Advanced Materials Research Vols. 962-965

Abstract: Study on burning particles in flame can help us to grasp the pyrotechnic decomposition mechanism. The high speed video (HSV) and particle image velocity (PIV) were used in this paper to analyze the flow field consist of high temperature burning particles during pyrotechnic combustion. The binary image was obtained through grayscale treatment and adaptive threshold segmentation from HSV and PIV data, by which the coordinate of each particle was marked. On the basis, the movement trajectory of each particle during combustion was pursued by the most recent guidelines algorithm of cancroids matching. Through the method proposed in this study, the velocity variation of each particle was obtained, the approximate distribution of particle quantity at each zone was visualized and the mathematical model of pyrotechnic particle velocity flow field was established.

Abstract: In the digital image processing, Otsu algorithm uses the criterion of maximum between-cluster to make image segmentation. In this paper, combined with drug defect detection requirements, the new threshold output functions is put forward which studies on the existing two-dimensional Otsu algorithm in a deep way from the computing complexity and integral effect. The improved algorithm improves the computing speed of the algorithm and optimizes the segmentation effect which is a good segmentation algorithm. The effectiveness of the proposed algorithm has been proved by relevant experiments, and the medicine image segmentation result show that the improved algorithm has a good application prospect in drug defect detection.

Abstract: It is obliged to do research on performance detection and analyzation technology of information system with deepen development of smart grid to avoid risk and investment. This paper proposed a method of information detection and analyzing based on data mining algorithm and performance evaluation model. It is constructed evaluation platform with related smart decision tools which provided by method mention above. Finally, it is proved that the platform has a very high practical value and promotion prospects.

Abstract: Binocular stereo vision is an important branch of robot vision technology, it use two cameras in different position or a camera which can be move or rotate to shoot the same scene images, by calculating the parallax of spatial point in two images, get the spatial location information. There is a study based on the binocular stereo vision for three-dimensional spatial reconstruction, in view of the problem of vision image acquisition, camera calibration,stereo matching and 3d reconstruction in the binocular stereo application technology.

Abstract: Because the decoding problem of general linear codes is an NPC problem, at the same time Goppa codes have fast decoding algorithm, McEliece first proposed a public-key cryptosystem based on error-correcting codes. This paper constructs the largest distance codes based on metric, then improves the McEliece cryptosystem based on the largest distance codes. We've proved that the cryptosystem is able to resist the attack of getting the private key by a known public key. Finally, through the analysis of existing viable attack method, we proved the feasibility and security of the improved McEliece cryptosystem.

Abstract: To solve the problem of traffic congestion, many cities have adopted a series of measures to develop public transportation. The bus speed is an important factor to measure the level of bus service, and it helps to determine the arrival time of bus, which can reduce the passengers’ anxiety and waiting time at bus stops. Kalman filter, whose mathematical structure is relatively simple, is a recursive solution to the discrete-data linear filter problem. It has the strengths of little calculation amount, low storage amount, high real-time characteristics. Therefore, this paper develops a prediction model of bus speed based on Kalman filter, gives a specific program to predict speed and verifies the model according to the real-time GPS measurements of No.84150 in Beijing on each Friday in March, 2013.The result shows that the proposed model has high accuracy and can be applied to the prediction of bus speed.

Abstract: With the development of rail transportation in many cities, how to build efficient, safe and comfortable operation network of urban development has become a major problem to be solved in rail transportation. Beijing subway network has been relatively mature, and the Beijing subway passenger traffic inherent rules has an important significance in other cities in the development of rail transit.Based on the Beijing subway AFC data, this paper is around the Beijing subway passenger laws and researches on the systematic work, aiming at finding its inherent laws with the adoption of data mining. This paper analyses the results in order to ensure the accuracy of early access to a large number of relevant documents, and makes adequate preparations on data processing

Abstract: This paper describes the development of the use of lubricant analytical programmes and trend analysis to optimise oil change intervals and to predict compressor failure. The various analytical methods are covered, as are the most frequently occurring lubricant applications where such condition monitoring programmes are most appropriate.

Abstract: When dealing with the relationship of inputs and outputs by using DEA model, people often find that part of decision units are non DEA efficient in numerous evaluated decision units. For each decision unit has various internal situations, and the external environment is also relatively complex, the strategy space that managers can take should also be diversified.Therefore,the improving methods must also be diversified. The paper will combine potential problems with the non-DEA efficient decision making units to study several different improving models.Managers can choose different improving methods according to different strategy space to enable the improvement more in line with the actual situation.

Abstract: We propose an efficient method for automatic detection of suspicious behavior in video surveillance data. First of all, we cluster a set of sequences labeled as normal or suspicious. Then, we assign new observation sequences to behavior clusters. We label a sequence as suspicious if it maps to an existing model of suspicious behavior or does not map to any existing model according to the corresponding HMMs. We evaluate our proposed method on a real-world video surveillance and find that the method is very effective at detecting suspicious behavior.