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Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

地球空间信息科学学报（英文版） 2024年第3期27卷 602-623页

作者： Roberto Tomás Qiming Zeng Juan M.Lopez-Sanchez Chaoying Zhao Zhenhong Li Xiaojie Liu María I.Navarro-Hernández Liuru Hu Jiayin Luo Esteban Díaz William T.Szeibert José Luis Pastor Adrián Riquelme Chen Yu Miguel Cano Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencePeking UniversityBeijingChina Institute for Computer Research(IUII) University of AlicanteAlicanteSpain College of Geological Engineering and Geomatics Chang'an UniversityXi'anChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain School of Civil Engineering Lanzhou University of TechnologyLanzhouChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Land Satellite Remote Sensing Application Center(LASAC) Ministry of Natural Resources of P.R.ChinaBeijingChina The First Topographic Surveying Brigade of Ministry of Natural Resources of the People's Republic of China Xi'anChina Sixense Departamento SatéliteL'Hospitalet de LlobregatBarcelonaSpain

Landslides are destructive geohazards to people and infrastructure,resulting in hundreds of deaths and billions of dollars of damage every ***,mapping the rate of deforma-tion of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated *** this paper,the main outcomes relevant to the joint European Space Agency(ESA)and the Chinese Ministry of science and Technology(MOST)Dragon-5 initiative cooperation project ID 59,339"Earth observation for seismic hazard assessment and landslide early warning system"are *** primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks,detect potential landslides in wide regions,and demonstrate EO-based landslide early warning system over selected *** work only focuses on the landslide hazard content of the project,and thus,in order to achieve these objectives,the following tasks were developed up to now:a)a procedure for phase unwrap-ping errors and tropospheric delay correction;b)an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements;c)the application of polarimetric SAR interferometry(PolInSAR)to increase the number and quality of monitoring points in landslide-prone areas;d)the semiautomatic mapping and preliminary classification of active displacement areas on wide regions;e)the modeling and identification of landslides in order to identify triggering factors or predict future displacements;and f)the application of an InSAR-based landslide early warning system on a selected *** achieved results,which mainly focus on specific sensitive regions,provide essential assets for planning present and future scientific activities devoted to identifying,mapping,characterizing,monitoring and predicting landslides,as well as for the implementation of early warning systems.

关键词： InSAR SAR offset tracking PolInSAR landslide mapping modeling early warning monitoring

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Design of Intelligent Backflow Prevention System

Design of Intelligent Backflow Prevention System

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Chinese Control Conference (CCC)

作者： Jiale Chen Tao Dong Shiyun Wang Guili Jiao Rongrong Zhang Xincheng Tian Center for Robotics Engineering Research Center of Intelligent Unmanned System Ministry of Education School of Control Science and Engineering Shandong University Jinan China Jinan China Shandong Tianyong Intelligent Technology Co. Ltd Jinan China Shandong Hydrology Center Jinan China

With the advent of the rainy season, the chances of urban flooding are increasing, to avoid the risk of flooding people and underground garages, this paper explains our reliance on video surveillance technology and 5G cloud data technology to collect the data of anti-backflow door equipment deployed in various parts of the city, and remote control and command in the management center, which can quickly give flood control measures and early warning to protect the personnel and property of underground garages, and reduced losses.

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Mutual Information-Based Temporal Difference Learning for Human Pose Estimation in Video

Mutual Information-Based Temporal Difference Learning for Hu...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Runyang Feng Yixing Gao Xueqing Ma Tze Ho Elden Tse Hyung Jin Chang School of Artificial Intelligence Jilin University Ministry of Education Engineering Research Center of Knowledge-Driven Human-Machine Intelligence China School of Computer Science University of Birmingham

Temporal modeling is crucial for multi-frame human pose estimation. Most existing methods directly employ optical flow or deformable convolution to predict full-spectrum motion fields, which might incur numerous irrelevant cues, such as a nearby person or background. Without further efforts to excavate meaningful motion priors, their results are suboptimal, especially in complicated spatio-temporal interactions. On the other hand, the temporal difference has the ability to encode representative motion information which can potentially be valuable for pose estimation but has not been fully exploited. In this paper, we present a novel multi-frame human pose estimation framework, which employs temporal differences across frames to model dynamic contexts and engages mutual information objectively to facilitate useful motion information disentanglement. To be specific, we design a multi-stage Temporal Difference Encoder that performs incremental cascaded learning conditioned on multi-stage feature difference sequences to derive informative motion representation. We further propose a Representation Disentanglement module from the mutual information perspective, which can grasp discriminative task-relevant motion signals by explicitly defining useful and noisy constituents of the raw motion features and minimizing their mutual information. These place us to rank No.1 in the Crowd Pose Estimation in Complex Events Challenge on benchmark dataset HiEve, and achieve state-of-the-art performance on three benchmarks PoseTrack2017, PoseTrack2018, and PoseTrack21.

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Optimal Energy Storage Operation under Demand Uncertainty: A Prospect Theory Analysis

Optimal Energy Storage Operation under Demand Uncertainty: A...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Qisheng Huang Jin Xu Peng Sun Bo Liu Costas Courcoubetis School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China School of Management Huazhong University of Science and Technology Wuhan China College of Computer Science and Electronic Engineering Hunan University Changsha China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper, we study the consumer’s optimal energy storage operation problem under demand uncertainty. Each consumer can purchase energy storage service from an independent energy storage aggregator to shift demand from peak periods to off-peak periods under time-of-use (ToU) pricing. Previous studies on energy storage operation and investment mainly focused on the expected cost-minimization problem of a risk-neutral consumer based on the expected utility theory (EUT). We propose a prospect theory (PT) model from behavioral economics to understand the consumer’s realistic energy storage operation behaviors considering their risk preferences. Although it is challenging to solve the PT-based non-convex optimization problem, we are able to characterize the optimal solution by exploiting the unimodal structure of the objective function. Theoretical and numerical results show that the consumer’s risk preferences have a significant impact on his decisions: 1) a PT-consumer with a low reference point is more willing to use energy storage to reduce risk compared with the EUT benchmark; and 2) a PT consumer is more willing to use the energy storage when the probability of high demand is small, due to the probability distortion.

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Deep Reinforcement Learning for Accurate Anomaly Detection in Multi-Cloud Environment 2

Deep Reinforcement Learning for Accurate Anomaly Detection i...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Chaudhary, Anisha Nalini Sunil, M. Tiwaskar, S.A. Vijayalakshmi, V.J. Sharma, Sorabh Senthurya, S. Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India

ISBN: (纸本)9798350388916

Intrusion Detection System (IDS) is used for identifying complicated Cloud assaults. A cooperative IDS may use an aggregation technique to decide on suspicious intrusions. Aggregation methods and IDS feedback slow the process. These restrictions make real-time cooperative IDS choices useless and unsustainable. We suggest a machine learning-based collaborative IDS that uses past feedback data to make proactive decisions to address these issues. DRL-based multi-cloud IDS was suggested. The suggested approach uses a DE noising Auto encoder (DA) to create a deep neural network. DA can rebuild IDS feedback from incomplete feedback. The DA network uses Q-learning to continuously learn features. This lets us respond on suspected incursions without IDS input. The GPU-enabled Tensor Flow model was tested on a real-world dataset. Our model achieves 95% detection accuracy in experiments. © 2024 IEEE.

关键词： Anomaly Detection Cloud Environment Deep Reinforcement DRL Effective Anomaly Identification and IDS Multi-Cloud Environment

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Wireless Network Interface Card Energy Management for Interactive Applications

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Chinese Journal of Electronics 2023年第1期20卷 45-50页

作者： Jing Wang Xuetao Guan Xu Cheng Jason Cong Shenzhen Graduate School of Peking University Shenzhen China Microprocessor Research and Development Center of Peking University Beijing China UCLA Computer Science Department PKU /UCLA Joint Research Institute in Science and Engineering Beijing China

Power management of Wireless network interface cards (WNIC) plays a significant role in the development of mobile and pervasive computing. However, current WNIC power management often substantially degrades performance or even increases overall energy consumption when used with interactive applications. This paper proposes a WNIC Energy management protocol for latency-sensitive interactive applications(ELIA). ELIA exploits features of user interactions for wireless network energy conservation. The effective user interaction analysis techniques recognize complex sequences of mouse clicks or keystrokes, and provide valuable hints to anticipate underlying network access patterns. The prediction algorithm uses dynamic time wrapping techniques to predict future network access based on those interaction hints, and then adapts power mode of the WNIC according to the prediction. Real-world evaluation results show that ELIA achieves a better balance of the competing goals of energy conservation, interactive performance, and application quality compared with state-of-the-art power management methods.

关键词： Pervasive computing Protocols Wireless networks Power system management Heuristic algorithms Energy conservation Prediction algorithms

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Multi-stream Fusion for Class Incremental Learning in Pill Image Classification 16th

Multi-stream Fusion for Class Incremental Learning in Pill...

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16th Asian Conference on computer Vision, ACCV 2022

作者： Nguyen, Trong-Tung Pham, Hieu H. Nguyen, Phi Le Nguyen, Thanh Hung Do, Minh VinUni-Illinois Smart Health Center VinUniversity Hanoi Viet Nam John von Neumann Institute University of Science VNU-HCM Ho Chi Minh City Viet Nam College of Engineering and Computer Science VinUniversity Hanoi Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam University of Illinois at Urbana-Champaign Champaign United States

ISBN: (纸本)9783031262838

Classifying pill categories from real-world images is crucial for various smart healthcare applications. Although existing approaches in image classification might achieve a good performance on fixed pill categories, they fail to handle novel instances of pill categories that are frequently presented to the learning algorithm. To this end, a trivial solution is to train the model with novel classes. However, this may result in a phenomenon known as catastrophic forgetting, in which the system forgets what it learned in previous classes. In this paper, we address this challenge by introducing the class incremental learning (CIL) ability to traditional pill image classification systems. Specifically, we propose a novel incremental multi-stream intermediate fusion framework enabling incorporation of an additional guidance information stream that best matches the domain of the problem into various state-of-the-art CIL methods. From this framework, we consider color-specific information of pill images as a guidance stream and devise an approach, namely "Color Guidance with Multi-stream intermediate fusion" (CG-IMIF) for solving CIL pill image classification task. We conduct comprehensive experiments on real-world incremental pill image classification dataset, namely VAIPE-PCIL, and find that the CG-IMIF consistently outperforms several state-of-the-art methods by a large margin in different task settings. Our code, data, and trained model are available at https://***/vinuni-vishc/CG-IMIF. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image classification

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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IEEE Conference on Wireless Communications and Networking

作者： Sarah Al–Shareeda Fusun Ozguner Keith Redmill Trung Q. Duong Berk Canberk Electrical and Computer Engineering Department Center for Automotive Research (CAR) The Ohio State University USA BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

关键词： 6G mobile communication Performance evaluation Identity-based encryption Authentication Deep reinforcement learning Reproducibility of results Resource management Cryptography Optimization Software development management

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An Efficient USE-Net Deep Learning Model for Cancer Detection

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International Journal of Intelligent Systems 2023年第1期2023卷

作者： Almutairi, Saad M. Manimurugan, S. Aborokbah, Majed M. Narmatha, C. Ganesan, Subramaniam Karthikeyan, P. Industrial Innovation & Robotics Center Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Electrical and Computer Engineering Oakland University Rochester112345 United States Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for fnding BrCa at an earlier stage and monitoring BrCa during treatment. Te computer-aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. Te main objective of this research is to classify the BrCa images for newly generated images. Te model performs preprocessing, segmentation, feature extraction, and classifcation. In preprocessing, a hybrid median fltering (HMF) is used to eliminate the noise in the images. Te contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Ten, ROI segmentation is performed using the USE-Net deep learning model. Te CafeNet model is used for feature extraction on the segmented images, and fnally, classifcation is made using the improved random forest (IRF) with extreme gradient boosting (XGB). Te model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specifcity, 98.96% precision, and 98.70% f1-score for ultrasound images. Te model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specifcity, 98.82% precision, and 99.05% f1-score for mammogram images. © 2023 John Wiley and Sons Ltd. All rights reserved.

关键词： Feature extraction

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A Comprehensive Survey of Blockchain-Powered Cybersecurity- A survey

A Comprehensive Survey of Blockchain-Powered Cybersecurity- ...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Chadi Altrjman Sumit Kumar Preeti Chaudhary Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Dept. AI and Robotics Institute Artificial Intelligence Engineering Near East University Nicosia Turkey Research Center for AI and IoT Faculty ofEngineering University of Kyrenia Kyrenia Turkey Waterloo University Canada InfoVision Pvt. Ltd. Computer Science and Engineering Graphic Era Hill University Dehradun

Text mining was used to examine the academic publications on blockchain technology and cybersecurity that were published in huge digital libraries. Automated text mining methods like topic modeling and key word extraction are utilized in this literature study to extract topics from a vast corpus of literature. This article emphasizes the cross-disciplinary character of blockchain technology in the context of cybersecurity. The outcomes also show the risks and security holes that emerge as blockchain technology matures. Nevertheless, the investigation this article also outlines research gaps in the area of computer security and recommends research directions for future work that will be necessary to create safe blockchain platforms.

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