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arXiv 2023年

作者： Zeng, Guangyang Mu, Biqiang Shi, Ling Chen, Jiming Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen China The Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Disentangled Feature Distillation for Light Field Super-Resolution with Degradations

Disentangled Feature Distillation for Light Field Super-Reso...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Linjie Zhou Wei Gao Ge Li Hui Yuan Tiesong Zhao Guanghui Yue School of Electronic and Computer Engineering Peking University Shenzhen China School of Control Science and Engineering Shandong University Jinan China Department of Communication Engineering Fuzhou University Fuzhou China School of Biomedical Engineering Shenzhen University Shenzhen China

Light field (LF) super-resolution has achieved remarkable results with the assumption of only downsampling. However, real-world LF scenes contain multiple degradation effects, which makes it difficult for existing methods to deal with hybrid distortions. In this paper, we propose a disentangled feature distillation framework for LF super-resolution with degradations. To reduce the learning difficulty, we propose a feature disentanglement mechanism to split the mixed reconstruction for both super-resolution and denoising into two single task learning processes. We also propose a feature enhancement strategy via knowledge distillation to transfer prior feature of each single reconstruction to our task of mixed reconstruction. Finally, the separate restored representations are fused to reconstruct a clean high-resolution LF. Experiments demonstrate the superior performance of our framework for different scale factors and noise levels. Additionally, our approach can also obtain excellent performance for joint super-resolution and deblurring, showing its gencralization for practical LF super-resolution applications.

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

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SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument Maintenance support Engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in the operation of axial compressors, causing reduced performance, increased wear and tear, and in some cases, complete machine failure. The proposed approach utilizes a neural network model based on the compressor's design data to forecast the behavior of the compressor's working map under off-design conditions. The model is verified using real data collected over five years during compressor operation, including the opening angle of the Inlet Guided Van (IGV), an important parameter often ignored or assumed to be fixed in previous studies. The results show that the neural network-based approach effectively predicts the compressor's behavior under different operating conditions, providing valuable insights into the onset of surge and offering a real-time surge prediction and control tool. The ability to predict the compressor's working map enables optimizing its performance and controlling instability. This model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As a result, this predictive model enables the safe operation of axial compressors by mitigating instability risks during operation. This study's findings highlight the model's effectiveness, as evidenced by the minimal error observed between the predicted and actual data collected across various compressor operating modes. The model exhibits a strong performance, characterized by a high regression factor of 0.92667 and a low mean squared error of 3.04465 x 10^-3. These numerical values underscore the reliability and pr

关键词： Forecasting

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

引用

SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument maintenance support engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

关键词： Forecasting

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OA-ECBVC: A Cooperative Collision-Free Encirclement and Capture Approach in Cluttered Environments

OA-ECBVC: A Cooperative Collision-Free Encirclement and Capt...

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IEEE Conference on Decision and control

作者： Xinyi Wang Yulong Ding Yizhou Chen Ruihua Han Lele Xi Ben M. Chen Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Department of Control Science and Engineering Tongji University Shanghai China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Department of Computer Science The University of Hong Kong Hong Kong China Hebei University of Science and Technology Shijiazhuang China

This article investigates the practical scenarios of chasing an adversarial evader in an unbounded environment with cluttered obstacles. We propose a Voronoi-based decentralized algorithm for multiple pursuers to encircle and capture the evader by reacting to collisions. An efficient approach is presented for constructing an obstacle-aware evader-centered bounded Voronoi cell (OA-ECBVC), which strictly ensures collision avoidance in various obstacle scenarios when pursuing the evader. The evader can be efficiently enclosed in a convex hull given random initial configurations. Furthermore, to cooperatively capture the evader, each pursuer continually compresses the boundary of its OA-ECBVC to quickly reduce the movement space of the evader while maintaining encirclement. Our OA-ECBVC algorithm is validated in various simulated environments with different dynamic systems of robots. Real-time performance of resisting uncertainties shows the superior reliability of our method for deployment on multiple robot platforms.

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Target-oriented Reference Construction for supervised cell type identification in scRNA-seq

Target-oriented Reference Construction for supervised cell t...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xin Wei Wenjing Ma Zhijin Wu Hao Wu Department of Biostatistics Brown University Department of Biostatistics University of Michigan Ann Arbor Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Cell type identification is a crucial step in single-cell RNA-seq (scRNA-seq) data analysis. The supervised cell type identification method is a preferred solution due to its accuracy and efficiency. The performance of such methods highly depends on the quality of the reference data. Although there are many supervised cell type identification tools, no method currently exists for constructing reference data. Here, we develop Target-Oriented Reference Construction (TORC), a widely applicable strategy for constructing references based on target datasets in scRNA-seq supervised cell type identification. TORC focuses on alleviating differences in cell type composition between the reference and target sets. Extensive benchmarks on simulated and real data analyses demonstrate consistent improvements in cell type identification with TORC.

关键词： Accuracy Data analysis Neural networks Benchmark testing Classification algorithms Complexity theory Bioinformatics

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Language-Conditioned Affordance-Pose Detection in 3D Point Clouds

arXiv

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arXiv 2023年

作者： Nguyen, Toan Vu, Minh Nhat Huang, Baoru Van Vo, Tuan Truong, Vy Le, Ngan Vo, Thieu Le, Bac Nguyen, Anh FPT Software AI Center Viet Nam Automation & Control Institute TU Wien Vienna Austria Imperial College London United Kingdom Department of Computer Science & Computer Engineering University of Arkansas United States Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science University of Science Viet Nam Department of Computer Science University of Liverpool United Kingdom

Affordance detection and pose estimation are of great importance in many robotic applications. Their combination helps the robot gain an enhanced manipulation capability, in which the generated pose can facilitate the corresponding affordance task. Previous methods for affodance-pose joint learning are limited to a predefined set of affordances, thus limiting the adaptability of robots in real-world environments. In this paper, we propose a new method for language-conditioned affordance-pose joint learning in 3D point clouds. Given a 3D point cloud object, our method detects the affordance region and generates appropriate 6-DoF poses for any unconstrained affordance label. Our method consists of an open-vocabulary affordance detection branch and a language-guided diffusion model that generates 6-DoF poses based on the affordance text. We also introduce a new high-quality dataset for the task of language-driven affordance-pose joint learning. Intensive experimental results demonstrate that our proposed method works effectively on a wide range of open-vocabulary affordances and outperforms other baselines by a large margin. In addition, we illustrate the usefulness of our method in real-world robotic applications. Our code and dataset are publicly available at https://***. © 2023, CC BY-NC-SA.

关键词： Robotics

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Optimized Fractional-Order PID controller for Temperature control of CSTH: A Comparative Study with PID Cascade controller

Optimized Fractional-Order PID Controller for Temperature Co...

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2025 International Conference on Energy, Power, Environment, control and Computing, ICEPECC 2025

作者： Alaswadi, Abdulaziz F. Alyazidi, Nezar M. AL-Aswadi, Fatima N. Ahmed, Amjed Abbas Department of Control and Instrumentation Engineering King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Center of Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia UCSI University Kuala Lumpur56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University Al Hudaydah Yemen Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Baghdad Iraq

ISBN: (纸本)9781837243235

controlling the temperature in chemical reactors, heating furnaces, distillation columns, and reboilers for circulating heating fluids is crucial across all industries. This project is a simulation-based study where both PID-cascade and fractional order PID (FO-PID) controllers were used to control the temperature of water in the continuously stirred tank heater system. The simulation was designed by using SIMULINK. Initially, the work reproduces results from a previous study that evaluated the performance of PID cascade controller applied for controlling the temperature of continuous stirred tank heater. Building on this foundation, a novel fractional-order PID (FO-PID) controller was designed and its parameters were optimized using the Whale Optimizer Algorithm (WOA). Results showed that the FO-PID outperforms the PID-cascade controller in terms of settling time and percentage overshoot. This study demonstrates significant advancements in process innovation and optimization, supporting sustainable industrial systems. The FO-PID controller in comparison to the Cascade PID controller reduced the percentage overshoot by approximately 97%, reduced settling time by 87% and maintained comparable steady-state error. © The Institution of engineering & Technology 2025.

关键词： Dynamic models

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Enhancing Effluent Quality Predictions in Wastewater Treatment with LSTM Neural Network

Enhancing Effluent Quality Predictions in Wastewater Treatme...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Daniel Voipan Iulian Vasiliev Larisa Diaconu Andreea-Elena Voipan Marian Barbu Department of Computer Science and Information Technology "Dunărea de Jos" University of Galați Galați Romania Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galați Galați Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate the dynamics of wastewater treatment more accurately and adjust operational strategies dynamically. By integrating LSTM networks, the research enables continuous prediction of Effluent Quality Index (EQI) variables under stochastic and deterministic scenarios, thereby improving the accuracy and efficiency of predicting pollutant levels. The research uses an LSTM model to learn from a comprehensive dataset derived from historical simulations of BSM2, where key parameters such as the oxygen transfer coefficient (K L a) are systematically varied to measure their impact on effluent quality. The LSTM's capability to handle complex, nonlinear data and its adaptability to time series forecasting significantly enhances model performance, offering a robust tool for real-time decision-making and process optimization in wastewater treatment facilities. This approach not only improves the accuracy and efficiency of predicting pollutant levels but also supports environmental compliance and operational sustainability, making it a valuable tool for environmental engineers and professionals in the field of wastewater treatment.

关键词： Adaptation models Accuracy Time series analysis Stochastic processes Effluents Predictive models Real-time systems Wastewater Wastewater treatment Long short term memory

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A Blockchain Approach to IoT Security and Reliability Analysis 3

A Blockchain Approach to IoT Security and Reliability Analys...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Jain, Nilesh Wahid, Nazia Al-Farhani, Liwa H. Manideep, A. Sai Bhardwaj, Vikash Sangeeth Kumar, M. Mandsaur University Department of Computer Applications Madhya Pradesh Mandsaur India Vishwakarma University Faculty of Science & Technology Department of Mathematics & Statistics Maharashtra Pune India RDUN University System Analysis Control and Information Processing Academy of Engineering Moscow Russia Vignan's Foundation for Science Technology and Research Department of Management Studies Andhra Pradesh Guntur India Dewan VS Institute of Engineering & Technology Uttar Pradesh Meerut India Sona College of Technology Mechanical Engineering Department Tamilnadu Salem India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

An investigation area of interest including the use of blockchain technology to address Internet of Things security concerns (IoT). Despite the fact that numerous relevant ideas have been offered, there are few published works that provide hypothetical and information backing. The examination of model building and execution assessment is the focus of this research. First, an IoT security paradigm is outlined, using blockchain and the Inter-Planetary-File-System (IPFS) in a view. Many of the security risks associated with traditional IoT designs can be avoided with this paradigm, and framework execution is entirely focused on distributed large limit stockpiling, simultaneity, and query. In addition, the suggested model's display is evaluated using normal idleness and throughput, which are important for further investigation and enhancement of this course. The blockchain-based security model's feasibility has been demonstrated via analysis and experiments. © 2022 IEEE.

关键词： Blockchain

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