In view of the difficulties in real-time monitoring of component contents in rare earth extraction process and the high time consumption and memory consumption of existing component content detection methods, a dynamic monitoring system for element component content based on time sequence characteristics of solution images was designed. Firstly, the image acquisition device was used to obtain the time sequence image of the extraction tank solution. Considering the color characteristics of the extracted liquid and the incompleteness of single color space, the time sequence characteristics of the image were extracted in the color space of the fusion of HSI (Hue, Saturation, Intensity) and YUV (Luminance-Bandwidth-Chrominance) by using Principal Component Analysis (PCA) method, and combined with the production index, the Whale Optimization Algorithm (WOA) based Least Squares Support Vector Machine (LSSVM) classifier was constructed to judge the status of the working condition. Secondly, when the working condition was not optimal, the color histogram and color moment features of the image were extracted in HSV (Hue, Saturation, Value) color space, and an image retrieval system was developed with the linear weighted value of the mixed feature difference between solution images as the similarity measurement to obtain the value of component content. Finally, the test of the mixed solution of the praseodymium/neodymium extraction tank was carried out, and the results show that this system can realize the dynamic monitoring of element component content.

A new algorithm was proposed to meet the instantaneous response requirements of the Boolean operation between large-scale mesh models in the product design. Discrete sampling was performed on mesh models to obtain the ray-segment point clound model and the three-dimensional Boolean operation among triangular mesh was then converted into one-dimensional one among ray segments; the intersection points could be accurately solved and interpolated around the overlapped regions of mesh models, so the Boolean operation was significantly speeded and the design efficiency of products of complex topology was greatly improved in turn. The point cloud model obtained by the proposed algorithm could be rendered with the same effect as that by the triangular mesh model. The proposed method can be adopted in engineering applications.

The Dynamic Time Warping (DTW) algorithm cannot keep high classification accuracy while improving the computation speed. In order to solve the problem, a Coarse-Granularity based Dynamic Time Warping (CG-DTW) algorithm based on the idea of naive granular computing was proposed. First of all, the better temporal granularities were obtained by computing temporal variance features, and the original series were replaced by granularity features. Then, the relatively optimal corresponding temporal granularity was obtained by executing DTW with dynamically adjusting intergranular elasticity of granularities compared. Finally, the DTW distance was calculated in the case of the corresponding optimal granularity. During this progress, an early termination strategy of lower bound function was introduced for further improving the CG-DTW algorithm efficiency. The experimental results show that, the proposed algorithm was better than classical algorithm in running rate with increasing by about 21.4%, and better than dimension reduction strategy algorithm in accuracy with increasing by about 32.3 percentage points.Especially for the long time sequences classification, CG-DTW takes consideration into both high computing speed and better classification accuracy. In actual applications, CG-DTW can adapt to long time sequences classification with uncertain length.

The inconsistence of normal curvature at vertex when reconstructing a coarse mesh model as a fine heavy one has been unsolved and this inconsistence will result in shadow in rendering. In this paper, the geometric condition of the normal curvature consistence at vertex was obtained and a novel algorithm was further proposed based on that condition refining coarse mesh models to be visually smooth parametric ones, which were presented collectedly as triangular Gregory patches. The constructed parametric model was G ¹ everywhere without the normal curvature inconsistence problem, so a good visual effect could be obtained. The experimental results show that the proposed algorithm can obtain a high quality visual effect with the 1%-2% vertexes of the original mesh model.

The object tracking algorithm based on discriminative classifier usually adopts complex appearance model to improve the tracking precision in complex scenes, which relatively influences the real-time performance of tracking. To solve this problem, a binary feature based on halftone was proposed to describe the object appearance and the kernel function of structured output Support Vector Machine (SVM) was improved, so as to realize fast updating and discriminating of discriminative model. In addition, a discriminative model updating strategy based on part matching was proposed, which can ensure the reliability of the training samples. In the experiments conducted on Benchmark, compared with the three algorithms including Compressive Tracking (CT), Tracking Detection Learning (TLD) and Structured Output Tracking with Kernels (Struck), the proposed algorithm had better performance in tracking speed with the increases of 0.2 times, 4.6 times and 5.7 times respectively. On the aspect of tracking precision, when overlap rate threshold was set to 0.6, the success rate of the proposed algorithm reached 0.62, which was higher than the success rates of the other three algorithms that were all less than 0.4;when the position error threshold was set to 10, the precision of the proposed algorithm reached 0.72,while the precisions of the other three algorithms were all less than 0.5. The experimental results show that the proposed algorithm obtains good robustness and real-time performance in complex scenes, such as illumination change, scale change, full occlusion and abrupt motion.

The research methods of mechanical geometry theorem proving were summed up into two categories, deterministic algorithms and probabilistic algorithms, and then an improved probabilistic algorithm was proposed to overcome the drawbacks such as poor efficiency or memory consumption in the existing methods. That was, the upper bounds of the degrees of variables in the pseudo-remainder were estimated by adopting an improved algorithm, and then on the basis of combining Schwartz-Zippel theorem and statistical theory, a geometric theorem could be proved by checking several random instances, the probability of error result could also be calculated and controlled within any given small range. Through the improved probabilistic algorithm, the Five Circles Theorem had already been proved successfully within two seconds which is quite difficult to be proved by existing algebra methods for its high complexity. Comparative experiment results also show that the improved probabilistic algorithm is high efficient.

In the garment industry, shortening idle travel of tool path is important to efficiently cut these patterns from a piece of cloth. As these cutting patterns are of complex shapes and distributed intensively, it is not trivial to obtain the optimal cutting tool path via the existing algorithms. Based on MAX-MIN Ant System (MMAS), a new algorithm was proposed to optimize the idle travel of tool path. The algorithm consists of four steps: 1) using standard MMAS algorithm to define the pattern order; 2) seek the node as the tool entrance on each pattern; 3) optimize the node sequence with MMAS; 4) repeat the step 2) and 3) to achieve the optimal tool path. The experiments show that the proposed algorithm can effectively generate optimal tool path. Compared with the line-scanning algorithm and Novel Ant Colony System (NACS) algorithm, the result has been improved by 60.15% and 22.44% respectively.

As the multimodal complex problem has many local optima, it is difficult for the basic Particle Swarm Optimization (PSO) to effectively solve this kind of problem. To conquer this defect, firstly, Monte Carlo method was used to simulate the fly trajectory of particle, and the reason for falling into local optima was concluded. Then, by defining distance, average distance and maximal distance between particles, an adaptive control factor named Adaptive Rejection Factor (ARF) for controlling local optimum position and global optimum position was proposed to increase the ability for escaping from local optima. In order to test the proposed strategy, three test benchmarks including Rosenbrock, Ackley and Griewank were selected to conduct the analysis of convergence property and statistical property. The 60 times independent runs show that the improved PSO based on ARF (ARFPSO) has the best value of 53.82, 2.1203 and 5.32E-004, which is better than the both contrast algorithms. The results show that ARFPSO can effectively avoid premature phenomenon, and the complexity analysis of the algorithm also shows that the introduced strategy does not increase computational complexity.

The existing identity-based hybrid signcryption schemes have either known security flaws or higher computational costs. With the vBNN-IBS signature algorithm, a new identity-based hybrid signcryption scheme was proposed. The new scheme, based on the Discrete Logarithm Problem (DLP) and the Computational Diffie-Hellman (CDH) problem on elliptic curve, did not use bilinear operation. So, the total computational costs of the new scheme were effectively reduced, and had only 7 scalar multiplication operations on elliptic curve, which were much lower than the other silimar schemes. In the random oracle model, the new scheme was verified safe enough to achieve the unforgeability and confidentiality. In addition, the paper also pointed out that the Sun et al's scheme (Sun Y X, Li H. Efficient certificateless hybrid signcryption. Journal of Software, 2011,22(7):1690-1698) did not satisfy the unforgeability.

In some industrial fields such as garments, shoes and metal building, it is often required to interpolate multiple specified set of skeleton curves using one loft developable surfaces (which can be developed onto plane without any distortion, tear or stretch). To solve this problem, one new algorithm based on Dijkstras algorithm was proposed to support the design of developable mesh surface: given multiple parametric curves, after adaptively sampling them, the objective is to search one globally optimal developable loft surface (given other specified objective surface energy function, varied corresponding loft mesh surfaces can be obtained of globally optimal topology). The problem was simplified into source route finding problem in Direct Acyclic Graph (DAG). The proposed algorithm is of practical engineering senses in designing loft surfaces in related engineering applications.

Currently, the collaborative filtering is one of the successful and better personalized recommendation technologies that have been applied to the personalized recommendation systems. As the number of users and items increase dramatically, the score matrix which reflects the users preference information is very sparse. The sparse matrix seriously affects the recommendation quality of collaborative filtering. To solve this problem, this paper presented a comprehensive mean optimal filling method. Compared to the default method and the mode method, this method has two advantages. First, the method takes account of user rating scale issues. Second, the method does not have the "multiple mode" and the "no mode" problems. On the same data set, using traditional user-based collaborative filtering to test the effectiveness of the method, and the results prove that the new method can improve the recommendation quality of recommendation systems.

With Digital Signal Processor (DSP) and Field Programmable Gate Array (FPGA) adopted as the kernel processors, the embedded network conversion gateway of high real-time was designed to realize the conversion between fieldbus and Ethernet. The conversion was finished on the physical layer and the data link layer, and the problem of direct communications between fieldbus and Ethernet was solved. The experimental results indicate that the feasibility and effectiveness of the design satisfy the requirements.

Ontology engineering provides appropriate ontology developing methods which can instruct the ontology building. The Track Methodology Model of Ontology Building (TMMOB) can support the different means of ontology building according to the characteristic of ontology in different knowledge system, and can evaluate the process of ontology developing. In order to improve the efficiency of ontology building, a multi-agent model for the TMMOB was created by using autonomy, collaboration and distribution of multi-agent. In the model, different agents, collaboration and communication between agents were defined and formalized. At the same time, the algorithms about operation and collaboration in agents, and the platform of the multi-agent running were implemented.

In this paper a new system that integrates the whole process of seismic information collection was presented. A model for data evaluation was designed and the system frame and the method dealing with mass information were presented. The experimental results show that the new developed system is able to efficiently control the quality of information collection.

The key of the utilization of flood resource is to know flood occurrence course and movement rule, and flood routing state can be reappeared. According to the research of three-dimension terrain simulation and visualization study of flood routing simulation, flood routing simulation system is established with GIS data, under (VC++) and OpenGL program environment. Flood routing simulation dynamically under given water level is realized, and strong base for the research of flood resources is established.

In the course of tracking IR motive targets, a large number of false alarm signals might appear in the target detection stage of an IR ATR system because of the non-prediction of IR targets signature. So the false alarm signals had to be filtered in the stage of holding down background clutters. A new FLIR image segmentation technique was presented based on wavelet neural networks, aiming to fusing both local characteristic of wavelet time-frequency and adaptive learning by neural networks, and resulting in the powerful abilities of approximation and tolerate error in IR image segmentation. This new algorithm was applied in a FLIR-ATR system, and got favorable results in achieving IR target contours and damping background noises.

To increase the optimization result and efficiency in floorplan, ant colony algorithm was used as optimization algorithm of floorplan. In this algorithm, the B*-tree structure was defined to describe the structure of layout, the utilization efficiency of modules was defined to be pheromone which strenthened the dependency relationship of partial modules that occupy less area, and mutation scheme was employed to improve the efficiency of convergence. Through experiments, ant colony algorithm can get better result and efficiency than simulate annealing algorithm in the flooplan of hard modules.