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arXiv 2021年

作者： Wang, Shengbo Wen, Shiping Shi, Kaibo Zhu, Song Huang, Tingwen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Science Program Texas A & M University at Qatar Doha23874 Qatar

The optimal control input for linear systems can be solved from algebraic Riccati equation (ARE), from which it remains questionable to get the form of the exact solution. In engineering, the acceptable numerical solutions of ARE can be found by iteration or optimization. Recently, the gradient descent based numerical solutions has been proven effective to approximate the optimal ones. This paper introduces this method to tracking problem for heterogeneous linear systems. Differently, the parameters in the dynamics of the linear systems are all assumed to be unknown, which is intractable since the gradient as well as the allowable initialization needs the prior knowledge of system dynamics. To solve this problem, the method named dynamic regressor extension and mix (DREM) is improved to estimate the parameter matrices in finite time. Besides, a discounted factor is introduced to ensure the existence of optimal solutions for heterogeneous systems. Two simulation experiments are given to illustrate the effectiveness. © 2021, CC BY-NC-ND.

关键词： Gradient methods

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Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation

arXiv

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arXiv 2021年

作者： Wang, Shengbo Lyu, Bo Wen, Shiping Shi, Kaibo Zhu, Song Huang, Tingwen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Science Program Texas A & M University at Qatar Doha23874 Qatar

Safety is always one of the most critical principles for a system to be controlled. This paper investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefited from the dynamic regressor extension and mixing (DREM), an extended element-wise parameter identification law is utilized to dismiss the uncertainty. On the one hand, it is shown that the proposed control scheme can always guarantee the safety in the identification process with noised signal injection excitation, which was not considered in the previous study. On the other hand, the element-wise estimation process in DREM can minimize conservatism of the safe adaptive process compared to other existing adaptive CBF algorithms. The stability as well as the forward invariance of the presented safe control-estimation scheme is proved. Furthermore, the robustness of the scheme under bounded disturbances is analyzed, where a robust CBF with modest conditions is used to ensure safety. The framework is illustrated by simulations on adaptive cruise control, where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances and the potential crash risk is avoided by the proposed safe control scheme. © 2021, CC BY-NC-ND.

关键词： Parameter estimation

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High Level Learning Approach for High Level Collision Avoidance Using Sensors

High Level Learning Approach for High Level Collision Avoida...

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Lamhot Naibaho Razauddin L. Vertivendan V. Karunakaran Ashish Oberoi N.C. Ajay Vishwath English Language Education Study Program Faculty of Letters and Languages Christian University of Indonesia Department of computer and information sciences Gulf Colleges Saudia Arabia Department of CSE Galgotias University Greater Noida Uttar Pradesh India Department of CSE Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore INDIA Department of CSE RIMT University Mandi Godindgarh India Aeronautical Department Parul Institute of Engineering and Technology Parul University

Motion planning for many unmanned aircraft is challenging because they have a larger configuration space than self-driving automobile development (Automated guided vehicles). Additionally, there are more significant uncertainties and disruptions in UAV environments, which makes inter automatic navigation more difficult. In this letter, I proposed a 2 recurrent neural networks (RL) oriented multi-UAV collision avoidance technique by modeling the unpredictability or noise in the environment. Our objective is to create a strategy that can construct a path without clashing with anything using local noisy data. But unlike supervised algorithms, RL lacks a steady training data set with floor labels, thus its collision avoidance strategies often show significant fluctuation and are difficult to reproduce. To solve these issues, we created a two-stage training method for RL-based collision avoidance. We first optimize the policy, and then we utilize a supervised learning methodology with a loss function that encourages the agents to adopt the well-known reciprocity obstacle detection mechanism. In the second step, we use transmission to fine-tune the policy. The complete computer simulation findings demonstrate that this approach can handle noisy local views with erratic sound levels and can design moment & accident paths under inadequate sensing. We review the impact of our policies in a variety of ways.

关键词： Training Uncertainty Supervised learning Training data Aircraft navigation Sensors Noise measurement

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied Science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Icd: A New Interpretable Cognitive Diagnosis Model for Intelligent Tutor Systems

SSRN

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SSRN 2022年

作者： Qi, Tianlong Ren, Meirui Guo, Longjiang Li, Xiaokun Li, Jin Zhang, Lichen Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an710119 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China Xi’an Key Laboratory of Culture Tourism Resources Development and Utilization Xi’an710062 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd Harbin150090 China

Cognitive diagnosis plays a crucial role in Intelligent Tutor Systems, which aims to diagnose learners' cognitive states according to learners' observable records, such as exercise records. However, this paper observes that the existing cognitive diagnosis models still have rooms for performance improvements: (1) they ignore the interaction between knowledge concepts;(2) they ignore the quantitative relation between exercises and concepts. In order to solve the aforementioned problems, this paper proposes a new interpretable cognitive diagnosis model named ICD based on a novel neural network designed by us, which can conduct answer records with different lengths. The neural network can fully utilize the interaction among concepts, the quantitative relation between exercises and concepts, and considers bias replacement by slip and guess, which makes the ICD more interpretable. The paper has conducted extensive experiments on four real publicity datasets, and the results show that both the performances and the interpretability of ICD are better than the latest state-of-the-art CDMs such as RCD, NCDM, and CDGK, and the classical CDMs such as DINA and MIRT. © 2022, The Authors. All rights reserved.

关键词： Neural networks

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Regularity and numerical approximation of fractional elliptic differential equations on compact metric graphs

arXiv

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arXiv 2023年

作者： Bolin, David Kovács, Mihály Kumar, Vivek Simas, Alexandre B. Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg GothenburgSE-41296 Sweden Department of Differential Equations Budapest University of Technology and Economics Mauegyetem rkp. 3. BudapestH-1111 Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Práter utca 50/a. BudapestH-1083 Hungary Theoretical Statistics and Mathematics Unit Indian Statistical Institute Bangalore Centre 8th Mile Mysore Road Karnataka Bangalore560059 India

The fractional differential equation Lβu = f posed on a compact metric graph is considered, where β > 0 and L = κ2 − ∇(a∇) is a second-order elliptic operator equipped with certain vertex conditions and sufficiently smooth and positive coefficients κ, a. We demonstrate the existence of a unique solution for a general class of vertex conditions and derive the regularity of the solution in the specific case of Kirchhoff vertex conditions. These results are extended to the stochastic setting when f is replaced by Gaussian white noise. For the deterministic and stochastic settings under generalized Kirchhoff vertex conditions, we propose a numerical solution based on a finite element approximation combined with a rational approximation of the fractional power L−β. For the resulting approximation, the strong error is analyzed in the deterministic case, and the strong mean squared error as well as the L2(Γ×Γ)error of the covariance function of the solution are analyzed in the stochastic setting. Explicit rates of convergences are derived for all cases. Numerical experiments for L = κ2 − ∆, κ > 0 are performed to illustrate the *** Codes 35R02, 35A01, 35A02, 60H15, 60H40 Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

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Prediction of Hydrogen Production Rate in Anaerobic Fermentation Using Grey Relation Analysis and Machine Learning

SSRN

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SSRN 2023年

作者： Wang, Yifan Wu, Jinghui Tseng, Yu-Yao Zhao, Chunliang Li, Keqing Wang, Xianze Wang, Ming-Hung Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment Green Development of Polluted Water in Northeast China Ministry of Education Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production has many environmental factors that limit microbial activity, but machine learning has enormous potential in handling the complexity of biological processes. This study explores the potential of machine learning in predicting hydrogen production rates (HPR) from anaerobic fermentation of biomass energy. Grey relation analysis was conducted to determine the correlation between operational parameters and HPR. Five machine learning algorithms, such as decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were then paired with operating conditions and water quality performance as features and HPR as a label, with mean squared error (MSE) and R2 as evaluation indexes. Butyric acid, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were found to play crucial roles in hydrogen production from sucrose anaerobic fermentation. XGBoost had the highest accuracy with R2 of 0.91 and MSE of 0.0052. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2024年第3期1卷

作者： Ashok Dahal Raphaël Huser Luigi Lombardo Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente Enschede The Netherlands Statistics Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, with frequency being rarely investigated. Frequency and intensity are dependent on each other because larger events occur less frequently and vice versa. However, due to the lack of multi-temporal inventories and joint statistical models, modeling such properties via a unified hazard model has always been challenging and has yet to be attempted. Here, we develop a unified model to estimate landslide hazard at the slope unit level to address such gaps. We employed deep learning, combined with extreme-value theory to analyze an inventory of 30 years of observed rainfall-triggered landslides in Nepal and assess landslide hazard for multiple return periods. We also use our model to further explore landslide hazard for the same return periods under different climate change scenarios up to the end of the century. Our model performs excellently (with an accuracy of 0.78 and an area under the curve of 0.86) and can be used to model landslide hazard in a unified manner. Geomorphologically, we find that under climate change scenarios (SSP245 and SSP585), landslide hazard is likely to increase up to two times on average in the lower Himalayan regions (Siwalik and lower Himalayas; ≈110–3,500 m) while remaining the same in the middle Himalayan region (≈3,500–5,000 m) whilst decreasing slightly in the upper Himalayan region (≳5,000 m) areas. A Landslide hazard model is developed by combining deep learning and the extended generalized Pareto distribution from extreme value theory Thirty years of past observations are used to model the current landslide hazard scenario under different return periods of precipitation Landslide hazard is predicted for multiple return peri

关键词： landslides statistics of extremes deep learning climate change modeling Nepal

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Paticipatory geographic information system (PGIS) for developed the online map of historical source in World War II, Pak Phraek Road community, Kanchanaburi Thailand 40

Paticipatory geographic information system (PGIS) for develo...

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40th Asian Conference on Remote Sensing: Progress of Remote Sensing technology for Smart Future, ACRS 2019

作者： Sriboonruang, Komsan Punkong, Narong Kumkrua, Malinee Faculty of Education Science 70 m.4 Nongbue Sub-District MuangKanchanaburi Kanchanaburi71190 Thailand Department of Academic Resource and Information Technology Business Computer Program Faculty of Management Science 70 m.4 Nongbue Sub-District MuangKanchanaburi Kanchanaburi71190 Thailand Business Computer Program Faculty of Management Science 70 m.4 Nongbue Sub-District MuangKanchanaburi Kanchanaburi71190 Thailand

The objectives of the research article were: To making the online Historical map of the Pak Phraek road community, Kanchanaburi. The samples and Key Informants included;villagers, philosopher of the Pak Phraek road community, historical scholars, and the government agencies. This research used the Geo-informatics concept, Participatory Geographic information System concept (PGIS), and the walking map concept were the main concept for this studies. The research results appeared as follows: The online Historical mapping of the Pak Phraek road community used the Geoinformatics information System Processing for developed the Pak Phraek historical map included;field work, data input, data edited, data management, data manipulation, data analysis and data display with The Participatory Geographic information System concept (PGIS) and Walking Map methodology on The free and open source software processing included;(1) Data edited from the field work in online map software, (2) Online map import testing and (3) Web page editing and displaying layers before show in The web server. © 2020 40th Asian Conference on Remote Sensing, ACRS 2019: "Progress of Remote Sensing technology for Smart Future". All rights reserved.

关键词： information management

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Handling Problems of Credit Data for Imbalanced Classes using SMOTEXGBoost

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Heru Mardiansyah Rahmat Widia Sembiring Syahril Efendi Graduate Program of Computer Science Department of Information Technology Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medam Indonesia

Some researchers find data with imbalanced class conditions, where there are data with a number of minorities and a majority. SMOTE is a data approach for an imbalanced classes and XGBoost is one algorithm for an imbalanced data problems. This research uses SMOTE and XGBoost or abbreviated as SMOTEXGBoost for handling data with an imbalanced classes. The results showed almost the same accuracy value between SMOTE and SMOTEXGBoost at 99%. While the value of AUC SMOTEXBoost has a more stable value than SMOTE that is equal to 99.89% for training and 98.51% for testing.

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