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International conference on control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Marina Zhuravlyova Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

The procedure of building control charts with variable control limits based on nonparametric bootstrap percentile method is proposed. It makes possible to construct control chart of any statistic with unknown continuous distribution and involves a large number of calculations. The order of building one of the scalar statistics to be monitored for the group of mutually dependent quality characteristics is considered. Formulas for obtaining variable control limits based on percentile method are shown. The issue of identifying the structure of relationships between quality characteristics is raised. Proposed control chart, Hotelling T2 and Shewhart usual control charts and their modifications for variable control limits have been constructed using actual data of two characteristics of the rolled steel manufacturing process. It is concluded that the development of the proposed approach can be useful in statistical process control.

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Experimental and numerical study of the delamination of Ceiba plywood under mode I, mode ii and mixed-mode (I plus ii) loading using the DCB, ELS and MMF tests

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INTERNATIONAL JOURNAL OF FRACTURE 2021年第1期231卷 1-20页

作者： El Moustaphaoui, Anouar Chouaf, Abdelkarim Kimakh, Khadija Hassan II Univ ENSEM Lab Control & Mech Characterizat Mat & Struct Casablanca Morocco

The characterization and modeling of Ceiba plywood delamination requires extensive experimental and numerical study. A data reduction scheme, based on the compliance method and the concept of equivalent crack, is utilized. This method has two main advantages over conventional methodologies: it does not require crack monitoring during propagation and takes into account the effect of the fracture process zone at the crack tip. The characterization of delamination and the mechanical behavior of adhesive joint are influenced not only by the adhesive properties but also by the characteristics of the interface. Various modeling methods have been proposed to characterize the delamination of laminated materials. Among the most widely used methods is the cohesive zone model, which is based on fracture mechanics and damage theory and allows for an adequate prediction of the onset and propagation of the delamination simultaneously. This method consists of introducing a cohesion law in finite element modeling. Often, the determination of these parameters is obtained iteratively by matching the numerical results with the experimental results. In this study, the cohesive parameters of the bilinear traction-separation laws in each pure mode were determined. This identification was achieved through the design of experiments method and the Kriging metamodel. A finite element modeling of the Ceiba plywood delamination was then performed. The experimental tests carried out make it possible to assess the delamination resistance under mode I, mode ii and mixed mode I + ii loading. Following the description of the experimental procedure, the experimental and numerical results are presented and compared. Finally, the response to the delamination was represented by a semi-empirical criterion and an analysis of fracture surfaces was performed and interpreted.

关键词： Delamination Fracture toughness FEM simulation Cohesive zone model Fracture surface

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Prediction method of dioxin emission concentration based on PCA and deep forest regression 40

Prediction method of dioxin emission concentration based on ...

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40th Chinese control conference (CCC)

作者： Xu, Wen Tang, Jian Xia, Heng Sun, Zijian Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

ISBN: (纸本)9789881563804

Municipal solid waste incineration (MSWI) is a widely used technology for reducing, harmless and recycling municipal solid waste. However, dioxin (DXN), a highly toxic pollutant in the exhaust gas of MSWI process, is the main factor that causes the "NIMBY effect" in building incineration power plants. The existing DXN detection method by combining long-period online sampling and off-line testing cannot meet the requirement of direct optimization control of DXN. To solve this problem, the prediction model of DXN emission concentration based on easy-to-measure process variables has become a research hotspot. However, due to the high dimension of process variables and the complex mechanism of DXN generation, adsorption and emission, it is difficult to effectively select input features of DXN model. Therefore, this paper proposes a modeling method based on principal component analysis (PCA) and deep forest regression (DFR). At first, the dioxin emission characteristics of MSWI process are described. Then, a modeling strategy and algorithm including PCA dimension reduction and DFR modeling are proposed. Finally, the effectiveness of the proposed method is verified by using the Benchmark data set and the actual DXN data.

关键词： Municipal solid waste incineration (MSWI) Dioxin (DXN) Principal component analysis (PCA) Deep forest regression (DFR)

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Digital Clones at the Adaptable control in the Agricultural Biotechnology

Digital Clones at the Adaptable Control in the Agricultural ...

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2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Ivashchuk, Olga A. Fedorov, Vyacheslav I. Berezhnoy, Vladislav A. Belgorod State National Research University Institute of Engineering Technology and Natural Sciences Dept of Information and Robotic Systems Belgorod Russia

ISBN: (纸本)9781665464291

In the article, results of the development of methods, models, and hardware/software solutions for creating and actualization of digital clones of crops with the complex structure in the form of a complex of 3D models are presented that ensure the possibility to perform virtual biological experiments consisting in the cultivation of agricultural plants in the context of in vitro conditions (in a test glass) with evaluation and forecasting of parameters that have an effect for the further field setting and adaptation of plants in conditions of the outdoor bed, and prevailing natural environment and climatic factors. For the segmentation of the plant using methods of machine learning, a segmenting neuron net with the U2-Net architecture was used. Good results of learning were obtained. A prototype of an automated installation has been developed that makes it possible to perform the complete cycle of the digital phenotyping and the analysis of obtained results based on digital clones of plants and implementation of the virtual process of in vitro cultivation. The obtained complex makes it possible to perform studies in that the microclimate inside of the test glass will not be disrupted;the data registration process is accelerated essentially;the human factor and the subjectivity are excluded during measurements. The knowledge base has been created that includes 792 units of 3D models for six crop species. © 2023 IEEE.

关键词： 3D modeling

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Improving Organizational Knowledge control with the use of NLP

Improving Organizational Knowledge Control with the use of N...

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Intelligent control, Computing and Communications (IC3), International conference on

作者： Susovan Kumar Pan Ankita Tiwari Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Knowledge management has emerged as a key enabler of organisational learning and innovation in the digital era. Over data helps organizations to collect, transform and act upon these insights towards process innovation and decision victory. Rooted in a dynamic capability lens of organizational learning, this study extends theory and practice by illustrating how knowledge management systems, powered by NLP, can provide transformational impact in terms of guiding organizational learning to achieve competitive advantage in the midst of evolving business scopes. Failure to capture, share and innovate organizational learning allows an organisation to learn from its best practices and adequately adapt to change. Traditional KM approaches have been biased to the codification of explicit knowledge which, by and large, have focused on the codification of explicit knowledge, in general, they did not pay much attention to tacit knowledge embedded in extracted information from the organization communication, documents, and external data stores. NLP technologies help fill this vacuum by enabling organizations to work with both structured and unstructured data in a manner capable of uncovering previously opaque patterns, insights and knowledge. Leveraging essential NLP techniques such as sentiment analysis, topic modeling, entity recognition, and text summarization, organizations can generate insights from vast datasets. As a few examples, qualitative analysis can be used to to measure employee satisfaction or customer feedback using sentiment analysis etc. or topic modeling can work to surface new trends or areas of knowledge that are critical for strategic initiatives. Entity recognition also facilitates the identification of key players, relations and resources, which streamlines the decision-making process. When paired with machine learning algorithms, this kind of NLP tooling provides insight into key topics in real time, automating repetitive tasks and enabling adaptive lea

关键词： Sentiment analysis Analytical models Technological innovation Virtual assistants Transforms Market research Natural language processing Knowledge management Real-time systems data mining

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Structural Connectivity analysis in Cognitive Decline: Insights from Graph Theory and Mass-Spring modeling

Structural Connectivity Analysis in Cognitive Decline: Insig...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Adebisi, Abdulyekeen T. Lee, Ho-Won Veluvolu, Kalyana C. Kyungpook National University School of Electronic and Electrical Engineering Daegu Korea Republic of Kyungpook National University School of Medicine Department of Neurology Daegu Korea Republic of Kyungpook National University Brain Science and Engineering Institute Daegu Korea Republic of Kyungpook National University School of Electronics Engineering Daegu Korea Republic of

ISBN: (纸本)9798350337488

The landscape of cognitive states and their underlying neurobiological mechanisms has been significantly illuminated through advancements in neuroimaging and computational modeling. This study introduces an integrated approach that harnesses network analysis and machine learning techniques to characterize and differentiate cognitive groups - Normal control (NC), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). Structural networks are formulated and analyzed based on diffusion tensor data through a fusion of graph theory and mass-spring model methodologies. Notably, features extracted from both graph theoretic and mass-spring model computations drive a two-step framework. This process commences with a random forest-based feature extraction, followed by a support vector-based classification approach, culminating in an impressive accuracy of 82.7% for classifying individuals across cognitive groups, with an AUC of 0.893. This study significance is underscored by the pressing need for enhanced cognitive impairment detection and differentiation strategies. The identified features offer nuanced insights into the intricate interplay among brain structure, dynamics, and cognitive function, thereby bridging gaps in our understanding of cognitive decline and neurodegeneration. By fortifying our diagnostic repertoire and facilitating personalized interventions, this research paves the way for refined clinical practices. © 2023 IEEE.

关键词： Alzheimer's disease Dementia related disorders Diffusion tensor imaging (DTI) Graph theory Mass-spring model Support vector machine (SVM)

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A Novel control Method Combining Multi-Level Dimension Reduction and NSGA-ii for Octane Number 40

A Novel Control Method Combining Multi-Level Dimension Reduc...

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40th Chinese control conference (CCC)

作者： Li, Guangze Zhang, Huimin Fan, Linqian Chu, Qingling Zhou, Ruijie Changan Univ Sch Elect & Control Engn Xian 710064 Shaanxi Peoples R China

ISBN: (纸本)9789881563804

Octane number is an important indicator reflecting the combustion performance of gasoline. To accomplish octane number modeling and its control variables optimization effectively, this paper proposes a novel control method MDR-NSGA-ii which combines the multi-level dimension reduction (MDR) and non-dominated sorting genetic algorithm ii (NSGA-ii). Firstly, based on the chemical data, the MDR method is used to select the control variables to obtain the main control variables. The MDR integrates the advantages of the distance correlation coefficient, the sparse representation method, and a machine learning fusion algorithm RF-XGBoost combining random forest (RF) and eXtreme gradient boosting (XGBoost). Secondly, the main control variables are controlled and optimized using NSGA-ii. Specially, an octane number loss model based on Elman neural network is established as a prediction model for the solution of NSGA-ii. Finally, the results of MDR-NSGA-ii are analyzed and evaluated, and it can be concluded that the proposed method can accomplish the octane modeling and its optimal control well.

关键词： octane number dimension reduction NSGA-ii process control

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Robust Event-Driven Interactions in Cooperative Multi-agent Learning 20th

Robust Event-Driven Interactions in Cooperative Multi-agent ...

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20th International conference on Formal modeling and analysis of Timed Systems (FORMATS)

作者： Ornia, Daniel Jarne Mazo, Manuel Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

ISBN: (纸本)9783031158391;9783031158384

We present an approach to safely reduce the communication required between agents in a Multi-Agent Reinforcement Learning system by exploiting the inherent robustness of the underlying Markov Decision process. We compute robustness certificate functions (off-line), that give agents a conservative indication of how far their state measurements can deviate before they need to update other agents in the system with new measurements. This results in fully distributed decision functions, enabling agents to decide when it is necessary to communicate state variables. We derive bounds on the optimality of the resulting systems in terms of the discounted sum of rewards obtained, and show these bounds are a function of the design parameters. Additionally, we extend the results for the case where the robustness surrogate functions are learned from data, and present experimental results demonstrating a significant reduction in communication events between agents.

关键词： Multi-Agent Systems Event-Triggered Communication Reinforcement Learning

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12th International conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

12th International Conference on Quality, Reliability, Risk,...

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12th International conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

ISBN: (纸本)9781839538360

The proceedings contain 337 papers. The topics discussed include: manufacturing process risk analysis model considering human and organizational factors;quality assessment of analog to digital converter chip using test data set;reliability prediction and evaluation model of smart electricity meter based on edge computing;wind turbine reliability analysis with fault-related weighted Bayesian networks;vibration reliability analysis of circular saw blade for Salix flat stubble;reliability prediction of temperature control circuit for solid rocket motor casting core insertion control system;deep learning-based satellite heat reliability analysis;heat reliability analysis of satellite based on graph neural network;reliability analysis of hydraulic system in a NC machining center based on bayes network;a two-phase wiener process model considering change process of degradation;an efficient reliability evaluation method involving correlated random variables under distribution parameter uncertainty;comprehensive hazard analysis of heavy CNC machine tools based on fusion fault correlation;and reliability modeling and analysis of lunar patrol probe moving subsystem.

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Cost control Algorithm of Prefabricated Building Based on BIM and Neural Network 22

Cost Control Algorithm of Prefabricated Building Based on BI...

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3rd Asia-Pacific conference on Image processing, Electronics and Computers, IPEC 2022

作者： Zhang, Qian Hubei Three Gorges Polytechnic Hubei Yichang443000 China

ISBN: (纸本)9781450395786

In the process of construction, various expenses fluctuate greatly affected by the external environment, and the cost control cannot be completed at one time. It needs to be dynamically adjusted with the actual construction progress and the external environment. The cost control of prefabricated construction is an important part of the construction cost. Because China's prefabricated buildings have not been popularized on a large scale and the relevant integrated design level is relatively backward, the cost of China's prefabricated buildings is relatively high compared with that of traditional buildings. Building cost modeling and control method based on Neural Network - neural network models and controls the building cost by simulating the thinking mode of human brain neurons. BIM technology is a technology integrating architectural design, construction management and project collaborative management, which can improve the efficiency of building management and reduce the construction cost. Based on BIM platform, this paper establishes a database of key factors influencing the cost of similar projects, uses the data in the database to train neural network, and collects real-time cost data of projects under construction based on BIM platform, carries out cost analysis, and completes this control and cost assessment. © 2022 ACM.

关键词： Architectural design

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