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Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study

BMC Medical Imaging 2024年第1期24卷 1-10页

作者： Chang, Joseph Lin, Bo-Ru Wang, Ti-Hao Chen, Chung-Ming Department of Biomedical Engineering College of Medicine and College of Engineering National Taiwan University No. 1 Sec. 1 Jen-Ai Road Taipei 100 Taipei 100 Taiwan EverFortune.AI Co. Ltd Taichung Taiwan The Data Science Degree Program College of Electrical Engineering and Computer Science National Taiwan University and Academia Sinica Taipei Taiwan Department of Radiation Oncology China Medical University Hospital Taichung Taiwan Department of Medicine China Medical University Taichung Taiwan

Background: The study aimed to develop and validate a deep learning-based computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. Methods: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. Results: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong’s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. Conclusion: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings. © The Author(s) 2024.

关键词： Active learning Chest radiographs Deep learning Pleural effusion X-rays

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Image reconstruction for the artificial compound eye based on deep learning 4

Image reconstruction for the artificial compound eye based o...

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AI and Optical Data sciences IV 2023

作者： Lee, Jioh Kim, Cheolsun Choi, Youngin Lee, Heung-No Artificial Intelligence Graduate School Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Smart Electronics Research Center Korea Electronics Technology Institute Jeollabuk-do Jeonju-si54853 Korea Republic of

ISBN: (纸本)9781510659810

The visual system of arthropods, called the compound eye, has distinctive features such as a wide field of view, high-speed motion detection, and infinite depth of field. These features have attracted researchers to build artificial compound eyes. However, the compound eye is limited in spatial resolution by its structural constraints such as the number and size of ommatidia that compose the compound eye. These constraints also can be found in the existing artificial compound eye. In previous work, a design method overcame these limitations and achieved resolution improvements by increasing the acceptance angle of ommatidia and using numerical optimization based on compressive sensing (CS). However, the limitation is that prior information such as a sparsifying basis is needed to solve the numerical optimization problem, and obtaining the solution to this problem is computationally time-consuming. In this paper, we propose a deep learning-based artificial compound eye. The deep learning architecture takes a measurement from the compound eye as input and learns how to reconstruct the original image. The experimental result demonstrates that the proposed deep learning approach provides improved performance in image reconstruction for the artificial compound eye. © 2023 SPIE.

关键词： Image reconstruction

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IoT-Enabled Deep Learning Algorithm for Estimation of State-of-Charge of Lithium-ion Batteries∗

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Journal of Circuits, Systems and computers 2024年第7期33卷

作者： Pushpavanam, B. Kalyani, S. Prasanna, M. Arul Sangaiah, Arun Kumar Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Kamaraj College of Engineering and Technology Madurai India Department of Electrical and Electronics Engineering Kamaraj College of Engineering and Technology Madurai India International Graduate Institute of AI National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging-discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis. © 2024 World Scientific. All rights reserved.

关键词： Battery management systems

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Investigation of Effective Electrode Structure for Metal Removal from Composite Materials Using Pulsed Discharge

Investigation of Effective Electrode Structure for Metal Rem...

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Pulsed Power Conference, International Conference on High-Power Particle Beams and International Symposium on Electromagnetic Launch Technology (EAPPC/BEAMS/EML), Euro-Asian

作者： Takuto Doi Shota Oishi Takashi Sakugawa Kaisei Nishimura Shinichiro Shobako Shinichi Shimasaki Tomohiko Yamashita Department of Computer Science & Electrical Engineering Graduate School of Science & Technology Kumamoto University Kumamoto Japan Department of Electro-Mechanical Systems Engineering National Institute of Technology Kagawa College Takamatsu Japan

ISBN: (数字)9789038661353

ISBN: (纸本)9798350352733

The authors investigate the potential of pulsed power technology in recycling of E-waste. Applying the pulsed discharge can separate composite materials into plastic and metal. In this study, pulsed discharge was applied to indium tin oxide (ITO) coated plastic films. We used two electrode types: rod-to-rod electrode and a pair of flat plate electrodes to investigate the effect of electrode structure on the metal removal area in detail. A series of single pulse discharges were applied at various electrode distances. As a result, it was revealed that metal can be removed over a wide range by the pulsed discharge using a pair of flat plate electrodes. When the gap between the electrodes was 30 mm, the removal area by the flat plate electrodes was approximately 3.4 times that by the rod electrodes. Analysis of the current density also revealed that the metal removal area was greatly affected by the current density.

关键词： Electrodes Particle beams Composite materials Shape Indium tin oxide Metals Plastic films Discharges (electric) Recycling Current density

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Set-based state estimation of a Li-ion cell using DC programming and constrained zonotopes

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IFAC-PapersOnLine 2023年第2期56卷 7147-7153页

作者： Angelo Tottoli Alesi A. de Paula Guilherme V. Raffo Bruno O.S. Teixeira Davide M. Raimondo Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

Advanced battery management systems (ABMSs) rely on mathematical models to ensure high battery safety and performance. One of the key tasks of a BMS is state estimation. In the following, we consider a single lithium-ion cell described with a dual polarization equivalent circuit model. To consider a realistic scenario, where the parameters have been identified from experimentally collected data, both parametric and measurement uncertainties are taken into account in the model. In particular, unknown but bounded uncertainties are assumed. In this setup, we address state estimation through a set-based approach using Constrained Zonotopes (CZ). Due to the model nonlinearities, a method able to propagate CZ through nonlinear mappings is demanded. Within this context, mean value and first-order Taylor CZ-based extensions were proposed which, however, might lead to conservative overestimation due to the sensitivity to the wrapping and dependency effects inherited from interval arithmetic. In the following, we suggest the use of DC programming as an alternative. The effectiveness of the proposed scheme is demonstrated in simulation for the considered Li-ion model.

关键词： Lithium-ion cells Nonlinear state estimation Uncertainty Set-based approach

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A Multi-UAV Charging Station Enabling Free Landing by Grid Pattern Transmitter

A Multi-UAV Charging Station Enabling Free Landing by Grid P...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Jungho Kim Hyunkyeong Jo Seoktae Seo Bonyoung Lee Hyungki Min Franklin Bien School of Electrical and Computer Engineering Ulsan National Institute of Science and Technology Ulsan Korea Program in Information & Communication Technology Convergence Ulsan National Institute of Science and Technology Ulsan Korea

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

This paper proposed a novel landing platform with wireless power transfer (WPT) to charge multiple unmanned aerial vehicles (UAVs) without restrictions on landing locations or alignment. A transmitter is designed using wires to charge a wide area. To overcome the disadvantages of system made by wires, a grid pattern transmitter and double power line are proposed. These structures provide multiple charging with free positioning of receivers and excite the transmitter lines in wide area properly. The optimized system is composed of 0.15m square cells, providing a 0.9 m by 0.9 m charging area. By experiment, it can efficiently charge four 0.1 m square receivers located anywhere in transmitter. The overall efficiency of four receivers is 37.5%. Also, the experimental results verify that it is unnecessary to align the receiver within the cell, as it has a 2.62% error due to rotation and a 2.94 % error due to position within the cell.

关键词： Coils Magnetic flux leakage Transmitters Wires Windings Receivers Wireless power transfer Autonomous aerial vehicles Current distribution Power electronics

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Experimental investigation of high-speed WDM-visible light communication using blue, green, and red InGaN µLEDs

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Optics Letters 2024年第16期49卷 4697-4700页

作者： Lu, Tingwei Dai, Yurong Lee, Tzu-Yi Wang, Shuhui Guo, Wenan Lai, Shouqiang Chen, Zhong Kuo, Hao-Chung Wu, Tingzhu National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen 361005 China College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Photonics and Graduate Institute of Electro-Optical Engineering Hsinchu 30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei 11492 Taiwan

Micro-light-emitting diodes (µLEDs) hold significant promise for applications in displays and visible light communication (VLC). This study substantiates the viability of a wavelength division multiplexing (WDM)-VLC system using InGaN blue, green, and red µLED devices. The devices exhibited notable color stability and high modulation bandwidth due to the weakly polarized electric field in the blue and green semipolar devices and the stress-optimized structure in the red device. The aggregated data rate reached 11.14 Gbps. Moreover, the blue, green, and red InGaN µLEDs exhibited a wide color gamut, encompassing 119.4% of the NTSC and 89.2% of the Rec. 2020 standards, affirming the potential of blue, green, and red InGaN µLEDs for applications in full-color display and WDM-VLC systems. © 2024 Optica Publishing Group.

关键词： Atomic layer deposition Bandpass filters Crosstalk Scanning electron microscopy Visible light communications Wavelength division multiplexing

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Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

arXiv

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arXiv 2024年

作者： Airiyoshi, Ryotai Hasegawa, Mikio Ohtsuki, Tomoaki Li, Aohan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Department of Electrical Engineering Tokyo University of Science Tokyo Japan Department of Information and Computer Science Keio University Yokohama Japan

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multiarmed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and ϵ-greedybased methods in terms of both transmission success rate and energy efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Fully Autonomous Distributed Transmission Parameter Selection Method for Mobile IoT Applications Using Deep Reinforcement Learning

Fully Autonomous Distributed Transmission Parameter Selectio...

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IEEE Conference on Vehicular Technology (VTC)

作者： Seiya Sugiyama Keigo Makizoe Maki Arai Mikio Hasegawa Tomoaki Otsuki Aohan Li Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Department of Electrical Engineering Tokyo University of Science Tokyo Japan Department of Information and Computer Science Keio University Yokohama Japan

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

With the rapid increase in Internet of Things (IoT) devices, packet collision has become a serious problem in Long Range (LoRa) communication. Furthermore, the rising demand for mobile IoT applications necessitates parameter allocation methods that take into account the mobility of End Devices (EDs). However, the existing transmission parameter selection methods considering mobility are almost centralized designs, which may increase the probability of interference and system latency. The decentralized design is few and requires prior information on ED, which may cause scalability issues, e.g., adding ED to an existing network. To solve the problems above, a method through a fully autonomous distributed design supposing the mobility of ED using Double Deep Q Network (DDQN) is proposed in this paper. In the proposed method, ED trains the model based on ACKnowledge (ACK) packets and its location information without any prior information. Performance evaluation results show that the proposed method can achieve a higher packet delivery rate than other autonomous distributed methods in mobile IoT scenarios.

关键词： Performance evaluation Vehicular and wireless technologies Scalability LoRa Interference Deep reinforcement learning Batteries

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