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SSRN 2023年

作者： Li, Yi Ndubisi, Nelson Oly Li, Gang Xu, Jinpeng School of Economics & Management Xidian University Xi’an China College of Business & Economics Qatar University Doha Qatar School of Management Xi’an Jiao tong University Xi'An China The State Key Lab for Manufacturing Systems Engineering Xi’an China The Key Laboratory of the Ministry of Education for Process Control and Efficiency Projects Xi’an China

In this study, we explore the causes of switching costs from both supplier and customer side, discuss the performance outcomes in the setting of new product development, and investigate the role that switching costs play as moderators and mediators in social capital-new product development performance *** on data from 214 Chinese manufacturers, we employ the structural equation model to examine our conceptual model and hypotheses. Our results indicate that relationship quality and customer involvement separately positively and negatively affect switching costs, respectively, which negatively affects new product development performance. Switching costs mediate and moderate the relationship between social capital and new product development performance. Our findings indicate that switching costs significantly and positively moderate the relationship between relationship quality and new product development performance. On the other hand, switching costs insignificantly and negatively moderate the relationship between customer involvement and new product development performance. © 2023, The Authors. All rights reserved.

关键词： Product development

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A Method for Automatic Identification of Natural Fracture Based on Machine Learning: A Case Study on the Dahebian Block of the Liupanshui Basin in Guizhou Province 13th

A Method for Automatic Identification of Natural Fracture Ba...

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13th International Field Exploration and Development Conference, IFEDC 2023

作者： Zhao, Wei-Guang Sang, Shu-Xun Cheng, De-Qiang Han, Si-Jie Zhou, Xiao-Zhi Zhang, Jin-Chao Zhao, Fu-Ping School of Resources and Geosciences China University of Mining and Technology Xuzhou221116 China Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Controland Utilization China University of Mining and Technology Xuzhou221008 China Carbon Neutrality Institute China University of Mining and Technology Xuzhou221008 China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process Ministry of Education China University of Mining and Technology Xuzhou221116 China Key Laboratory of Unconventional Natural Gas Evaluation and Development in Complex Tectonic Areas Ministry of Natural Resources Guiyang550009 China Guizhou Engineering Research Institute of Oil & Gas Exploration and Development Guiyang550009 China

ISBN: (纸本)9789819702718

Natural fractures are effective storage spaces and important seepage channels for oil and gas reservoirs. Accurately identifying natural fractures in reservoirs is crucial for the exploration and development of oil and gas resources. This article combines conventional and imaging logging data and uses machine learning to automatically identify natural fractures in reservoirs. The fracture labels of conventional logging come from imaging logging. Conventional logging data is decomposed through multi-scale wavelet to extract components that reflect fracture information, and further build the original data set. The AdaBoost model is trained based on a modified dataset of balanced samples for automatic fractures recognition in logging. The research results indicate that the approximate component and high-frequency component reflect the fluctuation of the formation and noise information respectively, and have little impact on the reservoir fractures identification;The medium frequency component can reflect the characteristic information of fractures and can be used for model training;After hyper-parameter optimization, the AdaBoost model has high accuracy and generalization ability, and can still accurately identify the types and distribution of natural fractures from the actual unbalanced logging data. This research has important guiding significance for the accurate characterization and construction of reservoir. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wavelet transforms

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Augmented Yield Analysis for Refined Elementary Mode Set of Penicillin Metabolic Network

Augmented Yield Analysis for Refined Elementary Mode Set of ...

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第33届中国过程控制会议

作者： Chenxi Tao Quan Li Zhonggai Zhao Fei Liu School of Internet of Things Engineering Jiangnan University Key Laboratory of Advanced Control of Light Industry Process (Ministry of Education) Institute of AutomationJiangnan University

Penicillin is usually produced by microbial growth and metabolism in a fermentation *** analysis and understanding of metabolic behavior are the keys to improving penicillin *** refined elementary model set(REMS) is a subset of the Elementary Models(EMs) necessary to describe specific metabolic *** plays an important role in mechanistic modeling and optimization of penicillin fermentation *** conventional yield analysis(YA) method can easily derive REMS using yield information,but the metabolic behavior described by the results may seriously deviate from *** this paper,we propose an augmented yield analysis(AYA) method to address this ***,basic metabolic pathways and metabolites characterizing their activities are identified;second,the production fluxes of the identified metabolites are combined with yield information to form an augmented yield space,into which the EMS is recoded;finally,the REMS is formed by a minimal set of EMs which can meet the preset requirements for metabolic *** REMS of the penicillin metabolic network is determined by AYA,and it can describe the actual metabolic behavior of penicillin,thereby verifying the effectiveness of AYA.

关键词： Penicillin metabolic network elementary mode analysis refined elementary mode set metabolic information augmented yield analysis

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Monitoring Wastewater Treatment processes Using a Maximum Likelihood-Based Narx Neural Network

SSRN

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SSRN 2023年

作者： Liu, Wentao Xiong, Weili Sun, Wenxin Chen, Hongtian Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Jiangnan University Wuxi214122 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

Wastewater treatment processes (WWTPs) contribute significantly to water recycling and energy conservation, because their failures can lead to the potential risk of adverse environmental impacts. WWTPs are acknowledged to be largely nonlinear systems with a series of biological reactors. Additionally, the presence of process uncertainties and measurement noises in WWTPs makes it difficult for the purpose of fault detection. This paper focuses on the data-driven realization of fault detection for sludge bulking and toxic impact in WWTPs. The fault detection model employs NARXbased neural networks to construct the residual generator through the maximum likelihood approach. The maximum likelihood-based NARX neural network can optimize both the model parameters and covariance matrix related to stochastic noises, based on which the model delivers more robustness performance against uncertainties than the existing work. Therefore, the nonlinearity and unknown system uncertainties of WWTPs can be processed simultaneously. The superiority and effectiveness of the maximum likelihood-based NARX neural network are validated through the benchmark simulation model no.1, where the successful detection rates for detecting sludge bulking faults and toxic impact faults are proved to be significantly higher than the traditional methods. © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Active Fault Diagnosis Method Based on Deep Forest and Its Industrial Application

Active Fault Diagnosis Method Based on Deep Forest and Its I...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Dong Li Kaixun He Lili Tao Hanlong Liu College of Electrical and Automation Engineering Shandong University of Science and Technology Qingdao China Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology China College of Intelligent Manufacturing and Control Engineering Shanghai Second University of Technology China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

Wind turbine equipment, as a key component in the process production control of thermal power plants, directly affects the power generation efficiency of the plant. Due to the non-linear and non-stationary nature of the signals acquired from wind turbine sensors, this paper presents a novel active fault diagnosis approach for wind turbine systems based on deep forest techniques. Initially, multi-dimensional time-domain features are extracted from the raw fault data. Simultaneously, important features are selected from multiple angles by integrating Pearson correlation coefficient and Borutashap algorithm, following the principles of active fault diagnosis. Additionally, the whale optimization algorithm (WOA) is utilised to search the optimal sinusoidal auxiliary signal parameter, thereby highlighting specific fault manifestations and improving the model’s diagnostic capability. The deep forest, with its strong classification ability and noise resistance, overcomes the overfitting issue in shallow machine learning algorithms and the complex parameter tuning in deep learning. Then, the designed sinusoidal auxiliary signal is used as an additional input to the deep forest model, enabling the final fault classification outcomes. Finally, experiments are conducted using a wind turbine power transmission system fault simulator. The experimental results demonstrate an improvement in fault diagnosis accuracy, confirming the effectiveness of the proposed method.

关键词： Fault diagnosis Deep learning Machine learning algorithms Feature extraction Whale optimization algorithms Regression analysis Wind turbines Random forests Tuning Power generation

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Thermodynamic Analysis of the Coal-Fired Combined Heat and Power Units Integrated with Steam Ejectors and Thermal Storage

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SSRN 2024年

作者： Zhang, Hao Sun, Li Zhao, Hongxia Zhang, Xiuao Xin, Gongming Shandong Engineering Research Center for High-Efficiency Energy Storage and Hydrogen Energy Utilization School of Energy and Power Engineering Shandong University Shandong Jinan250061 China Key Laboratory of Energy Thermal Conversion and Control Ministry of Education School of Energy and the Environment Southeast University Nanjing210096 China

The increasing penetration of intermittent renewable power requires the coal-fired combined heat and power (CHP) units to be of high flexibility for the purpose of peak load shifting. However, the ratio between the heat and power generation of the conventional CHP units is strictly coupled and limited within a narrow region, posing a great challenge in quickly responding to the load requirements. To this end, this paper proposes four improved CHP schemes by incorporating two types of steam ejectors hybrid with or without the thermal storage tank. The thermodynamic model is developed to evaluate the performance of the CHP schemes, whose accuracy is validated in comparison with the field data. The effect of the ejector extraction position on the system performance is studied, in terms of heat-power coupling and the energy-saving characteristics. The results show that the ejector extracting the steam from the reheater shows better peak-shaving capacity and higher energy efficiency than the schemes extracting steam from high-pressure turbine. The inclusion of thermal storage is able to significantly improve the system flexibility. The CHP scheme involving both reheater steam extraction and thermal storage reaches optimal peak shaving capacity of 67.15 MW. The energy efficiency and exergy efficiency can be improved by 20.7% and 1.21%, respectively, leading to a daily 584.4 tons of coal conservation compared to the conventional CHP system. © 2024, The Authors. All rights reserved.

关键词： Coal

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Two-Dimensional Broad Learning System for Data Analytic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第6期2卷 594-607页

作者： Dai, Wei Zhang, Rui Wang, Qianjin Cheng, Yuhu Wang, Xuesong The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The School of Electrical Engineering Yancheng Institute of Technology Yancheng221051 China

Recently, a novel learning algorithm called the broad learning system (BLS) has been established by using the theory of pseudoinverse and compressed sensing technology. As an alternative to deep learning, BLS has gained extensive attention in data analytics. Unlike the existing deep structures based learning tools (such as deep neural networks), it is able to rapidly achieve incremental learning, and does not require tedious retraining to remodel the system. However, given image data modeling tasks, 1-D BLS requires a conventional vectorization operation, the dimension of input will be quite large for high resolution image. This operation may result in huge computational complexity, thereby increasing the time of model construction. To resolve this issue, a 2-D version of BLS, namely 2-D-BLS, is first proposed to fast construct randomized learner models by using matrix inputs. Specifically, the proposed method employs left and right projecting vectors to replace the usual high dimensional input weight. Moreover, the theoretical analyses on the superiority of 2-D-BLS against BLS are presented in terms of the algorithm complexity and the difference of random parameter space. Empirical results on MNIST dataset, NORB 3-D object image recognition dataset, ORL dataset, YaleB dataset, and handwritten digit dataset demonstrate that the proposed 2-D-BLS outperforms the existing extensions of BLS and state-of-the-art 2-D machine learning methods in terms of the learning efficiency, showing good potential of 2-D-BLS for image data analytics. Impact Statement-as a novel learning tool, BLS has achieved great success in many fields. Image recognition is one of the most widely used fields of artificial intelligence. But, BLS is a 1-D model, the vectorization operation is necessary to convert matrix input data into a row or a column vector. This operation will lead to "explosion of dimensionality" and increase the time of model construction when it is coming to image data analytic

关键词： Learning systems

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Data-Driven Distributionally Robust Mixed-Integer control through Lifted control Policy

arXiv

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arXiv 2025年

作者： Ma, Xutao Ning, Chao Du, Wenli Shi, Yang Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China the Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China the Department of Mechanical Engineering University of Victoria VictoriaBCV8W 3P6 Canada

This paper investigates the finite-horizon distributionally robust mixed-integer control (DRMIC) of uncertain linear systems. However, deriving an optimal causal feedback control policy to this DRMIC problem is computationally formidable for most ambiguity sets. To address the computational challenge, we propose a novel distributionally robust lifted control policy (DR-LCP) method to derive a high-quality approximate solution to this DRMIC problem for a rich class of Wasserstein metric-based ambiguity sets, including the Wasserstein ambiguity set and its variants. In theory, we analyze the asymptotic performance and establish a tight non-asymptotic bound of the proposed method. In numerical experiments, the proposed DR-LCP method empirically demonstrates superior performance compared with existing methods in the literature. © 2025, CC BY.

关键词： Mixed-integer linear programming

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Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

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Science China(Information Sciences) 2021年第5期64卷 151-162页

作者： Xiang ZHANG Shuping HE Vladimir STOJANOVIC Xiaoli LUAN Fei LIU Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil EngineeringUniversity of Kragujevac Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable LyapunovKrasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters U, G and V. Finally, the filter gains and the optimal index α*are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

关键词： dissipative filtering hidden Markov model finite-time boundedness Markov jump systems conic-type nonlinearity

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Data-Driven Source Term Estimation of Hazardous Gas Leakages Under Variable Meteorological Conditions

SSRN

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SSRN 2024年

作者： Ni, Chuantao Lang, Ziqiang Wang, Bing Li, Ang Cao, Chenxi Du, Wenli Qian, Feng Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Automatic Control and System Engineering University of Sheffield SheffieldS1 3JD United Kingdom

Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs) is important for addressing environmental pollution and improving engineering safety and reliability. For this purpose, some least squares-based STE methods have recently been developed, which performs the real-time STE using an off-line determined response matrix that represents the relationship between the sensor measurements and strengths of hazardous gas leakages. However, these methods require the number and locations of potential hazardous gas leakage sources are known a priori, which is difficult in many practical applications. To resolve this issue, in the present study, a novel multi-sensor data-driven STE (MSDD-STE) approach is proposed, which overcomes the difficulties with existing least squares-based STE methods and can, for the first time, address the MSDD-STE problems in complicated scenarios where meteorological conditions such as wind directions change over a considerable range. A detailed analysis is introduced to evaluate the performance of the proposed approach. The effectiveness of the proposed approach is verified by comprehensive numerical simulation studies. © 2024, The Authors. All rights reserved.

关键词： Least squares approximations

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