This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedba...This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital port for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases： Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.展开更多
The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me...The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.展开更多
For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties i...For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique（feature selection methods） are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types： filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.展开更多
Infectious keratitis is the most common condition of corneal diseases in which a pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues.Infectious keratitis is a medical emergency ...Infectious keratitis is the most common condition of corneal diseases in which a pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues.Infectious keratitis is a medical emergency for which a rapid and accurate diagnosis is needed to ensure prompt and precise treatment to halt the disease progression and to limit the extent of corneal damage;otherwise,it may develop a sight-threatening and even eye-globe-threatening condition.In this paper,we propose a sequentiallevel deep model to effectively discriminate infectious corneal disease via the classification of clinical images.In this approach,we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis.In a comparison,the performance of the proposed sequential-level deep model achieved 80%diagnostic accuracy,far better than the 49.27%±11.5%diagnostic accuracy achieved by 421 ophthalmologists over 120 test images.展开更多
Spear Phishing Attacks(SPAs)pose a significant threat to the healthcare sector,resulting in data breaches,financial losses,and compromised patient confidentiality.Traditional defenses,such as firewalls and antivirus s...Spear Phishing Attacks(SPAs)pose a significant threat to the healthcare sector,resulting in data breaches,financial losses,and compromised patient confidentiality.Traditional defenses,such as firewalls and antivirus software,often fail to counter these sophisticated attacks,which target human vulnerabilities.To strengthen defenses,healthcare organizations are increasingly adopting Machine Learning(ML)techniques.ML-based SPA defenses use advanced algorithms to analyze various features,including email content,sender behavior,and attachments,to detect potential threats.This capability enables proactive security measures that address risks in real-time.The interpretability of ML models fosters trust and allows security teams to continuously refine these algorithms as new attack methods emerge.Implementing ML techniques requires integrating diverse data sources,such as electronic health records,email logs,and incident reports,which enhance the algorithms’learning environment.Feedback from end-users further improves model performance.Among tested models,the hierarchical models,Convolutional Neural Network(CNN)achieved the highest accuracy at 99.99%,followed closely by the sequential Bidirectional Long Short-Term Memory(BiLSTM)model at 99.94%.In contrast,the traditional Multi-Layer Perceptron(MLP)model showed an accuracy of 98.46%.This difference underscores the superior performance of advanced sequential and hierarchical models in detecting SPAs compared to traditional approaches.展开更多
Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques....Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.展开更多
Corrective control theory lays a novel foundation for the fault-tolerant control of asynchronous sequential machines. In this paper, we present a corrective control scheme for tolerating permanent state transition fau...Corrective control theory lays a novel foundation for the fault-tolerant control of asynchronous sequential machines. In this paper, we present a corrective control scheme for tolerating permanent state transition faults in the dynamics of asynchronous sequential machines. By a fault occurrence, the asynchronous machine may be stuck at a faulty state, not responding to the external input. We analyze the detectability of the considered faults and present the necessary and sufficient condition for the existence of a controller that overcomes any permanent transition faults. Fault tolerance is realized by using potential reachability and asynchronous mechanisms in the machine. A case study on an asynchronous counter is provided to illustrate the proposed fault detection and tolerance scheme.展开更多
Nondeterminism of PROLOG execution requires that a block of control information or a choice point for each procedure call be stored when there are other candidate clauses to be used.When the currently selected clause ...Nondeterminism of PROLOG execution requires that a block of control information or a choice point for each procedure call be stored when there are other candidate clauses to be used.When the currently selected clause fails,the bindings made by the clause must be undone and the stored choice point is reactivated,and then another clause of the candidate ones is chosen to run on it. Storing and reactivating choice points and undoing account for the great overhead are required to control PROLOG execution,which is quite different from conventional programs. This paper focuses on the techniques used in Sequential PROLOG Engine(SPE)to reduce the overhead of control operations.The control instructions of SPE store no more choice points than the necessary.Its architecture takes the approaches of analysing the potential parallelism in the con- trol operations and developing a fraction of it due to the cost-effect consideration.The results of executing two sample programs on SPE in the form of hand timings are presented,which favor the approach.展开更多
This paper presents a method for state simplification in incompletely specified sequential machines. The new method adopts Inclusive-OR operation of column vectors for multi-level output matrix E_k! Compared with othe...This paper presents a method for state simplification in incompletely specified sequential machines. The new method adopts Inclusive-OR operation of column vectors for multi-level output matrix E_k! Compared with other algorithms in use, this method is theoretically more strict, while its structure is simple and the results obtained are accurate.展开更多
To achieve an unmanned rice farm,in this study,a cotransporter system was developed using a tracked rice harvester and transporter for autonomous harvesting,unloading,and transportation.Additionally,two unloading and ...To achieve an unmanned rice farm,in this study,a cotransporter system was developed using a tracked rice harvester and transporter for autonomous harvesting,unloading,and transportation.Additionally,two unloading and transportation modes—harvester waiting for unloading(HWU)and transporter fol-lowing for unloading(TFU)—were proposed,and a harvesting-unloading-transportation(HUT)strategy was defined.By breaking down the main stages of the collaborative operation,designing module-state machines(MSMs),and constructing state-transition chains,a HUT collaborative operation logic frame-work suitable for the embedded navigation controller was designed using the concept and method of the finite-state machine(FSM).This method addresses the multiple-stage,nonsequential,and complex processes in HUT collaborative operations.Simulations and field-harvesting experiments were performed to evaluate the applicability of this proposed strategy and system.The experimental results showed that the HUT collaborative operation strategy effectively integrated path planning,path-tracking control,inter-vehicle communication,collaborative operation control,and implementation control.The cotrans-porter system completed the entire process of harvesting,unloading,and transportation.The field-harvesting experiment revealed that a harvest efficiency of 0.42 hm^(2)·h^(−1) was achieved.This study can provide insight into collaborative harvesting and solutions for the harvesting process of unmanned farms.展开更多
碳排放连续在线监测法作为一种高效、可溯源的方法,在我国碳计量领域中逐渐应用。然而,由于烟囱管道的大直径、复杂烟气流场,以及流量计检修维护、粉尘堵塞导致的监测数据中断与异常,烟气流量的准确监测成为一大挑战。为此,提出一种融...碳排放连续在线监测法作为一种高效、可溯源的方法,在我国碳计量领域中逐渐应用。然而,由于烟囱管道的大直径、复杂烟气流场,以及流量计检修维护、粉尘堵塞导致的监测数据中断与异常,烟气流量的准确监测成为一大挑战。为此,提出一种融合变量投影重要性分析(variable importance in projection,VIP)、最大信息系数(maximal information coefficient,MIC)及后向搜索(sequential backward selection,SBS)算法的联合筛选方法,结合支持向量机(support vector machine,SVM)构建烟气流量软测量模型。基于某F级燃气-蒸汽联合循环发电机组,通过VIP值评估辅助变量显著性,并结合MIC和SBS算法,进行变量冗余消除与优化选择,从而提升模型的预测精度和泛化能力。实验结果显示:SVM的表现优于长短时间记忆网络模型,与反向传播神经网络相比具有较好的泛化能力;当辅助变量数量为12时,模型性能最佳,测试集的均方根误差和平均绝对百分比误差均较低,验证了变量筛选方法的有效性;在稳态和非稳态工况下,模型预测值的平均绝对百分比误差小于0.7%,并有一定的滤波作用。展开更多
基金Foundation item： the National Natural Science Foundation of China （No. 61203337）
文摘This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital port for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases： Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.
基金supported by the National Natural Science Foundation of China (No.U1960202).
文摘The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.
基金Supported by Humanities and Social Science Programme in Hubei Province,China(Grant No.14Y035)National Natural Science Foundation of China(Grant No.71203170)National Special Research Project in Food Nonprofit Industry(Grant No.201413002-2)
文摘For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique（feature selection methods） are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types： filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.
基金supported by the Health Commission of Zhejiang Province(WKJ-ZJ-1905 and 2018ZD007)the Key Research and Development Projects of Zhejiang Province(2018C03082)the National Natural Science Foundation of China(61625107)。
文摘Infectious keratitis is the most common condition of corneal diseases in which a pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues.Infectious keratitis is a medical emergency for which a rapid and accurate diagnosis is needed to ensure prompt and precise treatment to halt the disease progression and to limit the extent of corneal damage;otherwise,it may develop a sight-threatening and even eye-globe-threatening condition.In this paper,we propose a sequentiallevel deep model to effectively discriminate infectious corneal disease via the classification of clinical images.In this approach,we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis.In a comparison,the performance of the proposed sequential-level deep model achieved 80%diagnostic accuracy,far better than the 49.27%±11.5%diagnostic accuracy achieved by 421 ophthalmologists over 120 test images.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under Grant Number(DGSSR-2023-02-02513).
文摘Spear Phishing Attacks(SPAs)pose a significant threat to the healthcare sector,resulting in data breaches,financial losses,and compromised patient confidentiality.Traditional defenses,such as firewalls and antivirus software,often fail to counter these sophisticated attacks,which target human vulnerabilities.To strengthen defenses,healthcare organizations are increasingly adopting Machine Learning(ML)techniques.ML-based SPA defenses use advanced algorithms to analyze various features,including email content,sender behavior,and attachments,to detect potential threats.This capability enables proactive security measures that address risks in real-time.The interpretability of ML models fosters trust and allows security teams to continuously refine these algorithms as new attack methods emerge.Implementing ML techniques requires integrating diverse data sources,such as electronic health records,email logs,and incident reports,which enhance the algorithms’learning environment.Feedback from end-users further improves model performance.Among tested models,the hierarchical models,Convolutional Neural Network(CNN)achieved the highest accuracy at 99.99%,followed closely by the sequential Bidirectional Long Short-Term Memory(BiLSTM)model at 99.94%.In contrast,the traditional Multi-Layer Perceptron(MLP)model showed an accuracy of 98.46%.This difference underscores the superior performance of advanced sequential and hierarchical models in detecting SPAs compared to traditional approaches.
基金This work was supported by the Research Grant of SEC E-Institute :Shanghai High Institution Grid and the Science Foundation ofShanghai Municipal Commission of Science and Technology No.00JC14052
文摘Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.
文摘Corrective control theory lays a novel foundation for the fault-tolerant control of asynchronous sequential machines. In this paper, we present a corrective control scheme for tolerating permanent state transition faults in the dynamics of asynchronous sequential machines. By a fault occurrence, the asynchronous machine may be stuck at a faulty state, not responding to the external input. We analyze the detectability of the considered faults and present the necessary and sufficient condition for the existence of a controller that overcomes any permanent transition faults. Fault tolerance is realized by using potential reachability and asynchronous mechanisms in the machine. A case study on an asynchronous counter is provided to illustrate the proposed fault detection and tolerance scheme.
基金SPE is partly supported by National Natural Science Foundation of China.
文摘Nondeterminism of PROLOG execution requires that a block of control information or a choice point for each procedure call be stored when there are other candidate clauses to be used.When the currently selected clause fails,the bindings made by the clause must be undone and the stored choice point is reactivated,and then another clause of the candidate ones is chosen to run on it. Storing and reactivating choice points and undoing account for the great overhead are required to control PROLOG execution,which is quite different from conventional programs. This paper focuses on the techniques used in Sequential PROLOG Engine(SPE)to reduce the overhead of control operations.The control instructions of SPE store no more choice points than the necessary.Its architecture takes the approaches of analysing the potential parallelism in the con- trol operations and developing a fraction of it due to the cost-effect consideration.The results of executing two sample programs on SPE in the form of hand timings are presented,which favor the approach.
文摘This paper presents a method for state simplification in incompletely specified sequential machines. The new method adopts Inclusive-OR operation of column vectors for multi-level output matrix E_k! Compared with other algorithms in use, this method is theoretically more strict, while its structure is simple and the results obtained are accurate.
基金the National Key Research and Development Program of China(2021YFD2000600)the National Natural Science Foundation of China(32071914)+1 种基金the Modern Agricultural Industry Technology System of China(CARS-170405)the Key Research and Development Program(Science and Technology Demonstration Project)project of Shandong Province(2022SFGC0202).
文摘To achieve an unmanned rice farm,in this study,a cotransporter system was developed using a tracked rice harvester and transporter for autonomous harvesting,unloading,and transportation.Additionally,two unloading and transportation modes—harvester waiting for unloading(HWU)and transporter fol-lowing for unloading(TFU)—were proposed,and a harvesting-unloading-transportation(HUT)strategy was defined.By breaking down the main stages of the collaborative operation,designing module-state machines(MSMs),and constructing state-transition chains,a HUT collaborative operation logic frame-work suitable for the embedded navigation controller was designed using the concept and method of the finite-state machine(FSM).This method addresses the multiple-stage,nonsequential,and complex processes in HUT collaborative operations.Simulations and field-harvesting experiments were performed to evaluate the applicability of this proposed strategy and system.The experimental results showed that the HUT collaborative operation strategy effectively integrated path planning,path-tracking control,inter-vehicle communication,collaborative operation control,and implementation control.The cotrans-porter system completed the entire process of harvesting,unloading,and transportation.The field-harvesting experiment revealed that a harvest efficiency of 0.42 hm^(2)·h^(−1) was achieved.This study can provide insight into collaborative harvesting and solutions for the harvesting process of unmanned farms.
文摘碳排放连续在线监测法作为一种高效、可溯源的方法,在我国碳计量领域中逐渐应用。然而,由于烟囱管道的大直径、复杂烟气流场,以及流量计检修维护、粉尘堵塞导致的监测数据中断与异常,烟气流量的准确监测成为一大挑战。为此,提出一种融合变量投影重要性分析(variable importance in projection,VIP)、最大信息系数(maximal information coefficient,MIC)及后向搜索(sequential backward selection,SBS)算法的联合筛选方法,结合支持向量机(support vector machine,SVM)构建烟气流量软测量模型。基于某F级燃气-蒸汽联合循环发电机组,通过VIP值评估辅助变量显著性,并结合MIC和SBS算法,进行变量冗余消除与优化选择,从而提升模型的预测精度和泛化能力。实验结果显示:SVM的表现优于长短时间记忆网络模型,与反向传播神经网络相比具有较好的泛化能力;当辅助变量数量为12时,模型性能最佳,测试集的均方根误差和平均绝对百分比误差均较低,验证了变量筛选方法的有效性;在稳态和非稳态工况下,模型预测值的平均绝对百分比误差小于0.7%,并有一定的滤波作用。
