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

作者： Hyemi Jang Junsung Park Dahuin Jung Jaihyun Lew Ho Bae Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Cyber Security Ewha Womans University Department of Electrical and Computer Engineering Seoul National University and Interdisciplinary Program in Artificial Intelligence Seoul National University

Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image pairs from the real world are extremely costly to gather, self-supervised learning, which utilizes noisy input itself as a target, has been studied. To prevent a self-supervised denoising model from learning identical mapping, each output pixel should not be influenced by its corresponding input pixel; This requirement is known as J-invariance. Blind-spot networks (BSNs) have been a prevalent choice to ensure J-invariance in self-supervised image denoising. However, constructing variations of BSNs by injecting additional operations such as downsampling can expose blinded information, thereby violating J-invariance. Consequently, convolutions designed specifically for BSNs have been allowed only, limiting architectural flexibility. To overcome this limitation, we propose PUCA, a novel J-invariant U-Net architecture, for self-supervised denoising. PUCA leverages patch-unshuffle/shuffle to dramatically expand receptive fields while maintaining J-invariance and dilated attention blocks (DABs) for global context incorporation. Experimental results demonstrate that PUCA achieves state-of-the-art performance, outperforming existing methods in self-supervised image denoising.

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Utilizing Battery Energy Storage Systems to Enhance the Integration of Photovoltaic Systems for Damping Oscillations

Utilizing Battery Energy Storage Systems to Enhance the Inte...

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IEEE Southeastcon

作者： Ilham Akbar Ariesa Budi Zakaria Mayur Basu Eduard Muliadi Suwarno Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Auburn USA Electrical and Computer Engineering Auburn University Auburn USA School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

As the integration of Variable Renewable Energy (VRE) generators into power systems increases, there is a potential decrease in overall system reliability, particularly in terms of stability. This research suggests the integration of a Battery Energy Storage System (BESS) into a power system with Variable Renewable Energy sources, such as a utility-scale Photovoltaic (PV) plant, to accelerate the damping of post-fault oscillations following a short-circuit disturbance. The objective is for a Power Oscillation Damping (POD) within the PV plant to address post-fault oscillations within a specific timeframe. The POD in the PV plant is designed to supply additional active and reactive power, responding to the system's demands after a disturbance. The addition of BESS to the system is anticipated to enhance the time required for the system to achieve a stable state, resulting in quicker system stabilization. Simulations conducted through PSCAD software can effectively depict the post-fault conditions of short-circuit disturbances, considering the impact of the integrated BESS.

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Power Oscillation Damping of a Wind Turbine Generation with Battery Energy to Smoothen Power System Stability

Power Oscillation Damping of a Wind Turbine Generation with ...

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IEEE Southeastcon

作者： Ariesa Budi Zakaria Ilham Akbar Mayur Basu Eduard Muljadi Suwano Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Auburn USA Electrical and Computer Engineering Auburn University Auburn USA School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

Integrating wind turbine generation (WTG) into the power grid presents challenges due to their variable and unpredictable output, creating instability. The intermittent nature of wind power and the reduced inertia of wind turbines pose significant challenges to maintaining power system stability, especially during grid faults and transient events. Intermittency can impact the electrical grid's stability and requires additional grid balancing and energy storage measures. This study proposes a Power Oscillation Damping (POD) control scheme integrated with battery energy to enhance WTG stability following grid faults. The proposed scheme combines a POD controller for WTG and a battery controller, demonstrating faster oscillation damping than a WTG without batteries. During damping control, the scheme prioritizes active power control of POD, utilizing the wind turbine's kinetic energy. Simultaneously, the reactive power control of POD adjusts based on the synchronous generator's rotational speed to maintain voltage stability. Feedback for both control loops is derived from the nearest synchronous generator, ensuring coordinated and responsive control. Adding a battery into the control scheme helps to smooth out power fluctuations and enhance stability. Therefore, it is possible to compensate for the intermittency of wind power and voltage drop after short-circuit events through various measures, such as advanced control systems and battery power. The effectiveness of this proposed control scheme, with the added integration of Battery Energy, is validated through the PSCAD simulator involving a short-circuit fault in a two-area power system. Results demonstrate a notable improvement in power system stability, particularly after grid faults.

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Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships 26th

Uncertainty Quantification Using Subsampling for Assembly-F...

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26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Rachtman, Eleonora Sarmashghi, Shahab Bafna, Vineet Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego La JollaCA United States Department of Electrical and Computer Engineering UC San Diego La JollaCA United States Department of Computer Science and Engineering UC San Diego La JollaCA United States

Computing the distance between two genomes, often without using alignments or even access to assembled sequences, is fundamental to many downstream analyses. © 2022, The Author(s), under exclusive license to Spri... 详细信息

ISBN: (纸本)9783031047480

关键词： Uncertainty analysis

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An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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Reducing Dependency on Imported Electricity: Utilizing Renewable Energy and Minimizing Fossil Fuel Power Plant Operations in West Kalimantan

Reducing Dependency on Imported Electricity: Utilizing Renew...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Ilham Akbar Ariesa Budi Zakaria Eduard Muljadi Suwarno Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Auburn USA Electrical and Computer Engineering Auburn University Auburn USA School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper discusses West Kalimantan's dependency on imported electricity and fossil fuels and advocates for integrating renewable energy sources, especially solar power. With abundant solar resources and its equatorial location, West Kalimantan presents a significant solar PV generation opportunity. However, its dependence on imported electricity from the ASEAN Grid initiative poses risks to energy security. By promoting solar power as an alternative energy source and reducing reliance on fossil fuels, the paper aims to improve energy independence and sustainability. Integrating solar PV requires assessing frequency stability to ensure system resilience. Moreover, the interconnection between the two grids enhances system resilience and facilitates renewable energy integration. In summary, integrating solar PV power plants into the West Kalimantan system aims to decrease dependence on electricity imports from Sarawak while preserving the system's frequency stability.

关键词： Photovoltaic systems Renewable energy sources Fault tolerance Electricity Power system stability Fossil fuels Security Sustainable development Thermal stability Research and development

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Mutual authentication method between PUFs

Mutual authentication method between PUFs

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2022 IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2022

作者： Choi, Yunyeong Lee, Jungwon Shin, Hyungsoon Sun, Wookyung Ewha Womans University Dept. of Electronic and Electrical Engineering Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of Ewha Womans University Smart Factory Multidisciplinary Program Seoul Korea Republic of

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

This paper proposes a new authentication method that enables mutual verification between two different PUFs without a server. The output signal (response) of one PUF is hidden in the opponent's input signal (challenge) list by interlinking the challenge and response information of the two PUFs. In this way, when sending and receiving authentication information, a challenge list index is used instead of exposing the challenge or response information to the network directly. The verification information for checking the validity of the received index is protected with encryption using a hash function. Also, two novel PUF systems, key-map PUF and cube PUF, are presented that improve the security of the PUF-based system using previous authentication information. The proposed authentication method and PUF systems can be used for IoT devices. © 2022 IEEE.

关键词： Hash functions

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Towards cross-modal causal structure and representation learning 2

Towards cross-modal causal structure and representation lear...

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2nd Machine Learning for Health Symposium, ML4H 2022

作者： Mao, Haiyi Liu, Hongfu Xiaotian Dou, Jason Benos, Panayiotis V. Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh United States Michtom School of Computer Science Brandeis University United States Department of Electrical and Computer Engineering University of Pittsburgh United States Department of Epidemiology University of Florida United States

Does the SARS-CoV-2 virus cause patients' chest X-Rays ground-glass opacities? Does an IDH-mutation cause differences in patients' MRI images? Conventional causal discovery algorithms, although well developed to uncover the cause-effect relationships on structured data, cannot elucidate causal relations between unstructured images and structured scalar variables due to the complexity of the former. In this paper, we consider causal discovery between images and structured (scalar) variables. Specifically, we derive low dimensional image representations to analyze with structured variables. We propose a two-module amortized variational algorithm named Cross-Modal Variational Causal representation and structure Learning (CMCL). CMCL jointly learns identifiable representations given a set of independent structured variables and causal relations via formulating latent representations and structured variables into a direct acyclic graph. Moreover, we further enforce counterfactual invariance/variance onto representations. We demonstrate that CMCL outperforms other related methods on synthetic datasets and validate causal relations on semi-synthetic datasets by visualization. © 2022 P.N. Argaw, E. Healey & I.S. Kohane.

关键词： Coronavirus

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Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark

arXiv

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arXiv 2024年

作者： Pazho, Armin Danesh Yao, Shanle Noghre, Ghazal Alinezhad Ardabili, Babak Rahimi Katariya, Vinit Tabkhi, Hamed Department of Electrical and Computer Engineering University of North Carolina Charlotte United States Public Policy Program University of North Carolina Charlotte United States

Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the complexity of diverse human behaviors, the rarity of anomalies, and ethical constraints. These challenges limit access to high-quality datasets and highlight the need for a dataset and framework supporting continual learning. Moving towards adaptive human-centric VAD, we introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time, bridging traditional training and real-world deployment. HuVAD prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes, offering over 5× more pose-annotated frames than previous datasets. Our standard and continual benchmarks, utilize a comprehensive set of metrics, demonstrating that UCAL-enhanced models achieve superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at https://***/TeCSAR-UNCC/HuVAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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