Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scal...Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cul-tural heritage image is one of thefine-grained images because each image has the same similarity in most cases.Using the classification technique,distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heri-tage content retrieval method using adaptive deep learning forfine-grained image retrieval.The key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image processing.We analyze the experimental results of incremental learning forfine-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar architecture.Using afine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning forfine-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods infine-grained image retrieval.展开更多
Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission.Recent research has focused on using semi-supervised learning mechani...Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission.Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry.However,real-time training and classifying network traffic pose challenges,as they can lead to the degradation of the overall dataset and difficulties preventing attacks.Additionally,existing semi-supervised learning research might need to analyze the experimental results comprehensively.This paper proposes XA-GANomaly,a novel technique for explainable adaptive semi-supervised learning using GANomaly,an image anomalous detection model that dynamically trains small subsets to these issues.First,this research introduces a deep neural network(DNN)-based GANomaly for semi-supervised learning.Second,this paper presents the proposed adaptive algorithm for the DNN-based GANomaly,which is validated with four subsets of the adaptive dataset.Finally,this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations,reconstruction error visualization,and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage,semi-supervised learning,and adaptive learning.Compared to other single-class classification techniques,the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13%and 8%of F1 scores and 4.17%and 11.51%for accuracy,respectively.Furthermore,experiments of the proposed adaptive learning reveal mostly improved results over the initial values.An analysis and monitoring system based on the combination of the three explainable methodologies is also described.Thus,the proposed method has the potential advantages to be applied in practical industry,and future research will explore handling unbalanced real-time datasets in various scenarios.展开更多
As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalizatio...As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.展开更多
This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and a...This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.展开更多
This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control(AILC) scheme is presented for a class of commens...This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control(AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance.To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control(ILC), a new boundary layer function is proposed by employing MittagLeffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function(CEF)containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.展开更多
The recent emergence of adaptive language learning systems calls for conceptual work to guide the design of assessment and learning in an adaptive environment.Although adaptive learning might have been touted as a uni...The recent emergence of adaptive language learning systems calls for conceptual work to guide the design of assessment and learning in an adaptive environment.Although adaptive learning might have been touted as a universal cure for learning problems,many adaptive language learning systems fall short of educators’expectations,partly due to a lack of standards and best practices in this area.To fill this gap,this paper proposes some major considerations in designing a high-quality assessment and learning experience in adaptive learning and ways to evaluate an adaptive learning system.The architecture of adaptive learning is decomposed,with a chain of inferences supporting the overall efficacy of an adaptive learning system presented,including user property representation,user property estimation,content representation,user interaction representation,and user interaction impact.A detailed analysis of key validity issues is provided for each inference,which motivates the major considerations in designing and evaluating assessment and learning.The paper first provides an overview of different types of assessment used in adaptive learning and an analysis of the assessment approach,priorities,and design considerations of each to optimize its use in adaptive learning.Then it proposes a framework for evaluating different aspects of an adaptive learning system.Some special connections are made to models,techniques,designs,and technologies specific to language learning and assessment,bringing more relevance to adaptive language learning solutions.Through establishing some guidelines on key aspects to evaluate and how to evaluate them,the work intends to bring more rigor to the field of adaptive language learning systems.展开更多
The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac arrest.While measuring cardiac signal it is contaminated with artifacts.The commonly encountered artifacts are Baseline wand...The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac arrest.While measuring cardiac signal it is contaminated with artifacts.The commonly encountered artifacts are Baseline wander(BW)and Muscle artifact(MA),these are physiological and nonstationary.As the nature of these artifacts is random,adaptive filtering is needed than conventional fixed coefficient filtering techniques.To address this,a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario.The proposed block least mean square(BLMS)algorithm is mathematically normalized with reference to data and error.This normalization leads,block normalized LMS(BNLMS)and block error normalized LMS(BENLMS)algorithms.Various adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological signals.The ability of these techniques is measured by calculating signal to noise ratio improvement(SNRI),Excess Mean Square Error(EMSE),and Misadjustment(Mad).Among the considered algorithms,the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter parts.Hence,this adaptive artifact canceller is suitable for real time applications like wearable,remove health care monitoring units.展开更多
Finding a suitable space is one of the most critical problems for dimensionality reduction. Each space corresponds to a distance metric defined on the sample attributes, and thus finding a suitable space can be conver...Finding a suitable space is one of the most critical problems for dimensionality reduction. Each space corresponds to a distance metric defined on the sample attributes, and thus finding a suitable space can be converted to develop an effective distance metric. Most existing dimensionality reduction methods use a fixed pre-specified distance metric. However, this easy treatment has some limitations in practice due to the fact the pre-specified metric is not going to warranty that the closest samples are the truly similar ones. In this paper, we present an adaptive metric learning method for dimensionality reduction, called AML. The adaptive metric learning model is developed by maximizing the difference of the distances between the data pairs in cannot-links and those in must-links. Different from many existing papers that use the traditional Euclidean distance, we use the more generalized l<sub>2,p</sub>-norm distance to reduce sensitivity to noise and outliers, which incorporates additional flexibility and adaptability due to the selection of appropriate p-values for different data sets. Moreover, considering traditional metric learning methods usually project samples into a linear subspace, which is overstrict. We extend the basic linear method to a more powerful nonlinear kernel case so that well capturing complex nonlinear relationship between data. To solve our objective, we have derived an efficient iterative algorithm. Extensive experiments for dimensionality reduction are provided to demonstrate the superiority of our method over state-of-the-art approaches.展开更多
It can be challenging to detect tumor margins during surgery for complete resection.The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptiv...It can be challenging to detect tumor margins during surgery for complete resection.The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model.Specifically,an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel.According to the output hypothesis of each pixel,the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights.The auto-encoder network is again trained based on these updated pixels.The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels,and thus can improve the detection performance.The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32%and a specificity of 91.31%in our animal experiments.This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection,especially,for the tumor whose margin is indistinct and irregular.展开更多
Establishing a system for measuring plant health and bacterial infection is critical in agriculture.Previously,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which ...Establishing a system for measuring plant health and bacterial infection is critical in agriculture.Previously,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been incorrect.Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light,both visible and eye using a drone.The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classification technique.Convolution neural networks in unmanned aerial vehi-cles image were used.To convey this interest,the rice’s health and bacterial infec-tion inside the photo were detected.The project entailed using pictures to identify bacterial illnesses in rice.The shape and distinct characteristics of each infection were observed.Rice symptoms were defined using machine learning and image processing techniques.Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria.The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.展开更多
In this paper,the problem of adaptive iterative learning based consensus control for periodically time-varying multi-agent systems is studied,in which the dynamics of each follower are driven by nonlinearly parameteri...In this paper,the problem of adaptive iterative learning based consensus control for periodically time-varying multi-agent systems is studied,in which the dynamics of each follower are driven by nonlinearly parameterized terms with periodic disturbances.Neural networks and Fourier base expansions are introduced to describe the periodically time-varying dynamic terms.On this basis,an adaptive learning parameter with a positively convergent series term is constructed,and a distributed control protocol based on local signals between agents is designed to ensure accurate consensus of the closed-loop systems.Furthermore,consensus algorithm is generalized to solve the formation control problem.Finally,simulation experiments are implemented through MATLAB to demonstrate the effectiveness of the method used.展开更多
In this study,We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion,where the leader's dynamics are unknown,and the controlled system's p...In this study,We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion,where the leader's dynamics are unknown,and the controlled system's parameters are uncertain.The multiagent systems are considered a kind of hybrid order nonlinear systems,which relaxes the strict requirement that all agents are of the same order in some existing work.For theoretical analyses,we design a composite energy function with virtual gain parameters to reduce the restriction that the controller gain depends on global information.Considering the stability of the controller,we introduce a smooth continuous function to improve the piecewise controller to avoid possible chattering.Theoretical analyses prove the convergence of the presented algorithm,and simulation experiments verify the effectiveness of the algorithm.展开更多
Robot soccer competition provides an excellent opportunity for robotics research. We have built a soccer robot system to participate in internal and oversea matches. Firstly, we propose a new learning control scheme a...Robot soccer competition provides an excellent opportunity for robotics research. We have built a soccer robot system to participate in internal and oversea matches. Firstly, we propose a new learning control scheme adaptive PID learning controller. It means to overcome the drawbacks of the conventional PID type control methods. Secondly, we introduce our vision recognition algorithm. It remarkably increases the speed of recognition. Finally, we refer the communication system. We adopt bulletin board system to prevent communication confusion.展开更多
Starting from a philosophical perspective,which states that the living structures are actually a combination between matter and information,this article presents the results on an analysis of the bipolar information-m...Starting from a philosophical perspective,which states that the living structures are actually a combination between matter and information,this article presents the results on an analysis of the bipolar information-matter structure of the human organism,distinguishing three fundamental circuits for its survival,which demonstrates and supports this statement,as a base for further development of the informational model of consciousness to a general informational model of the human organism.For this,it was examined the Informational System of the Human Body and its components from the perspective of the physics/information/neurosciences concepts,showing specific functions of each of them,highlighting the correspondence of these centers with brain support areas and with their projections in consciousness,which are:Center of Acquisition and Storing of Information(CASI)reflected in consciousness as memory,Center of Decision and Command(CDC)(decision),Info-Emotional Center(IES)(emotions),Maintenance Informational System(MIS)(personal status),Genetic Transmission System(GTS)(associativity/genetic transmission)and Info Genetic Generator(IGG)related by the body development and inherited behaviors.The Info Connection(IC),detected in consciousness as trust and confidence can explain the Near-Death Experiences(NDEs)and associated phenomena.This connection is antientropic and informational,because from the multitude of uncertain possibilities is selected a certain one,helping/supporting the survival and life.The human body appears therefore as a bipolar structure,connected to two poles:information and matter.It is argued that the survival,which is the main objective of the organism,is complied in three main ways,by means of:(i)the reactive operation for adaptation by attitude;(ii)the info-genetic integration of information by epigenetic processes and genetic transmission of information for species survival,both circuits(i)and(ii)being associated to the information pole;(iii)maintenance of the material body(defined as informed matter)and its functions,associated to the matter pole of the organism.It results therefore that the informational system of the human body is supported by seven informational circuits formed by the neuro-connections between the specific zones of the brain corresponding to the informational subsystems,the cognitive centers,the sensors,transducers and execution(motor/mobile)elements.The fundamental informational circuits assuring the survival are the reactive circuit,expressible by attitude,the epigenetic/genetic circuit,absorbing and codifying information to be transmitted to the next generations,and the metabolic circuit,connected to matter(matter pole).The presented analysis allows to extend the informational modeling of consciousness to an Informational Model of Consciousness and Organism,fully describing the composition/functions of the organism in terms of information/matter and neurosciences concepts.展开更多
Node classification has a wide range of application scenarios such as citation analysis and social network analysis.In many real-world attributed networks,a large portion of classes only contain limited labeled nodes....Node classification has a wide range of application scenarios such as citation analysis and social network analysis.In many real-world attributed networks,a large portion of classes only contain limited labeled nodes.Most of the existing node classification methods cannot be used for few-shot node classification.To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples,in this paper,we propose a local adaptive discriminant structure learning(LADSL)method for few-shot node classification.LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlargingginter-classdifferences.Extensiveexperiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.展开更多
The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling c...The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.展开更多
This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading ef...This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading effects of generators,carbon tax,and prohibited operating zones of generators,respectively.ASHLO algorithm,involves random learning operator,individual learning operator,social learning operator and adaptive strategies.To compare and analyze the computation performance of the ASHLO method,the proposed ASHLO method and other heuristic intelligent optimization methods are employed to solve OPF problem on the modified IEEE 30-bus and 118-bus AC/DC hybrid test system.Numerical results indicate that the ASHLO method has good convergent property and robustness.Meanwhile,the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.展开更多
An adaptive repetitive control scheme is proposed for trajectory-keeping of satellite formation flying in the leader–follower mode which is described by Lawden equation.The system is parameterised by power series app...An adaptive repetitive control scheme is proposed for trajectory-keeping of satellite formation flying in the leader–follower mode which is described by Lawden equation.The system is parameterised by power series approximation and the unknown timevarying parameters are estimated by adaptive repetitive learning law.Through rigorous analysis by constructing a Lyapunov-like composite energy function(CEF),the stability of the closed-loop system is proved.Finally,a simulation example is provided to illustrate the effectiveness of the control algorithms proposed in this paper.展开更多
Adaptive learning paths provide individual learning objectives that best match a learner’s characteristics.This is especially helpful when learners need to balance limited available learning time and multiple learnin...Adaptive learning paths provide individual learning objectives that best match a learner’s characteristics.This is especially helpful when learners need to balance limited available learning time and multiple learning objectives.The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest.However,most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions.There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible,which is a typical scenario in exanimation-oriented education systems.This study aims to solve this problem by introducing a new approach that builds on existing methods.First,the eight properties in Gardner’s multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects(LOs)and learners,thereby improving recommendation accuracy rates.Then,a novel adaptive learning path recommendation model is presented where viable knowledge topologies,knowledge bases and the previously-established properties relating to a learner’s ability are combined by Dempster-Shafer(D-S)evidence theory.A series of practical experiments were performed to assess the approach’s adaptability,the appropriateness of the selected evidence and the effectiveness of the recommendations.In the results,it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.展开更多
In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-doma...In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-domain representation is constructed to derive an error model with relative degree one for our purpose. And time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is constructed by using a first-order filter, which removes the requirement of strict positive real(SPR) condition and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function,the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.展开更多
基金This research was funded by King Mongkut’s University of Technology North Bangkok(Contract no.KMUTNB-62-KNOW-026).
文摘Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cul-tural heritage image is one of thefine-grained images because each image has the same similarity in most cases.Using the classification technique,distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heri-tage content retrieval method using adaptive deep learning forfine-grained image retrieval.The key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image processing.We analyze the experimental results of incremental learning forfine-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar architecture.Using afine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning forfine-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods infine-grained image retrieval.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by theKoreaGovernment(MOTIE)(P0008703,The CompetencyDevelopment Program for Industry Specialist).
文摘Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission.Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry.However,real-time training and classifying network traffic pose challenges,as they can lead to the degradation of the overall dataset and difficulties preventing attacks.Additionally,existing semi-supervised learning research might need to analyze the experimental results comprehensively.This paper proposes XA-GANomaly,a novel technique for explainable adaptive semi-supervised learning using GANomaly,an image anomalous detection model that dynamically trains small subsets to these issues.First,this research introduces a deep neural network(DNN)-based GANomaly for semi-supervised learning.Second,this paper presents the proposed adaptive algorithm for the DNN-based GANomaly,which is validated with four subsets of the adaptive dataset.Finally,this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations,reconstruction error visualization,and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage,semi-supervised learning,and adaptive learning.Compared to other single-class classification techniques,the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13%and 8%of F1 scores and 4.17%and 11.51%for accuracy,respectively.Furthermore,experiments of the proposed adaptive learning reveal mostly improved results over the initial values.An analysis and monitoring system based on the combination of the three explainable methodologies is also described.Thus,the proposed method has the potential advantages to be applied in practical industry,and future research will explore handling unbalanced real-time datasets in various scenarios.
文摘As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.
基金supported by the National Natural Science Foundation of China(61873013,61922007)。
文摘This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.
基金supported by the National Natural Science Foundation of China(60674090)Shandong Natural Science Foundation(ZR2017QF016)
文摘This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control(AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance.To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control(ILC), a new boundary layer function is proposed by employing MittagLeffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function(CEF)containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.
文摘The recent emergence of adaptive language learning systems calls for conceptual work to guide the design of assessment and learning in an adaptive environment.Although adaptive learning might have been touted as a universal cure for learning problems,many adaptive language learning systems fall short of educators’expectations,partly due to a lack of standards and best practices in this area.To fill this gap,this paper proposes some major considerations in designing a high-quality assessment and learning experience in adaptive learning and ways to evaluate an adaptive learning system.The architecture of adaptive learning is decomposed,with a chain of inferences supporting the overall efficacy of an adaptive learning system presented,including user property representation,user property estimation,content representation,user interaction representation,and user interaction impact.A detailed analysis of key validity issues is provided for each inference,which motivates the major considerations in designing and evaluating assessment and learning.The paper first provides an overview of different types of assessment used in adaptive learning and an analysis of the assessment approach,priorities,and design considerations of each to optimize its use in adaptive learning.Then it proposes a framework for evaluating different aspects of an adaptive learning system.Some special connections are made to models,techniques,designs,and technologies specific to language learning and assessment,bringing more relevance to adaptive language learning solutions.Through establishing some guidelines on key aspects to evaluate and how to evaluate them,the work intends to bring more rigor to the field of adaptive language learning systems.
文摘The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac arrest.While measuring cardiac signal it is contaminated with artifacts.The commonly encountered artifacts are Baseline wander(BW)and Muscle artifact(MA),these are physiological and nonstationary.As the nature of these artifacts is random,adaptive filtering is needed than conventional fixed coefficient filtering techniques.To address this,a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario.The proposed block least mean square(BLMS)algorithm is mathematically normalized with reference to data and error.This normalization leads,block normalized LMS(BNLMS)and block error normalized LMS(BENLMS)algorithms.Various adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological signals.The ability of these techniques is measured by calculating signal to noise ratio improvement(SNRI),Excess Mean Square Error(EMSE),and Misadjustment(Mad).Among the considered algorithms,the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter parts.Hence,this adaptive artifact canceller is suitable for real time applications like wearable,remove health care monitoring units.
文摘Finding a suitable space is one of the most critical problems for dimensionality reduction. Each space corresponds to a distance metric defined on the sample attributes, and thus finding a suitable space can be converted to develop an effective distance metric. Most existing dimensionality reduction methods use a fixed pre-specified distance metric. However, this easy treatment has some limitations in practice due to the fact the pre-specified metric is not going to warranty that the closest samples are the truly similar ones. In this paper, we present an adaptive metric learning method for dimensionality reduction, called AML. The adaptive metric learning model is developed by maximizing the difference of the distances between the data pairs in cannot-links and those in must-links. Different from many existing papers that use the traditional Euclidean distance, we use the more generalized l<sub>2,p</sub>-norm distance to reduce sensitivity to noise and outliers, which incorporates additional flexibility and adaptability due to the selection of appropriate p-values for different data sets. Moreover, considering traditional metric learning methods usually project samples into a linear subspace, which is overstrict. We extend the basic linear method to a more powerful nonlinear kernel case so that well capturing complex nonlinear relationship between data. To solve our objective, we have derived an efficient iterative algorithm. Extensive experiments for dimensionality reduction are provided to demonstrate the superiority of our method over state-of-the-art approaches.
基金This work was supported in part by NIH grants(R01CA204254,R01HL140325,and R21CA231911).
文摘It can be challenging to detect tumor margins during surgery for complete resection.The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model.Specifically,an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel.According to the output hypothesis of each pixel,the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights.The auto-encoder network is again trained based on these updated pixels.The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels,and thus can improve the detection performance.The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32%and a specificity of 91.31%in our animal experiments.This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection,especially,for the tumor whose margin is indistinct and irregular.
基金funded by King Mongkut’s University of Technology North Bangkok(Contract no.KMUTNB-63-KNOW-044).
文摘Establishing a system for measuring plant health and bacterial infection is critical in agriculture.Previously,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been incorrect.Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light,both visible and eye using a drone.The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classification technique.Convolution neural networks in unmanned aerial vehi-cles image were used.To convey this interest,the rice’s health and bacterial infec-tion inside the photo were detected.The project entailed using pictures to identify bacterial illnesses in rice.The shape and distinct characteristics of each infection were observed.Rice symptoms were defined using machine learning and image processing techniques.Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria.The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.
基金supported by the National Natural Science Foundation of China(Grant Nos.62203342,62073254,92271101,62106186,and62103136)the Fundamental Research Funds for the Central Universities(Grant Nos.XJS220704,QTZX23003,and ZYTS23046)+1 种基金the Project funded by China Postdoctoral Science Foundation(Grant No.2022M712489)the Natural Science Basic Research Program of Shaanxi(Grant Nos.2023-JC-YB-585 and 2020JM-188)。
文摘In this paper,the problem of adaptive iterative learning based consensus control for periodically time-varying multi-agent systems is studied,in which the dynamics of each follower are driven by nonlinearly parameterized terms with periodic disturbances.Neural networks and Fourier base expansions are introduced to describe the periodically time-varying dynamic terms.On this basis,an adaptive learning parameter with a positively convergent series term is constructed,and a distributed control protocol based on local signals between agents is designed to ensure accurate consensus of the closed-loop systems.Furthermore,consensus algorithm is generalized to solve the formation control problem.Finally,simulation experiments are implemented through MATLAB to demonstrate the effectiveness of the method used.
基金the National Natural Science Foundation of China(Grant Nos.62203342,62073254,92271101,62106186,and 62103136)the Fundamental Research Funds for the Central Universities(Grant Nos.XJS220704,QTZX23003,and ZYTS23046)+1 种基金the Project Funded by China Postdoctoral Science Foundation(Grant No.2022M712489)the Natural Science Basic Research Program of Shaanxi(Grant No.2023-JC-YB-585)。
文摘In this study,We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion,where the leader's dynamics are unknown,and the controlled system's parameters are uncertain.The multiagent systems are considered a kind of hybrid order nonlinear systems,which relaxes the strict requirement that all agents are of the same order in some existing work.For theoretical analyses,we design a composite energy function with virtual gain parameters to reduce the restriction that the controller gain depends on global information.Considering the stability of the controller,we introduce a smooth continuous function to improve the piecewise controller to avoid possible chattering.Theoretical analyses prove the convergence of the presented algorithm,and simulation experiments verify the effectiveness of the algorithm.
文摘Robot soccer competition provides an excellent opportunity for robotics research. We have built a soccer robot system to participate in internal and oversea matches. Firstly, we propose a new learning control scheme adaptive PID learning controller. It means to overcome the drawbacks of the conventional PID type control methods. Secondly, we introduce our vision recognition algorithm. It remarkably increases the speed of recognition. Finally, we refer the communication system. We adopt bulletin board system to prevent communication confusion.
文摘Starting from a philosophical perspective,which states that the living structures are actually a combination between matter and information,this article presents the results on an analysis of the bipolar information-matter structure of the human organism,distinguishing three fundamental circuits for its survival,which demonstrates and supports this statement,as a base for further development of the informational model of consciousness to a general informational model of the human organism.For this,it was examined the Informational System of the Human Body and its components from the perspective of the physics/information/neurosciences concepts,showing specific functions of each of them,highlighting the correspondence of these centers with brain support areas and with their projections in consciousness,which are:Center of Acquisition and Storing of Information(CASI)reflected in consciousness as memory,Center of Decision and Command(CDC)(decision),Info-Emotional Center(IES)(emotions),Maintenance Informational System(MIS)(personal status),Genetic Transmission System(GTS)(associativity/genetic transmission)and Info Genetic Generator(IGG)related by the body development and inherited behaviors.The Info Connection(IC),detected in consciousness as trust and confidence can explain the Near-Death Experiences(NDEs)and associated phenomena.This connection is antientropic and informational,because from the multitude of uncertain possibilities is selected a certain one,helping/supporting the survival and life.The human body appears therefore as a bipolar structure,connected to two poles:information and matter.It is argued that the survival,which is the main objective of the organism,is complied in three main ways,by means of:(i)the reactive operation for adaptation by attitude;(ii)the info-genetic integration of information by epigenetic processes and genetic transmission of information for species survival,both circuits(i)and(ii)being associated to the information pole;(iii)maintenance of the material body(defined as informed matter)and its functions,associated to the matter pole of the organism.It results therefore that the informational system of the human body is supported by seven informational circuits formed by the neuro-connections between the specific zones of the brain corresponding to the informational subsystems,the cognitive centers,the sensors,transducers and execution(motor/mobile)elements.The fundamental informational circuits assuring the survival are the reactive circuit,expressible by attitude,the epigenetic/genetic circuit,absorbing and codifying information to be transmitted to the next generations,and the metabolic circuit,connected to matter(matter pole).The presented analysis allows to extend the informational modeling of consciousness to an Informational Model of Consciousness and Organism,fully describing the composition/functions of the organism in terms of information/matter and neurosciences concepts.
基金supported by the National Key R&D Program of China(2018YFB1402600)the National Natural Science Foundation of China(Grant Nos.61802028,62192784,61877006,and 62002027)。
文摘Node classification has a wide range of application scenarios such as citation analysis and social network analysis.In many real-world attributed networks,a large portion of classes only contain limited labeled nodes.Most of the existing node classification methods cannot be used for few-shot node classification.To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples,in this paper,we propose a local adaptive discriminant structure learning(LADSL)method for few-shot node classification.LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlargingginter-classdifferences.Extensiveexperiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.
文摘The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.
基金supported by National Natural Science Foundation of China(No.51377103)the technology project of State Grid Corporation of China:Research on Multi-Level Decomposition Coordination of the Pareto Set of Multi-Objective Optimization Problem in Bulk Power System(No.SGSXDKYDWKJ2015-001)the support from State Energy Smart Grid R&D Center(SHANGHAI)
文摘This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading effects of generators,carbon tax,and prohibited operating zones of generators,respectively.ASHLO algorithm,involves random learning operator,individual learning operator,social learning operator and adaptive strategies.To compare and analyze the computation performance of the ASHLO method,the proposed ASHLO method and other heuristic intelligent optimization methods are employed to solve OPF problem on the modified IEEE 30-bus and 118-bus AC/DC hybrid test system.Numerical results indicate that the ASHLO method has good convergent property and robustness.Meanwhile,the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.
基金This work was supported by National Natural Science Foundation of China under Grant(NSFC number 60705030).
文摘An adaptive repetitive control scheme is proposed for trajectory-keeping of satellite formation flying in the leader–follower mode which is described by Lawden equation.The system is parameterised by power series approximation and the unknown timevarying parameters are estimated by adaptive repetitive learning law.Through rigorous analysis by constructing a Lyapunov-like composite energy function(CEF),the stability of the closed-loop system is proved.Finally,a simulation example is provided to illustrate the effectiveness of the control algorithms proposed in this paper.
基金supported by the National Natural Science Foundation of China(61972133)Plan for“1125”Innovation Leading Talent of Zhengzhou City(2019)the Opening Foundation of Yunnan Key Laboratory of Smart City in Cyberspace Security(202105AG070010)
文摘Adaptive learning paths provide individual learning objectives that best match a learner’s characteristics.This is especially helpful when learners need to balance limited available learning time and multiple learning objectives.The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest.However,most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions.There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible,which is a typical scenario in exanimation-oriented education systems.This study aims to solve this problem by introducing a new approach that builds on existing methods.First,the eight properties in Gardner’s multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects(LOs)and learners,thereby improving recommendation accuracy rates.Then,a novel adaptive learning path recommendation model is presented where viable knowledge topologies,knowledge bases and the previously-established properties relating to a learner’s ability are combined by Dempster-Shafer(D-S)evidence theory.A series of practical experiments were performed to assess the approach’s adaptability,the appropriateness of the selected evidence and the effectiveness of the recommendations.In the results,it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.
基金the National Natural Science Foundation of China(No.61273058)
文摘In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-domain representation is constructed to derive an error model with relative degree one for our purpose. And time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is constructed by using a first-order filter, which removes the requirement of strict positive real(SPR) condition and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function,the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.
