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Adaptive finite-time command-filtered backstepping sliding mode control for stabilization of a disturbed rotary-inverted-pendulum with experimental validation

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JVC/Journal of Vibration and Control 2023年第5-6期29卷 1431-1446页

作者： Mofid, Omid Alattas, Khalid A Mobayen, Saleh Vu, Mai The Bouteraa, Yassine Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia University of Sfax Sfax3038 Tunisia

In this paper, the finite-time stabilization of the disturbed and uncertain rotary-inverted-pendulum system is studied based on the adaptive backstepping sliding mode control procedure. For this purpose, first of all, the dynamical equation of the rotary-inverted-pendulum system is obtained in the state-space form in the existence of external disturbances and model uncertainties with unknown bound. Afterward, a novel command filter is defined to enhance the control strategy by consideration of a virtual control input. Therefore, the differential signal is replaced by the output of the command filter to reduce the complicated computing in the control process. Hence, the finite-time convergence of the sliding surface to the origin is attested by using the backstepping sliding mode control scheme according to the Lyapunov theory. Besides, the unknown upper bound of the exterior perturbation and uncertainty is approximated providing the adaptive control technique. Finally, simulations and experimental results are done to demonstrate the impression and proficiency of the suggested method. © The Author(s) 2022.

关键词： Inverted pendulum

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Real-Time Non-Gaussianity Evaluation for Anomaly Detection in Noisy Power Grid Data Stream 7

Real-Time Non-Gaussianity Evaluation for Anomaly Detection i...

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7th International Conference on Energy Conservation and Efficiency, ICECE 2024

作者： Liu, Maohua Ahmad, Faraz Ahmad, Irshad Beyette, Fred R. School of Electrical and Computer Engineering College of Engineering University of Georgia Athens30602 United States University of Engineering and Technology Department of Electrical Engineering Lahore38600 Pakistan

ISBN: (纸本)9798350370898

In response to concerns regarding energy sustainability and increasing energy demand, a substantial number of renewable energy systems (RESs) are being integrated into the power grid. Unlike conventional power plants that can be adjusted to meet demand, RESs rely on weather conditions, introducing numerous unstable factors and abnormal events. Anomaly detection is a critical data analysis task, beneficial for pinpointing grid irregularities, failures, or intrusions. By examining anomaly patterns in integrated energy systems, a deeper comprehension of their operational status can be attained, leading to enhanced reliability and efficiency of the power grid. This paper suggests a statistical algorithm based on assessing nonGaussianity. In contrast to computationally expensive learning-based anomaly detection, the proposed algorithm can efficiently detect anomalies by evaluating kurtosis and skewness of the time series. It proves to be both time and resource-efficient, making it suitable for real-time and resource-constrained scenarios. © 2024 IEEE.

关键词： Anomaly detection

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Topic Modeling Based Clustering of Disaster Tweets Using BERTopic

Topic Modeling Based Clustering of Disaster Tweets Using BER...

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2024 MIT Art, Design and technology school of Computing International Conference, MITADTSoCiCon 2024

作者： Rachel J, Julanta Leela Bhuvaneswari, A. Kumudha, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India T.John Institute of Technology Dept. of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350362879

The essence of catastrophe management is in the capacity to promptly and effectively addressing unforeseen circumstances, as well as to implement premeditated and flexible strategies in accordance with established protocols and norms. Twitter has become a prominent means of disaster communication, particularly in nations where it has a substantial presence. Twitter can provide valuable information regarding the effects of infrastructure on emergency management in the event of significant disasters. However, the process of sifting through numerous unrelated tweets can be laborious and time-consuming. Prior research has successfully recognized the various categories of communications present on social media platforms during times of disaster. However, there is a dearth of recommended methodologies aimed at effectively extracting valuable information from these messages. In this study, we propose a framework that may be efficiently utilized to promptly deliver disaster effect information derived from social media sources. The methodology involves the application of certain techniques to process the unprocessed Twitter data by employing keyword filtering, location-based filtering, and analysis of tweet properties. Subsequently, Bidirectional Encoder Representations from Transformers (BERT) technique is utilized to categorize the tweets originating from disaster-Affected regions into distinct subjects that are relevant and valuable for emergency management. BERT analysis yielded a total of 376 themes within the dataset. Among these topics, a significant proportion of 196 were found to be closely associated with the impacts of disasters. These impacts encompassed many aspects such as outages, closures, flooded roadways, and damaged infrastructure. © 2024 IEEE.

关键词： Disasters

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Trajectory Design and Resource Allocation for UAV-Assisted Computation Offloading 7

Trajectory Design and Resource Allocation for UAV-Assisted C...

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7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Xu, Yunxin Wang, Jiale Wang, Junhao Que, Xingle Lu, Dandan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350350760

This paper studies air-ground cooperative communication based on unmanned aerial vehicles (UAVs) and device-to-device (D2D) technology. This field has become a research hotspot due to its susceptibility to passive eavesdropping and privacy leakage. To enhance data transmission security, this paper proposes a full-duplex UAV-assisted scheme to address potential ground eavesdropping risks in UAV-assisted computation offloading tasks. The scheme transmits interference signals while receiving computation tasks to counteract eavesdroppers. Given the limited energy of UAVs and the inadequacies of existing energy optimization research, this paper establishes a UAV-assisted mobile edge computing (MEC) communication system model. To address the non-convex optimization problem within the model, a successive convex approximation algorithm is employed for simplification and decomposition. User scheduling, power allocation, and UAV trajectory design subproblems are solved through an alternating iteration method. Simulation results validate that the proposed method can effectively improve the system's secure communication performance even in the presence of uncertain eavesdropper locations. ©2024 IEEE.

关键词： Convex optimization

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PartDensE: Knowledge Graph Embedding with Joint Use of Specific Learning and Relation-aware Functions 6

PartDensE: Knowledge Graph Embedding with Joint Use of Speci...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Dan, Qiuyue Cui, Delong Peng, Zhiping Li, Qirui He, Jieguang Qiu, JinBo Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China Guangdong University of Petrochemical Technology School of Electronic and Information Engineering Maoming China Jiangmen Polytechnic Jiangmen China Guangdong University of Petrochemical Technology School of Computer Maoming China

ISBN: (纸本)9798350386974

Knowledge Graph Embedding (KGE) is important to the value of the Knowledge Graph (KG) in its application field. Currently, neural network-based models achieve the most advanced performance. However, these models rarely consider the potential information between entities and relations in KGs, and also suffer from the problems of complex structure and high consumption of computing resources. In this paper, we propose the simple and efficient model PartDensE by using dense network, combining specific sharing layer and relational awareness layer. Specifically, our model learns all entities and relationships individually and can mine hidden features to enrich global information. Also, learn all the information based on each relation and be able to accurately establish associations between entities and relations. PartDensE can capture global and local features and displayed and hide information, retaining as much valuable information as possible. We evaluated PartDensE with 4 indicators across on two baseline datasets. Numerous experimental data prove the effectiveness of our model. © 2024 IEEE.

关键词： Knowledge graph

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Aorta localization in Computed Tomography images: A YoloV9 segmentation approach 9

Aorta localization in Computed Tomography images: A YoloV9 s...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Smyrlis, Panagiotis N. Stancu, Gabriela R. Dimaraki, Foteini Ntetska, Aimilia Karamitsou, Thomai Tzimourta, Katerina D. Tsipouras, Markos G. Angelidis, Pantelis University of Western Macedonia Laboratory of Biomedical Technology and Digital Health School of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350342482

In this study, state-of the art segmentation methods were tested for use in biomedical CTA images to precisely highlight aorta instances. A set of YOLO architectures was trained on a newly annotated dataset of 120 transverse view images and detailed cross-validation results were obtained to measure the detection and precise localization ability of the models. The recent YOLOV9 algorithm flavors V9-C and V9-E were studied and compared to the previous V8-X model. The V9 version did score remarkable results to Mean Average Precision metrics, achieving 76.15% segmentation mAP50-95 score in the YOLOV9-C testing. © 2024 IEEE.

关键词： Convolutional neural networks

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Women Infertility: A Systematic Review of Effects and Causes

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SN computer Science 2023年第2期4卷 1-11页

作者： Sahana Devi, K.J. Yendapalli, Vamsidhar Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM University Karnataka Bengaluru India

Recently, infertility has been affecting a large number of women than men. It is the male or female reproductive system’s disease. Consequently, after 12 months or more of usual insecure sexual intercourse, if they fail to attain pregnancy then that failure is defined as infertility. As per the National Institute of Child Health and Human Development, people in the USA have infertility issues that are found in 11% of women along with 9% of men. Women are more fertile in their 20s, while in their 30s it reduces to half of it. After 35 years, the probability of getting pregnant is diminished in women. For the woman to get pregnant, the proper functioning of the ovaries, fallopian tubes, and uterus is required. So, infertility occurs due to any issues with the above body parts. Women's infertility is caused by many factors, but the most vital cause is polycystic ovary syndrome (PCOS), which is a hormonal disorder commonly found among reproductive-aged women. So, women infertility, effects of women infertility, causes of women infertility, PCOS and detection of PCOS by general and machine learning (ML) techniques had been discussed in this paper. The accuracy attained in the detection of PCOS utilizing ML techniques is analyzed. Among people as of the top five responding countries that incorporate the USA, UK, Australia, India, together with the Philippines, the percentage of women with physician-assured PCOS against women exclusive of PCOS is analyzed. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Causes Detection of PCOS Effects ML techniques PCOS Women infertility

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BanijyaPrayukti: E-Commerce Recommendation Engine for Sustainable Products 7

BanijyaPrayukti: E-Commerce Recommendation Engine for Sustai...

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7th IET Smart Cities Symposium, SCS 2023

作者： Pradhan, Aditya Ghosh, Amartya Mondal, Rintu Saxena, Kumar Abhi Dutta, P.K. Mitra, Anirban Dept. of Computer Science and Engineering Amity School of Engineering and Technology Kolkata Kolkata India Dept. of Electronics and Communication Engineering Amity School of Engineering and Technology Kolkata Kolkata India

ISBN: (纸本)9781839539831

The rapid growth of e-commerce has revolutionized the way businesses operate and interact with customers. E- commerce has revolutionized the way businesses operate, making it easier than ever to reach customers and sell products. However, with this convenience comes a challenge: how can we promote sustainable products and encourage consumers to make eco- friendly choices? The primary objective of this project is to pro- mote sustainability by utilizing artificial intelligence techniques in recommending eco-friendly alternatives. Our goal is to provide users with recommendations on sustainable products based on their shopping history, behavior patterns, and demographic information. The system continuously learns from user interactions, enabling it to provide increasingly accurate and personalized recommendations over time. © The Institution of engineering & technology 2023.

关键词： Electronic commerce

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M-GhostNet: A Lightweight CNN Model Combined with Coordinate Attention Mechanism for Identifying Pests and Diseases 12

M-GhostNet: A Lightweight CNN Model Combined with Coordinate...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Ma, Chi Xiao, Yu Hu, Hui Li, Jingyan Wu, Jie The School of Computer Science and Engineering Huizhou University Huizhou516007 China The School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9798400707988

Plant pests and diseases have always been an important factor affecting agricultural production, and how to automatically identify them is one of the current research hotspots. Although traditional deep learning models can achieve good results, they are difficult to deploy into small devices due to their large number of parameters. In this paper, we design a lightweight convolutional neural network M-GhostNet based on the coordinate attention mechanism, which improves the bottleneck structure in the original GhostNet and and effectively extracts the spatial information of the image. We compare and ablate the M-GhostNet model on the IP102 dataset and the Embrapa dataset. Compared with the existing lightweight neural networks, M-GhostNet achieves the best results of 67.15%, 65.04%, and 71.41% on the IP102 dataset, respectively, and 92.67%, 95.65%, 93.74%, and 96.52% on the Embrapa dataset in terms of Mrec, Mpre, MF1, and Acc. The final results show that M-GhostNet can achieve good performance in both pest and disease identification. © 2023 ACM.

关键词： Convolutional neural networks

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A Hybrid UNet Model Incorporating Attention Mechanism for Adaptive Brain Tumor Magnetic Resonance Segmentation 7

A Hybrid UNet Model Incorporating Attention Mechanism for Ad...

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7th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： He, Weihua Gopalakrishnan, Anilkmar Kothalil Vincent Mar School of Engineering Science and Technology Assumption University Department of Computer Science Bangkok10240 Thailand

ISBN: (纸本)9798350370782

The segmentation of medical images is crucial, particularly in brain tumor MR imaging, as it aids doctors in accurate diagnosis and treatment planning. However, conventional UNet models often face limitations due to their inability to capture intricate features, resulting in suboptimal segmentation performance. This study addresses these challenges by proposing a UNet model enhanced with a Convolutional Block Attention Module (CBAM). This advanced network leverages spatial and channel attention mechanisms that work together to focus more effectively on important image regions, refining feature representation. Experiments conducted on the TCGA brain tumor MR dataset demonstrate that our proposed UNet+CBAM model outperforms the standard UNet, achieving a 2.5% improvement in the Dice coefficient. This significant enhancement underscores the efficacy of integrating attention mechanisms into medical image segmentation. We conclude that our UNet+CBAM model offers an improved and reliable solution for brain tumor segmentation, potentially advancing its clinical application. © 2024 IEEE.

关键词： Magnetic resonance imaging

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