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International Conference on Electrical and electronics engineering, ELECO

作者： Burak Makav Volkan Kılıç Electrical and Electronics Engineering Graduate Program Izmir Katip Celebi University Izmir Turkey

Automatic caption generation in natural language to describe the visual content of an image has attracted an increasing amount of attention in the last decade due to its potential applications. It is a challenging task to generate captions with proper linguistics properties as it requires an advanced level of image understanding that goes far beyond image classification and object detection. In this paper, we propose to use the Stanford CoreNLP model to generate a caption after images are trained using VGG16 deep learning architecture. The visual attributes of images are extracted with the VGG16, which conveys richer content, and then they are fed into the Stanford model for caption generation. Experimental results on the MSCOCO dataset show that the proposed model significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.

关键词： Hidden Markov models Measurement Visualization Deep learning Object oriented modeling Computer architecture Sports

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A Study on Phase Shift Keying on 2-Sphere

A Study on Phase Shift Keying on 2-Sphere

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Sho Kakuwa Masahiro Fujii Graduate Program in Information Electrical and Electronics Systems Engineering Utsunomiya University Utsunomiya JAPAN

In this paper, we propose new modulation schemes to achieve high transmit energy efficiency. Although one of them has a cube shape on a 2-sphere and is equivalent to the conventional QPSK, the others whose symbol constellations are its twisted and crushed shapes can outperform QPSK. In comparison with QPSK, the proposed signal designs can reduce the required E b /N 0 by more than 0.25 dB at 10 -7 of the BER because it is possible to extend the minimum distance.

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Hardware Implementation of a GMDH Controller for Mobile Robot Obstacle Following/Avoidance

Hardware Implementation of a GMDH Controller for Mobile Robo...

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IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana L. H. Oliveira M. S. Santana V. C. Oliveira Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

Proportional, integral, and derivative (PID) controllers have been widely adopted for industrial applications. However, these controllers are not very efficient for non-linear systems. Artificial neural networks (ANN) based on the Group Method of Data Handling (GMDH) have great potential to replace the PID controllers due to their polynomial structure, allowing complex non-linear systems to be controlled. This work presents a hardware architecture of a GMDH network applied to speed control of a mobile robot platform. The proposed GMDH controller was implemented using a 16-bit floating-point arithmetic representation and was mapped on a Zynq7020 device. A hardware-in-the-loop based on the Universal Direct Memory Access methodology was developed to validate the proposed circuits, allowing for performance comparisons between a classical PID and the GMDH controllers for different simulated scenarios.

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Implementation of a PID Controller using Online Tuning Applied to a Mobile Robot Obstacle Following/Avoidance

Implementation of a PID Controller using Online Tuning Appli...

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Argentine Conference on electronics (CAE)

作者： M. A. Pastrana L. H. Oliveira D. A. Mendes Douglas. L. Silva Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

The implementation of a bioinspired PID-based controller with online tuning capabilities is developed for obstacle following/avoidance of a nonholonomic mobile robot. An innovative mixture of the conventional PID controller and particle swarm optimization (PSO), which has a strong capability of adaptation across unknown scenarios, reduced objective error, brings us a novel online tuning methodology that can derive optimal controller parameters for a given scenario. It is appropriate for a kind of plant without prior knowledge or established mobile robot model. Computer simulation for obstacle following/avoidance is carried out in four different scenarios using the CoppeliaSim software and the EVA mobile robot platform. The effectiveness of the proposed control algorithm is demonstrated trough the simulation experiment, which shows its superior performance in the training scenario, including an overshoot of 20.1 %, a settling time of 10 seconds, and a steady-state error of 0.76%.

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Planning Optimization Platform for Cluster Type Micro-grid Installations and Operations

Planning Optimization Platform for Cluster Type Micro-grid I...

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2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019

作者： Iwamura, Kazuaki Nakanishi, Yosuke Lewlomphaisarl, Udom Estoperez, Noel Lomi, Abraham Waseda University Graduate School of Environment and Energy Engineering Tokyo Japan National Electronics and Computer Technology Center Advanced Automation and Electronics Research Unit Bangkok Thailand Mindanao State University Illigan Institute of Technology Iligan City Philippines National Institute of Technology Renewable Energy Research Center and Graduate Program Malang Malang Indonesia

ISBN: (纸本)9781728149301

This paper proposes an architecture for a planning optimization platform called the Grid of Grids Optimal Designer (GGOD) to facilitate the optimization of cluster type micro-grid installation and operation. Micro-grid clusters are groups of micro-grids with renewable energy that operate autonomously without power supply from main grids. The GGOD simulates micro-grid optimizations based on a service-oriented architecture and comprises three layers, i.e., end user, service broker, and service supply layers. The enduser layer corresponds to the client system, and the GGOD corresponds to the service broker and service supply layers. The service broker layer is for service management, and the service supply layer comprises various optimization components. Combinable optimization components are activated based on request text in extensible markup language format from end users. By combining optimization components, different optimizations are executed for a single request, the results of the same type of optimization methods are compared, and sudden operation environment changes are considered. Geospatial integrated resource planning and micro-grid facility planning are demonstrated as examples of GGOD applications. © 2019 IEEE.

关键词： Simulation platform

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ClusteredSHAP: Faster GradientExplainer based on K-means Clustering and Selections of Gradients in Explaining 12-Lead ECG Classification Model 23

ClusteredSHAP: Faster GradientExplainer based on K-means Clu...

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13th International Conference on Advances in Information Technology, IAIT 2023

作者： Mo, Bo-Yu Nuannimnoi, Sirapop Baskoro, Angger Khan, Azam Ariesta Dwi Pratiwi, Jasmine Huang, Ching-Yao Department of Electronics Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Eecs International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan International Health Program National Yang-Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798400708497

The vast majority of healthcare systems are operating at or near their full capacity. Providing an inaccurate diagnosis is a further prevalent issue. Although it is common knowledge that physicians get significant training, it is nevertheless possible for them to misdiagnose patients, overlook warning signs, or commit any number of other human blunders. Deep Learning (DL) has emerged as a helpful tool in medical diagnostics, particularly in ECG (electrocardiogram) signal classification, enabling efficient and precise detection of cardiac abnormalities. However, the inherent black-box nature of DL poses challenges for its direct implementation in real-world medical settings. To address this, Explainable AI (XAI) techniques such as SHAP, LIME, and CAM have been introduced, aiming to render the complex decisions of neural networks interpretable. Among these, SHAP is recognized for its robustness and comprehensive explanation capabilities. Nonetheless, the high computational demands of traditional SHAP methods hinder their real-time application, especially in urgent medical scenarios. In this paper, we propose an optimized SHAP approach leveraging K-Means clustering to group gradients by importance, namely ClusteredSHAP. Our methodology focuses on a select cluster of high-magnitude gradients, enhancing the efficiency of the GradientExplainer. Our evaluation encompasses both the computational efficiency and the usability of the resulting explanations, through a custom questionnaire and explanation usability scores inspired by established user experience metrics and medical tool usability standards. The results show that our proposed ClusteredSHAP not only provides significantly faster explanations, but also achieves a similar level of average explanation usability scores with the GradientExplainer. Through this work, we strive to bridge XAI with clinical practice, ensuring timely, transparent, and effective patient care. © 2023 ACM.

关键词： 12-Lead ECG Classification Explainable AI Heart Disease Diagnosis K-means Clustering SHapley Additive exPlanations

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Development of artificial intelligence system for dangerous object recognition in X-ray baggage images

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Transactions of the Korean Institute of Electrical Engineers 2020年第7期69卷 1067-1072页

作者： Lee, Jeong-Nam Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

The importance of flight safety has been highlighted lately due to the increase of aviation industry. Baggage screening tasks are still difficult and the failures of dangerous object detection are frequent despite the improvement of screening equipment. The purpose of this study to develop the AI system on dangerous object detection for improving aviation safety. The convolutional neural network model, Xception, were applied to perform dangerous object recognition using X-ray baggage image dataset which contains 25,405 images of twelve items. Based on experiments, the accuracy and F1-score are 0.9939 and 0.9942. The significantly high success rate makes the model a very effective advisory or early warning tool, and an approach that could be further expanded to support a dangerous object identification system to operate in real airport screening process. © Korean Institute of Electrical Engineers

关键词： Airport security

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Utilizing Mobile Robots to Teach Dengue and Chikungunya Prevention in Brazilian Schools

Utilizing Mobile Robots to Teach Dengue and Chikungunya Prev...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： R. B. Costa Pedro H. L. Gomes Luiz F. Rodriguez Silva M. A. Pastrana Maria C. C. Gontijo M. S. Luiz Daniel M. Muñoz Faculty of Gama Aerospace Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program Department of Mechanical Engineering Mechatronics Graduate Program

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

This paper introduces a novel approach to combating the spread of Dengue and Chikungunya, two prevalent arboviruses in tropical regions, especially in Brazil. The research presents a low-cost differential topology robot designed as an educational tool to raise awareness about mosquito breeding grounds among students and teachers in public schools across the Federal District, Brazil. Developed at the University of Brasilia, the robot is deployed in collaboration with the Federal District Health Department to support prevention campaigns in schools. The paper evaluates the effectiveness of this innovative tool in enhancing community engagement and improving health outcomes. The findings suggest that integrating robotics into health education can significantly elevate awareness and strengthen prevention strategies against arboviral diseases.

关键词： Pediatrics Prevention and mitigation Education Collaboration Robot sensing systems Topology Public healthcare Robots Viruses (medical) Diseases

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ANALYSIS OF TUNNEL LIGHTING CALCULATION USING LUMEN METHOD

ANALYSIS OF TUNNEL LIGHTING CALCULATION USING LUMEN METHOD

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The 7th Lighting Conference of China, Japan and Korea(CJK 2014)

作者： Yoo,Seong-sik Lee,Se-jong Lee,Min-wook Kim,Hoon BIT Medical Convergence Graduate Program Department of Electrical and Electronics Engineering Kangwon National University

In Korea,tunnel lighting has been designed using the lumen method based illuminance *** to the illuminance using lighting calculation software for the tunnel lighting designed using lumen method,low illuminance was *** figure out the causes of this result,this study compared the lumen method calculating process with the results of the calculation using simulation software and analyzed what factors caused *** on the results of analysis proposed a method of improving.

关键词： ANALYSIS OF TUNNEL LIGHTING CALCULATION USING LUMEN METHOD

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Nano functional neural interfaces

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Nano Research 2018年第10期11卷 5065-5106页

作者： Yongchen Wang Hanlin Zhu Huiran Yang Aaron D. Argall Lan Luan Chong Xie Liang Guo Department of Biomedical Engineering The Ohio State University Columbus 43210 USA Department of Biomedical Engineering The University of Texas at Austin Austin 78712 USA Department of Electrical and Computer Engineering The Ohio State University Columbus 43210 USA Key Laboratory of Flexible Electronics and Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University Nanjing 211816 China Biomedical Sciences Graduate Program The Ohio State University Columbus 43210 USA Department of Neuroscience The Ohio State University Columbus 43210 USA

Engineered functional neural interfaces （fNIs） serve as essential abiotic-biotic transducers between an engineered system and the nervous system. They convert external physical stimuli to cellular signals in stimulation mode or read out biological processes in recording mode. Information can be exchanged using electricity, light, magnetic fields, mechanical forces, heat, or chemical signals. fNIs have found applications for studying processes in neural circuits from cell cultures to organs to whole organisms, fNI-facilitated signal transduction schemes, coupled with easily manipulable and observable external physical signals, have attracted considerable attention in recent years. This enticing field is rapidly evolving toward miniaturization and biomimicry to achieve long-term interface stability with great signal transduction efficiency. Not only has a new generation of neuroelectrodes been invented, but the use of advanced fNIs that explore other physical modalities of neuromodulation and recording has begun to increase. This review covers these exciting developments and applications of fNIs that rely on nanoelectrodes, nanotransducers, or bionanotransducers to establish an interface with the nervous system. These nano fNIs are promising in offering a high spatial resolution, high target specificity, and high communication bandwidth by allowing for a high density and count of signal channels with minimum material volume and area to dramatically improve the chronic integration of the fNI to the target neural tissue. Such demanding advances in nano fNIs will greatly facilitate new opportunities not only for studying basic neuroscience but also for diagnosing and treating various neurological diseases.

关键词： neural interface neurotechnology nanoelectrode nanomaterial neural recording neural stimulation

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