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Advanced Pigmented Facial Skin Analysis Using Conditional Generative Adversarial Networks

IEEE Access 2024年 12卷 46646-46656页

作者： Tsai, An-Chao Huang, Patrick Po-Han Wu, Zhong-Chong Wang, Jhing-Fa National Pingtung University International Master Program of Information Technology and Application Pingtung91201 Taiwan Huang Ph Dermatology and Aesthetics Kaohsiung80458 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

In recent years, artificial intelligence (AI) approaches in computer vision and medical technology have been combined to create various convenient and accurate tools to assist medical treatments. In this work, we propose conditional generative adversarial networks (conditional GANs)-based pigmented facial skin analysis system for melasma diagnosis. In the past, melasma diagnosis was based on subjective diagnoses from doctors, and there were few automatic melasma analysis methods. The proposed system helps to determine the region according to the melasma's severity. Areas associated with melasma and hemoglobin are detected to determine whether they may require special treatments. Furthermore, the proposed work cooperates with HUANGDERM dermatology to collect a facial skin pigmented dataset. We divide the dataset into 3,000 groups for training datasets and 678 groups for testing. Each group contains four categories of images: standard white light, polarized light, melanin and hemoglobin distribution. As a result, the proposed system successfully generates melasma and hemoglobin images and performs well with respect to subjective and objective evaluations. © 2013 IEEE.

关键词： Hemoglobin

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Identification of User's Interests by Deep-Learning Based Pose Estimation with Angle Relationship from Keypoints for Smart Advertising Displays

Identification of User's Interests by Deep-Learning Based Po...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Lee, Yu-En Fan, Chih-Peng National Chung Hsing University Master Program in Semiconductor and Green Technology Taichung Taiwan National Chung Hsing University Department of Electrical Engineering Taichung Taiwan

ISBN: (纸本)9798331530839

In this paper, a deep-learning based pose estimation technology for recognizing user's interests on interactive advertising displays is developed by leveraging the YOLO-Pose architecture. The primary goal is to predict user's attention areas by analyzing angular relationships with human body poses. The methodology uses the YOLO-Pose model to identify 17 keypoints on the human body and the angles among the selected body keypoints are calculated. The proposed design incorporates the left and right hand identification and the height determination to address accuracy issues from variations in user's height or body size. By integrating the estimation data for angles, user's height, and handedness, the horizontal and vertical SVM classifiers will be pre-trained. After the SVM classifiers are well trained, the combination results from both SVM classifiers enables the prediction of the user's area of interests. Experimental results with four, six, and eight grid divisions on the display reveals that the average identifying accuracies for horizontal and vertical grid classification can be up to 94% and 83%, respectively. By height adjusted information, the horizontal and vertical classification accuracies of the SVM classifiers can improve by 11% and 20% respectively within corresponding height ranges. For the embedded device implementation, the YOLO model and SVM classifier are deployed on the Jetson AGX Orin platform to achieve an average processing speed of 15 frames per second. © 2024 IEEE.

关键词： Marketing

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Usability evaluation of high-fidelity simulation manikin for cardiopulmonary resuscitation training for medical students

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Research on Biomedical engineering 2024年第1期40卷 253-264页

作者： Silva, Lucas de Brito Gonçalves, Fabrícia Torres Arantes, Henrique Pierotti Garcia, Rodrigo Alves Gasparin, Maria Regina Franco Ribeiro dos Reis, Melissa Mariane Andrade, Adriano de Oliveira Pereira, Adriano Alves Milagre, Selma Terezinha Santos, Fernando Pasquini Faculty of Electrical Engineering Graduate Program in Biomedical Engineering Federal University of Uberlândia Campus Santa Mônica Bloco 1E MG Uberlândia38400-902 Brazil Instituto Master de Ensino Presidente Antônio Carlos MG Araguari Brazil

Purpose: With the increasing incidence of cardiac arrests in non-critical areas, it is crucial to provide comprehensive training to healthcare professionals. Simulation-based training using manikins offers a morally sound alternative to traditional methods and allows students to apply clinical skills in realistic scenarios. Usability evaluation, including assessment of efficiency, effectiveness, and user satisfaction, is essential for ensuring the effectiveness of the training. This research focuses on evaluating the usability of the simulation manikin for teaching cardiopulmonary resuscitation to medical students. Methods: Twenty-one volunteers participated in the experiments: 18 students and three teaching doctors (one instructor professor and two evaluator professors). The instructor professor was instructed to follow the same procedure used in the classroom setting. The evaluating professors were provided with appropriately labeled data collection instruments to evaluate the usability of manikins. The student performed the tasks as instructed, and after completion, they were directed to the debriefing room, where they filled out the feedback form. Results: The experimental data and the statistical analyses showed that regarding effectiveness, the study findings indicated that participants encountered difficulties in certain tasks, as evidenced by the need for significant intervention from the instructor professor. In relation to efficiency, the results demonstrated a clear correlation between the successful execution of tasks and the duration of instruction provided by the instructor, thereby indicating the presence of a usability problem associated with the simulation manikin. And users are not entirely satisfied with the manikin, considering the room for improvement indicated by the SUS score. Conclusion: The experiment and the results reported in this paper contribute to addressing the existing gap in the literature concerning the usability of high-fidelity s

关键词： Efficiency

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Optimal Energy Dispatch Analysis Using the Inclusion of Virtual Power Plants Based on Dynamic Power Flows 3rd

Optimal Energy Dispatch Analysis Using the Inclusion of Vi...

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3rd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

作者： Canacuan, Darwin Carrión, Diego Montalvo, Iván Master’s Program in Electricity Universidad Politécnica Salesiana Quito170525 Ecuador Electrical Engineering Program Universidad Politécnica Salesiana Quito170525 Ecuador

ISBN: (纸本)9783031322129

The growing manifestation of non-conventional renewable energies, in modern electrical power systems, creates a great challenge on the normal operation of the system and planning the dispatch of active power, in economic terms. In order to achieve the objective of economic dispatch and be competitive in the electricity market, the concept of virtual power plant (VPP) has been introduced. It considers electrical elements and equipment with the aim of working as a single set that generates economic and operational benefits to the electrical system, taking into account the features and restrictions for an energy dispatch scenari0. This paper proposes a case of study that considers the use of a virtual power plant in order to analyze the effects caused along the network. The optimal dynamic flow of AC power (ODFAC) uses a non-linear programming algorithm and how it is affected not only for the restrictions of the electrical elements and equipment but also for the restrictions of the topology and formation of the electrical power system. The IEEE 9-bar system is used to illustrate the efficiency of the method, comparing the results of the system with and without the integration of a virtual power plant. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual Power Plants

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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary Study 3

Building a Simple COVID-19 Knowledge Graph in Bahasa Indones...

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3rd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023

作者： Muharram, Arief Purnama Taufikulhakim, Farhan Hilmi Purwarianti, Ayu Universitas Indonesia Faculty of Medicine Jakarta Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Master Program of Informatics Bandung Indonesia

ISBN: (纸本)9798350302424

COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels, respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development. © 2023 IEEE.

关键词： COVID-19

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Self-Tuning Neural Network Controller Based on Fuzzy Logic for Multiple Positions Tracking of a Pneumatic Driven Soft Endoscope Actuator 10

Self-Tuning Neural Network Controller Based on Fuzzy Logic f...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Acosta, Renzo Tafur, Julio Canahuire, Ruth Pontificia Universidad Catolica del Peru Control and Automation Engineering Master Degree Program Enginnering Department Lima Peru Universidad de Ingenieria y Tecnologia - UTEC Department of Electrical and Mechatronics Engineering Lima Peru

ISBN: (纸本)9798350394245

This paper presents the dynamic modelling and end effector position control of a soft endoscope. Soft endoscope system under study consists mainly of a pneumatic driven soft actuator (PDSA) with four independently chambers. Recurrent neural network (RNN) and dynamic back propagation (DBP) training algorithm are used to obtain PDSA dynamic model. PDSA position controller is based on a feedforward neural network (FNN) and it is trained using PDSA closed loop system (CLS) and DBP training algorithm. CLS stability analysis concludes that it is not possible control PDSA end effector position using one position controller for different end effector initial and desired positions. Fuzzy logic methodology is used to integrate several positions controllers in a one controller valid for all operation range. The controller implementation and close loop system simulation are performed in MATLAB for different end effector desired position to validate system performance in all range of operation. © 2024 IEEE.

关键词： Pneumatics

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Exploring the Impact of Word Embedding Dimensions on Depression Data Classification Using BiLSTM Model 8

Exploring the Impact of Word Embedding Dimensions on Depress...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Lestandy, Merinda Abdurrahim Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang Malang65144 Indonesia Informatics Master Program Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia

Depression is a mental health issue that impacts over 300 million individuals worldwide. Daily anxiety impacts the relationships of depressed individuals with their family and friends, affects their health, and, in the worst-case scenario, leads to suicide. Due to the an increase of social networks, a vast majority of people now express their thoughts, feelings, and emotions via social media. The researchers mainly focused on features and building predictions, making it difficult for them to detect depression via social media. Word embedding offers a potential solution for processing and transforming unstructured data into meaningful representations. Embedded words represent textual data as numeric numerals with equivalent meaning. Using the Bidirectional Long Short-Term Memory (Bi-LSTM) paradigm, this investigation attempts to experiment with various weighting dimensions using the Word2Vec and GloVe methods. The data used is Depression: Reddit Dataset labelled: depressed and non-depressed. The experimental results of word weight dimensions using Word2Vec with a dimension of 500 are better than Glove. The results of accuracy, precision, recall and f1-score for the Word2Vec method were 96.22%, 97.02%, 95.30% and 96.15%, while the Glove method was 95.91%, 96.40%, 95.30% and 95.85% with a dimension of 200. Overall, Word Embedding Dimensions influences the results of this study. Based on these findings, it is concluded that the proposed Word2Vec method is a significant approach. © 2023 The Authors. Published by Elsevier B.V.

关键词： BiLSTM Depression Social Media Word Embedding

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Java Bali Grid Mean Time To Failure Prediction using Machine Learning 1

Java Bali Grid Mean Time To Failure Prediction using Machine...

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1st International Conference on Technology, engineering, and Computing Applications, ICTECA 2023

作者： Sesotyo, Priyo Adi Pandjaitan, M.M. Lanny W. Lukas Universitas Katolik Indonesia Atma Jaya Professional Engineer Study Program Faculty of Engineering Jakarta12930 Indonesia Universitas Semarang Electrical Engineering Dept. Semarang50196 Indonesia Universitas Katolik Indonesia Atma Jaya Electrical Engineering Master Program Jakarta12930 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350360554

The role of Mean Time To Failure (MTTF) is critical to maintain cost-efficient in smart grids, as the predictive maintenance means for ensuring their reliability. Java Bali grid is the most important power network in Indonesia, because it holds 75% energy sales and the foundation of the Indonesian government, business and industrial sectors. One method for prediction in the machine learning is the Linear Regression, as part of the Supervised Learning technique. The regression model extracts the grid overload variable from MV distribution system, where connected load (MVA) divided by the number of MV customers. The model constructs the Linear Regression function, where the slope and intercept values found from the correlation between predicted response and actual response. After confronting the function f(x) and the coefficient of determination (R2), the test results for the MTTF prediction values for Java-Bali's region shows the higher accuracy when the R2 shows highest value. © 2023 IEEE.

关键词： Forecasting

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Development of LED Lighting for Microalgae Propagation with Emission Spectrum Having Broader Emission Peaks

Development of LED Lighting for Microalgae Propagation with ...

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2024 IEEE Sustainable Smart Lighting World Conference and Expo, LS24 2024

作者： Hosoda, Nobuaki Murata, Takashi Misu, Takayuki Kanagawa Institute of Technology Graduate School of Engineering Master's Degree Program in Electrical and Electronic Engineering Atsugi Kanagawa243-0292 Japan Kanagawa Institute of Technology Faculty of Engineering Department of Applied Chemistry and Bioscience Atsugi Kanagawa243-0292 Japan Kanagawa Institute of Technology Faculty of Engineering Department of Electrical and Electronic Engineering Atsugi Kanagawa243-0292 Japan

ISBN: (纸本)9798331540197

In this study, we aimed to develop LED lighting with an emission spectrum effective for microalgae propagation, and fabricated LED lighting with Phosphor layer RGB, which has peaks in the red, green, and blue wavelength bands, and Phosphor layer RG'B', which has broader peaks in the red, green, and blue wavelength bands. In order to examine the effect of microalgae propagation, Closterium ehrenbergii samples prepared at different dilution rates were irradiated with LED illumination light of Phosphor layer RG'B' and Phosphor layer RGB, respectively, for 7 days, and their propagation rates were compared. The results of the experiment showed that Phosphor layer RG'B' produced a higher propagation rate than Phosphor layer RGB. © 2024 IEEE.

关键词： Light emitting diodes

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Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

TechRxiv

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TechRxiv 2024年

作者： Muharram, Arief Purnama Sajid, Fahmi Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XGBoost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F-1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies. © 2024, CC BY.

关键词： Self-supervised learning

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