityNingbo 315211China Key Laboratory of Soft Chemistry and Functional Materials of MOE School of Chemistry and Chemical Engineering Nanjing University of Science and TechnologyNanjing 210094China Center for Integrated Nanotechnologies Los Alamos National LaboratoryLos Alamos 87545New MexicoUSA National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Academy for Advanced Interdisciplinary Studies&Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and TechnologyShenzhen 518055China Materials Science and Technology Division Los Alamos National LaboratoryLos Alamos 87545New MexicoUSA

Direct synthesis of layer-tunable and transfer-free graphene on technologically important substrates is highly valued for various electronics and device *** of the art in the field is currently a two-step process:a high-quality graphene layer synthesis on metal substrate through chemical vapor deposition(CVD)followed by delicate layer transfer onto device-relevant ***,we report a novel synthesis approach combining ion implantation for a precise graphene layer control and dual-metal smart Janus substrate for a diffusion-limiting graphene formation to directly synthesize large area,high quality,and layer-tunable graphene films on arbitrary substrates without the post-synthesis layer transfer ***(C)ion implantation was performed on Cu-Ni film deposited on a variety of device-relevant substrates.A well-controlled number of layers of graphene,primarily monolayer and bilayer,is precisely controlled by the equivalent fluence of the implanted C-atoms(1 monolayer~4×10^(15)C-atoms/cm^(2)).Upon thermal annealing to promote Cu-Ni alloying,the pre-implanted C-atoms in the Ni layer are pushed toward the Ni/substrate interface by the top Cu layer due to the poor C-solubility in *** a result,the expelled C-atoms precipitate into a graphene structure at the interface facilitated by the Cu-like alloy *** removing the alloyed Cu-like surface layer,the layer-tunable graphene on the desired substrate is directly *** layer-selectivity,high quality,and uniformity of the graphene films are not only confirmed with detailed characterizations using a suite of surface analysis techniques but more importantly are successfully demonstrated by the excellent properties and performance of several devices directly fabricated from these graphene *** dynamics(MD)simulations using the reactive force field(ReaxFF)were performed to elucidate the graphene formation mechanisms in this novel synthesis *** the wide use of ion

关键词： device applications dual-metal smart Janus substrate growth mechanism Ion implantation layer-tunable and transfer-free graphene

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images 3

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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3rd International Conference on Automation, Computing and Renewable systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive Learning

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of Computer science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Developing an Explainable AI Model for Predicting Patient Readmissions in Hospitals 2

Developing an Explainable AI Model for Predicting Patient Re...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Chandre, Pankaj Vanarote, Viresh Kuri, Moushmee Uttarkar, Amit Dhore, Abhishek Pathan, Shafi Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350347579

The objective of this study is to develop an AI model that can correctly identify which patients are most likely to require hospital readmission within a predetermined window of time after being discharged. Given that readmissions are linked to higher healthcare costs and poorer patient outcomes;this is a crucial problem in healthcare. The model must, nonetheless, also be explicable, which means that healthcare professionals must be able to comprehend the rationale behind why it made certain predictions. This is essential for establishing the model's credibility and making sure it is being used properly. To do this, the study may employ a range of machine learning methods renowned for their interpretability, like decision trees or random forests. Additionally, the study could investigate how to generate feature importance plots or partial dependence plots to visualize the model's decision-making process. Overall, by enhancing patient outcomes and fostering openness and confidence in the use of AI, this research subject has the potential to have a significant impact on healthcare. © 2023 IEEE.

关键词： Hospitals

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