For the actual background of the shortage of self-owned vehicles of the transporters in the early stage of emergency, the combinatorial optimization problem of hybrid vehicle paths with transportation mode of joint distribution of self-owned vehicles and vehicles rented by third-party was studied. Firstly, with the different interests between demand points and transporters considered, a multi-objective hybrid vehicle routing optimization model with soft time windows was established with the goal of maximizing system satisfaction and minimizing system delivery time and total cost. Secondly, the shortcomings of NSGA-Ⅱ algorithm in solving this kind of problems such as poor convergence and uneven distribution of Pareto frontiers were considered, the heuristic strategy and pheromone positive feedback mechanism of ant colony algorithm were used to generate offspring population, non-dominated sorting strategy model was used to guide the multi-objective optimization process, and the variable neighborhood descent search was introduced to expand the search space. A multi-objective non-dominated sorting ant colony algorithm was proposed to break through the bottleneck of the original algorithm. The example shows that the proposed model can provide reference for decision makers to choose reasonable paths according to different optimization objectives in different situations, and the proposed algorithm shows better performance in solving different scale problems and different distribution type problems.
For the problem of time, effort and money consuming to obtain a large number of samples by conventional means faced by Artificial Intelligence (AI) application research in different fields, a variety of sample augmentation methods have been proposed in many AI research fields. Firstly, the research background and significance of data augmentation were introduced. Then, the methods of data augmentation in several common fields (including natural image recognition, character recognition and discourse parsing) were summarized, and on this basis, a detailed overview of sample acquisition or augmentation methods in the field of medical image assisted diagnosis was provided, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) images. Finally, the key issues of data augmentation methods in AI application fields were summarized and the future development trends were prospected. It can be concluded that obtaining a sufficient number of broadly representative training samples is the key to the research and development of all AI fields. Both the common fields and the professional fields have conducted sample augmentation, and different fields or even different research directions in the same field have different sample acquisition or augmentation methods. In addition, sample augmentation is not simply to increase the number of samples, but to reproduce the existence of real samples that cannot be completely covered by small sample size as far as possible, so as to improve sample diversity and enhance AI system performance.
Ranking function detection is one of the most important methods to analyze the termination of loop program. Some tools have been developed to detect linear ranking functions corresponding to linear loop programs. However, for polynomial loops with polynomial loop conditions and polynomial assignments, existing methods for detecting their ranking functions are mostly incomplete or with high time complexity. To deal with these weaknesses of existing work, a method was proposed for detecting polynomial ranking functions for polynomial loop programs, which was based on extended Dixon resultants (the KSY (Kapur-Saxena-Yang) method) and Successive Difference Substitution (SDS) method. Firstly, the ranking functions to be detected were seen as polynomials with parametric coefficients. Then the detection of ranking functions was transformed to the problem of finding parametric coefficients satisfying the conditions. Secondly, this problem was further transformed to the problem of determining whether the corresponding equations have solutions or not. Based on extended Dixon resultants in KSY method, the problem was reduced to the decision problem whether the polynomials with symbolic coefficients (resultants) were strictly positive or not. Thirdly, a sufficient condition making the resultants obtained strictly positive were found by SDS method. In this way, the coefficients satisfying the conditions were able to be obtained and thus a ranking function satisfying the conditions was found. The effectiveness of the method was proved by experiments. The experimental results show that polynomial ranking functions including d-depth multi-stage polynomial ranking functions are able to be detected for polynomial loop programs. This method is more efficient to find polynomial ranking functions compared with the existing methods. For loops whose ranking functions cannot be detected by the method based on Cylindrical Algebraic Decomposition (CAD) due to high time complexity, their ranking functions are able be found within a few seconds with the proposed method.
The basic Ant Colony Optimization (ACO) has slow searching speed at prior period and being easy to fall into local optimum at later period. To overcome these shortcomings, the initial pheromone distribution strategy and local optimization strategy were proposed, and a new pheromone updating rule was put forward to strengthen the effective accumulation of pheromone. The improved ACO was used in QoS-based Web service composition optimization problem, and the feasibility and effectiveness of it was verified on QWS2.0 dataset. The experimental results show that, compared with the basic ACO, the improved ACO which updates the pheromone with the distance of the solution and the ideal solution, and the improved genetic algorithm which introduces individual domination strength into the environment selection, the proposed ACO can find more Pareto solutions, and has stronger optimizing capacity and stable performance.
Concerning the problem of underwater bearings-only system target tracking with incomplete measurements when the probability of sensor detection is less than 1,an improved extended Kalman filtering algorithm for target state estimation was presented. First, the mathematical model of underwater bearings-only system for target tracking with incomplete measurements was established. Second, based on the sensor's incomplete measurement data, the previous update data was used to compensate for the incomplete date and then to perform the filtering. Finally, two evaluation criteria including Cramer-Rao Low Bound (CRLB) and Root Mean Square Errors (RMSE) were used to evaluate the proposed algorithm. The simulation results show that the proposed extended Kalman filtering algorithm for target tracking has higher real-time property with desired tracking precision in the problem of underwater bearings-only system target tracking with incomplete measurements.
In order to filter out Gaussian noise and impulse noise at the same time, and get high resolution image in super-resolution reconstruction, a method with L1 and L2 mixed norm and Bilateral Total Variation (BTV) regularization was proposed for sequence images super-resolution. Firstly, multi-resolution optical flow model was used to register low-resolution sequence images and the registration precision was up to sub-pixel level, then the complementary information was used to raise image resolution. Secondly, taking advantage of L1 and L2 mixed norm, BTV regularization algorithm was used to solve the ill-posed problem. Lastly, the proposed algorithm was used to sequence images super-resolution. Experimental results show that the method can decrease the mean square error and increase Peak Signal-to-Noise Ratio (PSNR) by 1.2 dB to 5.2 dB. The algorithm can smooth Gaussian and impulse noise, protect image edge information and improve image identifiability, which provides good technique basis for license plate recognition, face recognition, video surveillance, etc.
Concerning the problem that the efficiency of serial PageRank algorithm is low in dealing with mass Web data, a PageRank parallel algorithm based on Web link classification was proposed. Firstly, the Web was classified according to its Web link, and the weights of different Web which was from diverse websites were set variously. Secondly, with the Hadoop parallel computation platform and MapReduce which has the characteristics of dividing and conquering, the Webpage ranks were computed parallel. At last, a data compression method of three layers including data layer, pretreatment layer and computation layer was adopted to optimize the parallel algorithm. The experimental results show that, compared with the serial PageRank algorithm, the accuracy of the proposed algorithm is improved by 12% and the efficiency is improved by 33% in the best case.
To improve the accuracy of bird sounds recognition in low Signal-to-Noise Ratio (SNR) environment, a new bird sounds recognition technology based on Radon Transform (RT) and Translation Invariant Discrete Wavelet Transform (TIDWT) from spectrogram after the noise reduction was proposed. First, an improved multi-band spectral subtraction method was presented to reduce the background noise. Second, short-time energy was used to detect silence of clean bird sound, and the silence was removed. Then, the bird sound was translated into spectrogram, RT and TIDWT were used to extract features. Finally, classification was achieved by Support Vector Machine (SVM) classifier. The experimental results show that the method can achieve better recognition effect even the SNR belows 10dB.