[Show abstract][Hide abstract] ABSTRACT: This paper introduces the structure function, main feature and implementation principles of a vehicle monitor system. The system use MapX controls to match electronic map for implementing vehicle monitor, and adopts MSComm controls to obtain information of SMS of GPS. It realizes the function of the located data delivering and the electronic map matching.
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a structure that automatically analyzes the parameters of Chinese test items. This structure utilizes latent semantic analysis (LSA) to analyze the relationships of keywords among all test items in an item bank. It also uses the similarity measure to calculate the similarity degree of keywords. We also propose an algorithm, which is similar to the graph-theoretic algorithm, for keyword clustering. The concept weights of each test item can be generated automatically with the proposed automatic generate concept weights.
[Show abstract][Hide abstract] ABSTRACT: This paper discusses an approach to multi-document summarization that builds on understanding word as feature deeply. We created 7 basic word features using the frequency, position information, event information and topic information. Then choose logistic regression model to compute words value. The summarizer gives a score of sentence by words value, and combines score and redundancy of sentence to produce summarization. The evaluation of summaries uses three parameters which are N-gram co-occurrence statistics, term word coverage and high frequency word coverage. The experiment results show the systempsilas has more effectiveness and feasibility.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propose a new method for text summarization. The system finds topic word and event word firstly, and then recalculates word weight. Using recalculated word weight to compute similarly of paragraphs to search local topics units. The most representative sentences in each local topic unit are selected as the summary sentences. By analyzing semantic structure of the documents first, the summary sentences are not redundancy and the coverage of each local topic is balanced. Experimental results show that our approach is effective and efficient, and performance of the system is reliable.
[Show abstract][Hide abstract] ABSTRACT: Urban traffic control is very complicated, so it is very difficult to build a precise mathematical model. In this paper, we propose a fuzzy Actor-Critic reinforcement leaning algorithm to control the traffic signal, thus the decision can be made dynamically according to real-time traffic state information, and the change of environment can be adapted automatically; In order to solve the curse of the dimensionality problem, we applied fuzzy radial basis function (FRBF) neural network to approximate the state value function. By training self-adapted non-linear processing unit, and realizing online and adaptive constructing of state space, the approximation is improved, thus the control of traffic signal at single intersections is solved. The simulation results show that the effectiveness of the new control algorithm is obviously better than traditional sliced time allocation methods.
[Show abstract][Hide abstract] ABSTRACT: An improved collision detection algorithm based on AABB is presented. During the global search, each axis is cut into a series of segments containing the same number of AABBs' projection intervals, and Shell sort is adopted to sort projection lists, not insertion sort. This will avoid needless intersecting test of AABB. During the local detection, the amount of byte of AABB bounding-volume for internal node is reduced according to the constructing process of AABB tree, and leaf nodes are wiped from tree structure The storage of AABB tree is compressed. This method can save a large amount of space and speed up the algorithm. Experiments indicate that the improved algorithm reduce detection time for the same models.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propose a practical approach for extracting the most relevant sentences from the original document to form a summary. The idea of our approach is to obtain summary based on similarity of thematic sentences, which use terms as features rather than words, and employs term length term frequency (TLTF) to compute weight of terms to obtain features. Furthermore, it uses an improved k-means method to cluster sentences, and compute similarity of thematic sentences according to clustering results. Experimental results indicate a clear superiority of the proposed method over the traditional method under the proposed evaluation scheme.