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Multi-Module Collaborative Remote Control System for Robot with Target recognition-Tracking-Capture

Multi-Module Collaborative Remote Control System for Robot w...

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IEEE international conference on Real-time computing and Robotics (RCAR)

作者： Deshan Yang Xuesong Wu Kangjia Fu Chong Sun Xiang Zhang Qi Zhang Northwestern Polytechnical University in Xi'an China National Innovation Institute of Defense Technology Beijing China National Innovation Institute of Defense Technology China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

This paper introduces a multi-module collaborative remote control system for real-time soft robot operation, integrating visual recognition, tracking, and capture methods. The system comprises a client-side, including visual, connection, control, and main program modules, and a server-side. The visual module captures real-time images using a camera and performs target recognition assisted with ArUco markers. The connection module manages communication with the server, transmitting real-time images and control commands. The control module comprises a scanning module, a real-time visual tracking module and a capture module. Specifically, under the scheduling of the main program module, the scanning module carries out a global scan of the environment, the visual tracking module identifies and tracks the target in real-time, and the capture module completes the capture of the target. The main program module serves as the core control unit of the entire system. The simulation environment of the robotic arm is carried out on the server side to verify the correctness of the control commands sent and received. Finally, we conduct systematic tests to evaluate the impact of two key parameters that affect the performance of this system, namely tracking precision and cycle interval based on a soft robot designed by ourselves. Experimental results verify the system’s practicability and reliability in target recognition, tracking, and capture tasks.

关键词： Visualization Target tracking Systematics Target recognition Collaboration soft robotics Real-time systems

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international conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2023

International Conference on Intelligent Manufacturing and En...

引用

international conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2023

ISBN: (纸本)9789819967735

The proceedings contain 53 papers. The special focus in this conference is on Intelligent Manufacturing and Energy Sustainability. The topics include: Computational Studies on Single-Wall Rotating Vaneless Diffuser of a Centrifugal Compressor Stage;conceptual Design and Structural Analysis of a Multipurpose Agricultural Robot;condition Based Assessment and Diagnostics of Transformer in Smart Grid Network Using Adaptive Neuro Fuzzy Inference System Framework;Cooling High-Powered LEDs Using an Innovative Fully Automated Heat Pipe System;coupling Code for Real-Time Simulation of a Wave Energy Converter;damage Identification on Aircraft Wing Using Convolutional Neural Network Based pattern recognition;design and Numerical Analysis of Electromagnetic Rail Gun for Defense Applications;design of Energy Efficient Reversible Full Adder and Ripple Carry Adder for Digital computing Applications;Design of Low Power 11T SRAM Cell Using CNTFET Technology;a Review of Biodegradable Oil Derived from Biomass for Internal Combustion Engine Lubrication;designing of Photovoltaic System Using Matlab;development of a Pellet Rotation System for Automated Visual Inspection;development of Conceptual Model for the Introduction Stage of Lean Intelligent Manufacturing Using Simulation Model Based on the Identification of the Appropriate Technologies of Industry 5.0;DSP Based Inbuilt Active PFC Battery Charger;effect of Rotor Tip and Stator Hub Clearance on Aero Performance in Axial Flow Compressors for Aero Gas Turbine Engines;efficiency Enhancement and Energy Optimization in Alloy Steel Drilling: An Experimental and Statistical Analysis;Efficient and Cost-Effective Renewable Energy Integration of Photovoltaic and Hydro in Rural India Using HOMER Pro: A Case Study of Chupki, Punjab.

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Genetic Encryption of Safeguarding IoT Image Data with DNA-Based Security Measures

Genetic Encryption of Safeguarding IoT Image Data with DNA-B...

引用

Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： R. Yuvarani R. Mahaveerakannan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

In order to secure Internet of Things (IoT) devices, this research has developed a revolutionary lightweight encryption method that draws inspiration from the concepts of DNA. Our approach incorporates secret code utilization, SHA-256 hashing, and harnesses the inherent randomness of DNA sequences to generate robust keys resistant to attacks and difficult to decipher. A fundamental aspect of cryptography, the avalanche effect, is crucially addressed, ensuring that even minor alterations in plaintext produce vastly distinct ciphertext, thereby thwarting pattern recognition by potential attackers. Through comprehensive experimentation, the proposed algorithm has demonstrated exceptional performance across key metrics including key size, encryption speed, resistance to deformation, and efficacy of the avalanche effect, outperforming conventional encryption methods.

关键词： Resistance Measurement PSNR DNA Green computing Encryption pattern recognition

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Understanding the Influence of Extremely High-Degree Nodes on Graph Anomaly Detection 27th

Understanding the Influence of Extremely High-Degree Nodes...

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27th international conference on pattern recognition, ICPR 2024

作者： Sun, Xun Xiao, Xi Wang, Zhaoguo Hu, Guangwu Jiang, Xuhui Zhang, Bin Li, Hao Shenzhen International Graduate School Tsinghua University Shenzhen China Ministry of Education Chengdu China Shenzhen China Shenzhen Institute of Information Technology Shenzhen China Institute of Computing Technology CAS Beijing China Peng Cheng Laboratory Shenzhen China National Key Laboratory of Advanced Communication Network Shijiazhuang China

ISBN: (纸本)9783031781827

Graph Anomaly Detection (GAD) has attracted considerable attention for its potential in detecting anomalies. However, an overlooked issue in prior research is the presence of extremely high-degree node, which can introduce noise into GAD, escalate computational costs, and intensify the problem of over-smoothing. To tackle this issue, this paper first presents a novel graph anomaly dataset, NFTGraph, characterized by a notable presence of extremely high-degree nodes. A series of experiments on this dataset sheds light on the influence of such nodes on GAD. Moreover, we introduce a novel model, the Super Node-Aware Graph Neural Network (SNGNN), designed to mitigate the noise emanating from extremely high-degree nodes. Experimental results demonstrate that SNGNN outperforms extant models, achieving an average improvement of over 2% in the Area Under the ROC Curve (AUROC), and effectively reducing noise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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A Proposal of an Improved Maximum Bayes Boundary-Ness Training Method 22

A Proposal of an Improved Maximum Bayes Boundary-Ness Traini...

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5th international conference on Signal Processing and Machine Learning, SPML 2022

作者： Kishishita, Koki Katagiri, Shigeru Ohsaki, Miho Division of Information Science Nara Institute of Science and Technology Japan Graduate School of Science and Engineering Doshisha University Kyoto Japan

ISBN: (纸本)9781450396912

One ultimate objective of statistical pattern recognition is to achieve the optimal status that produces Bayes error and its corresponding Bayes boundary. To compute such parameters of pattern recognizers without requiring unrealistically long computation times and strict parameter assumptions, the Maximum Bayes Boundary-ness (MBB) training method was proposed, and its effectiveness was demonstrated. This method focuses on the fact that the posterior probabilities on the Bayes boundary are equal among the classes that comprise the boundary and aim to directly achieve it without estimating the Bayes error. In the MBB training method, a loss function is defined for each sample, its weighted average was defined as an empirical average loss, and its minimization was a training objective. However, the loss is defined slightly excessively using a convex function. In this paper, we demonstrate the high utility of a simplified MBB training that is defined by correcting or simplifying the formalization of conventional MBB training by removing the convex function in the loss. We experimentally compared five datasets and gained speed in most of them. © 2022 Association for computing Machinery.

关键词： pattern recognition

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Research on Fast Steel Surface Deficiency Detection System based on FPGA 3

Research on Fast Steel Surface Deficiency Detection System b...

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3rd international conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Wang, Yaoyao Liu, Dengfeng School of Artificial Intelligence and Computer Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

Convolutional neural network-related applications have now been developed on a variety of platforms, including CPUs, GPUs, and others, but most of them sacrificed energy consumption to achieve good performance. Therefore, in recent years, more and more hot spots have shifted to how to achieve related applications such as low energy consumption research methods. In order to make the steel surface defect detection system meet the requirements of real-time detection, this article proposes that VGG16 is used as the main network to complete the design of the FPGA's rapid detection and identification system for the FPGA. This system conducts software and hard-hard design based on the ZYNQ-7000 platform (1) The design of parallelization of convolution on the PL side The hardware acceleration is achieved by accelerating the data flow design. Each IP core accelerated with the PL end. (2) In the method of quantifying the data, in the case of almost little loss of data accuracy, the use of resources on FPGA films has been greatly reduced to achieve acceleration of algorithms. The final experimental results show that compared with the CPU, this algorithm has increased by 6 times. The power consumption ratio of the CPU platform and the FPGA platform is 12.6, and the power consumption ratio of the GPU platform and the FPGA platform is 38.2, which is more suitable for applications on the embedded platform. © 2023 SPIE.

关键词： Field programmable gate arrays (FPGA)

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Intrusion Detection System based on pattern recognition using CNN

Intrusion Detection System based on Pattern Recognition usin...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： k. Azarudeen S.Harish Kumar T.V. Aswin Vijay P. Thirukumaran V.S Barath Balaji Department of CSE Velammal college of engineering and technology Madurai India

The Intrusion Detection System (IDS) is a security tool that uses pattern recognition algorithms to detect unusual activity in a network. IDS-based pattern recognition techniques search through network traffic for patterns that might indicate a security issue. IDS-based pattern recognition may find attacks of various types, such as malware infections, brute-force attacks, and insider threats. IDS-based pattern recognition techniques may be trained using methods based on machine learning, which allows the system to learn from previous attacks and improve its accuracy. Even though signature-based IDS analyzes network data for known attack patterns, anomaly-based IDS looks for anomalous network behaviour. IDS uses pattern recognition-based machine learning methods to enhance detection precision while decreasing false positives. The ability to identify previously unidentified attacks and the flexibility to shifting attack patterns are just two advantages of pattern recognition-based IDS. The 2 categories of pattern recognition-based IDS are signature-based and anomaly-based systems. Security staff can get alerts and notifications, that allows them to respond quickly to reduce security hazards. IDS-based pattern recognition systems rely on training data to learn what constitutes a security threat. If the training data is biased or incomplete, this can lead to inaccurate or incomplete detection. For example, if the training data only includes examples of attacks from a specific geographic region or industry sector, the system may not be able to detect attacks that occur outside of these parameters

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pattern Storage & Recalling Using Hopfield Neural Network and HOG Feature Based SVM Classifier: An Experiment with Handwritten Odia Numerals 5

Pattern Storage & Recalling Using Hopfield Neural Network an...

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5th international conference on Emerging Smart computing and Informatics, ESCI 2023

作者： Sharma, Naman Kalra, Karan Sarangi, Pradeepta Kumar Rani, Lekha Saxena, Merry Kumar, Santosh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Siksha 'o' Anusandhan University Bhubaneshwar India

ISBN: (纸本)9781665475242

Handwritten Odia text identification is very useful for the society and also a challenging task as it may include a variety of writing styles, complicated character-touching, and abundant categories of character class. This work implements two different classifiers for handwritten Odia numerals. The first experiment involves the implementation of a Hopfield neural network with binary image as the feature set. Using the feature extraction technique based on the Histogram of Oriented Gradients (HOG), a Support Vector Machine (SVM) classifier is implemented in a second experiment. Different cell sizes have been used to extract HOG features and finally the best cell size with optimal features have been used. The data set has been created on Microsoft Paint by drawing the digits manually and also collected from different users. The Hopfield model has been implemented with four different data sets (0% noise, 10% noise, 20% noise and 30% noise). Addition of noise to the data has been done through programming. Both the models (Hop field and SVM) have been implemented through MATLAB and the data set has been used for testing the models. Results prove that the Hopfield model's performance is higher than the SVM classifier whereas, the recognition accuracy for Hopfield model is decreasing with increase in noise percentage and could be considered as poor performance. © 2023 IEEE.

关键词： Binary images

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Supervised Machine Learning: Discriminative Learning Methods for pattern Classification using HELM 5

Supervised Machine Learning: Discriminative Learning Methods...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Shirisha, N. Kiran, Ajmeera Prashanthi, A. Reddy, MrKamjula Lakshmi Kanth Mlr Institute of Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Computer Science and Engineering India Anurag University Department of Artificial Intelligence Hyderabad India

ISBN: (纸本)9798350321425

The ability to forecast the data is significantly helped by machine learning's involvement. At the present time, data analysis is playing a very significant part in both the Information Technology (IT) industry and academic institutions. Numerous real-time applications, such as translation in real time, facial recognition, musical compositions, and so on, have been developed through the various initiatives that have been carried out up to this point. According to the standards that must be met by every data analyst, data must first be preprocessed before any predictions can be made on that data. Predictions must also be based exclusively on the preprocessed data. This work demonstrates the data pre-processing along with the prediction of a churn model. The model contains a set of records of various customers, including information regarding whether or not the client possesses credit cards and whether or not the consumer is an active member. This research works on the construction of a hybrid ensemble learning model (abbreviated HELM) to collect heterogeneous weak learners in order to anticipate such analyses. In order to work on the Churn Model classification, a variety of machine learning algorithms are integrated with one another. The findings revealed the efficiency of each machine learning algorithm that was used, in addition to HELM's operational effectiveness. © 2023 IEEE.

关键词： Forecasting

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Generative Approach Using soft-Labels to Learn Uncertainty in Predicting Emotional Attributes 9

Generative Approach Using Soft-Labels to Learn Uncertainty i...

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9th international conference on Affective computing and Intelligent Interaction (ACII)

作者： Sridhar, Kusha Lin, Wei-Cheng Busso, Carlos Univ Texas Dallas Multimodal Signal Proc MSP Richardson TX 75080 USA

ISBN: (纸本)9781665400190

This paper presents a novel speech emotion recognition (SER) method to capture the uncertainty in predicting emotional attributes using the true distribution of scores provided by annotators as ground truth (i.e., soft-labels). Reliable, generalizable, and scalable SER systems are important in areas such as healthcare, customer service, security, and defense. A barrier to build these systems is the lack of quality labels due to the expensive annotation process, leading to poor generalization. To address this limitation, this study proposes a semi-supervised generative modeling approach using a variational autoencoder (VAE) with an emotional regressor at the bottleneck trained with soft-labels of emotional attributes. We demonstrate that estimating uncertainties in predicting emotional attribute scores is possible with soft-labels. We analyze the benefits of uncertainty estimation with a reject option formulation, where the model can abstain from predicting emotion when it is less confident. At 60% test coverage, we achieve relative improvements in concordance correlation coefficient (CCC) up to 16.85% for valence, 7.12% for arousal, and 8.01% for dominance. Furthermore, we propose an uncertainty transfer learning strategy where uncertainties learned from one attribute are used as a sample re-ordering criterion for another attribute, achieving additional improvements in prediction performance for valence. We also demonstrate the generalization power of our method in comparison to other uncertainty estimating methods using cross-corpus evaluations. Finally, we demonstrate that our method has lower computational complexity than alternative approaches.

关键词： Generative Models Variational Autoencoder Speech Emotion recognition Emotional Attributes

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