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Signal processing for sound synthesis:: Computer-generated sounds and music for all

IEEE SIGNAL PROCESSING MAGAZINE 2007年第2期24卷 8-+页

作者： Valimaki, Vesa Rabenstein, Rudolf Rocchesso, Davide Serra, Xavier Smith, Julius O. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China Key Lab of Radar Signal Processing Xidian University Xi'an China

Almost everybody uses sound synthesis in their daily lives, although many may not know it.

关键词： Editors signal processing Music Synthesis of Sound

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Path planning with danger model immune system

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Information Technology Journal 2013年第18期12卷 4659-4665页

作者： Zhao, Lihua Pan, Jin Key Lab of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an 710071 China Xi'an Communications Institute Xi'an 710106 China

In this study, we present a Danger Model immune algorithm based path planning algorithm (DMIA-PP) for robot path planning. Different with the traditional immune algorithm, the system is not based on self-nonself mechanism, but on danger model theory. In a dynamic environment, if the changes of the obstacles affect the found optimal path, a danger signal will be send to the system. And the memory cell will be stimulated to propagate and to mutate, so as to a new optimization circle. The experimental results have shown the effectiveness and accuracy of the method in a complex searching map. © 2013 Asian Network for Scientific Information.

关键词： Motion planning

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Contour Knowledge-aware perception Learning for Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第5期35卷 4560-4575页

作者： You, Chao Jiao, Licheng Li, Lingling Liu, Xu Liu, Fang Ma, Wenping Yang, Shuyuan Xidian University Key Lab. Of Intelligent Perception And Image Understanding Of The Ministry Of Education Of China International Research Center Of Intelligent Perception And Computation School Of Artificial Intelligence Xi'an710071 China

The diversity of contextual information is of great importance for accurate semantic segmentation. However, most methods focus on single spatial contextual information, which results in an overlap of the semantic content of categories and a loss of contour information of objects. In this article, we propose a novel contour knowledge-aware perception learning network (CKPL-Net) to capture diverse contextual information by space-category aggregation module (SCAM) and contour-aware calibration module (CACM). First, SCAM is introduced to enhance intraclass consistency and interclass differentiation of features. By integrating space-aware and category-aware attention, SCAM reduces the redundancy of features from a categorical perspective while maintaining spatial correlation of pixels, substantially avoiding the overlap of the semantic content in categories. Second, CACM is designed to maintain the integrity of objects by perceiving contour contextual information. It develops a novel contour-aware knowledge and adaptively transforms the grid structure of convolutions for boundary pixels, which effectively calibrates the representation of features near boundaries. Finally, the quantitative and qualitative analyses on the three public datasets: ISPRS Potsdam dataset, ISPRS Vaihingen dataset, and WHDLD dataset, demonstrate that the proposed CKPL-Net achieves superior performance compared with prevalent methods, which indicates diverse contextual information is beneficial for accurate segmentation. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Influence of jumping rate on opposition-based differential evolution using the current optimum

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Information Technology Journal 2013年第5期12卷 959-966页

作者： Zhao, Lihua Xu, Qingzheng Pan, Jin Key Lab of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an 710071 China Xi'An Communication Institute Xi'an 710106 China

Diverse forms of opposition are already existent virtually everywhere and utilizing opposite numbers to accelerate an optimization method is a new idea. In this study, Differential Evolution (DE) and opposition-based differential evolution using the current optimum (COODE) are compared for different jumping rates. Experiments on 58 widely used benchmark problems show that, the higher jumping rate leads to faster convergence to global optimum and smaller success rate for most problems. © 2013 Asian Network for Scientific Information.

关键词： Optimization

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SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

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Journal of Systems Engineering and Electronics 2009年第3期20卷 519-526页

作者： Zhang Wenge~(1,2),Liu Fang~(1,2),Jiao Licheng~(2,3)& Gao Xinbo~(2,3) 1.School of Computer Science and Technology,Xidian Univ.,Xi’an 710071,P.R.China 2.Key lab.of intelligent perception and image understanding of Ministry of Education of China,Xi’an 710071,P.R.China 3.Inst,of intelligent Information Processing,Xidian Univ.,Xi’an 710071,P.R.China School of Computer Science and Technology Xidian Univ. Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 P. R. China

To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.

关键词： computer image processing synthetic aperture radar speckle edge detection nonsubsampled second generation bandelet transform Canny operator threshold shrinkage.

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Two-phase clustering algorithm with density exploring distance measure

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CAAI Transactions on Intelligence Technology 2018年第1期3卷 59-64页

作者： Jingjing Ma Xiangming Jiang Maoguo Gong Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University PO Box 224 Xi'an 710071People's Republic of China

Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance （DED） measure. In the first phase, the fast global K-means clustering algorithm is used to obtain the cluster number and the prototypes. Then, the prototypes of all these clusters and representatives of points belonging to these clusters are regarded as the input data set of the second phase. Afterwards, all the prototypes are clustered according to a DED measure which makes data points locating in the same structure to possess high similarity with each other. In experimental studies, the authors test the proposed algorithm on seven artificial as well as seven UCI data sets. The results demonstrate that the proposed algorithm is flexible to different data distributions and has a stronger ability in clustering data sets with complex non-convex distribution when compared with the comparison algorithms.

关键词：密度探索距离聚类算法计算方法人工智能

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A Special Point and Transfer Component Analysis Based Dynamic Multi-objective Optimization Algorithm 11th

A Special Point and Transfer Component Analysis Based Dynami...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Liu, Ruochen Li, Nanxi Peng, Luyao Wu, Kai Key Lab of Intelligent Perception and Image Understanding of Ministry of Education International Center of Intelligent Perception and Computation Xidian University Xi’an710071 China

ISBN: (纸本)9783030720612

To solve a dynamic multi-objective optimization problem better, algorithms need to quickly adapt to environmental changes and track its changing Pareto fronts fast. In this paper, an algorithm (SPTr-RMMEDA) based on special point and transfer component analysis is presented, in which, by using some special points and their neighborhoods solutions, the prediction strategy are used to generated the transfer learning prediction model, together with part new initial solutions to generate next population when change occurs. In order to better adapt to environmental changes, in addition to reusing some important historical information by transfer component analysis, the algorithm also introduces the adaptive diversity introduction strategy. The algorithm performs on 12 groups test problems. And the experimental results show that the proposed algorithm is superior to other four algorithms, such as Tr-NSGA-II, Tr-RMMEDA, DNSGA-II-A and DNSGA-II-B on most test problems in term of convergence and computation complexity. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Hybrid immune algorithm with Lamarckian local search for multi-objective optimization

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Memetic Computing 2010年第1期2卷 47-67页

作者： Gong, Maoguo Liu, Chao Jiao, Licheng Cheng, Gang Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University 710071 Xi'an China

Lamarckian learning has been introduced into evolutionary computation as local search mechanism. The relevant research topic, memetic computation, has received significant amount of interests. In this study, a novel Lamarckian learning strategy is designed for improving the Nondominated Neighbor Immune Algorithm, a novel hybrid multi-objective optimization algorithm, Multi-objective Lamarckian Immune Algorithm (MLIA), is proposed. The Lamarckian learning performs a greedy search which proceeds towards the goal along the direction obtained by Tchebycheff approach and generates the improved progenies or improved decision vectors, so single individual will be optimized locally and the newcomers yield an enhanced exploitation around the nondominated individuals in less-crowded regions of the current trade-off front. Simulation results based on twelve benchmark problems show that MLIA outperforms the original immune algorithm and NSGA-II in approximating Pareto-optimal front in most of the test problems. When compared with the state of the art algorithm MOEA/D, MLIA shows better performance in terms of the coverage of two sets metric, although it is laggard in the hypervolume metric. © 2009 Springer-Verlag.

关键词： Artificial immune system Lamarckian learning Memetic algorithm Multi-objective optimization

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Community detection in dynamic social networks based on multiobjective immune algorithm

Community detection in dynamic social networks based on mult...

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作者： Gong, Mao-Guo Zhang, Ling-Jun Ma, Jing-Jing Jiao, Li-Cheng Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

Community structure is one of the most important properties in social networks, and community detection has received an enormous amount of attention in recent years. In dynamic networks, the communities may evolve over time so that pose more challenging tasks than in static ones. Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms. In this paper, a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks. It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method. Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms. © 2012 Springer Science+Business Media, LLC & Science Press, China.

关键词： Multiobjective optimization

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Constrained layout optimization in satellite cabin using a multiagent genetic algorithm

Constrained layout optimization in satellite cabin using a m...

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9th International Conference on Simulated Evolution and Learning, SEAL 2012

作者： Liu, Jing Key Lab. of Intelligent Perception and Image Understanding Ministry of Education of China Xidian University 710071 China

ISBN: (纸本)9783642348587

One application of constrained layout optimization problems (CLOPs) is to lay out the instruments in satellite cabin (CLOPssc), which concerns the two dimensional physical placement of a collection of objects within a satellite cabin. CLOPssc are not only of significant theoretical interest, but also of real practical significance in industrial applications. In this paper, we use a multiagent genetic algorithm with a simple technique of handling constraints to solve CLOPssc. In experiments, the performance of the new algorithm is compared with existing algorithms on three benchmark problems with different complexities. Experimental results show that the new algorithm has a better global searching ability. Especially, it found a currently best solution for the constrained layout problem with 40 objects. © 2012 Springer-Verlag.

关键词： Genetic algorithms

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