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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Tong Krstic, Miroslav Jiang, Zhong-Ping New York University Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering BrooklynNY11201 United States University of California Department of Mechanical and Aerospace Engineering San DiegoCA92093-0411 United States

ISBN: (纸本)9798350316339

This paper studies the distributed feedback optimization problem for linear multi-agent systems without precise knowledge of local costs and agent dynamics. The proposed solution is based on a hierarchical approach that uses upper-level coordinators to adjust reference signals toward the global optimum and lower-level controllers to regulate agents' outputs toward the reference signals. In the absence of precise information on local gradients and agent dynamics, an extremum-seeking mechanism is used to enforce a gradient descent optimization strategy, and an adaptive dynamic programming approach is taken to synthesize an internal-model-based optimal tracking controller. The whole procedure relies only on measurements of local costs and input-state data along agents' trajectories. Moreover, under appropriate conditions, the closed-loop signals are bounded and the output of the agents exponentially converges to a small neighborhood of the desired extremum. A numerical example is conducted to validate the efficacy of the proposed method. © 2024 IEEE.

关键词： Dynamic programming

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Fatigue Analysis in Assembly Line Workers Using Brain computer Interface

Fatigue Analysis in Assembly Line Workers Using Brain Comput...

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IEEE Southeastcon

作者： Faraz Ahmad Maohua Liu Fred R. Beyette School of Electrical and Computer Engineering College of Engineering University of Georgia Athens USA

In contemporary industrial manufacturing, the assembly line remains a pivotal component, offering significant employment opportunities for human workers engaged in the production of a broad spectrum of products. The efficiency of assembly line operations is intricately tied to the effectiveness of the workforce, particularly in executing tasks that involve repetitive and often strenuous movements, predominantly using the arms. The resultant physical and mental fatigue poses a substantial challenge, diminishing productivity and overall operational efficiency. This paper addresses the pressing issue of worker fatigue on assembly lines, attributing considerable economic costs and safety concerns to this pervasive problem. Current methods for evaluating fatigue, such as questionnaire surveys, prove inadequate due to their inherent inefficiencies and susceptibility to under-reporting by workers. To tackle these limitations, there is a critical need for a real-time fatigue assessment device capable of capturing the moment-to-moment impact of fatigue on assembly line workers. This research project proposes the utilization of Brain-computer Interface (BCI) technology, specifically employing the Event-Related Potential (ERP) technique with Electroencephalography (EEG) technology. The focus is on identifying the P300 ERP signal, known to alter the onset of fatigue. By leveraging an ambulatory EEG system, this non-invasive approach enables the real-time capture of ERP signals as assembly line workers execute their routine tasks. This methodology aims to overcome the biases associated with post-shift surveys, providing a more accurate and immediate assessment of worker fatigue.

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Real-Time Anomaly Detection in Large-Scale Sensor networks using Isolation Forests

Real-Time Anomaly Detection in Large-Scale Sensor Networks u...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Rawat, Ruchira Al-Attab, Kassem Dixit, Krishna Kant Deepak, A. Pushkarna, Gaurav Harikrishna, M. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Electrical Engineering Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research delves into real-time anomaly detection in enormous-scope sensor networks, employing Isolation Forest, One-Class SVM, Local Outlier Factor, and Recursive Partitioning algorithms. The review features Isolation Forest as a champion entertainer, accomplishing an accuracy of 0.92 and a review of 0.88. One-Class SVM and Local Outlier Factor display cutthroat abilities with accuracy upsides of 0.87 and 0.89 , and review upsides of 0.82 and 0.85 , individually. Recursive Partitioning, stressing interpretability, keeps a good accuracy of 0.82 and a review of 0.76. The examination stretches out to related work, investigating imaginative methodologies, for example, Subspace Occasional Grouping, profound exchange learning for modern shortcoming findings, and anomaly detection in unambiguous spaces like lodgings and microservicebased frameworks. Multimodal information combination, AI for 5G-based IoT network security, and novel structures like TCFTrans add to the rich scene of anomaly detection. © 2024 IEEE.

关键词： Anomaly detection

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： network coding

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PerSHOP - A Persian dataset for shopping dialogue systems modeling

arXiv

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arXiv 2024年

作者： Mahmoudi, Keyvan Faili, Heshaam School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Nowadays, dialogue systems are used in many fields of industry and research. There are successful instances of these systems, such as Apple Siri, Google Assistant, and IBM Watson. Task-oriented dialogue system is a category of these, that are used in specific tasks. They can perform tasks such as booking plane tickets or making restaurant reservations. Shopping is one of the most popular areas on these systems. The bot replaces the human salesperson and interacts with the customers by speaking. To train the models behind the scenes of these systems, annotated data is needed. In this paper, we developed a dataset of dialogues in the Persian language through crowd-sourcing. We annotated these dialogues to train a model. This dataset contains nearly 22k utterances in 15 different domains and 1061 dialogues. This is the largest Persian dataset in this field, which is provided freely so that future researchers can use it. Also, we proposed some baseline models for natural language understanding (NLU) tasks. These models perform two tasks for NLU: intent classification and entity extraction. The F-1 score metric obtained for intent classification is around 91% and for entity extraction is around 93%, which can be a baseline for future research. © 2024, CC BY-NC-ND.

关键词： Crowdsourcing

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Robot Wheelchair Convoys for Assistive Human Transportation

Robot Wheelchair Convoys for Assistive Human Transportation

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Smart Mobility (SM), IEEE International Conference on

作者： Helio Perroni Filho Jenny Ren Mohammad Akhavan Yuqian Hou Walleed Khan Arian Haghparast Julian Forsyth Kimia Rajaeifar Michael Jenkin James H. Elder Department of Electrical Engineering & Computer Science Lassonde School of Engineering York University Toronto Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350390131

ISBN: (纸本)9798350390148

We present AirChair, a semi-autonomous human transportation system composed of multiple wheelchairs operating as a convoy. The first wheelchair follows an on-foot human guide, the second wheelchair follows the first, and so on. Each wheelchair independently tracks its target with the help of an RGBD camera, and performs motion planning to follow along while steering clear of obstacles. The guide manages the convoy through a mobile control interface, allowing them to intervene as needed to ensure passenger safety. The effectiveness of combining automation technologies with an engaged operator is demonstrated by experiments in uncontrolled, real-world environments, which also suggest directions for further development.

关键词： Automation Target tracking Filtering Wheelchairs System performance Transportation Switches Cameras Safety Mobile robots

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Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

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computers, Materials & Continua 2023年第7期76卷 665-686页

作者： Wadhah Mohammed M.Aqlan Ghassan Ahmed Ali Khairan Rajab Adel Rajab Asadullah Shaikh Fekry Olayah Shehab Abdulhabib Saeed Alzaeemi Kim Gaik Tay Mohd Adib Omar Ernest Mangantig School of Computer Sciences Universiti Sains MalaysiaUSM11800PenangMalaysia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia86400JohorMalaysia IPPT Universiti Sains MalaysiaUSM11800PenangMalaysia

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood *** severe forms,it can lead to *** genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for ***,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia *** Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health *** individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the *** effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main *** represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social *** contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform *** this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,*** tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.

关键词： Social media platform Twitter screening thalassemia lexicon-based VADER

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Yoga in digital Age: A Hybrid Approach using ML and computer vision 11

Yoga in digital Age: A Hybrid Approach using ML and Computer...

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11th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Achaiah, K.K. Pramod, Arjun S. Tesfahun, Abebe Department of Computer Science Amrita School Of Computing Amrita Vishwa Vidyapeetham Mysuru campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

—In today’s fast-paced world, stress and pressure affect people of all ages, both physically and mentally. Yoga has emerged as a highly sought-after solution due to its numerous benefits. In the present technology-dependent era of digital age, it’s only natural that we turn to technology for assistance in their yoga journey. This is where the proposed article comes into play, serving as one’s digital Yoga Guru. In this paper, seven classifiers, out of which six are Machine Learning classifiers namely Support Vector Machine, Naïve Bayes, Random Forest, K-Nearest Neighbor, Logistic Regression, and Ensembler are employed to classify the dataset. These classifiers exhibit a promising average accuracy exceeding 90%. Additionally, the proposed method incorporates a Deep Learning model, specifically the Multi-Perceptron Neural network, which has been trained to predict outputs with an impressive accuracy of 92%. This Paper utilizes the power of MediaPipe, a cutting-edge technology that plays a pivotal role in this innovative approach to yoga. MediaPipe is capable of identifying individuals on the screen and provides 33 key-points, including critical markers like facial key-points, and main keypoints of the body such as shoulders, elbows, knees, feet, wrists, and etc. These key points are essential for pose recognition, and assessing the correctness of yoga postures. What sets the proposed method apart is its real-time, live tracing feature, boasting an impressive accuracy rate of 99%. As one engages in yoga practice, this technology tracks their movements, and compares them to the ideal yoga postures. This immediate feedback ensures that one performs each pose correctly, promoting better results and minimizing the risk of injury. In essence, the proposed method bridges the gap between ancient yoga wisdom and modern technology, offering a digital Yoga Guru that guides one to achieve physical and mental well-being with precision and confidence. ©2023 IEEE.

关键词： Decision trees

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Enhance Image-to-Image Generation with LLaVA-generated Prompts 5

Enhance Image-to-Image Generation with LLaVA-generated Promp...

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5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Ding, Zhicheng Li, Panfeng Yang, Qikai Li, Siyang Columbia University Fu Foundation School of Engineering and Applied Science New York United States University of Michigan Department of Electrical and Computer Engineering Ann Arbor United States University of Illinois Urbana-Champaign Department of Computer Science Urbana United States Pace University Lubin School of Business New York United States

ISBN: (纸本)9798350352719

This paper presents a novel approach to enhance image-to-image generation by leveraging the multimodal capabilities of the Large Language and Vision Assistant (LLaVA). We propose a framework where LLaVA analyzes input images and generates textual descriptions, hereinafter LLaVA-generated prompts. These prompts, along with the original image, are fed into the image-to-image generation pipeline. This enriched representation guides the generation process towards outputs that exhibit a stronger resemblance to the input image. Extensive experiments demonstrate the effectiveness of LLaVA-generated prompts in promoting image similarity. We observe a significant improvement in the visual coherence between the generated and input images compared to traditional methods. Future work will explore fine-tuning LLaVA prompts for increased control over the creative process. By providing more specific details within the prompts, we aim to achieve a delicate balance between faithfulness to the original image and artistic expression in the generated outputs. © 2024 IEEE.

关键词： Visual languages

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Automatic Identification of Spinal Vertebrae in CT Scan Images Using A Hybrid ResNet + U-Net Neural network

Automatic Identification of Spinal Vertebrae in CT Scan Imag...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Nima Namazi Reza A. Zoroofi School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

In medical diagnostics, particularly in the analysis of medical images, computational and automated methods have been employed to perform various tasks. Over the past few years, the use of artificial intelligence and machine learning-based methods has significantly improved the accuracy and effectiveness of medical diagnostics. This research presents an automated approach for identification and labeling spinal vertebrae in CT Scan images. Deep learning approaches are classified into 2D and 3D methods, each with its own strengths and computational considerations. While 3D methods often offer higher accuracy, they require more computational resources. To reduce this, creating 2D representations of 3D CT scan data has emerged as an effective strategy, narrowing the performance gap between 2D and 3D approaches. The neural network architecture presented in this study is a combination of ResNet and U-Net structures, both suitable for medical image applications. Their suitability stems from the ability to create deeper networks that mitigate the vanishing gradient problem. These structures introduce skip connections between network layers, enabling the simultaneous flow and use of information from different image dimensions. This work facilitates the use of information from all stages of downsampling. To evaluate the performance of the proposed network, the recognition rate metric was used, showing superior performance with a rate of $\mathbf{9 1 . 4 2 \%}$ compared to J-CNN, DI2IN, and BtrflyNet models in the labeling task. Notably, the integration of long and short skip connections positively impacts network performance.

关键词： Learning systems Deep learning Three-dimensional displays Accuracy Computed tomography Neural networks computer architecture Labeling Medical diagnosis Medical diagnostic imaging

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