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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Manasis, Apostolos S. Papageorgiou, Panos C. Konstantopoulos, George C. University of Patras Department of Electrical and Computer Engineering Rion26504 Greece

ISBN: (纸本)9798350316339

Fast charging stations for electric vehicles (EVs) often consist of charging units with multiple modules connected in parallel to achieve high power ratings and can suffer from cyber-attacks in the modern smart grid architecture or measurement/sensor/communication failure. In this manuscript, an EV charging unit consisting of multiple single active bridge (SAB) converters connected in parallel is examined and a new nonlinear resilient controller is suggested to realize accurate power sharing among the converters with inherent current limitation, while enhancing the entire charging unit resilience under irregular scenarios. By taking into account the dynamic model of the parallel SAB converters and using invariant set and nonlinear ultimate boundedness theory, it is proven that the current injected to the EV battery by each converter is limited below a maximum value at all times. Furthermore, based on the novel dynamic structure of the controller, it is shown that when an unexpected event (e.g. setpoint attack, sensor fault) occurs at one or more converters, the inherent current-limiting property protects the corresponding devices and the remaining non-compromised units automatically share the remaining power to the battery, thus enhancing the EV charger resilience. An EV charging unit with 4 modules is simulated to verify the effectiveness and increased resilience of the proposed control approach under setpoint attacks and sensor/measurement faults. © 2024 IEEE.

关键词： Charging stations

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The Cybernetic Artificial Intelligence (CAI): Α new scientific field for modelling and controlling Complex Dynamical Systems 22

The Cybernetic Artificial Intelligence (CAI): Α new scienti...

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22nd IFAC Conference on Technology, Culture and International Stability, TECIS 2024

作者： Groumpos, Peter P. Department of Electrical and Computer Engineering University of Patras Greece26550 Greece

Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. The Fuzzy Cognitive Map approach is used for creating new knowledge. An algorithm for developing the CAI approach is proposed. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence (CAI). The importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values and the acceptance of the CAI as a viable new scientific field. © 2024 The Authors. This is an open access article under the CC BY-NC-ND license.

关键词： Adversarial machine learning

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Advancing Agricultural Sustainability Through an AI Powered Classification Framework of Plant Pests and Diseases 13th

Advancing Agricultural Sustainability Through an AI Powered...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Hasan, Hadi Al Kakoun, Razan Massaad, Gaby Awad, Mariette Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon

ISBN: (纸本)9783031632266

Plants pests and diseases can cause significant economic losses for farmers, and accurate identification and classification of these issues is crucial. Deep learning techniques, have demonstrated promising results in correctly identifying and classifying plant pests and diseases. However, the accuracy of these models heavily relies on the quality and size of the training dataset. Gathering a large and diverse dataset of plant pests and diseases can be challenging due to the variation in the appearance of diseased plants and the high cost of data collection. To tackle this issue, a web portal has been developed to gather datasets of plant pests and diseases, making it simpler for researchers to contribute data and enhance the accuracy of these models. In addition, Generative Adversarial Networks (GANs) have been used to expand the current datasets by creating synthetic images that closely resemble real-world data. This has resulted in improved accuracy and robustness of CNN and YOLO models in identifying and classifying plant pests and diseases. © IFIP International Federation for Information Processing 2024.

关键词： Deep learning

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Integrating Low Power Data Encoding in Intel Advanced Interface Bus

Integrating Low Power Data Encoding in Intel Advanced Interf...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Kotoulas, Emmanouil Pavlidis, Vasilis F. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

ISBN: (纸本)9798350318845

The ever-growing need for computational power has led to advanced packaging technologies for integrated circuits. One such technology is 2.5D integration, which reduces the distance between chiplets in a single package, resulting in a reduction of delay and power during data transfer. To connect chiplets, Intel created the Advanced Interface Bus (AIB), an open-source interface, as a universal solution for low power chiplet communication through a wide parallel channel with a low complexity circuit and an active redundancy scheme. However, with the increase in frequency of the interconnect, a considerable part of the total system power is used for data transfers. To reduce the power consumed by the interconnect, data encoding schemes are deployed. This paper explores the integration of our encoding scheme Adaptive Word Reordering (AWR) in Intel's AIB, evaluating the energy savings that can result by combining an emerging industrial interface with a modern data encoding technique. The results show that with the integration of AWR in AIB, the total power is reduced by 5.5% whereas the included Data Bus Invert (DBI) encoding scheme does not achieve noticeable improvement for the evaluated workloads. © 2024 IEEE.

关键词： Data transfer

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A Federated Mining Framework for Complete Erasable Itemsets

A Federated Mining Framework for Complete Erasable Itemsets

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hong, Tzung-Pei Kuo, Meng-Jui Chen, Chun-Hao Li, Katherine Shu-Min National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350362480

In this study, we develop an innovative federated framework for erasable itemset mining to address the challenges of horizontal federated learning in data mining and resolve the shortcomings of the previous algorithm. The framework is based on a client-server architecture, in which clients from multiple data sources collaborate to effectively integrate information. The proposed algorithm is divided into two parts, including the client-side mining and the server-side aggregation. During the client-side mining stage, the algorithm introduces quasi-erasable itemsets to collect additional useful information, facilitating the integration of results on the server side. In the server-side aggregation stage, the algorithm employs a boundary strategy, effectively utilizing the clients' quasi-erasable and erasable itemsets to improve the accuracy of the results. Experimental results demonstrate that the proposed method not only effectively mines complete knowledge but also ensures data-privacy protection. © 2024 IEEE.

关键词： Federated learning

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A comparative study of recommender systems in different domains 4th

A comparative study of recommender systems in different doma...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Rohit Sharma, Aditi Kumar, Aditya Singh, Prabal Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India

ISBN: (纸本)9781032911571

Recommender systems are techniques designed to enhance user experience in various domains. They suggest relevant items to users based on their behavior and preferences (Linyuan et al. Feb 2012). These systems are being extensively utilized in domains such as e-commerce, social media, and video streaming platforms. A recommender system aims to provide personalized recommendations to users based on their past behavior, preferences, and other similar data. The recommender system receives input data from users. Several recommender systems are available nowadays, making it challenging to select one. It would be beneficial to conduct a comparative analysis of various recommendation systems. The research paper attempts to conclude the performance of various recommender systems by using a variety of evaluation metrics. Evaluation metrics provide insights into the effectiveness of the recommendation algorithms. This guides the selection of the most appropriate model for a given application (Oona et al. 2024). The comparative study aims to examine and analyze different recommender systems across various domains. We evaluate the performance of these systems in improving user experiences and increasing user interaction. By studying the performance of recommender systems in different domains, we gain insights into the strengths and limitations of each approach. © 2025 the Author(s).

关键词： collaborative content-based evaluation metric hybrid recommendation recommender

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Federated learning techniques in the healthcare area 4th

Federated learning techniques in the healthcare area

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Kumar, Sanjay Virk, Ishpreet Singh Puri, Renu Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Federated understanding techniques have actually shown prospective, in the medical care sector allowing cooperation as well as information sharing while promoting personal privacy and also safety and security steps. This evaluation explores making uses of federated knowing in health care showcasing its capacity to boost results development medical study and also sustain tailored medication. The evaluation covers the advantages along with barriers of incorporating federated discovering in medical care together with opportunities for expedition as well as development in this area. Furthermore it deals with difficulties as well as chances for research study on medical care applications. The goal is to inspire a journey of linked expertise in health care contexts for it to come to be an approach for securing fragile professional information. The future pattern suggests an expanding fostering of linked knowing in setups, causing sophisticated procedures that focus on personal privacy and also safety and security while successfully using this innovation to resolve crucial problems within the medical care field. © 2025 the Author(s).

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UltraMAE: Multi-modal Masked Autoencoder for Ultrasound Pre-training 7

UltraMAE: Multi-modal Masked Autoencoder for Ultrasound Pre-...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Rahman, Aimon Patel, Vishal M. Department of Electrical and Computer Engineering Johns Hopkins University United States

Pre-training on a large dataset such as ImageNet followed by supervised fine-tuning has brought success in various deep learning-based tasks. However, the modalities of natural images and ultrasound images have considerable differences, making pre-training on natural images ineffective for ultrasound-related tasks. In this paper, we introduce a unified masking-based model for both ultrasound images and videos that learns better visual representation than the network with single-modality representations. This is the first large-scale generalized ultrasound pre-training network that simultaneously utilizes 100,000+ videos and images of different parts of the human anatomy such as the liver, bones, heart, thyroids, nerves, etc, making the network an effective benchmark pretrained model for any ultrasound-specific downstream tasks. We propose a novel method for ultrasound image analysis that utilizes an ultrasound-specific confidence map to guide low-level representation learning through masked feature acquisition. Our pre-trained network has demonstrated remarkable efficacy and versatility in tackling both classification and segmentation tasks across a range of ultrasound pathologies, highlighting its potential for widespread adoption and impact in the ultrasound field. In addition, we show that our pre-training model can be leveraged to learn efficiently with a small number of labeled ultrasound images. © 2024 CC-BY 4.0, A. Rahman & V.M. Patel.

关键词： Ultrasonic imaging

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MDDC: melanoma detection using discrete wavelet transform and convolutional neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12959-12966页

作者： Asadi, Omid Yekkalam, Aref Manthouri, Mohammad Electirical and Computer Engineering Department North Tehran Branch Islamic Azad University Tehran Iran Computer Engineering Department Yadegar Emam Branch Islamic Azad University Tehran Iran Electrical Engineering Department Shahed University Tehran Iran

Detection of melanoma in the early stage can provide the patients with more promising treatments. Numerous methods have been proposed for detecting melanoma using computational approaches;however, the application of deep learning in this field is incipient and needs to be assessed further. In this paper, we introduce MDDC, a four-step deep learning-based pipeline for melanoma detection using digital images. MDDC removes image noise using discrete wavelet transform, pre-processes and extends the generalizability using a Gaussian blur filter and image transformation techniques, and finally computes the probability of melanoma in digital images using a convolutional neural network. The model performance is evaluated by considering various CNN architectures and three validation scenarios to find the best CNN architecture. Moreover, we used the transfer learning technique to improve the model performance and generalize it for working on another dataset. MDDC can handle noisy images and is insensitive to image transformations. The validation of MDDC led to 0.96 Accuracy, 0.96 Recall, 0.954 Specificity, 0.99 AUC, and 0.99 AUPR. In addition, further evaluation of the proposed method with state-of-the-art methods verifies that MDDC outperforms other methods. Consequently, MDDC can efficiently and quickly detect melanoma in digital images. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Discrete wavelet transforms

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Modeling and Optimization of a Novel Design of CIGS Tandem Solar Cells, Utilizing Mole Fraction Variation 52

Modeling and Optimization of a Novel Design of CIGS Tandem S...

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52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Filippou, Filippos Michael, Sherif Department of Electrical and Computer Engineering MontereyCA93943 United States

ISBN: (纸本)9781665464260

This study thoroughly explored the potential of a high efficiency multi-junction photovoltaic design comprising layers of Copper Indium Gallium Selenide (CIGS) solar cells. Leveraging the advanced design principles of tandem solar cells, the theoretical lattice match between semiconductors based on CIGS and the ability to tailor the energy gaps of each CIGS layer were the principal ideas that drove to the success of this research. Utilizing the Silvaco ATLAS simulation toolbox, an advanced virtual wafer-fabrication program, various physical models were simulated, and overall design optimization was conducted. Building upon prior research at the Naval Postgraduate School, which provided the background on modeling CIGS solar cells with Silvaco ATLAS and validating its results, two main models were simulated, exploring a triple-junction and a quad-junction tandem solar cell respectively. Optimization primarily involved adjusting the thicknesses and the mole fractions of the individual CIGS layers to match the corresponding generated currents while maximizing the output voltages. Results indicate promising performance for the triple-junction cell, achieving an efficiency of 27%, whereas the quad-junction cell reached lower standards, with an efficiency staying just below 19%. However, a notable drawback for both configurations was the necessity of significantly increasing the thickness of the bottom layers to enhance efficiency. © 2024 IEEE.

关键词： Gallium compounds

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