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13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019

作者： Dao, Thi-Kien Chu, Shu-Chuan Pan, Jeng-Shyang Nguyen, Trong-The Ngo, Truong-Giang Nguyen, Trinh-Dong Tran, Huu-Trung Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

ISBN: (纸本)9789811533075

Bat Algorithm (BA) is one of the fundamental algorithms for solving optimization problems. However, the BA still exists weaknesses in terms of exploitation and exploration. In this paper, an enhancing capability of exploration and exploitation for BA by hybridizing BA with Ant Lion Optimizer (ALO) is proposed for the global optimization problems. In the experimental section, several benchmark functions are used to test the performance of the proposed approach. Compared results with other algorithms literature show that the proposed method provides a new competitive algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Global optimization

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APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

arXiv

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arXiv 2023年

作者： Park, Hyun Patel, Parth Haas, Roland Huerta, E.A. Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign Illinois Urbana61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these novel AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a novel computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers, and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. Copyright © 2023, The Authors. All rights reserved.

关键词： Supercomputers

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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A Power Balance Control Strategy of Wireless Sensor Network with Collaborating Heuristic 16th

A Power Balance Control Strategy of Wireless Sensor Network ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, applications and Tools, FITAT 2020

作者： Yu, Jie Dao, Thi-Kien Ngo, Truong-Giang Nguyen, Trong-The College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong18000 Viet Nam

ISBN: (纸本)9789813367562

In order to effort a strong enough signal, nodes in wireless sensor networks (WSN) have to increase their transmission power that continues to maintain the transmission power. However, a vicious circle is iterated that causes a decline in the overall network performance, low utility, and network life cycle shorten. This paper presents a solution to the power balance control strategy for WSN with collaborating heuristic. A distance between the weight factors and obtained nodes interference value is used to establish a useful interference model for enhancing the signal-to-interference-noise ratio (SINR). The utility function of the nodes residual energy and transmission rate is modeled by apply heuristic strategy. The optimal transmission power is obtained after several iterations of the heuristic algorithm. Simulation results show that the proposed approach can prolong the network life cycle and achieve higher network utility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Heuristic algorithms

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An Optimizing Multilevel Thresholding for Image Segmentation Based on Hybrid Swarm Computation Optimization 16th

An Optimizing Multilevel Thresholding for Image Segmentation...

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作者： Dao, Thi-Kien Wang, Hong-Jiang Yu, Jie Nguyen, Huu-Quynh Ngo, Truong-Giang Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Image segmentation

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Short-term household load forecasting model based on variational mode decomposition and gated recurrent unit with attention mechanism

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 143-153页

作者： Lin, Fu Liu, Shi-Jian Chao, Han-Chieh Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The household load is an important part of the load. Accurate short-term household load forecasting is of great help to issues such as power price formulation, demand response, or power transmission. This paper proposed a forecasting model based on Variational Mode Decomposition (VMD) and Gated Recurrent Unit (GRU) combined with self-attention mechanism (SAM) and attention mechanism (AM) for short-term household load forecasting. First, we use VMD to decompose household electricity load data, extract sub-sequences with strong periodic information, and reorganize the original data sets with sub-sequences and data pre-processed. The new data sets contain more abundant information. Secondly, GRU combined with SAM and AM forecasting model for learning and training to obtain results. Finally, RMSE and MAPE evaluation functions are used for method evaluation to verify the superiority of the proposed scheme. The RMSE and MAPE of the proposed method are 0.193 kW and 15.567%, respectively, which are more favorable than traditional GRU. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Variational mode decomposition

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Flood extent mapping in SAR images using semi-supervised approach

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Results in Engineering 2025年 26卷

作者： S, Girisha G, Savitha P, Sughosh Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Civil Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Floods pose a significant threat to both human populations and critical infrastructure. They are caused by excessive precipitation, snowmelt, or infrastructure failures. Precise mapping of flood levels is essential for directing emergency response, allocating resources as efficiently as possible, and estimating the degree of damage. Accurate semantic segmentation of flood-zones from SAR images is necessary for disaster management as it reduces human error and facilitates fast decision-making. However, the complexity of SAR images and the scarcity of annotated datasets make it difficult to develop efficient computer vision algorithms for this use. The paper employs consistency regularization and pseudo-label generation to overcome these issues and presents a novel hybrid semi-supervised semantic segmentation method. In particular, the study presents a student-teacher paradigm in which robust feature learning is ensured by training the teacher model through consistency regularization and self-supervised learning. An attention module-based tailored student model is developed to precisely identify flood-zones. Auxiliary decoders are also added throughout the training phase to reinforce consistency regularization and help the model capture extensive hierarchical characteristics. The suggested method was assessed with publicly accessible datasets, and the outcomes show notable gains in both qualitative and quantitative measures. The findings highlight the method's effectiveness in improving flood-zone segmentation accuracy, leading to more efficient disaster management. © 2025 The Authors

关键词： Semantic Segmentation

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Single and Multi-Hop Question-Answering datasets for Reticular Chemistry with GPT-4-Turbo

arXiv

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arXiv 2024年

作者： Rampal, Nakul Wang, Kaiyu Burigana, Matthew Hou, Lingxiang Al-Johani, Juri Sackmann, Anna Murayshid, Hanan S. Al-Sumari, Walaa Abdullah Al-Abdulkarim, Arwa M. Al-Hazmi, Nahla Eid Al-Awad, Majed O. Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA94720 United States Kavli Energy Nanoscience Institute University of California BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Data Services Librarian University of California Berkeley Library United States Department of Mathematics University of California BerkeleyCA94720 United States Department of Statistics University of California BerkeleyCA94720 United States Riyadh12354 Saudi Arabia Hydrogen Technologies Institute King Abdulaziz City for Science and Technology P.O. Box 6086 Riyadh11442 Saudi Arabia KACST−UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce 'RetChemQA,' a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 Q&As for each type. The questions have been extracted from an extensive corpus of literature containing about 2,530 research papers from publishers including NAS, ACS, RSC, Elsevier, and Nature Publishing Group, among others. The dataset has been generated using OpenAI's GPT-4 Turbo, a cutting-edge model known for its exceptional language understanding and generation capabilities. In addition to the Q&A dataset, we also release a dataset of synthesis conditions extracted from the corpus of literature used in this study. The aim of RetChemQA is to provide a robust platform for the development and evaluation of advanced machine learning algorithms, particularly for the reticular chemistry community. The dataset is structured to reflect the complexities and nuances of real-world scientific discourse, thereby enabling nuanced performance assessments across a variety of tasks. The dataset is available at the following link: https://***/nakulrampal/RetChemQA. © 2024, CC BY.

关键词： Machine learning

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Study of PSO Optimized BP Neural Network and Smith Predictor for MOCVD Temperature Control in 7nm 5G Chip Process 6th

Study of PSO Optimized BP Neural Network and Smith Predictor...

引用

6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Chang, Kuo-Chi Zhou, Yu-Wen Wang, Hsiao-Chuan Lin, Yuh-Chung Chu, Kai-Chun Hsu, Tsui-Lien Pan, Jeng-Shyang School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Shandong China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

The key industries of information technology in the semiconductor integrated circuit industry will have an important role after 2020, In the third generation of advanced semiconductor of 7nm 5G chips GaN is be used, MOCVD is a key technology for preparing high-quality communication semiconductor crystals. This study proposes PID controller of PSO and BP neural network algorithm to improve the control ability of MOCVD temperature. From the research results, it is found that the method proposed in this study adopts the PSO and BP neural network intelligent PID algorithm controller with Smith predictor, which has better dynamic performance, and the value from 150 to 500s is stable from 0 to 1 no vibration, any overshoot and short adjustment time, ideal control. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Controllers

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A method of fundamental matrix estimation based on nsga-ii and improved quasi-affine transform

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 313-327页

作者： Fan, Yi-Kai Liu, Shi-Jian Kong, Ling-Ping Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China VSB-Technical University of Ostrava Czech Republic Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Information Management Chaoyang University of Technology Taiwan

The fundamental matrix is the mainstream solution to computer vision problems such as 3D reconstruction, real-time location and map building. Accuracy and efficiency are two main measurement indexes in fundamental matrix estimation. When the accuracy is not enough, it often needs to be corrected through back-end optimization and other costly ways, and low efficiency will affect the real-time performance of the system. In order to solve this problem, this paper proposes a new estimation method of fundamental matrix based on improved quasi affine transformation. Specifically, based on the QUATRE algorithm, this method first proposes a population cooperation method based on a specific "gene-chromosome" pattern. Secondly, combining the advantages of NSGA-II in solving multi-objective problems, the Pareto dominance relationship of the population chromosome was firstly calculated according to the objective function of mean polarity distance and internal points in the way of NSGA-II, and the crowding degree was also calculated. The operations of population initialization, mutation and crossover in the discrete solution space represented by the homogeneous coordinate system are redefined. The selection operation is then performed according to the elite policy of NSGA-II. In addition, a confidence-based method to determine the number of iterations is proposed to accelerate the algorithm. Experimental results show that the proposed method can effectively eliminate noise and mismatching, and is superior to the current mainstream methods in accuracy and efficiency, and can effectively solve the problem of fundamental matrix estimation. © 2021 Global Research Online. All rights reserved.

关键词： Efficiency

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