With the increasing popularity of mobile internet devices,speech emotion recognition has become a convenient and valuable means of human-computer interaction.The performance of speech emotion recognition depends on th...With the increasing popularity of mobile internet devices,speech emotion recognition has become a convenient and valuable means of human-computer interaction.The performance of speech emotion recognition depends on the discriminating and emotion-related utterance-level representations extracted from speech.Moreover,sufficient data are required to model the relationship between emotional states and speech.Mainstream emotion recognition methods cannot avoid the influence of the silence period in speech,and environmental noise significantly affects the recognition performance.This study intends to supplement the silence periods with removed speech information and applies segmentwise multilayer perceptrons to enhance the utterance-level representation aggregation.In addition,improved semisupervised learning is employed to overcome the prob-lem of data scarcity.Particular experiments are conducted to evaluate the proposed method on the IEMOCAP corpus,which reveals that it achieves 68.0%weighted accuracy and 68.8%unweighted accuracy in four emotion classifications.The experimental results demonstrate that the proposed method aggregates utterance-level more effectively and that semisupervised learning enhances the performance of our method.展开更多
In this paper, based on a stochastic mode! for inputs and weights, and in view of the disturbance of correlative and large input and weight errors, a general algorithm to obtain the output error characteristics of a c...In this paper, based on a stochastic mode! for inputs and weights, and in view of the disturbance of correlative and large input and weight errors, a general algorithm to obtain the output error characteristics of a class of multilayered perceptrons with threshold functions is proposed by using statistical approach. Furthermore, the formula to calculate the robustness of the networks is also given. The result of computer simulation indicates the correctness of the algorithm.展开更多
This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configura...This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.展开更多
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
Learning of the feedforward multilayer perceptron (MLP) networks is to adapt all synaptic weights in such a way that the discrepancy between the actual output signals and the desired signals, averaged over all learnin...Learning of the feedforward multilayer perceptron (MLP) networks is to adapt all synaptic weights in such a way that the discrepancy between the actual output signals and the desired signals, averaged over all learning examples (training patterns), is as small as possible. The backpropagation, or variations thereof, is a standard method applied to adjust the synaptic weights in the network in order to minimize a given cost function. However as a steepest descent approach, BP algorithm is too slow for many applications. Since late 1980s lots of efforts have been reported in the literature aimed at improving the efficiency of the algorithm. Among them a recently proposed learning strategy based on linearization of the nonlinear activation functions and optimization of the multilayer perceptron layer by layer (OLL) seems promising. In this paper a modified learning procedure is presented which tries to find a weight change vector at each trial iteration in the OLL algorithm more efficiently. The proposed learning procedure can save expensive computation efforts and yield better convergence rate as compared to the original OLL learning algorithms especially for large scale networks. The improved OLL learning algorithm is applied to the time series prediction problems presented by the OLL authors, and demonstrates a faster learning capability.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The ...This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The IHLOA algorithm introduces three key innovations:(1)chaotic initialization to enhance population diversity and global search capability,(2)adaptive random walk strategies to escape local optima,and(3)a cross-strategy mechanism to accelerate convergence and enhance fault detection accuracy and robustness.The system comprises both hardware and software components.The hardware includes sensors such as the BH1750 light intensity sensor,DS18B20 temperature sensor,and INA226 current and voltage sensor,all interfaced with the STM32F103C8T6 microcontroller and the ESP8266 module for wireless data transmission.The software,developed using QT Creator,incorporates an IHLOA-MLP model for fault diagnosis.The user-friendly interface facilitates intuitive monitoring and scalability for multiple systems.Experimental validation on a PV array demonstrates that the IHLOA-MLP model achieves a fault detection accuracy of 94.55%,which is 2.4%higher than the standard MLP,while reducing variance by 63.64%compared to the standard MLP.This highlights its accuracy and robustness.When compared to other optimization algorithms such as BKA-MLP(94.10%accuracy)and HLOA-MLP(94.00%accuracy),the IHLOA-MLP further reduces variance to 0.08,showcasing its superior performance.The system selects voltage as a feature vector to maintain circuit stability,avoiding efficiency impacts from series current sensors.This combined hardware and software approach further reduces false alarms to 0.1%through a consecutive-judgment mechanism,significantly enhancing practical reliability.This work provides a cost-effective and scalable solution for improving the stability and safety of PV systems in real-world applications.展开更多
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by pred...In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.展开更多
As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and...As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.展开更多
In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the...In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the identification of these weak influence parameters in the complex systems and propose a identification model based on the parameter recursion.As an application,three parameters of the steam generator are identified,that is,the valve opening,the valve CV value,and the reference water level,in which the valve opening and the reference water level are weak influence parameters under most operating conditions.Numerical simulation results show that,in comparison with the multi-layer perceptron(MLP),the identification error rate is decreased.Actually,the average identification error rate for the valve opening decreases by 0.96%,for the valve CV decreases by 0.002%,and for the reference water level decreases by 12%after one recursion.After two recursions,the average identification error rate for the valve opening decreases by 11.07%,for the valve CV decreases by 2.601%,and for the reference water level decreases by 95.79%.This method can help to improve the control of the steam generator.展开更多
It is of great significance in clinical diagnosis and treatment evaluation to accurately identify the lesion tissue and further extract its characteristics and depth location. In this study, we construct tissue phanto...It is of great significance in clinical diagnosis and treatment evaluation to accurately identify the lesion tissue and further extract its characteristics and depth location. In this study, we construct tissue phantoms for three lesion types: fibrosis (FT), organelle proliferation (OPT), and pigmentation (PT). These phantoms allow for the quantitative regulation of mimicked disease depth. The experimental results show that the parameter Kc, combined with MMT parameters, can effectively distinguish the presence of lesions and their abnormal types. Further, the study extracts depth-sensitive polarization feature parameters (DSPFPs) for specific lesion types. Through experiments of tissue phantoms with various depth settings, the established machine learning regression models based on DSPFPs demonstrate their depth retrieval capabilities.展开更多
Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusio...Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.展开更多
The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ...The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.展开更多
Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy sys...Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.展开更多
Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limite...Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows.展开更多
Existing icing detection technologies face challenges when applied to small and medium-sized aircraft,especially electric vertical take-off and landing(eVTOL)aircraft that meet the needs of low-altitude economic devel...Existing icing detection technologies face challenges when applied to small and medium-sized aircraft,especially electric vertical take-off and landing(eVTOL)aircraft that meet the needs of low-altitude economic development.This study proposes a data-driven icing detection method based on rotor performance evolution.Through dry-air baseline tests and dynamic icing comparative experiments(wind speed 0—30 m/s,rotational speed 0—3000 r/min,collective pitch 0°—8°)of a 0.6 m rotor in the FL-61 icing wind tunnel,a multi-source heterogeneous dataset containing motion parameters,aerodynamic parameters,and icing state identifiers is constructed.An innovative signal processing architecture combining adaptive Kalman filtering and moving average cascading is adopted.And a comparative study is conducted on the performance of support vector machine(SVM),multilayer perceptron(MLP),and random forest(RF)algorithms,achieving real-time identification of icing states in rotating components.Experimental results demonstrate that the method exhibits a minimum detection latency of 6.9 s and 96%overall accuracy in reserved test cases,featuring low-latency and low false-alarm,providing a sensor-free lightweight solution for light/vertical takeoff and landing aircraft.展开更多
To address the high-quality forged videos,traditional approaches typically have low recognition accuracy and tend to be easily misclassified.This paper tries to address the challenge of detecting high-quality deepfake...To address the high-quality forged videos,traditional approaches typically have low recognition accuracy and tend to be easily misclassified.This paper tries to address the challenge of detecting high-quality deepfake videos by promoting the accuracy of Artificial Intelligence Generated Content(AIGC)video authenticity detection with a multimodal information fusion approach.First,a high-quality multimodal video dataset is collected and normalized,including resolution correction and frame rate unification.Next,feature extraction techniques are employed to draw out features from visual,audio,and text modalities.Subsequently,these features are fused into a multilayer perceptron and attention mechanisms-based multimodal feature matrix.Finally,the matrix is fed into a multimodal information fusion layer in order to construct and train a deep learning model.Experimental findings show that the multimodal fusion model achieves an accuracy of 93.8%for the detection of video authenticity,showing significant improvement against other unimodal models,as well as affirming better performance and resistance of the model to AIGC video authenticity detection.展开更多
The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is ...The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.展开更多
Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based ...Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based on the 3D U-Net architecture for liver region segmentation. Our model replaces the last two layers of the 3D U-Net with a sliding window-based multilayer perceptron(SMLP), enabling better extraction of local image features. We also design a high-and low-level feature fusion dilated convolution block that focuses on local features and better supplements the surrounding information of the target region. This block is embedded in the entire encoding process, ensuring that the overall network is not simply downsampling. Before each feature extraction, the input features are processed by the dilated convolution block. We validate our experiments on the liver tumor segmentation challenge 2017(Lits2017) dataset, and our model achieves a Dice coefficient of 0.95, which is an improvement of 0.015 compared to the 3D U-Net model. Furthermore, we compare our results with other segmentation methods, and our model consistently outperforms them.展开更多
文摘With the increasing popularity of mobile internet devices,speech emotion recognition has become a convenient and valuable means of human-computer interaction.The performance of speech emotion recognition depends on the discriminating and emotion-related utterance-level representations extracted from speech.Moreover,sufficient data are required to model the relationship between emotional states and speech.Mainstream emotion recognition methods cannot avoid the influence of the silence period in speech,and environmental noise significantly affects the recognition performance.This study intends to supplement the silence periods with removed speech information and applies segmentwise multilayer perceptrons to enhance the utterance-level representation aggregation.In addition,improved semisupervised learning is employed to overcome the prob-lem of data scarcity.Particular experiments are conducted to evaluate the proposed method on the IEMOCAP corpus,which reveals that it achieves 68.0%weighted accuracy and 68.8%unweighted accuracy in four emotion classifications.The experimental results demonstrate that the proposed method aggregates utterance-level more effectively and that semisupervised learning enhances the performance of our method.
基金National Science Foundation of Chinathe Doctoral Fund of the State Education Commission of China
文摘In this paper, based on a stochastic mode! for inputs and weights, and in view of the disturbance of correlative and large input and weight errors, a general algorithm to obtain the output error characteristics of a class of multilayered perceptrons with threshold functions is proposed by using statistical approach. Furthermore, the formula to calculate the robustness of the networks is also given. The result of computer simulation indicates the correctness of the algorithm.
文摘This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
基金supported by the National Natural Sciences Foundation of China.
文摘Learning of the feedforward multilayer perceptron (MLP) networks is to adapt all synaptic weights in such a way that the discrepancy between the actual output signals and the desired signals, averaged over all learning examples (training patterns), is as small as possible. The backpropagation, or variations thereof, is a standard method applied to adjust the synaptic weights in the network in order to minimize a given cost function. However as a steepest descent approach, BP algorithm is too slow for many applications. Since late 1980s lots of efforts have been reported in the literature aimed at improving the efficiency of the algorithm. Among them a recently proposed learning strategy based on linearization of the nonlinear activation functions and optimization of the multilayer perceptron layer by layer (OLL) seems promising. In this paper a modified learning procedure is presented which tries to find a weight change vector at each trial iteration in the OLL algorithm more efficiently. The proposed learning procedure can save expensive computation efforts and yield better convergence rate as compared to the original OLL learning algorithms especially for large scale networks. The improved OLL learning algorithm is applied to the time series prediction problems presented by the OLL authors, and demonstrates a faster learning capability.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金supported by the National Natural Science Foundation of China(12064027,12464010)2022 Jiangxi Province High-level and Highskilled Leading Talent Training Project Selected(No.63)+1 种基金Jiujiang"Xuncheng Talents"(No.JJXC2023032)Jiujiang Natural Science Foundation Project(Key Technologies Research on Autonomous Cruise Solar-Powered UAV-2025-1).
文摘This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The IHLOA algorithm introduces three key innovations:(1)chaotic initialization to enhance population diversity and global search capability,(2)adaptive random walk strategies to escape local optima,and(3)a cross-strategy mechanism to accelerate convergence and enhance fault detection accuracy and robustness.The system comprises both hardware and software components.The hardware includes sensors such as the BH1750 light intensity sensor,DS18B20 temperature sensor,and INA226 current and voltage sensor,all interfaced with the STM32F103C8T6 microcontroller and the ESP8266 module for wireless data transmission.The software,developed using QT Creator,incorporates an IHLOA-MLP model for fault diagnosis.The user-friendly interface facilitates intuitive monitoring and scalability for multiple systems.Experimental validation on a PV array demonstrates that the IHLOA-MLP model achieves a fault detection accuracy of 94.55%,which is 2.4%higher than the standard MLP,while reducing variance by 63.64%compared to the standard MLP.This highlights its accuracy and robustness.When compared to other optimization algorithms such as BKA-MLP(94.10%accuracy)and HLOA-MLP(94.00%accuracy),the IHLOA-MLP further reduces variance to 0.08,showcasing its superior performance.The system selects voltage as a feature vector to maintain circuit stability,avoiding efficiency impacts from series current sensors.This combined hardware and software approach further reduces false alarms to 0.1%through a consecutive-judgment mechanism,significantly enhancing practical reliability.This work provides a cost-effective and scalable solution for improving the stability and safety of PV systems in real-world applications.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
基金supported by National Natural Science Foundation of China(Nos.62266054 and 62166050)Key Program of Fundamental Research Project of Yunnan Science and Technology Plan(No.202201AS070021)+2 种基金Yunnan Fundamental Research Projects(No.202401AT070122)Yunnan International Joint Research and Development Center of China-Laos-Thailand Educational Digitalization(No.202203AP140006)Scientific Research Foundation of Yunnan Provincial Department of Education(No.2024Y159).
文摘In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.
基金supported in part by the Guangxi Science and Technology Major Program under grant AA22068067the Guangxi Natural Science Foundation under grant 2023GXNSFAA026236 and 2024GXNSFDA010064the National Natural Science Foundation of China under project 62172119.
文摘As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.
文摘In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the identification of these weak influence parameters in the complex systems and propose a identification model based on the parameter recursion.As an application,three parameters of the steam generator are identified,that is,the valve opening,the valve CV value,and the reference water level,in which the valve opening and the reference water level are weak influence parameters under most operating conditions.Numerical simulation results show that,in comparison with the multi-layer perceptron(MLP),the identification error rate is decreased.Actually,the average identification error rate for the valve opening decreases by 0.96%,for the valve CV decreases by 0.002%,and for the reference water level decreases by 12%after one recursion.After two recursions,the average identification error rate for the valve opening decreases by 11.07%,for the valve CV decreases by 2.601%,and for the reference water level decreases by 95.79%.This method can help to improve the control of the steam generator.
基金supported by the Science and Technology Research Program of Shenzhen(No.JCYJ20200109142820687 and JCYJ20210324120012035)the Cross-research Innovation Fund of In-ternational Graduate School at Shenzhen,Tsinghua University(No.JC2021001)the Russian Sci-ence Foundation(No.23-14-00287)。
文摘It is of great significance in clinical diagnosis and treatment evaluation to accurately identify the lesion tissue and further extract its characteristics and depth location. In this study, we construct tissue phantoms for three lesion types: fibrosis (FT), organelle proliferation (OPT), and pigmentation (PT). These phantoms allow for the quantitative regulation of mimicked disease depth. The experimental results show that the parameter Kc, combined with MMT parameters, can effectively distinguish the presence of lesions and their abnormal types. Further, the study extracts depth-sensitive polarization feature parameters (DSPFPs) for specific lesion types. Through experiments of tissue phantoms with various depth settings, the established machine learning regression models based on DSPFPs demonstrate their depth retrieval capabilities.
基金supported in part by the National Natural Science Foundation of China(No.62001333)the Scientific Research Project of Education Department of Hubei Province(No.D20221702).
文摘Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.
基金supported by the National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154)the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)+1 种基金the Young and Middle-Aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province,China(Grant No.T2023007)the National Natural Science Foundation of China(Grant No.U23A20318).
文摘The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.
基金the Deanship of Graduate Studies and Scientific Research at University of Bisha,Saudi Arabia for funding this research work through the Promising Program under Grant Number(UB-Promising-42-1445).
文摘Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.
基金supported by the National Key R&D Program of China(2022YFA1305700 to Li J)the“Dawn”Program of Shanghai Education Commission,China(21SG33 to Li J)The National Natural Science Foundation of China(82272793 to Gao X).
文摘Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows.
基金supported in part by the National Key R&D Program of China(No.2022YFE0203700)the Aeronautical Science Foundation of China(No.2023Z010027001)。
文摘Existing icing detection technologies face challenges when applied to small and medium-sized aircraft,especially electric vertical take-off and landing(eVTOL)aircraft that meet the needs of low-altitude economic development.This study proposes a data-driven icing detection method based on rotor performance evolution.Through dry-air baseline tests and dynamic icing comparative experiments(wind speed 0—30 m/s,rotational speed 0—3000 r/min,collective pitch 0°—8°)of a 0.6 m rotor in the FL-61 icing wind tunnel,a multi-source heterogeneous dataset containing motion parameters,aerodynamic parameters,and icing state identifiers is constructed.An innovative signal processing architecture combining adaptive Kalman filtering and moving average cascading is adopted.And a comparative study is conducted on the performance of support vector machine(SVM),multilayer perceptron(MLP),and random forest(RF)algorithms,achieving real-time identification of icing states in rotating components.Experimental results demonstrate that the method exhibits a minimum detection latency of 6.9 s and 96%overall accuracy in reserved test cases,featuring low-latency and low false-alarm,providing a sensor-free lightweight solution for light/vertical takeoff and landing aircraft.
文摘To address the high-quality forged videos,traditional approaches typically have low recognition accuracy and tend to be easily misclassified.This paper tries to address the challenge of detecting high-quality deepfake videos by promoting the accuracy of Artificial Intelligence Generated Content(AIGC)video authenticity detection with a multimodal information fusion approach.First,a high-quality multimodal video dataset is collected and normalized,including resolution correction and frame rate unification.Next,feature extraction techniques are employed to draw out features from visual,audio,and text modalities.Subsequently,these features are fused into a multilayer perceptron and attention mechanisms-based multimodal feature matrix.Finally,the matrix is fed into a multimodal information fusion layer in order to construct and train a deep learning model.Experimental findings show that the multimodal fusion model achieves an accuracy of 93.8%for the detection of video authenticity,showing significant improvement against other unimodal models,as well as affirming better performance and resistance of the model to AIGC video authenticity detection.
文摘The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.
基金supported by the Shandong Provincial Natural Science Foundation (Nos.ZR2023MF062 and ZR2021MF115)the Introduction and Cultivation Program for Young Innovative Talents of Universities in Shandong (No.2021QCYY003)。
文摘Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based on the 3D U-Net architecture for liver region segmentation. Our model replaces the last two layers of the 3D U-Net with a sliding window-based multilayer perceptron(SMLP), enabling better extraction of local image features. We also design a high-and low-level feature fusion dilated convolution block that focuses on local features and better supplements the surrounding information of the target region. This block is embedded in the entire encoding process, ensuring that the overall network is not simply downsampling. Before each feature extraction, the input features are processed by the dilated convolution block. We validate our experiments on the liver tumor segmentation challenge 2017(Lits2017) dataset, and our model achieves a Dice coefficient of 0.95, which is an improvement of 0.015 compared to the 3D U-Net model. Furthermore, we compare our results with other segmentation methods, and our model consistently outperforms them.
