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43rd Chinese Control Conference, CCC 2024

作者： Wang, Xiaoxue Su, Housheng Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

Knowledge Graph (KG) is a structured knowledge representation that encompasses abundant entity and relationship information, offering rich contextual insights for named entity recognition(NER). This study proposes the LIGNER model, which utilizes the Lattice-LSTM to integrate KG information into the process of NER for Chinese text. Lattice-LSTM is a neural network structure based on deep learning, which offers a flexible framework for organizing and representing entities. The LIGNER model integrates external information based on the Lattice framework, which helps to integrate structured knowledge from KG and improves the accuracy of NER. To address the challenge of low accuracy in long entity recognition, a new approach incorporating part-of-speech and position information is introduced. Experimental results on Chinese NER datasets demonstrate that LIGNER achieves advanced performance and efficiency compared with competitive methods. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Knowledge graph

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Load Frequency Control of Interconnected Microgrid Enhanced by Deep Reinforcement Learning SAC-PID 43

Load Frequency Control of Interconnected Microgrid Enhanced ...

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43rd Chinese Control Conference, CCC 2024

作者： Liu, Zheng Zhu, Lijun Li, Bolin Wang, Xuhui Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

In recent years, the incorporation of sustainable energy resources such as wind power has had a significant impact on the stability of microgrids. In this context, our research introduces a proficient method for load frequency regulation utilizing deep reinforcement learning (DRL). Firstly, a two-area interconnected microgrid frequency control model is constructed, including wind power generation, reheat steam turbines, generators and loads. Secondly, the proposed method uses a soft actor critic (SAC) with a double Q-network to tailor the parameters of the proportional integral derivative (PID) controller, to train a DRL agent to obtain optimal controller parameters for this two-area interconnection, and we used the area control error (ACE) as the controller's input to reduce fluctuations in both grid frequency and interconnection line power flows. Furthermore, we designed a reward function via the square of the ACE to obtain the best-suited parameters for the controller. Finally, the trained controllers are tested under step load disturbance and random load disturbance, verifying that the designed SAC-PID controller provides improved frequency control performance for microgrids compared with the classical PID controller and the PID controller optimized through the particle swarm optimization (PSO) algorithm. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Three term control systems

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IR-CM: The Fast and General-purpose Image Restoration Method Based on Consistency Model 38

IR-CM: The Fast and General-purpose Image Restoration Method...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Xiaoxuan Ma, Jie School of Artificial Intelligence and Automation HUST Huazhong University of Science and Technology Wuhan China

This paper proposes a fast and general-purpose image restoration method. The key idea is to achieve few-step or even one-step inference by conducting consistency distilling or training on a specific mean-reverting stochastic differential equations. Furthermore, based on this, we propose a novel linear-nonlinear decoupling training strategy, significantly enhancing training effectiveness and surpassing consistency distillation on inference performance. This allows our method to be independent of any pre-trained checkpoint, enabling it to serve as an effective standalone image-to-image transformation model. Finally, to avoid trivial solutions and stabilize model training, we introduce a simple origin-guided loss. To validate the effectiveness of our proposed method, we conducted experiments on tasks including image deraining, denoising, deblurring, and low-light image enhancement. The experiments show that our method achieves highly competitive results with only one-step inference. And with just two-step inference, it can achieve state-of-the-art performance in low-light image enhancement. Furthermore, a number of ablation experiments demonstrate the effectiveness of the proposed training strategy. our code is available at https://***/XiaoxuanGong/IR-CM. © 2024 Neural information processing systems foundation. All rights reserved.

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ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models 13

ELO-Rated Sequence Rewards: Advancing Reinforcement Learning...

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Ju, Qi Hei, Falin Fang, Zhemei Luo, Yunfeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350361674

Reinforcement Learning (RL) is highly dependent on the meticulous design of the reward function. However, accurately assigning rewards to each state-action pair in Long-Term RL (LTRL) challenges is formidable. Consequently, RL agents are predominantly trained with expert guidance. Drawing on the principles of ordinal utility theory from economics, we propose a novel reward estimation algorithm: ELO-Rating based RL (ERRL). This approach is distinguished by two main features. Firstly, it leverages expert preferences over trajectories instead of cardinal rewards (utilities) to compute the ELO rating of each trajectory as its reward. Secondly, a new reward redistribution algorithm is introduced to mitigate training volatility in the absence of a fixed anchor reward. Our method demonstrates superior performance over several leading baselines in long-term scenarios (extending up to 5000 steps), where conventional RL algorithms falter. Furthermore, we conduct a thorough analysis of how expert preferences affect the outcomes. © 2024 IEEE.

关键词： Reinforcement learning

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An inter-community epidemic model incorporating population mobility and control measures 4

An inter-community epidemic model incorporating population m...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Zhang, Jiamin Ouyang, Min Hong, Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510681637

Continued outbreaks of infectious diseases have underscored the urgency of devising and implementing effective control measures to mitigate infection rates. Among the strategies employed, the isolation of communities harboring potential infection vectors stands out as a key method to curtail the spread of such diseases. In this vein, this study introduces a refined SEAIR (Susceptible, Exposed, Asymptomatic, Infectious, and Recovered) transmission model that integrates contact patterns determined by inter-community population movements. The robustness of the model is evidenced by its strong fit with empirical infection data. The results underscore a pronounced degree of interaction within individual neighborhoods, underscoring the critical role of targeted local public health strategies and interventions. The model predicts that heightened isolation measures within communities experiencing infections can effectively control the spread of infectious diseases within three weeks. This finding accentuates the pivotal role of isolation at the community level as a viable public health response to infectious disease outbreaks. © 2024 SPIE.

关键词： COVID-19

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Empowering E-Commerce Decisions Through AI-Enhanced Review Analytics

Empowering E-Commerce Decisions Through AI-Enhanced Review A...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Tembhurnikar, Atharv Rahinj, Shreyash Mali, Siddhesh Desai, Vishwajeet Yadav, Gitanjali Vishwakarma Institute of Information Technology Dept. of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331527495

Due to huge customer market product reviews are large in number and complicated. Thus, understanding the actual essence of the feedback is difficult. The system proposed by us is complex but it tackles the issues faced by previous systems. The paper serves as guideline for creating such system. For gathering data the method of web scraping is used, followed by preprocessing data using Natural Language Processing (NLP) techniques. For prediction purpose deep learning models LSTM and Convolutional Neural Networks (CNNs) are used along with Classification model XGBoost. Through our system we have presented the results visually so that consumers as well as Business stakeholders can take actionable insights. The system has great potential in variety of sectors as it can capture the sentiment of the reviews for a product and also its genuineness with precise accuracy. It has achieved fabulous training accuracy of 89.19% and validation accuracy of 81.49%. It represents the true sentiment of the customer feedback eliminating the fake reviews. © 2025 IEEE.

关键词： Emotion Recognition

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Short-Term Residential Load Forecasting via Pooling-Ensemble Model with Smoothing Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第7期5卷 3690-3702页

作者： Xiao, Jiang-Wen Fang, Hongliang Wang, Yan-Wu Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this article proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole short-term load forecasting framework in this article contains three steps. Specifically and first, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Second, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Third, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2020 IEEE.

关键词： Probability distributions

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Integrated Farm Security System

Integrated Farm Security System

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Franciska, W Arockia Harshavardhini, C. Arockia Shermila, S. Beulah Patturose, J Gold St.Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798331529833

India's agriculture, the nation's largest industry, faces significant challenges due to current economic conditions, with crop loss from man-wildlife conflicts being a pressing issue. Traditional methods like employing guards have proven inadequate and unsustainable. This technical abstract proposes a technology-driven solution to safeguard crops, wildlife, and human welfare. By deploying an advanced setup integrating machine learning and computer vision, the system can detect and identify animals and humans in agricultural areas, enabling proactive measures to prevent crop damage. Sophisticated algorithms differentiate between various threats, allowing targeted, ethical responses that prioritize coexistence. This approach addresses immediate challenges while contributing to the long-term sustainability of agriculture and biodiversity conservation. Continuous monitoring and collaboration among farmers, wildlife conservationists, and technology experts aim to establish a holistic framework for managing man-wildlife conflicts in Indian agriculture, ensuring the well-being of both humans and wildlife. © 2025 IEEE.

关键词： Smart agriculture

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Accuracy Assessment and Calibration of a 6-DOF Robotic Arm Through Simulation data Analysis 6th

Accuracy Assessment and Calibration of a 6-DOF Robotic Arm T...

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6th International Conference on Computational intelligence in Pattern Recognition, CIPR 2024

作者： Kailash, M. Jones, Aida Babu, K. Dinesh Praveen, S. Department of Artificial Intelligence and Data Science KCG College of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

This study focuses on refining the real-time positional accuracy of a 6-degree-of-freedom (6-DOF) robotic arm’s end effector by comparing the actual X, Y, and Z axis positions with those calculated from joint angles using DH parameters. Through the application of linear regression analysis, variations between these datasets are identified and quantified. The derived variations are then used to adjust the DH parameter-based calculations, resulting in significantly improved accuracy for the robotic arm’s end-effector positioning. This approach offers a systematic method for enhancing the precision of calculated positional data, thereby contributing to the advancement of robotic arm operations across diverse fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Degrees of freedom (mechanics)

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Healthcare data Protection and Biometric Access on Cloud Platforms 6

Healthcare Data Protection and Biometric Access on Cloud Pla...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Gayathri, S. Devaraj, S. Muthupandi, B. Vishwaraj, S. Department of Artificial Intelligence and Data Science K S Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798331522667

This paper discusses about various fingerprint recognition methods, each founded on different principles, in search of a robust and efficient technique. The research concentrates on designing an easy yet efficient alternative for fingerprint recognition. The technique is divided into two main stages: the first is the acquisition of human fingerprint samples, and the second is the development and implementation of an advanced fingerprint recognition system. One of the features of the method is feature extraction using multiple iterations of the two-dimensional discrete cosine transform (DCT) to extract robust features. This method combines thumbprints from both hands for better recognition. The experiments show that the proposed technique is capable of yielding a good recognition accuracy. © 2025 IEEE.

关键词： Cloud platforms

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