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data-driven modeling Using System Integration Scaling Factors and Positioning Performance of an Exposure Machine System

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IEEE ACCESS 2017年 5卷 7826-7838页

作者： Tsai, Jinn-Tsong Chang, Cheng-Chung Chen, Wen-Ping Chou, Jyh-Horng Natl Pingtung Univ Dept Comp Sci Pingtung 900 Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Met Ind Res & Dev Ctr Kaohsiung 811 Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Elect Engn Kaohsiung 824 Taiwan Kaohsiung Med Univ Dept Healthcare Adm & Med Informat Kaohsiung 807 Taiwan

A data-driven modeling approach is proposed for using system integration scaling factors and positioning performance of an exposure machine system to build models for predicting positioning errors and for analyzing parameter sensitivity. The proposed approach uses a uniform experimental design (UED), multiple regression (MR), back-propagation neural network (BPNN), adaptive neuro-fuzzy inference system (ANFIS), and analysis of variance (ANOVA). The UED reduces the number of experimental runs needed to collect data for modeling. The MR, BPNN, and ANFIS are used to construct positioning models of an exposure machine system. The significant system integration scaling factors are determined by ANOVA. The inputs to the data-driven model are system integration scaling factors f(x), f(y), and f(q), and the output is the positioning error. The UED was used to collect 41 experimental data, which comprised 0.0595% of the full-factorial experimental data. Performance tests demonstrated the excellent performance of the UED in collecting data used to build the MR, BPNN, and ANFIS data-driven models. The data-driven models can accurately predict positioning errors during validation. In addition, a sensitivity analyses of parameters showed that design parameters f(x) and f(y) have the greatest influence on positioning performance.

关键词： data-driven modeling system integration scaling factors exposure machine uniform experimental design multiple regression back-propagation neural network adaptive neuro-fuzzy inference system

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Establishing Video Game Genres Using data-driven modeling and Product databases

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GAMES AND CULTURE 2018年第1期13卷 20-43页

作者： Faisal, Ali Peltoniemi, Mirva Aalto Univ HIIT Dept Informat & Comp Sci Espoo Finland Aalto Univ Dept Neurosci & Biomed Engn Espoo Finland Univ Jyvaskyla Jyvaskyla Sch Business & Econ Seminaarinkatu 15 Jyvaskyla 40014 Finland Aalto Univ Dept Ind Engn & Management Espoo Finland

Establishing genres is the first step toward analyzing games and how the genre landscape evolves over the years. We use data-driven modeling that distils genres from textual descriptions of a large collection of games. We analyze the evolution of game genres from 1979 till 2010. Our results indicate that until 1990, there have been many genres competing for dominance, but thereafter sport-racing, strategy, and action have become the most prevalent genres. Moreover, we find that games vary to a great extent as to whether they belong mostly to one genre or to a combination of several genres. We also compare the results of our data-driven model with two product databases, Metacritic and Mobygames, and observe that the classifications of games to different genres are substantially different, even between product databases. We conclude with discussion on potential future applications and how they may further our understanding of video game genres.

关键词： game genre data-driven modeling Metacritic Mobygames game corpus topic model quantitative

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Research on data driven modeling method of grinding process based on RBF neural network 31

Research on data driven modeling method of grinding process ...

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31st Chinese Control And Decision Conference (CCDC)

作者： Liu, Bingyu Liu, Bojian Hao, Dezhi Gao, Xianwen Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781728101057

Beneficiation is a complex industrial process, current beneficiation methods are carried out by the difference in the nature of the minerals and gangues inside the ore, which needs the separation of gangue and minerals by grinding process. The production and investment consumption of grinding accounts for a large proportion of the total consumption of the dressing plant, and the grinding process is a key process for providing raw materials for mineral sorting. Therefore, the design and operation of the grinding process directly affects the economic indicators of the dressing plant. In this paper, the research is conducted on the background of a certain dressing plant, and the mechanism of the grinding process is analyzed in order to analyze the state of the grinding process and the parameter variables. Aiming at the situation that the ore is a mixed ore of various ores, the influence of different mineral contents on the results is fully considered. The mathematical model of the grinding process yield and the particle size distribution characteristics of the grinding products is established by RBF neural network. Simulation results demonstrate the effectiveness of the model.

关键词： Grinding data-driven modeling RBF neural network

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data-driven Human modeling by Sparse Representation

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COMPUTER-AIDED DESIGN 2020年 128卷 102913-102913页

作者： Wu, Yiu-Bun Liu, Bin Liu, Xiuping Wang, Charlie C. L. Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Dalian Univ Technol Sch Math Sci Dalian Peoples R China

data-driven methods for modeling the realistic shapeof 3D human bodies need to access datasets that contain a large amount of 3D human models. We develop a method based on sparse representation in this paper to represent 3D human models as signals of patches. Unlike the general mesh compression approaches, all mesh models used in a data-driven human modeling framework have the same mesh connectivity. By using this property, we segment a human model into patches containing the same number of vertices. L-0-learning algorithm is selected to train an overcomplete dictionary matrix, which in turn introduces sparse representation of the dataset. Patch signals of individual human models can then be extracted by using the dictionary matrix. With the ease of balance control between sparsity and accuracy that is featured by the chosen learning algorithm, a representation with high compression ratio and low shape-approximation error can be determined. The results have been compared with the widely used statistic representation based on principal component analysis (PCA) to verify the effectiveness of our approach. Moreover, the method for using sparse representation in the regression-based statistical modeling of 3D human models has been presented at the end of the paper. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Human models Sparse representation Regression data-driven modeling

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Review on data-driven modeling and monitoring for plant-wide industrial processes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2017年 171卷 16-25页

作者： Ge, Zhiqiang Zhejiang Univ State Key Lab Ind Control Technol Inst Ind Proc Control Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

data-driven modeling and applications in plant-wide processes have recently caught much attention in both academy and industry. This paper provides a systematic review on data-driven modeling and monitoring for plant-wide processes. First, methodologies of commonly used data processing and modeling procedures for the plant-wide process are presented. Detailed research statuses on various aspects for plant-wide process monitoring are reviewed since 2000. After that, extensions, opportunities, and challenges on data-driven modeling for plant wide process monitoring are discussed and highlighted for future research.

关键词： Plant-wide process data-driven modeling Process monitoring

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data-driven modeling for UGI Gasification Processes via an Enhanced Genetic BP Neural Network With Link Switches

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2016年第12期27卷 2718-2729页

作者： Liu, Shida Hou, Zhongsheng Yin, Chenkun Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing 100044 Peoples R China

In this brief, an enhanced genetic back-propagation neural network with link switches (EGA-BPNN-LS) is proposed to address a data-driven modeling problem for gasification processes inside United Gas Improvement (UGI) gasifiers. The online-measured temperature of crude gas produced during the gasification processes plays a dominant role in the syngas industry;however, it is difficult to model temperature dynamics via first principles due to the practical complexity of the gasification process, especially as reflected by severe changes in the gas temperature resulting from infrequent manipulations of the gasifier in practice. The proposed data-driven modeling approach, EGA-BPNN-LS, incorporates an NN-LS, an EGA, and the Levenberg-Marquardt (LM) algorithm. The approach cannot only learn the relationships between the control input and the system output from historical data using an optimized network structure through a combination of EGA and NN-LS but also makes use of the networks gradient information via the LM algorithm. EGA-BPNN-LS is applied to a set of data collected from the field to model the UGI gasification processes, and the effectiveness of EGA-BPNN-LS is verified.

关键词： data-driven modeling genetic algorithm (GA) link switches neural networks UGI gasifier

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data-driven Predictive modeling of Neuronal Dynamics Using Long Short-Term Memory

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ALGORITHMS 2019年第10期12卷 203-203页

作者： Plaster, Benjamin Kumar, Gautam Univ Idaho Dept Chem & Mat Engn Moscow ID 83844 USA

modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions have been of interest to engineers, mathematicians and physicists over the last several decades. With the motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

关键词： long short-term memory brain dynamics data-driven modeling complex systems

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data-driven stochastic modeling for multi-purpose reservoir simulation

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JOURNAL OF APPLIED WATER ENGINEERING AND RESEARCH 2018年第1期6卷 40-47页

作者： Khadr, Mosaad Schlenkhoff, Andreas Tanta Univ Fac Engn Tanta 31111 Egypt Univ Wuppertal Fac Engn Pauluskirchstr 7 D-42285 Wuppertal Germany

This paper presents an integration of data-driven modeling and stochastic models for simulation of reservoir operation. The simulation model developed in this study was applied to the Ruhr river reservoirs system in Germany. An adaptive neurofiuzzy inference system, Thomas-Fiering model and hidden Markov model were integrated in a simulation model. The set of model input included the time of the year, reservoir storage, inflow and Standardized Precipitation Index: and the target output was the reservoir release. Predicted and observed release values were evaluated using several common evaluation criteria. Results of model performance showed that the proposed model is capable of simulating reservoir operation and provides reliable reservoir release prediction. Results showed also that the proposed approach could be a good tool at the real-time operation stage to quickly check operational alternatives due to emergency events or planning and real-time incongruence.

关键词： data-driven modeling stochastic modeling reservoir operation drought Standardized Precipitation Index

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data-driven modeling of Anisotropic Haptic Textures: data Segmentation and Interpolation 10th

Data-Driven Modeling of Anisotropic Haptic Textures: Data Se...

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10th International Conference on Haptics - Perception, Devices, Control, and Applications (EuroHaptics)

作者： Abdulali, Arsen Jeon, Seokhee Kyung Hee Univ Dept Comp Sci & Engn Yongin South Korea

ISBN: (纸本)9783319423241;9783319423234

This paper presents a new data-driven approach for modeling haptic responses of textured surfaces with homogeneous anisotropic grain. The approach assumes unconstrained tool-surface interaction with a rigid tool for collecting data during modeling. The directionality of the texture is incorporated in modeling by including 2 dimensional velocity vector of user's movement as an input for the data interpolation model. In order to handle increased dimensionality of the input, improved input-data-space-based segmentation algorithm is introduced, which ensures evenly distributed and correctly segmented samples for interpolation model building. In addition, new Radial Basis Function Network is employed as interpolation model, allowing more general and flexible data-driven modeling framework. The estimation accuracy of the approach is evaluated through cross-validation in spectral domain using 8 real surfaces with anisotropic texture.

关键词： data-driven modeling Anisotropic texture RBF networks

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data-driven modeling of Coastal Water Quality using the Bayesian Method for Coastal Management

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JOURNAL OF COASTAL RESEARCH 2016年第75期000卷 647-651页

作者： Kim, Jinah Choi, Jungwoon KIOST Operat Oceanog Res Ctr Ansan South Korea

To understand and a predict a coastal water quality system, a data-driven statistical model has been proposed using the Bayesian method and applied to the Saemangeum tidal lake. To describe a coastal water quality system, a multivariate statistical model was derived by determining observed variables and their interrelationships such as sea surface temperature, salinity, Chl-a, DO, pH, TN, TP, COD, NH4N, NO2N, NO3N, PO4O, and SiO2Si for parameters of coastal marine environments, coastal water quality, and nutrients using observed field data. To estimate this statistical model, a Bayesian approach using Markov chain Monte Carlo method was applied to identify an optima] data-driven model. There are no limitations of statistical assumptions for samples using the Bayesian method, which is required in a frequentist approach, such as the maximum likelihood method. The Saemangeum tidal lake's coastal water quality system was quantitatively described and assessed by interpreting coefficients of model parameters with relation among variables from a derived structural equation model. Moreover, a prediction for coastal management was possible by Bayesian inference. Thus, there are new findings on the salinity threshold necessary to maintain optimal water by improving degraded water quality. Based on the findings, a quantity of water mixing (exchaning fresh water through sluice gates) can be applied while continuing construction of land reclamation.

关键词： data-driven modeling Bayesian method multivariate statistical model coastal wetter quality the Saemangeum coast

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