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Conference on Power, Energy and Innovations (ICPEI)

作者： Pratya Phanomupatum Ravee Phromloungsri Rachata Tama Tiwat Pongthavornkamol Decha Wongpatsa Somchat Sonasang Department of Electronics Faculty Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Location and Automatic Identification System Research Team Communication and Network Research Group National Electronic and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Electrical Elements and Wireless Devices Unit (EEWSU) Faculty Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand

ISBN: (数字)9798350356779

ISBN: (纸本)9798350356786

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency.

关键词： Power dividers Technological innovation Microwave integrated circuits Radio astronomy Phase shifters Insertion loss Microwave circuits Microstrip Proposals Substrates

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On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods

arXiv

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arXiv 2023年

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence the need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT due to its incompatible rounding procedure, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε), which is better in Ε than revised Sinkhorn. The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. © 2023, CC BY.

关键词： Gradient methods

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Exploring Text-Guided Single Image Editing for Remote Sensing Images

arXiv

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arXiv 2024年

作者： Han, Fangzhou Si, Lingyu Dong, Hongwei Zhang, Lamei Chen, Hao Du, Bo The Department of Information Engineering Harbin Institute of Technology Harbin China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China The Hubei Luojia Laboratory National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. During the generation stage, consistency in content and details between the original and edited images must be maintained, while in the editing stage, controllability and accuracy of the edits should be ensured. For natural images, these challenges can be tackled by training generative backbones on large-scale benchmark datasets and using text guidance based on vision-language models (VLMs). However, these previously effective approaches become less viable for RSIs due to two reasons: First, existing generative RSI benchmark datasets do not fully capture the diversity of remote sensing scenarios, particularly in terms of variations in sensors, object types, and resolutions. Consequently, the generalization capacity of the trained backbone model is often inadequate for universal editing tasks on RSIs. Second, the large spatial resolution of RSIs exacerbates the problem in VLMs where a single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics when using text to guide RSI editing. To solve above problems, this paper proposes a text-guided RSI editing method that is controllable but stable, and can be trained using only a single image. It adopts a multi-scale training approach to preserve consistency without the need for training on extensive benchmark datasets, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability in the text-guided editing process. High-quality RSI editing results can be achieved by first applying multi-scale training to the target image, followed by text-guided fine-tuning using RSI pre-trained VLMs and PE. Experi

关键词： Semantics

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Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients

引用

International Journal of Information technology and Decision Making 2024年第1期23卷 197-268页

作者： AlSereidi, Abeer Salih, Sarah Qahtan M. Mohammed, R.T. Zaidan, A.A. Albayati, Hassan Pamucar, Dragan Albahri, A.S. Zaidan, B.B. Shaalan, Khaled Al-Obaidi, Jameel Albahri, O.S. Alamoodi, Abdulah Majid, Nazia Abdul Garfan, Salem Al-Samarraay, M.S. Jasim, A.N. Baqer, M.J. Faculty of Engineering & IT The British university in Dubia United Arab Emirates United Arab Emirates Department of Computer Center Middle Technical University's Baghdad Iraq Sulaymaniyah Iraq Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq University of Defence in Belgrade Department of Logistic Pavla Jurisica Sturma 33 Belgrade11000 Serbia Baghdad Iraq Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliu64002 Taiwan Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Perak 35900 Malaysia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Institute of Biological Sciences Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Foundation of Alshuhda Baghdad Iraq

Context: When the epidemic ¯rst broke out, no speci¯c treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment e®orts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The ¯rst is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes ¯ve sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates

关键词： Coronavirus

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Silicon carbide-graphene nanocomposite electrodes for enhanced desalination via capacitive deionization

引用

Ceramics International 2025年

作者： Yousef, Ayman Hameed, R.M. Abdel Maafa, Ibrahim M. Abutaleb, Ahmed El-Halwany, M.M. Department of Chemical Engineering College of Engineering and Computer Sciences Jazan University Jazan45142 Saudi Arabia Chemistry Department Faculty of Science Cairo University Giza Egypt Engineering and Technology Research Center Jazan University P.O.Box 114 Jazan82817 Saudi Arabia Department of Mathematics and Physics Engineering Faculty of Engineering Mansoura University El-Mansoura Egypt Faculty of Engineering Mansoura National University Egypt

Graphene nanoflakes [GNFs] were ultrasonically modified with silicon carbide nanopowder with subsequent hydrothermal heating at 120 °C. A number of these SC-GNFs nanocomposites was fabricated based on altering their metallic carbide content [5, 10 and 15 wt%] in relation to that of carbonaceous substrate. The chemical composition, morphology and interaction with water droplets of SC-GNFs nanostructures were examined using suitable physical characterization tools. The ability of these carbide containing graphene nanomaterials to desalinate water samples was evaluated through varied electrochemical measurements. Outstanding specific capacitances could be measured at different SC-GNFs nanocomposites when related to those of bare components of SiC and GNFs. Incorporating 10 wt% SiC between graphene nanoflakes could significantly promote their capacitance by about nine folds. Galvanostatic charge-discharge experiments of SC-GNFs nanocomposite revealed good stability attitude within longer periods. Increased salt removal efficiencies were also demonstrated at all SC-GNFs nanopowders up to 83.33 % when contrasted to those of SiC [10.42 %] and GNFs [12.50 %]. These findings at graphene nanoflakes surfaces after their treatment with SiC nanoparticles might suggest further studies for relevant metallic carbides onto convenient carbonaceous based supports for valuable CDI systems. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Deionized water

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A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging

arXiv

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arXiv 2025年

作者： Wang, Junjia Xiong, Bo Zhou, You Cao, Xun Ma, Zhan School of Electronic Science and Engineering Nanjing University Nanjing210023 China National Engineering Research Center of Visual Technology Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China Medical School Nanjing University Nanjing210093 China

The emergence of virtual staining technology provides a rapid and efficient alternative for researchers in tissue pathology. It enables the utilization of unlabeled microscopic samples to generate virtual replicas of chemically stained histological slices, or facilitate the transformation of one staining type into another. The remarkable performance of generative networks, such as CycleGAN, offers an unsupervised learning approach for virtual coloring, overcoming the limitations of high-quality paired data required in supervised learning. Nevertheless, large-scale color transformation necessitates processing large field-of-view images in patches, often resulting in significant boundary inconsistency and artifacts. Additionally, the transformation between different colorized modalities typically needs further efforts to modify loss functions and tune hyperparameters for independent training of networks. In this study, we introduce a general virtual staining framework that is adaptable to various conditions. We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities, termed the Value Mapping Generative Adversarial Network (VM-GAN). Meanwhile, we present a confidence-based tiling method to address the challenge of boundary inconsistency arising from patch-wise processing. Experimental results on diverse data with varying staining protocols demonstrate that our method achieves superior quantitative indicators and improved visual perception. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Photovoltaic Array Fault Detection Based on Manifold Learning and Neural Network

Photovoltaic Array Fault Detection Based on Manifold Learnin...

引用

computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Kai Wang Yanru Zhong Xiaonan Luo Guangxi Key Laboratory of Intelligent Processing of computer Images and Graphic Guilin University of Electronic Science and Technology Guilin China Navigation Positioning and Location Services National and Local Joint Engineering Research Center Guilin University of Electronic Science and Technology Satellite Guilin China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

This paper endeavors to devise an ingenious methodology rooted in artificial intelligence, which, in turn, can be effectively employed to identify sundry categories of faults that are closely associated with photovoltaic arrays. This novel approach amalgamates the manifold learning algorithm with a formidable multilayer perceptron neural network (MLP). Intriguingly, it substantiates that the initial reduction in data dimensionality is achieved through the remarkable Isomap algorithm. Consequently, this compressed data is seamlessly channeled into the MLP, thereby yielding remarkable outcomes concerning fault detection. To validate and scrutinize the aforementioned proposition, a quintessential circuit representing a photovoltaic array is initially simulated utilizing the venerable Matlab/Simulink tool. During this simulation, an extensive corpus of both normal and faulty data is meticulously generated. Subsequently, the manifold learning algorithm is artfully applied to drastically diminish the dimensionality of this extensive dataset The resultant compressed data is then deftly fed into the MLP, allowing it to discern the salient characteristics pertaining to the sundry fault types. Through incessant iterations of intensive training, these faults can be astutely detected and discerned with remarkable accuracy. The results conclusively demonstrate the efficacy of combining manifold-based learning and multilayer perceptron neural network in detecting faults in photovoltaic arrays. The proposed method achieves high precision and effectiveness, surpassing the traditional approach by 2.52%.

关键词： Photovoltaic systems Training Accuracy Fault detection Computational modeling Multilayer perceptrons Mathematical models

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L-CAD: language-based colorization with any-level descriptions using diffusion priors 23

L-CAD: language-based colorization with any-level descriptio...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zheng Chang Shuchen Weng Peixuan Zhang Yu Li Si Li Boxin Shi School of Artificial Intelligence Beijing University of Posts and Telecommunications National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University and National Engineering Research Center of Visual Technology School of Computer Science Peking University International Digital Economy Academy

Language-based colorization produces plausible and visually pleasing colors under the guidance of user-friendly natural language descriptions. Previous methods implicitly assume that users provide comprehensive color descriptions for most of the objects in the image, which leads to suboptimal performance. In this paper, we propose a unified model to perform language-based colorization with any- level descriptions. We leverage the pretrained cross-modality generative model for its robust language understanding and rich color priors to handle the inherent ambiguity of any-level descriptions. We further design modules to align with input conditions to preserve local spatial structures and prevent the ghosting effect. With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios. Extensive experimental results demonstrate our advantages of effectively handling any-level descriptions and outperforming both language-based and automatic colorization methods. The code and pretrained models are available at: https://***/changzheng123/L-CAD.

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RAID45: Hybrid Parity-Based RAID for Reducing Parity Write Wear on High-Density SSDs

RAID45: Hybrid Parity-Based RAID for Reducing Parity Write W...

引用

IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Jialin Liu Yujiong Liang Yunpeng Song Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

High-densitysolid-state drives (SSDs), such as triple-level cell (TLC) or quad-level cell (QLC) flash, are adopted in parity-based RAID systems to achieve high reliability with low redundancy. However, the parity writes cause high write wear, which is unfriendly to such high-density SSDs with low write endurance. Conversely, high-performance SSDs, such as ZNAND, XL-Flash, have high write endurance but their high cost per bit hinders their deployment in RAID. This paper proposed a novel hybrid RAID structure, RAID45, to reduce parity writes for highdensity SSDs. Specifically, RAID45 uses high-performance SSD to store the parity of write-intensive stripes to absorb as much of the wear of parity writes on high-density SSDs as possible. Experimental results on real platform show that RAID45 achieves encouraging parity write reduction on the high-density SSDs.

关键词： Costs Redundancy Reliability

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Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs

Cross-Modality Time-Variant Relation Learning for Generating...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jingyi Wang Jinfa Huang Can Zhang Zhidong Deng Department of Computer Science Tsinghua University Beijing China School of Electronic and Computer Engineering Peking University China Department of Computer Science Beijing National Research Center for Information Science and Technology (BNRist) THUAI State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomous navigation, and task planning of self-driving vehicles and mobile robots. In the process of temporal and spatial modeling during dynamic scene graph generation, it is particularly intractable to learn time-variant relations in dynamic scene graphs among frames. In this paper, we propose a Time-variant Relation-aware TRansformer (TR 2 ), which aims to model the temporal change of relations in dynamic scene graphs. Explicitly, we leverage the difference of text embeddings of prompted sentences about relation labels as the supervision signal for relations. In this way, cross-modality feature guidance is realized for the learning of time-variant relations. Implicitly, we design a relation feature fusion module with a transformer and an additional message token that describes the difference between adjacent frames. Extensive experiments on the Action Genome dataset prove that our TR2 can effectively model the time-variant relations. TR2 significantly outperforms previous state-of-the-art methods under two different settings by 2.1 % and 2.6% respectively.

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