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Effects of dietary Lactobacillus postbiotics and bacitracin on the modulation of mucosaassociated microbiota and pattern recognition receptors affecting immunocompetence of jejunal mucosa in pigs challenged with enterotoxigenic F18+Escherichia coli 认领 引用 被引量：5
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作者 Marcos Elias Duarte Zixiao Deng Sung Woo Kim 《Journal of Animal Science and Biotechnology》 SCIE CAS CSCD 2025年第1期282-299,共18页
Background Enterotoxigenic Escherichia coli(E.coli)is a threat to humans and animals that causes intestinal dis-orders.Antimicrobial resistance has urged alternatives,including Lactobacillus postbiotics,to mitigate th... Background Enterotoxigenic Escherichia coli(E.coli)is a threat to humans and animals that causes intestinal dis-orders.Antimicrobial resistance has urged alternatives,including Lactobacillus postbiotics,to mitigate the effects of enterotoxigenic E.coli.Methods Forty-eight newly weaned pigs were allotted to NC:no challengeo supplement;PC:F18+E.coli chal-lengeo supplement;ATB:F18+E.coli challenge/bacitracin;and LPB:F18+E.coli challenge/postbiotics and fed diets for 28 d.On d 7,pigs were orally inoculated withF18+E.coli.At d 28,the mucosa-associated microbiota,immune and oxidative stress status,intestinal morphology,the gene expression of pattern recognition receptors(PRR),and intestinal barrier function were measured.Data were analyzed using the MIXED procedure in SAS 9.4.Results PC increased(P<0.05)Helicobacter mastomyrinus whereas reduced(P<0.05)Prevotella copri and P.ster-corea compared to NC.The LPB increased(P<0.05)P.stercorea and Dialister succinatiphilus compared with PC.The ATB increased(P<0.05)Propionibacterium acnes,Corynebacterium glutamicum,and Sphingomonas pseudosanguinis compared to PC.The PC tended to reduce(P=0.054)PGLYRP4 and increased(P<0.05)TLR4,CD14,MDA,and crypt cell proliferation compared with NC.The ATB reduced(P<0.05)NOD1 compared with PC.The LPB increased(P<0.05)PGLYRP4,and interferon-γand reduced(P<0.05)NOD1 compared with PC.The ATB and LPB reduced(P<0.05)TNF-αand MDA compared with PC.Conclusions TheF18+E.coli challenge compromised intestinal health.Bacitracin increased beneficial bacteria show-ing a trend towards increasing the intestinal barrier function,possibly by reducing the expression of PRR genes.Lac-tobacillus postbiotics enhanced the immunocompetence of nursery pigs by increasing the expression of interferon-γand PGLYRP4,and by reducing TLR4,NOD1,and CD14. 展开更多
关键词 Escherichia coli Immunocompetence Intestinal health Pattern recognition receptors Pigs
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Pattern recognition-based analysis of the material basis of five flavors of Chinese herbal medicines in Lamiaceae 认领 引用
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作者 ZHANG Chuanyao WANG Xiao +4 位作者 SHI Gaoxiang ZHOU Qing BU Feifei ZHANG Xiaojun WANG Peng 《Journal of Traditional Chinese Medicine》 SCIE CAS CSCD 2025年第3期597-609,共13页
OBJECTIVE:To study the correlation between five flavors(Wuwei)and the chemical substances of Chinese herbal medicines in Lamiaceae and to establish five flavors identification models.METHODS:A total of 245 herbs belon... OBJECTIVE:To study the correlation between five flavors(Wuwei)and the chemical substances of Chinese herbal medicines in Lamiaceae and to establish five flavors identification models.METHODS:A total of 245 herbs belonging to the Lamiaceae family were selected from the Pharmacopoeia of the People's Republic of China 2020 and Chinese Materia Medica.A database of the chemical substances of these herbs was constructed,with the chemical substances obtained from the professional literature and databases.A three-level classification system of the material components was established on the basis of the molecular structure and biosynthetic pathway of these substances.Apriori association rule analysis and feature selection were employed to obtain the material basis of the five flavors.A multiple logistic regression analysis method was employed to establish identification models for the five flavors.RESULTS:The association rule analysis revealed 34 high-value groups and 30 specific groups for the main flavors,and 39 high-value groups and 36 specific groups for the combined flavors.Sixteen groups of chemical components were the decisive groups for the main flavors,and 13 groups were the decisive groups for the combined flavors.Multiple logistic regression analysis was used to successfully establish identification models with an overall accuracy of 88.8%for the main flavors and 87%for the combined flavors.CONCLUSIONS:Five flavors are often characterized by the interaction of multiple classes of substances,and a single class of substances cannot be used to characterize flavors.The organic combination of multiple classes of substances is the material basis of the five flavors,both the main and combined flavors.Significant differences exist in the material basis of the main and combined flavors,suggesting that the“natural flavor”and“functional flavor”may have different material bases. 展开更多
关键词 Lamiaceae pattern recognition five flavors chemical substances identification model
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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction 认领 引用
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作者 Zijian Sun Yaqian Li +2 位作者 Haoran Liu Haibin Li Wenming Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第5期3187-3210,共24页
In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and healthcare.Traditional methods,which rely heavily on handcrafted fe... In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and healthcare.Traditional methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation capabilities.To address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification features.The proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic information.Additionally,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio data.Specifically,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced modeling.The guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification accuracy.Experimental results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,respectively.These results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing approaches.By addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems. 展开更多
关键词 Audio pattern recognition raw audio end-to-end network feature fusion
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Pattern Recognition and Leading Edge Extraction for the Backscatter Ionograms by YOLOX 认领 引用
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作者 MA Jiasheng YANG Guobin +1 位作者 JIANG Chunhua LIU Tongxin 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2025年第1期69-78,共10页
The ionosphere is an important component of the near Earth space environment.The three common methods for detecting the ionosphere with high frequency(HF)radio signals are vertical detection,oblique detection,and obli... The ionosphere is an important component of the near Earth space environment.The three common methods for detecting the ionosphere with high frequency(HF)radio signals are vertical detection,oblique detection,and oblique backscatter detection.The ionograms obtained by these detection methods can effectively reflect a large amount of effective information in the ionosphere.The focus of this article is on the oblique backscatter ionogram obtained by oblique backscatter detection.By extracting the leading edge of the oblique backscatter ionogram,effective information in the ionosphere can be inverted.The key issue is how to accurately obtain the leading edge of the oblique backscatter ionogram.In recent years,the application of pattern recognition has become increasingly widespread,and the YOLO model is one of the best fast object detection algorithms in one-stage.Therefore,the core idea of this article is to use the newer YOLOX object detection algorithm in the YOLO family to perform pattern recognition on the F and Es layers echoes in the oblique backscatter ionogram.After image processing,a single-layer oblique backscatter echoes are obtained.It can be found that the leading edge extraction of the oblique backscatter ionogram obtained after pattern recognition and image processing by the YOLOX model is more fitting to the actual oblique backscatter leading edge. 展开更多
关键词 ionosphere backscatter ionogram leading edge YOLOX pattern recognition
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Evaluation of water quality and water resources carrying capacity using a varying fuzzy pattern recognition model: A case study of small watersheds in Hilly Region 认领 引用
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作者 Su-duan Hu Wen-da Liu +6 位作者 Jun-jian Liu Jiang-Yulong Wang Jun-jie Yang Zhao-yi Li Zhi-yang Tang Guo-qiang Wang Tian-cun Yu 《Journal of Groundwater Science and Engineering》 2025年第4期386-405,共20页
Water scarcity and environment deterioration have become main constraints to sustainable economic and social development.Scientifically assessing Water Resources Carrying Capacity(WRCC)is essential for the optimal all... Water scarcity and environment deterioration have become main constraints to sustainable economic and social development.Scientifically assessing Water Resources Carrying Capacity(WRCC)is essential for the optimal allocation of regional water resources.The hilly area at the northern foot of Yanshan Mountains is a key water conservation zone and an important water source for Beijing,Tianjin and Hebei.Grasping the current status and temporal trends of water quality and WRCC in representative small watersheds within this region is crucial for supporting rational water resources allocation and environment protection efforts.This study focuses on Pingquan City,a typical watershed in northern Hebei Province.Firstly,evaluation index systems for surface water quality,groundwater quality and WRCC were estab-lished based on the Pressure-State-Response(PSR)framework.Then,comprehensive evaluations of water quality and WRCC at the sub-watershed scale were conducted using the Varying Fuzzy Pattern Recogni-tion(VFPR)model.Finally,the rationality of the evaluation results was verified,and future scenarios were projected.Results showed that:(1)The average comprehensive evaluation scores for surface water and groundwater quality in the sub-watersheds were 1.44 and 1.46,respectively,indicating that both met the national Class II water quality standard and reflected a high-quality water environment.(2)From 2010 to 2020,the region's WRCC steadily improved,with scores rising from 2.99 to 2.83 and an average of 2.90,suggesting effective water resources management in Pingquan City.(3)According to scenario-based predic-tion,WRCC may slightly decline between 2025 and 2030,reaching 2.92 and 2.94,respectively,relative to 2020 levels.Therefore,future efforts should focus on strengthening scientific management and promoting the efficient use of water resources.Proactive measures are necessary to mitigate emerging contradiction and ensure the long-term stability and sustainability of the water resources system in the region.The evalua-tion system and spatiotemporal evolution patterns proposed in this study can provide a scientific basis for refined water resource management and ecological conservation in similar hilly areas. 展开更多
关键词 Varying fuzzy pattern recognition model Dynamic assessment Small watershed Water qual-ity evaluation Water resources carrying capacity
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TEACHING AI IN ARCHAEOLOGY:PRACTISING PATTERN RECOGNITION WITH ORANGE DATA MINING 认领 引用
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作者 Elisabeth Günther 《Journal of Ancient Civilizations》 2025年第2期229-246,251,共18页
AI applications have already-irreversibly-entered the fields of Humanities,Classics,and archaeological research,but are rarely taught in class.This paper attempts to encourage teachers and students of Classical Archae... AI applications have already-irreversibly-entered the fields of Humanities,Classics,and archaeological research,but are rarely taught in class.This paper attempts to encourage teachers and students of Classical Archaeology,Classics,Art History,and related fields,to practically explore,evaluate,and critically discuss pattern recognition(here focusing on Greek vase-painting),without requiring previously acquired coding skills.For this task,I will outline the potential of the open access program“Orange Data Mining”for academic teaching,based on a seminar taught by myself at Heidelberg University in the winter semester of 2024/25.I will introduce four different pattern recognition exercises(“Image Grid,”“Image Clustering,”“Image Classification,”and“Prediction”)that are easily accessible for classicists(please,try this at home!)and report the results and experiences which came out of our seminar.Furthermore,I will evaluate how the“hands-on”use of Orange Data Mining in class enables students to access the current debate on the chances and limitations of AI for research in ancient studies. 展开更多
关键词 Digital Archaeology Digital Classics pattern recognition AI teaching
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A Novel Gait Pattern Recognition Method Based on LSTM-CNN for Lower Limb Exoskeleton 认领 引用 被引量：12
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作者 Chao-feng Chen Zhi-jiang Du +3 位作者 Long He Yong-jun Shi Jia-qi Wang Wei Dong 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第5期1059-1072,共14页
This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb exoskeleton.The Inertial Measurement Unit(IMU)installed on the exos... This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb exoskeleton.The Inertial Measurement Unit(IMU)installed on the exoskeleton to collect motion information,which is used for LSTM-CNN input.This article considers five common gait patterns,including walking,going up stairs,going down stairs,sitting down,and standing up.In the LSTM-CNN model,the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features.To optimize the deep neural network structure proposed in this paper,some hyperparameter selection experiments were carried out.In addition,to verify the superiority of the proposed recognition method,the method is compared with several common methods such as LSTM,CNN and SVM.The results show that the average recognition accuracy can reach 97.78%,which has a good recognition eff ect.Finally,according to the experimental results of gait pattern switching,the proposed method can identify the switching gait pattern in time,which shows that it has good real-time performance. 展开更多
关键词 Lower limb exoskeleton Gait pattern recognition LSTM-CNN Recognition accuracy Real-time performance
Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors 认领 引用 被引量：22
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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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A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression 认领 引用 被引量：13
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作者 ZHANG Xiu-ling ZHANG Shao-yu +1 位作者 TAN Guang-zhong ZHAO Wen-bao 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2012年第3期25-30,共6页
To adapt to the new requirement of the developing flatness control theory and technology,cubic patterns were introduced on the basis of the traditional linear,quadratic and quartic flatness basic patterns.Linear,quadr... To adapt to the new requirement of the developing flatness control theory and technology,cubic patterns were introduced on the basis of the traditional linear,quadratic and quartic flatness basic patterns.Linear,quadratic,cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns.In order to over-come the defects live in the existent recognition methods based on fuzzy,neural network and support vector regres-sion(SVR)theory,a novel flatness pattern recognition method based on least squares support vector regression(LS-SVR)was proposed.On this basis,for the purpose of determining the hyper-parameters of LS-SVR effectively and enhan-cing the recognition accuracy and generalization performance of the model,particle swarm optimization algorithm with leave-one-out(LOO)error as fitness function was adopted.To overcome the disadvantage of high computational complexity of naive cross-validation algorithm,a novel fast cross-validation algorithm was introduced to calculate the LOO error of LDSVR.Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy,high speed and strong generalization ability. 展开更多
关键词 flatness pattern recognition least squares support vector regression cross-validation
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Overcoming the Limits of Cross-Sensitivity:Pattern Recognition Methods for Chemiresistive Gas Sensor Array 认领 引用 被引量：6
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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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Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition 认领 引用 被引量：19
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作者 LEUNG Yeuk-Lan Alice GUAN Binghe +4 位作者 CHEN Shuang CHAN Hoyin KONG Kawai LI Wenjung SHEN Jiangang 《Digital Chinese Medicine》 CAS 2021年第1期1-8,共8页
Artificial intelligence(AI) aims to mimic human cognitive functions and execute intellectual activities like that performed by humans dealing with an uncertain environment. The rapid development of AI technology provi... Artificial intelligence(AI) aims to mimic human cognitive functions and execute intellectual activities like that performed by humans dealing with an uncertain environment. The rapid development of AI technology provides powerful tools to analyze massive amounts of data, facilitating physicians to make better clinical decisions or even replace human judgment in healthcare.Advanced AI technology also creates novel opportunities for exploring the scientific basis of traditional Chinese medicine(TCM) and developing the standardization and digitization of TCM pulse diagnostic methodology. In the present study, we review and discuss the potential application of AI technology in TCM pulse diagnosis. The major contents include the following aspects:(1) a brief introduction of the general concepts and knowledge of TCM pulse diagnosis or palpation,(2) landmark developments in AI technology and the applications of common AI deep learning algorithms in medical practice,(3) the current progress of AI technology in TCM pulse diagnosis,(4) challenges and perspectives of AI technology in TCM pulse diagnosis. In conclusion, the pairing of TCM with modern AI technology will bring novel insights into understanding the scientific principles underlying TCM pulse diagnosis and creating opportunities for the development of AI deep learning technology for the standardization and digitalization of TCM pulse diagnosis. 展开更多
关键词 Artificial intelligence(AI) Traditional Chinese medicine(TCM) Palpation Pulse pattern recognition Pulse diagnosis
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Roles of pattern recognition receptors in diabetic nephropathy 认领 引用 被引量：5
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作者 Zhi-feng ZHOU Lei JIANG +4 位作者 Qing ZHAO Yu WANG Jing ZHOU Qin-kai CHEN Jin-lei LV 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2020年第3期192-203,共12页
Diabetic nephropathy(DN)is currently the most common complication of diabetes.It is considered to be one of the leading causes of end-stage renal disease(ESRD)and affects many diabetic patients.The pathogenesis of DN ... Diabetic nephropathy(DN)is currently the most common complication of diabetes.It is considered to be one of the leading causes of end-stage renal disease(ESRD)and affects many diabetic patients.The pathogenesis of DN is extremely complex and has not yet been clarified;however,in recent years,increasing evidence has shown the important role of innate immunity in DN pathogenesis.Pattern recognition receptors(PRRs)are important components of the innate immune system and have a significant impact on the occurrence and development of DN.In this review,we classify PRRs into secretory,endocytic,and signal transduction PRRs according to the relationship between the PRRs and subcellular compartments.PRRs can recognize related pathogen-associated molecular patterns(PAMPs)and danger-associated molecular patterns(DAMPs),thus triggering a series of inflammatory responses,promoting renal fibrosis,and finally causing renal impairment.In this review,we describe the proposed role of each type of PRRs in the development and progression of DN. 展开更多
关键词 Diabetic nephropathy Innate immunity Pattern recognition receptor Pathogenesis
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Visualization of flatness pattern recognition based on T-S cloud inference network 认领 引用 被引量：4
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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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Authentication and distinction of Shenmai injection with HPLC fingerprint analysis assisted by pattern recognition techniques 认领 引用 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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A novel approach for VOC source apportionment combining characteristic factor and pattern recognition technology in a Chinese industrial area 认领 引用 被引量：3
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作者 Deji Jing Nana Cheng +6 位作者 Cheng Zhang Ziwei Chen Xingnong Cai Sujing Li Jingkai Zhao Qiaoli Wang Wei Li 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2022年第11期25-37,共13页
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 ... 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. 展开更多
关键词 VOC Source apportionment Characteristic factors Pattern recognition Industrial areas
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Application of extension neural network to safety status pattern recognition of coalmines 认领 引用 被引量：7
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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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Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition 认领 引用 被引量：9
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作者 任春涛 李畅游 +3 位作者 贾克力 张生 李卫平 曹有玲 《Chinese Journal of Oceanology and Limnology》 2008年第3期339-344,共6页
Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clu... Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application. 展开更多
关键词 transitive closure method ISODATA clustering algorithm fuzzy pattern recognition method partitioning of water quality
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Establishment of a pattern recognition metabolomics model for the diagnosis of hepatocellular carcinoma 认领 引用 被引量：2
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作者 Peng-Cheng Zhou Lun-Quan Sun +3 位作者 Li Shao Lun-Zhao Yi Ning Li Xue-Gong Fan 《World Journal of Gastroenterology》 SCIE CAS 2020年第31期4607-4623,共17页
BACKGROUND Early diagnosis of hepatocellular carcinoma may help to ensure that patients have a chance for long-term survival;however,currently available biomarkers lack sensitivity and specificity.AIM To characterize ... BACKGROUND Early diagnosis of hepatocellular carcinoma may help to ensure that patients have a chance for long-term survival;however,currently available biomarkers lack sensitivity and specificity.AIM To characterize the serum metabolome of hepatocellular carcinoma in order to develop a new metabolomics diagnostic model and identifying novel biomarkers for screening hepatocellular carcinoma based on the pattern recognition method.METHODS Ultra-performance liquid chromatography-mass spectroscopy was used to characterize the serum metabolome of hepatocellular carcinoma(n=30)and cirrhosis(n=29)patients,followed by sequential feature selection combined with linear discriminant analysis to process the multivariate data.RESULTS The concentrations of most metabolites,including proline,were lower in patients with hepatocellular carcinoma,whereas the hydroxypurine levels were higher in these patients.As ordinary analysis models failed to discriminate hepatocellular carcinoma from cirrhosis,pattern recognition analysis was used to establish a pattern recognition model that included hydroxypurine and proline.The leaveone-out cross-validation accuracy and area under the receiver operating characteristic curve analysis were 95.00%and 0.90[95%Confidence Interval(CI):0.81-0.99]for the training set,respectively,and 78.95%and 0.84(95%CI:0.67-1.00)for the validation set,respectively.In contrast,forα-fetoprotein,the accuracy and area under the receiver operating characteristic curve were 65.00%and 0.69(95%CI:0.52-0.86)for the training set,respectively,and 68.42%and 0.68(95%CI:0.41-0.94)for the validation set,respectively.The Z test revealed that the area under the curve of the linear discriminant analysis model was significantly higher than the area under the curve ofα-fetoprotein(P<0.05)in both the training and validation sets.CONCLUSION Hydroxypurine and proline might be novel biomarkers for hepatocellular carcinoma,and this disease could be diagnosed by the metabolomics model based on pattern recognition. 展开更多
关键词 Hepatocellular carcinoma Pattern recognition Metabolomics Biomarkers
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Efficiency of sample-based indices for spatial pattern recognition of wild pistachio(Pistacia atlantica) trees in semi-arid woodlands 认领 引用 被引量：2
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作者 Yousef Erfanifard Joachim Saborowski +1 位作者 Kerstin Wiegand Katrin M.Meyer 《Journal of Forestry Research》 SCIE CAS CSCD 2016年第3期583-594,共12页
The efficiency of sample-based indices proposed to quantify the spatial distribution of trees is influenced by the structure of tree stands, environmental heterogeneity and degree of aggregation. We evaluated 10 commo... The efficiency of sample-based indices proposed to quantify the spatial distribution of trees is influenced by the structure of tree stands, environmental heterogeneity and degree of aggregation. We evaluated 10 commonly used distance-based and 10 density-based indices using two structurally different stands of wild pistachio trees in the Zagros woodlands, Iran, to assess the reliability of each in revealing stand structure in woodlands. All trees were completely stem-mapped in a nearly pure（40 ha） and a mixed（45 ha） stand. First, the inhomogeneous pair correlation function [g（r）] and the Clark-Evans index（CEI） were used as references to reveal the true spatial arrangement of all trees in these stands. The sampled data were then evaluated using the 20 indices.Sampling was undertaken in a grid based on a square lattice using square plots（30 m 9 30 m） and nearest neighbor distances at the sample points. The g（r） and CEI statistics showed that the wild pistachio trees were aggregated in both stands, although the degree of aggregation was markedly higher in the pure stand. Three distance- and six density-based indices statistically verified that the wild pistachio trees were aggregated in both stands. The distance-based Hines and Hines statistic（ht） and the densitybased standardised Morisita（Ip）, patchiness（IP） and Cassie（CA） indices revealed aggregation of the trees in the two structurally different stands in the Zagros woodlands and the higher clumping in the pure stand, whereas the other indices were not sensitive enough. 展开更多
关键词 Density-based indices Distance-basedindices Pattern recognition Wild pistachio Woodland
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New pattern recognition system in the e-nose for Chinese spirit identification 认领 引用 被引量：7
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作者 曾慧 李强 谷宇 《Chinese Physics B》 SCIE EI CAS CSCD 2016年第2期164-169,共6页
This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbala... This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively. 展开更多
关键词 new pattern recognition system polymer quartz piezoelectric crystal sensor e-nose principle com-ponents analysis （PCA） back propagation （BP） algorithm Chinese spirit identification
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