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An RDF Data Set Quality Assessment Mechanism for Decentralized Systems

Data Intelligence 2020年第4期2卷 529-553页

作者： Li Huang Zhenzhen Liu Fangfang Xu Jinguang Gu Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and TechnologyWuhan 430065China College of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content State Administration of Radio and TelevisionBeijing 100038China

With the rapid growth of the linked data on the Web,the quality assessment of the RDF data set becomes particularly important,especially for the quality and accessibility of the linked *** most cases,RDF data sets are shared online,leading to a high maintenance cost for the quality *** also potentially pollutes Internet *** blockchain technology has shown the potential in many *** the blockchain storage quality assessment results can reduce the centralization of the authority,and the quality assessment results have characteristics such as *** this end,we propose an RDF data quality assessment model in a decentralized environment,pointing out a new dimension of RDF data *** use the blockchain to record the data quality assessment results and design a detailed update strategy for the quality assessment *** have implemented a system DCQA to test and verify the feasibility of the quality assessment *** proposed method can provide users with better cost-effective results when knowledge is independently protected.

关键词： Decentralization Quality assessment Blockchain RDF data set

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RDF Multi-query Optimization Algorithm Based on Triple Pattern Reordering 15th

RDF Multi-query Optimization Algorithm Based on Triple Patte...

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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Wang, Manzi Xu, Fangfang Fu, Haidong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Key Laboratory of Intelligent Information Processing and Real-Time Industrial System in Hubei Province Wuhan430065 China

ISBN: (纸本)9783030325909

Under the premise of accelerating statistics by RDF storage index and narrowing the scope of semantic pruning, the distance-based triple pattern reordering algorithm is used to obtain the optimal connection order of a single query. After converting each query to a left deep tree, the left deep tree identification algorithm is used to find the common subtree and evaluate its cost. Establish materialized view and corresponding update replacement mechanism for valuable common subtrees. While optimizing a single query to a certain extent, it improves the possibility of the existence of common result sets among multiple queries. By making full use of query sharing, the overall execution cost of query set can be reduced. The experimental results show that the algorithm of this paper has better query performance than the existing query schemes, whether it is on single query or multiple query. Especially in the case that the RDF data set is large in scale, the number of queries in the query set is large, and the query statement is more complicated, the multi-query optimization method of this paper is better. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Foundation PIT deformation measurement technique based on ground-based interferometric radar

Foundation PIT deformation measurement technique based on gr...

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IET International Radar Conference (IRC 2023)

作者： Changjun Wang Hanpu Zhou Jian Wang Jiake Gao Yunkai Deng Weiming Tian Beijing Building Research Institute Co. Ltd. of CSCEC Beijing 100076 People's Republic of China Radar Research Lab School of lnformation and Electronics Beijing lnstitute of Technology Beijing People's Republic of China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing People's Republic of China Chongqing Innovation Center Beijing Institute of Technology People's Republic of China Advanced Technology Research Institute Beijing Institute of Technology Jinan 250300 People's Republic of China

ISBN: (数字)9781837240982

The deformation monitoring data of foundation pit is an important criterion for forecasting and strengthening measures in the field of building safety. During the construction period, the foundation pit will be affected by load, temperature change, random disturbance and other effects to produce deformation. Due to the complex deformation process of foundation pit, it is hard to accurately describe and separate the load trend deformation and elastic thermal deformation by mathematical expression. Excessive thermal deformation may cause cracks in the surrounding pavement and affect the judgment of load deformation. This paper first introduces the deformation measurement method of foundation pit based on differential interferometry technology using ground-based interferometric radar. Based on the obtained deformation type, the simulation verifies the separation effect of wavelet transform on different deformation trends and periodic components. Through the foundation pit deformation monitoring experiment of Tongzhou infrastructure project, the wavelet transform method is used to decompose the foundation pit deformation curve, and the extracted periodic component has good consistency with the temperature information.

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An Sgx-Based key Protection Solution for Ethereum Blockchain

SSRN

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SSRN 2023年

作者： Ren, Zhengwei He, Guangxin Yu, Rongwei Tong, Yan Xu, Shiwei Tang, Jinshan School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Health Informatics George Mason University FairfaxVA United States

The security of Ethereum digital assets mainly depends on users’ private keys. Cryptocurrency wallets are usually used to provide key management and protection for blockchain systems. However, the security of most existing software wallets is weak and hardware wallets are susceptible to man-in-the-middle attacks, exacerbating their security risks. In this paper, we propose an SGX-based key protection solution for the Ethereum blockchain. Operations related to private keys are enforced in Enclaves. Private keys that are not in service are sealed and stored locally. The corresponding sealed private key will be loaded into an Enclave only when a transaction needs to be signed, ensuring that private keys can be used securely and continuously. By employing this approach, we can significantly enhance the security of Ethereum key generation, storage, and usage without disrupting the standard functionalities of Ethereum. Furthermore, an efficient batch scheme is also proposed, designed to handle situations where multiple transactions from the same user need to be processed within a short time frame. We have implemented the prototype system and verified the functionalities and performances through rigorous testing. The experimental results show that the proposed solution can work normally in Ethereum with a reasonable performance overhead, validating its feasibility. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Slow-time MIMO Radar Clutter Mitigation Based on Robust Pulse-to-Pulse Coding 6

Slow-Time MIMO Radar Clutter Mitigation Based on Robust Puls...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Xu, Feng Xue, Junqi Yang, Xiaopeng Yang, Fawei Wang, Zhanze Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781728129129

Slow-time MIMO radar is investigated to deal with some cost sensitive applications like UAVs detection to avoid the use of arbitrary waveform generators and digital receivers. But this method suffers from the deterioration of Signal to Noise Ratio (SNR) caused by superposition of clutter. To solve this problem, a robust pulse-to-pulse coding method is proposed to mitigate clutter as well as suppress the sidelobe level in Doppler domain. In our proposed method, the problem of optimizing weighting vector is transformed into a typical MVDR problem and the design of new weighting vector is obtained by the combination of MVDR solution and a sequence of weight coefficients which are calculated by Fourier inversion. This proposed method can mitigate the clutter adaptively while the sidelobe is suppressed. Both simulation and experiments results validate the reliability and effectiveness of this method. © 2019 IEEE.

关键词： MIMO radar

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Video-Based 3D Human Pose Estimation Research

Video-Based 3D Human Pose Estimation Research

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Siting Tao Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China China

ISBN: (纸本)9781665409858

At present, most of the video-based 3D human pose estimation methods process the video frame by frame, ignoring the timing information of the upper and lower frames of the video. When there is a single-frame estimation error caused by self-occlusion, the occlusion part will be Some strange movements appear due to information loss, and the estimated results make it difficult to produce accurate motion sequences. Therefore, considering the continuity of the upper and lower frames of the video, this paper adopts the HMR algorithm and introduces a bidirectional long-term memory network (BI-LSTM) in the encoding part to process the video sequence, and then outputs the parameters of the human body model through the SMPL parameters and inputs them into the discriminator for confrontation, train. In order for the discriminator to also take into account the semantic information of the temporal context, the discriminator in this paper also introduces a BI-LSTM network to improve the authenticity of the generated poses. Experimental results show that the accuracy of the model is improved when self-occlusion occurs.

关键词： Training Industrial electronics Solid modeling Estimation error Three-dimensional displays Biological system modeling Pose estimation

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Semantic Knowledge Sharing Mechanism Based on Blockchain 15th

Semantic Knowledge Sharing Mechanism Based on Blockchain

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作者： Zhang, Botao Li, Xingzhou Ren, Hui Gu, Jinguang School of Computer Science and Technology Key Laboratory of Intelligent Information Wuhan University of Science and Technology Wuhan China Processing and Real-Time Industrial System in Hubei Province Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030325909

In the context of big data, the construction and query of existing knowledge graph are realized by gathering scattered knowledge into a centralized big data platform. However, with the advent of the era of knowledge sharing 3.0, the knowledge value awareness of knowledge owners has generally increased, and it has become increasingly difficult to concentrate knowledge on centralized big data platforms. Blockchain technology is a decentralized and distrusting technology solution proposed in recent years. Combined with blockchain, a decentralized semantic knowledge sharing mechanism based on blockchain is proposed. The decentralized global knowledge graph is constructed by using the inter-node communication mechanism, and the query mode and transaction flow design based on the mechanism are realized. The experimental results show that the semantic knowledge sharing mechanism based on blockchain proposed is feasible, and compared with the traditional centralized knowledge graph, the construction time and query rate are greatly improved. © 2020, Springer Nature Switzerland AG.

关键词： Big data

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Building Energy Efficiency Assessment Base on Predict-Center Criterion Under Diversified Conditions

SSRN

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SSRN 2023年

作者： Liao, Xuechao Zheng, Xiujuan Ping, Zouwei Zhao, Haoyi Wang, Ning Zhang, Yong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430080 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430080 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430080 China National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan430030 China WISDRI City Construction Engineering &Research Incorporation Ltd. Wuhan430030 China Changjiang Institute of Survey Planning Design and Research Wuhan430010 China

With the rapid expanding of urban economy and population, how to reduce the energy consumption and improve energy efficiency of public buildings has become a key issue for energy conservation and urban development. However, due to the diversity of building employing condition, energy consumption data often has problems with nonlinearity, outliers, and diversity, which will greatly reduce the accuracy of energy consumption prediction and energy conservation assessment. To overcome the above drawbacks, a regression prediction model of building energy consumption is designed, which cluster the same type of data characteristics to form a subset of equipment through Gaussian mixture clustering, and design the specific normalized adaptive loss function to determine the optimal control parameters of regression model. Furthermore, a novel predict-center energy efficiency evaluation criterion is proposed, and the cumulative distribution pattern is further statistically analyzed. Finally, the energy benchmark evaluation framework is proposed to determine the energy efficiency evaluation grade. Through comparative experimental analysis, it can be seen that the proposed prediction model can adapt to different types of building data, and have better prediction accuracy and convergence speed. The consistency and robustness of the benchmark evaluation framework and method are optimal. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Spaceborne Large-Squint Terrain-Matching Synthetic Aperture Radar: Concept and technology 6

Spaceborne Large-Squint Terrain-Matching Synthetic Aperture ...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wang, Yan Li, Zhe DIng, Zegang Zeng, Tao Radar Research Lab Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728129129

This paper discusses a new spaceborne large-squint terrain-matching synthetic aperture radar (LSTM-SAR) to effectively image the terrains that do not spread along satellite orbit. Different from the traditional spaceborne SAR, the swath of the LSTM-SAR spreads along the target terrain, instead of the satellite orbit, to perform terrain-matched imaging with both high resolution and short data acquisition period. Aiming at giving a sketch of how the LSTM-SAR works, the paper focuses on discussing the following three main aspects: the data acquisition geometry, azimuth sampling manner and raw data focusing algorithm. Specifically, an optimum squint geometry is firstly suggested for two dimensional swath optimization;then two non-uniform azimuth sampling methods are discussed to avoid data loss induced by transmission blockage;lastly, a wide nonlinear chirp scaling based algorithm is discussed to focus the spatially variant spaceborne LSTM-SAR echo. The presented approach is validated via the computer simulations. © 2019 IEEE.

关键词： Synthetic aperture radar

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Multi-Level Drowsiness Detection Based on Deep Feature Fusion of Eye and Head Pose

Multi-Level Drowsiness Detection Based on Deep Feature Fusio...

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Symposium of Image, Signal processing, and Artificial Vision (STSIVA)

作者： Fang Ye Shunxin Li Xin Yuan Longfei Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665426565

Drowsiness detection is a significant problem, most existing non-intrusive methods estimate drowsiness only by single images, without leveraging the temporal information available in the frame sequence. The lack of temporal information leads to the inability of drowsiness detection to indicate consecutive behaviors. To this end, we present a drowsiness detection method, which takes into account both eye and head pose deep feature representation by conducting feature fusion. Then, the fused feature is fed into the LSTM (Long Short-Term Memory) network to enhance the accuracy of the drowsiness detection model through temporal information. The experimental results on the NHTU-DDD dataset and the self-constructed dataset show that the proposed method outperforms six existing advanced approaches.

关键词： Head Conferences Feature extraction Informatics

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