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Overcoming the Limits of Cross-Sensitivity:Pattern Recognition Methods for Chemiresistive Gas Sensor Array
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作者 Haixia Mei Jingyi Peng +4 位作者 Tao Wang Tingting Zhou Hongran Zhao Tong Zhang Zhi Yang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2024年第12期285-341,共57页
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... 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 area.Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors.It 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 results.In this review,we analyze the sensing mechanism of crosssensitivity for chemiresistive gas sensors.We further examine the types,working principles,characteristics,and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays.Additionally,we report,summarize,and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification.At 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 diagnosis.In conclusion,this study anticipates future research prospects by considering the existing landscape and challenges.It 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. 展开更多
关键词 pattern recognition Sensor array Chemiresistive gas sensor CROSS-SENSITIVITY Artificial olfactory
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Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM
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作者 WANG Yanxia YANG Lisha +1 位作者 HUANG Xiaoyuan ZHOU Ruliang 《Journal of Mountain Science》 SCIE CSCD 2024年第6期2025-2042,共18页
Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are n... Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources. 展开更多
关键词 High temperature increase Mountain areas MODIS Spatial pattern recognition Raster window measurement Threshold selection
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AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis
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作者 Mohd Asif Hajam Tasleem Arif +2 位作者 Akib Mohi Ud Din Khanday Mudasir Ahmad Wani Muhammad Asim 《Computers, Materials & Continua》 SCIE EI 2024年第11期2077-2131,共55页
The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant par... The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants. 展开更多
关键词 pattern recognition artificial intelligence machine learning deep learning image processing plant leaf identification
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Dynamic Signature Verification Using Pattern Recognition
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作者 Emmanuel Nwabueze Ekwonwune Duroha Austin Ekekwe +1 位作者 Chinyere Iheakachi Ubochi Henry Chinedu Oleribe 《Journal of Software Engineering and Applications》 2024年第5期214-227,共14页
Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key mot... Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used. 展开更多
关键词 VERIFICATION SECURITY BIOMETRICS SIGNATURE AUTHENTICATION Model pattern recognition Dynamic
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Expression and clinical significance of pattern recognition receptor-associated genes in hand, foot and mouth disease
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作者 Muqi Wang Huiling Deng +7 位作者 Yuan Chen Yikai Wang Yufeng Zhang Chenrui Liu Meng Zhang Ting Li Shuangsuo Dang Yaping Li 《Asian Pacific Journal of Tropical Medicine》 SCIE CAS 2024年第4期173-183,I0001-I0003,共14页
Objective:To explore which pattern recognition receptors(PRRs)play a key role in the development of hand,foot,and mouth disease(HFMD)by analyzing PRR-associated genes.Methods:We conducted a comparative analysis of PRR... Objective:To explore which pattern recognition receptors(PRRs)play a key role in the development of hand,foot,and mouth disease(HFMD)by analyzing PRR-associated genes.Methods:We conducted a comparative analysis of PRR-associated gene expression in human peripheral blood mononuclear cells(PBMCs)infected with enterovirus 71(EV-A71)which were derived from patients with HFMD of different severities and at different stages.A total of 30 PRR-associated genes were identified as significantly upregulated both over time and across different EV-A71 isolates.Subsequently,ELISA was employed to quantify the expression of the six most prominent genes among these 30 identified genes,specifically,BST2,IRF7,IFI16,TRIM21,MX1,and DDX58.Results:Compared with those at the recovery stage,the expression levels of BST2(P=0.027),IFI16(P=0.016),MX1(P=0.046)and DDX58(P=0.008)in the acute stage of infection were significantly upregulated,while no significant difference in the expression levels of IRF7(P=0.495)and TRIM21(P=0.071)was found between different stages of the disease.The expression levels of BST2,IRF7,IFI16 and MX1 were significantly higher in children infected with single pathogen than those infected with mixed pathogens,and BST2,IRF7,IFI16 and MX1 expression levels were significantly lower in coxsackie B virus(COXB)positive patients than the negative patients.Expression levels of one or more of BST2,IRF7,IFI16,TRIM21,MX1 and DDX58 genes were correlated with PCT levels,various white blood cell counts,and serum antibody levels that reflect disease course of HFMD.Aspartate aminotransferase was correlated with BST2,MX1 and DDX58 expression levels.Conclusions:PRR-associated genes likely initiate the immune response in patients at the acute stage of HFMD. 展开更多
关键词 pattern recognition receptors(PRRs) Hand foot and mouth disease(HFMD) IMMUNE Enterovirus 71(EV-A71)
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Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network 被引量:2
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作者 Yuhong Jin Lei Hou +1 位作者 Zhenyong Lu Yushu Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第2期180-197,共18页
The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics cause... The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown. 展开更多
关键词 Hollow shaft rotor Breathing crack Radial basis function network pattern recognition neural network Machine learning
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Numerical Comparison of Shapeless Radial Basis Function Networks in Pattern Recognition 被引量:1
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作者 Sunisa Tavaen Sayan Kaennakham 《Computers, Materials & Continua》 SCIE EI 2023年第2期4081-4098,共18页
This work focuses on radial basis functions containing no parameters with themain objective being to comparatively explore more of their effectiveness.For this,a total of sixteen forms of shapeless radial basis functi... This work focuses on radial basis functions containing no parameters with themain objective being to comparatively explore more of their effectiveness.For this,a total of sixteen forms of shapeless radial basis functions are gathered and investigated under the context of the pattern recognition problem through the structure of radial basis function neural networks,with the use of the Representational Capability(RC)algorithm.Different sizes of datasets are disturbed with noise before being imported into the algorithm as‘training/testing’datasets.Each shapeless radial basis function is monitored carefully with effectiveness criteria including accuracy,condition number(of the interpolation matrix),CPU time,CPU-storage requirement,underfitting and overfitting aspects,and the number of centres being generated.For the sake of comparison,the well-known Multiquadric-radial basis function is included as a representative of shape-contained radial basis functions.The numerical results have revealed that some forms of shapeless radial basis functions show good potential and are even better than Multiquadric itself indicating strongly that the future use of radial basis function may no longer face the pain of choosing a proper shape when shapeless forms may be equally(or even better)effective. 展开更多
关键词 Shapeless RBF-neural networks pattern recognition large scattered data
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Visualization of flatness pattern recognition based on T-S cloud inference network 被引量:2
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作者 张秀玲 赵亮 +1 位作者 臧佳音 樊红敏 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第2期560-566,共7页
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 nov... 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 designed.Therefore, 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. 展开更多
关键词 pattern recognition T-S cloud inference network cloud model mixed programming virtual reality visual recognition
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Hierarchical pattern recognition of landform elements considering scale adaptation
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作者 XU Yue-xue ZHU Hong-chun +1 位作者 LI Jin-yu ZHANG Sheng-jia 《Journal of Mountain Science》 SCIE CSCD 2023年第7期2003-2014,共12页
Landform elements with varying morphologies and spatial arrangements are recognized as feature indicator of landform classification and play a critical role in geomorphological studies.Differential geometry method has... Landform elements with varying morphologies and spatial arrangements are recognized as feature indicator of landform classification and play a critical role in geomorphological studies.Differential geometry method has been extensively applied in prior landform element research,while its efficacy in differentiating similar morphological characteristics remains inadequate to date.To reduce reliance on geomorphometric variables and increase awareness of landform patterns,geomorphons method was generated in previous study corresponding to specific landform reclassification map based on lookup table.Besides,to address the problem of feature similarity,hierarchical classification was proposed and effectively utilized for terrain recognition through the analytical strategy of fuzzy gradient features.Thus,combining the advantages of these two aspects,a hierarchical framework was proposed in this study for landform element pattern recognition considering the morphology and hierarchy factors.First,the local triplet patterns derived from geomorphons were enhanced by setting the flatness threshold,and subsequently adopted for the primary landform element recognition.Then,as geomorphic units with the same morphology possess different spatial analytical scales,the unidentified landform elements under the principle of scale adaptation were determined by calculating the spatial correlation and entropy information.To ensure the effectiveness of this proposed method,the sampling points were randomly selected from NASADEM data and then validated against a real 3D terrain model.Quantitative results of landform element pattern recognition demonstrate that our approach can reach above 77%average accuracy.Additionally,it delineates local details more effectively than geomorphons in visual assessment,resulting in a 7%accuracy improvement in overall scale. 展开更多
关键词 DEM Landform elements Hierarchical classification Scale adaptation pattern recognition
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Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors
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作者 Kainat Ibrar Abdul Muiz Fayyaz +4 位作者 Muhammad Attique Khan Majed Alhaisoni Usman Tariq Seob Jeon Yunyoung Nam 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期2351-2368,共18页
Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substa... Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits. 展开更多
关键词 Human personality GAIT pattern recognition smartphone sensors
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Fuzzy pattern recognition model of geological sweetspot for coalbed methane development
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作者 LIU Gaofeng LIU Huan +3 位作者 XIAN Baoan GAO Deli WANG Xiaoming ZHANG Zhen 《Petroleum Exploration and Development》 SCIE 2023年第4期924-933,共10页
From the perspective of geological zone selection for coalbed methane(CBM) development, the evaluation parameters(covering geological conditions and production conditions) of geological sweetspot for CBM development a... From the perspective of geological zone selection for coalbed methane(CBM) development, the evaluation parameters(covering geological conditions and production conditions) of geological sweetspot for CBM development are determined, and the evaluation index system of geological sweetspot for CBM development is established. On this basis, the fuzzy pattern recognition(FPR) model of geological sweetspot for CBM development is built. The model is applied to evaluate four units of No.3 Coal Seam in the Fanzhuang Block, southern Qinshui Basin, China. The evaluation results are consistent with the actual development effect and the existing research results, which verifies the rationality and reliability of the FPR model. The research shows that the proposed FPR model of geological sweetspot for CBM development does not involve parameter weighting which leads to uncertainties in the results of the conventional models such as analytic hierarchy process and multi-level fuzzy synthesis judgment, and features a simple computation without the construction of multi-level judgment matrix. The FPR model provides reliable results to support the efficient development of CBM. 展开更多
关键词 coalbed methane development geological sweetspot evaluation index system analytic hierarchy process multi-level fuzzy synthesis judgment fuzzy pattern recognition
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DPT‐tracker:Dual pooling transformer for efficient visual tracking
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作者 Yang Fang Bailian Xie +3 位作者 Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第4期948-959,共12页
Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model compl... Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input images.To alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time dimensions.MCPT aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking prediction.DPT tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking methods.Extensive experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency. 展开更多
关键词 human‐computer interfacing image motion analysis pattern recognition signal processing TRACKING
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Classification of Chinese Traditional Drug-"Beimu" (Bulbus Fritillariae) by Pyrolysis High Resolution Gas Chromatography-Pattern Recognition 被引量:2
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作者 房杏春 李萍 +1 位作者 田琳 安登魁 《Journal of Chinese Pharmaceutical Sciences》 CAS 1992年第2期65-72,共8页
The combination of pyrolysis high resolution gas chromatography and pat- tern recognition techniques is a powerful tool for the classification of traditional Chinese drug.A study has been completed on 55 Beimu samples... The combination of pyrolysis high resolution gas chromatography and pat- tern recognition techniques is a powerful tool for the classification of traditional Chinese drug.A study has been completed on 55 Beimu samples of five different geographic origins: Eastern China.Central China.South-western China,North-western China and North-eastern China.Principal component analysis and SIMCA are applied to effectively classifying the samples according to the origin of the plants.The chemical information contained in the high resolution gas chromatographic data is sufficient to characterize the geographic origin of sam- pies. 展开更多
关键词 Beimu FRITILLARIA Pyrolysis High Resolution Gas Chromatography pattern recognition
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On similarity measures of interval-valued intuitionistic fuzzy sets and their application to pattern recognitions 被引量:29
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作者 徐泽水 《Journal of Southeast University(English Edition)》 EI CAS 2007年第1期139-143,共5页
The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets (IVIFSs) is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized H... The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets (IVIFSs) is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized Hamming distance, the weighted Hamming distance, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance, etc. Then, by combining the Hausdorff metric with the Hamming distance, the Euclidean distance and their weighted versions, two other similarity measures between IVIFSs, i. e., the weighted Hamming distance based on the Hausdorff metric and the weighted Euclidean distance based on the Hausdorff metric, are defined, and then some of their properties are studied. Finally, based on these distance measures, some similarity measures between IVIFSs are defined, and the similarity measures are applied to pattern recognitions with interval-valued intuitionistic fuzzy information. 展开更多
关键词 interval-valued intuitionistic fuzzy set SIMILARITY pattern recognition
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A Hybrid Neural Network for Spatiotemporal Pattern Recognition
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作者 曹元大 陈一峰 《Journal of Beijing Institute of Technology》 EI CAS 1996年第1期1-6,共6页
A hybrid network is presented for spatio-temporal feature detecting, which is called TS-LM-SOFM. Its top layer is a novel single layer temporal sequence recognizer called TS which can transform sparse temporal sequen... A hybrid network is presented for spatio-temporal feature detecting, which is called TS-LM-SOFM. Its top layer is a novel single layer temporal sequence recognizer called TS which can transform sparse temporal sequential pattern into abstract spatial feature representations. The bottom layer of TS-LM-SOFM, a modified self-organizing feature map, is used as a spatial feature detector. A learning matrix connects the two layers. Experiments show that the hybrid network can well capture the spatio-temporal features of input signals. 展开更多
关键词 neural networks pattern recognition spatio-temporal pattern
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Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors 被引量:16
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作者 Zuojun Liu Wei Lin +1 位作者 Yanli Geng Peng Yang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第4期651-660,共10页
Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we deve... Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis. 展开更多
关键词 Above-knee prosthesis hidden Markov model(HMM) intra-class correlation coefficient(ICC) intent pattern recognition sensor fusion
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Application of support vector machine in trip chaining pattern recognition and analysis of explanatory variable effects 被引量:2
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作者 杨硕 邓卫 程龙 《Journal of Southeast University(English Edition)》 EI CAS 2017年第1期106-114,共9页
In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purpos... In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods: the support vector machine (SVM) model, the radial basis function neural network (RBFNN) model and the multinomial logit (MNL) model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical. 展开更多
关键词 trip chaining patterns support vector machine recognition performance sensitivity analysis
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Authentication and distinction of Shenmai injection with HPLC fingerprint analysis assisted by pattern recognition techniques 被引量:5
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作者 Xue-Feng Lu Kai-Shun Bi +1 位作者 Xu Zhao Xiao-Hui Chen 《Journal of Pharmaceutical Analysis》 CAS 2012年第5期327-333,共7页
In this paper, the feasibility and advantages of employing high performance liquid chromatographic (HPLC) fingerprints combined with pattern recognition techniques for quality control of Shenmai injection were inves... In this paper, the feasibility and advantages of employing high performance liquid chromatographic (HPLC) fingerprints combined with pattern recognition techniques for quality control of Shenmai injection were investigated and demonstrated. The Similarity Evaluation System was employed to evaluate the similarities of samples of Shenmai injection, and the HPLC generated chromatographic data were analyzed using hierarchical clustering analysis (HCA) and soft independent modeling of class analogy (SIMCA). Consistent results were obtained to show that the authentic samples and the blended samples were successfully classified by SIMCA, which could be applied to accurate discrimination and quality control of Shenmai injection. Furthermore, samples could also be grouped in accordance with manufacturers. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of Shenmai injection. 展开更多
关键词 Shenmai injection High performance liquidchromatography FINGERPRINT pattern recognition
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Application of extension neural network to safety status pattern recognition of coalmines 被引量:6
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作者 周玉 W.Pedrycz 钱旭 《Journal of Central South University》 SCIE EI CAS 2011年第3期633-641,共9页
In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of... In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production. 展开更多
关键词 safety status pattern recognition extension neural network coal mines
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Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments 被引量:2
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作者 Amani Tahat Jordi Marti +1 位作者 Ali Khwaldeh Kaher Tahat 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第4期410-421,共12页
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 'occu... 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. 展开更多
关键词 pattern recognition proton transfer chart pattern data mining artificial neural network empiricalvalence bond
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