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| 1 | Fuzzy c-means clustering with non local spatial information for noisy image segmentation显示文摘 | Feng Zhao (1) add_zf1119@hotmail.com Licheng Jiao (1) Hanqiang Liu (1) | 2011 | Frontiers of Computer Science2011，5，1： | 31 |
| 2 | Immune secondary response and clonal selection inspired optimizers显示文摘The immune system's ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen,immune system usually calls for a more rapid and larger response to the antigen,where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response,two novel artificial immune system algorithms,secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA),are presented for solving single and multi-objective optimization problems,respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the clonal selection theory,CSO reproduces individuals and selects their improved maturated progenies after the affinity mat-uration process. SRO copies certain antibodies to a secondary pool,whose members do not participate in CSO,but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand,decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimiza-tion problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor edit-ing process. Compared with some existing evolutionary algorithms,such as OGA/Q,IEA,IMCPA,BGA and AEA,SRCPA is shown to be able to solve complex optimization problems,such as high-dimensional function optimizations,with better performance. On the other hand,SRCMOA combines the Pareto-strength based fitness assignment strategy,CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA,NSGA-Ⅱ,SPEA,and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto-optimal fronts with wide distributions. | Maoguo Gong Licheng Jiao Lining Zhang Haifeng Du | 2009 | Progress in Natural Science:Materials International2009，19，2： | 24 |
| 3 | Optimal approximation of linear systems by artificial immune response显示文摘This paper puts forward a novel artificial immune response algorithm for op-timal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm. | GONG Maoguo DU Haifeng JIAO Licheng | 2006 | Science in China(Series F)2006，49，1： | 21 |
| 4 | The Impact of the Linked Factors of Provenance,Tectonics and Climate on Potash Formation:An Example from the Potash Deposits of Lop Nur Depression in Tarim Basin,Xinjiang,Western China显示文摘Potash deposits commonly accumulate in highly restricted settings at the final stage of brine evaporation. This does not mean that potash deposits are formed simply as a result of the evaporation concentration of seawater or lake water, but rather as a coupling result of particular provenance, tectonics and climate activities. In this paper, we focus on the formative mechanism of the potash deposits of Lop Nur depression in Tarim Basin to interpret the detailed coupling mechanism among provenance, tectonics and climate. In terms of the provenance of Lop Nur Lake, the water of the Tarim River which displays 'potassium-rich' characteristics play an important role. In addition, the Pliocene and Lower-Middle Pleistocene clastic beds surrounding Lop Nur Lake host a certain amount of soluble potassium and thus serves as 'source beds' for potash formation. During the late Pliocene, the Lop Nur region has declined and evolved into a great lake from the previous piedmont and diluvial fan area. Since the mid Pleistocene, the great-united Lop Nur Lake has been separated and has generated a chain system consisting of Taitema Lake, Big Ear Lake and Luobei Lake which has turned into the deepest sag in Lop Nur Lake. Dry climate in Lop Nur region has increased since the Pliocene, and became extreme at the late Pleistocene. The study implies that potash formation in Lop Nur Lake depends on the optimal combination of extreme components of provenance, tectonics and climate during a shorter-term period. The optimal patterns of three factors are generally characterized by the long-term accumulation and preliminary enrichment of potassium, the occurrence of the deepest sub-depression and the appearance of an extremely arid climate in Lop Nur region. These factors have been interacting synergistically since the forming of the saline lake and in the later stages strong 'vapor extraction' caused by extremely arid climate is needed to trigger large scale mineralization of potash deposits. | LIU Chenglin JIAO Pengcheng Lü Fenglin WANG Yongzhi SUN Xiaohong ZHANG Hua WANG Licheng YAO Fojun | 2015 | Acta Geologica Sinica(English Edition)2015，89，6： | 18 |
| 5 | Memetic computation based on regulation between neural and immune systems: the framework and a case study显示文摘Lamarckian learning has been introduced into evolutionary computation to enhance the ability of local search. The relevant research topic, memetic computation, has received significant amount of interest. In this study, a novel memetic computational framework is proposed by simulating the integrated regulation between neural and immune systems. The Lamarckian learning strategy of simulating the unidirectional regulation of neural system on immune system is designed. Consequently, an immune memetic algorithm based on the Lamarckian learning is proposed for numerical optimization. The proposed algorithm combines the advantages of immune algorithms and mathematical programming, and performs well in both global and local search. The simulation results based on ten low-dimensional and ten high-dimensional benchmark problems show that the immune memetic algorithm outperforms the basic genetic algorithm-based memetic algorithm in solving most of the test problems. | GONG MaoGuo , JIAO LiCheng, LIU Fang & YANG Jie Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China | 2010 | Science China(Information Sciences)2010，53，8： | 16 |
| 6 | Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework显示文摘Generative adversarial network(GAN)has achieved great success in many fields such as computer vision,speech processing,and natural language processing,because of its powerful capabilities for generating realistic samples.In this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian domains.However,in many specific scenarios,we can’t obtain enough labeled data,which cause failure of deep learning methods because they are easy to fall into over-fitting.Fortunately,semi-supervised learning(SSL)can leverage the large amount of unlabeled data to enhance the classification performance of classifiers,especially in scenarios with limited amount of labeled data.We present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal data.According to the characteristics of the electromagnetic signal,we propose a weighted loss function,leading to an effective classifier to realize the end-to-end classification of the electromagnetic signal.We validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System(ACARS)signal dataset.Extensive experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies. | Huaji Zhou Licheng Jiao Shilian Zheng Lifeng Yang Weiguo Shen Xiaoniu Yang | 2020 | China Communications2020，17，10： | 8 |
| 7 | Multiobjective optimization using an immunodominance and clonal selection inspired algorithm显示文摘Based on the mechanisms of immunodominance and clonal selection theory,we propose a new multiobjective optimization algorithm,immune dominance clonal multiobjective algorithm(IDCMA).IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure,termed as Ab-Ab affinity,between the dominated individuals and one of the nondominated individuals found so far.According to the values of Ab-Ab affin-ity,all dominated individuals(antibodies) are divided into two kinds,subdominant antibodies and cryptic antibodies.Moreover,local search only applies to the sub-dominant antibodies,while the cryptic antibodies are redundant and have no func-tion during local search,but they can become subdominant(active) antibodies during the subsequent evolution.Furthermore,a new immune operation,clonal proliferation is provided to enhance local search.Using the clonal proliferation operation,IDCMA reproduces individuals and selects their improved maturated progenies after local search,so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space.The performance comparison of IDCMA with MISA,NSGA-Ⅱ,SPEA,PAES,NSGA,VEGA,NPGA,and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution. | GONG MaoGuo JIAO LiCheng MA WenPing DU HaiFeng | 2008 | Science in China(Series F)2008，51，8： | 6 |
| 8 | Soft spectral clustering ensemble applied to image segmentation显示文摘 | Jianhua Jia (12) jjh163yx@163.com Bingxiang Liu (1) Licheng Jiao (2) | 2011 | Frontiers of Computer Science2011，5，1： | 6 |
| 9 | Adaptive chaos clonal evolutionary programming algorithm显示文摘Based on the chaos movement and the clonal selection theory, a novel artificial immune system algorithm, Adaptive Chaos Clonal Evolutionary Programming Algorithm (ACCEP), is proposed in this paper. The new algorithm uses the Logistic Sequence to control the mutation scale and uses the Chaos Mutation Operator to control the clonal selection. Compared with SGA and Clonal Selection Algorithm, ACCEP can enhance the precision and stability, avoid prematurity to some extent, and have the high convergence speed. The results of the experiment indicate that ACCEP has the capability to solve complex machine learning tasks, like Multimodal Function Optimization. | DU Haifeng GONG Maoguo LIU Ruochen JIAO Licheng | 2005 | Science in China(Series F)2005，48，5： | 5 |
| 10 | Image Segmentation via Mean Shift and Loopy Belief Propagation显示文摘This paper presents a novel approach that can quickly and effectively partition images based on fully exploiting the spatially coherent property. We propose an algorithm named iterative loopy belief propagation(iLBP) to integrate the homogenous regions and prove its convergence. The image is first segmented by mean shift(MS) algorithm to form over-segmented regions that preserve the desirable edges and spatially coherent parts. The segmented regions are then represented by region adjacent graph(RAG) . Motivated by k-means algorithm,the iLBP algorithm is applied to perform the minimization of the cost function to integrate the over-segmented parts to get the final segmentation result. The image clustering based on the segmented regions instead of the image pixels reduces the number of basic image entities and enhances the image segmentation quality. Comparing the segmentation result with some existing algorithms,the proposed algorithm shows a better performance based on the evaluation criteria of entropy especially on complex scene images. | JIA Jianhua,JIAO Licheng,CHANG Xia Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China/Institute of Intelligent Information Processing,Xidian University,Xi’an 710071,Shaanxi,China | 2010 | Wuhan University Journal of Natural Sciences2010，15，1： | 5 |
| 11 | Intelligent multi-user detection using an artificial immune system显示文摘Artificial immune systems (AIS) are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations. | GONG MaoGuo, JIAO LiCheng, MA WenPing & MA JingJing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China | 2009 | Science in China(Series F)2009，52，12： | 5 |
| 12 | Artificial immune kernel clustering network for unsupervised image segmentation显示文摘An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the sup- port vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coeffcient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algo- rithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predfined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained. | Wenlong Huang Licheng Jiao | 2008 | Progress in Natural Science:Materials International2008，18，4： | 4 |
| 13 | Metric Learning for Tensor High-Dimensional Data显示文摘 | SHI Jiarong JIAO Licheng SHANG Fanhua | 2011 | Chinese Journal of Electronics2011，20，3： | 4 |
| 14 | A Wavelet-Based Fuzzy Neural Network for Interpolation of Fuzzy If-Then Rules显示文摘In this paper, a wavelet-based fuzzy neural network with its structure and a learningalgorithm is proposed and the simulation results are given to prove its feasibility. | Jiao Licheng Liu Fang Wang Ling & Zhang Yanning(State Key Lab. of RSP and Center for Neural Networks, Xidian University, Xi’an 710071, P. R. China) (This project was partly supported by the National Thud of Intercent. Expert and partly supported bythe | 1998 | Journal of Systems Engineering and Electronics1998，9，4： | 3 |
| 15 | Multiuser detection and independent component analysis——Progress and perspective显示文摘The latest progress in the multiuser detection and independent component analysis (ICA) is reviewed systematically. Then two novel classes of multiuser detection methods based on ICA algorithms and feedforward neural networks are proposed. Theoretical analysis and computer simulation show that ICA algorithms are effective to detect multiuser signals in code-division multiple-access (CDMA) system. The performances of these methods are not identical entirely in various channels, but all of them are robust, efficient, fast and suitable for real-time implementations. | JIAO Licheng , MA Haibo and LIU Fang( State Key Laboratory for Radar Signal Processing, Xidian University, Xi’an 710071, China) | 2002 | Progress in Natural Science:Materials International2002，12，7： | 3 |
| 16 | Immunodominance and clonal selection inspired multiobjective clustering显示文摘The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested. | Wenping Ma , Licheng Jiao, Maoguo Gong Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China | 2009 | Progress in Natural Science:Materials International2009，19，6： | 3 |
| 17 | SAR image despeckling based on edge detection and nonsubsampled second generation bandelets显示文摘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 are 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). | 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 | 2009 | Journal of Systems Engineering and Electronics2009，20，3： | 3 |
| 18 | SAR Image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain显示文摘The edge and contour details in SAR images are important for subsequent processing tasks. The multiscale geometric analysis method — Nonsubsampled contourlet transform(NSCT) is able to capture the geometric information of SAR images effectively. Describing the aggregation behavior of the neighborhoods coefficients, the scale mixtures of Gaussians model has exhibited favorable performances. A novel SAR image despeckling method is presented by constructing the scale mixtures of Gaussians model of NSCT. This method models the SAR images using the multiscale and multidirection information in NSCT domain. The dependency relationship of NSCT neighborhoods coefficients are also taken into consideration in our model. The speckle noise coefficients are shrinkaged by statistical prior estimation based on SAR image model constructed. Experimental results demonstrate that our method is advantageous at directional information preservation and the speckle restraint. | CHANG Xia JIAO Licheng LIU Fang SHA Yuheng | 2015 | Chinese Journal of Electronics2015，24，1： | 2 |
| 19 | Image fusion based on a new contourlet packet显示文摘 | Shuyuan Yang Min Wang Licheng Jiao Ruixia Wu Zhaoxia Wang | 2009 | Information Fusion2009，，2： | 2 |
| 20 | Community detection in networks by using multiobjective evolutionary algorithm with decomposition显示文摘 | Maoguo Gong Lijia Ma Qingfu Zhang Licheng Jiao | 2012 | Physica A: Statistical Mechanics and its Applications2012，，15： | 2 |
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