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International conference on control, Automation, Robotics and Vision (ICARCV)

作者： XueHong Tao Yuan Miao Guanhua Wang Swinburne University of Technology Melbourne VIC Australia Victoria University Melbourne VIC Australia

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Many human-computer interactions and automated processes are dependent on data. We need to correctly store data and retrieve data to support these processes. Large Language Models (LLMs) have made significant progress in comprehension and reasoning. However, we have found that their ability to handle professional data is weak, which significantly limits their applications in automated processes and professional applications. These scenarios often involve a large number of similar data entries, which are challenging for LLMs such as ChatGPT-4 Omni. In this paper, we propose a Rational Intelligence Model that comprehends human experts' knowledge of data structure and process requirements of data, automatically extracts data from conversations with end users, and effectively stores the data and retrieves it for supporting the interaction and process. Experiments show that ChatGPT-4o can achieve 0% error in simple queries, 2.6% errors when data entries are out of order, and 38% errors when the queries are reasonably complex. However, with our proposed Rational Intelligence Model (RIM), we can achieve 0% error rate in all tests. RIM fundamentally changes software engineering and expert system development approaches. Instead of having a software engineer understand expert knowledge of data processing, this is now achieved by RIM, which means it is much more flexible, lower in cost, and requires much less development time.

关键词： Solid modeling Costs Accuracy Error analysis Large language models data processing data models Software Cognition Software engineering

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Research on Model of Graphite Spraying Powder Feeder of control Optimization

Research on Model of Graphite Spraying Powder Feeder of Cont...

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Computer Science, Electronic Information Engineering and Intelligent control Technology (CEI), IEEE International conference on

作者： Yan Zhang Ying Wang Heilongjiang University of Technology JiXi China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Along with the development process of intelligentization, graphite spraying technology has been applied in many fields, such as manufacturing, new energy and so on. The amount of graphite powder transported through the powder feeder directly affects the quality of the spraying process, so the optimization study of powder transmission in the powder feeder is very significant. Firstly, a suitable linear regression analysis model was established through the analysis of 21 groups of sample data, having found an optimal match between the powder feeder inlet flow and the motor speed, based on the small number of sampling data in a single set of samples, the grey system GM (1, 1) was selected to establish the prediction model. After verification, the prediction model has high accuracy and effectiveness, which will have certain reference value for future research on powder feeders.

关键词： Analytical models Powders Computational modeling Spraying Graphite Predictive models Motors

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Research on the Detection of False data Injection Attacks in DC Microgrids Based on Attention Mechanism and DAE-CNN-BiLSTM

Research on the Detection of False Data Injection Attacks in...

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Electrical Power Systems and Intelligent control (EPSIC), International conference on

作者： Zhuoyu Guan Gengwu Zhang Xiaoqing Han Taiyuan University of Technology College of Electrical and Power Engineering Shanxi Taiyuan China

ISBN: (数字)9798350366075

ISBN: (纸本)9798350366082

Distributed DC microgrid is a typical power information physical system (CPS) that is vulnerable to cyber-attacks. False data Injection Attack (FDIA) on the information side can change the system's operational state and even destabilize the microgrid system. Most of the existing microgrid cyber attack detection methods in view of linear DC microgrid models. However, there are constant power loads (CPLs) in DC microgrids, which present negative resistive and nonlinear characteristics and are generally nonlinear. For this reason, in this paper, a distributed DC microgrid model with constant power loads is developed and a nonlinear DC microgrid false data injection attack detection method based on the attention mechanism and convolutional bi-directional recurrent neural network is proposed. After adding a known constant power load during the DC microgrid stabilization process, the distributed units suffering from FDIA are identified by comparing the discrepancy between the estimated value of the offline-trained neural network and the actual measured value to distinguish between the load change and the false data injection attack. Finally, a 4-node DC microgrid model is built in MATLAB/Simulink for simulation experiments to validate the effectiveness of the proposed assay.

关键词： Estimation error Attention mechanisms Distributed databases Microgrids Vectors MATLAB Transient analysis Time-domain analysis Cyberattack Load modeling

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modeling coordinated enzymatic control of saccharification and fermentation by Clostridium thermocellum during consolidated bioprocessing of cellulose

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BIOTECHNOLOGY AND BIOENGINEERING 2021年第5期118卷 1898-1912页

作者： Ahamed, Firnaaz Song, Hyun-Seob Ho, Yong Kuen Monash Univ Malaysia Sch Engn Chem Engn Discipline Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Univ Nebraska Dept Biol Syst Engn Lincoln NE USA Univ Nebraska Dept Food Sci & Technol Nebraska Food Hlth Ctr Lincoln NE 68583 USA Monash Univ Malaysia Monash Ind Palm Oil Educ & Res Platform Jalan Lagoon Selatan Bandar Sunway Selangor Malaysia

Consolidated bioprocessing (CBP) of cellulose is a cost-effective route to produce valuable biochemicals by integrating saccharification, fermentation and cellulase synthesis in a single step. However, the lack of understanding of governing factors of interdependent saccharification and fermentation in CBP eludes reliable process optimization. Here, we propose a new framework that synergistically couples population balances (to simulate cellulose depolymerization) and cybernetic models (to model enzymatic regulation of fermentation) to enable improved understanding of CBP. The resulting framework, named the unified cybernetic-population balance model (UC-PBM), enables simulation of CBP driven by coordinated control of enzyme synthesis through closed-loop interactions. UC-PBM considers two key aspects in controlling CBP: (1) heterogeneity in cellulose properties and (2) cellular regulation of competing cell growth and cellulase secretion. In a case study on Clostridium thermocellum, UC-PBM not only provides a decent fit with various exometabolomic data, but also reveals that: (i) growth-decoupled cellulase-secreting pathways are only activated during famine conditions to promote the production of growth substrates, and (ii) starting cellulose concentration has a strong influence on the overall flux distribution. Equipped with mechanisms of cellulose degradation and fermentative regulations, UC-PBM is practical to explore phenotypic functions for primary evaluation of microorganisms' potential for metabolic engineering and optimal design of bioprocess.

关键词： cellulose Clostridium thermocellum consolidated bioprocessing lumped hybrid cybernetic model metabolic pathway analysis multi‐ layered population balance model

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Stability and Hopf Bifurcation of a Class of Genetic Regulatory Network with Multiple Delays

Stability and Hopf Bifurcation of a Class of Genetic Regulat...

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data Driven control and Learning Systems (DDCLS)

作者： Wenyu Zhang Zunshui Cheng School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Gene expression can be divided in transcription and translation, the genetic regulatory network describes this process appropriately. Stability and Hopf bifurcation of two-gene regulatory network with multiple delays is studied in this paper. Firstly, the model with multiple delays can be introduced and the conditions of stability are given in positive equilibrium $(M_analysis^{*},\ P_analysis^{*},M_analysis^{*},\ P_analysis^{*})^{T}$ . Secondly, because of the actual significance of gene expression, different time delay is utilized as bifurcation parameter to discuss the stability and Hopf bifurcation. By analysis, the relationship between the two delays have also been provided. Then, in order to validate the effectiveness of theories, two numerical examples can be used. The conclusion has been presented in final.

关键词： Learning systems Sufficient conditions Delay effects Biological system modeling Bifurcation Genetics Stability analysis

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Construction and Empirical Study of Regional Logistics Demand Prediction Model Based on BP-RBF Neural Network

Construction and Empirical Study of Regional Logistics Deman...

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control, Electronics and Computer Technology (ICCECT), IEEE International conference on

作者： Ren Yujia School of Management Tianjin University of Technology Tianjin China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

To accurately and efficiently predict regional logistics requirements, a prediction model based on BP-RBF neural network is proposed. Considering the characteristics of nonlinear changes in logistics demand, in the modeling process, BP and RBF are first used to establish a single prediction submodel respectively, and then the weighted combination prediction model is further constructed, and finally the empirical analysis of logistics demand prediction in Shanghai is conducted. From the results it is clear that the combined logistics demand prediction model will have higher prediction accuracy for a single logistics demand prediction model, which greatly reduces the possibility of error, makes the prediction results more reasonable and accurate, and can provide help for the planning after regional logistics.

关键词： Economics Analytical models Neural networks Demand forecasting Predictive models data models Planning

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Transformer-Based Predictive analysis and Maintenance for Aluminum Foil Mill

Transformer-Based Predictive Analysis and Maintenance for Al...

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第43届中国控制会议

作者： Jiuzheng Su Wenshan Hu Guo-Ping Liu Zhongcheng Lei School of Electrical Engineering and Automation Wuhan University Center for Control Science and Technology Southern University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The aluminum foil mill is an important industrial production equipment. To reduce operation and maintenance costs and prevent breakdowns in the rolling mill, it is necessary to analyze and predict the data of different sensors of the mill, including voltage, current, temperature and others. In this paper, the problem analysis and modeling of the actual rolling mill production process are carried out, and the typical data of sensors are analyzed based on the Transformer model. Experimental results are compared with other typical time series prediction models. The final results show that the Transformer model has great potential in the predictive maintenance of aluminum foil mill.

关键词： Transformer model Aluminum foil mill Predictive maintenance Time series prediction

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Using Free-Choice Nets for process Mining and Business process Management 16

Using Free-Choice Nets for Process Mining and Business Proce...

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16th conference on Computer Science and Intelligence Systems (FedCSIS)

作者： van der Aalst, Wil M. P. Rhein Westfal TH Aachen Proc & Data Sci Informat 9 Aachen Germany Fraunhofer Inst Angew Informat Tech FIT St Augustin Germany

ISBN: (纸本)9788395918384

Free-choice nets, a subclass of Petri nets, have been studied for decades. They are interesting because they have many desirable properties normal Petri nets do not have and can be analyzed efficiently. Although the majority of process models used in practice are inherently free-choice, most users (even modeling experts) are not aware of free-choice net theory and associated analysis techniques. This paper discusses free-choice nets in the context of process mining and business process management. For example, state-of-the-art process discovery algorithms like the inductive miner produce process models that are free-choice. Also, hand-made process models using languages like BPMN tend to be free-choice because choice and synchronization are separated in different modeling elements. Therefore, we introduce basic notions and results for this important class of process models. Moreover, we also present new results for free-choice nets particularly relevant for process mining. For example, we elaborate on home clusters and lucency as closely-related and desirable correctness notions. We also discuss the limitations of free-choice nets in process mining and business process management, and suggest research directions to extend free-choice nets with non-local dependencies.

关键词： Computer science Analytical models Annotations Computational modeling Petri nets data models Business process management

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data-driven deviation generation for non-ideal surfaces of Skin Model Shapes 32

Data-driven deviation generation for non-ideal surfaces of S...

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32nd CIRP Design conference, CIRP Design 2022

作者： Qie, Yifan Anwer, Nabil Université Paris-Saclay Ens Paris-Saclay Lurpa Gif-sur-Yvette91190 France

The digitalization techniques enable quality control as well as performance simulation of mechanical products in the Digital Twin era. In the product design process, realistic models regarding surface geometrical deviations are essential for further functional analysis. In the context of ISO standards on geometrical product specifications and verification (GPS), the Skin Model Shapes (SMSs) concept stemmed from the Skin Models paradigm is put forward to represent deviations of mechanical parts in accordance with the nature of geometric deviations on the surfaces. Contributions have been made to generate SMSs by combining both systematic deviations and random deviations on the nominal model. However, the non-ideal surface generation process is limited by the reuse of geometric deviation's knowledge. Existing geometric deviations that are predicted or observed on the parts cannot be applied for other SMSs generation without new parameter settings using different methods. In this paper, a framework for data-driven geometric deviation generation is proposed for non-ideal surface modeling in tolerancing. A database that contains a variety of deviations generated by different methods is constructed. Deviations that are classified as systematic and random types are represented and stored as samples in the dataset. Deviation pattern identification is addressed by transfer learning using AlexNet and it is used for different types of non-ideal surface generation. Different approaches regarding both prediction and observation stage within SMS schema are included for constructing the deviation samples. The mapping process for both systematic and random deviation samples are considered. Therefore, there is no need to re-design deviations with parameters for the surfaces. A case study is used to illustrate the non-ideal surface generation process by the propose method. © 2022 The Authors.

关键词： Quality control

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analysis on Artificial Intelligence based Human Resource Computer Management System

Analysis on Artificial Intelligence based Human Resource Com...

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Sentiment analysis and Deep Learning (ICSADL), International conference on

作者： Subrahmanya Bhat T. Padmavathi Kumuda P R Nuzhat Fatima Rizvi Suganya Bharathi S C. Arunkumar Madhuvappan Swami Vivekanand Vidyaprasarak Mandal's College of Commerce Bori Ponda Goa CVR College of Engineering Telangana Department of Finance and BFSI M S Ramaiah Institute of Management Bengaluru Symbiosis International (Deemed) University Pune India Faculty of Management SRM Institute of Science and Technology Chengalpattu Tamil Nadu Department of Biomedical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Human resource management and competitiveness are becoming increasingly crucial in the growth of businesses. This study examines the factors of the enterprise survival environment in the process model of human asset management from the perception of control and computing. Based on systematic analysis, this study employs the SWOT is an acronym that stands for Strengths, Weaknesses, Opportunities, Threats, and analysis algorithm to creatively examine the contact of corresponding environmental elements on human resource management mode, and formulates a comprehensive set of human resource development plan. Simultaneously, in light of the control and computer challenges that exist in the human resource model, this study proposes specific implementation rules and methods, and theoretically verifies the efficacy and reliability of the related rules. By eliminating bias, automating tedious procedures, and empowering HR staff to make data-driven judgments, AI may expedite the hiring process. AI may save HR teams time and resources by, for instance, scheduling interviews, reviewing resumes, and performing preliminary candidate screening. It makes possible a data-driven strategy for hiring, promoting, and retaining people that aims to reduce bias and improve the experiences of both job searchers and workers.

关键词： Analytical models Sentiment analysis Systematics processor scheduling Terminology Computational modeling Environmental factors

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