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A Combination of Soft Attention-aided CNN Models using Dempster-Shafer Theory for Skin Cancer Classification

A Combination of Soft Attention-aided CNN Models using Demp...

引用

International Conference on Current Problems of Applied Mathematics and computer Systems, CPAMCS 2023

作者： Sarkar, Sujan Ray, Amartya Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031640094

Skin cancer is one of the most deadly forms of cancer in the world, causing hundreds of deaths every year. Researchers have been developing various computer-aided diagnosis (CAD) systems to help medical professionals classify the types of skin cancer using different image modalities. In this paper, we propose a model, which combines the outcomes of three separately trained attention-based convolutional neural networks (CNNs) using the Dempster-Shafer theory. The three base learners used in this paper are ResNet50, InceptionResNetV2 and Dense-Net201, each of which is aided by a soft attention module. For evaluating the proposed model, we have considered a publicly accessible skin cancer dataset, called HAM10000. This is a challenging dataset to work with as it is a class imbalanced dataset. Our proposed approach yields an accuracy of 0.932, which is better than many recently published methods found in the literature. The source code is available at: https://***/Cmatermedicalimageanalysis/DST_Combination © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Beyond the network-based multi-omics data integration in COPD: A pathway-centric analysis

Beyond the network-based multi-omics data integration in COP...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sibilio, Pasquale Conte, Federica Paci, Paola Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Karolinska Institutet Stockholm17177 Sweden

ISBN: (纸本)9798350386226

Chronic obstructive pulmonary disease (COPD) is a lung disease causing hundred thousand of death each year worldwide and defined as a respiratory and airflow impairment majorly due to large and small airways dysfunctions. COPD could be considered as a syndrome that includes disease axes of variable pathological and clinical conditions. Despite recent advances, the comprehension of the molecular mechanisms responsible for COPD disease spectrum is far to be reached. Moreover, we should also consider continuous smoking exposure, which could lead to variability in the disease mechanisms and progression. Network-based multi-omics data integration can help to study the association between molecular determinants on diverse biomolecular layers in a disease context. In a previous study, we leveraged lung RNA-seq and DNA-methylation data of a COPD-control cohort to build a correlation-based integrated network (called coupled network), that helped to unveil genes involved in immune and inflammatory modulation of COPD. Therefore, in our previous study we highlighted the most important genes of the coupled network and inspected their single contribution to disease-related pathways. In this study we aim to overcome this limitation by performing a pathway activity analysis by considering the expression and DNA methylation profiles of coupled network genes. Moreover, we exploit this analysis to study the possible contribution of coupled network genes to the differential disease progression between current and former smokers patients. © 2024 IEEE.

关键词： Lung cancer

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Optimizing Thermo-Electric Generation with Quasi-Z-Source Converter in Energy Harvesting for Wearable Devices 3

Optimizing Thermo-Electric Generation with Quasi-Z-Source Co...

引用

3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Lotfy, Mostafa Wageh Hussein, Aziza I. Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (纸本)9781643685649

Recent advances in semiconductors industry and microelectronics have created new opportunities for integrating various technologies in energy harvesting projects. These advancements have simplified the process of capturing energy from different sources, such as thermal energy, and converting it into electrical power. Thermo-Electric Generators (TEGs) are commonly used in many applications such as wearable devices. However Thermo-Electric Generators produce very low output voltages, which is insufficient for most low-power electronic circuits to operate. Therefore, a boost converter is essential to step up the voltage output from the TEG to a usable level. In this paper, a DC-DC converter with a high performance in terms of voltage gain is implemented. The proposed quasi-Z-source (q-ZSC) DC-DC converter not only offers high voltage gain but also several significant advantages. The proposed q-ZSC is capable of both boosting and regulating the voltage output efficiently, even under variable input conditions, making it highly suitable for low-power energy harvesting systems such as Thermo-Electric Generators. The design is simulated using MATLAB Simulink software, focusing on integrated circuits, with a strong emphasis on optimizing the circuit's efficiency. This work underscores critical factors in improving energy harvesting circuits, enhancing their performance and effectiveness in low-power applications for wearable technology. © 2024 The Authors.

关键词： DC generator motors

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High-resolution sample size enrichment of single-cell multi-modal low-throughput Patch-seq datasets

High-resolution sample size enrichment of single-cell multi-...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Martini, Lorenzo Bardini, Roberta Savino, Alessandro Carlo, Stefano Di Politecnico di Torino Control and Computer Engineering Department Torino10129 Italy

ISBN: (纸本)9781665468190

Single-cell multimodal technologies are becoming the hot topic of single-cell heterogeneity and function studies, promising to unravel the hidden relationship and functionalities of different aspects of the cells. Among the plethora of single-cell technologies, interesting is the patch-seq technology, which simultaneously performs Patch clamp measures and scRNA-seq on the same cells. However, given the experimental limitations of throughput of Patch clamp, the scRNA-seq analysis is challenging because it requires more samples to investigate cellular heterogeneity. Usually, the solution is associating the cells with the cell types in an existing scRNA-seq dataset. However, doing so loses part of the single cell resolution of the multimodal technique. Therefore, this work proposes a procedure leveraging the Seurat Integration process to find from a reference dataset t he most similar cells to the ones from the patch-seq. The similarity is how much gene expression profiles are identical, and to evaluate that, this work defines various etrics based on R and Index. In this way, one obtains a selection of suitable Reference cells to enrich the number of cells on which to perform multimodal investigation. © 2022 IEEE.

关键词： Gene expression

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Green and Low Carbon Load Side Carbon Emission Measurement in Power Systems Based on Opportunity Constraints 11

Green and Low Carbon Load Side Carbon Emission Measurement i...

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11th China International Conference on Electricity Distribution, CICED 2024

作者： Xu, Zhiyuan Cheng, Liang School of Control and Computer Engineering North China Electric Power University Beijing China Institute of Software Chinese Academy of Sciences Tca Lab Beijing China

ISBN: (纸本)9798350368345

In the context of global warming, traditional carbon measurement methods have failed to completely capture the carbon emission dynamics of the power system under various operating states, resulting in biased carbon emission data. Moreover, there is still significant research space for measurement models on the power load side. To improve the accuracy and real-time performance of carbon emission measurement, this study designs a power system load side carbon emission measurement model based on opportunity constraints. The proposed model is built on opportunity constraints and fairness principles to allocate carbon emission responsibilities on the load side. On this basis, considering the dynamic characteristics of the power system, a two-stage carbon emission measurement model is raised, which uses dynamic carbon emission measurement method and node carbon potential method to calculate carbon emissions in the first and second stages, respectively. The outcomes validated that the carbon emissions of Unit 1 in the fixed carbon emission factor model were 620.07 tons, while they were reduced to 470.11 tons in the proposed model. After implementing demand response measures, the total carbon emissions of the system during peak electricity usage decreased by 3130 kg. The fixed carbon emission factor econometric model, traditional carbon market model, and the proposed model resulted in a total carbon emissions of 18306.61 tons, 16939.79 tons, and 19045.21 tons, respectively. Therefore, the proposed model performs better in regulating demand, can more accurately measure carbon emissions on the load side of the power system, and contribute to promoting green and low-carbon development of the energy system. © 2024 IEEE.

关键词： Low emission

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Research on Autonomous Decision-Making of Multi-UAV Air Combat Based on Deep Reinforcement Learning 7th

Research on Autonomous Decision-Making of Multi-UAV Air Comb...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhou, Zhiming Qi, Hongmin Liu, Zhen Qian, Dianwei Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733354

To solve the autonomous decision-making problem of multi-UAV air combat, a multi-UAV reinforcement learning self-game autonomous decision-making method for air combat with parameter sharing is proposed. Firstly, a self-game policy pool training framework is designed. The game ability of the multi-UAV autonomous decision-making model is improved by self-game confrontation with a policy pool containing different training generations. Secondly, a parameter-sharing mechanism is proposed to train homogeneous UAVs to improve the efficiency of reinforcement learning training and the utilization of interactive data. Finally, a multi-UAV autonomous decision-making model using the multi-agent deep deterministic policy gradient algorithm is designed so that it can explore effectively in the continuous action space. In addition, experiments are designed to verify the effectiveness and migration of the designed algorithm and training framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Enlightening the Student in Knowledge Distillation 48

Enlightening the Student in Knowledge Distillation

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zheng, Yujie Wang, Chong Chen, Yi Qian, Jiangbo Wang, Jun Wu, Jiafei Ningbo University Faculty of Electrical Engineering and Computer Science China China University of Mining and Technology School of Information and Control Engineering China SenseTime Research China

ISBN: (纸本)9781728163277

Knowledge distillation is a common method of model compression, which uses large models (teacher networks) to guide the training of small models (student networks). However, the student may find a hard time absorbing the knowledge from a sophisticated teacher due to the capacity and confidence gaps between them. To address this issue, a new knowledge distillation and refinement (KDrefine) framework is proposed to enlighten the student by expending and refining its network structure. In addition, a confidence refinement strategy is utilized to generate adaptive soften logits for efficient distillation. The experiments show that the proposed framework outperforms state-of-the-art methods on both CIFAR-100 and Tiny-ImageNet datasets. The code is available at https://***/YujieZheng99/KDrefine. © 2023 IEEE.

关键词： knowledge distillation model compression structural re-parameterization

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Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication 10

Multi-Agent Coordination for a Partially Observable and Dyna...

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10th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2023

作者： Affinita, Daniele Volpi, Flavio Spagnoli, Valerio Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico D. Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Faculty of Political Science and Sociopsychological Dynamics UNINT University Rome Italy

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings. © 2024 Copyright for this paper by its authors.

关键词： Anthropomorphic robots

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From Product Sheet to Text and Video: A NLG Pipeline to Transform Structured Data into Comprehensive Descriptions 32

From Product Sheet to Text and Video: A NLG Pipeline to Tran...

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Avignone, Andrea Fiori, Alessandro Chiusano, Silvia Rizzo, Giuseppe Dept. of Control and Computer Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy LINKS Foundation Torino Italy

The recent improvement of powerful Large Language Models is the key to automatically produce satisfactory written and spoken language, in contrast to the constraints of conventional template-based solutions. However, the most advanced models can be costly and complex to integrate into practical applications, especially in business contexts where the output quality significantly matters. This study presents a tailored pipeline for data-to-text and text-to-speech generation, primarily harnessing the availability of open source pre-trained language models and leveraging established Natural Language Processing tasks. As a use case, we worked on the automatic generation of both textual and video product descriptions from the structured information about the product features. The pipeline involves all the required steps, providing the final trained and customized model. The obtained descriptions showed the capability of replicating the overall semantic, lexical and linguistic style of the corresponding human counterpart, despite being based on a cost-effective model. © 2024 Copyright for this paper by its authors.

关键词： Semantics

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Analysis of the Characteristics of a Dualchannel Three-phase Induction Motor 36

Analysis of the Characteristics of a Dualchannel Three-phase...

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36th International Conference on Electrical Drives and Power Electronics, EDPE 2023

作者： Korkosz, Mariusz Kutsyk, Andriy Krzywdzinska-Kornak, Krystyna Suliga, Mateusz Semeniuk, Mykola Rzeszow University of Technology Faculty of Electrical and Computer Engineering Rzeszow Poland Lviv Polytechnic National University Institute of Power Engineering and Control System Lviv Ukraine

ISBN: (纸本)9798350322750

The paper analyzes the characteristics of a dual-channel three-phase induction motor. Motors designed for multi-channel power supply are dedicated to applications requiring greater reliability. Each channel allows a certain degree of increase in the ability to continue operation after the failure states. The use of a dual channel power supply is the most common way to increase drive train redundancy. This paper presents the tests of the behaviour of a dual-channel three-phase induction motor in the event of loss of power to one of the phases (in one or two channels) and single-channel operation. A correctly designed motor allows for continuous operation in single-channel power mode. Numerical and laboratory tests confirmed this assumption. It has been demonstrated that the loss of power to one phase of a channel should result in the channel being turned off. © 2023 IEEE.

关键词： Fault tolerance

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