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Underwater image enhancement via histogram similarity-oriented color compensation complemented by multiple attribute adjustment

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intelligent Marine Technology and Systems 2023年第1期1卷 1-15页

作者： Wang, Hao Frery, Alejandro C. Li, Mingjie Ren, Peng College of Control Science and Engineering China University of Petroleum (East China) Qingdao China College of Mathematics and Statistics Victoria University of Wellington Wellington New Zealand Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi’an China South China Sea Institute of Planning and Environmental Research State Oceanic Administration Guangzhou China College of Oceanography and Space Informatics China University of Petroleum (East China) Qingdao China

Underwater images are often influenced by color casts, low contrast, and blurred details. We observe that images taken in natural settings typically have similar histograms across color channels, while underwater images do not. To improve the natural appearance of an underwater image, it is critical to improve the histogram similarity across its color channels. To address this problem, we develop a histogram similarity-oriented color compensation method that corrects color casts by improving the histogram similarity across color channels in the underwater image. In addition, we apply the multiple attribute adjustment method, including max-min intensity stretching, luminance map-guided weighting, and high-frequency edge mask fusion, to enhance contrast, saturation, and sharpness, effectively addressing problems of low contrast and blurred details and eventually enhancing the overall appearance of underwater images. Particularly, the method proposed in this work is not based on deep learning, but it effectively enhances a single underwater image. Comprehensive empirical assessments demonstrated that this method exceeds state-of-the-art underwater image enhancement techniques. To facilitate public assessment, we made our reproducible code available at https://***/wanghaoupc/UIE_HS2CM2A.

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An Evolutionary Real-Time 3D Route Planner for Aircraft

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Chinese Journal of Systems Engineering and Electronics 2003年第1期000卷

A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time,with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.

关键词： Evolutionary computation Route planning Route constraints Real time Aircraft

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Switched High-Gain Based Interval Observer Design for Harmonic Oscillator System

Switched High-Gain Based Interval Observer Design for Harmon...

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Chinese control Conference (CCC)

作者： Tianhang Ming Xiaoling Wang Bo Liu Housheng Su College of Automation & AI Nanjing University of Posts and Telecommunications Nanjing P. R. China Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing China College of Information Engineering Minzu University of China Beijing P. R. China Key Laboratory of Image Processing and Intelligence Control of Ministry of Education Huazhong University of Science and Technology Wuhan P. R. China

In this paper, we revisit the interval observer design problem of harmonic oscillator system with unknown but bounded external disturbance and measurement noise. With the bounding information of the external disturbance and measurement noise, a high-gain interval observer is firstly defined and constructed for the harmonic oscillator system to realize the interval estimation on the state and the robust control of the system. Then, a switched high-gain interval observer is proposed to reduce the influence of the measurement noise for a tighter interval width on the state estimation. Finally, some numerical simulations are given to verify the theoretical results.

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control Selection Based on state Change of Chaotic System

Control Selection Based on State Change of Chaotic System

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International Conference on Computer and Communications (ICCC)

作者： Jiaxi Zhou Wei Zhang Yuming Feng B. O. Onasanya School of Electronics and Information Engineering Chongqing Three Gorges University Wanzhou Chongqing China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China Department of Mathematics University of Ibadan Ibadan Oyo State Nigeria

Considering that the system may need to meet more needs in the control process, that is, to accelerate or slow down the control intensity according to the system state, the control mode that combines the control cycle and the control method has a wider range of applications. In this paper, the model for state detection and judgment in the second half of the control cycle is studied, and appropriate measures are taken to achieve various control effects of the system. The simulation of the application example illustrates the effectiveness of the controller.

关键词： Analytical models Chaotic communication Process control control systems Numerical models Optimization

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Novel 3D Geometry-Based Stochastic Models for Non-Isotropic MIMO Vehicle-to-Vehicle Channels

arXiv

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arXiv 2023年

作者： Yuan, Yi Wang, Cheng-Xiang Cheng, Xiang Ai, Bo Laurenson, David I. The Joint Research Institute for Signal and Image Processing School of Engineering & Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom The Research Institute for Modern Communications School of Electronics Engineering & Computer Science Peking University Beijing100871 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Joint Research Institute for Signal and Image Processing Institute for Digital Communications University of Edinburgh EdinburghEH9 3JL United Kingdom

This paper proposes a novel three-dimensional (3D) theoretical regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels. The proposed RS-GBSM, combining line-of-sight (LoS) components, a two-sphere model, and an elliptic-cylinder model, has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics, and jointly considers the azimuth and elevation angles by using the von Mises Fisher distribution. Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator. Based on the proposed 3D theoretical RS-GBSM and its SoS simulation model, statistical properties are derived and thoroughly investigated. The impact of the elevation angle in the 3D model on key statistical properties is investigated by comparing with those of the corresponding two-dimensional (2D) model. It is demonstrated that the 3D model is more accurate to characterize real V2V channels, in particular for pico cell scenarios. Finally, close agreement is achieved between the theoretical model, SoS simulation model, and simulation results, demonstrating the utility of the proposed models. © 2023, CC BY.

关键词： Stochastic systems

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Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

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Journal of Physics: Conference Series 2019年第1期1229卷

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education China

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Shi, Yang Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China The Department of Mechanical Engineering University of Victoria VictoriaBC V8P 5C2 Canada

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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High Information Density and Low Coverage Data Storage in DNA with Efficient Channel Coding Schemes

arXiv

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arXiv 2024年

作者： Ding, Yi He, Xuan Nguyen, Tuan Thanh Song, Wentu Yakhini, Zohar Yaakobi, Eitan Pan, Linqiang Tang, Xiaohu Cai, Kui Information Coding and Transmission Key Lab of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China Cluster Singapore University of Technology and Design 487372 Singapore Faculty of Computer Science Technion - Israel Institute of Technology Haifa3200003 Israel School of Computer Science RUNI Herzliya4615200 Israel Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

DNA-based data storage has been attracting significant attention due to its extremely high data storage density, low power consumption, and long duration compared to conventional data storage media. Despite the recent advancements in DNA data storage technology, significant challenges remain. In particular, various types of errors can occur during the processes of DNA synthesis, storage, and sequencing, including substitution errors, insertion errors, and deletion errors. Furthermore, the entire oligo may be lost. In this work, we report a DNA-based data storage architecture that incorporates efficient channel coding schemes, including different types of error-correcting codes (ECCs) and constrained codes, for both the inner coding and outer coding for the DNA data storage channel. We also carried out large scale experiments to validate our proposed DNA-based data storage architecture. Specifically, 1.61 and 1.69 MB data were encoded into 30,000 oligos each, with information densities of 1.731 and 1.815, respectively. It has been found that the stored information can be fully recovered without any error at average coverages of 4.5 and 6.0, respectively. This experiment achieved the highest net information density and lowest coverage among existing DNA-based data storage experiments (with standard DNA), with data recovery rates and coverage approaching theoretical optima. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel coding

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Self-adaptation of chimera states

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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

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Causal Invariant Hierarchical Molecular Representation for Out-of-distribution Molecular Property Prediction

Causal Invariant Hierarchical Molecular Representation for O...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlong Wen Yifei Guo Shuoying Wei Wenhan Long Lida Zhu Rongbo Zhu Huazhong Agricultural University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Huazhong University of Science and Technology Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model’s capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://***/Chertuion/CHiMoGNN.

关键词： Representation learning Limiting Codes Predictive models Feature extraction Iron Graph neural networks Drug discovery Data mining Compounds

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