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The automatic generation of Chinese enterprise abbreviations is a task that extracts enterprise abbreviations to represent the enterprise full name. Traditional methods do not divide abbreviations in detail, which leads to a poor generation effect of irregular Chinese enterprise abbreviations generation, and the best selection method of candidate abbreviations among traditional methods is still coarse-grained relationship modeling. To solve the problem of irregular abbreviation generation and abbreviation screening, this paper proposes a two-channel Chinese enterprise automatic abbreviation generation method. First, in the two-channel method, the enterprise component channel outputs regular candidate abbreviations, and the single-character channel outputs irregular candidate abbreviations to improve the processing effect of the method on irregular abbreviations. Then we design a Bayesian filtering model based on the position relationship of abbreviations in enterprise components to improve the final effect of the automatic generation of Chinese enterprise abbreviations. The results show that our effect is the best in the data performance of Chinese enterprises.

Mobile Crowdsourcing, which uses a crowd of workers to complete computer-complexity tasks, has become more and more popular. How to maximize the total profit, which is the difference between total task utility and total worker costs, is a key design goal in such crowdsourcing system. However, this problem is extremely hard because even in the offline situation, this problem has been proved to be NP-hard. In the online situation, we need to consider not only future unknown task arrivals, but also the heterogeneity of both tasks and workers. In this paper, we address this challenging problem from following aspects. To solve this problem, we first build an optimization framework to formulate this problem by explicitly modelling task utility and worker costs. Then, under this framework, we design efficient online assignment algorithms to assign tasks to workers. To analyze the efficiency of our proposed algorithms, we develop a dual-fitting method to analyze both primal objective and dual objective. We prove that our designed algorithms can achieve a constant competitive ratio of 2. Finally, we conduct extensive trace-driven simulations to demonstrate that the online algorithms can outperform baseline algorithms by nearly 20% in terms of the total profit achieved.

Recently, most wireless power transmission (WPT) systems have used a “one-to-one” charging method of one receiving coil to one transmitting coil. However, when the receiving coil is offset in this way, the mutual inductance between the transmitting and receiving coils will be weakened, and the flexibility of the WPT system application will also be reduced. Therefore, how to realize the “one-to-many” transmission mode of one transmitting coil to multiple receiving coils and ensure the power stability of multiple loads is an urgent problem to be solved. In order to solve the above problems, the LCC-S topology is combined with the three-phase circuit, and a three-phase omnidirectional WPT system coil scheme is proposed based on the power characteristics of the three-phase LCC-S topology in this paper. Firstly, the influence of the resonance compensation topology on power characteristics is analyzed through circuit theory and a more suitable LCC-S topology is chosen. Then, the optimal coil scheme of the three-phase omnidirectional WPT system is proposed based on the law of mutual inductance. Compared with the original scheme, the theoretical calculation results show that the power fluctuation of the system is reduced by 75.13%. Finally, a set of three-phase WPT system model and test systems similar to the theory are established, which effectively verify the improvement of the proposed scheme, and verify the correctness and feasibility of the scheme.

In this paper, a novel symmetrical topology for the small power high frequency bidirectional AC-DC converter has been proposed, which is applied to the bi-directional wireless power transmission system based on the magnetic coupling resonance. It has dual functions of the inverter and rectifier. The research is mainly reflected in the following aspects: First, based on analyzing the principle of the wireless power bidirectional transmission technology, the operation mechanism of the topology circuit is analyzed emphatically. Second, the feasibility of the designed converter is verified by the simulation of the PSPICE software. Third, the hardware system has been operated at 200 kHz in the modes of the constant current, the constant voltage and the constant resistance, respectively. The results show the maximal output power of the designed circuit is 7.8 W, meeting the design requirement. This circuit has the advantages of the simple circuit structure and simple control. It's suitable for the bidirectional wireless power charging of the small power portable devices.

For a clamp-on ultrasonic flowmeter based on transit-time differential method, the flow rate calculation model with flow velocity correction is established and verified by experiments carried out in a flow standard facility. Based on established flow rate calculation model, a systematic methodology incorporating the parametric uncertainty distribution is proposed to analyze the influences of multiple parameters with uncertainty on measurement error by establishing the sample-based stochastic model. Monte Carlo sampling (MCS) method is used to randomly select the value of each parameter from its prescribed Gaussian distribution and then combining them together as one sample, and the number of selected samples is determined by conducting stochastic convergence analysis of mean value and the standard deviation of input and output parameters. And finally the influence degrees of multiple parameters are analyzed and compared quantitatively on the same dimension to find out which parameters are influential and which are negligible. The results reveal the different influences of multiple parameters with uncertainty, which can be used as reference for performance improvement and measuring error analysis of the clamp-on ultrasonic flowmeter in industrial local measurement.

Local community detection, only considering the regional information of the large network, can be used to identify a densely connected community containing the seed node in a network, aiming to address the efficiency problem faced by global community detection. However, most existing studies in local community detection did not account for the higher-order structures crucial to the network, but rather have simply focused single nodes or edges. Moreover, existing higher-order solutions are not purely local methods, as they still use global search to find the best local community, which leads to a global search problem. Furthermore, the quality of the detected community depends on the location of the seed node, which leads to a seed-dependent problem. Thus, in this paper, we proposed a fuzzy agglomerative algorithm (FuzLhocd) for local higher-order community detection based on different fuzzy membership functions. To solve the global search problem, we introduce a novel, purely localized metric called local motif modularity. Based on this local metric, FuzLhocd only needs to visit a limited number of neighborhoods around the seed node. To solve the seed-dependent problem, we systematically studied the formation of the local community, divided the process of local community detection into three stages and employed various fuzzy membership functions at different stages. Our extensive experiments based on both real-world and synthetic networks demonstrated that FuzLhocd not only runs efficiently locally but also effectively solves the seed-dependent problem and achieves a high accuracy as well. We concluded that our local motif modularity metric and FuzLhocd algorithm is highly effective for local higher-order community detection.

Data mining task is a challenge on finding a high-quality community structure from largescale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1-1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007.

Community detection is a key technique for identifying the intrinsic community structures of complex networks. The distance dynamics model has been proven effective in finding communities with arbitrary size and shape and identifying outliers. However, to simulate distance dynamics, the model requires manual parameter specification and is sensitive to the cohesion threshold parameter, which is difficult to determine. Furthermore, it has difficulty handling rough outliers and ignores hubs (nodes that bridge communities). In this paper, we propose a robust distance dynamics model, namely, Attractor++, which uses a dynamic membership degree. In Attractor++, the dynamic membership degree is used to determine the influence of exclusive neighbors on the distance instead of setting the cohesion threshold. In addition, considering its inefficiency and low accuracy in handling outliers and identifying hubs, we design an outlier optimization model that is based on triangle adjacency. By using optimization rules, a postprocessing method further judges whether a singleton node should be merged into the same community as its triangles or regarded as a hub or an outlier. Extensive experiments on both real-world and synthetic networks demonstrate that our algorithm more accurately identifies nodes that have special roles (hubs and outliers) and more effectively identifies community structures.