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SSRN 2023年

作者： Fan, Haiju Qin, Xiaona Shum, Hubert P.H. Li, Ming College of Computer and Information Engineering Henan Normal University Henan453007 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang453007 China Department of Computer Science Durham University Durham United Kingdom

To improve storage and transmission, images are generally compressed. Vector quantization (VQ) is a popular compression method as it has a high compression ratio that suppresses other compression techniques. Despite this, existing adversarial attack methods on image classification are mostly performed in the pixel domain with few exceptions in the compressed domain, making them less applicable in real-world scenarios In this paper, we propose a novel one-index attack method in the VQ domain to generate adversarial images by a differential evolution algorithm, successfully resulting in image misclassification in victim models. The one-index attack method modifies a single index in the compressed data stream so that the decompressed image is misclassified. It only needs to modify a single VQ index to realize the attack, which limits the number of perturbed indexes. The proposed method belongs to the black-box attack, which is more in line with the actual attack scenario. We apply our method to attack three popular image classification models, i.e., Resnet, NIN and VGG16. On average, 55.9% and 77.4% of the images in CIFAR-10 and Fashion MINIST, respectively, are successfully attacked, with a high level of misclassification confidence and a low level of image perturbation. © 2023, The Authors. All rights reserved.

关键词： Image classification

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Air Purification Robotics using Cloud and Deep Q Networks for Autonomous Systems

Air Purification Robotics using Cloud and Deep Q Networks fo...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Elangovan Guruva Reddy T. S. Balaji Damodhar Yuvaraj S B. Santha Vathani S. Sivakumar N. Malathi Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Management Studies Mahalashmi Women's College of Arts and Science Chennai Tamil Nadu India Department of Computer Science Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues.

关键词： Cloud computing Technological innovation Purification Autonomous systems Robot kinematics Decision making Urban areas

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Integration of PSO and Deep Learning for Trend Analysis of Meta-Verse 2

Integration of PSO and Deep Learning for Trend Analysis of M...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Veeraiah, Vivek Khan, Huma Kumar, Arhath Ahamad, Shahanawaj Mahajan, Anagha Gupta, Ankur Adichunchanagiri University Dept. of R&d Computer Science Karnataka Mandya India Rungta College of Engineering and Technology Dept. of CSE Chhattisgarh Bhilai India Nmam Institute of Technology Dept. of Mca Nitte India College of CSE University of Hail Hail City Saudi Arabia Dr. Vishwnath Karad Mit World Peace University Dept. of Education Pune India Vaish College of Engineering Dept. of CSE Haryana Rohtak India

ISBN: (纸本)9781665437899

Meta-verse is a latest type of application that incorporates a number of cutting-edge technologies. It's multi-technological, social, and hyper-spatiotemporal in nature. Deep learning techniques have made significant advances in recent years. Particle swarm optimization of nonlinear functions was introduced. A major goal of the proposed research was to verify the accuracy of PSO and deep learning for trend analysis of meta-verse. In this study, the precision of the proposed work was compared to that of previous work. Meta-verse, Deep learning, PSO, and Trending Analysis will be used in a real-world scenario in the proposed work. The proposed work provides a wide range of possibilities and adaptability. © 2022 IEEE.

关键词： Deep learning

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Fil-Cad: A Fault Detection Method for Iiot Based on Federated Incremental Learning with Class Accuracy Distillation

SSRN

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SSRN 2024年

作者： Liu, Yanhua Fang, Wenyu Huang, Wei Wang, Xiaofeng Zhao, Baokang Liu, Ximeng College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer National University of Defense Technology Changsha410073 China

Fault detection is a research topic attracting much attention in IIoT. However, the continuous accumulation and growth of IIoT data leads to the catastrophic forgetting problem in the fault detection model and data heterogeneity, which creates a bottleneck for the performance and generalization of federated learning in fault detection. To improve the fault detection performance of federated learning models in data incremental scenarios, we propose a federatedincremental-learning-based fault detection method for IIoT, namely FIL-CAD. Firstly, comparing the knowledge distillation, the accuracies of different data classes in the global and local models are chosen as the model lightweight learning features, and the loss function is weighted. Secondly, we introduce the concept of data learning depth. It computes different model weights in the aggregation based on their degrees of data sample learning and model training participation and obtains the global model through weighted aggregation. Additionally, FIL-CAD supports the combination of differential privacy to protect data privacy. The proposed method is compared with four benchmark methods on four datasets, which include CWRU and MFPT bearing datasets. Then, the experimental results show that FIL-CAD performs optimally in accuracy and F1-Score, and the average accuracy is improved by 1.32%-18.52%, demonstrating the superiority of FIL-CAD. © 2024, The Authors. All rights reserved.

关键词： Fault detection

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Making informed decisions in cutting tool maintenance in milling: A KNN-based model agnostic approach

arXiv

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arXiv 2023年

作者： Wahul, Revati M. Rahalkar, Aditya M. Khare, Om M. Patange, Abhishek D. Soman, Rohan N. Department of Computer Engineering Modern Education Society’s Wadia College of Engineering Maharashtra Pune411001 India Department of Mechanical Engineering COEP Technological University Pune Wellesley Rd Shivajinagar Maharashtra Pune411005 India Department of Computer Engineering & Information Technology COEP Technological University Pune Wellesley Rd Shivajinagar Maharashtra Pune411005 India Department of Mechanical Engineering Cummins College of Engineering for Women Pune Karve Nagar Maharashtra Pune411052 India Institute of Fluid Flow Machinery Polish Academy of Sciences 14 Fiszera St. Gdansk80-231 Poland

In machining processes, monitoring the condition of the tool is a crucial aspect to ensure high productivity and quality of the product. Using different machine learning techniques in Tool Condition Monitoring (TCM) enables a better analysis of the large amount of data of different signals acquired during the machining processes. The real-time force signals encountered during the process were acquired by performing numerous experiments. Different tool wear conditions were considered during the experimentation. A comprehensive statistical analysis of the data and feature selection using decision trees was conducted, and the KNN algorithm was used to perform classification. Hyperparameter tuning of the model was done to improve the model’s performance. Much research has been done to employ machine learning approaches in tool condition monitoring systems;however, a model agnostic approach to increase the interpretability of the process and get an in-depth understanding of how the decision making is done is not implemented by many. This research paper presents a KNN-based white box model, which allows us to dive deep into how the model performs the classification and how it prioritizes the different features included. This approach helps in detecting why the tool is in a certain condition and allows the manufacturer to make an informed decision about the tool maintenance. © 2023, CC0.

关键词： Milling (machining)

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Minimizing Age of Collection for Multiple Access in Wireless Industrial Internet of Things

arXiv

引用

arXiv 2023年

作者： Liang, Jiaxin Chan, Tse-Tin Pan, Haoyuan Computer Network and Protocol Research Laboratory Huawei Technologies Shenzhen518060 China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This paper investigates the information freshness of Industrial Internet of Things (IIoT) systems, where each IoT device makes a partial observation of a common target and transmits the information update to a central receiver to recover the complete observation. We consider the age of collection (AoC) performance as a measure of information freshness. Unlike the conventional age of information (AoI) metric, the instantaneous AoC decreases only when all cooperative packets for a common observation are successfully received. Hence, effectively allocating wireless time-frequency resources among IoT devices to achieve a low average AoC at the central receiver is paramount. Three multiple access schemes are considered in this paper: time-division multiple access (TDMA) without retransmission, TDMA with retransmission, and frequency-division multiple access (FDMA). First, our theoretical analysis indicates that TDMA with retransmission outperforms the other two schemes in terms of average AoC. Subsequently, we implement information update systems based on the three schemes on software-defined radios. Experimental results demonstrate that considering the medium access control (MAC) overhead in practice, FDMA achieves a lower average AoC than TDMA with or without retransmission in the high signal-to-noise ratio (SNR) regime. In contrast, TDMA with retransmission provides a stable and relatively low average AoC over a wide SNR range, which is favorable for IIoT applications. Overall, we present a theoretical-plus-experimental investigation of AoC in IIoT information update systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis

Development of a Calibration-Free Brain-Computer Interface U...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Korhan Cengiz Shreyas J Udayaprasad P K Gururaj H L Dilip Kumar S M College of Information Technology University of Fujairah UAE Faculty of Organization and Informatics University of Zagreb Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology.

关键词： Training Machine learning algorithms Artificial neural networks Electroencephalography Brain-computer interfaces Calibration Reliability

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DDoS Detection and Defense Based on FLAD and SDN

DDoS Detection and Defense Based on FLAD and SDN

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jie Dong Wenyu Fang Wanling Zheng Jinkun Liu Yanhua Liu College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm.

关键词： Training Data privacy Network topology Denial-of-service attack Feature extraction Real-time systems Topology Labeling computer crime Long short term memory

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Design and Development of Intelligent Voice Personal Assistant using Python 3

Design and Development of Intelligent Voice Personal Assista...

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3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Appalaraju, Vadaboyina Rajesh, V. Saikumar, K. Sabitha, P. Kiran, K Ravi Department of Electronics and Computer Engineering Andhra Pradesh Visakhapatnam530049 India Dep of ECE Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram522502 India Department of CSE St. Martin's Engineering College Department of IT Gokaraju Rangaraju Institute of Engineering and Technology India

ISBN: (纸本)9781665438117

In this work, describe a virtual personal assistant for smart phones and other devices that acts as a helping hand to improve various personal responsibilities, allowing people to focus on more essential matters. It completes the assignment based on a variety of different questions provided by the voice assistant. As a result, the design and development of an intelligent voice personal assistant is in high demand to make our lives easier and simpler. The current paper assists us in completing user activities such as searching for information about a query, movie details, writing a note, sending a message to a specific person at a specific time, providing location, playing music, date and time, and opening files. It necessitates the usage of a microphone to receive user input. An attempt is made here to construct an intelligent voice personal assistant using Python, which provides the capacity to operate devices based on voice or speech for extracting information to conduct activities on a desktop or other smart device. © 2021 IEEE.

关键词： Speech recognition

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Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising

arXiv

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arXiv 2023年

作者： Zhu, Yuting He, Qiang Yao, Yudong Teng, Yueyang College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07102 United States

Deep learning is a very promising technique for low-dose computed tomography (LDCT) image denoising. However, traditional deep learning methods require paired noisy and clean datasets, which are often difficult to obtain. This paper proposes a new method for performing LDCT image denoising with only LDCT data, which means that normal-dose CT (NDCT) is not needed. We adopt a combination including the self-supervised noise2noise model and the noisy-as-clean strategy. First, we add a second yet similar type of noise to LDCT images multiple times. Note that we use LDCT images based on the noisy-as-clean strategy for corruption instead of NDCT images. Then, the noise2noise model is executed with only the secondary corrupted images for training. We select a modular U-Net structure from several candidates with shared parameters to perform the task, which increases the receptive field without increasing the parameter size. The experimental results obtained on the Mayo LDCT dataset show the effectiveness of the proposed method compared with that of state-of-the-art deep learning methods. The developed code is available at https://***/XYuan01/Self-supervised-Noise2Noise-for-LDCT. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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