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Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 4071-4083页

作者： Bui, Trong-An Lee, Pei-Jun Liang, Chun-Sheng Hsu, Pei-Hsiang Shiu, Shiuan-Hal Tsai, Chen-Kai National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Liscotech System Company Ltd. Department of Research and Development Taipei100213 Taiwan

Edge computing enables rapid data processing and decision-making on satellite payloads. Deploying deep learning-based techniques for low-light image enhancement improves early detection and tracking accuracy on satellite platforms, but it faces challenges due to limited computational resources. This article proposes an edge-computing-enabled inference model specifically designed onboard satellites. The proposed model follows an encoder-decoder architecture to generate the illumination map with low multiplication matrix complexity, 25.52 GMac of $1920 \times 1200$ image size. To reduce nanosatellite hardware consumption with a single-precision floating-point format, the edge-computing-enabled inference model proposes a quantized convolution that computes signed values. The proposed inference model is deployed on Arm Cortex-M3 microcontrollers onboard satellite payload (86.74 times faster than normal convolution model) but also has a similar quality with the low-light enhanced in full-precision computing of lightweight training model by using the peak signal-to-noise ratio (average of 28.94) and structural similarity index (average of 0.85) metrics. © 2008-2012 IEEE.

关键词： Image enhancement

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Drawing Order Diagrams Through Two-Dimension Extension

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Journal of Graph Algorithms and Applications 2023年第9期27卷 783-802页

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Knowledge & Data Engineering Group Research Center for Information System Design & Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order diagrams provide an important tool for understanding and thus discovering knowledge in such data. Easily readable drawings of such order diagrams are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work, we present the novel algorithm DimDraw to decompose an ordered set (e.g., a concept hierarchy) in linear orders and to produce a corresponding order diagram. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of its transitive incompatibility graph. To evaluate the quality of the algorithm, a user study was conducted where generated drawings were compared with ones from state-of-the-art drawing algorithms. © 2023 Brown University. All rights reserved.

关键词： Set theory

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Smart Agriculture with Enhanced Accuracy for Fruit Detection Using Conv2D 6

Smart Agriculture with Enhanced Accuracy for Fruit Detection...

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6th IEEE International Conference on Artificial Intelligence in engineering and technology, IICAIET 2024

作者： Kamran, Yashfa Tauqeer, Mahnur Ahmad, Moiz Tauqeer, Syed Muhammad Hamza National University of Science and Technology Department of Electrical Engineering Islamabad Pakistan Tum School of Engineering and Design Technical University of Munich Munich Germany University of Engineering and Technology Department of Electronics Engineering Taxila Pakistan

ISBN: (纸本)9798350389692

With the rise of Deep learning and Machine Learning, immense breakthroughs in the area of agricultural automation have now become possible. Most importantly, it is the kind of fruit ripeness that may be the main issue as it touches both the retailing and farming sectors. In this paper, Convolutional Neural Network (CNN) approach and 2-Dimensional Convolution (Conv2D) model is applied as a main part of differentiating between ripe and unripe fruits because of their main distinguishing feature. The model is refined through the use of image processing techniques like resizing and normalization, which make its generalization ability more robust. Using the Adam optimizer and the binary cross entropy loss function, the model is tuned for the binary classification task which we deal with. The results of the model can be observed through fruit maturity classification with training accuracy 99 percent and validation accuracy reaches 84 percent, Area Under curve (AUC) equals 0.92. © 2024 IEEE.

关键词： Fruits

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Evaluating different levels of information on the calibration of building energy simulation models

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Building Simulation 2024年第4期17卷 657-676页

作者： Siyu Cheng Zeynep Duygu Tekler Hongyuan Ji Wenxin Li Adrian Chong Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore117566Singapore School of Civil Engineering and Architecture Chongqing University of Science and TechnologyChongqing401331China School of Energy and Environment Southeast UniversityNanjing210096China

A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between information-gathering efforts and achieving sufficient model credibility is crucial but often obscured by ambiguities. To address this gap, we model and calibrate a test bed with different levels of information (LOI). Beginning with an initial model based on building geometry (LOI 1), we progressively introduce additional information, including nameplate information (LOI 2), envelope conductivity (LOI 3), zone infiltration rate (LOI 4), AHU fan power (LOI 5), and HVAC data (LOI 6). The models are evaluated for accuracy, consistency, and the robustness of their predictions. Our results indicate that adding more information for calibration leads to improved data fit. However, this improvement is not uniform across all observed outputs due to identifiability issues. Furthermore, for energy-saving analysis, adding more information can significantly affect the projected energy savings by up to two times. Nevertheless, for ECM ranking, models that did not meet ASHRAE 14 accuracy thresholds can yield correct retrofit decisions. These findings underscore equifinality in modeling complex building systems. Clearly, predictive accuracy is not synonymous with model credibility. Therefore, to balance efforts in information-gathering and model reliability, it is crucial to (1) determine the minimum level of information required for calibration compatible with its intended purpose and (2) calibrate models with information closely linked to all outputs of interest, particularly when simultaneous accuracy for multiple outputs is necessary.

关键词： calibration building energy simulation(BES) energy conservation measure(ECM) level of information field measurements

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A Composite Decomposition Method for Large-Scale Global Optimization

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6304-6319页

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China East China University of Science and Technology Engineering Research Center of Process System Engineering Ministry of Education Shanghai200237 China Westlake University School of Engineering Hangzhou310030 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74048 United States

Cooperative coevolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of nonadditively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups nonseparable variables by recursively considering the interactions between each nonseparable variable and the formed nonseparable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a nonseparable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group nonseparable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG-series designs. © 2024 IEEE.

关键词： Computational efficiency

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Tamper-forgery Image Detection in Security Digital Image: A Review 3

Tamper-forgery Image Detection in Security Digital Image: A ...

引用

3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Thorat, Nilesh N. Dhotre, Prashant Kulal, Nilesh Pimpalkar, Aarti P Patil, Vijaya S. Kolekar, Sachin M. Mit Art Design and Technology University Mit School of Computing Computer Science & Engineering Department Pune India Mit Art Design and Technology University Mit School of Computing Information Technology Department Pune India

ISBN: (纸本)9798350354348

Concerns with image security exist in every sector that uses digital photographs. Historically, public safety and forensics have depended on images from the crime scene, biometric photos, suspect photos, and other sources. With the development of digital imaging, there has been a significant increase in the use of digital images in this field. Although the use of digital image processing has led to the creation of several new forensic methods of inquiry, it has also simplified picture modification. Digital picture validity is an issue since several snipping and image alteration programs are freely available to the public. It serves as solid proof in many different types of crimes and is utilized as documentation for many purposes. Creating and modifying photos has become easier and more accessible with the development of image manipulation and editing tools. The two most common methods of photo counterfeiting are copy-and-move forgery and image splicing. An image can be spliced, or combined from two photos, to hide or show an error scenario. A section of a picture can be copied and pasted into another image. The many kinds of digital image frauds are examined in this study along with software that detects them. An analysis was done on the methods currently in use for spotting phony photos. © 2024 IEEE.

关键词： Anonymity

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Air-Ground Collaborative Control for Angle-Specified Heterogeneous Formations

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Chen, Liangming Xiao, Jiaping Teo, Clarence Wei Rui Li, Jianqing Feroskhan, Mir School of System Design and Intelligent Manufacturing and Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China school of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore College of Communication Engineering Hangzhou Dianzi University Hangzhou China

The need for better search and rescue capabilities has led to the increased demand for collaborative aerial-ground multi-robot deployments. However, most existing solutions require high communication bandwidth or bulky sensor equipment that is not suitable especially for aerial robots. To make the execution of these tasks more efficient, this paper proposes an angle-specified heterogeneous leader-follower formation framework consisting of unmanned aerial vehicles (UAVs) flying in a 3D space and ground robots moving in a 2D plane. The UAVs are controlled to maintain a desired angle-specified formation with the first three UAVs as the leaders forming a triangular shape and the remaining UAVs as the followers. The follower UAVs only need direction measurements to track the leader UAVs. For the ground robots, two leader robots track the UAV group and determine the orientation and scale of the ground formation, while the follower robots track the leader robots using only direction measurements. The proposed heterogeneous formation framework is energy-efficient as most robots only require low-cost and lightweight direction measurements. The stability of the proposed formation control algorithms is proved and validated through various physical application experiments. IEEE

关键词： Multi agent systems

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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction systems in High-Speed Trains

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Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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Sufficient learning: mining denser high-quality pixel-level labels for edge detection

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Neural Computing and Applications 2025年第14期37卷 8245-8260页

作者： Chen, Xiaodiao Yang, Wenya Wu, Wen Tao, Xiuting Mao, Xiaoyang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Media and Design Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science and Engineering University of Yamanashi Kofu400-8511 Japan

The success of deep neural networks can largely be attributed to large-scale datasets with accurate annotations. In many practical applications, labels are annotated by multiple annotators, resulting in ambiguous labels. To mitigate the impact of such labels, existing deep edge detectors tend only to exploit sparsely reliable samples with high-confident labels by simple voting mechanism. However, lacking sufficient pixel-level learnable samples would severely degrade detection performance. To combat this issue, this work designs a sufficient learning framework for edge detection (SLED) to sufficiently explore reliable samples for model learning, which consists of three stages: sample selection, label refinement, and retraining. Firstly, we propose a sample selection scheme founded on history prediction, which aims to identify more reliable samples and locate unreliable regions containing potentially reliable samples. Secondly, we design an efficient label refinement strategy based on a graph convolutional network (GCN) to propagate reliable label information to unreliable nodes. This strategy can generate "new training targets" with denser, reliable labels for all images. Finally, we retrain the network with these new targets, obtaining a more robust model. Experimental results on three widely used benchmarks show that the proposed detector outperforms all compared SOTA methods. Our code will be released at https://***/wenya1994/SLED. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Pixels

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STUDY ON BENDING PERFORMANCE OF COLUMN-TO-BEAM CONNECTION WITH SAO-SHACHI JOINT FOR TRADITIONAL WOODEN BUILDINGS

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AIJ Journal of technology and design 2024年第76期30卷 1257-1262页

作者： Urakubo, Mikiya Miyamoto, Mitsuhiro Utsunomiya, Naoki Nakao, Masato Ohashi, Yoshimitsu Kita Shigenori Structural Design Office Co. Ltd Japan Faculty of Engineering and Design Kagawa Univ. Japan Dept. of Architectural System Technology Kinki Polytechnic College Japan Dept. of Architecture and Building Science Yokohama National Univ Japan Tokyo City Univ Japan

Sao-shachi joint is used for the joint at column-to-beam connections with large cross-sectional areas, providing relatively high resistance and exerting a significant impact on the overall building load-bearing capacity. Therefore, the seismic performance evaluation method for column-to-beam connections with Sao-shachi is important in designing traditional wooden buildings. This study aims to develop a formula for evaluating the bending performance of such joints based on full-scale experiments. The comparison of estimated values obtained from the proposed method with experimental results generally revealed accurate predictions of failure modes and moment and rotation angle relationships. © 2024 Architectural Institute of Japan. All rights reserved.

关键词： Wooden buildings

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