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RIME-SVM for Electromagnetic Radiation Signal Identification in Coal Mining Environment

RIME-SVM for Electromagnetic Radiation Signal Identification...

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IEEE international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Pengxue Zhu Shiqi Huang Le Liu Zheng Zhang China West Normal University Nanchong China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In this study, an innovative signal classification and recognition system is proposed for the early warning needs of disasters such as impact ground pressure in coal mining. The RIME-SVM model is introduced, which combines the Rime Optimization Algorithm and Support Vector Machine to optimize the classifier parameter configuration and significantly improve the classification performance. Experiments have demonstrated that the model has excellent signal classification ability in industrial environments, which is especially suitable for the identification of electromagnetic radiation signals in coal mine environments. Compared with the traditional method, RIME-SVM significantly improves the classification accuracy, demonstrates the efficiency and accuracy of processing complex industrial signal data, and brings important progress to the field of coal mine safety monitoring technology.

关键词： Support vector machines Time-frequency analysis Accuracy pattern classification Electromagnetic radiation Data models Coal mining Classification algorithms Optimization Monitoring

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5th international conference on Inventive Computation Technologies, ICICT 2022 - Proceedings

5th International Conference on Inventive Computation Techno...

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5th international conference on Inventive Computation Technologies, ICICT 2022

ISBN: (纸本)9781665408370

The proceedings contain 243 papers. The topics discussed include: provably secure pseudonyms based authentication protocol for wearable ubiquitous computing environment;analysis of dc motor speed control and interfacing it with drowsiness detection;height and weight estimation of an individual from virtual visuals;projective synchronization of uncertain chaotic systems with different dimensions and external noise disturbance;design and implementation of a rescue and surveillance robot using cross-platform application;design of voice controlled multifunctional computer numerical control (CNC) machine;Bangla font recognition using transfer learning method;electroencephalography (EEG) based home automation for physically challenged people using brain computer interface (BCI);optimization of redirection algorithm based on cache technology in programming implementation of college students' mental health education information platform;a resistance detection device for microelectronic semiconductors: from signal detection to fusion analysis;and research on positioning accuracy of welding robot based on visual data mining and pattern fusion analysis algorithm.

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Theoretical Framework for Integrating IoT and Explainable AI in a Smart Home Intrusion Detection System

Theoretical Framework for Integrating IoT and Explainable AI...

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Contemporary computing and Communications (InC4), IEEE international conference on

作者： Mahi Dixit Swetha Maria Siby Jenefa J Divya Vetriveeran Rakoth Kandan Sambandam Vinodha D Department of Computer Science and Engineering CHRIST University Bengaluru India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Using IoT devices in smart homes brings benefits and security dangers. This study extensively examines various intrusion detection methods within smart home environments. It also suggests a novel hybrid intrusion detection theoretical framework integrating IoT data with Explainable Artificial Intelligence (XAI) approaches. Using information from multiple IoT devices, including motion sensors, door/window sensors, cameras, and temperature sensors, our theoretical framework can create a comprehensive image of the home environment. By effectively detecting new threats, it offers anomaly detection utilizing unsupervised learning approaches to discover potential breaches without tagged data.

关键词： Temperature sensors Temperature measurement Explainable AI Intrusion detection Smart homes Motion detection pattern recognition

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Feature Fusion of LBP, HELBP & RD-LBP for Face recognition

Feature Fusion of LBP, HELBP & RD-LBP for Face Recognition

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international conference on Distributed computing and Optimization Tech-niques (ICDCOT)

作者： Karanwal, Shekhar Diwakar, Manoj Graph Era Univ CSE Dept Dehra Dun India

ISBN: (纸本)9789811922817;9789811922800

As literature suggests that single descriptor fails to produce desired results therefore the proposed work launches novel Fused Local Descriptor (FLD), by merging 3 state of art local descriptors. The services of FLD was taken from LBP, HELBP & RD-LBP. As all these 3 descriptors adopts different concepts therefore their integration is done. In LBP, the gray value (neighbors of patch (3 x 3 size)) are involved in comparison to center gray value. In HELBP, the gray value (horizontal neighbors of patch (3 x 5 size)) are involved in comparison to center gray value and in RD-LBP, the different scale pixels are compared. PCA is bring next for reduction and classification is further done by SVMs. FLD attains staggering rates than either of LBP, HELBP & RD-LBP. FLD also proves its significance in front of several other literatures based ones. ORL & GT are used for evaluation in MATLAB R2018a environment.

关键词： Horizontal Elliptical LBP (HELBP) Local Binary pattern (LBP) PCA Radial Difference LBP (RD-LBP) SVMs

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Research on Artificial Intelligence (AI) recognition Automated soft-Shell Crab Breeding System under “Internet +” Technology

Research on Artificial Intelligence (AI) Recognition Automat...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Lei Zheng Mingmin Gong Zhenyang Li Mingming Yang School of Business Shenzhen Institute of Technology Shenzhen China School of Information Engineering Wuhan College Wuhan China School of Information and Communication Shenzhen Institute of Technology Shenzhen China

This project aims to use artificial intelligence technology to raise crabs in land equipment, so as to easily obtain soft-shell crabs, to increase the supply of new aquatic products in the market, to meet the needs of market consumption upgrading, and finally to achieve the goal of economic benefits. This project is based on artificial intelligence to identify automatic soft-shell crab cultivation technology, combined with scientific breeding management methods, the general crab to soft-shell crab cultivation. The equipment uses the Internet of Things, big data, cloud computing, computer vision and other technologies to design five systems, including soft shell crab mounting identification system, water quality monitoring system, three-dimensional breeding system, automatic feeding system, integrated control system, to realize the local and remote all-round intelligent monitoring of soft-shell crab breeding base. In this paper, STC15F2K60S2 as the core of the design of MCU based hardware platform is presented. RS485 communication mode is used to collect data of each sensor in real time, and multiple environmental parameters can be monitored in real time. Using video surveillance technology, the pond can be secured, and the pond can be photographed on the surface and underwater. The feeding device is controlled by PLC and the feeding machine and aerator are controlled intelligential. The network uses ESP8266 wireless network to realize network connection to AP. Through the establishment of the “server” management software, we enable users to remote query through the computer browser, mobile APP and other ways in the case of network.

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Intelligent Diagnosis of Pipeline Defects Based on Extreme Learning Machine 2

Intelligent Diagnosis of Pipeline Defects Based on Extreme L...

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2nd international conference on Electronics, Communications and Information Technology, CECIT 2021

作者： Xiong, Jingyi Liang, Wei Ding, Yu Yao, Junming CUP Beijing China Jiangsu Xukuang Energy CO.LTD Safety Supervision Xuzhou China

ISBN: (纸本)9781665437578

In order to carry out effective defect diagnosis of in-service oil and gas pipelines to ensure the safe and stable operation of oil and gas pipelines, this article proposes a pipeline defect intelligent diagnosis method based on particle swarm optimization and extreme learning machine. First, extract the defect signal features to form a feature vector set;then use the particle swarm algorithm to optimize the extreme learning machine to obtain hidden node thresholds;finally, divide the feature vector set into a testing set and a training set, and use improved extreme learning The machine is used as a pattern recognition algorithm for defect pattern recognition. Experimental results show that the method proposed in this paper can effectively perform fault pattern recognition, with a recognition accuracy of 92%, and is suitable for intelligent diagnosis of pipeline defects. © 2021 IEEE.

关键词： pattern recognition

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Enhanced Classification of Gravitational Wave Images using CNN Model

Enhanced Classification of Gravitational Wave Images using C...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Aditya Sharma Inderdeep Kaur Paurav Goel Master of Engineering - Artificial Intelligence Chandigarh University Chandigarh India Department of Computer Science Chandigarh University Chandigarh India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The discovery of gravitational waves resulting from the merger of binary black holes revolutionized physics and astronomy. Amid signal complexity and noise capture, data performance is critical. This work presents a convolutional neural network (CNN) algorithm using real-time data from the "Gravity Spy" dataset at https://***/datasets/tentotheminus9/gravity-spy-gravitational-waves. A combination of CNN model and Fourier transform accuracy of gravitational wave signal expression. It is worth noting that frequency domain signal analysis is better than time domain signal analysis in capturing signal characteristics. Experimental results demonstrate the effectiveness of CNN in improving signal recognition by achieving an accuracy of 94.63%. This research helps advance the exploration of the void by proposing a deeper study, thus promoting a deeper understanding of astrophysical phenomena.

关键词： Deep learning Analytical models Accuracy Gravitational waves pattern classification Real-time systems Convolutional neural networks

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Instance Segmentation in Mobile computing Environments for Identification of Specific Characteristics in Endangered Species 17

Instance Segmentation in Mobile Computing Environments for I...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Ribeiro, Morgana C. O. Monteiro, Rhayane S. Moreira, Mario W. L. Rodrigues, Joel J. P. C. Fed Inst Educ Sci & Technol Ceara IFCE Aracati CE Brazil Fed Univ Piaui UFPI Teresina PI Brazil Inst Telecomunicacoes Aveiro Portugal

ISBN: (纸本)9781728186160

The segmentation of instances is a key topic in image processing and computer vision. There are numerous applications such as medical image analysis, video surveillance, image compression among others, in which its algorithms show significant results. Due to the COVID-19 pandemic, most countries have been affected mainly by their economy. In the food production sector, including fishing and aquaculture, it was no different. In this context, this research has as main objective to contribute to the 2030 Agenda for Sustainable Development suggested by the United Nations (UN), through a fish detection and segmentation model based on the framework Detectron2, optimizing the time of professionals in identifying specific characteristics of a particular species. To achieve this objective, this research seeks to facilitate the recognition of patterns of parts of the fish from the segmentation of instances and to stimulate scientific research in the area through the morphological information collection of certain species. The results present an accuracy, based on the Intersection over Union (IoU) indicator, of 88.4%, providing an effective solution for the collection of these characteristics.

关键词： Mobile computing pattern recognition Instance Segmentation Convolutional Neural Networks Detectron2

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Detection of Region-of-Interest Based on Noise Filtering in High-Speed Images 1

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4th international conference on Innovative computing, IC 2021

作者： Jang, Seok-Woo Anyang University Anyang14028 Korea Republic of

ISBN: (数字)9789811642586

ISBN: (纸本)9789811642579

Relatively inexpensive cameras have become common that can film even minute movements of moving objects at high speeds. This paper introduces a method of removing noise pixels abnormally generated from the ultra-high-speed images entered, and then detecting the skin color area of a person, the area of interest that represents personal information from the images of noise removed. The proposed algorithm eliminates noises by applying bilateral filters from the high-speed images that are first accepted. Then, using color models created through learning, the human skin area, which is an area of interest, is accurately extracted from the image of noise removed. The experimental results show that the proposed algorithm effectively removes noises from the high-speed images entered and then robustly detects the area of interest, the skin color area. The method introduced in this study is expected to be useful in many applications related to pattern recognition, such as image analysis and understanding, video pre-processing, noise removal, etc. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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Comprehensive Analysis of Deep Learning for Accurate Knee Joint Segmentation and Classification

Comprehensive Analysis of Deep Learning for Accurate Knee Jo...

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Electrical Electronics and computing Technologies (ICEECT), international conference on

作者： Bhagyashree Maharana Avinash Kumar Sharma Khushi Samridhi Yojna Arora Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida U.P. India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Knee joint segmentation and classification are critical tasks in medical imaging, having applications in diagnosis, treatment planning, and surgical navigation. The intricate architecture of the knee joint and the variation in knee joint imaging, however, can make these tasks difficult. Deep learning has become a viable method for classifying and segmenting knee joints. From massive knee joint picture datasets, deep learning models may be trained to recognize intricate patterns, and they can produce state-of-the-art performance on certain tasks. In order to divide and classify knee joints, this paper discusses current developments in deep learning. The paper talks about the various training approaches and deep learning architectures applied to these challenges. Furthermore, the paper provides an extensive summary of the cutting-edge functionality of deep learning.

关键词： Deep learning Training Image segmentation Accuracy Navigation Surgery Bones Planning pattern recognition Tumors

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