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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Rahul Pradhan Ch. Prathiba Timur Kholmukhamedov B.N. Patil S. Suma Christal Mary I Infant Raj Department of Computer Engineering and Applications GLA University Mathura India Department of CSM Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Automatic Control and Computer Engineering Department Turin Polytechnic University Tashkent Uzbekistan Department of Applied Physics Yeshwantrao Chavan College of Engineering Nagpur India Department of Information Technology Panimalar Engineering College Poonamalle Chennai India Department of CSE K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramount for the success of smart city initiatives. The current state of sensor communication faces challenges like suboptimal adaptability, inefficient energy usage, and limited fault detection. This study proposes a solution through the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS), identifying these challenges as opportunities for improvement. The novelty lies in ANFIS’s introduction as an intelligent and adaptive solution. Its ability to learn from sensor data, adapt to changing conditions, and optimize communication parameters sets it apart in enhancing cognitive sensor communication. The research methodology involves a systematic approach, encompassing data collection, pre-processing, and ANFIS model development. The model is rigorously trained, validated, and tested for real-world effectiveness. Integration into the smart city communication system is explored, emphasizing applications in context-aware communication, energy-efficient protocols, and fault detection. Performance is evaluated using metrics like accuracy, reliability, and efficiency, demonstrating the model’s efficacy in addressing drawbacks and enhancing overall sensor communication in smart cities. The findings contribute to advancing intelligent systems for urban infrastructure and services.

关键词： Adaptation models Protocols Adaptive systems Systematics Smart cities Fault detection Computational modeling Scalability Energy efficiency Intelligent systems

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A Unified Planning Model for Microgrids Considering Typical Scenarios and Operational Simulations

A Unified Planning Model for Microgrids Considering Typical ...

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Power and Electrical Technology Conference (APET), Asia

作者： Mengmeng Su Jun Peng Yong Zhang Min Li Zhenwei Li Mingfei Ban North China Branch of State GRID Corporation of China Beijing China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The structure of microgrids and the models considered have a significant impact on the planning and operational outcomes of micro grids. Additionally, micro grids encompass various models of primary equipment, including wind, solar, diesel, and storage, while the demand-side response mechanisms are continually being refined. This necessitates the construction of diverse load types, thereby increasing the complexity of micro grid planning and highlighting the need for further research into microgrid planning models. This paper presents various typical mathematical models for microgrids, employs spectral clustering algorithms to obtain representative scenarios, and constructs a unified planning model for microgrids. Furthermore, it elucidates the model adjustment methods under different planning objectives and conducts case analyses of microgrid planning issues.

关键词： Analytical models Costs Asia Clustering algorithms Microgrids Mathematical models Planning Complexity theory Optimization Load modeling

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An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework 2

An Efficient Detection of Intracranial Hematoma Using Window...

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Vidhya, V. Gudigar, Anjan Raghavendra, U. Basak, Sudipta Mallappa, Sankalp Hegde, Ajay Menon, Girish Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Instrumentation and Control Engineering Manipal576104 India Department of Neurosurgery Manipal Hospitals Karnataka India Department of Neurosurgery Manipal Academy of Higher Education Kasturba Medical College Manipal576104 India

ISBN: (纸本)9798350306811

Traumatic brain injury (TBI) is a burgeoning medical disorder across the world particularly among young adults and children. TBI can cause intracranial hematoma (ICH), a lethal condition which requires prompt and accurate interpretation of computed tomography (CT) images for timely diagnosis and treatment. Since manual detection of CT images is a tedious and operator-dependent task, a deep learning framework is proposed for locating and categorizing ICH for improved diagnostic performance. Firstly, the input images are processed by using various techniques such as local directional pattern, local binary pattern, and windowing. Then the single stage YOLOv5 object detection model with faster spatial pyramid pooling is applied to detect hematoma regions of various types in the brain. The proposed model using windowing approach realized an overall mAP of 0.969, precision of 0.945 and recall of 0.943. The experimental results from the research study demonstrated that the suggested framework can help radiologists in strategic decision-making and improve quality care to the patients. © 2023 IEEE.

关键词： Object detection

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A Comprehensive Methodology of Field-Oriented control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Busarello, Tiago Davi Curi Bubshait, Abdullah Varaprasad, Oruganti V. S. R. Alsaleem, Abdulhakeem Simoes, Marcelo Godoy Federal University of Santa Catarina Department of Control Automation and Computation Engineering Florianopolis88040-900 Brazil King Faisal University College of Engineering Department of Electrical Engineering Al Ahsa31982 Saudi Arabia Group Department of Electrical Computer and Software Engineering OshawaONL1G 0C5 Canada Qassim University College of Engineering Department of Electrical Engineering Buraydah52571 Saudi Arabia University of Vaasa School of Technology and Innovations Vaasa65200 Finland

This paper proposes a comprehensive methodology for Field-Oriented control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness. © 2013 IEEE.

关键词： Vector control (Electric machinery)

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A Prediction and Weak Coevolution-Based Dynamic Constrained Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Gong, Dunwei Rong, Miao Hu, Na Wang, Yan Pedrycz, Witold Yang, Shengxiang College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao Shandong China School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Electrical and Computer Engineering Dept University of Alberta Edmonton Canada School of Computer Science and Informatics De Montfort University Leicester U.K

Dynamic multi-objective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with dynamic multi-objective optimization problems (DMOPs). However, existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this paper, we propose a prediction and weak coevolutionary multi-objective optimization algorithm (PWDCMO) to handle dynamic constrained multi-objective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multi-objective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with four popular dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO. IEEE

关键词： Heuristic algorithms

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Robust Multivariate Detection and Estimation with Fault Frequency Content Information

arXiv

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arXiv 2023年

作者： Dong, Jingwei Pan, Kaikai Pequito, Sérgio Esfahani, Peyman Mohajerin The Delft Center for Systems and Control Delft University of Technology Netherlands The College of Electrical Engineering Zhejiang University China The Department of Electrical and Computer Engineering University of Lisbon Lisbon Portugal

This paper studies the problem of fault detection and estimation (FDE) for linear time-invariant (LTI) systems with a particular focus on frequency content information of faults, possibly as multiple disjoint continuum ranges, and under both disturbances and stochastic noise. To ensure the worst-case fault sensitivity in the considered frequency ranges and mitigate the effects of disturbances and noise, an optimization framework incorporating a mixed H/H2 performance index is developed to compute the optimal detection filter. Moreover, a thresholding rule is proposed to guarantee both the false alarm rate (FAR) and the fault detection rate (FDR). Next, shifting attention to fault estimation in specific frequency ranges, an exact reformulation of the optimal estimation filter design using the restricted H∞ performance index is derived, which is inherently non-convex. However, focusing on finite frequency samples and fixed poles, a lower bound is established via a highly tractable quadratic programming (QP) problem. This lower bound together with an alternating optimization (AO) approach to the original estimation problem leads to a suboptimality gap for the overall estimation filter design. The effectiveness of the proposed approaches is validated through applications of a non-minimum phase hydraulic turbine system and a multi-area power system. Copyright © 2023, The Authors. All rights reserved.

关键词： Hydraulic motors

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Xception CNN-Ensemble Learning Based Facial Emotion Recognition

Xception CNN-Ensemble Learning Based Facial Emotion Recognit...

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International Conference on Computing Communication control and Automation (ICCUBEA)

作者： Supriya Shirsath Vaishali Vikhe Pratap Vikhe Dept. of Computer Engineering Pravara Rural Engineering College Ahmednagar India Dept. of Instrumentation & Control Engineering Pravara Rural Engineering College Ahmednagar India

ISBN: (纸本)9781665484527

Emotions in human is an effective medium to study the mindset of a person. Since, expression on the face of human is significant approach to understand the condition and communicate with him to release the pressure or stress of person. The emotions and behavior have a strong relationship and is noteworthy in non-verbal form of communication. The advancement in medical science and use of image processing tools like Artificial Intelligence (AI) and Machine Learning (ML), can be helpful to recognize emotion, detect stress and depression level of a person. Thus, in this research work emotion recognition system is developed using Convolution Neural Networks (CNNs) with increased depth and width. CNNs have been shown to enhance prediction accuracy. In terms of proper emotion categorization and accurate prediction, the suggested ensemble technique is effective. In this paper Xception CNN architecture is proposed for accurate facial emotions prediction along with an ensemble model, using Max Voting ensemble technique, mainly contributes in accurate classification. In proposed technique trained CNN models were loaded and for each trained model prediction probabilities were generated. Furthermore, it is then used for Max Voting to generate final emotion prediction. The CNN models are trained and evaluated on FER-2013 dataset.

关键词： Training Emotion recognition Computational modeling Face recognition Anxiety disorders Neural networks Machine learning

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach 25

Real-Time Object Detection and Robotic Manipulation for Agri...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Zhao, Hongyu Tang, Zezhi Li, Zhenhong Dong, Yi Si, Yuancheng Lu, Mingyang Panoutsos, George Imperial College The Department Of Physics London United Kingdom University Of Sheffield The Department Of Automatic Control And Systems Engineering SheffieldS1 3JD United Kingdom University Of Manchester The Department Of Electrical And Electronic Engineering Manchester United Kingdom University Of Southampton The Department Of Electronics And Computer Science Southampton United Kingdom Fudan University The Department Of Economics Shanghai200433 China AmesIA50011 United States

ISBN: (纸本)9798350340266

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation. © 2024 IEEE.

关键词： Crops

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Learning-Based Defect Recognitions for Autonomous UAV Inspections

arXiv

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arXiv 2023年

作者： Liu, Kangcheng Han, Xiaodong Chen, Ben M. The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong The College of Computer and Control Engineering Minjiang University Fuzhou China

Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and its performance of it is compared with other state-of-the-art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmark dataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed and will be established for the crack inspection tasks of various concrete structures. All our self-established dataset and codes are open-sourced at: https://***/KangchengLiu/Crack-Detection- and-Segmentation-Dataset-for-UAV-Inspection. © 2023, CC BY.

关键词： Network architecture

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SELM: From Efficient Autonomous Exploration to Long-term Monitoring in Semantic Level

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Lang, Fang Qin, Yongsen Wang, Yinchuan Liu, Jin Wang, Chaoqun Song, Wei Zhu, Qiuguo Song, Rui Zhejiang University Polytechnic Institute Hangzhou310015 China Shandong University School of Control Science and Engineering Jinan China Zhejiang University Robotics Institute Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

Maintaining up-to-date environmental models from initial deployment through long-term autonomy in service is critical for applications like navigation and task planning. To address the challenges of persistent monitoring in unknown environments, we introduce a Two-Stage monitoring strategy, termed the Semantic-level autonomous Exploration and Long-term environment Monitoring (SELM) framework. In the first stage, we introduce a novel semantic exploration method to adapt to new environments quickly. Leveraging the semantic information within the incrementally constructed 3D Scene Graph (3DSG), we combine the Next-Best-View (NBV) selection with room semantics, introducing a more efficient and comprehensive approach for multi-room indoor environment exploration. In addition, the exploration provides patrol routes, the room distance-connectivity graph, and complete environment initial states for subsequential monitoring. The monitoring stage aims to persistently patrol to update the world model in the presence of dynamic changes, including changes in objects' positions. We formulate the long-term monitoring problem as the Partially Observable Markov Decision Process (POMDP) to cope with the environmental uncertainty. To solve the POMDP, we propose the Graph Attention Bidirectional long short-term memory Proximal Policy Optimization (GABPPO) algorithm for the optimal patrol strategy. The feasibility and effectiveness of the proposed SELM framework are verified through extensive experiments. © 2016 IEEE.

关键词： Environmental monitoring

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