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Insurance Contract for High Renewable Energy Integration

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arXiv 2022年

作者： Zhao, Dongwei Wang, Hao Huang, Jianwei Lin, Xiaojun The MIT Energy Initiative Massachusetts Institute of Technology United States The Department of Data Science and Artificial Intelligence Monash University Australia The School of Science and Engineering The Chinese University of Hong Kong Shenzhen and Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Elmore Family School of Electrical and Computer Engineering Purdue University United States

The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users’ reliability preference is private information;and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users’ private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users’ total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty. © 2022, CC BY.

关键词： Insurance

Learned descriptor using dynamical exponential algorithm 2

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Learned descriptor using dynamical exponential algorithm

2nd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2021

作者： Yin, Jianhua Liu, Cong Wen, Jie Chen, Junhong Jiang, Jun Liu, Hui Zhu, Longzhen School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Hengfeng Bank Co. Ltd. Shanghai200000 China

ISBN: (纸本)9783800756162

Recent works improve the performance of learned descriptors by the triplet loss function and make some effort in finding the hard negative samples. However, these methods pay less attention to the weight parameter of the loss function to improve the stability of learned descriptors. Unstable descriptors can cause mismatching. To solve the problem, we design a dynamical exponential algorithm (DEA) and introduce the algorithm into the triplet loss function. This algorithm can make that the distance between nonmatching descriptors is exceeded to the distance between matching descriptors. It can improve the stability of the learned descriptor by increasing the difference between two distances. We demonstrate that the performance of the consistent descriptors reached state-of-the-art on various datasets. © 2021 AIIPCC 2021 - 2nd International Conference on Artificial Intelligence, Information Processing and Cloud Computing. All rights reserved.

关键词： Artificial intelligence

Unsupervised Cross-Domain 3D Human Pose Estimation via Pseudo-Label-Guided Global Transforms

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arXiv 2025年

作者： Liu, Jingjing Wang, Zhiyong Fan, Xinyu Dadashzadeh, Amirhossein Liu, Honghai Mirmehdi, Majid School of Computer Science University of Bristol United Kingdom State Key Laboratory of Robotics and Systems Harbin Institute of Technology Shenzhen China School of Aerospace Engineering Xiamen University China

Existing 3D human pose estimation methods often suffer in performance, when applied to cross-scenario inference, due to domain shifts in characteristics such as camera viewpoint, position, posture, and body size. Among these factors, camera viewpoints and locations have been shown to contribute significantly to the domain gap by influencing the global positions of human poses. To address this, we propose a novel framework that explicitly conducts global transformations between pose positions in the camera coordinate systems of source and target domains. We start with a Pseudo-Label Generation Module that is applied to the 2D poses of the target dataset to generate pseudo-3D poses. Then, a Global Transformation Module leverages a human-centered coordinate system as a novel bridging mechanism to seamlessly align the positional orientations of poses across disparate domains, ensuring consistent spatial referencing. To further enhance generalization, a Pose Augmentor is incorporated to address variations in human posture and body size. This process is iterative, allowing refined pseudo-labels to progressively improve guidance for domain adaptation. Our method is evaluated on various cross-dataset benchmarks, including Human3.6M, MPI-INF-3DHP, and 3DPW. The proposed method outperforms state-of-the-art approaches and even outperforms the target-trained model. Copyright © 2025, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

OPTIMIZING FOREST FIRE PREVENTION: INTELLIGENT SCHEDULING ALGORITHMS FOR DRONE-BASED SURVEILLANCE SYSTEM

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arXiv 2023年

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah11952 Saudi Arabia MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Higher School of Economic and Commercial Sciences Tunis University of Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones’ capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones’ assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023, CC BY.

关键词： Fires

Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement

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arXiv 2021年

作者： Li, Zhaoqun Liang, Xu Fan, Dandan Li, Jinxing Jia, Wei Zhang, David The School of Data Science The Chinese University of Hong Kong Shenzhen The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China The Bio-Computing Research Center School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Computer and Information Hefei University of Technology Hefei230009 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

With the growing demand for hand hygiene and convenience of use, palmprint recognition with touchless manner made a great development recently, providing an effective solution for person identification. Despite many efforts that have been devoted to this area, it is still uncertain about the discriminative ability of the touchless palmprint, especially for large-scale datasets. To tackle the problem, in this paper, we build a large-scale touchless palmprint dataset containing 2334 palms from 1167 individuals. To our best knowledge, it is the largest touchless palmprint image benchmark ever collected with regard to the number of individuals and palms. Besides, we propose a novel deep learning framework for touchless palmprint recognition named 3DCPN (3D Convolution Palmprint recognition Network) which leverages 3D convolution to dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of the Gabor filter is embedded into the first layer for enhancement of curve feature extraction. With a well-designed ensemble scheme, low-level 3D features are then convolved to extract high-level features. Finally on the top, we set a region-based loss function to strengthen the discriminative ability of both global and local descriptors. To demonstrate the superiority of our method, extensive experiments are conducted on our dataset and other popular databases TongJi and IITD, where the results show the proposed 3DCPN achieves state-of-the-art or comparable performances. Copyright © 2021, The Authors. All rights reserved.

关键词： Palmprint recognition

Feature Selection for Precise Anomaly Detection in Substation Automation Systems

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Feature Selection for Precise Anomaly Detection in Substatio...

IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Xuelei Wang Colin Fidge Ghavameddin Nourbakhsh Ernest Foo Zahra Jadidi Calvin Li School of Computer Science Queensland University of Technology Brisbane Australia School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia School of Information and Communication Technology Griffith University Brisbane Australia Asset & Operations Jemena Sydney Australia

ISBN: (纸本)9781665448796

With the rapid advancement of the electrical grid, substation automation systems (SASs) have been developing continuously. However, with the introduction of advanced features, such as remote control, potential cyber security threats in SASs are also increased. Additionally, crucial components in SASs, such as protection relays, usually come from third-party vendors and may not be fully trusted. Untrusted devices may stealthily perform harmful or unauthorised behaviours which could compromise or damage SASs, and therefore, bring adverse impacts to the primary plant. Thus, it is necessary to detect abnormal behaviours from an untrusted device before it brings about catastrophic impacts. Anomaly detection techniques are suitable to detect anomalies in SASs as they only bring minimal side-effects to normal system operations. Many researchers have developed various machine learning algorithms and mathematical models to improve the accuracy of anomaly detection. However, without prudent feature selection, it is difficult to achieve high accuracy when detecting attacks launched from internal trusted networks, especially for stealthy message modification attacks which only modify message payloads slightly and imitate patterns of benign behaviours. Therefore, this paper presents choices of features which improve the accuracy of anomaly detection within SASs, especially for detecting “stealthy” attacks. By including two additional features, Boolean control data from message payloads and physical values from sensors, our method improved the accuracy of anomaly detection by decreasing the false-negative rate from 25% to 5% approximately.

关键词： Performance evaluation Substation automation Systems operation Sensor phenomena and characterization Feature extraction Mathematical models Relays

A Human-Human Physical Interaction System for Training Handwriting

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A Human-Human Physical Interaction System for Training Handw...

IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： S. Buscaglione A. Noccaro C. Provenzale C. Bonsignori E. Ivanova L. Sparaci F. Taffoni E. Burdet D. Formica NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit Università Campus Bio-Medico di Roma Italy Neurorobotics Lab Newcastle University Newcastle Upon Tyne UK Advanced Robotics and Human-Centred Technologies-CREO Laboratory Università Campus Bio-Medico di Roma Italy Department of Bioengineering Imperial College of Science Technology and Medicine UK School of Electric Engineering and Computer Science Queen Mary University of London UK Institute of Cognitive Sciences and Technologies (ISTC) National Research Council of Italy (CNR) Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Handwriting training in childhood is known to influence academic success, foster self-esteem, and contribute to overall development. Previous studies underscored the positive impact of haptic feedback on handwriting learning using one-way communication systems that provided the trainee with haptic feedback based on pre-recorded trainer's movements. In this study, we propose a system involving bidirectional physical coupling (haptic communication) between a trainee and a trainer to exploit the advantages of haptic communication for motor task improvement, proved in tracking tasks. The system, composed of an interactive display and a coupling spring, was tested in a handwriting task performed by a dyad of healthy adults with different cursive handwriting backgrounds. The participants' performance demonstrated the feasibility of using this system for handwriting, opening the way for further studies to assess its benefits in handwriting improvement and learning.

关键词： Training Couplings Tracking Communication systems Biomechatronics Motors Haptic interfaces Springs Robots

A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

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A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Frank Kulwa Oluwarotimi Williams Samuel Mojisola Grace Asogbon Tolulope Tofunmi Oyemakinde Olumide Olayinka Obe Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Computer Science Federal University of Technology Akure Nigeria

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

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Mitigation Methods of Reducing DDOS Attack Frequency on Blockchain

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Mitigation Methods of Reducing DDOS Attack Frequency on Bloc...

Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R. Sathees Kumar Phanikanth Chintamaneni Madarapu Naresh Kumar Ankita Joshi Bajila Swathi Joshuva Arockia Dhanraj Department of CSE Narasaraopeta Institute of Technology JNTUK University Narasaraopet Andhra Pradesh Department Of CSE Koneru Lakshmaiah Education Foundation Scientist B National Informatics Centre Ministry of Electronics and Information Technology (MEITY) New Delhi National Informatics Centre Ministry of Electronics and Information Technology (MEITY) New Delhi Uttaranchal Institute of Technology Uttaranchal University India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai India

A serious risk to genuine organization demands is a distributed denial of service (DDoS) attack. The main DDoS attack was archived in 1996, however, from that point forward, their intricacy and refinement have consistently expanded. A 2 TBps attack on a crucial foundation, including the monetary area, was accounted for in August 2020 during the Coronavirus pandemic. In the accompanying two years, it is anticipated that the number of attacks would twofold, stretching around 15 million. Since its beginning, many relief plans have been made, however, the intricacy is rising, requiring modern arrangements in view of new innovation. A potential and common-sense method for DDoS relief is blockchain. The point of the review is to give an exhaustive examination of the fundamental and intrinsic properties of blockchain, including their decentralization, inside and outer trust lessness, unchanging nature, honesty, obscurity, and evidence, which have demonstrated to be a fantastic contender for battling this destructive cyber threat.

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