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arXiv 2022年

作者： Fei, Ben Yang, Weidong Chen, Wen-Ming Li, Zhijun Li, Yikang Ma, Tao Hu, Xing Ma, Lipeng Shanghai Key Laboratory of Data Science The School of Computer Science Fudan University Shanghai200433 China The Academy for Engineering and Technology Fudan University Shanghai200433 China The Department of Automation University of Science and Technology of China Hefei230060 China Shanghai AI Laboratory Shanghai China School of Optical-electrical Information and Computer Engineering University of Shanghai for Science and Technology 516 JunGong Road Shanghai China

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications of 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, view-based, convolution-based, graph-based, generative model-based, transformer-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Development and Improving Energy Management System in Smart Grid through Integration with Renewable Energy System with AI and Internet of Things

Development and Improving Energy Management System in Smart ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： R Bhavya P Manjuladevi. E. Sri Santhoshini M. Ramya R. Dhanapal New Horizon College of Engineering Bangalore Karnataka Bangalore technological Institute Bangalore Karnataka Department of Artificial Intelligence K.Ramakrishnan College of Technology Trichy Tamilnadu Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore Tamilnadu

An innovative development in the energy substructure is defined as smart grid. The process of maintaining the stability between the supply side and the demand side is denoted as energy management system in smart grid. Further various cutting end solutions are obtained through the internet of things which helps in the overall conversion of conventional power grid into a smart grid network. This leads to rise in the energy internet which is formulated through the interrelationship between electricity producers cum consumers with renewable energy sources. This includes real time implementation with bidirectional flow of power through communication network and considering cost parameter. Thus the various challenges in the electricity production and distribution are done smartly through incorporating artificial intelligence and internet of things.

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Artificial Neural Network and Implementation of Robotic Arm for Mineral Extraction in Mines with Permanent Magnet Synchronous Motor

Artificial Neural Network and Implementation of Robotic Arm ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： G. Yadagir M. Sudha vidya Sagar Reddy G D.Shobha Rani S Sophia J Femila Roseline Collegiate Education Jayashankar Vidya Bhavan Hyderabad Telangana India Department of Electronics and Communication engineering Paavai Engineering College (Autonomous) Namakkal Tamil Nadu India School of Architecture Koneru Lakshmaiah Educational Foundation Guntur Andhra Pradesh India Department of Computer Science and Engineering Chadalawada Ramanamma Engineering college Tirupathi Tamil Nadu India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

The implementation of a robotic arm for mineral extraction in mines is a challenging task that requires advanced technology and innovation. This paper proposes a solution using artificial intelligence (AI) and a permanent magnet synchronous motor (PMSM) to control the robotic arm's movement. A robotic arm with several degrees of freedom, a PMSM to move the arm, and AI algorithms to manage its motion make up the proposed system. The PMSM gives the arm's movements excellent control and enhances the precision and effectiveness of the mineral extraction procedure. The AI algorithms are in charge of directing the robotic arm's motion by examining sensor data and making choices following the situation at hand. The system is capable of adapting to its surroundings to improve the efficiency of mineral extraction. Compared to conventional mining methods, the suggested system has several benefits, including greater safety, increased efficiency, and lower costs. The system can completely transform the mining sector and can be applied in a variety of mining applications. Using a robotic arm and AI technologies, the suggested method offers a novel approach to mineral extraction in mines. The technology has several advantages over conventional mining methods and has the potential to greatly increase the productivity and security of the mining sector.

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Dynamical causality under invisible confounders

arXiv

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arXiv 2024年

作者： Yan, Jinling Zhang, Shao-Wu Zhang, Chihao Huang, Weitian Shi, Jifan Chen, Luonan MOE Key Laboratory of Information Fusion Technology School of Automation Northwestern Polytechnical University Xi’an710072 China Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai200031 China NCMIS CEMS RCSDS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematics Sciences University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Institute of Intelligence Science and Technology Hengqin Guangdong Zhuhai519031 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences Hangzhou310024 China

Causality inference is prone to spurious causal interactions, due to the substantial confounders in a complex system. While many existing methods based on the statistical methods or dynamical methods attempt to address misidentification challenges, there remains a notable lack of effective methods to infer causality, in particular in the presence of invisible/unobservable confounders. As a result, accurately inferring causation with invisible confounders remains a largely unexplored and outstanding issue in data science and AI fields. In this work, we propose a method to overcome such challenges to infer dynamical causality under invisible confounders (CIC method) and further reconstruct the invisible confounders from time-series data by developing an orthogonal decomposition theorem in a delay embedding space. The core of our CIC method lies in its ability to decompose the observed variables not in their original space but in their delay embedding space into the common and private subspaces respectively, thereby quantifying causality between those variables both theoretically and computationally. This theoretical foundation ensures the causal detection for any high-dimensional system even with only two observed variables under many invisible confounders, which is actually a long-standing problem in the field. In addition to the invisible confounder problem, such a decomposition actually makes the intertwined variables separable in the embedding space, thus also solving the non-separability problem of causal inference. Extensive validation of the CIC method is carried out using various real datasets, and the experimental results demonstrates its effectiveness to reconstruct real biological networks even with unobserved confounders. © 2024, CC BY-NC-ND.

关键词： Time series

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Predicting circRNA-disease associations using similarity assessing graph convolution from multi-source information networks

Predicting circRNA-disease associations using similarity ass...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Li Xue-Gang Hu Pei-Pei Li Lei Wang Zhu-Hong You School of Computer Science and Information Engineering Hefei University of Technology Hefei China Big Data and Intelligent Computing Research Center Guangxi Academy of Sciences) Nanning China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Circular RNA (circRNA), a novel endogenous noncoding RNA molecule with a closed-loop structure, can be used as a biomarker for many complex human diseases. Determining the relationship between circRNAs and diseases helps us to understand the diagnosis, treatment, and pathogenesis of complex diseases, which plays a critical role in clinical research. Nevertheless, the discovery of new circRNA-disease associations by wet-lab methods is not only time-consuming and costly but also randomized and blinded, which is also limited to small-scale studies. Thus, there is an urgent need to establish efficient and reliable computational methods to infer potential circRNA-disease associations on a large scale to effectively reduce costs and save time, and avoid high false-positive rates. In this paper, we propose a novel computational method for predicting circRNA-disease association based on the Similarity Assessing Graph Convolution Network (SAGCN) algorithm, which combines the multi-source similarity network constructed by circRNA and disease. Firstly, we fuse the multi-source similarity information of circRNAs and diseases and construct the multi-source similarity network respectively. Then we use the SAGCN algorithm to extract the hidden feature representations of circRNAs and diseases efficiently and objectively in the way of measuring the similarity between different nodes in the network. Finally, the obtained high-level features of circRNAs and diseases are fed to the multilayer perceptron (MLP) classifier for accurate prediction. Using the 5-fold cross-validation method, the AUC scores of the four SAGCN algorithms, on the benchmark circR2Disease dataset are 93.30%, 92.98%, 92.22% and 91.94%, respectively. Furthermore, case studies further validated that the proposed model was supported by biological experiments, and 25 of the top 30 circRNA-disease associations with the highest scores were confirmed by recent literature. Based on these reliable results, it can be anticip

关键词： Convolution Computational modeling Biological system modeling RNA Predictive models Prediction algorithms Feature extraction

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite Systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Expression of Concern for: Construction of Malaria Disease Prediction System using Deep Learning

Expression of Concern for: Construction of Malaria Disease P...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： J Sivakumar Sudhagar D Vithya V T Jaichandran R K. Narasimha Raju C. S. Sundar Ganesh Department of Electronics and Communication Engineering St.Joseph'sCollegeof Engineering Chennai Tamil Nadu India Department of Information Technology Jerusalem College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering AarupadaiVeedu Institute of Technology Vinayaka Mission's Research Foundation Paiyanoor Tamil Nadu India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering Visakhapatnam Andhra Pradesh India Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9781665460842

ISBN: (纸本)9781665460859

Malarial fever is an infectious sickness that influences a large number of lives each year. Malarial fever is as yet a significant worldwide wellbeing trouble, with more than 3.2 billion individuals in 91 nations staying in danger of the infection. Precisely recognizing intestinal sickness from different illnesses, particularly simple malarial fever from non-malarial contaminations, stays a test. Customary analysis of malarial fever in lab requires an accomplished individual and cautious assessment to segregate solid and contaminated red platelets. Profound learning-based models have had extraordinary accomplishment in object recognition; however, the cutting-edge models have not yet been generally applied to natural picture informationIn this article, item location model recently utilized on normal pictures to distinguish cells is appliedand perceive their stages in brightfield microscopy pictures of malarial fever tainted blood This paper, is incorporated with Spotting of Malarial fever from the output images of tiny malign by employing CNN (Convolutional Neural Network) model. The datasets are collected from the Kaggle and used for testing and training. In this model, malarial parasites are discerned from infinitesimal output images with 97.76% of accuracy. In this paper, the CNN model is evaluated using Accuracy and Loss.

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Analysis of blockchain protocol against static adversarial miners corrupted by long delay attackers

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science China(Information sciences) 2020年第3期63卷 49-63页

作者： Quan YUAN Puwen WEI Keting JIA Haiyang XUE Key Laboratory of Cryptologic Technology and Information Security Ministry of EducationShandong University Department of Computer Science and Technology Tsinghua University State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences Data Assurance and Communication Security Research Center Chinese Academy of Sciences

Bitcoin, which was initially introduced by Nakamoto, is the most disruptive and impactive cryptocurrency. The core Bitcoin technology is the so-called blockchain protocol. In recent years, several studies have focused on rigorous analyses of the security of Nakamoto’s blockchain protocol in an asynchronous network where network delay must be considered. Wei, Yuan, and Zheng investigated the effect of a long delay attack against Nakamoto’s blockchain protocol. However, their proof only holds in the honest miner setting. In this study, we improve Wei, Yuan and Zheng’s result using a stronger model where the adversary can perform long delay attacks and corrupt a certain fraction of the miners. We propose a method to analyze the converge event and demonstrate that the properties of chain growth, common prefix, and chain quality still hold with reasonable parameters in our stronger model.

关键词： blockchain bitcoin random oracle delay consensus protocol

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Towards Faster Fully Homomorphic Encryption Implementation with Integer and Floating-point Computing Power of GPUs

Towards Faster Fully Homomorphic Encryption Implementation w...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Guang Fan Fangyu Zheng Lipeng Wan Lili Gao Yuan Zhao Jiankuo Dong Yixuan Song Yuewu Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing China Ant Group Hangzhou China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Cyber Security University of Science and Technology of China Hefei China

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without knowledge of the plaintext message and currently has been the focus of both academia and industry. However, the performance issue hinders its large-scale application, highlighting the urgent requirements of high-performance FHE *** noticing the tremendous potential of GPUs in the field of cryptographic acceleration, this paper comprehensively investigates how to convert the available computing resources residing in GPUs into FHE workhorses, and implement a full set of low-level and middle-level FHE primitives based on two arithmetic units (i.e., INT32 and FP64 units) with three types of data precision (i.e., INT32, INT64 and FP64). This paper gives a comprehensive evaluation and comparison based on each road-map. Our implementations of fundamental functions outperform the implementations on the same platform by 1.7× to 16.7×. Taking CKKS FHE schemes as a case study, our implementation of homomorphic multiplication achieves 3.2× speedup over the state-of-the-art GPU-based implementation, even considering the difference of platforms. The detailed evaluation and comparison of this paper would offer a vital reference for the follow-up work to choose appropriate underlying arithmetic units and important primitive optimizations in GPU-based FHE implementations.

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Cross-Image Distillation for Semi-Supervised Semantic Segmentation

Cross-Image Distillation for Semi-Supervised Semantic Segmen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Nan Zhang Fan Xiao Junlin Hou Ruiwei Zhao Xiaobo Zhang Rui Feng Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China

Semi-supervised semantic segmentation approaches have drawn much more attention in recent years, which aim to exploit a large amount of unlabeled data together with a small number of labeled data. However, existing models usually regarded segmentation as pixel-wise classification, neglecting global semantic relations among pixels across various images. Moreover, scarce annotated data usually exhibits a biased distribution against the desired one, hindering performance improvement. To address these challenging problems, we propose a novel cross-image distillation framework for semi-supervised semantic segmentation. Specifically, we introduce a relation distillation module to model inter-channel correlations between features of labeled samples and unlabeled samples. In addition, we propose a style distillation strategy to explicitly calibrate the learned feature distributions of labeled and unlabeled data to be aligned. Experimental results on two popular benchmarks demonstrate that our proposed approach achieves superior performance over other state-of-the-art methods. We will release the code soon.

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