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3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Putra, Karisma Trinanda Surahmat, Indar Nazilah Chamim, Anna Nur Ramadhan, Muhammad Zhafran Wicaksana, Darma Dhea Namyra Alissa, Raden Ajeng Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia

ISBN: (纸本)9798350327762

Diabetes mellitus is a prevalent health condition affecting approximately one in eight individuals worldwide. Insufficient management of this disease can lead to various medical complications, including vision loss and limb amputation. Continuous glucose monitoring (CGM) is crucial for effective diabetes management. However, current one-time glucose monitoring methods still rely on invasive techniques, causing pain and lacking continuous monitoring capability. This study aims to develop a non-invasive technology for measuring blood glucose levels that can be conveniently monitored through a smartphone using wireless communication. The device utilizes an ESP32 microprocessor and a PPG MAX 30102 sensor capable of recording pulse waves, a biomarker, at the fingertips. In this preliminary study, non-invasive data from 22 participants were used and divided into three classes according to conventional invasive measurements. artificial intelligence (AI) methods, specifically Convolutional Neural Networks (CNN) and artificial Neural Networks (ANN) models, were utilized to achieve convergence between the conventional invasive measurements and the proposed approach. The trained model demonstrated the highest accuracy of 0.944, with a minimum model loss of 0.177. In the validation phase, the proposed approach proved its reliability by achieving an average accuracy of 0.750. The non-invasive approach has the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly enhancing diabetes management and improving the overall quality of life for those affected by this condition. © 2023 IEEE.

关键词： Glucose

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Design and Calibration of Dual Sensor: Towards Simultaneous Instrument Tracking and Deformable Shape Sensing in Computer-Integrated Surgery 46

Design and Calibration of Dual Sensor: Towards Simultaneous ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Jeon, Sangseo Hnin, Khaing Thandar Jung, Yong Gi Lim, Sunghwan Korea Institute of Science and Technology Center for Healthcare Robotics Artificial Intelligence and Robotics Institute Seoul02792 Korea Republic of Sungkyunkwan University School of Medicine Samsung Medical Center Department of Otorhinolaryngology - Head and Neck Surgery Seoul06351 Korea Republic of

ISBN: (纸本)9798350371499

We introduce a novel integration of vision sensors in the form of a dual sensor system. The dual sensor system combines three distinct units: an optical localizer and two RGB-D sensors. This system is uniquely capable of concurrently providing real-time 3D shape information for deformable objects and accurate pose tracking data for medical instruments. This capability positions the dual sensor with immense potential for application in image-guided surgical navigation, robotic systems, and robot-assisted automation for surgical procedures involving deformable target objects. This paper provides an overview of the design, fabrication and a one-time calibration methodology of the dual sensor. An experiment was performed to assess the calibration accuracy of the dual sensor. Experimental results indicated that the dual sensor system delivers a level of accuracy that renders it suitable for deployment within the envisioned scope of *** relevance - The presented dual sensor provides real-time shape scanning of intra-operatively deforming objects and instrument tracking capabilities simultaneously, contributing to successful outcomes of diverse computer-integrated surgeries dealing with non-rigid surgical targets such as breast, face, and incised skin area © 2024 IEEE.

关键词： Robotic surgery

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Video Tracking and Auxiliary Obstacle Avoidance of Power Plant Inspection Robot based on Deep Learning and 3D Digital Twin System 6

Video Tracking and Auxiliary Obstacle Avoidance of Power Pla...

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6th International Conference on robotics, Intelligent Control and artificial intelligence, RICAI 2024

作者： Chen, Quanjie Lin, Qing Liu, Weitao Liu, Xuanyi Jia, Xiaoyan Yu, Rongdong Wang, Zhan Department of Artificial Intelligence and Robotics Zhejiang Energy Digital Technology Co. Ltd Hangzhou China Department of Engineering Delivery Center Zhejiang Energy Digital Technology Co. Ltd Hangzhou China

ISBN: (纸本)9798331541699

The highly intricate landscape of power plant equipment and personnel demands sophisticated solutions, rendering robot-based automation control and the digitalization of production information indispensable for the realization of plant intelligence and smart operational paradigms. Traditional inspection robots, despite their proficiency in gathering information and executing predefined tasks, confront substantial hurdles in terms of adaptability and robustness within the confines of complex environments. The limitations of onboard sensors, such as the narrow fields of view offered by chassis-mounted devices and head-mounted cameras, result in the creation of numerous blind spots that make the robots vulnerable to collisions. Additionally, these robots are frequently perceived as standalone entities, rather than as integral components of the intricate power plant ecosystem. In response to these challenges, this article introduces an innovative control method that leverages the refined Unicorn video tracking algorithm. This algorithm provides real-time tracking, obstacle warnings, and collision pre-alerts for inspection robots in edge cases and unforeseen situations, thereby significantly enhancing their adaptability and preprocessing capabilities in complex environments. Furthermore, the process of collecting and annotating virtual datasets has been greatly simplified through the utilization of a 3D digital twin system, which facilitates the preparation of training datasets with minimal effort. © 2024 IEEE.

关键词： Electric power plant equipment

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Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning

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Chinese Journal of Chemical Engineering 2022年第9期35卷 234-244页

作者： Jia Ren Zengqiang Chen Mingwei Sun Qinglin Sun Zenghui Wang College of Artificial Intelligence Nankai UniversityTianjin 300350China Key Lab of Intelligent Robotics of Tianjin Tianjin 300350China Department of Electrical and Mining Engineering University of South AfricaFlorida 1710South Africa

The high-purity distillation column system is strongly nonlinear and coupled,which makes it difficult to *** disturbance rejection control(ADRC)has been widely used in distillation systems,but it has limitations in controlling distillation systems with large time delays since ADRC employs ESO and feedback control law to estimate the total disturbance of the system without considering the large time *** paper designs a proportion integral-type active disturbance rejection generalized predictive control(PI-ADRGPC)algorithm to control the distillation column system with large time *** replaces the PD controller in ADRC with a proportion integral-type generalized predictive control(PI-GPC),thereby improving the performance of control systems with large time *** the proposed controller has many parameters and is difficult to tune,this paper proposes to use the grey wolf optimization(GWO)to tune these parameters,whose structure can also be used by other intelligent optimization *** performance of GWO tuned PI-ADRGPC is compared with the control performance of GWO tuned ADRC method,multi-verse optimizer(MVO)tuned PI-ADRGPC and MVO tuned *** simulation results show that the proposed strategy can track reference well and has a good disturbance rejection performance.

关键词： Proportion integral-type active disturbance rejection generalized predictive control Grey wolf optimization Parameter tuning Distillation Process control Prediction

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Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding (Extended Abstract) 17th

Traffic Flow Optimisation for Lifelong Multi-Agent Path Find...

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17th International Symposium on Combinatorial Search, SoCS 2024

作者： Chen, Zhe Harabor, Daniel Li, Jiaoyang Stuckey, Peter J. Department of Data Science and Artificial Intelligence Monash University Melbourne Australia Robotics Institute Carnegie Mellon University Pittsburgh United States OPTIMA Australian Research Council ITTC Melbourne Australia

ISBN: (纸本)9781577358916

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics that asks us to compute collision-free paths for a team of agents, all moving across a shared map. Existing scalable approaches struggle as the number of agents grows, as they typically plan free-flow optimal paths, which creates congestion. To tackle this issue, we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths. Empirically, we report large improvements in overall throughput for lifelong MAPF while coordinating more than ten thousand agents. © 2024, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Multi agent systems

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Histogram of Oriented Gradients Feature Extraction in Enhanced Convolutional Neural Networks Method for Palm Vein Recognition 4

Histogram of Oriented Gradients Feature Extraction in Enhanc...

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4th International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2024

作者： Wulandari, Meirista Irmawati Hayati, Nur Universitas Tarumanagara Department of Electrical Engineering Jakarta Indonesia Universitas Multimedia Nusantara Department of Information System Tangerang Indonesia Universitas Muhammadiyah Yogyakarta Center of Artificial Intelligence and Robotics Studies Department of Electrical Engineering Bantul Indonesia

ISBN: (纸本)9798350378368

Histogram of Oriented Gradients, or HOG, is an efficient method for extracting features from images that shows to be reliable in recognizing and detecting objects in a variety of conditions. HOG is a very useful tool in image analysis because it is invariant to brightness, rotation, and scale transformations and can generate detailed local descriptors. By exploiting the advantages of HOG in feature extraction, the palm vein recognition system can overcome the weaknesses of palm vein images, which are sensitive to changes in brightness and rotation. By analyzing the distribution of orientation gradients, HOG can extract important features from palm vein images generated from infrared light, enabling reliable biometric recognition. We proposed a method applying HOG into palm vein biometric system with Convolutional Neural Network (CNN) which has strong hierarchical representation capabilities, automatic feature extraction, and the ability to handle high-dimensional data. Our proposed model is tested on three public contactless palm vein datasets (CASIA and VERA). We evaluated the results using various performance biometric measures, such as correct recognition rate (CRR) accuracy, and area under curve (AUC) of the receiver operating characteristic. The simulation results showed that HOG features could improve accuracy from pretrained CNN such as AlexNet, VGG-16, ResNet-50 and EfficientNet. © 2024 IEEE.

关键词： Convolutional neural networks

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A Deep Learning Method for Detecting Phone Call Behaviors of Bidding Evaluation Expert 2

A Deep Learning Method for Detecting Phone Call Behaviors of...

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2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022

作者： He, Xingwei Lin, Qing Liu, Xuanyi Yu, Rongdong Zhou, Zhihao Xu, Jie Chen, Jiemin Wang, Zhan Zhejiang Energy Digital Technology Co. Ltd Department of Artificial Intelligence and Robotics Hangzhou China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

With the development of information technology, bidding evaluation process has changed from the traditional offline paper-based mode to online digital mode. To ensure an open and fair environment for online bidding evaluation, an intelligent supervision system is needed, which includes the phone call behavior detection. In this paper, we proposed a deep learning method for bidding evaluation expert's phone call behaviors detection. The proposed method consists of object detection and ROI analysis. In the detection stage, the phone and head are detected with our proposed YOLOv5+Transformer network. The phone call behavior is then recognized with self-defined ROI analysis. The experimental results show that the mAP is increased by 4.8% with the proposed network when compared with original YOLOv5s. In addition, the proposed method is used to analyze the video stream comprehensively, which can effectively solve the problems of object scale variation, occlusion, and accurately detect the phone call behavior. © 2022 IEEE.

关键词： Object detection

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Exploring Intermittent Dynamics of Neural Activity in a Single-Pair System of Excitatory and Inhibitory Neurons 3

Exploring Intermittent Dynamics of Neural Activity in a Sing...

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3rd International Conference on Emerging Techniques in Computational intelligence, ICETCI 2023

作者： Sugawara, Akio Nobukawa, Sou Wagatsuma, Nobuhiko Inagaki, Keiichiro Chiba Institute of Technology Department of Computer Science Narashino Japan Toho University Department of Information Science Funabashi Japan Chubu University Department of Artificial Intelligence and Robotics Kasugai Japan

ISBN: (纸本)9798350300604

A long-Tailed property has been observed at various levels of the brain, ranging from neural population activity to the level of cognitive neurodynamics. Specifically, at the cognitive neurodynamics level, a phenomenon called perceptual alternation displays perceptual durations that follow a gamma or log-normal distribution. Moreover, this perceptual alternation becomes nondeterministic. The occurrence of alternation phenomena has also been noticed in neural populations at the cognitive neurodynamics level. Even in a system consisting of a single pair of excitatory and inhibitory neurons, referred to as chaos-chaos intermittency (CCI), a similar intermittent alternation of neural activity emerges, involving intermittent transitions between multiple isolated attractors. In this study, our hypothesis was that nondeterminism and long-Tailed properties can emerge in this single-pair system. To test this hypothesis, we evaluated the determinism of two types of dynamics in the system that couples excitatory and inhibitory neurons: 1) transitions between attractors and 2) behavior within attractors. This evaluation was performed on multiple time scales using multi-scale entropy analysis (MSE) and an iterated amplitude-Adjusted Fourier transform (IAAFT) surrogate. The results demonstrated that the transitions between attractors were nondeterministic, while the behavior within attractors was deterministic. Furthermore, a long-Tailed property manifested in the duration of the transitions between attractors, possibly resulting from long-Term evaluation in the chaotic unstable orbit. The emergence of this long-Tailed property was attributed to the low frequency CCI around the attractor-merging bifurcation. This discovery contributes to the understanding of the long-Tailed properties exhibited by neural activity across multiple levels. © 2023 IEEE.

关键词： Brain

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Continuous Query-based Data Trading

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Performance Evaluation Review 2024年第1期52卷 73-74页

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Owner/Author.

关键词： Integer programming

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and Engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical Sciences. All rights reserved.

关键词： Empirical mode decomposition

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