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Information Entropy-Based Fault Detection for Quality-Related Processes

Information Entropy-Based Fault Detection for Quality-Relate...

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data driven control and learning systems (ddcls)

作者： Yongye Zhu Jinglin Zhou College of Information Science & Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Canonical Correlation Analysis (CCA) is currently very popular in the field of data-driven fault detection. the CCA method usually assumes that the data obeys a Gaussian distribution, but the sample data collected in the actual complex industrial production process often has non-Gaussian characteristics, and the CCA method is not very suitable for modeling this kind of data. In order to extend the applicability of the CCA method from Gaussian data space to non-Gaussian data space, this paper proposes an information entropy-based quality-related process fault detection method (ECCA) based on CCA. First, the ECCA approach takes as its objective function to maximize the mutual information of typical pairs of variables in the space of process and quality variables. the objective function is also optimized using Particle Swarm Optimization (PSO) algorithm to solve for the optimal pairs of typical variables; Second, when considering the extraction of the next pair of independent typical variable pairs, the extraction of independent typical variable pairs can be achieved by resorting to the framework of the Partial Least Squares (PLS) algorithm in combination with the minimization of the information entropy of the residuals in the space of the process and quality variables; third, a truncation criterion for the number of typical variables is identified; Fourth, T 2 and Te 2 statistics are constructed to monitor the system; Finally, the ECCA method is applied to synthetic data and TE process data for comparison with the traditional CCA method. Experimental validation shows that the ECCA method has better monitoring performance than the traditional CCA method in monitoring quality-related process faults.

关键词： Correlation Fault detection Process control Linear programming data mining Object recognition Information entropy

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model

Structure-constrain Source Free Domain Adaptation with Regul...

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data driven control and learning systems (ddcls)

作者： Dan Yang Xin Peng Cheng Su Dayu Tan Chaoyang Chen Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness.

关键词： Fault diagnosis learning systems Adaptation models data privacy Transfer learning Prototypes Predictive models

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data-driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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data driven control and learning systems (ddcls)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And data Acquisition (SCADA) system. then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries

Exponential Weighted Moving Average-Based Variational Mode D...

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data driven control and learning systems (ddcls)

作者： Penghao Wu Hongfeng Tao Engang Tian Tangwen Yin Hongtian Chen Yiyang Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. this paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. the proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. this process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. the resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. the collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods.

关键词： Fault diagnosis learning systems Employee welfare Sensitivity Power supplies Noise Electric vehicles

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Camouflaged Object Detection through Feature Selection and Enhancement

Camouflaged Object Detection Through Feature Selection and E...

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data driven control and learning systems (ddcls)

作者： Yin Yuan Kanjian Zhang Jinxia Zhang The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing P. R. China Southeast University Shenzhen Research Institute Shenzhen China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Camouflaged object detection is a widely explored problem, the goal of which is to completely segment objects hidden in the background. Compared with general object segmentation and salient object detection tasks, this is very challenging. Existing methods are trying their best to mine the characteristics of the camouflaged objects or obtain some auxiliary information to find the discriminative features that distinguish the targets from the background. However, these methods of explicitly supplementing information have limited exploration and analysis of camouflaged features, and modules designed for specific information are needed. the effectiveness of these modules largely affects the final results. So in this paper, an FSENet (Feature Selection and Enhancement Network) is designed based on an implicit feature selection strategy and a feature enhancement strategy. this method comprehensively utilizes the advantages of CNN and Transformer to obtain the local features and global features of the target and capture as much detail as possible. Moreover, an Implicit Feature Selection (IFS) module based on feature channels is proposed to adaptively select more important features to retain and discard unimportant feature channels to reduce interference. In addition, stacked Multi-scale Region Attention (MRA) modules are designed to enrich the multi-scale information of features and maintain the consistency of multi-scale information. Our method is evaluated on 3 challenging benchmark datasets and outperforms 20 previous state-of-the-art methods under four widely used evaluation metrics.

关键词： Measurement learning systems Object detection Interference Object segmentation Benchmark testing Feature extraction

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Deep Metric learning with Impulsive Neural Network

Deep Metric Learning with Impulsive Neural Network

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data driven control and learning systems (ddcls)

作者： Hong-Zhou Chen Bin Hu Long Chen Zhi-Hong Guan School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan P. R. China School of Future Technology South China University of Technology Guangzhou P. R. China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Neural networks with siamese and triplet structures can discriminate between samples by explicitly learning the distance measure. But the dominant metric learning model based on deep neural networks (DNN) has issues with low energy efficiency and slow on-chip operating speed. Motivated by this, we put forth novel impulsive neural networks (INN) based deep metric learning approachs. the loss function is proposed based on the Euclidean distance between the membrane potentials of the neurons in the output layer of the sub-networks composed by Leaky integrate-and-fire (LIF) neurons. Furthermore, the back-propagation through time (BPTT) technique can be effectively implemented and utilized for network optimization through the surrogate functions. Experiments on multiple datasets show that the proposed model can learn the difference between images with performance very close to DNN-based models. At the same time, we conducted an intuitive energy consumption analysis of our model. the results show that compared with DNN-based models, the proposed model has higher energy efficiency. Besides, it only needs an extremely short time step T=10 to complete inference process, which makes the low-latency on-chip operation possible.

关键词： Measurement Neurons Process control Membrane potentials Artificial neural networks Energy efficiency System-on-chip

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CGA-MAE: Camera-Guided Alignment to Auxiliary Point Cloud Masked Autoencoders for 3D Object Detection

CGA-MAE: Camera-Guided Alignment to Auxiliary Point Cloud Ma...

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data driven control and learning systems (ddcls)

作者： Haolong Peng Yibo Chen Yuxi Chen Ke Zhu Jianming Zhang Zhejiang Polytechnic institute Polytechnic institute Zhejiang University Hangzhou China Zhejiang University Robotics Institute Yuyao China College of control science and engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Masked Autoencoders have demonstrated remarkable proficiency in acquiring robust visual representations and achieving cutting-edge outcomes across diverse independent modalities, However, only a handful of studies have delved into their efficacy within multi-modality environments. In this work, we focus on LiDAR point cloud and Camera image, two modalities commonly coexistent in real-world scenarios, aiming to explore their intricate interactions. To improve upon the cross-modal synergy in existing works, we project the feature in the bird's eye view(BEV) plane to receive more geometry information and propose CGA-MAE, a simple and effective cross-modal fusion self-supervised pretraining method based on Camera-Guided Alignment (CGA), which can bridge the BEV feature learning gap between LiDAR and Camera. We initially recognize the significance of masking strategies between these sources, leveraging a projection module, we harmonize the alignment of masks and visible tokens inherent in both modalities. then, we propose a new Modality Feature Fusion Model called MFFM to promote the interaction between the LiDAR feature and the Camera Feature. through extensive experiments performed on large NuScenes dataset, we discover it is nontrivial to interactively learn LiDAR-Camera features, where Our approach significantly advances the State-Of-the-Art (SOTA), achieving a mean Average Precision (mAP) of 67.8% and a nuScenes Detection Score (NDS) of 71.0%.

关键词： Point cloud compression Representation learning learning systems Geometry Visualization Laser radar three-dimensional displays

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Adaptive control for Autonomous Underwater Vehicles without Kinetic Model using Deep Neural Network

Adaptive Control for Autonomous Underwater Vehicles without ...

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data driven control and learning systems (ddcls)

作者： Shihui Jiang Tianbo Zhang Jing Fang Xing Liu Dong Shen CSSC Systems Engineering Research Institute Beijing P. R. China Department of Automation Beijing National Research Center for Info. Sci. & Tech. (BNRist) Tsinghua University Beijing P. R. China School of Mathematics Renmin University of China Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper concerns the trajectory tracking control of autonomous underwater vehicles (AUVs) at the kinematic and kinetic levels. Considering the accurate kinetic model of AUVs unavailable in practice, an adaptive control using deep neural network (ACDNN) method is first proposed for 4-DOF AUVs. Specifically, only the inertial matrix information, which is the most accessible, is utilized to achieve tracking tasks while the Coriolis and centripetal force matrix and damping coefficient matrix are unknown. the unknown matrices are approximated and learned by the deep neural network. Compared with conventional adaptive neural network control, which is usually based on radial basis functions, the proposed method learns unknown kinetic features by the weights of nonlinear compositions of weighted features. It provides a good way to tackle the complex unknown AUV dynamics in complex marine environments. In addition, a separate time-scaled strategy is adopted to ensure online computing. the uniformly ultimate stability is achieved by Lyapunov analysis. Simulation results verify the effectiveness of the proposed method.

关键词： Damping Adaptation models Autonomous underwater vehicles Simulation Force Artificial neural networks Kinetic theory

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Time Series Prediction Problems Under Covariate Drift

Time Series Prediction Problems Under Covariate Drift

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data driven control and learning systems (ddcls)

作者： Xiaoxue Liang Kuangrong Hao Lei Chen Lihong Ren Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Donghua University Shanghai China College of Information Sciences and Technology Donghua University Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the real world, time series data are ubiquitous, and the prediction task of time series data is very important. Most real-world time series data do not satisfy the assumption of independent and identical distribution (i.i.d.) due to environmental changes, that is, the distribution of the training datasets is different from the distribution of the test datasets, $P(X_{i})= P(X_{j})$ , but the conditional distribution is usually considered to be unchanged, $P(y\vert x_{i})= P(y\vert x_{j})$ . In this case, it is defined as covariate drift. However, most of the existing prediction algorithms are based on the assumption of i.i.d., so these algorithms have great limitations in the prediction of time series data under covariant drift. therefore, the AEIF-MLP model is proposed, a time series data prediction model based on MLP and causal structures. the model mainly consists of two modules. Based on the principle of maximum entropy, we propose an adaptive environment segmentation module to separate different environments in the training datasets. Based on the causal structure, we propose an invariant feature learning module to learn common invariant features in different environments to train the model to deal with test datasets of unknown distribution. In summary, this method solves the problem of time series prediction under covariate drift. the validity of the model is verified by drift data sets in different environments for the first time, and the validity of the model is further verified on two real data sets.

关键词： Training Representation learning learning systems Adaptation models Time series analysis Predictive models Prediction algorithms

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Beyond Nash Equilibrium: Achieving Bayesian Perfect Equilibrium with Belief Update Fictitious Play

Beyond Nash Equilibrium: Achieving Bayesian Perfect Equilibr...

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data driven control and learning systems (ddcls)

作者： Qi Ju Zhemei Fang Yunfeng Luo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan P. R. China National Key Laboratory of Science and Technology on Multispectral Information Processing Wuhan P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the domain of machine learning and game theory, the quest for Nash Equilibrium (NE) in extensive-form games with incomplete information is challenging yet crucial for enhancing AI's decision-making support under varied scenarios. Traditional Counterfactual Regret Minimization (CFR) techniques excel in navigating towards NE, focusing on scenarios where opponents deploy optimal strategies. However, the essence of machine learning in strategic game play extends beyond reacting to optimal moves; it encompasses aiding human decision-making in all circumstances. this includes not only crafting responses to optimal strategies but also recovering from suboptimal decisions and capitalizing on opponents' errors. Herein lies the significance of transitioning from NE to Bayesian Perfect Equilibrium (BPE), which accounts for every possible condition, including the irrationality of opponents. To bridge this gap, we propose Belief Update Fictitious Play (BUFP), which innovatively blends fictitious play with belief to target BPE, a more comprehensive equilibrium concept than NE. Specifically, through adjusting iteration stepsizes, BUFP allows for strategic convergence to both NE and BPE. For instance, in our experiments, BUFP(EF) leverages the stepsize of Extensive Form Fictitious Play (EFFP) to achieve BPE, outperforming traditional CFR by securing a 48.53% increase in benefits in scenarios characterized by dominated strategies.

关键词： Training learning systems Monte Carlo methods Navigation Games Machine learning Nash equilibrium

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