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作者： Aguirre, Matthew University of Michigan

学位级别：博士

This dissertation presents a comprehensive analysis of human behaviors with an emphasis on practical methods that generate a positive societal impact with regards to driving safety and business management decisions. Human behaviors were explored through three major works: (1) automatic clustering of kinematic driving data patterns to enhance the understanding of drivers' normal maneuvering behaviors; (2) detecting distracted driving behaviors to enhance human driving safety through the fusion of kinematic data and driving models; and (3) modeling and predicting consumers' behaviors to assist airline management decisions. The major contributions are summarized as follows. The first work introduces a new multi-model fusion and detection algorithm through effectively synthesizing three different state-space models, each of which represents a driver's typical kinematic motion pattern. The kinematic motion trajectory can be dynamically updated and predicted by using the Kalman filtering method. A normalized likelihood is further proposed for model fusion by weighing the contribution of each kinematic motion model. The results are then used to build two monitoring control charts. Specifically, both an Exponentially Weighted Moving Average (EWMA) control chart and a Cumulative Sum (CUSUM) control chart are developed to automatically detect and assess distracted driving behavior. This work is attractive in that it can be used to generate real-time warning signals during driving to avoid risky distracted driving behaviors. The second major work aims to identify typical drivers' normal maneuvering behaviors from a collected naturalistic driving dataset. This is done by improving the existing Hierarchical Dirichlet process Hidden Semi-Markov Model (HDP-HSMM) inference method. In literature, a HDP prior can cause consistency issues, which harms the estimation of duration probabilities, transition probabilities, and total number of states estimated. Many solutions have been prop

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RESEARCH AND DEVELOPMENT OF DEFAULT VALUE ANALYSES AND MANAGEMENT PLATFORM FOR NUCLEAR POWER PLANT control SYSTEM 29

RESEARCH AND DEVELOPMENT OF DEFAULT VALUE ANALYSES AND MANAG...

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2022 29th International conference on Nuclear Engineering, ICONE 2022

作者： Zhao, Hongbin Liu, Zhiyong Liu, Hemin Liu, Weifang China Nuclear Power Engineering Company Ltd Shenzhen China

ISBN: (纸本)9780791886472

The research and development of default value development and management platform of nuclear power plant control system can realize the functions of default value analysis, value selection, query and data management. The software standardizes the process and judgment factors of default value analysis process, makes qualitative and quantitative judgment on value selection factors, and combines the value selection algorithm embedded in the software, The auxiliary designer can automatically get the default value. The use of software can effectively improve the rationality and standardization of default value analysis. The software platform solves the problems of different default value analysis standards and chaotic data management in nuclear power plants. As the first special software for signal default value analysis and management of nuclear power control system in China, it has improved the intelligent level of the third generation nuclear power HPR1000. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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Research on new energy grid-connected human-machine cooperative training system applying parallel control and transformer modeling 3

Research on new energy grid-connected human-machine cooperat...

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3rd International conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Li, Miao Wang, Jing Zhuang, Yuan Training Center of Jilin Province State Grid Corporation of China Changchun China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regulators with simulation exercises, intelligent deductions, decision-making references, Q&A services, which provide intelligent reference solutions for precise regulation under new energy access scenarios with high percentage of new energy under a new type of power system. In order to enhance training effectiveness and operational efficiency, it is need to apply intelligent technology and data-driven models instead of experience and human labor. Interactive Q&A application such as Chat GPT has subversively changed the implementation mode of the training process, making the traditional teaching of theory and basic knowledge easier and faster. However, these commercial Q&A systems still have limitations in terms of accuracy, security, as well as professionalism, which makes them inefficient in professional learning area. In this paper, we use the idea of parallel control and the transformer model to construct a human-computer cooperative training system adapted to new energy grid-connected electric power systems, which realize a human-computer cooperative training model that highly integrates the Q&A services with the real trainer, the real trainees, the computer simulation system. By constructing a large model of human-computer system, a training computing experiment platform, as well as a system with a closed loop of training reality, the parallel training will help training program planning, training teaching design, training arrangements, teaching interaction and other key training links, and realize automated and intelligent training design and execution. As a training and management model adapted to the situation of artificial intelligence, parallel training will bring brand new possibilities for the development of the training industry in the intellige

关键词： Decision making

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28th International conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023

28th International Conference on Computer-Aided Architectura...

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28th International conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023

ISBN: (纸本)9789887891802

The proceedings contain 150 papers. The special focus in this conference is on Computer-Aided Architectural Design Research in Asia. The topics include: COMMUNICATION WITH DETROIT: Machine Learning in Open Source Community Housing Design;PREDICTION AND OPTIMISATION OF THE TYPICAL AIRPORT TERMINAL CORRIDOR FAÇADE SHADING USING INTEGRATED MACHINE LEARNING AND EVOLUTIONARY ALGORITHMS;RESEARCH ON ARCHITECTURAL SKETCH TO SCHEME IMAGE BASED ON CONTEXT ENCODER;‘;TEXT-TO-GARDEN: Generating Traditional Chinese Garden Design From Text-Descriptions at Scale With Multimodal Machine Learning;INTEGRATION OF EEG AND DEEP LEARNING ON DESIGN DECISIONMAKING: A data-Driven Study of Perception in Immersive Virtual Architectural Environments;INCORPORATING PHYSICAL EXPERIMENTATION INTO CREATIVE DL-DRIVEN DESIGN SPACE EXPLORATION;AN IMAGE-BASED MACHINE LEARNING METHOD FOR URBAN FEATURES PREDICTION WITH THREE-DIMENSIONAL BUILDING INFORMATION;AN INTEGRATED APPLICATION OF BUILDING INFORMATION modeling, COMPUTER-AIDED MANUFACTURING, MACHINE LEARNING, AND THE INTERNET OF THINGS: A Hybrid Stadium as a Case Study;CROSS-DISCIPLINARY SEMANTIC BUILDING FINGERPRINTS: Knowledge Graphs To Store Topological Building Information Derived From Semantic Building Models (Bim) To Apply Methods Of Artificial Intelligence (Ai) Throughout The Life Cycle Of Buildings;SYNTHESIZING STYLE-SIMILAR RESIDENTIAL FACADE FROM SEMANTIC LABELING ACCORDING TO THE USER-PROVIDED EXAMPLE;VARIABILITY IN MACHINE LEARNING FOR MULTI-CRITERIA PERFORMANCE analysis;SKYWAYS VERSUS SIDEWALKS: Evaluating the Perceptual Qualities and Environmental Features of Elevated Pedestrian Systems in Hong Kong;BESPOKE 3D PRINTED CHAIR: Research On The Digital Design And Fabrication Method Of Multi-body Pose Fusion;TRADITIONAL CHINESE VILLAGE MORPHOLOGICAL FEATURE EXTRACTION AND CLUSTER analysis BASED ON MULTI-SOURCE data AND MACHINE LEARNING;GESTURE modeling: In Between Nature and control;PAINTERLY EXPANSION.

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Crow Search Algorithm-Based Optimization of Feedforward/Feedback controller for Fed-Batch Cultivation process control

Crow Search Algorithm-Based Optimization of Feedforward/Feed...

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Mediterranean conference on Embedded Computing (MECO)

作者： Olympia Roeva Tsonyo Slavov Jordan Kralev Asparuh Markovski Department of Bioinformatics and Mathematical Modelling Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Sofia Bulgaria Department of Systems and Control Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798331513412

ISBN: (纸本)9798331513429

In this paper, for the first time, the Crow Search Algorithm is applied to tune a nonlinear feedforward/feedback controller for fed-batch cultivation process control. The designed controller consists of a nonlinear feedforward part, a linear two-degree-of-freedom PID controller as a feedback part and an extended Kalman filter that estimates unmeasurable process variables. Based on the ten different nonlinear cultivation process models, ten controllers are tuned by optimisation. Their performance is investigated by cross-simulation experiments (each controller is simulated with each process model). In simulation, for more realistic results, the measurement delay of Flow Injection analysis system, process and measurement noises are taken into account. The obtained results show the robustness of the developed control systems.

关键词： Biological system modeling Noise Metaheuristics process control control systems Robustness Biomass Noise measurement Tuning Substrates

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Reconstruction of Greyscale X-Ray for Boundary analysis of Anatomical Structure 7

Reconstruction of Greyscale X-Ray for Boundary Analysis of A...

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2023 7th International conference On Computing, Communication, control And Automation, ICCUBEA 2023

作者： Javale, Deepali Talreja, Govinda Somatkar, Amey Mohite, Neha Khot, Mukta School of Computer Engineering and Technology Dr. Vishwanath Karad Mit World Peace University Pune India

ISBN: (纸本)9798350304268

In medical imaging, accurate boundary detection of anatomical structures is crucial for disease diagnosis and treatment planning. Traditional X-ray imaging, however, generates 2D greyscale images that frequently lack the clarity and resolution required for precise boundary analysis. In order to improve the boundary analysis of anatomical structures, we provide a unique method for reconstructing greyscale X-ray pictures in this study. Our method makes better use of internal structure visualization and boundary identification by reconstructing 3D volumetric data from multiple 2D X-ray images using Deep Learning techniques and N eural Networks. This research paper focuses on the Reconstruction of Greyscale X-ray images to facilitate Boundary analysis of Anatomical Structures for orthopaedic surgeries. Our study utilizes the X-ray images in DICOM format from the LIDC-IDRI dataset obtained from the Cancer Imaging Archive, employing a series of steps to reconstruct 3D images and detect boundaries within the lung region. Basic pre-processing techniques are applied to the acquired X-ray images to enhance their quality and improve subsequent analysis. Subsequently, segmentation is performed to isolate the bounded lung area from the images. This segmentation process enables focused analysis and accurate boundary detection. Sinograms are generated from the segmented images through the utilization of the Inverse Radon Transform technique. This step captures X-ray attenuation measurements from different angles, providing the foundation for subsequent image reconstruction. The reconstruction part of our project involves the implementation of both the Algebraic Reconstruction Technique and Filtered Back Projection. FBP, a widely used algorithm in CT imaging, is used to reconstruct the 3D image based on the filtered sinogram data. The ART algorithm, known for its iterative nature, refines the image reconstruction through an iterative process that incorporates acquired and synthetic

关键词： Algebraic Reconstruction Technique (ART) Anatomical Structures Boundary analysis Filtered Back Projection (FBP) Greyscale X-ray Image Reconstruction Inverse Radon Transform LIDC-IDRI dataset Lung Segmentation Orthopaedic Surgery

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SCADA-BASED LONG-TERM WIND TURBINE PERFORMANCE DEGRADATION analysis BASED ON DEEP LEARNING TECHNIQUES 69

SCADA-BASED LONG-TERM WIND TURBINE PERFORMANCE DEGRADATION A...

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69th ASME Turbomachinery Technical conference and Exposition (ASME Turbo Expo) (GT)

作者： Peng, Dandan Terzi, Ludovico Desmet, Wim Gryllias, Konstantinos Katholieke Univ Leuven Dept Mech Engn LMSD Leuven Belgium Flanders Make KU Leuven Leuven Belgium Leuven AI KU Leuven Inst AI B-3000 Leuven Belgium ENGIE Italia Milan Italy

ISBN: (纸本)9780791888094

In the context of the rapid development of wind energy as a renewable resource, ensuring effective monitoring and maintenance of wind turbines is increasingly important. Despite the large volume of data collected by Supervisory control and data Acquisition (SCADA) systems, extracting key feature information to construct successful condition monitoring models remains a complex and challenging task. This study introduces a deep learning approach, called Convolutional Neural Network-based Support Vector data Description (CNN-SVDD), aiming at effectively monitoring the long-term health status of wind turbines and providing timely warning for potential faults. The method employs Convolutional Neural Network (CNN) to deeply analyze time-series features and the correlations between sensors from SCADA data, capable of overcoming the challenges posed by environmental noise and changes in operational conditions. Subsequently, within the deep feature space learned by CNN, the SVDD classifier constructs a soft-boundary hypersphere. It allows for some samples to reside outside the hypersphere, thereby enhancing robustness against potential outliers in the SCADA data. Notably, the optimization processes of CNN and SVDD are linked, forming together an end-to-end process. The anomaly score generated by SVDD may act as an intelligent health indicator for assessing long-term performance degradation in wind turbines. Additionally, the radius of the hypersphere serves as a threshold for potential fault warnings. Extensive experiments on SCADA data acquired from multiple wind turbines over an 11-year period demonstrate the effectiveness of this method in early fault warning and long-term wind turbine performance degradation monitoring.

关键词： Wind Turbines Condition Monitoring Convolutional Neural Networks Support Vector data Description Long-Term Performance Degradation Fault Detection

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Active Fault-Tolerant control to Prevent Hanger Bending During Configuration Transformation of 3D Cable System in Suspension Bridges

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3249-3249页

作者： Bai, Yunteng Wang, Xiaoming Zhao, Zhiyan Wang, Huan Changan Univ Sch Highway Xian 710018 Peoples R China State Grid Xixian New Area Elect Power Supply Co Xian 712000 Peoples R China Sichuan Highway Planning Survey Design & Res Inst Chengdu 610041 Peoples R China

Effectively preventing hanger bending damage during the configuration transformation of the spatial main cables in suspension bridges is a critical challenge, particularly under the influence of construction errors. This study proposes an active fault-tolerant control method that integrates real-time data feedback, tolerance interval inversion technology, and a suspended lateral bracing (SLB) system to mitigate the risk of hanger bending damage in real time. The method establishes a dynamic inversion mechanism, utilizing data feedback, constraint function reconstruction, and secondary optimization to compensate for construction errors. This ensures that hangers remain undamaged throughout the transformation process. Construction errors are quantified as intervals, with the lower bound of the reliability interval used to account for extreme disturbances. This transforms the inversion process into a multi-objective optimization problem constrained by the worst reliability conditions. By integrating finite element analysis (FEA), reliability analysis, surrogate modeling, and interval analysis, the proposed approach establishes a direct relationship between design variables and the lower bound of the reliability interval. Case study results demonstrate that the proposed method not only ensures structural performance and hanger safety, but also significantly enhances the constructability of the configuration transformation. Additionally, it provides a larger fault tolerance margin, thereby improving the overall efficiency and safety of the construction process.

关键词： self-anchored suspension bridge configuration transformation hanger bending damage active fault-tolerant control suspended lateral bracing system multi-objective optimization

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Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews 24

Node-weighted Graph Convolutional Network for Depression Det...

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Interspeech conference

作者： Burdisso, Sergio Villatoro-Tello, Esau Madikeri, Srikanth Motlicek, Petr Idiap Res Inst Martigny Switzerland Brno Univ Technol Fac Informat Technol Brno Czech Republic

We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews. To this end, we use a GCN for modeling non-consecutive and long-distance semantics to classify the transcriptions into depressed or control subjects. The proposed method aims to mitigate the limiting assumptions of locality and the equal importance of self-connections vs. edges to neighboring nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capabilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outperforms the vanilla GCN model as well as previously reported results, achieving an F1=0.84% on both datasets. Finally, a qualitative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.

关键词： depression detection graph neural networks node weighted graphs limited training data interpretability

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On the Mathematical modeling and Optimization for the Energy Efficiency Performance of CSMA-NOMA Random Access Networks With Channel Inversion

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第4期22卷 2867-2884页

作者： Cao, Shengbin Hou, Fen Univ Macau Dept Elect & Comp Engn State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Guangdong Hong Kong Macau Joint Lab Smart Cities Hong Kong Guangdong Peoples R China

The paradigm of upcoming 5G and beyond is the massive machine type communications (mMTC), where a large number of devices automatically operate wireless communications. Due to their automatic and energy-intensive operations, energy efficiency (EE) becomes crucial, but there is a lack of investigation for EE of random access networks, which is the underlying platform for mMTC. In this paper, we focus on the EE of carrier sense multiple access-based non-orthogonal multiple access (NOMA) random access networks. We first construct mathematical models. Instead of investigating all combinations regarding successful decoding events as in previous works, by pivoting around the decoding process of a specific signal, a hidden pattern of NOMA decoding process is unveiled, which can largely decrease analytical complexity. Then, together with this feature, by adopting Markov chain and Q-function approximation, closed-form formulation for EE is derived. Subsequently, to efficiently solve the complicated non-convex EE maximization problem built via the constructed models, we employ an approach that unifies complementary geometric programming (CGP) and difference of convex programming (DCP) to optimize all the controllable parameters at device side, namely, transmission probability, power, and data rate, with a tightest lower bound strategy to guarantee seamless EE improvement and very fast convergence to local optimal points even in the worst case. Simulation experiments verify the accuracy of mathematical models and efficiency of optimization scheme.

关键词： NOMA Decoding Mathematical models Wireless communication Optimization Power control Multiaccess communication Non-orthogonal multiple access (NOMA) energy efficiency random access performance analysis complementary geometric programming (CGP) difference of convex programming (DCP) CSMA CA

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