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Bilateral Contract for Load Frequency and Renewable Energy Sources Using Advanced Controller

computers, Materials & Continua 2022年第11期73卷 3165-3180页

作者： Krishan Arora Gyanendra Prasad Joshi Mahmoud Ragab Muhyaddin Rawa Ahmad H.Milyani Romany F.Mansour Eunmok Yang School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwara144001PunjabIndia School of Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia King Abdullah City for Atomic and Renewable Energy(K.A.CARE) RiyadhSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoul02707Korea

Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated ***,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation *** regular active load and inefficiency of best measures among varied associates is a huge *** sudden load deviation will give rise to immediate amendment in frequency and tie-line power *** is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the ***,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power *** the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks *** proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test *** experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s *** model can also be leveraged to examine the decision boundaries of the Bilateral case.

关键词： Bilateral contract load frequency control optimization harris hawks optimizer

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Micromolded honeycomb scaffold design to support the generation of a bilayered RPE and photoreceptor cell construct

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Bioactive Materials 2023年第12期30卷 142-153页

作者： In-Kyu Lee Ruosen Xie Agustin Luz-Madrigal Seunghwan Min Jingcheng Zhu Jiahe Jin Kimberly L.Edwards M.Joseph Phillips Allison L.Ludwig David M.Gamm Shaoqin Gong Zhenqiang Ma Department of Electrical and Computer Engineering University of Wisconsin-MadisonMadisonWI53706USA Wisconsin Institute for Discovery University of Wisconsin-MadisonMadisonWI53715USA Department of Ophthalmology and Visual Sciences University of Wisconsin-MadisonMadisonWI53705USA Department of Biomedical Engineering University of Wisconsin-MadisonMadisonWI53706USA McPherson Eye Research Institute University of Wisconsin-MadisonMadisonWI53705USA Waisman Center University of Wisconsin-MadisonMadisonWI53705USA Department of Comparative Biomedical Sciences University of Wisconsin–MadisonMadisonWI53706USA Department of Materials Science and Engineering University of Wisconsin-MadisonMadisonWI53706USA Department of Engineering Physics University of Wisconsin-MadisonMadisonWI53706USA Grainger Institute for Engineering University of Wisconsin-MadisonMadisonWI53706USA

Age-related macular degeneration(AMD)causes blindness due to loss of retinal pigment epithelium(RPE)and photoreceptors(PRs),which comprise the two outermost layers of the *** the small size of the macula and the importance of direct contact between RPE and PRs,the use of scaffolds for targeted reconstruction of the outer retina in later stage AMD and other macular dystrophies is particularly *** developed microfabricated,honeycomb-patterned,biodegradable poly(glycerol sebacate)(PGS)scaffolds to deliver organized,adjacent layers of RPE and PRs to the subretinal ***,an optimized process was developed to photocure PGS,shortening scaffold production time from days to *** resulting scaffolds robustly supported the seeding of human pluripotent stem cell-derived RPE and PRs,either separately or as a dual celllayered *** advanced,economical,and versatile scaffolds can accelerate retinal cell transplantation efforts and benefit patients with AMD and other retinal degenerative diseases.

关键词： Microfabrication Retina Scaffolds Stem cells Tissue engineering

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Fully Dynamic Algorithms for Knapsack Problems with Polylogarithmic Update Time 41

Fully Dynamic Algorithms for Knapsack Problems with Polyloga...

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41st IARCS Annual Conference on Foundations of Software Technology and Theoretical computer science, FSTTCS 2021

作者： Eberle, Franziska Megow, Nicole Nölke, Lukas Simon, Bertrand Wiese, Andreas Faculty of Mathematics and Computer Science University of Bremen Germany IN2P3 Computing Center CNRS Villeurbanne France Department of Industrial Engineering University of Chile Santiago Chile

ISBN: (纸本)9783959772150

Knapsack problems are among the most fundamental problems in optimization. In the Multiple Knapsack problem, we are given multiple knapsacks with different capacities and items with values and sizes. The task is to find a subset of items of maximum total value that can be packed into the knapsacks without exceeding the capacities. We investigate this problem and special cases thereof in the context of dynamic algorithms and design data structures that efficiently maintain near-optimal knapsack solutions for dynamically changing input. More precisely, we handle the arrival and departure of individual items or knapsacks during the execution of the algorithm with worst-case update time polylogarithmic in the number of items. As the optimal and any approximate solution may change drastically, we maintain implicit solutions and support polylogarithmic time query operations that can return the computed solution value and the packing of any given item. While dynamic algorithms are well-studied in the context of graph problems, there is hardly any work on packing problems (and generally much less on non-graph problems). Motivated by the theoretical interest in knapsack problems and their practical relevance, our work bridges this gap. © Franziska Eberle, Nicole Megow, Lukas Nölke, Bertrand Simon, and Andreas Wiese.

关键词： Approximation algorithms

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DRSM: EFFICIENT NEURAL 4D DECOMPOSITION FOR DYNAMIC RECONSTRUCTION IN STATIONARY MONOCULAR CAMERAS

arXiv

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arXiv 2024年

作者： Xie, Weixing Dong, Xiao Yang, Yong Lin, Qiqin Chen, Jingze Yao, Junfeng Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Department of Computer Science BNU-HKBU United International College China Department of Computer Science The University of Texas Dallas United States

With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation. © 2024, CC BY.

关键词： Cameras

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BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images

TechRxiv

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TechRxiv 2022年

作者： Li, Jiabao Li, Yuqi Wang, Chong Ye, Xulun Heidrich, Wolfgang The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315600 China Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application China The Visual Computing Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Hyperspectral images (HSIs) provide rich spectral information that has been widely used in numerous computer vision tasks. However, their low spatial resolution often prevents their use in applications such as image segmentation and recognition. Fusing low-resolution HSIs with high-resolution RGB images to reconstruct high-resolution HSIs has attracted great research attention recently. In this paper, we propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-of-the-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model. The experimental results demonstrate the effectiveness of our method both in simulations and in our real experiments. The proposed method outperforms other state-of-the-art nonblind and blind fusion methods on two popular HSI datasets. Our related code and data is available at https://***/CPREgroup/Real-Spec-RGB-Fusion. © , CC BY-NC-SA.

关键词： Image fusion

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DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimizing Radiotherapy Treatment Plans in Oropharyngeal Cancer

DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimi...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Bhuvanashree Murugadoss J Amudha Vijayan Sugumaran Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Bengaluru Karnataka India Center for Data Science and Big Data Analytics Oakland University Rochester MI USA Department of Decision and Information Sciences Oakland University Rochester MI USA

Automated radiotherapy treatment planning aims to improve treatment accuracy and efficiency. However, the prevalent Knowledge-Based Planning (KBP) method faces issues like lengthy manual problem formulation and challenges in accurately modeling human anatomy and processing high-dimensional data. This work focuses on treatment plan optimization and considers deep learning as a potential solution. Deep learning algorithms, by virtue of their ability to learn from large quantities of data and model complex relationships, can automate the formulation of the optimization problem in KBP, saving significant time and effort. However, despite these compelling advantages, it should be noted that the current convolution-based encoder-decoder models used for radiotherapy treatment plan optimization have a limited capability in capturing long-range dependencies between distant voxels. This work aims to introduce ‘DE-ConvGraph 3D UNet’, a novel deep learning model, to address these limitations and optimize radiotherapy treatment plans for oropharyngeal cancer. The proposed ‘DE-ConvGraph 3D UNet’ model includes a Graph Convolutional Network (GCN) component to capture long-range dependencies between distant voxels. Furthermore, the dual-encoder structure of the model combines the strengths of GCN and 3D Convolutional U-Net, enabling global relationships and local pattern capturing in a 3D patient volume. Experiments were conducted using the benchmark dataset - OpenKBP-Opt. The proposed model shows improvements in performance in comparison to state-of-the-art U-Net variants in terms of mean squared voxel-wise error, dose volume histogram points and clinical criteria satisfaction.

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Abdelrahman Altigani Muhammad Rukunuddin Ghalib Huwaida T. Elshoush Muawia A. Elsadig Nashat Alrefai Shiraz Naserelden Computer Information Science Higher Colleges of Technology Al Ain UAE School of Engineering & Computing De Monfort University Dubai UAE Computer Science Department Faculty of Mathematical Sciences and Informatics University of Khartoum Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam KSA Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam KSA Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption.

关键词： Ciphers Schedules Heuristic algorithms Dynamics Force Dynamic scheduling Encryption computer security Time complexity Standards

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Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection

arXiv

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arXiv 2023年

作者： Xiao, Meng Wang, Dongjie Wu, Min Wang, Pengfei Zhou, Yuanchun Fu, Yanjie Computer Network Information Center Chinese Academy of Sciences China University of Chinese Academy of Sciences China Department of Computer Science University of Central Florida United States Singapore Arizona State University School of Computing and AI United States

The goal of Feature Selection - comprising filter, wrapper, and embedded approaches - is to find the optimal feature subset for designated downstream tasks. Nevertheless, current feature selection methods are limited by: 1) the selection criteria of these methods are varied for different domains, leading them hard to be generalized;2) the selection performance of these approaches drops significantly when processing high-dimensional feature space coupled with small sample size. In light of these challenges, we pose the question: can selected feature subsets be more robust, accurate, and input dimensionality agnostic? In this paper, we reformulate the feature selection problem as a deep differentiable optimization task and propose a new research perspective: conceptualizing discrete feature subsetting as continuous embedding space optimization. We introduce a novel and principled framework that encompasses a sequential encoder, an accuracy evaluator, a sequential decoder, and a gradient ascent optimizer. This comprehensive framework includes four important steps: preparation of features-accuracy training data, deep feature subset embedding, gradient-optimized search, and feature subset reconstruction. Specifically, we utilize reinforcement feature selection learning to generate diverse and high-quality training data and enhance generalization. By optimizing reconstruction and accuracy losses, we embed feature selection knowledge into a continuous space using an encoder-evaluator-decoder model structure. We employ a gradient ascent search algorithm to find better embeddings in the learned embedding space. Furthermore, we reconstruct feature selection solutions using these embeddings and select the feature subset with the highest performance for downstream tasks as the optimal subset. Finally, extensive experimental results demonstrate the effectiveness of our proposed method, showcasing significant enhancements in feature selection robustness and accuracy. To improve t

关键词： Feature Selection

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Human-centered GeoAI foundation models: where GeoAI meets human dynamics

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Urban Informatics 2025年第1期4卷 1-15页

作者： Ye, Xinyue Du, Jiaxin Li, Xinyu Shaw, Shih-Lung Fu, Yanjie Dong, Xishuang Zhang, Zhe Wu, Ling Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Computer Science and Engineering Texas A&M University College Station USA College of Computing Grand Valley State University Allendale USA Department of Geography & Sustainability University of Tennessee Knoxville USA School of Computing and AI Arizona State University Tempe USA Department of Electrical & Computer Engineering Prairie View A&M University Prairie View USA Department of Geography Texas A&M University College Station USA Department of Justice Studies Prairie View A&M University Prairie View USA

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

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AIM-CP: AI-Based Multi-visual Components Analysis for Crowd Dynamics Prediction

AIM-CP: AI-Based Multi-Visual Components Analysis for Crowd ...

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International Conference on Image and Vision computing New Zealand, IVCNZ

作者： Elham Basunduwah Muhammad Mohzary Sejun Song Baek-Young Choi School of Science and Engineering University of Missouri-Kansas City MO USA College of Engineering and Computing in Al-Qunfudhah Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Jazan University Jazan Saudi Arabia Engineering and Technology Research Center Jazan University Jazan Saudi Arabia School of Computer and Cyber Science Augusta University Augusta GA USA

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Accurate prediction of crowd mobility dynamics is essential for effective crowd safety management. Traditional models, such as the Random Waypoint Model (RWM), have been widely employed for mobility simulation. However, they suffer inherent limitations, including non-uniform node distribution and speed decay, which reduce their predictive accuracy. Although vision-based crowd mobility tracking methods show promise, movement prediction still faces significant challenges in achieving accuracy, scalability, generalization, and computational efficiency. This paper introduces a novel AI-based multi-visual components analysis approach for crowd dynamics prediction called AIM-CP. This approach combines the YOLOv8 object detection system with a rule-based expert system to enhance prediction accuracy. AIM-CP integrates multi-modal data from face, body, and pose movements across 12 distinct classes, improving real-time processing capabilities and leveraging more scalable and robust AI models. To support this approach, we developed high-resolution datasets, including mall datasets, the CrowdHuman dataset, selected video frames from YouTube, and images from Unsplash. Experimental results demonstrate that AIM-CP is highly effective in detecting mobility dynamics, significantly improving single-modal prediction models with an accuracy of 90.12% in real-world mobility scenarios. These findings suggest that AIM -CP offers a powerful tool for improving crowd safety and mobility management in dynamic environments.

关键词： Accuracy Video on demand Computational modeling Object detection Predictive models Real-time systems Web sites Expert systems Artificial intelligence Faces

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