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Associating Semantic Sensor Web with Domain Ontology : The Way to Obtain Meaningful Sensor Data
Chaoqun Ji,Jin Liu,Xiaofeng Wang 보안공학연구지원센터 2014 International Journal of u- and e- Service, Scienc Vol.7 No.5
To realize the sharing and reuse of sensor data and improve interoperability, semantic sensor web(SSW)is proposed to add semantics information to existing sensor networksby utilizing domain, spatial and temporal anthologies and other related semantic technology.However these is seldom research on how to fully utilize the sensor data through a semantic way such as domain ontology based inference. This paperpresents stateofthe art of SSWin various aspects,and proposesthe method to associate data from semantic sensor web with domain ontology to realize the communication between different domain ontologies and SSW. In addition, this paperalso proposes a new calculation method of semantic similarity amongdifferent entities in different ontology. Experiments show that this method can effectively find the similar entitiesandrealize the knowledge sharing and ontologies reuse.
Integrated aero-structural optimization design of pre-bend wind turbine blades
Xiaofeng Guo,Xiaoli Fu,Huichao Shang,Jin Chen 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.11
In the optimization design of a pre-bend wind turbine blade, there is a coupling relationship between blade aerodynamic shape and structural layup. The evaluation index of a wind turbine blade not only shows on conventional ones, such as Annual energy production (AEP), cost, and quality, but also includes the size of the loads on the hub or tower. Hence, the design of pre-bend wind turbine blades is a true multi-objective engineering task. To make the integrative optimization design of the pre-bend blade, new methods for the blade’s pre-bend profile design and structural analysis for the blade sections were presented, under dangerous working conditions, and considering the fundamental control characteristics of the wind turbine, an integrated aerodynamic-structural design technique for pre-bend blades was developed based on the Multi-objective particle swarm optimization algorithm (MOPSO). By using the optimization method, a three-dimensional Pareto-optimal set, which can satisfy different matching requirements from overall design of a wind turbine, was obtained. The most suitable solution was chosen from the Pareto-optimal set and compared with the original 1.5 MW blade. The results show that the optimized blade have better performance in every aspect, which verifies the feasibility of this new method for the design of pre-bend wind turbine blades.
Lightweight Single Image Super-Resolution Convolution Neural Network in Portable Device
( Jin Wang ),( Yiming Wu ),( Shiming He ),( Pradip Kumar Sharma ),( Xiaofeng Yu ),( Osama Alfarraj ),( Amr Tolba ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.11
Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.
Attribute-Based Data Sharing with Flexible and Direct Revocation in Cloud Computing
( Yinghui Zhang ),( Xiaofeng Chen ),( Jin Li ),( Hui Li ),( Fenghua Li ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.11
Attribute-based encryption (ABE) is a promising cryptographic primitive for implementing fine-grained data sharing in cloud computing. However, before ABE can be widely deployed in practical cloud storage systems, a challenging issue with regard to attributes and user revocation has to be addressed. To our knowledge, most of the existing ABE schemes fail to support flexible and direct revocation owing to the burdensome update of attribute secret keys and all the ciphertexts. Aiming at tackling the challenge above, we formalize the notion of ciphertext-policy ABE supporting flexible and direct revocation (FDR-CP-ABE), and present a concrete construction. The proposed scheme supports direct attribute and user revocation. To achieve this goal, we introduce an auxiliary function to determine the ciphertexts involved in revocation events, and then only update these involved ciphertexts by adopting the technique of broadcast encryption. Furthermore, our construction is proven secure in the standard model. Theoretical analysis and experimental results indicate that FDR-CP-ABE outperforms the previous revocation-related methods.
Simultaneous Entities and Relationship Extraction from Unstructured Text
Jingtai Zhang,Jin Liu,Xiaofeng Wang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.6
Entity recognition and entity relationship extraction are two very important tasks in information extraction. Most research work in the literature treats these two work independently when processing the text. This paper proposes a novel method for performing entity recognition and entity relationship extraction simultaneously from unstructured text based on Conditional Random Fields (CRFs). This method makes use of entity features, entity relationship features and features of the triples which is composed of entities and their relationship to conduct the model training. Experiment results show that this method can recognize entity and extract entity relationship effectively.
Wang Yan,Wang Jin,Zhang Xue,Li Nan,Wang Junxia,Liang Xiaofeng 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.7
Series of unequal quantity Nd/Ce co-doped ceramic nuclear waste forms, (Gd, Nd)2(Zr, Ce)2O7, were prepared to tailor its ordered pyrochlore or disordered fluorite structure. The phase transition, microtopography, and elemental composition of the ceramic samples were systematically investigated, especially the effect of order-disorder structure on the chemical stability. It was confirmed that unequal quantity of Nd/Ce could synchronously replace the Gd/Zr-sites of Gd2Zr2O7. And the phase transition of order-disorder structure could be successfully tailored by regulating the average cationic radius ratio of (Gd, Nd)2(Zr, Ce)2O7 series. The elements of Gd, Nd, Zr, and Ce are uniformly distributed in the ordered or disordered structures. The MCC-1 leaching results showed that (Gd, Nd)2(Zr, Ce)2O7 pyrochlore ceramic nuclear waste forms had excellent chemical stability, whose elements' normalized leaching rates were as low as 104 -107 g‧m2 ‧d1 after 7 days. In particular, the chemical stability of disordered structure was superior to that of ordered structure. It was proposed that the force constant and the closest packing were changed with the structure transformation resulting the chemical stability difference
Xu, Wei,Xu, Jinli,Yan, Xiaofeng The Korean Institute of Power Electronics 2020 JOURNAL OF POWER ELECTRONICS Vol.20 No.1
Accurate estimation of the state of charge (SOC) of a lithium-ion battery is one of the most crucial issues of battery management system (BMS). Existing methods can achieve accurate estimation of the SOC under stable working conditions. However, they may result in inaccuracy under unstable working conditions such as dynamic cycles and different temperature conditions. This is due to the fact that the dynamic behaviors of battery states have not been considered by the parameter identification methods. In this paper, a SOC and parameter joint estimation method is put forward, where the battery model parameters are identified in real time by a particle filter (PF) with consideration of the battery states. Meanwhile, a cubature Kalman filter (CKF) is used to estimate SOC. Then, experiments under dynamic cycles and different temperature conditions are undertaken to assess the performance of the proposed algorithm when compared with the existing joint estimations. The results show that the proposed joint method can achieve a high accuracy and robustness for SOC estimation.