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pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

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Chinese Physics B 2014年第4期23卷 410-421页

作者： Amani Tahat Jordi Marti Ali Khwaldeh Kaher Tahat Department of Physics and Nuclear Engineering Technical University of Catalonia-Barcelona Tech B4-B5North Campus UPC08034 Barcelona Catalonia Spain Department of Computer Engineering Faculty of Engineering Philadelphia University Department of Graduate Business Studies Waterford Institute of TechnologyMain Campus Cork RoadWaterford Ireland

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

关键词： pattern recognition proton transfer chart pattern data mining artificial neural network empiricalvalence bond

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pattern recognition of Modulation Signal Classification Using Deep Neural Networks

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Computer Systems Science & Engineering 2022年第11期43卷 545-558页

作者： D.Venugopal V.Mohan S.Ramesh S.Janupriya Sangsoon Lim Seifedine Kadry Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Saranathan College of EngineeringTrichy620012India Department of Computer Science and Engineering Krishnasamy College of Engineering&TechnologyCuddalore607109India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Engineering Sungkyul UniversityAnyangKorea Department of Applied Data Science Noroff University CollegeKristiansandNorway

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,*** becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex *** works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so *** addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal *** this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated *** proposed CSM-FFDNN model involves two major processes namely FE and *** proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated ***,the extracted features are fed into the DNN model for modulation signal *** improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN *** experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

关键词： pattern recognition signal modulation communication signals deep learning feature extraction

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pattern recognition of Non-Stationary Time Series with Finite Length

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Tsinghua Science and Technology 2006年第5期11卷 611-616页

作者：费万春白伦 Faculty of Textile Science and Technology Shinshu University Ueda 368-8567 Japan Material Engineering College Soochow University

Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time varying parameter auto-regressive （TVPAR） model. After the system was taught to recognize the size data, the system correctly recognized the size of series of cocoon filaments as much as 96.95% of the time for a single series and 98.72% of the time for the mean of two series. The correct recognition rate was higher after suitable filtering. The theory and method can be used to analyze other types of non-stationary finite length time series.

关键词： time series analysis non-stationarity pattern recognition size series of cocoon filaments

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pattern recognition and Forecast of Coal and Gas Outburst

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Journal of China University of Mining and Technology 2005年第3期15卷 251-254页

作者： LI Sheng ZHANG Hong-wei College of Resource and Environment Engineering Liaoning Technical University Fuxin Liaoning 123000China School of resource and Envtronment Engineering Beijing UniversiO of Science and Technology Beijing 100083 China

Coal and gas outburst is a complicated dynamic phenomenon in coal mines, Multi-factor pattern recognition is based on the relevant data obtained from research achievements of Geo-dynamic Division, With the help of spatial data management, the Neuron Network and Cluster algorithm are applied to predict the danger probability of coal and gas outburst in each cell of coal mining district. So a coal-mining district can be divided into three areas： dangerous area, minatory area, and safe area. This achievement has been successfully applied for regional prediction of coal and gas outburst in Hualnan mining area in China.

关键词： coal and gas outburst probability prediction pattern recognition geo-dynamic division

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Overcoming the Limits of Cross-Sensitivity:pattern recognition Methods for Chemiresistive Gas Sensor Array

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Nano-Micro Letters 2024年第12期16卷 285-341页

作者： Haixia Mei Jingyi Peng Tao Wang Tingting Zhou Hongran Zhao Tong Zhang Zhi Yang Key Lab Intelligent Rehabil&Barrier Free Disable(Ministry of Education) Changchun UniversityChangchun 130022People’s Republic of China Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power EngineeringEast China University of Science and TechnologyShanghai 200237People’s Republic of China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Department of Micro/Nano ElectronicsSchool of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012People’s Republic of China

As information acquisition terminals for artificial olfaction,chemiresistive gas sensors are often troubled by their cross-sensitivity,and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing *** recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas *** is crucial to choose an appropriate pattern recognition method for enhancing data analysis,reducing errors and improving system reliability,obtaining better classification or gas concentration prediction *** this review,we analyze the sensing mechanism of crosssensitivity for chemiresistive gas *** further examine the types,working principles,characteristics,and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing ***,we report,summarize,and evaluate the outstanding and novel advancements in pattern recognition methods for gas *** the same time,this work showcases the recent advancements in utilizing these methods for gas identification,particularly within three crucial domains:ensuring food safety,monitoring the environment,and aiding in medical *** conclusion,this study anticipates future research prospects by considering the existing landscape and *** is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

关键词： pattern recognition Sensor array Chemiresistive gas sensor Cross-sensitivity Artificial olfactory

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Molten salt phase diagram evaluation by pattern recognition:Part I Divalent rare earth halide and alkali metal halide binary systems

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Rare Metals 2002年第1期21卷 36-42页

作者： SUNYimin QIAOZhiyu InstituteofChemistryandMaterialsScience AnhuiNormalUniversityWuhu241000China DepartmentofPhysicalChemistry UniversityofScienceandTechnologyBeijingBeijing100083China

At present CALPHAD (CALculation of PHAse Diagram) technique is notcapable of predicting whether there exists intermediate compound,much less predicting the formulae, the number, and the meltingcongruence of intermedi- ate compounds. To solve this problem, a newapproach called the phase diagram evaluation by pattern recognition(PDEPR) was improved. The micro-parameters. Such as the radius andthe electronegativity of the different features in multi-dimensionalspace.

关键词： design of materials intermediate compound pattern recognition Phasediagram

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Biomolecule-templated photochemical synthesis of silver nanoparticles： Multiple readouts of localized surface plasmon resonance for pattern recognition

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Nano Research 2018年第6期11卷 3213-3221页

作者： Fang Pu Xiang Ran Meng Guan Yanyan Huang Jinsong Ren Xiaogang Qu Laboratory of Chemical Biology and State Key Laboratory of Rare Earth Resources Utilization Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun 130022 China Cancer Center FirstAffiliated Hospital Jilin University Changchun 130061 China University of Chinese Academy of Sciences Beijing 100039 China

Silver nanoparticles （AgNPs） with distinct localized surface plasmon resonance （LSPR） absorption spectra can be synthesized using different proteins as templates upon irradiation by light. We utilized the multiple readouts of LSPR signals of AgNPs to construct sensor arrays for pattern recognition of proteins. Room temperature, aqueous solutions, and lack of harsh reducing reagents make the whole process inherently ＂green＂. Meanwhile, the strategy efficiently simplified the process of array-receptor preparation and data acquisition, leading to lower time consumption, sample use, and cost. Furthermore, the system can differentiate proteins using flexible and alterable sensor elements by choosing different combinations of LSPR signals at different wavelengths. The principle of the sensor design can also be further extended to differentiate other biomolecules. The study provides a new method to construct feasible, economical, and general nanoparficle-based sensing arrays for pattern recognition.

关键词： localized surface plasmon resonance silver nanoparticles pattern recognition biomolecule photochemical synthesis

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Conversion of a single-layer ANN to photonic SNN for pattern recognition

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Science China(Information Sciences) 2024年第1期67卷 261-270页

作者： Yanan HAN Shuiying XIANG Tianrui ZHANG Yahui ZHANG Xingxing GUO Yuechun SHI State Key Laboratory of Integrated Service Networks State Key Discipline Laboratory of Wide Bandgap Semiconductor TechnologyXidian University Yongjiang Laboratory

This work presents a complete conversion scheme for photonic spiking neural networks(SNNs).We verified that the output of an artificial neural network(ANN) trained with the simulated optical activation function can be directly converted into the spike rate of a photonic spiking neuron *** reveal the feasibility of hardware implementation,we considered the effects of different bit precisions of data and weight,noise level,and bias current mismatch on the converted *** proposed scheme was evaluated using the Deterding vowel,IRIS,TIDIGITS,and MNIST datasets for pattern recognition,and achieved mean accuracies of 95.80%,98.67%,96.19%,and 92.33%,*** proposed scheme can convert an ANN into a photonic SNN with almost no precision loss,and the performance was comparable to that of an ANN trained with the rectified linear unit *** proposed scheme can enable the high-performance implementation of photonic SNNs.

关键词： photonic SNN conversion optical computing pattern recognition artificial neural network

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Visualization of flatness pattern recognition based on T-S cloud inference network

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Journal of Central South University 2015年第2期22卷 560-566页

作者：张秀玲赵亮臧佳音樊红敏 Key Laboratory of Industrial Computer Control Engineering of Hebei Province (Yanshan University) National Engineering Research Center for Equipment and Technology of Cold Strip Rolling

Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is ***, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.

关键词： pattern recognition T-S cloud inference network cloud model mixed programming virtual reality visual recognition

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A novel approach for VOC source apportionment combining characteristic factor and pattern recognition technology in a Chinese industrial area

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Journal of Environmental Sciences 2022年第11期34卷 25-37页

作者： Deji Jing Nana Cheng Cheng Zhang Ziwei Chen Xingnong Cai Sujing Li Jingkai Zhao Qiaoli Wang Wei Li Key Laboratory of Biomass Chemical Engineering of the Ministry of Education Institute of Industrial Ecology and EnvironmentCollege of Chemical and Biological EngineeringZhejiang University(Yuquan Campus)Hangzhou 310027China College of Environment Zhejiang University of TechnologyHangzhou 310032China

Volatile organic compound(VOC) emission control and source apportionment in small-scale industrial areas have become key topics of air pollution control in China. This study proposed a novel characteristic factor and pattern recognition(CF-PR) model for VOC source apportionment based on the similarity of characteristic factors between sources and receptors.A simulation was carried out in a typical industrial area with the CF-PR model involving simulated receptor samples. Refined and accurate source profiles were constructed through in situ sampling and analysis, covering rubber, chemicals, coating, electronics, plastics, printing, incubation and medical treatment industries. Characteristic factors of n-undecane,styrene, o-xylene and propane were identified. The source apportionment simulation results indicated that the predicted contribution rate was basically consistent with the real contribution rate. Compared to traditional receptor models, this method achieves notable advantages in terms of refinement and timeliness at similar accuracy, which is more suitable for VOC source identification and apportionment in small-scale industrial areas.

关键词： VOC Source apportionment Characteristic factors pattern recognition Industrial areas

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