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2nd International Conference on Cloud Computing, big data Application and Software engineering, CBASE 2023

作者： Zhang, Lin Peng, Tong Zhejiang Ocean University School of Information Engineering Zhoushan China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9798350331448

In order to solve the problem that light is absorbed by different scales and scattered by particles in water, resulting in overall color deviation, low contrast and unclear details of the image, an underwater image enhancement algorithm based on improved adaptive MSRCR(Multi-Scale Retinex with Color Restoration) and two-dimensional gamma function based on details is proposed. Firstly, the weight of the convolution function in different scales is redistributed according to the image gradient information as the selection factor, and then the guided filter and gaussian filter are used as the adaptive MSRCR surround function and linear fusion to solve the problems of color decay and insufficient sharpness. Finally, the improved two-dimensional gamma function with CLAHE(Contrast Limited Adaptive Histogram Equalization) algorithm is used to correct the image contrast after fusion. The experimental results show that the enhanced image processed by this method has good enhancement effect in underwater image elimination of color distortion, image detail enhancement and obtaining real and natural restored image, and has obvious advantages over other contrast algorithms. © 2023 IEEE.

关键词： Color

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LogADCH: Hyperspheric-Based Log Message Anomaly Detection And Classification

LogADCH: Hyperspheric-Based Log Message Anomaly Detection An...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： He, Shiming Xiao, Jinpan Chen, Bowen Lei, Ying Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Web services have been integrated with all walks of life in society. Abnormalities in the network and services seriously affect user experience and company revenue. The system log records various information of the system, and the information-rich log messages usually show different anomalies. It is particularly important to accurately and timely identify the anomalies of the log messages and classify the anomalies. In addition, most of the current existing work only detects anomalies and does not classify them, and a few existing works require anomaly labels to classify anomalies. Based on these potential challenges, we propose an unsupervised hypersphere-based log message anomaly detection and classification method, LogADCH. We use the hypersphere loss function to let the BERT model learn compact representations of log messages and perform anomaly detection. Finally, K-Means clustering is performed on the semantic feature vectors detected as anomalies to form clusters of different types of anomalies. Compared with other works, LogADCH further classifies the abnormal types after detecting the abnormal logs, helping the operation and maintenance personnel to quickly locate the fault. We compare our method with other methods on two public datasets, and the experimental results show that LogADCH has superior performance in anomaly detection as well as anomaly classification. © 2023 IEEE.

关键词： Classification (of information)

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Segmentation and Detection of Industrial Surface Defect Based on Deep Neural Network 7th

Segmentation and Detection of Industrial Surface Defect Base...

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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Hou, Yi Qian, Songrong School of Mechanical Engineering Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

ISBN: (纸本)9789819927296

In the manufacturing of industrial products, traditional methods of surface inspection do not meet the increasing quality standards in manufacturing. In this paper, first, we propose a lightweight convolutional neural network framework for the detection of subtle surface defects using deep neural network algorithms. The detection framework consists of two stages: pixel-based defect segmentation and defect classification, where the defect segmentation algorithm segments and extracts the defect-containing areas of an industrial image, and the defect classification algorithm predicts whether the image contains defects based on the results of the previous segmentation. Then, we improved the You Only Look Once (YOLO) algorithm for industrial surface defect detection. The improved algorithm, defined as DR-YOLO, identifies defects and their approximate locations from multi-target images. Subsequently, we use data augmentation to address the problems of small sample size and uneven distribution. The experimental results show that our method outperforms other detection algorithms in terms of evaluation metrics, as well as being more advantageous in terms of rapidity of detection, and the results of defect segmentation are close to industrial reality. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pixels

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Mechanical properties of thermoelectric generators

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Journal of Materials Science & Technology 2023年第17期148卷 64-74页

作者： Xin Bao Shuaihang Hou Zhixin Wu Xiaodong Wang Li Yin Yijie Liu Huolun He Sichen Duan Baolin Wang Jun Mao Feng Cao Qian Zhang School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin150001China School of Science Harbin Institute of TechnologyShenzhen518055China

To satisfy the requirements of practical applications,thermoelectric generators should be highly efficient and mechanically ***,progress in designing high-performance thermoelectric generators has been ***,the mechanical properties of thermoelectric generators are still *** this review,studies on the mechanical properties of thermoelectric generators are *** me-chanical properties of bulk thermoelectric generators will be first *** this section,the mechan-ical properties of thermoelectric materials and the strategies for improving their mechanical properties are *** the device’s failure usually occurs at the interface between the thermoelectric ma-terials and electrode,the joint strength of electrodes and thermoelectric materials will be *** that,the mechanical properties of the inorganic thin-film thermoelectric devices will be *** the figure of merit for the flexibility of thermoelectric materials depends on the film thickness,elastic modulus,and yield strength,the synthesis methods of thin-film thermoelectric materials will be ***,this review will be concluded with a discussion on flexible organic thermoelectric de-vices and flexible devices using bulk legs.

关键词： Thermoelectric generator Mechanical properties Interface Joint strength

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FOSS: A Self-Learned Doctor for Query Optimizer 40

FOSS: A Self-Learned Doctor for Query Optimizer

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhong, Kai Sun, Luming Ji, Tao Li, Cuiping Chen, Hong Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Shanghai Yunxi Technology Co. Ltd. China Engineering Research Center of Database and Business Intelligence MOE China

ISBN: (纸本)9798350317152

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks. © 2024 IEEE.

关键词： Reinforcement learning

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Uncertainty-guided Mutual Consistency Training for Semi-supervised Biomedical Relation Extraction

Uncertainty-guided Mutual Consistency Training for Semi-supe...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Mao, Bing Jia, Chang Huang, Yucheng He, Kai Wu, Jialun Gong, Tieliang Li, Chen Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Biomedical relation extraction seeks to automatically extract biomedical relations from biomedical text, which plays an important role in biomedical studies. However, constructing high-quality biomedical annotation data is not only time-consuming but also requires a high level of knowledge in the biomedical field. To alleviate this problem, Semi-supervised Biomedical Relation Extraction aims to extract relation facts from the limited labeled data and the more readily available unlabeled samples. Existing works can be roughly categorized as self-training methods and self-ensembling methods. The former aims to generate pseudo labels, which may lead to the gradual drift problem. The latter aims to encourage the output of one model to be consistent with the other model, where the acquisition of the model is tedious. To alleviate these issues, we propose a novel Uncertainty-Guided Mutual Consistency Training framework(UG-MCT) for semi-supervised Biomedical relation extraction. Specifically, our framework consists of two models with the same structure, which differ only when updating their weights, and then an intersecting pseudo-label mechanism is designed to convert the prediction discrepancies of the two models into mutual consistency training loss, thus promoting the consistency of model predictions. In addition, we utilize uncertainty as guided information to assist the model in focusing on the confident pseudo labels and mitigate the noise of inaccurate pseudo labeling during training. Thus, our model is very simple and efficient while mitigating the noise introduced by pseudo-labels. UG-MCT is evaluated on multiple datasets in different settings and the experimental results demonstrate that our method is highly effective in semi-supervised biomedical relation extraction compared to the state-of-the-art. © 2022 IEEE.

关键词： Extraction

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Integrated multi-dimensional communication system for dam and bank risk situations on the Yellow River

Integrated multi-dimensional communication system for dam an...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Dang, Lanxue Zhang, Miaoxin Xie, Yi Zuo, Xianyu Zhang, Lei Fan, Minghu 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)9781510664616

Due to the terrain, the frequent floods of the Yellow River have posed a great threat to the cities along the Yellow River. At present, although the danger alarm system and video surveillance system are widely installed along the Yellow River Basin, there is a lack of linkage between the two, which wastes monitoring resources. In addition, the push rules of different types of alarm signals are relatively complex, and the coupling between rules and system implementation is high, which is not conducive to rule modification and expansion;moreover, there are many cities along the Yellow River, if each city customizes its own system, it will lead to more overhead. Therefore, this problem can be solved by improving the hierarchical scalability of the system. So, based on the Drools framework, this paper uses the rule engine to realize the unified processing of different rules and the linkage integration of sensors and cameras, and designs a recursive model to simplify the development process, so as to realize the accurate positioning of alarm information at all levels of provinces, cities and counties, and the hierarchical processing of various alarm information. © 2023 SPIE.

关键词： Alarm systems

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Kubernetes-based Scripted Remote Sensing Process Service Platform 6

Kubernetes-based Scripted Remote Sensing Process Service Pla...

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6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Zhou, Liming Liu, Mengyu Yang, Wencheng Liu, Zhehao Zhao, Hang Han, Hongyu Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781665460033

With the formation of the high-resolution satellite system, the information carried in remote sensing data is becoming more and more abundant, and the types of data are also increasing. In order to realize the efficient processing of remote sensing data, the construction of remote sensing process is very important. At the same time, higher requirements are put forward for the remote sensing system in terms of dynamic resource scheduling, easy maintenance, elastic expansion, controllable complexity, and flexible deployment. In response to the above problems, this paper first designs a scripted process construction technology, which realizes the efficient construction of various complex processes. And introduce Docker container technology and Kubernetes container cluster management technology, split the remote sensing system into different micro-service modules, run independently in mutually isolated containers, so that the system has the characteristics of flexible load scheduling and elastic scaling. © 2023 IEEE.

关键词： Containers

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Design and implementation of highly scalable IoT platform based on Netty

Design and implementation of highly scalable IoT platform ba...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Yuan, Bo Li, Shilong Dang, Lanxue Lin, Yinghao Han, Hongyu 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)9781510664616

With the development of network technology and people's growing need for a better life, IoT technology has been integrated into more and more people's daily lives. At the same time, due to the different needs of individual users and the differences in the function of networking equipment spawned a variety of Internet of things products. Hence an IoT platform that is convenient, efficient and easy to expand is particularly important. This article designs and implements an integrated IoT platform for many types of persistent connected IoT devices on the market. The platform supports concurrent access of large-scale IoT devices with multiple sources and protocols, and supports remote management of devices through instructions. Monitor the health of the equipment in real time and alarm in real time in case of abnormalities. © 2023 SPIE.

关键词： Internet of things

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Fast Multivariate Time Series Anomaly Detection Based on Matrix Completion

Fast Multivariate Time Series Anomaly Detection Based on Mat...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： He, Shiming Yi, Tongzhijian Yang, Bo Guo, Chenxi Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Increasingly complex systems contain large numbers of devices that generate great number of multivariate time series that are monitored and recorded. For anomaly detection of these complex time series, deep learning technology is often used to build a learning model for anomaly detection. The method based on deep learning usually takes a long time to train data in practice, and the online service system is deployed or changed very frequently, which makes the online service system need to frequently spend a lot of time updating the anomaly detection model. To overcome this challenge, this paper proposes a fast multivariate time series anomaly detection (FMAD) approach based on matrix completion. After sampling the primitive low-rank matrix, FMAD uses matrix completion to reconstruct the original low-rank matrix to detect anomalies in multivariate time series. We conducted related experimental analysis and discussion. The experimental results on three real data sets show that FMAD has improved the problem of too long model training time and improved the detection efficiency. © 2023 IEEE.

关键词： Anomaly detection

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