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Enhancing Electricity Consumption Forecasting with Artificial Intelligence on Small Datasets: A Comparative Study

Enhancing Electricity Consumption Forecasting with Artificia...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Alexandru-George Berciu Dan Doru Micu Eva-Henrietta Dulf Automation Department EnTReC Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Electrotechnics and Measurement EnTReC Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Accurate electricity consumption forecasting is essential for efficient resource allocation and grid management, particularly with limited data. the present paper investigates how artificial intelligence techniques can enhance forecasting in small datasets and addresses data quality concerns in the educational sector. It reviews challenges in traditional forecasting, explores artificial intelligence methods such as support vector machines, autoregressive integrated moving average, and long short-term memory networks, and evaluates their performance using real-world data from four educational buildings belonging to the Technical University of Cluj-Napoca. By combining theoretical insights with empirical results, the present study advances artificial intelligence-driven forecasting for electricity consumption in small datasets, offering insights for future research and industry applications in energy management and policy formulation.

关键词： Support vector machines Accuracy Electricity Buildings Transfer learning Predictive models Ensemble learning Forecasting Artificial intelligence Long short term memory

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SATYA: Smart AI-driven Talent Yield Analyzer for Resume Screening and Recommendation Evaluation

SATYA: Smart AI-driven Talent Yield Analyzer for Resume Scre...

引用

robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Nisarg Bhavsar Sachish Singla Divyansh Sharma Rwik Dey Subarno Maji Indian Institute of Technology Kharagpur India

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

the hiring process is becoming increasingly complex, as HR departments are overwhelmed with large volumes of resumes and recommendations that require in-depth analysis. this paper presents SATYA (Smart AI-driven Talent Yield Analyzer), a comprehensive AI system that automates the resume screening process, validates recommendations, and enhances the accuracy of talent acquisition. By leveraging Natural Language Processing (NLP), Machine Learning (ML), and graph-based algorithms, SATYA assesses resumes, verifies skill claims, evaluates recommendation credibility, and generates final candidate scores. Trained on a dataset of 1000 resumes manually scored by HR experts, SATYA achieved an accuracy of 91.4%, significantly outperforming traditional resume screening methods. this paper describes the architecture of SATYA, detailing the resume scoring model, recommendation network analysis, skill validation process, and multi-objective candidate evaluation. the system's scalable design ensures that it can be integrated into existing Applicant Tracking Systems (ATS) to improve talent acquisition efficiency.

关键词： Analytical models Accuracy Machine learning algorithms automation Network analyzers Machine learning Natural language processing Intelligent systems Robots

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State and Disturbance Estimations of Typical Nonlinear Systems in Electromechanical Engineering

State and Disturbance Estimations of Typical Nonlinear Syste...

引用

robotics, Control and automation Engineering (RCAE), international conference on

作者： Tang Ye Wang Dan Zhao Xuliang Dong Sitong Xiang Yue Shenghui Guo Marketing Service Center State Grid Liaoning Electric Power Co. Ltd. Shenyang China School of Electronics and Information Engineering Suzhou University of Science and Technology Suzhou China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Taking a single-link flexible system driven by a DC motor as an example, the Takagi-Sugeno (T-S) fuzzy model is used to study the state and disturbance estimation methods for nonlinear systems, which are commonly present in the field of electromechanical engineering. By analyzing the working mechanism of the system, a nonlinear model of the system is established and then expressed as a T-S fuzzy model. By utilizing system state extension, the original system is transformed into a formal generalized system to achieve both system state estimation and estimation of system output disturbances. A robust proportional-integral observer is designed to achieve system state and disturbance estimation. the solution for the observer gain matrix is obtained by solving linear matrix inequalities. the system is studied separately with and without disturbance, and the correctness and effectiveness of the proposed method are verified through simulation results.

关键词： Analytical models Motor drives automation Simulation Observers DC motors Linear matrix inequalities Takagi-Sugeno model Nonlinear systems Robots

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Enhancing Simultaneous Arrival at a Dynamic Target: A Hybrid Approach with Proximal Policy Optimization and Expert Rules

Enhancing Simultaneous Arrival at a Dynamic Target: A Hybrid...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Yifei Lei Jinwen Hu Zhao Xu Chenqi Gao Jiatong Li School of Automation Northwestern Polytechnical University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

this paper addresses the complex challenge of ensuring simultaneous arrival of multiple agents at a dynamic target, a critical requirement for operations such as roundups and saturation attacks. Traditional guidance systems have faced significant hurdles due to inaccurate flight time estimates and difficulties adapting to high-speed maneuvers. To overcome these limitations, we introduce a novel framework that utilizes Proximal Policy Optimization (PPO) and expert rules. this approach leverages distributed computing to enable autonomous decision-making among agents, thereby simplifying the deep reinforcement learning model and reducing computational overhead, which enhances scalability and adaptability. Additionally, we incorporate an angular velocity reward into the reward function, improving the predictability and effectiveness of maneuvers, particularly for targets with high-speed and irregular trajectories. the proposed methods have been rigorously tested through numerous simulations and high-fidelity scenarios, confirming their robustness and superior performance over traditional and enhanced proportional guidance systems.

关键词： Adaptation models Scalability Computational modeling Decision making Angular velocity Robustness Trajectory Distributed computing Optimization Testing

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A Space Information-Enhanced Dense Video Caption for Indoor Human Action Recognition

A Space Information-Enhanced Dense Video Caption for Indoor ...

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robotics, Control and automation (ICRCA), international conference on

作者： Bin CHEN Yugo NAKAMURA Shogo FUKUSHIMA Yutaka ARAKAWA ISEE. Kyushu University Kyushu University Fukuoka Japan

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

Dense video captioning tasks are used to detect interesting events and provide descriptive text for these events from untrimmed videos. this technology has the potential to be used in security surveillance and human care applications. However, current methods often overlook the relationships between objects in the video, which limits their applicability and makes it challenging to adapt them to specific domains, such as video summarization for indoor human activities. In these scenarios, human activities are closely intertwined with the objects in the scene. In this paper, we propose a plug-and-play module designed to enhance existing dense video captioning methods with spatial information. Specifically, we extract spatial information about the interesting objects using Red-Green-Blue-Depth (RGB-D) images and the results of image segmentation. We then integrate this information into the captions generated by the Dense Video Captioning (DVC) method using a fine-tuned Large Language Model (LLM). We evaluate the performance of our model on a custom dataset and demonstrate that our system provides a convenient and effective approach for obtaining space-enhanced captions.

关键词： Image segmentation Surveillance Large language models Pipelines Information retrieval Security Human activity recognition

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A Dual-Graph Learning Framework with Sparse Adaptive Embedding for EEG Emotion Recognition

A Dual-Graph Learning Framework with Sparse Adaptive Embeddi...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Wenkang Liu Fangkun Li Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

EEG brain networks capture the complex relationships between multiple brain regions, providing rich information for emotion recognition. However, most existing methods rely on dynamically random initialized adjacent matrix, which limits their ability to effectively model emotional features across different brain regions. Additionally, these methods typically ignore the importance of synchronously modeling both adjacent and long-distance channels, resulting to suboptimal emotion recognition performance. In this paper, we propose a novel dual-graph learning framework with sparse adaptive embedding (DGLF-SAE) for EEG-brain-network emotion recognition, which includes a global hypergraph sparse adaptive embedding (GHG-SAE) module and a local adjacency graph sparse adaptive embedding (LAG-SAE) module. Specifically, the GHG-SAE module is designed to capture global relationships by associating long-distance channels, while the LAG-SAE module focuses on learning local relationships by modeling adjacent channels. We validate the effectiveness of our proposed DGLF-SAE on the SEED and SEED-IV datasets, with experimental results (98.05% and 92.83% on SEED and SEED-IV datasets, respectively) demonstrating that our approach outperforms state-of-the-art methods.

关键词： Emotion recognition Adaptation models Technological innovation Control engineering Service robots Graph convolutional networks Benchmark testing Brain modeling Electroencephalography Sparse matrices

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Modeling the Variable Pitch Screws by Using the Envelope theory Based on Cubic H-Bézier

Modeling the Variable Pitch Screws by Using the Envelope The...

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Yanwei Wu Gengbin Zhang Yu Ling Changqing Song Tie Zhang Guangzhou Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangzhou China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

this paper presents a novel approach to derive analytical expressions for variable pitch screw threads by employing three-segment composite H-Bézier curves as transitional curves at the beginning and end of the screw thread. Additionally, it introduces a methodology to represent the material surface as enveloping single-parameter surface families, enabling the calculation of the helical surface of variable pitch screw threads. the geometric model developed through this method finds applications in post-processing tasks such as material property analysis, numerical control machining, finite element analysis, and more. Finally, the proposed design and calculation methods for variable pitch screw threads are modeled and simulated within the MATLAB environment, demonstrating their practical value and feasibility.

关键词： Shape Design methodology Geometric modeling Machining Fasteners Harmonic analysis Polynomials Numerical models Trajectory Optimization

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Sparse Causal Residual Neural Network for Linear and Nonlinear Concurrent Causal Inference and Root Cause Diagnosis

Sparse Causal Residual Neural Network for Linear and Nonline...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jiawei Chen Chunhui Zhao Youxian Sun School of Mechanical Engineering Zhejiang University Hangzhou China the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Reliable and effective fault diagnosis methods are necessary for complex industrial processes that consists of various units. After a process fault is detected, it remains a challenging task to locate the root cause unit and determine the propagation path of the fault. In this paper, a novel method, termed Sparse Causal Residual Neural Network (SCRNN), is proposed and applied for modern industrial root cause diagnosis. the advantage of SCRNN lies in that it can not only recognize linear and nonlinear causal relationships in parallel, but also automatically determine the causality lags and deduce the time delay of causal transmission. Besides, due to the specially designed sparse constraint and optimization algorithm, the SCRNN model can realize the function of key dependent variable selection, avoiding the high computational complexity and complicated procedure brought by pairwise comparison. the feasibility of the proposed method is illustrated through the benchmark TE process.

关键词： Time series analysis Predictive models Optimization Reactive power Fitting Fault diagnosis Residual neural networks

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A Control Algorithm for an Aerial Co-Manipulation System

A Control Algorithm for an Aerial Co-Manipulation System

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Fan Chen Xinyue Zhang Yuxin Li Yixin Wang Electronic Information Engineering Southwest University of Science and Technology Mianyang China Communication Engineering Southwest University of Science and Technology Mianyang China Robotics Engineering Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

During aerial co-manipulation, addressing the disturbances caused by the motion of the lower UAV on the aerial dexterous manipulation platform and the perturbations induced by the manipulation platform's own movement, as well as the issue of the inadequate number of existing simulation platforms, this study introduces an adaptive sliding mode control approach that leverages radial basis function neural networks and establishes a simulation platform for aerial comanipulation within the Gazebo environment. In the control method, the Lagrangian equation is employed for standardized modeling, and the model is simplified through projection. the universal approximation property of neural networks is utilized for adaptive rate optimization. Additionally, a sliding mode algorithm grounded on the exponential reaching law is implemented to bolster robustness. To further mitigate chattering, the hyperbolic tangent function is employed. Finally, the results were verified through trajectory tracking in Matlab/Simulink, ensuring that the system converged in about 1 second and achieved micron-level multi-channel tracking error accuracy, effectively reducing chatter. the Gazebo simulation platform sets up three aerial dexterous manipulation platforms capable of performing basic aerial tasks, and control stability is verified using PID algorithms.

关键词： Adaptation models Accuracy Uncertainty Trajectory tracking Heuristic algorithms Visual systems Mathematical models Stability analysis Sliding mode control Lyapunov methods

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Comparison of Different Flux-Weakening Strategies of AC-Excited Hybrid Excitation Synchronous Motor

Comparison of Different Flux-Weakening Strategies of AC-Exci...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Y. W. Deng K. Wang J. Li T. Wang College of Automation Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

Compared with permanent magnet synchronous motor (PMSM), hybrid excitation synchronous motor (HESM) adds a set of electric excitation windings, so the controllable excitation current is introduced, which increases the control freedom of the system, and the magnetic field adjustment is convenient and reliable. It is suitable for aerospace, electric vehicles and other occasions. In this paper, a new type of AC-excited hybrid excitation synchronous motor (AC-EHESM) is studied. Firstly, according to the characteristics of the motor, the mathematical model of the motor is built, and the operation limits of the motor are analyzed. On this basis, three different flux-weakening control methods are compared and studied. these three control strategies can widen the operating area of the motor to a certain extent, and do not depend on the motor parameters, and can be applied to other HESM.

关键词： Simulation Windings Air gaps Synchronous motors Permanent magnet motors Mathematical models Hybrid power systems

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