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Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 15-28页

作者： Li, Dengao Feng, Ran Wu, Fanming Zhao, Jinhua Zhao, Jumin College of Computer Science and Technology College of Data Science Taiyuan University of Technology Shanxi Taiyuan China College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan University of Technology Shanxi Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China

In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations. © 2024 - IOS Press. All rights reserved.

关键词： Long short-term memory

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Research on the Implementation Path of data Assetization in Electric Power Enterprises 5

Research on the Implementation Path of Data Assetization in ...

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5th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Yang, Tianguang Yan, Weisi Kang, Chunting Data Consulting Business Center Beijing SGITG-Accenture Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9798331507992

Under the background of the state actively promoting data assetization, this study proposes a four-stage evolution path of 'data resourceization-resource productization-product assetization-asset valorization' by analyzing the concepts of data assets and data assetization. By systematically sorting out the status quo and problems of data assetization in electric power enterprises, this study proposes the implementation path of electric power data assets from identification, quality and value assessment to operation and management based on the principle of implementable on the ground, so as to guide enterprises to manage data assets in a comprehensive manner and realize the value creation of assetization in a step-by-step manner. © 2024 IEEE.

关键词： data assetization data assets data assets management data products

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A proposed PMU-based voltage stability and critical bus detection method using artificial neural network

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Energy Informatics 2024年第1期7卷 1页

作者： Putranto, Lesnanto Multa Azhar, Izzuddin Fathin Department of Electrical and Information Engineering Engineering Faculty Universitas Gadjah Mada Jl. Grafika No 2 Engineering Faculty Complex Yogyakarta55281 Indonesia Center for Data and Information Technology Secretariat General Ministry of Transportation Jakarta10110 Indonesia

Voltage stability detection is currently still becoming the main issue in the modern integrated renewable energy power systems. To assess the voltage stability, the classical methods based on continuation power flow (CPF) technique were used to show nose curve. However, the classical methods require complete model of power system and long computation time. data driven analysis and synchronized real time measurement technologies currently are developing in power systems monitoring, including the stability detection. The detection method is built based on the historical event model and uses the real time measurement as an input. For that reason, the algorithm to detect the voltage instability and critical bus is proposed using the artificial neural network (ANN) technique to represent the historical event model using the PMU measurement data. The ANN model architecture for this application is developed by creating seven hidden layers consisting of one normalization, four rectifier linear unit, one softmax and one sigmoid layer. To warrant the accuracy, the k-fold cross-validation is used. The algorithm is simulated on modified IEEE 14 test system which consider different loading scenario, line contingency, number of PMU and Photovoltaic (PV) integration. To mimic the actual historical data, the synthetic data is generated and labelled. The result shows that the proposed method can represent the complete power system model by giving high accuracy which for voltage stability detection is > 97% and critical buses detection is > 96% for all scenarios. Moreover, the required computation time is between 16 and 18 s per detection which makes the scalability to the real time detection is reasonable. © 2024, The Author(s).

关键词： Neural networks

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Tone-Mapping Resilient HDR Image Watermarking based on Multi-Transforms and Saliency Detection

Tone-Mapping Resilient HDR Image Watermarking based on Multi...

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2024 International Conference on Electronics, information, and Communication, ICEIC 2024

作者： Khan, Ahmed Wong, KokSheik Kuribayashi, Minoru Baskaran, Vishnu Monn School of Information Technology Monash University Malaysia Tohoku University Center for Data-driven Science and Artificial Intelligence Japan

ISBN: (纸本)9798350371888

High dynamic range images are shared widely online, however they might be used improperly. Some HDR image-based watermarking (HDR-IW) techniques were proposed to secure these images. They did, however, inherit the same issue found in the traditional IW approaches for standard dynamic range images, in which only certain trade-offs among robustness, imperceptibility, and capacity are achieved. To con-currently improve robustness, imperceptibility, and payload, a novel saliency (eye-catching region) detection-based trade-off independent HDR-IW is proposed in this study. The host HDR image is first converted into an SDR image and the salient objects are detected to create a saliency map, which is then used to separate the host image's foreground and background. With the use of the same mask, the watermark (binary image) is then split into the foreground and background regions. Next, both regions are encrypted using multiple homomorphic algorithms. Subsequently, discrete wavelet and lifting transforms followed by the fast Fourier transforms are applied to the host image to generate the real parts of the subbands. Finally, the first 4 bits of each coefficient in the LL and HH subbands of the host image are utilized to embed watermark bits. Experiment results suggest that the proposed approach performs better at resolving competing criteria than cutting-edge techniques. © 2024 IEEE.

关键词： Watermarking

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Abnormal Clustering and Cross Slicing Transformer for Insect Fine-Grained Image Classification 2

Abnormal Clustering and Cross Slicing Transformer for Insect...

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Mei, Aokun Huo, Hua Big Data and Computing Intelligence Engineering Technology Research Center School of Information Engineering Henan University of Science and Technology Big Data Analysis Laboratory of Henan Medical Luoyang471023 China

ISBN: (纸本)9798350390254

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the Vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the Vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images. © 2024 IEEE.

关键词： Image segmentation

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Privacy-preserving deep learning techniques for wearable sensor-based big data applications

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 210-222页

作者： Rafik HAMZA Minh-Son DAO Integrated Big Data Research Center|NICT-National Institute of Information and Communications Technology TokyoJapan

Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)*** technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's *** the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security *** this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable *** highlight the fundamental features of security and privacy for wearable device ***,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable *** also present a case study on privacy-preserving machine learning ***,we theoretically and empirically evaluate the privacy-preserving deep learning framework's *** explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption ***,we explore the obstacles and gaps in the deployment of practical real-world *** a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.

关键词： Wearable technology Augmented reality Privacy-preserving Deep learning Big data Secure prediction service

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. Computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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Partitioned Edge Learning over Fast Fading Channels

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IEEE Transactions on Vehicular technology 2025年第6期74卷 8561-8576页

作者： Jiang, Zhihui Wen, Dingzhu Liu, Shengli Zhu, Guangxu Yu, Guanding Zhejiang University College of Information Science and Electronic Engineering Hangzhou China ShanghaiTech University School of Information Science and Technology Shanghai China Shanghai University School of Communication and Information Engineering Shanghai China Shenzhen Research Institute of Big Data Shenzhen International Center for Industrial and Applied Mathematics Shenzhen China

The implementation of the partitioned edge learning (PARTEL) framework in practical fast-fading wireless systems with time-varying channels is investigated in this paper. By exploiting the benefit of only training and transmitting a light-size sub-model on devices, PARTEL enjoys both enhanced computation and communication efficiency compared to federated edge learning framework where each device needs to train and transmit all model parameters. In this work, we aim at further enhancing the learning efficiency via minimizing the training latency of each training round in practical fast fading channels, where each round includes multiple channel time-coherence time durations. The challenges arise from the unknown channel state information (CSI) of future durations and the coupling between load balancing and bandwidth allocation among devices. To this end, an equivalent Markov decision process (MDP) problem is derived, where each decision step corresponds to one channel coherence-time duration. The learning load balancing is first determined based on an existing design for static channels by using the expected channel gains. Then, the bandwidth allocation for uploading the local sub-model updates in each duration is sequentially determined by the proposed optimization algorithm. The spectrum efficiency is enhanced since the bandwidth is adaptively allocated to all devices according to their communication and computation statuses in each coherence-time duration. Finally, extensive simulations are conducted to show the superiority of our proposed algorithms over the existing benchmarks. Specifically, the proposed algorithm can reduce the one-round latency by up to 25.51% for the bandwidth of 70 MHz. © 1967-2012 IEEE.

关键词： Bandwidth

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AOP: Towards Adaptive Offloading Point Approach in a Federated Learning Framework for Edge AI Applications 29

AOP: Towards Adaptive Offloading Point Approach in a Federat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Khoa, Tran Anh Nguyen, Do-Van Dao, Minh-Son Zettsu, Koji National Institute of Information and Communications Technology Big Data Integration Research Center Tokyo Japan

ISBN: (纸本)9798350330717

The study discusses challenges in deploying Artificial Intelligence (AI) on Internet-of-Things (IoT) devices and introduces a solution called Adaptive Offloading Point (AOP) for Federated Learning (FL) in Edge AI. AOP accelerates local training on resource-constrained devices and uses reinforcement learning-based clustering (RL) to determine which deep neural network (DNN) layers to offload to the server. Test results show that, in the VIT transformer model, AOP significantly reduces training time compared to classical FL and the baseline method called FedAdapt, making it a promising solution for Edge AI applications. © 2023 IEEE.

关键词： Adaptive offloading point Edge AI Federated learning Reinforcement learning Transformer

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GRAPH CONVOLUTIONAL NETWORKS & ADVERSARIAL TRAINING FOR JOINT EXTRACTION OF ENTITY AND RELATION

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2023年第3期85卷 213-224页

作者： Qu, Xiaolong Zhang, Yang Tian, Ziwei Li, Yuxun Li, Dongmei Zhang, Xiaoping School of Information Science and Technology Beijing Forestry University China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical Sciences China

Entity recognition and relation extraction are the core tasks in information extraction. Currently, supervised deep learning extraction methods are mainly divided into two categories: pipeline and joint entity-relation extraction. The pipeline method has problem of exposure bias, information redundancy, error accumulation and interaction missing. To solve the problems, researchers proposed joint entity-relation extraction method. However, the joint entity-relation extraction method based on sequence annotation does not effectively process entity overlapping, and relation overlapping. Therefore, we propose a joint extraction model GcnJere based on graph convolutional neural network to solve existing problems in the pipeline method and further improve the processing effect of entity overlapping and relation overlapping. Furthermore, we combine the advantages of adversarial training and propose GcnJereAT to improve the generalization ability and robustness of GcnJere. Finally, the performance of the proposed two models is verified in the public benchmark dataset. The experimental results indicate that the computational performance of the two models is superior to the comparison models. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Convolution

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