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
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
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 challenge/no supplement;PC:F18^(+)E.coli chal-lenge/no 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
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 E_(s) 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
Deep neural networks,especially face recognition models,have been shown to be vulnerable to adversarial examples.However,existing attack methods for face recognition systems either cannot attack black-box models,are n...Deep neural networks,especially face recognition models,have been shown to be vulnerable to adversarial examples.However,existing attack methods for face recognition systems either cannot attack black-box models,are not universal,have cumbersome deployment processes,or lack camouflage and are easily detected by the human eye.In this paper,we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles.To achieve visual camouflage,we use a generative adversarial network(GAN).The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior,the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability,and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks.Sufficient visualization results demonstrate that compared with existing methods,the proposed method can generate samples with camouflage and adversarial characteristics.Meanwhile,extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models.展开更多
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
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
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
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
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
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
For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-inpu...For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition.展开更多
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
A new pattern recognition method of shape was presented based on artificial neural network theory.The method avoids the defects of shape pattern recognition with polynomials and it has strong disturbance resistance.It...A new pattern recognition method of shape was presented based on artificial neural network theory.The method avoids the defects of shape pattern recognition with polynomials and it has strong disturbance resistance.It has been proved to be superior in recognizing different shape patterns by identifying many sorts of working sample books which the results are known.展开更多
A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a n...A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features （i.e., the dynamics） of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases： the identification （training） phase and the recognition （test） phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt （PTB） database are included to demonstrate the effectiveness of the approach.展开更多
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
Structural Health Monitoring(SHM) suggests the use of machine learning algorithms with the aim of understanding specific behaviors in a structural system. This work introduces a pattern recognition methodology for ope...Structural Health Monitoring(SHM) suggests the use of machine learning algorithms with the aim of understanding specific behaviors in a structural system. This work introduces a pattern recognition methodology for operational condition clustering in a structure sample using the well known Density Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm.The methodology was validated using a data set from an experiment with 32 Fiber Bragg Gratings bonded to an aluminum beam placed in cantilever and submitted to cyclic bending loads under 13 different operational conditions(pitch angles). Further, the computational cost and precision of the machine learning pipeline called FA + GA-DBSCAN(which employs a combination of machine learning techniques including factor analysis for dimensionality reduction and a genetic algorithm for the automatic selection of initial parameters of DBSCAN) was measured. The obtained results have shown a good performance, detecting 12 of 13 operational conditions, with an overall precision over 90%.展开更多
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
We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensit...We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.展开更多
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
基金the European Union’s Horizon 2020 research and innovation programme.
文摘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.
基金the National Natural Science Foundation of China:Research on the Identification of Cold-Hot Properties in Lamiaceae Herbs based on Infrared Spectroscopy Holistic Component Characteristic Markers(No.81673622)the Anhui Provincial Natural Science Foundation:Research on the Extraction and Identification of Holistic Compositional Characteristics of Warm-Hot Properties of Lamiaceae Herbs(No.1508085MH202)the Anhui Provincial Natural Science Research Project of Higher Education:Research on the Material Basis of Cold-Hot Properties of Lamiaceae Herbs based on Pattern Recognition and Energy Metabolism(No.2023AH050773)。
文摘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.
文摘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 challenge/no supplement;PC:F18^(+)E.coli chal-lenge/no 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.
基金Supported by the National Natural Science Foundation of China(42104151,42074184,42188101,41727804)。
文摘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 E_(s) 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.
基金the National Key Research and Development Program of China(No.2022ZD0210500)the National Natural Science Foundation of China(Nos.61972067,U21A20491,and 62103437)the Dalian Outstanding Youth Science Foundation(No.2022RJ01)。
文摘Deep neural networks,especially face recognition models,have been shown to be vulnerable to adversarial examples.However,existing attack methods for face recognition systems either cannot attack black-box models,are not universal,have cumbersome deployment processes,or lack camouflage and are easily detected by the human eye.In this paper,we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles.To achieve visual camouflage,we use a generative adversarial network(GAN).The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior,the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability,and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks.Sufficient visualization results demonstrate that compared with existing methods,the proposed method can generate samples with camouflage and adversarial characteristics.Meanwhile,extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models.
基金supported by the National Natural Science Foundation of China(62106214)the Hebei Natural Science Foundation(D2024203008)the Provincial Key Laboratory Performance Subsidy Project(22567612H).
文摘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.
基金financially supported by China Geological Survey Project(No.DD20220954)Open Funding Project of the Key Laboratory of Groundwater Sciences and Engineering,Ministry of Natural Resources(No.SK202301-4)+2 种基金Science and Technology Innovation Foundation of Comprehensive Survey&Command Center for Natural Resources(No.KC20240003)Yanzhao Shanshui Science and Innovation Fund of Langfang Integrated Natural Resources Survey Center,China Geological Survey(No.YZSSJJ202401-001)Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements(No.2022KFKTC009).
文摘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.
文摘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.
基金The Fundamental Research Funds for the Central Universities,the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX_0177)
文摘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.
基金The National Natural Science Foundation of China (No70571087)the National Science Fund for Distinguished Young Scholarsof China (No70625005)
文摘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.
文摘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.
基金Item Sponsored by National Natural Science Foundation of China and Shanghai Baosteel Group Co(50675186)Provincial Natural Science Foundation of Hebei Province of China(E2006001038)
文摘For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition.
基金supported in part by the National Nature Science Fundation(61174009,61203323)Youth Foundation of Hebei Province(F2016202327)+3 种基金the Colleges and Universities in Hebei Province Science and Technology Research Project(ZC2016020)supported in part by Key Project of NSFC(61533009)111 Project(B08015)Research Project(JCYJ20150403161923519)
文摘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.
基金Project Sponsored by Excellent Youth Teacher Foundation of Education Ministry of China and Provincial Natural Science Foundation of Hebei(598275)
文摘A new pattern recognition method of shape was presented based on artificial neural network theory.The method avoids the defects of shape pattern recognition with polynomials and it has strong disturbance resistance.It has been proved to be superior in recognizing different shape patterns by identifying many sorts of working sample books which the results are known.
基金supported by the National Science Fund for Distinguished Young Scholars of China(No.61225014)
文摘A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features （i.e., the dynamics） of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases： the identification （training） phase and the recognition （test） phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt （PTB） database are included to demonstrate the effectiveness of the approach.
基金supported by the Pre-research project in the manned space field.Project Number 020202,China.
文摘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.
基金supported by the Centro de Investigación para el Desarrollo y la Innovación (CIDI) from Universidad Pontificia Bolivariana (No. 636B-06/16–57)。
文摘Structural Health Monitoring(SHM) suggests the use of machine learning algorithms with the aim of understanding specific behaviors in a structural system. This work introduces a pattern recognition methodology for operational condition clustering in a structure sample using the well known Density Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm.The methodology was validated using a data set from an experiment with 32 Fiber Bragg Gratings bonded to an aluminum beam placed in cantilever and submitted to cyclic bending loads under 13 different operational conditions(pitch angles). Further, the computational cost and precision of the machine learning pipeline called FA + GA-DBSCAN(which employs a combination of machine learning techniques including factor analysis for dimensionality reduction and a genetic algorithm for the automatic selection of initial parameters of DBSCAN) was measured. The obtained results have shown a good performance, detecting 12 of 13 operational conditions, with an overall precision over 90%.
基金supported by National Key Scientific Project for New Drug Discovery and Development of China (Grant no. 2009ZX09301-012)
文摘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.
基金Project supported by the National High Technology Research and Development Program of China(Grant No.2013AA030901)
文摘We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘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.
