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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Lei Zhang Xuefei Chen Yuxiang Ma School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Mobile devices face the problem of handover signal sources in mobile communication networks. It is particularly difficult to address this problem in 5G networks, because the coverage area of 5G base stations is smaller than that of traditional mobile communication networks, e.g., 4G networks. Therefore, when a device is moving, it needs to frequently handover base stations to maintain the connection with the network in the 5G network. Given that 5G networks have high requirements for low latency and high reliability, new methods need to be proposed to optimize the selection of gNBs for devices. The prediction of the UEs’ trajectory can reduce redundant handovers between devices and base stations. However, the prediction of the UEs’ movement trajectory requires sufficient user movement data to achieve the desired prediction accuracy. For users with less trajectory data, inaccurate predictions may cause negative effects and even increase the number of handovers. In this paper, we propose a gNB handover model based on user similarity. The evaluation results show that our method can reduce the number of handovers by 50% compared with existing related solutions, which means that the proposed method will effectively reduce the transmission delay and enhance the robustness of the gNB.

关键词： Base stations 5G mobile communication Handover Predictive models Prediction algorithms data models Robustness

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Base Station handover Based on User Trajectory Prediction in 5G Networks

Base Station handover Based on User Trajectory Prediction in...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Yuxiang Ma Xuefei Chen Lei Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In the 5G era, user equipment connected to 5G base stations can obtain better communication services. However, due to the limited coverage of base stations, the movement of users may cause frequent handover of base stations. With the widespread deployment of 5G base stations, how to reduce unnecessary handover times when users connect to the base station becomes particularly important. In recent years, user trajectory data has been mined and applied to many scenarios. In the 5G network, by judging the user’s movement trajectory, the number of handovers required for the user to connect to the 5G base station can be effectively reduced. In this paper, we propose a 5G base station handover method based on trajectory prediction. A CNN-LSTM neural network, which combines a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has been proposed to predict the user’s trajectory. The evaluation results show that our mechanism can effectively reduce the number of base station handovers and improve the efficiency of users using the network. In addition, the stability of 5G networks can be improved by reducing inefficient base station handover.

关键词： Base stations 5G mobile communication Time series analysis Handover Predictive models Prediction algorithms Feature extraction

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key-Point Based Distribution Probability Optimization for Object Detection in Remote Sensing Images

Key-Point Based Distribution Probability Optimization for Ob...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yufei Wang Xiaopan Chen Danyang Li Jitong Zhang Yulong Cai School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781665409513

Recently, object detection in remote sensing images has been an important topic in computer vision. As a mainstream approach to detectors, single-stage detectors usually compute the important points of the bounding box to get the location of the target, and optimize them by Focal Loss. One of the development trends for one-stage detectors is to combine with Localization Quality Estimation (LQE), which can provide accurate ranking scores, and thus bring benefits to the Non-Maximum Suppression as well as the following detection results. In this paper, we propose a one-stage detector, named key-point based Distribution Probability Optimization (KDPO), for remote sensing object detection. Specially, we first classify and locate the object through five key points. Then, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification. Next, we utilize a vector to represent arbitrary distribution of box locations. The improved representations avoid the inconsistency risk and accurately describe the flexible distribution of the data. Meanwhile, we use the improved Focal Loss for the succeed optimization. The proposed method is tested on two popular aerial image datasets, including HRSC2016 and DOTA. In all experiments, our approach achieves promising performance comparing to the state-of-the-art detectors.

关键词： Location awareness Heating systems Computer vision Estimation Detectors Object detection Prediction algorithms

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Design of Offline analysis System for Remote Sensing data Service Based on Hive

Design of Offline Analysis System for Remote Sensing Data Se...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Hongkuo Zhang Jiaxiang Zhang Meng Tian Baojun Qiao School of Computer and Information Engineering Heanan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9781665406314

With the wide application of remote sensing data service platform, the platform accumulates more and more data, and the remote sensing users' demand for remote sensing data service becomes more and more diverse. Therefore, how to deal with remote sensing service data, make statistics and analyze its potential value, and meet the diverse needs of remote sensing users becomes particularly important. Based on big data technology, this paper firstly collects and stores remote sensing user behavior data and remote sensing service platform business data, then uses Hive to build offline data warehouse, performs dimensional modeling and hierarchical design of the data, and finally obtains relevant index data values by statistical analysis. Through the statistical analysis of remote sensing service data can not only provide data support for active service mode, but also provide support for the decision of remote sensing application department managers.

关键词： Process design Analytical models Statistical analysis data warehouses data models Indexes Intelligent systems

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An Algorithm of data Classification Based on PCA and K-Means++

An Algorithm of Data Classification Based on PCA and K-Means...

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Telecommunications, Optics and Computer Science (TOCS), IEEE Conference on

作者： Ruirui Huang Qianshuai Cheng Zhengquan Chen Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665424998

Principal component analysis (PCA) and K-means++ algorithm are used to classify and analyze eight main variables of the average annual consumption expenditure of urban households in 31 Provinces and cities in China in a given year, so as to better build the reasonable consumption level of each area. In this paper, a data classification method based on PCA and improved K-means++ is proposed. In this method, PCA was used to extract eight variable characteristics of consumer expenditure, and K-means++ algorithm was used to classify the data. The experimental results show that this method reduces the dependence of the characteristic dimension of variable data and the k-means++ algorithm on the initial classification center of gravity, and improves the accuracy and stability of data classification results.

关键词： Dimensionality reduction Conferences Urban areas Optics Stability analysis Classification algorithms Telecommunications

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Graph Structure-Aware Bi-directional Graph Convolution Model for Semantic Role labeling

Graph Structure-Aware Bi-directional Graph Convolution Model...

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IEEE International Conference on big data

作者： Wenke Ding Congcong Zhang Gaofei Xie Xiaojie Hu Xiajiong Shen Yatian Shen School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781665445993

Dependency tree contains rich syntactic structure information, which is very effective for semantic role labeling. Semantic role labeling is a task with strong syntactic dependence. How to effectively use subgraphs to predict surrounding graph structures for context of dependency tree and encode the dependency relationships between predicates and arguments in the sentence which to be annotated is still a challenging problem. In this paper, we propose a bidirectional graph convolution model based on distribution of graph structure and the attention mechanism, which is used to make better use of all syntactic information in the sentence dependency tree and the dependencies between predicates and arguments, so as to improve the accuracy of semantic role labeling. Without heavy feature engineering and extra knowledge of a particular language, our model obtained 88.9% F1 value on the WSJ test set of public dataset CoNLL2005, and 0.7% improvement compared with the existing best model.

关键词： Knowledge engineering Convolution Conferences Semantics Bidirectional control Syntactics big data

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Diffusion Model-based Contrastive Learning for Human Activity Recognition

arXiv

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arXiv 2024年

作者： Xiao, Chunjing Han, Yanhui Yang, Wei Hou, Yane Shi, Fangzhan Chetty, Kevin The School of Computer and Information Engineering Henan University Kaifeng475004 China University College London LondonWC1E 6BT United Kingdom The School of Computer and Information Engineering Henan University China The Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China The Department of Security and Crime Science University College London LondonWC1E 6BT United Kingdom

WiFi Channel State Information (CSI)-based activity recognition has sparked numerous studies due to its widespread availability and privacy protection. However, when applied in practical applications, general CSI-based recognition models may face challenges related to the limited generalization capability, since individuals with different behavior habits will cause various fluctuations in CSI data and it is difficult to gather enough training data to cover all kinds of motion habits. To tackle this problem, we design a diffusion model-based Contrastive Learning framework for human Activity Recognition (CLAR) using WiFi CSI. On the basis of the contrastive learning framework, we primarily introduce two components for CLAR to enhance CSI-based activity recognition. To generate diverse augmented data and complement limited training data, we propose a diffusion model-based time series-specific augmentation model. In contrast to typical diffusion models that directly apply conditions to the generative process, potentially resulting in distorted CSI data, our tailored model dissects these condition into the high-frequency and low-frequency components, and then applies these conditions to the generative process with varying weights. This can alleviate data distortion and yield high-quality augmented data. To efficiently capture the difference of the sample importance, we present an adaptive weight algorithm. Different from typical contrastive learning methods which equally consider all the training samples, this algorithm adaptively adjusts the weights of positive sample pairs for learning better data representations. The experiments suggest that CLAR achieves significant gains compared to state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Design and Research of Remote Sensing Process Language based on XML

Design and Research of Remote Sensing Process Language based...

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Information Technology,big data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xianyu Zuo Heng Li Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665428781

In the era of rapid development of remote sensing technology, in order to improve the production efficiency of remote sensing data processing flow and further optimize the editing of remote sensing processes in the visual interface. In this paper we have analyzed the characteristics and attributes of remote sensing processes and algorithms. We made the production of processes and algorithms into scripts, and realizes to create remote sensing processes by inputting scripting language in the visual interface. A remote sensing language based on XML is designed to read and store key information. This language will use XML to drive the production of remote sensing process. With the language we design, a remote sensing process customization system has been implemented. It can improve the editing and production efficiency of remote sensing data processing flow.

关键词： Visualization Flowcharts XML Production Programming data processing Sensors

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Prediction model of physical activity level and hypertension based on artificial neural network 4

Prediction model of physical activity level and hypertension...

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2021 4th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Hua, Liang Yu, Wang Jinying, Li Wanjun, Zhang Yiting, Wang School of Physical Education Henan University Kaifeng Henan475001 China Department of Health Management People's Hospital of Henan Province People's Hospital of Henan University People's Hospital of Zhengzhou University Zhengzhou450003 China Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng475004 China School of Foreign Language Henan University Kaifeng Henan475001 China

Purpose: Taking the population of henan Provincial People's Hospital Chronic Disease Control Center from July 1, 2020 to December 31, 2020 who came to the hospital for physical examination as the research object, analyze and explore the role and contribution of physical activity level in the prediction of hypertension by using the LSTM network model, which can provide references for the clinical diagnosis of hypertension. Methods: Randomly select 2000 physical examination data, remove missing and invalid data, and preprocess them. Finally, select factors such as gender, age, body mass index, weight grade, waist-to-hip ratio, physical activity level, body fat percentage and other factors to establish a neural network prediction model. Then test and study the model, focusing on exploring the contribution of physical activity level to prediction. Result: The level of physical activity has certain advantages in predicting the prevalence of hypertension, but the predictive ability in the later stage is insufficient © Published under licence by IOP Publishing Ltd.

关键词： Disease control

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Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge

arXiv

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arXiv 2023年

作者： Zhang, Yufeng Wang, Meng-Xiang Yu, Jianxing School of Artificial Intelligence Sun Yat-sen University Zhuhai519082 China Guangdong Key Laboratory of Big Data Analysis and Processing 510006 China China National Institute of Standardization 100088 China Pazhou Lab Guangzhou510330 China

This paper proposes a new task in the field of Answering Subjective Induction Question on Products (SUBJPQA). The answer to this kind of question is non-unique, but can be interpreted from many perspectives. For example, the answer to ‘whether the phone is heavy’ has a variety of different viewpoints. A satisfied answer should be able to summarize these subjective opinions from multiple sources and provide objective knowledge, such as the weight of a phone. That is quite different from the traditional QA task, in which the answer to a factoid question is unique and can be found from a single data source. To address this new task, we propose a three-steps method. We first retrieve all answer-related clues from multiple knowledge sources on facts and opinions. The implicit commonsense facts are also collected to supplement the necessary but missing contexts. We then capture their relevance with the questions by interactive attention. Next, we design a reinforcement-based summarizer to aggregate all these knowledgeable clues. Based on a template-controlled decoder, we can output a comprehensive and multi-perspective answer. Due to the lack of a relevant evaluated benchmark set for the new task, we construct a large-scale dataset, named SupQA, consisting of 48,352 samples across 15 product domains. Evaluation results show the effectiveness of our approach. © 2023, CC BY.

关键词： Reinforcement learning

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