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Arc plasma-deposited Co single-atom catalysts supported on an aligned carbon nanofiber for hydrogen peroxide electrosynthesis and an electro-Fenton process

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Carbon Energy 2024年第11期6卷 234-255页

作者： Chang‐Kyu Hwang Sooyeon Kim Ki ro Yoon Thao Thi Le Chinh V.Hoang Jae Won Choi Wenjun Zhang Sae Yane Paek Chung Hyeon Lee Ji Hyun Lee Keun Hwa Chae Sohee Jeong Seung Yong Lee Byeong-Kwon Ju Sang Hoon Kim Sang Soo Han Jong Min Kim Materials Architecturing Research Center Korea Institute of Science and Technology (KIST))seculSecrngbuk-guRepublic of Korea Department of Micro/Nano Systems Korea UniversitySeoulSeongbuk-guRepublic of Korea Computational Science Research Center Korea Institute of Science and Technology (KIST)SeculSecrngbuk-guRepublic of Korea Advanced Textile R&D Department Korea Institute of Industrial Technology (KITECH)AmsanGyeonggi-doRepublic of Korea Extreme Materials Research Center Korea Institute of Science and Technology (KIST)SeoulSeongbuk:guRepubic of Korea Nanoscience and Technology KIST School University of Science and TechnologySeoulRepublic of Korea Advanced Analysis and Data Center Korea Institute of Science and TechnologySeoulSeongbuk-guRepublic of Korea Display and Nanosensor Laboratory School of Electrical Engineering Korea UniversitySeoulSeongbuk guRepublic of Korea KHU-KIST Department of Converging Science and Technology Kyung Hee UniversitySeoulSeongbuk-guRepubic of Korea

Atomically dispersed single-atom catalysts(SACs)on carbon supports show great promise for H_(2)O_(2) electrosynthesis,but conventional wet chemistry methods using particulate carbon blacks in powder form have limited their potential as two-electron(2^(e−))oxygen reduction reaction(Orr)***,we demonstrate high-performance Co SACs supported on a free-standing aligned carbon nanofiber(CNF)using electrospinning and arc plasma deposition(APd).Based on the surface oxidation treatment of aligned CNF and precise control of the deposition amount in a dry-based APd process,we successfully form densely populated Co SACs on aligned *** experimental analyses and density functional theory calculations,we reveal that Co SAC has a Co–N_(2)–O_(2) moiety with one epoxy group,leading to excellent 2^(e−)Orr ***,the aligned CNF significantly improves mass transfer in flow cells compared to randomly oriented CNF,showing an overpotential reduction of 30 mV and a 1.3-fold improvement(84.5%)in Faradaic efficiency,and finally achieves an outstanding production rate of 15.75 mol gcat^(−1) h^(−1) at 300 mA cm^(−2).The high-performance Co SAC supported on well-aligned CNF is also applied in an electro-Fenton process,demonstrating rapid removal of methylene blue and bisphenol F due to its exceptional 2e^(−)Orr activity.

关键词： arc plasma deposition electro-Fenton flow reactor hydrogen peroxide production oxygen reduction reaction single-atom catalysts

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Estimating Smart Grid Stability with Hybrid rNN+LSTM deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing recurrent Neural Networks (rNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of rNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Image Caption Generation using CNN and Audio Conversion

Image Caption Generation using CNN and Audio Conversion

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： K. Priya r.Vijay Babu M.Muralidhar reddy T.H. Mohan reddy M. Maanesh Department of Artificial Intelligence & Data Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science Arizona state university Tempe United states

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

This research focuses on developing a system that can generate descriptive captions for images and convert them into audio output. The system employs a Convolutional Neural Network (CNN) to extract visual features from images, which are then fed into a Gated recurrent Unit (GrU) to generate textual descriptions. The generated captions are subsequently converted into audio using text-to-speech techniques. By training the model on a large dataset of image-caption pairs, the system learns to associate visual information with textual descriptions, enabling accurate and coherent caption generation. This research contributes to the advancement of image understanding and generation, with potential applications in various fields such as image search, accessibility, and content creation.

关键词： Training Visualization Multimedia systems Logic gates Speech enhancement Feature extraction robustness Text to speech Internet Convolutional neural networks

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Underwater Object detection using Image Enhancement and deep Learning Models 11

Underwater Object Detection using Image Enhancement and Deep...

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11th European Workshop on Visual Information Processing, EUVIP 2023

作者： Tarekegn, Adane Nega Cheikh, Faouzi Alaya Ullah, Mohib Sollesnes, Erik Tobias Alexandru, Cornelia Azar, Saeed Nourizadeh Erol, Erdeniz Suciu, George Norwegian University of Science and Technology Department of Computer Science Gjovik Norway Usea Ocean Data Oslo Norway Beia Consult International Research & Development Department Bucharest Romania Obss Technology R&d and Ai Department Istanbul Turkey Elkon Elektrik San. Tic. A.S Istanbul Turkey

ISBN: (纸本)9798350342185

Autonomous underwater vehicles (AUVs) are efficient robotic tools, offering a wide range of applications in ocean exploration and research, such as oceanographic mapping, environmental monitoring, and archaeology. Incorporating an automatic object detection system with AUVs can substantially improve their ability to perceive and recognize objects in a complicated and often hazardous environment. Currently, detecting underwater objects relied on a man-in-the-loop approach, where AUVs captured vast amounts of data and saved them in memory for offline processing. This study investigates the use of deep learning for automatic image preprocessing and object detection, evaluating and comparing three state-of-the-art YOLO (You Only Look Once) models, including YOLOv8, YOLOv7, and YOLOv5. Extensive experiments were conducted using publicly available underwater image datasets, revealing that the pre-trained models attain superior performance on the Brackish dataset. YOLOv5 and YOLOv8 achieved the highest mean average precision (mAP) with a score of 99%, while YOLOv7 scored 89%. Furthermore, an underwater image enhancement algorithm is employed on the UrPC2021 dataset, significantly improving the detection accuracy with a 3% increase in mAP across all three models. In terms of inference speed, YOLOv5 demonstrated the highest frames per second (FPS), while maintaining comparable performance in mAP and recall. © 2023 IEEE.

关键词： Image enhancement

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Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation

arXiv

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arXiv 2024年

作者： Zhang, Irina denholm, Jim Hamidinekoo, Azam Ålund, Oskar Bagnall, Christopher Huix, Joana Palés Sulikowski, Michal Vito, Ortensia Lewis, Arthur Unwin, robert Söderberg, Magnus Burlutskiy, Nikolay Qaiser, Talha Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca United Kingdom Pathology Clinical Pharmacology & Safety Sciences R&D AstraZeneca Sweden Translational Science and Experimental Medicine AstraZeneca United Kingdom KTH Royal Institute of Technology Sweden

Accurate segmentation of glomerulus instances attains high clinical significance in the automated analysis of renal biopsies to aid in diagnosing and monitoring kidney disease. Analyzing real-world histopathology images often encompasses inter-observer variability and requires a labor-intensive process of data annotation. Therefore, conventional supervised learning approaches generally achieve sub-optimal performance when applied to external datasets. Considering these challenges, we present a semi-supervised learning approach for glomeruli segmentation based on the weak-to-strong consistency framework validated on multiple real-world datasets. Our experimental results on 3 independent datasets indicate superior performance of our approach as compared with existing supervised baseline models such as U-Net and SegFormer. © 2024, CC BY.

关键词： diagnosis

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Bus Sub-station Lines Optimization Based on Analysis of Main Passenger Flow in the Same direction

Bus Sub-station Lines Optimization Based on Analysis of Main...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Wei, Qingbo Xuan, Qipeng Su, Kui Sha, Zhiren Su, Yuejiang Guangzhou Public Transport Data Management Center Co. Ltd. Guangzhou510620 China Guangzhou Municipal Transportation Bureau Guangzhou510620 China Guangzhou Fundway Smart Transportations R&D Co. Ltd. Guangzhou510000 China School of Civil Engineering and Transportation South China University of Technology Guangzhou510641 China

ISBN: (纸本)9789819639601

In traditional design of bus sub-station lines, most are based on experience model, which is easy to distribute the same direction lines at different sub-stations, resulting in a decline in passenger travel experience. To solve this problem, this study proposes an automatic calculation method for the optimal bus sub-station lines. Based on the actual travel Od of bus passengers, stopping lines and their departure frequencies, distribution of downstream bus station, and the stopping capacity of each sub-station, a Genetic Algorithm (GA) model is constructed to solve the optimal scheme. In the GA algorithm, the fitness function is "passengers with the same Od board at the same sub-station as much as possible, and travels of the same direction board at the same sub-station as much as possible", and in the process of selection, crossover and mutation, limitation of each sub-station’s stopping capacity is also fully considered. Finally, some sub-stations in Guangzhou is taken as the test object, with "the total number of passengers boarding at the same sub-station" and other indicators are taken as the test index. results show that after optimization, the stopping sub-station of bus lines in the same direction are more concentrated, and the number of passengers who fully enjoy taking the line at the same station increases by 71%. Overall, this study can provide engineering application solutions for automatic design of bus sub-station lines. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Genetic algorithms

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IoT-Enabled Asthma Trackers for Children with KNN for Improved Breathing Control 5

IoT-Enabled Asthma Trackers for Children with KNN for Improv...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Sathyanathan, P. Balachander, K. Pandey, Pramod Srinivasan, V. Prasanna Ganeshbabu, T.r. Muthulekshmi, M. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Managing asthma in children is a complex task that needs constant vigilance and personalized treatments to get the best potential control. For better respiratory management, an Internet of Things (IoT)-enabled pediatric asthma tracking system that uses machine learning (ML) methods, particularly for K-nearest neighbors (KNN), is presented. The system's integration of wearable sensors, which continuously monitor vital signs and environmental conditions, provides a comprehensive view of the child's respiratory health. Asthma exacerbation causes, and trends may be discovered by analyzing the obtained data using KNN algorithms. Analysis allows for the generation of real-time warnings and feedback, allowing caregivers to take proactive steps in reducing risks and optimizing treatment procedures. Our IoT-enabled method for improving respiratory control and decreasing asthma-related consequences in children is feasible and effective in pilot research, including children with asthma. Our research highlights the promise of IoT devices and ML algorithms to transform how pediatric asthma is treated by providing tailored, evidence-based treatments that lead to better health outcomes. © 2025 IEEE.

关键词： Pediatrics

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Artificial Intelligence based Facial Emotion recognition with deep Neural GAN Augmentation 8

Artificial Intelligence based Facial Emotion Recognition wit...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Menaka, d. Priya, d. Karthika Lenus, C. reeda Ashwini, A. Shermila, P. Josephin Prathaban, Banu Priya Noorul Islam Centre for Higher Education Department of Electronics and Instrumentation Engineering Kumaracoil India Easwari Engineering College Department of Artificial Intelligence and Data Science Ramapuram Chennai India S.A.Engineering College Department of Physics Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India R.M.K.College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Srm Institute of Science and Technology Kattankulathur School of Computing Department of Networking and Communication Chennai India

ISBN: (纸本)9798350367904

Facial recognition system in computer vision is widely used in human-computer interface and clinical diagnostics. Nonetheless, achieving high accuracy in this task still poses a challenge due to issues of inadequate amount and imbalance of data to train the models as well as issues of generalization and robustness. The findings of this research propose the use of Generative Adversarial Network (GAN) data augmentation for face emotion classification specifically for this approach. The proposed strategy is based on the generation of artificial 'emotional' images on the face using deep Neural Self Attention GANs (dNSAGANs). This way, to capture all the variations of facial expressions the GAN takes a large number of images and thus makes the augmented data richer and more realistic. This larger data set is then used to train a deep learning architecture that performs the face emotion classification thereby enabling the network to learn enhanced discriminative features. To collect face photos the FEr2013 dataset is used. The training process, entrained the generator and discriminator to minimize the mean squared error while the discriminator was simultaneously trained to robustly distinguish generated images from real ones. Altogether, the proposed Adam optimizer was more accurate than Adagrad and Adadelta for all epochs. The percentage of accuracy is about 88.64 % for 100 epochs using the proposed optimizer techniques. © 2024 IEEE.

关键词： Generative adversarial networks

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Strengthening Authentication: The role of Multi-Level Hashing in OSPA Protocol 5

Strengthening Authentication: The Role of Multi-Level Hashin...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Hariharasudhan, S. Varghese, Vineetha Malarvizhi, K. Santhoshkumar, S.P. Nalini, T. Vikram, d. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Jct College of Engineering and Technology Department of Computer Science Al Business Systems Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence Data Science Coimbatore India

ISBN: (纸本)9798331518097

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture. © 2024 IEEE.

关键词： Authentication Protocol

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revolutionizing Cardiovascular Attack Prediction: A Comprehensive Machine Learning Approach for Accurate and Timely detection 4

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： durai, S. Jaganathan, d. Prabhu, V. Harini, r. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India R.M.K. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350385793

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), random Forest (rF), decision Tree (dT), and Logistic regression (Lr), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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