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A Visual Impaired Simulator to achieve Embedded Accessibility Designs

A Visual Impaired Simulator to achieve Embedded Accessibilit...

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2009 IEEE international conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Konstantinos Votis theofanis Oikonomou Peter Korn Dimitrios Tzovaras Spiridon Likothanassis Informatics and Telematics Institute 6th km Charilaou-Thermi RoadThessalonikiGR-57001 Greece Pattern Recognition Laboratory Computer Engineering and InformaticsUniversity of Patras RioPatrasGreece

the ever increasing of software technologies has brought closer technology to users with visual impaired *** diversification poses a real challenge to "authors" (developers and designers) when creating software that has to cope with a myriad of interaction situations,as well as specific directives for ensuring an accessible *** an advanced accessibility simulation tool,"authors" can obtain a better understanding of the accessibility constraints for visual impaired users within a Java Swing application by simulating each *** proposed NetBeans-enabled plugin tool will assist them,with a minimal effort,to explore user-centered design and important accessibility issues for their Java Swing implementations

关键词： Human Computer Interaction,Web Accessibility,Simulation,software design,User-centered design

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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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Defect Identification During Pulse Mode Laser Welding Process through the pattern recognition Analysis of the Acquired Sound Frequency Spectrum 6th

Defect Identification During Pulse Mode Laser Welding Proces...

引用

6th international conference in Mechanical Engineering Research, ICMER 2021

作者： Yusof, M.F.M. Ishak, M. Salleh, M.N. Ghazali, M.F. Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Pahang Pekan Malaysia Centre for Research in Advanced Manufacturing University Malaysia Pahang Pahang Pekan Malaysia

ISBN: (纸本)9789811914560

Problems on laser weld quality still remain as vital issue even though the process was done with optimized condition which results the demand on robust monitoring method during the process. Until recently, many methods have been explored and air-borne acoustic are among of methods that have been proven to be able to detect the presence of defect. However, despite detection, it is essential if the type of defect could be identified as it gives different severity level to the development of failure. this work presents the identification of defect during pulse mode laser welding through the analysis of sound. In achieving the goal of this study, bead on plate weld have been done onto the 22MnB5 boron steel plate repeatedly based on 3 different set of experiment with the variation in the level of parameters. Simultaneously, time-series sound signal was acquired along the process before it was converted into frequency spectrum before further analysis. According to the result, it was recorded that the variation of parameters level in pulse mode laser welding process lead to the presence of porosity and crack. Relatively, the trend of sound frequency spectrum were also significantly changes its trend in respond to the parameters level variation. It was discovered that the dominant frequency for the signals acquired from the process which produce good quality weld, porosity and crack recorded the same range which was between 5 to 7 kHz. Uniquely, the existence of porosity could be identified by the occurrence of peak at around 9 kHz while the presence of crack could be recognized by the occurrence of peak at 8 kHz and 11 kHz. this trend was proven to be consistent in repeated experiment according to the result from principal component analysis. Based from the result in this study, it could be conclude that the identification of defect could be done by the analysis of the acquired sound during the process. Significantly, this would expand the ability of acoustic method for q

关键词： Laser beam welding

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Normalization: A Tool for Controlling Concentration Effects in Fuzzy Set Aggregation

Normalization: A Tool for Controlling Concentration Effects ...

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Electrical Engineering and Control Technologies (CEECT), conference on

作者： Xiaofei Zhao Fanglin Guo School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou China School of Information Engineering Lanzhou Petrochemical University of Vocational Technology Lanzhou China Digital Business Department State Grid Gansu Electric Power Company Lanzhou China

ISBN: (数字)9798331528096

ISBN: (纸本)9798331528102

this paper addresses the concentration effect, a common issue in fuzzy set aggregation, particularly when using operators like the minimum, which can overemphasize lower membership degrees. We propose normalization as a simple yet effective technique to mitigate this problem, introducing two distinct approaches: pre-aggregation normalization, where individual fuzzy sets are normalized before aggregation, and postaggregation normalization, where the aggregated set is normalized. We analyze the impact of these methods on key aggregation properties, such as idempotency and monotonicity, revealing a trade-off between property preservation and concentration reduction. Using min-max normalization as a case study, we demonstrate through experimental results that both approaches, especially post-aggregation normalization, effectively counteract concentration effects, often matching or outperforming more complex techniques while maintaining computational efficiency. the paper provides a detailed algorithmic implementation of both approaches, enabling easy integration into existing fuzzy logic systems. the simplicity, adaptability, and effectiveness of normalization make it a valuable tool for enhancing the accuracy, interpretability, and robustness of fuzzy set aggregation in diverse applications, including multi-criteria decision making, pattern recognition, fuzzy control, and image processing.

关键词： Fuzzy logic Electrical engineering Fuzzy sets Fuzzy control Accuracy Image processing Robustness pattern recognition MCDM Computational efficiency

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Intrusion Detection on Industrial Networks Using Neuromorphic Computing

Intrusion Detection on Industrial Networks Using Neuromorphi...

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international conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Ali Mehrabi André Van Schaik International Centre for Neuromorphic Systems Western Sydney University

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

Cyber-attacks on Industrial Control Systems (ICS) present critical risks to operational stability, public safety, and national security. As industrial networks become more integrated and interconnected, their susceptibility to sophisticated cyber threats increases. Traditional security measures often fall short in detecting evolving attack patterns. this paper investigates the application of neuromorphic computing, which emulates the human brain's pattern recognition capabilities, as an innovative approach to intrusion detection in industrial networks. We introduce a hardware-based Spiking Neural Network (SNN) engineered to monitor and analyze communication packets in real time, detecting anomalies indicative of potential intrusions. the neuromorphic approach offers a promising solution to the growing challenge of cyber-attacks in critical infrastructure. the proposed system is implemented on an FPGA, focusing initially on monitoring key packet types such as LLDP, ARP, UDP, TCP, and Profibus RT, with scalability to accommodate additional protocols. the results demonstrate the efficacy of this SNN-based intrusion detection system in providing real-time, efficient, and accurate monitoring of industrial networks.

关键词： Neuromorphic engineering Scalability Intrusion detection Spiking neural networks Real-time systems Stability analysis Public security Security Monitoring Cyberattack

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Searching Efficient Models for Human Activity recognition 21

Searching Efficient Models for Human Activity Recognition

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2021 6th international conference on Biomedical Imaging, Signal Processing

作者： Shamisa Kaspour Nikhil Raj Alankrit Mishra Abdulsalam Yassine thiago Eustaquio Alves De Oliveira Department of Computer Science Lakehead University Canada Department of Software Engineering Lakehead University Canada

ISBN: (纸本)9781450385817

Human Activity recognition (HAR) can be measured in various ways in a new era of growing technologies. this paper studies different classification and data processing tasks. this paper proposes using HAR to monitor the elderly while being power-efficient and respecting an individual’s privacy, allowing it to be run on mobile devices like smartphones or smartwatches. Upon reviewing other methods of HAR by sensor data, we realized that they severely lacked in the areas mentioned earlier. Moreover, they either used older classification techniques or made too complex and over-the-top models for the same. We tested a total of nine methods to find the best model/method, from simple support vector machines (SVM) and convolutional neural networks (CNN) to hybrid models. the best results were produced by a simple, fully connected network (multi-layer perceptron) with the data condensed using Fisher’s linear discriminant analysis (FLDA) that gave us 98.6% accuracy. Our final model satisfies both the requirements we had set; it is simplified and produces benchmark results.

关键词： pattern recognition Linear Discriminant Analysis Smartphone Sensor Data Human Activity recognition

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Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks

Every Corporation Owns Its Image: Corporate Credit Ratings v...

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international conference on Computer and Communications (ICCC)

作者： Bojing Feng Wenfang Xue Bindang Xue Zeyu Liu Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China School of Astronautics Beihang University Beijing China Tianjin Academy for Intelligent Recognition Technologies Tianjin China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. there have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently.

关键词： Neural networks Machine learning Predictive models Feature extraction Data models Convolutional neural networks Reliability

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Promo-Zen: A Natural Language Processing Based Promotional Text Message Management System

Promo-Zen: A Natural Language Processing Based Promotional T...

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Advancements in Computing (ICAC), international conference on

作者： Chamath Udugampola Nethmi Weerasingha Department of Computer and Data Science NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Promotional Short Message Service (SMS) messages, which provide frequent updates about deals and discounts to consumers, are crucial in developing countries like Sri Lanka, as they help alleviate financial pressure during economic crises. However, poor practices by the brands, SMS technology limitations and human memory limitations challenge the ability of consumers to make effective use of this facility. As a solution, this project aimed to develop an Android application to organize and enhance the readability of promotional messages, while also providing mechanisms to prevent users from forgetting promotions. these objectives were achieved by creating a Kotlin app that displays promotional information in a date-sorted weekly dashboard, offers deal-expiration reminders, and optimizes message content for readability. the system utilizes a novel workflow/pipeline combining a Natural Language Processing (NLP) model with Named Entity recognition (NER) capabilities, Regular Expressions and custom logic, to understand the context of and extract the crucial information from the promotional messages. User-acceptance testing carried out demonstrates positive results, reducing traditional access times to promotions of 8–10 seconds to approximately 2 seconds through this application. Additionally, user-testers reported multiple occurrences of the reminders feature ensuring that promotions weren't forgotten before use.

关键词： Named entity recognition Developing countries Message services Feature extraction Logic Data mining pattern matching Testing Context modeling

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Person Attributes Extraction in Profiles Based on SVM and pattern

Person Attributes Extraction in Profiles Based on SVM and Pa...

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2015 6th IEEE international conference on Software Engineering and Service Science(ICSESS 2015)

作者： Zhen Zhu Yuan Sun School of Information Engineering Minzu University of China

this paper is an exploration to find a way to get the person attributes in profiles. Considering those attributes exists in large volume of unstructured data, and it is very difficult to gain in a short time. So, we use a method combing the pattern and SVM to extract the person attributes. Firstly, we collect many raw profiles in websites by our configurable crawler. Secondly, we use statistic methods to do pre-processing works include lexical analysis and name recognition. thirdly, we build the patterns, which can use in model to extract the person attributes. Also we generalize those patterns to SVM features. Finally, we use SVM assisted with pattern-based method to predict the person attributes. the results prove the method is effective and the data we extracted is useful in building specificareas' expert database and information retrieval.

关键词： Person attributes extraction Expert database SVM pattern-based extraction

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Blink Detection Algorithm Based on BP Neural Network 20

Blink Detection Algorithm Based on BP Neural Network

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Proceedings of the 6th international conference on Robotics and Artificial Intelligence

作者： Zhenghao Hu Hao Zhai Aomei Li Zhizhong Xi Zhaozhen Jiang Beiyao Cui Army artillery Air Defense Academy China

ISBN: (纸本)9781450388597

Blinking is a very important behavior feature for fatigue driving detection. It is of great significance to detect the blink times regularly for reducing traffic accidents. therefore, this paper proposes a blink detection algorithm based on BP neural network algorithm. the algorithm collects the driver image data by raspberry pie camera, uses AdaBoost detection algorithm with Haar feature to locate the position of face and eye, and extracts the information of human eye. the extracted eye information is used as the input neuron of BP neural network, and LM algorithm is used to train the neural network model to achieve the function of blinking detection. the experimental results show that the algorithm can recognize blink better, the accuracy rate can reach 98.34%, and the speed is about 20 times of the eye feature point aspect ratio (EAR) algorithm.

关键词： fatigue driving BP neural network pattern recognition vehicle equipment blink detection

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