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Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys), IEEE international conference on

作者： Qiuxian Li Quanxing Zhou Qixin Pan Lingyun Wang Hongfa Ding College of Big Data Engineering Kaili University Kaili China College of Information Guizhou University of Finance and Economics Guiyang China

ISBN: (数字)9781728176512

ISBN: (纸本)9781728176529

In order to solve the problem of low efficiency of traditional secret sharing communication, we design a new rational secret sharing scheme based on BP neural network under the framework of game theory. We first propose an extended system game model for rational secret sharing, and define the set of participants, information set, feasible strategies, and utility functions of the secret sharing game. Secondly, using the characteristics of pattern recognition and classification of BP neural network, the secret input and output training patterns are correlated to realize nonlinear mapping in the neural network, thereby identifying the sub-secrets owned by honest and rational participants and recovering the correct secret. We design a communication channel by using the random function of bilinear pair on elliptic curve to ensure interactive communication among participants and reduce the communication complexity of the solution. Finally, the analysis proves the effectiveness and security of this scheme. By comparing with the existing scheme, this scheme not only has higher communication efficiency, but also meets its security requirements.

关键词： Training Neural networks Games Resists pattern recognition Cryptography Game theory

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OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features

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JOURNAL OF bioinformatics AND COMPUTATIONAL BIOLOGY 2016年第1期14卷 1650006-1650006页

作者： Pian, Cong Zhang, Jin Chen, Yuan-Yuan Chen, Zhi Li, Qin Li, Qiang Zhang, Liang-Yun Nanjing Agr Univ Coll Sci Nanjing 210095 Jiangsu Peoples R China

MicroRNAs (miRNAs) are a set of short (21-24 nt) non-coding RNAs that play significant regulatory roles in the cells. Triplet-SVM-classifier and MiPred (random forest, RF) can identify the real pre-miRNAs from other hairpin sequences with similar stem-loop (pseudo pre-miRNAs). However, the 32-dimensional local contiguous structure-sequence can induce a great information redundancy. therefore, it is essential to develop a method to reduce the dimension of feature space. In this paper, we propose optimal features of local contiguous structure-sequences (OP-Triplet). these features can avoid the information redundancy effectively and decrease the dimension of the feature vector from 32 to 8. Meanwhile, a hybrid feature can be formed by combining minimum free energy (MFE) and structural diversity. We also introduce a neural network algorithm called extreme learning machine (ELM). the results show that the specificity (S-p) and sensitivity (S-n) of our method are 92.4% and 91.0%, respectively. Compared with Triplet-SVM-classifier, the total accuracy (ACC) of our ELM method increases by 5%. Compared with MiPred (RF) and miRANN, the total accuracy (ACC) of our ELM method increases nearly by 2%. What is more, our method commendably reduces the dimension of the feature space and the training time.

关键词： RNA germ cells pattern recognition algorithm

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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...

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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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Enhancing Typing Dynamics Emotion recognition: A Multi-Class XGBoost Approach for Accurate Sentiment Detection

Enhancing Typing Dynamics Emotion Recognition: A Multi-Class...

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Advancements in Computing (ICAC), international conference on

作者： Sudesh Bandara tharindu Dasath Senula Nanayakkara Samadhi Rathnayake thusithanjana thilakarthna Faculty of Computing Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

this research paper aims to enhance the accuracy and efficiency of emotion recognition through keystroke dynamics by utilizing a multi-class XGBoost model. the study addresses the limitations observed in previous models, such as Support Vector Machines and Logistic Regression, which struggled with the complex, high-dimensional nature of keystroke data. the primary objective was to develop a robust model capable of capturing the subtle variations in typing behavior associated with different emotional states. To achieve this, a comprehensive approach was adopted, involving the collection of detailed keystroke datasets, advanced feature engineering, and systematic hyperparameter tuning. the XGBoost model was chosen for its superior ability to manage complex interactions within large datasets. through rigorous evaluation, the XGBoost model outperformed traditional models in accuracy, precision, and recall, demonstrating significant improvements in emotion classification tasks. the findings of this study reveal that XGBoost, when combined with meticulous feature engineering and hyperparameter optimization, offers a substantial advancement in the field of emotion recognition via keystroke dynamics. the research not only confirms the effectiveness of XGBoost in this domain but also provides new insights into optimizing machine learning models for emotion detection. these results have broad implications for the development of more emotionally aware computing systems, potentially improving user interaction across various applications.

关键词： Support vector machines Emotion recognition Logistic regression Adaptation models Accuracy Systematics Computational modeling Keystroke dynamics pattern recognition Tuning

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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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Hypervelocity damage pattern recognition on Aluminum alloy based on acoustic emission

Hypervelocity damage pattern recognition on Aluminum alloy b...

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international conference on Information Science and Control Engineering (ICISCE)

作者： Kai Zhang Baojun Pang Runqiang Chi Hongyu Jiao Department of Mechanical Engineering Changshu institute of technology Changshu China Department of Space Engineering Mechanics Harbin institute of technology China

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

Space debris is a threat to manned spacecraft due to its high velocity, a method based on BP neural network is presented to estimate the degree of hypervelocity impact damage. In this research, projectiles were fired by two stage light gas gun to impact single aluminum plate, the damage formed on target plate was a crater or a perforating hole. the impact signals were obtained by ultrasonic sensors. According to the signal characteristics of different damage mode, signal amplitude, S2 modal energy, proportion of high-frequency energy and signal propagation distance were chosen as the input characteristic parameters to build a neural network. Experimental results showed that the trained network can be used to identify damage patterns, the correct rate was higher than 85%. It can also predict the depth of crater and hole diameter.

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Analysis of Complex Network Topological Features for EEG Signals Classification

Analysis of Complex Network Topological Features for EEG Sig...

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Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Ping Zhen Yuanyuan Yue Qingshan Liu School of Mathematics Southeast University Nanjing Jiangsu China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Electroencephalogram (EEG) signals provide direct insights into the physiological activities of brain, making EEG analysis valuable for understanding cognitive processes and neurological disorders. the main objective of this study is to investigate the effectiveness of extracting combined topological features for epilepsy recognition based on EEG data. We utilize visibility graphs and persistent diagrams to extract both graph features and homological features. these features are then combined to enhance the classification performance, surpassing the results achieved by using graph features alone. Notably, our approach achieves high accuracy rates of 100% for classifying Z and S, 99.75% for N and S, and 97.88% for F and S. these findings significantly contribute to the field of EEG-based epilepsy classification and provide valuable insights for further research and investigation.

关键词： Neurological diseases Time series analysis Epilepsy pattern classification Feature extraction Electroencephalography Physiology Data mining Signal analysis Optimization

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U-stroke pattern modeling for end user identity verification through ubiquitous input device

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2015年 9339卷 219-230页

作者： Bhattasali, Tapalina Chaki, Nabendu Saeed, Khalid Chaki, Rituparna Department of Computer Science and Engineering University of Calcutta Kolkata India Bialystok University of Technology Bialystok Poland A.K.Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (纸本)9783319243689

Identity verification on ubiquitous input devices is a major concern to validate end-users, because of mobility of the devices. User device interaction (UDI) is capable to capture end-users’ behavioral nature from their device usage pattern. the primary goal of this paper is to collect heterogeneous parameters of usage patterns from any device and build personal profile with good-recognition capability. this work mainly focuses on finding multiple features captured from the usage of smart devices;so that parameters could be used to compose hybrid profile to verify end-users accurately. In this paper, U-Stroke modeling is proposed to capture behavioral data mainly from smart input devices in ubiquitous environment. In addition to this, concept of CCDA (capture, checking, decision, and action) model is proposed to process U-Stroke data efficiently to verify enduser’s identity. this proposal can draw attention of many researchers working on this domain to extend their research towards this direction. © IFIP international Federation for Information Processing 2015.

关键词： Touch screens

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Transformer Partial Discharge recognition Based on Phase-Preserving Data Augmentation

Transformer Partial Discharge Recognition Based on Phase-Pre...

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Energy, Power and Grid (ICEPG), international conference on

作者： Shifang He Mingtao Yu Zongzhen Yang Binghui Lei Department of China Yangtze Power Co.Ltd. Yichang Xiling Road 1 Hubei China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

the identification of transformer Partial Discharge (PD) is crucial for the safe and stable operation of modern power systems. Traditional PD identification methods rely on manual feature extraction, which has limited generalization capability. Moreover, when applying deep learning algorithms to Phase-Resolved Partial Discharge (PRPD) patterns, conventional data augmentation techniques may disrupt the phase distribution, leading to the loss of critical features and affecting identification accuracy. To address this issue, this paper proposes a PD identification method based on phase-preserving data augmentation techniques. By incorporating Gaussian noise addition, phase shift processing, and segmented perturbation processing, the proposed method effectively retains the phase information in PRPD patterns, avoiding the loss of critical features during data augmentation. Experimental results show that the proposed method significantly outperforms traditional methods in terms of accuracy and stability, achieving a maximum accuracy of 96.39%, demonstrating great potential for practical engineering applications.

关键词： Partial discharges Accuracy Perturbation methods Gaussian noise Power system stability Data augmentation Data models Stability analysis Robustness pattern recognition

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Classification Model of Seed Cotton Grade Based on Least Square Support Vector Machine Regression Method

Classification Model of Seed Cotton Grade Based on Least Squ...

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2012 IEEE 6th international conference on Information and Automation for Sustainability（ICIAFS2012）

作者： Si Chen Department of Electrical Enginnering University at Buffalo the State University of New York Buffalo New York US Ling Li-na 1 Yuan Rong-chang 2 Sun Long-qing 3 1 3 China Agricultural University 2 Power Automation Department

Grade classification of seed cotton is a major problem that has an significant impact on the agricultural *** to characteristics like impurities,yellowness and brightness that extract from images of seed cotton,constructing classification model of seed cotton base on the least square *** support vector machine regression to come up with a well improved *** full learning,seed cotton classification accuracy satisfy the actual application needs.

关键词： seed cotton fuzzy math pattern recognition least square method support vector machine

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