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36th International Conference on Information Networking (ICOIN)

作者： Lim, Yuto Zhou, Chenmian Tan, Yasuo Fang, Yuan Singh, Manmeet Mahinderjit Japan Adv Inst Sci & Technol 1-1 Asahidai Nomi Ishikawa 9211292 Japan Dalian Polytech Univ Dalian 116034 Liaoning Peoples R China Univ Sains Malaysia Usm Penang 11800 Malaysia

ISBN: (纸本)9781665413329

A personal thermal comfort (PTC) model is a novel approach to predict the thermal sensation of an individual rather than a group of people. The relationship between the environmental and human factors of this model is not well investigated in the smart home domain. Moreover, the difficulties on collecting feedback of personal thermal sensation, especially neutral comfort, and the requirements of data size can lead to the failure of thermal comfort system to fulfill the individual's comfort preference. In this paper, we present an incomplete supervised learning method for an enhanced PTC model to be more personalized to predict real-time personal thermal sensation in Cyber-Physical Human Centric System. In particular, the psychological parameters are always available for the continuous satisfactory control of a heating, ventilating, and air conditioning (HVAC) system in a timely manner. In the enhanced PTC model, we propose a personalized predictive classifier (PPC), which uses a learning algorithm from incomplete supervision to predict the personal thermal sensation. We also explore the PPC that uses two cascaded Random Forest classifiers can result a better performance and a faster learning speed in the implementation of energy efficient thermal comfort control (EETCC) system to improve individual's thermal comfort level while optimizing the HVAC energy consumption at the same time. Through the experiment and evaluation studies, we conclude that the enhanced PTC model with PPC is able to offer psychological parameters inference to a continuous satisfactory control system in smart homes.

关键词： Cyber-Physical Systems Smart Homes Personal Thermal Comfort supervised learning method

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Indirect Prediction of Spindle Rotation Error Through Vibration Signal Based on supervised Local Mean Decomposition Filter Fusion and Bi-LSTM Classification Network

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING 2024年第2期10卷 021102页

作者： Liang, Jianhong Wang, Liping Yu, Guang Wu, Jun Wang, Dong Lin, Song Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China Southwest Univ Sci & Technol Coll Informat Engn Mianyang 621000 Peoples R China Southwest Univ Sci & Technol Coll Informat Engn Mianyang 621000 Peoples R China

Spindle rotation error directly correlates with the quality of mechanical processing. Currently, the error was mainly converted through measuring the distance information of standard component installed at the tool position, and it can't complete the normal machining because the tool is occupied. Therefore, a novel self-adaptive supervised learning method through easy-collected vibration signal that don't affect the machining to indirect predict the error. This method includes three steps: First, the original vibration signal is decomposed by local mean decompression (LMD) method to obtain two critical components;subsequently, the two components are fused as a signal by a weighted-average approach;finally, the fused signal and corresponding error are self-adaptive supervised trained by the setting termination condition to modify fusion coefficient and network parameters. The method is used to analyze the data-set of spindle platform, which has collected the experimental data at speeds 1000, 2000, 3000, and 4000 more than 170 groups, and the indirect prediction accuracy reached 94.12%, 92.35%, 97.68%, and 90.59%, respectively. Additionally, the experimental results were compared and demonstrated by three aspects with current different algorithms.

关键词： supervised learning method spindle rotation error vibration signal LMD Bi-LSTM classification network

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Continuous quality control evaluation during manufacturing using supervised learning algorithm for Industry 4.0

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年 1-10页

作者： Shafiq, Muhammad Thakre, Kalpana Krishna, Kalluri Rama Robert, Noel Jeygar Kuruppath, Ashok Kumar, Devendra Qujing Normal Univ Sch Informat Engn Qujing Yunnan Peoples R China Marathwada Mitra Mandals Coll Engn Dept Comp Engn Pune India Vasavi Coll Engn A Dept Informat Technol Hyderabad India VIT Univ Sch Comp Sci & Engn Chennai Campus Chennai Tamil Nadu India New Horizon Coll Engn Dept Comp Sci & Engn Bengaluru 560103 India ABES Engn Coll Dept Comp Applicat Ghaziabad UP India

Smart industries use modern technologies such as machine learning and big data to maintain supply chain management and increase productivity but still the main challenge faced during quality control as this might affect the production rate. Smart industries are completely based on supervised learning that enables better inspection and effectively controls the parameter involved in the production process. Smart industries choose the mechanism that improves production and assures maximum quality. The various kernel function is initially used to select and extract a parameter. Support vector machine (SVM) is a supervised learning approach used in manufacturing industries to evaluate quality control. The SVM model uses the kernel function, namely RBF, along with Neural Networks, in identifying the parameter involved in quality management and undergoes the classification process. SVM consists of C-SVM and V-SVM classifier models involved in the classification process and undergoes training to handle the multiple numbers of consequence aroused during manufacturing. The performance of SVM classifiers and RBF NNs is evaluated. Different kernel functions, such as polynomial, linear, sigmoid, RBF, and over-varying gamma coefficient values, are tested in the experimental evaluation concerned with the comparative analysis of the continuous quality control function of the SVM classifier. Experimental results demonstrate the superiority of the SVM classifier in terms of the estimated computational time (88.1%), F1-measure (89.4%), ROC (65%), and accuracy (94.6%). The goal of the proposed model is to monitor the manufacturing process and control fault occurrence.

关键词： Kernel function Neural networks (NNs) Quality control Radial basis function (RBF) supervised learning method Support vector machine (SVM)

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supervised learning method and quality capability of process used in an optical transmission inspection of on-line nonwoven basis weight

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OPTICS COMMUNICATIONS 2012年第8期285卷 2106-2112页

作者： Huang, Ding-Kuo Chen, Chi-Feng Natl Cent Univ Dept Mech Engn Jhongli 32054 Taiwan Taiwan Text Res Inst Dept Text Technol & Prod Dev Taipei 231 Taiwan Natl Cent Univ Inst Optomechatron Engn Jhongli 32054 Taiwan

The supervised learning method and quality capability of process used in an on-line optical transmission inspection system of the basis weight for nonwoven material are investigated. A near-infrared light transmission inspection is applied in the production machine of a nonwoven fabric to detect the basis weight and support the producing quality. Using least squares method, the parameter transfer equations of the piecewise ploynomials functions between the measured voltage and the nonwoven basis weight are found. supervised learning method is adopted to improve the producing capability. It is shown that the capability index of process Cp and Cpk is testing samples when the supervised learning algorithm is used. Obvious, the supervised learning method is effective to improve the producing capability and support the producing quality. (C) 2011 Elsevier B.V. All rights reserved.

关键词： supervised learning method Nonwoven material Piecewise polynomials equation Quality capability of process Basis weight

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Which exogenous driver is informative in forecasting European carbon volatility: Bond, commodity, stock or uncertainty?

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ENERGY ECONOMICS 2023年 117卷

作者： Wang, Jiqian Guo, Xiaozhu Tan, Xueping Chevallier, Julien Ma, Feng Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China Serv Sci & Innovat Key Lab Sichuan Prov Chengdu Peoples R China Shanghai Jiao Tong Univ Sch Environm Sci & Engn Shanghai Peoples R China China Univ Min & Technol Sch Econ & Management Xuzhou Peoples R China IPAG Business Sch IPAG Lab 184 bd St Germain F-75006 Paris France Univ Paris 08 LED 2 rue Liberte F-93526 St Denis France

This study relies on 45 exogenous drivers to improve the accuracy in forecasting EUA volatility. Several popular linear and nonlinear predictive regressions, including individual factor analysis, the combination forecast method, the diffusion index model and the supervised learning method, are used to generate volatility forecasts at the monthly frequency. Our empirical results reveal that the diffusion index model and combination forecast method can hardly drive the EUA volatility in a data-rich world owing to worse forecasting performance of individual factors;however, the supervised learning method can successfully predict the EUA volatility. Addi-tionally, the WilderHill new energy global innovation index, Euro corporate bond return spread, GSCI gold index and Euro Area government bond yield spread can extremely drive EUA volatility in terms of individual factor analysis, frequency of variable selection and factor importance. Our findings provide crucial implications to market participants and emission companies, who should pay more attention to the price movement of European bond market, gold and clean energy.

关键词： European carbon market Volatility forecasting Exogenous driver supervised learning method

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DEEP GROUPED NON-NEGATIVE MATRIX FACTORIZATION method FOR IMAGE DATA REPRESENTATION 20

DEEP GROUPED NON-NEGATIVE MATRIX FACTORIZATION METHOD FOR IM...

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20th International Conference on Machine learning and Cybernetics (ICMLC)

作者： Zhan, Zihao Chen, Wen-Sheng Pan, Binbin Chen, Bo Shenzhen Univ Coll Math & Stat Shenzhen Peoples R China Shenzhen Univ Guangdong Key Lab Media Secur Shenzhen Peoples R China Shenzhen Univ Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen Peoples R China

ISBN: (纸本)9781665466080

Non-negative matrix factorization (NMF) is an unsupervised learning method that can be exploited for parts-based image representation due to non-negativity constraints. However, singer-layer NMF cannot capture the latent hierarchical structure features from images, while deep features play more important roles in the image representation and recognition tasks. To overcome the limitation of NMF, this paper proposes a novel deep grouped NMF (DGNMF) approach to learn different level attributes of the data. It is interesting that DGNMF approach automatically makes the data from distinct classes share different basis images and the feature vectors among different classes are mutually orthogonal at the same layer. Meanwhile, to preserve the local information, our DGNMF model establishes the objective function with graph regularization, and its optimization problem is solved using gradient descent method. The developed DGNMF algorithm is proved to be convergent and is finally evaluated on face datasets for classification. Compared with some state-of-the-art deep NMF variants, the results demonstrate the proposed DGNMF algorithm achieves surpassing performances using different layer features.

关键词： Deep Non-Negative Matrix Factorization supervised learning method Image Data Representation

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Physics-guided deep neural network for structural damage identification

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OCEAN ENGINEERING 2022年 260卷

作者： Huang, Zhou Yin, Xinfeng Liu, Yang Changsha Univ Sci & Technol Sch Civil Engn Changsha 410114 Peoples R China

The physics-driven method via finite element model and the data-driven methods via supervised learning is commonly used in the analysis of structural damage identification. The finite element model can be susceptible to environmental noise and modeling errors, which leads to model identification results deviating from the real damage. The supervised learning method faces the challenge of insufficient labeled damage data, which limits the learned model in identifying damage scenarios that are not contained in the training data. In this study, a novel physics-guided data-driven method is proposed to incorporate physical perception into the data-driven method for identifying structural damage. Specifically, the physics from the finite element model (e.g., structural dynamic characteristics) is incorporated into the neural network model to guide the damage feature learning from scarce measured data. In addition, a loss function with physical significance is developed to evaluate the discrepancy between the finite element model and the measured data, which guides the neural network to learn the damage features of the real structure. The learned neural network model has the potential to improve the damage detection results. The proposed methodology is validated by a numerical case study of a suspension bridge model and an experimental study of a frame structure.

关键词： Damage identification Vibration response Finite element model supervised learning method Physics-guided neural network

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Motion control of unmanned underwater vehicles via deep imitation reinforcement learning algorithm

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第7期14卷 764-774页

作者： Chu, Zhenzhong Sun, Bo Zhu, Daqi Zhang, Mingjun Luo, Chaomin Shanghai Maritime Univ Shanghai Engn Res Ctr Intelligent Maritime Search Shanghai 201306 Peoples R China Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

In this study, a motion control algorithm based on deep imitation reinforcement learning is proposed for the unmanned underwater vehicles (UUVs). The algorithm is called imitation learning (IL) twin delay deep deterministic policy gradient (DDPG) (TD3). It combines IL with DDPG (TD3). In order to accelerate the training process of reinforcement learning, the supervised learning method is used in IL for behaviour cloning from the closed-loop control data. The deep reinforcement learning employs actor-critic architecture. The actor part executes the control strategy and the critic part evaluates current control strategy. The training efficiency of IL-TD3 is compared with DDPG and TD3. The simulation results show that the training results of IL-TD3 converge faster and the training process is more stable than both of them, the convergence rate of IL-TD3 algorithm during training is about double that of DDPG and TD3. The control performance via IL-TD3 is superior to PID in UUVs motion control tasks. The average track error of IL-TD3 is reduced by$70\percnt $70%than PID control. The average tracking error under thruster fault is almost the same as under normal condition.

关键词： neurocontrollers intelligent robots learning (artificial intelligence) three-term control closed loop systems motion control remotely operated vehicles gradient methods autonomous underwater vehicles control engineering computing training process IL-TD3 algorithm DDPG control performance PID control unmanned underwater vehiclesViadeep imitation reinforcement learning algorithm motion control algorithm deep imitation reinforcement learning unmanned underwater vehicles imitation learning twin deep deterministic policy gradient supervised learning method closed-loop control data deep reinforcement learning actor-critic architecture actor part critic part current control strategy UUV motion control tasks

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Research on learning mechanism designing for equilibrated bipolar spiking neural networks

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第7期53卷 5189-5215页

作者： Yang, Xu Lin, Jiajun Zheng, Wenhao Zhao, Jinfeng Ji, Mengyao Lei, Yunlin Chai, Zenghao Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

Artificial Intelligence (AI) has become very popular due to both the increasing demands from applications and the booming of computer techniques. Spiking Neural Network (SNN), as the third generation of Artificial Neural Network, receives more and more attention in the field of AI. With the high similarity to biological neural network, SNN has the potential to break through the barriers of strong AI. However, the using of SNNs on practical scenarios is rather limited, as a result of the lack of high efficient learning algorithms. Nowadays, learning methods of SNNs are designed mainly based on previous biological discoveries. The fact that there are both excitatory neurons and inhibitory neurons in the biological neural network has stimulated the motive of this research. The existence of inhibitory neurons could strengthen the self-regulation ability of neural networks and improve learning efficiency. Inspired by the ancient Chinese "Yin and Yang" Theory, we first presented our effort at constructing SNN structure with equilibrated excitatory neurons and inhibitory neurons. Then an ensemble learning optimized supervised learning method is designed and tailored for this SNN structure. Experiments are conducted using MNIST data sets, and results show that, with the designed learning mechanism, our equilibrated bipolar SNN structure could gain reasonable accuracy with much more compact structure and much more sparse synapse connections.

关键词： Artificial intelligence Spiking neural network supervised learning method Ensemble learning Excitatory and inhibitory

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SGD-Based Wiener Polynomial Approximation for Missing Data Recovery in Air Pollution Monitoring Dataset 15th

SGD-Based Wiener Polynomial Approximation for Missing Data R...

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15th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Izonin, Ivan Ml, Michal Gregus Tkachenko, Roman Logoyda, Mykola Mishchuk, Oleksandra Kynash, Yurii Lviv Polytech Natl Univ Lvov Ukraine Comenius Univ Bratislava Slovakia

ISBN: (纸本)9783030205218;9783030205201

This paper describes the developed SGD-based Wiener polynomial approximation method for the missing data recovery of air pollution monitoring tasks. The main steps of algorithmic implementation of the method have been described and the necessity of a combination of both of these tools is substantiated. The basic parameters of the method (the degree of the polynomial, the loss function of the SGD algorithm) for design an optimal variant of it are experimentally investigated. One out of four studied loss functions was chosen for the practical implementation of the method for the design of the future applied air pollution monitoring system. It is founded that high degrees of the Wiener polynomial significantly increase the training time with a slight increase in accuracy. That's why a second-degree polynomial was chosen. The simulation of the method showed high as accuracy (based on MAPE, RMSE, MAE) and low computation time. Comparison of the developed method's results with the existing regression analysis methods (Adaptive Boosting, GRNN, SVR with different kernels) confirmed the high efficiency of its work. The proposed combination of the method allows obtaining an effective result from the point of view of accuracy-speed for the large volumes of data processing. The developed method will be useful when solving different tasks, for example, for a smart home or a smart city, medicine, economics, etc. That is, for those tasks where the problem of missing data does not allow conducting further effective intellectual analysis.

关键词： Air pollution Missing data recovery Regression approach Wiener polynomial Stochastic gradient descent supervised learning method

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