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The rapid growth of event data motivates the developing of decomposed and parallel process discovery, which solves process discovery from a large event log by decomposing the log into multiple small event logs, discovering process models from these small logs in parallel, and merging discovered process models. As such, process discovery from a large event log can be solved in less time. Currently, the passage and maximal decomposition based techniques are successful in the activity partition by decomposing causal graph structure derived from a large event log. In this paper, we propose a five-step framework, based on which we can build various decomposed and parallel process discovery techniques by simply combining and adapting existing techniques. Then, we propose a technique, RPSTHD, based on the framework using the refined process structure tree (RPST), heuristic miner, process mining using integer linear programming (ILP), etc. An experimental evaluation shows that our technique significantly outperforms the state-of-the-art decomposed discovery techniques in both efficiency and effectiveness.

The invention discloses an elevator group control method based on face recognition and belongs to the technical field of elevator control. Through the face recognition technology, the current number of passengers who wait for an elevator can be obtained more accurately, and by analyzing the better-timeliness current number of passengers who wait for the elevator and the elevator running information, the elevator can run more effectively. An elevator group control system comprises a communication module, a control module, an elevator calling unit, a video capture module and a back-end server, wherein the video capture module is in communication with the control module through the communication module; the elevator calling module is in communication with the control module; and the control module is in communication with the back-end server through the communication module. The elevator group control method based on face recognition is applied to group control of elevators.

This paper presents an intelligent positioning algorithm for transmitting station. By using GPS and RSSI, the influence of RSSI value can be reduced. The advantages of multi angle positioning method and centroid localization method are combined. The algorithm required less precision positioning device, also the least square method is used to reduce the error of location in urban areas. The method is simple and convenient, and the simulation results show the practicability of the algorithm.

We are concerned with the relay selection in a decode-and-forward cooperative network to minimize the bit error rate (BER) in a radio cell. This problem can be solved using maximum weighted (MW) matching algorithm, greedy matching algorithm or worst-link-first (WLF) matching algorithm. Among the algorithms, WLF matching algorithm has achieved much attention for its being less complex while the achieved performance is high. In order to further reduce the computational complexity, we propose an improved WLF relay selection scheme, in which relays are selected according to instantaneous channel state information (CSI) and a cooperative threshold is set up to eliminate some unsatisfying candidate relays from the set of alternative candidate relays and thus reduce the amount of calculation. Theory analysis and simulation results both show that the improved WLF relay selection scheme proposed can be easily implemented and achieve almost the same BER performance as that of WLF scheme while the computational complexity is much lower.

For the low accuracy of the spectrum sensing of the single node, this paper discusses several popular cooperative spectrum sensing technology. It also proposes a clustering double threshold cooperative spectrum sensing algorithm does two threshold detection based on the double threshold cooperative spectrum sensing algorithm. This algorithm applies or data fusion rule to realize the combination of the data among clusters on the basis of the improvement of the double threshold detection technology. The simulation results show that this algorithm can effectively reduce false alarm rate and undetected rate, and it can improve the overall detection performance.

For the least connections algorithm currently used cannot solve the problem of inaccurately reflecting the load capacity of servers caused by the situation that the difference of resources consumed by different requests is very big, this paper designs a new algorithm named UCM algorithm. It means use the unused rate of the CPU and Memory as the actor to calculate the weight of the server, and introduces a new parameter named load redundant parameter to judge the ability to deal with new requests. By simulation, it simulates a circumstance that many requests are sent synchronously. The least-connections algorithm and UCM algorithm are used in the system. The result indicates that using the UCM algorithm has a shorter response time, and it means saving the time that the system deals with the service requests.

Along with correspond by letter the development of the profession, the wireless signal blast-off the station is gradually small scaled to turn and easily operate. It results in the illegal member made use of it to carry out to break the law the crime activity, influencing the people to usually produce the living work. The want for is related to examine and position the equipment in fight break the law shoot stand break the law the activity of the criminal offence. The huge inconvenience of the traditional wireless monitor equipment physical volume in take. Positioned the calculate way through the RSSI and GPS the Base station to design the fixed position the hardware host of the instrument and the kit software of the smart terminal. Should position the instrument assurance fixed position accuracy to guarantee the equipment physical volume in the meantime of small scaled turn and then hold to turn. The examination and the fixed position function that carries out to stand to the illegal blast-off, only to the experiment and the analysis that the illegal GSM shot station to carry on examination and position.

Human detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detect human in video sequences has grown steadily. This paper proposed a novel approach to improve the capability of human detection. After investigating the image texture analysis methods, we adopt the new good texture descriptor, Local Binary Pattern histogram Fourier (LBPHF). Then we combine the LBPHF with the Histograms of Oriented Gradients (HOG) as feature sets, and use linear SVM to train our classifier. In the experiment on the INRIA personal dataset which is well known relatively good human detection's dataset, it is shown that our detector combining with the LBPHF significantly outperforms the other methods. Moreover, the time cost is much less and the dimension is reduced.

This paper architectures the system that recognizes and controls the P to P flow on the platform that is based on the existing network software platform. It introduces a two-layer P to P flow recognition means, and it also designs and realizes the basic control of the P to P flow on the Net filter structure of Linux. The P to P flow will be recognized when the flow passes through the system, and the system that focuses on the P to P flow will limit the flow and govern the bandwidth according to the strategy.