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9th Symposium on Novel Photoelectronic Detection technology and applications

作者： Li, Ye Li, Dongxue Gao, Wenfei Xu, Kaiseng Qing, Dangsheng Zhang, Jinming Wang, Hongyu School of Artificial Intelligence Shenyang University of Technology Shenyang China Beijing Institute of Computer Technology and Application Beijing100854 China

ISBN: (纸本)9781510664432

OSR is a photosensitive material commonly used in aerospace photovoltaic displays, and it is difficult to detect the quality of OSR sheets due to its extremely high specular reflectivity. The mirror target is a common target in industrial production, such as the patch on the surface of photovoltaic cells, and its compact and neat arrangement affects the photosensitive quality. Therefore, the automatic perception and measurement of its gap is of great significance. In this paper, the gap measurement method of specular target based on binocular vision is proposed by taking the gap of a high reflectivity OSR sheet as the measurement target. In this method, the binocular camera is first calibrated to determine the relative geometric position of the binocular camera and its structural parameters. Then, the Canny algorithm that uses bilateral filtering instead of Gaussian filtering extracts the edge information in the image, and the LSD straight line detection algorithm is used to extract the extracted edge information in a straight line. Finally, the parallax map or depth map is obtained through the parallax change of the binocular camera, the virtual point is synthesized, and the fixed height constraint binocular matching method and triangulation method are used to solve the periodic blurring problem of boundary registration, and the gap width of the high reflectivity specular target is calculated. Binocular visual quality inspection experiments were designed to verify the feasibility and practicability of the proposed method. © 2023 SPIE.

关键词： Triangulation

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IoT based Smart wheelchair For Disabled Person

IoT based Smart wheelchair For Disabled Person

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Lalit Kushwaha, Ajay Shriram Sherawat, Kartikeya Siddique, Zishan IILM University School of Computer Scienceand Engineering Department of Computer Application Uttar Pradesh Greater Noida India Sharda University Sharda School of Engineringand Technology Department of Computer Scienceand Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350378092

Smart wheelchairs are revolutionizing mobility for disabled individuals, offering enhanced independence and improved quality of life. These advanced devices incorporate cutting-edge technologies like obstacle avoidance systems, voice control, and self-navigation capabilities, transforming the traditional wheelchair into an intelligent mobility solution. Obstacle avoidance systems utilize sensors to detect and avoid obstacles, ensuring safe navigation in complex environments. Voice control enables users to operate the wheelchair hands-free, providing greater convenience and control. Self-navigation capabilities allow the wheelchair to autonomously navigate to pre-programmed destinations, reducing user burden and enhancing independence. Smart wheelchairs also offer connectivity features, enabling remote monitoring and control by caregivers. This provides an added layer of safety and support for users, ensuring timely assistance when needed. The integration of these advanced technologies into smart wheelchairs is transforming mobility for disabled individuals, empowering them with greater independence, safety, and control over their movement. As smart wheelchair technology continues to evolve, the possibilities for enhanced mobility and improved quality of life for disabled individuals are limitless. © 2024 IEEE.

关键词： Disabled persons

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A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

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Journal of computer Science & technology 2023年第6期38卷 1203-1222页

作者：刘嘉聿汪飞马海平黄振亚刘淇陈恩红苏喻 School of Data Science University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Cognitive Intelligence Hefei 230088China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230026China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Computer Science and Artificial Intelligence Hefei Normal UniversityHefei 230061China

Cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge *** existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning *** this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis ***,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness ***,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model *** UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.

关键词： cognitive diagnosis probabilistic graphical model item response theory(IRT) stochastic process expectation maximization(EM)algorithm

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Automatic Controlled Robotic Car Based on Node MCU for Real-Time Obstacle and Explosive Detection 4

Automatic Controlled Robotic Car Based on Node MCU for Real-...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Dutt, Krishan Misra, Alok Tiwari, Raj Gaurang Singh, Ravinder Mishra, Mohit Kaur, Amninder School of Computer Application Lovely Professional University Punjab Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350387490

Automatic vehicles have significant challenges when it comes to detecting and avoiding obstacles in real-time. By considering hardware, software, and communication settings, this study shows the design and implementation of a robotic automobile that can recognize and avoid obstacles in real-time. Robotic vehicles that are programmed to drive themselves utilizing platform-based sensors are detailed in this research. Working together, an Android-based smartphone and this automated gadget are developed. The Arduino Uno has functioned as the robot's central processing unit. The robot is comprised of a multitude of hardware components, such as buzzers, infrared sensors, ultrasonic sensors, and Bluetooth modules. It also contains the software component that is used by mobile applications. Through a smartphone app, the driver of the autonomous vehicle may choose the desired route or driving mode and then order the vehicle to follow it. The robot may be set to work in conjunction with your smart device, or it can be just left in autonomous mode so it will go where it wants to go. Consequently, the robot can detect life and get away from the obstacle. The purpose of this paper is to alert both military and civilian personnel to the possibility of terrorist attacks, with a focus on those branches of the military that have live, detectable sensors. © 2024 IEEE.

关键词： Ultrasonic sensors

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Research on a combinatorial control method for coaxial rotor aircraft based on sliding mode

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Defence technology（防务技术） 2022年第2期18卷 280-292页

作者： Yi-ran Wei Hong-bin Deng Zhen-hua Pan Ke-wei Li Han Chen School of Mechatronical Engineering Beijing Institute of TechnologyBeijingChina School of Automation Beijing Institute of TechnologyBeijingChina Beijing Institute of Computer and Electronics Application BeijingChina Hong Kong Polytechnic University Hong KongChina

Aiming at the position and attitude tracking of coaxial rotor aircraft(CRA),this paper proposes a combinatorial control method of sliding mode control(SMC)coupled with proportional-integralderivative control(PIDC).Considering the complete description of flight dynamics,aerodynamics and airflow interference,the dynamical model of CRA is *** dynamical model is simplified according to the actual flight,then the simplified dynamical model is divided into two subsystems:a fully-actuated subsystem and an under-actuated *** controller of the fully-actuated subsystem consists of a SMC controller coupled with a rate bounded PIDC controller,while the controller of the under-actuated subsystem is composed of a SMC *** sliding manifold is defined by combining the position and velocity tracking errors of the state variables for each *** stability theory is used to verify the stability of the sliding mode controller,which ensures that all state trajectories of the system can reach and stay on the sliding mode surface,the uncertainty and external interference of the model are *** and experiment compared with the conventional PIDC are carried out,the results demonstrate the effectiveness and the robustness of the proposed control method of this paper.

关键词： Coaxial rotor aircraft Sliding mode control Position and attitude tracking Simulation Experiment

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Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data

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Big Data Mining and Analytics 2023年第4期6卷 465-477页

作者： Ankit Kumar Neeraj Varshney Surbhi Bhatiya Kamred Udham Singh Department of Computer Engineering and Application GLA UniversityMathura 281406India. College of Computer Sciences and Information Technology King Faisal UniversityHofuf 31982Saudi Arabia School of Computing Graphic Era Hill UniversityDehradun 248002India.

We live in an age where everything around us is being *** generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery *** model functionality is used for creating a cluster parallel,distributed algorithm,and large *** MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster *** results are obtained in the work and the exam conducted under this job;many of them are scheduled to set schedules,match matrices’data positions,clustering before determining to click,and accurate mapping and internal reliability to be closed together to avoid running and execution *** output and proponents have been implemented,and the map has been used to reduce the *** execution input key/value pair and output key/value pair have been *** paper focuses on evaluating this technique for the efficient retrieval of large volumes of *** technique allows for capabilities to inform a massive database of information,from storage and indexing techniques to the distribution of queries,scalability,and performance in heterogeneous *** results show that the proposed work reduces the data processing time by 30%.

关键词： big data hadoop mapreduce resource allocation query management

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APWF: A Parallel Website Fingerprinting Attack with Attention Mechanism

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computers, Materials & Continua 2025年第2期82卷 2027-2041页

作者： Dawei Xu Min Wang Yue Lv Moxuan Fu Yi Wu Jian Zhao College of Computer Science and Technology Changchun UniversityChangchun130022China School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing100081China Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled Changchun UniversityChangchun130022China China Academy of Information and Communications Technology Beijing100083China Key Laboratory of Mobile Application Innovation and Governance Technology Ministry of Industry and Information TechnologyBeijing100846China

Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF model innovatively adopts a parallel structure, fusing temporal features related to both the front and back of the fingerprint sequence, along with spatial features captured through channel attention enhancement, to enhance the accuracy of the attack. Meanwhile, the APWF method introduces isomorphic migration learning and adjusts the model by freezing the optimal model weights and fine-tuning the parameters so that only a small number of the target, samples are needed to adapt to web page changes. A series of experiments show that the attack model can achieve 83% accuracy with the help of only 10 samples per category, which is a 30% improvement over the traditional attack model. Compared to comparative modeling, APWF improves accuracy while reducing time costs. After further fine-tuning the freezing model, the method in this paper can maintain the accuracy at 92.4% in the scenario of 56 days between the training data and the target data, which is only 4% less loss compared to the instant attack, significantly improving the robustness and accuracy of the model in coping with conceptual drift.

关键词： Website fingerprinting attack transfer learning concept drift

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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry 2

A Machine Learning Approach to Compare Causal Inference Mode...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Singh, Sanskriti Sanjay Kumar Kumar Sinha, Apoorv Pandey, Trilok Nath Acharya, Biswa Mohan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SOA University ITER Dept. of Computer Application Bhubaneswar India

ISBN: (纸本)9798350344882

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

关键词： Sales

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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