A measurement fusion method for nonlinear system identification using a cooperative learning algorithm

Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.     

Urban expansion modeling using an enhanced decision tree algorithm

Decision tree (DT) algorithms have been applied for classification and change detection in various geospatial studies and more recently, for urban expansion and land use/land cover (LULC) change modeling. However, these studies have not elaborated on specification of DT algorithms regarding data sampling, predictor variables, model configuration, and model evaluation. The focus of this study is to explore several balanced and unbalanced sampling methods, various predictor variables, different configurations of stopping rules, and reliable evaluation metrics to enhance the performance of classification and regression tree (CART), one of the most efficacious DT algorithms, for urban expansion modeling. The implementation of the model in the Triangle Region, North Carolina (NC) State, over the period of 2001 to 2011 demonstrates a striking performance with the training accuracy of 97%, the testing accuracy of 94%, and the Kappa value of 0.80. This performance was achieved using a training dataset containing all changed land cells and three times of that randomly selected from unchanged land cells and regulating the minimum number of records in a leaf node equal to 1, the minimum number of records in a parent node equal to 2, and the value of 10,000 for the maximum number of splits. The CART DT algorithm indicates that proximity to built areas, proximity to highways, current LULC type, elevation, and distance to water bodies are the most significant predictor variables for the urban expansion prediction in the study area.

     

A successive approximation algorithm for an undiscounted Markov decision process

In this paper we consider a completely ergodic Markov decision process with finite state and decision spaces using the average return per unit time criterion. An algorithm is derived which approximates the optimal solution. It will be shown that this algorithm is finite and supplies upper and lower bounds for the maximal average return and a nearly optimal policy with average return between these bounds.     

A decision tree using ID3 algorithm for English semantic analysis

Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results.     

A query-by-example music retrieval system using feature and decision fusion

Multimedia Tools and Applications - An attractive topic of Music Information Retrieval (MIR) is focused on query-by-example (QBE), which receives a user-provided query and aims to find the target...     

Multi sensor image fusion for surveillance applications using hybrid image fusion algorithm

Multimedia Tools and Applications - Image fusion is the process of integrating several source images into a single image that provides more reliable information along with reduced redundancy. In...     

基于序贯检测的无线传感器网络决策融合算法

数据融合是能源有限的无线传感器网络应用中必不可少的信息处理手段.目前的数据融合方法多着眼于固定参与节点数目下的数据融合,由于融合时数据传输量与参与节点数目呈正比,所以,最佳的节能策略应是满足一定性能条件下参与节点数目可变.将序贯检测理论应用于无线传感器网络数据融合问题上,并建立其数学模型.考虑无线信道的衰减和物理层调制解调特性,进一步提出基于序贯检测的无线传感器网络数据融合判决(WDASD)方法,并通过仿真实验验证了WDASD算法的性能.     

A hybrid decision tree training method using data streams

Classical classification methods usually assume that pattern recognition models do not depend on the timing of the data. However, this assumption is not valid in cases where new data frequently become available. Such situations are common in practice, for example, spam filtering or fraud detection, where dependencies between feature values and class numbers are continually changing. Unfortunately, most classical machine learning methods (such as decision trees) do not take into consideration the possibility of the model changing, as a result of so-called concept drift and they cannot adapt to a new classification model. This paper focuses on the problem of concept drift, which is a very important issue, especially in data mining methods that use complex structures (such as decision trees) for making decisions. We propose an algorithm that is able to co-train decision trees using a modified NGE (Nested Generalized Exemplar) algorithm. The potential for adaptation of the proposed algorithm and the quality thereof are evaluated through computer experiments, carried out on benchmark datasets from the UCI Machine Learning Repository.     

A trend based investment decision approach using clustering and heuristic algorithm

This paper proposes a stock price trend clustering and trend investment decision model by using a genetic algorithm to search for optimal solutions and the best investment strategies for different stock price trends.The new price trend clustering model identifies three types of stock price movements:uptrends,sideways trends,and downtrends.Unfortunately,trends discovered through stock price movements or technical indicator graphs are typically subjective and unquantifiable.This paper takes daily stock prices and trading volume data from the China Shanghai Stock Exchange Composite Index(SSECI)from January 2,1997 to August31,2012,to examine the performance of the proposed trend clustering model.The proposed model is also compared to other popular stock market investment strategies to verify its validity.Research result shows that the proposed trend clustering model correctly identifies three different trends in the stock market.Furthermore,the trend investment strategy model developed by using genetic algorithm methodology performs better than other investment strategies,namely,Granville’s rule,the KD indicator strategy,the buys and holds strategy,and GMA rules,in both bull and bear market periods.Research results prove the proposed new model to be a stable and valid investment strategy.     

A PSO-based intelligent decision algorithm for VLSI floorplanning

Floorplanning is an important issue in the very large-scale integrated (VLSI) circuit design automation as it determines the performance, size, yield and reliability of VLSI chips. This paper proposes a novel intelligent decision algorithm based on the particle swarm optimization (PSO) technique to obtain a feasible floorplanning in VLSI circuit physical placement. The PSO was applied with integer coding based on module number and a new recommended value of acceleration coefficients for optimal placement solution. Inspired by the physics of genetic algorithm (GA), the principles of mutation and crossover operator in GA are incorporated into the proposed PSO algorithm to make this algorithm to break away from local optima and achieve a better diversity. Experiments employing MCNC and GSRC benchmarks show that the proposed algorithm is effective. The proposed algorithm can avoid local minimum and performs well in convergence. The experimental results of the proposed method in this paper can also greatly help floorplanning decision making in VLSI circuit design automation.     

一种基于静态小波变换的图像融合算法

给出了一种基于静态小波多尺度边缘检测的图像融合算法,是一种适用于微光和红外图像融合的多分辨率方法。该方法选择á trous多孔算法为基础,并选取不同尺度参数的LOG算子作边缘检测,利用了图像的多尺度边缘信息确定图像的边缘位置进行融合。在小波域中,对高频信息依据边缘检测的边缘点进行融合,对低频信息利用取加权法进行融合,再进行小波逆变换重构融合图像。通过红外和微光图像的融合实验结果表明,该方法能有效地突出边缘细节,提高图像分辨效果和人眼对场景目标的发现和识别概率。     

基于融合的显微图像Gabor边缘提取算法

不同参数Gabor滤波器都具有各自的频率选择和方向选择特性,图像中纹理基元可以利用多个方向和中心频率Gabor滤波器组提取出来的频谱值来表示.据此提出一种适应于显微图像的Gabor滤波边缘检测算法.使用特定不同方向Gabor滤波边缘提取算子对图像进行边缘信息提取,获得不同方向上边缘特征信息,对其进行分析和融合提取图像边缘.运用该方法时所获不同方向的图像边缘进行自适应融合,获得的图像边缘较理想,模糊的边缘得到增强,并有效地消除了噪声.实验结果表明,该算法对显微图像处理有效,检测到的边缘清晰.     

A new fast motion estimation algorithm using fast mode decision for high-efficiency video coding standard

High-efficiency video coding is the latest standardization effort of the International Organization for Standardization and the International Telecommunication Union. This new standard adopts an exhaustive algorithm of decision based on a recursive quad-tree structured coding unit, prediction unit, and transform unit. Consequently, an important coding efficiency may be achieved. However, a significant computational complexity is resulted. To speed up the encoding process, efficient algorithms based on fast mode decision and optimized motion estimation were adopted in this paper. The aim was to reduce the complexity of the motion estimation algorithm by modifying its search pattern. Then, it was combined with a new fast mode decision algorithm to further improve the coding efficiency. Experimental results show a significant speedup in terms of encoding time and bit-rate saving with tolerable quality degradation. In fact, the proposed algorithm permits a main reduction that can reach up to 75 % in encoding time. This improvement is accompanied with an average PSNR loss of 0.12 dB and a decrease by 0.5 % in terms of bit-rate.     

A perception correlated comparison method for dynamic meshes

There are multiple areas of computer graphics where triangular meshes are being altered in order to reduce their size or complexity, while attempting to preserve the original shape of the mesh as closely as possible. Recently, this area of research has been extended to cover even a dynamic case, i.e., surface animations which are compressed and simplified. However, to date very little effort has been made to develop methods for evaluating the results, namely the amount of distortion introduced by the processing. Even the most sophisticated compression methods use distortion evaluation by some kind of mean squared error while the actual relevance of such measure has not been verified so far. In this paper, we point out some serious drawbacks of the existing error measures. We present results of the subjective testing that we have performed, and we derive a new measure called Spatiotemporal edge difference (STED) which is shown to provide much better correlation with subjective opinions on mesh distortion.     

A fast algorithm for the alpha-connected two-center decision problem

This paper considers the problem of determining whether a set of points can be covered by two discs with centers p and q and common radius r, such that the ratio d(p,q)/r is bounded below by a specified constant, α. An O(n²log²n) algorithm for solving this problem is also presented.     

An interactive algorithm for multicriteria decision making: theattainable reference point method

An interactive algorithm, the attainable reference point method, is proposed for finding a satisfactory solution to a general multicriteria decision making problem. The decision-maker is only required to modify the reference value of the satisfactory objectives to generate a new attainable reference point in each iteration step. The lexicographic weighted Tchebycheff program associated with the attainable reference point is constructed to guarantee the efficiency of all the discussed points. The value of the unsatisfactory objective chosen by the decision-maker is improved to be satisfactory. Thus its reference value does not need to be modified again in later iterations, and a satisfactory solution can be derived in finite steps. A numerical example is presented to demonstrate the feasibility and efficiency of the proposed method     

A method for obtaining randomized algorithms with small tail probabilities

We study strategies for converting randomized algorithms of the Las Vegas type into randomized algorithms with small tail probabilities.Supported by ESPRIT U Basic Research Actions Program of the EC under Contract No. 3075 (project ALCOM).Supported by ESPRIT II Basic Research Actions Program of the EC under Contract No. 3075 (Project ALCOM).Research supported by NSF Grant No. CCR-9005448.Partially supported by a Wolfson Research Award administered by the Israel Academy of Sciences and Humanities.     

A classification method using a hybrid genetic algorithm combined with an adaptive procedure for the pool of ellipsoids

This paper presents a hybrid classification method that utilizes genetic algorithms (GAs) and adaptive operations of ellipsoidal regions for multidimensional pattern classification problems with continuous features. The classification method fits a finite number of the ellipsoidal regions to data pattern by using hybrid GAs, the combination of local improvement procedures and GAs. The local improvement method adaptively expands, rotates, shrinks, and/or moves the ellipsoids while each ellipsoid is separately handled with a fitness value assigned during the GA operations. A set of significant features for the ellipsoids are automatically determined in the hybrid GA procedure by introducing “don’t care” bits to encode the chromosomes. The performance of the method is evaluated on well-known data sets and a real field classification problem originated from a deflection yoke production line. The evaluation results show that the proposed method can exert superior performance to other classification methods such as k nearest neighbor, decision trees, or neural networks. Ki K. Lee received the B.S. degree from Han Yang University, Seoul, Korea in 1994, and the M.S. and Ph.D. degrees in industrial engineering from Korea Advanced Institute Science and Technology (KAIST), Daejeon, Korea in 1996 and 2005, respectively. From 2001 to 2004, he was a senior research engineer in telecommunication systems laboratory of LG Electronics Inc. Since 2005, he has been an assistant professor in the School of Management at Inje University, Kimhae, Korea. His research interests include intelligent decision support systems, soft computing, and pattern recognition. Wan C. Yoon received the B.S. degree from Seoul National University, Korea in 1977, the M.S. degree from KAIST, Korea in 1979, and the Ph.D. degree in industrial and systems engineering from Georgia Institute of Technology in 1987. He is professor of the Department of Industrial Engineering at KAIST, Korea. His research interests include application of artificial intelligence, human decision-making and aiding, information systems, and joint intelligent systems. Dong H. Baek received the B.S. degree from Han Yang University, Seoul, Korea in 1992, and the M.S. and Ph.D. degrees in industrial engineering from Korea Advanced Institute Science and Technology (KAIST), Daejeon, Korea in 1994 and 1999, respectively. He is an assistant professor in management information systems at department of business administration, Hanyang University, Korea. His research interests include management information systems, system engineering, and machine learning.     

Random forest-based scheme using feature and decision levels information for multi-focus image fusion

Often captured images are not focussed everywhere. Many applications of pattern recognition and computer vision require all parts of the image to be well-focussed. The all-in-focus image obtained, through the improved image fusion scheme, is useful for downstream tasks of image processing such as image enhancement, image segmentation, and edge detection. Mostly, fusion techniques have used feature-level information extracted from spatial or transform domain. In contrast, we have proposed a random forest (RF)-based novel scheme that has incorporated feature and decision levels information. In the proposed scheme, useful features are extracted from both spatial and transform domains. These features are used to train randomly generated trees of RF algorithm. The predicted information of trees is aggregated to construct more accurate decision map for fusion. Our proposed scheme has yielded better-fused image than the fused image produced by principal component analysis and Wavelet transform-based previous approaches that use simple feature-level information. Moreover, our approach has generated better-fused images than Support Vector Machine and Probabilistic Neural Network-based individual Machine Learning approaches. The performance of proposed scheme is evaluated using various qualitative and quantitative measures. The proposed scheme has reported 98.83, 97.29, 98.97, 97.78, and 98.14 % accuracy for standard images of Elaine, Barbara, Boat, Lena, and Cameraman, respectively. Further, this scheme has yielded 97.94, 98.84, 97.55, and 98.09 % accuracy for the real blurred images of Calendar, Leaf, Tree, and Lab, respectively.