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A novel metagrating is proposed to achieve single order diffraction. The proposed unit has electric and magnetic hybrid response. The unit is composed of asymmetrical patch. By introducing magnetic response, the element will break the symmetry of diffraction. One of the first order diffraction is canceled and the energy is totally reflected into the other order at 10GHz. Besides, the diffraction efficiency is higher than 80% from 9.4GHz to 11.1GHz. The simulated far field pattern shows good agreement with prediction. The proposed design provide a highly efficient method to suppress 0 and +1 order diffraction and simplify the difficulty of design, paving the way for further applications of beam steering.

Significant control and modulation delay presents in the current regulation of high-power voltage source converters. The proportional-resonant controller (PR) with equalized phase correction (EPC), commonly used in the active power filter, can be referred here to suppress the negative sequence current better than the PI controller, and increase the phase margin with EPC. After introducing the current feedback (CF), proved to increase the tracking performance similarly to that in the dq-axis, the double-sided frequency domain becomes asymmetric, making EPC less effective. In this paper, the PR controller with differentiated phase correction (DPC) is proposed, with two independent phase correction angles and gains at the positive and negative poles. Analyzed with double-sided bode diagram, the stability margins are improved with the two extra freedom degrees. Moreover, the function related to the conjugate symmetry about the fundamental frequency is used to quantitively describe the response of such asymmetric system. Finally, simulation and experiment are implemented to verify the improved dynamic performance of the proposed one versus the compared schemes.

A CNN based algorithm is proposed for the estimation of nonlinear phase shift. Simulations show that the mean relative errors of it can achieve 2%, and it is insensitive to the changing of baud-rate.

Current evaluation methods for soccer player performance either relies on rating from soccer experts or structured statistics of the match, such as shots and tackles. The former needs a lot of manpower and the evaluation is inevitably subjective. The latter can only record the quantity of a player's match events, but cannot reflect the quality (e.g., a wonderful shot or a terrible shot is regarded as a shot). To solve the above problems, an improved evaluation method for soccer player performance using affective computing is proposed. On the basis of statistics, our method also takes advantage of the text information of post-match reports, and employ the affective computing technology to quantify the quality of events. In this way, both the quantity and quality of events are considered. All the players in the Chinese Super League 2019 season are selected as evaluation objects, and the results show that the improved method can evaluate player performance more effectively and reasonably.

RDF knowledge graphs have received more attention in recent years. Many graph similarity search approaches are proposed to help users get the desired knowledge. However, few of them consider storage architecture and semantic similarity search together. We believe that a complete query engine requires not only an efficient search approach, but also a reliable storage architecture to support. In this paper, we design a semantic-based graph similarity search engine SimG with carefully designed architecture and data structures over an RDF Knowledge graph, which can quickly get the best k answers even given a simplified queries (e.g., Basic Graph Pattern (BGP)) by users. The outstanding features of SimG are as follows: (1) To improve RDF data management efficiency and reduce storage overhead, we first divide the RDF knowledge graph into multiple topic graphs based on the type similarity model. Then we store these topic graphs in a distributed manner(e.g., all topic-graphs are managed and maintained independently). (2) In order to manage topic graphs efficiently, we utilize the adjacency list as the fundamental data structure to store each topic graph. Moreover, we design a skip list based index to accelerate the data accessing. On the top of this topic graph storage, we implement several basic APIs such as access, add, delete and update to support the following semantic query approach. (3) The semantic similarity query module is deployed on the topic graph storage, which returns topk answers by considering the semantic feature based on the APIs implemented above. Finally, extensive experiments on our query algorithm and storage architecture confirm the effectiveness and efficiency of SimG.

In recent researches, SiC & Si hybrid active neutral point clamped (ANPC) converter is developed to address the critical requirements in the drive system aiming to future green transportation. In this paper, a space vector modulation (SVM) scheme is proposed for the hybrid ANPC converter. By applying the proposed SVM, the IGBT parts of the circuit can always keep low frequency switching to minimize the switching losses. Thus the hybrid ANPC converter can achieve better efficiency and cost performance at the same time. On the other hand, advanced control strategies can still be available because of the realization of SVM still be compatible with the conventional schemes. In this paper, the basic and proposed SVM schemes are given. Simulation and experimental results are demonstrated to show the results of modulation.

Large scale RDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired search results efficiently over such RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the "schema-free" feature of RDF KG (e.g. there are several ways to represent the similar knowledge). Furthermore, it is also a challenge to get all possible search results for a simple SQPRQL expression. In this paper, we study the problem of relaxing SPARQL queries over RDF KG in order to acquire semantic approximate results for an "incomplete query". The basic idea behind this paper is to conduct a text-based learning for achieving the semantics of entities and relations and then take use of these semantic information to generate the relaxed SPARQL query avoid user intervention. We first propose a corpus translating algorithm to transform the discrete RDF triples into the trainable corpus with high quality. And then we use the CBOW model to train the semantic vector for each entities and relations. Thirdly, we relax the SPARQL query by taking use of the semantic vectors and obtain the semantic approximate results over RDF KG. Experiments on DBpedia datasets confirm the superiority of our solution.

Online aggregation is a commonly used technology to return approximate query results over random samples, which provides a fast way for users to obtain a trade-off between time and accuracy. The key issue of online aggregation is how to guarantee the efficiency and effectiveness of random sample collection. However, the state-of-the-art approaches either adopt the random sampling method or adopt the sequential sampling with preprocessing to obtain the uniform samples. The former one suffers from the inefficient random access to the whole dataset especially for skewed data distribution, and the later one is limited by the time-consuming preprocessing. To make the sampling more efficient, we propose a scalable sampling algorithm called logic-partition based Gaussian sampling. The basic idea of our solution is convert the random sampling into a near-sequential sampling without any extra preprocessing, and achieve a balance between the sampling efficiency and sample quality. Extensive experiments using the TPC-H benchmark for skewed data distribution have demonstrated the superior performance of our solution.

A sampling-based approximate query processing (AQP) method provides a fast way for users to obtain a trade-off between accuracy and time consumption by executing the query on a sample of data rather than the whole dataset. There are two major AQP methods: the (1) central limit theorem (CLT)-based online aggregation; and the (2) bootstrap method. The former is very efficient but is only suitable for simple aggregation queries, while the latter is quite general but has relatively high computational overhead. Both methods suffer from the possible estimation failure. However, there is no technology that can both support simple/complex queries within an acceptable time coupled with carefully considering the estimation failure. To make the current AQP method much more general and efficient, we propose a hybrid approximate query framework called AQP++ to combine the advantages of both methods and eliminate the limitations as far as possible. According to this hybrid framework, an estimation parameters adjustment method is presented for CLT-based online aggregation to improve its usability for much more complex aggregation queries. Then, an execution cost model is proposed to describe the computational overhead of the two AQP methods, which can be used to support our dynamic scheduling mechanism of AQP++ and make the whole system more efficient and flexible. Moreover, we have implemented our AQP++ prototype and conducted extensive experiments on the TPC-H benchmark for skewed data distribution. Our results demonstrate that our AQP++ can produce acceptable approximate results for both simple and complex queries within a much shorter time compared with the original CLT-based online aggregation and bootstrap method.

Providing accurate information about human's state, activity is one of the most important elements in Ubiquitous Computing. Various applications can be enabled if one's state, activity can be recognized. Due to the low deployment cost, non-intrusive sensing nature, Wi-Fi based activity recognition has become a promising, emerging research area. In this paper, we survey the state-of-the-art of the area from four aspects ranging from historical overview, theories, models, key techniques to applications. In addition to the summary about the principles, achievements of existing work, we also highlight some open issues, research directions in this emerging area.