A boosting approach for supervised Mahalanobis distance metric learning

Determining a proper distance metric is often a crucial step for machine learning. In this paper, a boosting algorithm is proposed to learn a Mahalanobis distance metric. Similar to most boosting algorithms, the proposed algorithm improves a loss function iteratively. In particular, the loss function is defined in terms of hypothesis margins, and a metric matrix base-learner specific to the boosting framework is also proposed. Experimental results show that the proposed approach can yield effective Mahalanobis distance metrics for a variety of data sets, and demonstrate the feasibility of the proposed approach.     

A self-organization mining based hybrid evolution learning for?TSK-type fuzzy model design

In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method.     

Reduction of cochannel interference in WCDMA cellular systems

In this paper, a novel cochannel interference reduction technique is proposed for wideband code division multiple access (WCDMA) cellular systems. Cochannel interference for the proposed cellular architecture is considered and analyzed. An analytic expression for the proposed method is derived. Simulation results demonstrate that the proposed method provides better signal-to-noise (S/N) ratio than the existing cochannel interference reduction methods. A significant reduction of cochannel interference is achieved compared to sectoring and omnidirectional architectures in the proposed microzoning architecture. In particular, it is shown here that the proposed architecture exhibits a larger number of users per cell while maintaining an adequate S/N ratio in comparison with other architectures.     

A universal modification of the linear coupling method

In the late sixties, N. Shor and B. Polyak independently proposed optimal first-order methods for solving non-smooth convex optimization problems. In 1982 A. Nemirovski proposed optimal first-order methods for solving smooth convex optimization problems, which utilized auxiliary line search. In 1985 A. Nemirovski and Yu. Nesterov proposed a parametric family of optimal first-order methods for solving convex optimization problems with intermediate smoothness. In 2013 Yu. Nesterov proposed a universal gradient method which combined all good properties of the previous methods, except the possibility of using auxiliary line search. One can typically observe that in practice auxiliary line search improves performance for many tasks. In this paper, we propose the apparently first such method of non-smooth convex optimization allowing the use of the line search procedure. Moreover, it is based on the universal gradient method, which does not require any a priori information about the actual degree of smoothness of the problem. Numerical experiments demonstrate that the proposed method is, in some cases, considerably faster than Nesterov's universal gradient method.     

A novel algebraic method for kernel-based object tracking

In the present paper, a new tracking method based on kernel tracking is proposed. The proposed method employs a novel algebraic algorithm to get the kernel movement. In contrast to the mean-shift method which uses a weighted kernel to reduce the effect of the background, the algebraic algorithm of the proposed method allows dividing the candidate area into two parts in order to identify the object and background regions. To detect the object and background regions, we propose measuring the similarity of weighted histogram for each part. The experiments show the superiority of the proposed method for the removal of the background. The effect of noise and background clutter is reduced by segmentation of the object which produces the narrow histogram. In conclusion, the ability of the proposed method for tracking in crowded and cluttered scenes is demonstrated.     

Sequential approximation optimization assisted particle swarm optimization for expensive problems

In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses the optima obtained by sequential approximation optimization in local regions to replace the personal historical best particles and then runs the basic particle swarm optimization procedures. Compared with particle swarm optimization, the proposed algorithm is more efficient because the optima provided by sequential approximation optimization can direct swarm particles to search in a more accurate way. In addition, a space partition strategy is proposed to constraint sequential approximation optimization in local regions. This strategy can enhance the swarm diversity and prevent the preconvergence of the proposed algorithm. In order to validate the proposed algorithm, a lot of numerical benchmark problems are tested. An overall comparison between the proposed algorithm and several other optimization algorithms has been made. Finally, the proposed algorithm is applied to an optimal design of bearings in an all-direction propeller. The results show that the proposed algorithm is efficient and promising for optimization of the expensive problems.     

Genetic algorithm for the one-commodity pickup-and-delivery traveling salesman problem

In this paper, we proposed a genetic algorithm for the one-commodity pickup-and-delivery traveling salesman problem. In the proposed algorithm, we designed a new tour constructing heuristic to generate the initial population, and proposed a novel pheromone-based crossover operator that utilizes both local and global information to construct offspring. In addition, a local search procedure was embedded into the genetic algorithm to accelerate convergence. The proposed genetic algorithm was tested on benchmark instances with up to 500 customers, and the computational results show that it gives a faster and better convergence than existing heuristics.     

A filter based neuron model for adaptive incremental learning of self-organizing maps

In this work a learning algorithm is proposed for the formation of topology preserving maps. In the proposed algorithm the weights are updated incrementally using a higher-order difference equation, which implements a low-pass digital filter. It is shown that by suitably choosing the filter the learning process can adaptively follow a specific dynamic. Numerical results, for time-varying and static distributions, show the potential of the proposed method for unsupervised learning.     

Novel recommendation system based on long-term composition for adaptive web services

In the era of digital web services, composition of features on the fly is inevitable. The Long-term Composed Service (LCS) entertains the composition of features to any extent, since it has an open-ended lifetime. In the proposed research work, we have intended to provide service support to run the business toward a long time commitment. Structure-based recommended system for LCSs (RS-LCSs) is proposed, where user queries and recent updation/requirements are considered for exhibiting the response through the system. In the proposed system, business has been regulated according to the time constraints. We have tested our proposed system on the standard benchmark dataset and quantitative metrics show our proposed method has performed well against the compared methods. The forecasting of business has been done through our model to address the recent queries and new requirements issues to provide an adaptive web service for the business development.     

Fast exponentiation based on common-multiplicand-multiplication and minimal-signed-digit techniques

In this paper, an improved common-multiplicand-multiplication algorithm is proposed, utilizing the binary exponentiation method and minimal-signed-digit recoding technique for fast exponentiation. By adopting the recoding technique on the common signed-digit representations in the grouped exponent substrings, the proposed algorithm provides an efficient exponentiation method. The proposed algorithm achieves better performance in modern exponentiation methods by decreasing the overall computational complexity. In particular, the proposed method is very suitable for parallel processing systems to improve the efficiency of exponentiation operation.     

A new approach for solving fuzzy transportation problems using generalized trapezoidal fuzzy numbers

In the literature, several algorithms are proposed for solving the transportation problems in fuzzy environment but in all the proposed algorithms the parameters are represented by normal fuzzy numbers. Chen [Operations on fuzzy numbers with function principal, Tamkang Journal of Management Science 6 (1985) 13-25] pointed out that in many cases it is not to possible to restrict the membership function to the normal form and proposed the concept of generalized fuzzy numbers. There are several papers in the literature in which generalized fuzzy numbers are used for solving real life problems but to the best of our knowledge, till now no one has used generalized fuzzy numbers for solving the transportation problems. In this paper, a new algorithm is proposed for solving a special type of fuzzy transportation problems by assuming that a decision maker is uncertain about the precise values of transportation cost only but there is no uncertainty about the supply and demand of the product. In the proposed algorithm transportation costs are represented by generalized trapezoidal fuzzy numbers. To illustrate the proposed algorithm a numerical example is solved and the obtained results are compared with the results of existing approaches. Since the proposed approach is a direct extension of classical approach so the proposed approach is very easy to understand and to apply on real life transportation problems for the decision makers.     

A GPU parallel computing method for LPUSS

We discuss the effective implementation of parallel processing for linear prediction-based uniform state sampling (LPUSS). In previous work, we proposed LPUSS as an optimization algorithm for mechanical motions that assures high optimality of the solutions and computational efficiency. In parallel computation, LPUSS requires balanced memory allocation and managed processing timing. In this paper, we propose an effective parallel computing method that assures high optimality and calculation efficiency in parallel processing using GPU processor. We conducted two experiments to validate the proposed method. In the first experiment, we compared single-thread processing for LPUSS and the proposed parallel processing. As a result of this experiment, calculation speed of LPUSS was about 4–20 times faster than that with single-thread CPU. In the second experiment, we applied the proposed method to the optimization of sixtuple inverted pendulum. As a result, the proposed method optimized the motion within 40 minutes. According to our survey, there is no other optimization method that is applicable to higher than quadruple inverted pendulum models with standard constraints.     

A global Particle Swarm-Based-Simulated Annealing Optimization technique for under-voltage load shedding problem

In this paper, a new approach based on hybrid Particle Swarm-Based-Simulated Annealing Optimization technique (PSO-B-SA) is proposed for solving under-voltage load shedding (UVLS) problem. Under-voltage load shedding (UVLS) is one of the most important tools for avoiding voltage instability. In this paper, the UVLS problem is formulated based on the concept of the static voltage stability margin and its sensitivity at the maximum loading point or the collapse point. The voltage stability criterion is modeled directly into the load-shedding scheme. In any UVLS scheme finding the global point is very important for having cost effective economy. The proposed PSO-B-SA methodology is implemented in the under-voltage load shedding scheme for IEEE 14 and 118 bus test systems. In addition to having better solution, the global property of the proposed approach plays an important role in on-line applications. Simulation results show the efficacy and advantage of the proposed scheme.     

QFT prefilter design for multivariable systems using interval constraint satisfaction technique

In this paper, a computationally efficient method is proposed for automated design of the prefilters for multivariable systems. In quantitative feedback theory （QFT） method, proposed by Horowitz, the prefilter is designed to achieve the desired tracking specifications. In the proposed approach, we pose the prefilter design problem as an interval constraint satisfaction problem and solve it using the well-established interval constraint satisfaction techniques. The proposed method finds optimal values of the parameters of fixed structure prefilter within the initial search domain. An approach based on prefilter synthesis for single-input single-output is already developed. The purpose of this paper is to extend this approach to QFT prefilter design for general multivariable systems. To validate the above design approach, we applied the method to a laboratory setup of magnetic levitation system.     

On the Design of Event‐Triggered Suboptimal Controllers for Nonlinear Systems

In this paper, two suboptimal event‐triggered control techniques are proposed for both the regulation and the tracking problems in a broad class of nonlinear networked control systems. The proposed techniques are based on the state‐dependent Riccati equation (SDRE) methodology. In the case of the regulation problem, the asymptotic stability of the origin of the closed‐loop system under the proposed event‐triggered control law is investigated. In addition, for the tracking problem, it is proved that the tracking error between the system output and its desired trajectory converges asymptotically to zero under some mild conditions. It is shown that the proposed methods can considerably reduce the information exchange between the controller and the actuator. Due to the implementation procedures of the proposed techniques, no Zeno behavior is occurred. Three numerical simulations are provided to demonstrate the design procedure and the flexibility of the proposed event‐triggered control techniques.     

基于Boosting RBF神经网络的人体行为识别

提出一种基于Boosting RBF神经网络的人体行为识别方法,该方法利用规范化的运动历史图像（MHI）进行图像序列表示,从中提取Zernike矩的统计描述特征,然后提出Adaboost算法自适应地选择图像序列的特征作为RBF神经网络的输入,为了进一步提高神经网络的泛化能力,采用一种调整权值分布,限制权重扩张的改进的Boosting方法,分类器以加权投票方式进行分类决策。实验结果表明,提出的方法能够有效地识别人体运动类别。     

Spatial error concealment: A novel exemplar-based approach using segmentation

In this paper, the problem of spatial error concealment for real-time applications is addressed. The proposed method can be categorized in exemplar-based error concealment approaches. In this category, a patch of corrupted pixels are replaced by another patch of the image that contains correct pixels. For splitting the erroneous block to different patches, a novel context-dependent exemplar-based algorithm based on a previously proposed segmentation method is proposed. The capability of the proposed method for concealment in diverse image regions is depicted. Our detailed conducted experiments show that the proposed method outperforms the state-of-the-art spatial error concealment methods in terms of output quality.     

Towards functional verifying a family of SystemC TLMs

It is often the case that in the development of a system-on-a-chip(SoC)design,a family of SystemC transaction level models(TLM)is created.TLMs in the same family often share common functionalities but differ in their timing,implementation,configuration and performance in various SoC developing phases.In most cases,all the TLMs in a family must be verified for the follow-up design activities.In our previous work,we proposed to call such family TLM product line(TPL),and proposed feature-oriented(FO)design methodology for efficient TPL development.However,developers can only verify TLM in a family one by one,which causes large portion of duplicated verification overhead.Therefore,in our proposed methodology,functional verification of TPL has become a bottleneck.In this paper,we proposed a novel TPL verification method for FO designs.In our method,for the given property,we can exponentially reduce the number of TLMs to be verified by identifying mutefeature-modules(MFM),which will avoid duplicated veri-fication.The proposed method is presented in informal and formal way,and the correctness of it is proved.The theoretical analysis and experimental results on a real design show the correctness and efficiency of the proposed method.     

Combined discrete event and discrete time simulation framework and its application to the block erection process in shipbuilding

In this study, a simulation framework which can support to develop various simulation systems for process planning in shipbuilding is proposed. In addition, a simulation kernel which is a key component of the proposed simulation framework is implemented according to the concept of the combined discrete event and discrete time simulation. To evaluate the efficiency and applicability of the simulation framework proposed in this study, it is applied to the block erection process in shipbuilding. The result shows that the proposed simulation framework can provide the consistent, integrated development environment for a simulation system.     

基于改进的基因表达式编程算法在作业车间调度问题中的应用

文章提出一种新颖的方法一改进的基因表达式编程算法来求解作业车间调度问题。作业车间调度问题是许多实际生产调度问题的简化模型,基因表达式编程算法结合了遗传算法和遗传编程的优点,具有更强的解决问题能力,对基因表达式编程算法进行改进使其在作业车间调度问题的应用上更加有效;最后应用一个实例来验证提出方法的有效性。