Optimization techniques applied to multiple manipulators for path planning and torque minimization

This paper presents the formulation and application of a strategy for the determination of an optimal trajectory for a multiple robotic configuration. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used as the optimization techniques and results obtained from them compared. First, the motivation for multiple robot control and the current state-of-art in the field of cooperating robots are briefly given. This is followed by a discussion of energy minimization techniques in the context of robotics, and finally, the principles of using genetic algorithms and simulated annealing as an optimization tool are included. The initial and final positions of the end effector are specified. Two cases, one of a single manipulator, and the other of two cooperating manipulators carrying a common payload illustrate the proposed approach. The GA and SA techniques identify the optimal trajectory based on minimum joint torque requirements. The simulations performed for both the cases show that although both the methods converge to the global minimum, the SA converges to solution faster than the GA.     

Dynamic scheduling techniques for heterogeneous computing systems

There has been a recent increase of interest in heterogeneous computing systems, due partly to the fact that a single parallel architecture may not be adequate for exploiting all of a program's available parallelism. In some cases, heterogeneous systems have been shown to produce higher performance for lower cost than a single large machine. However, there has been only limited work on developing techniques and frameworks for partitioning and scheduling applications across the components of a heterogeneous system. In this paper we propose a general model for describing and evaluating heterogeneous systems that considers the degree of uniformity in the processing elements and the communication channels as a measure of the heterogeneity in the system. We also propose a class of dynamic scheduling algorithms for a heterogeneous computing system interconnected with an arbitrary communication network. These algorithms execute a novel optimization technique to dynamically compute schedules based on the potentially non-uniform computation and communication costs on the processors of a heterogeneous system. A unique aspect of these algorithms is that they easily adapt to different task granularities, to dynamically varying processor and system loads, and to systems with varying degrees of heterogeneity. Our simulations are designed to facilitate the evaluation of different scheduling algorithms under varying degrees of heterogeneity. The results show improved performance for our algorithms compared to the performance resulting from existing scheduling techniques.     

Soft computing techniques for web services brokering

To enhance and improve the interoperability of meteorological Web Services, we are currently developing an Integrated Web Services Brokering System (IWB). IWB uses a case-based classifier to automatically discover Web Services. In this paper, we explore the use of rough set techniques for selecting features prior to classification. We demonstrate the effectiveness of this feature technique by comparing it with a leading non-rough set (Information Gain) feature selection technique.     

Performance-based path determination for interprocessorcommunication in distributed computing systems

The different types of messages used by a parallel application program executing in a distributed computing system can each have unique characteristics so that no single communication network can produce the lowest latency for all messages. For instance, short control messages may be sent with the lowest overhead on one type of network, such as Ethernet, while bulk data transfers may be better suited to a different type of network, such as Fibre Channel or HIPPI. This work investigates how to exploit multiple heterogeneous communication networks that interconnect the same set of processing nodes using a set of techniques we call performance-based path determination (PBPD). The performance-based path selection (PBPS) technique selects the best (lowest latency) network among several for each individual message to reduce the communication overhead of parallel programs. The performance-based path aggregation (PBPA) technique, on the other hand, aggregates multiple networks into a single virtual network to increase the available bandwidth. We test the PBPD techniques on a cluster of SGI multiprocessors interconnected with Ethernet, Fibre Channel, and HiPPI networks using a custom communication library built on top of the TCP/IP protocol layers. We find that PBPS can reduce communication overhead in applications compared to using either network alone, while aggregating networks into a single virtual network can reduce communication latency for bandwidth-limited applications. The performance of the PBPD techniques depends on the mix of message sizes in the application program and the relative overheads of the networks, as demonstrated in our analytical models     

一种单计算参数的自学习路径规划算法

针对当前机器人路径规划算法存在计算参数多的问题,提出一种单计算参的自学习蚁群算法。该算法使用一种改进的栅格法完成环境建模,种群中个体使用8-geometry行进规则,整个种群的寻优过程使用了自学习和多目标搜索策略。其特点在于整个算法只需进行一个计算参数设置。蚂蚁个体可使用1、■、2、■、■步长行进,一次搜索可以发现多条可行路径,提高了算法计算效率。仿真实验表明,在复杂的工作空间,该算法可以迅速规划出一条安全避碰的最优路径,效率优于已存在算法。     

Advances in soft computing techniques for visual information-based systems

Multimedia Tools and Applications -     

Clustering techniques for dynamic location management in mobile computing

This paper presents a novel approach based on clustering algorithms in combination with the location area (LA) scheme to solve the mobility management problem. Users’ movement history is used by the network to predict future paging decisions. This approach integrates the LAs scheme and efficient clustering algorithms to find a network topology which can lead to massive savings in the number of signals made to locate users in the network. The approach is tested with several networks to show its advantages to the current GSM standards. The results provide new insights into the mobility management problem.     

Soft computing techniques and applications for intelligent multimedia systems

Multimedia Tools and Applications -     

A path sampling identity for computing the Kullback-Leibler and J divergences

Estimating normalizing constants is a common and often difficult problem in statistics, and path sampling (PS) is among the most powerful methods that have been put forward to this end. Using an identity that arises in the formulation of PS, we derive expressions for the Kullback-Leibler (KL) and J divergences between two distributions from possibly different parametric families. These expressions naturally stem from PS when the geometric path is used to link the two extreme densities. We examine the use of the KL and J divergence measures in PS in a variety of model selection examples. In this context, one challenging aspect of PS is that of selecting an appropriate auxiliary density that will yield a high quality estimate of the marginal likelihood without incurring excessive computational effort. The J divergence is shown to be helpful for choosing auxiliary densities that minimize the error of the PS estimators. These results increase appreciably the usefulness of PS.     

云计算及其关键技术

云计算是一种新兴的计算模型,它是在网格计算的基础上发展而来的。介绍了云计算的发展历史和应用场景,比较了现有的云计算的定义并给出了新的定义,以谷歌的云计算技术为例,总结了云计算的关键技术：数据存储技术（Google File System）、数据管理技术（BigTable）、编程模型和任务调度模型（Map-Reduce）等,分析了云计算和网格计算以及传统超级计算的区别,并指出了云计算的广阔发展前景。     

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.     

Response to “Clarification on the short communication “On computing the shortest path in a multiply-connected domain having curved boundaries” ”

In this letter, we briefly comment upon the response of the authors of Bharath Ram and Ramanathan (2013) to our earlier technical note Wei and Joneja (2013), which was itself addressing the work reported originally in Bharath Ram and Ramanathan (2013).     

Optimization of rigid-plastic shallow curved beams

The minimum weight problem of a shallow circular beam is studied in the case when the beam has a piece-wise constant thickness. The minimum of the weight is sought under the condition that the deflections of the beam of piece-wise constant thickness do not exceed those of the reference beam of constant thickness for given values of the external loading. The beam is subjected to uniformly distributed transverse pressure and to axial dead load. The material of the beam is assumed to be ideally rigid-plastic. Moderately large deflections are taken into account. Necessary optimality conditions are derived and used in order to establish the optimal values of the design parameters.     

Task allocation techniques for distributed computing systems: a review

An efficient strategy to allocate tasks to processors in distributed computer systems is required for proper utilization of available computational power. Various task allocation models developed so far have the basic assumption that an application program is suitably divided into a number of subtasks or modules and the execution costs of modules and intermodule communication costs are also known. The subdivision of a given program into modules is assumed to be carried out by a software mechanism which forms a separate discipline of research work.The task allocation models developed earlier did not consider various constraints like load balancing, precedence relations, timing constraints, etc. Most of the recent works in the formulation of task allocation strategies take into account a number of constraints. This paper presents a brief outline of the various available models and algorithms for task allocation with suggestions for possible future directions in the field.     

Multilevel threshold selection for image segmentation using soft computing techniques

Multilevel thresholding is the method applied to segment the given image into unique sub-regions when the gray value distribution of the pixels is not distinct. The segmentation results are affected by factors such as number of threshold and threshold values. Hence, this paper proposes different methods for determining optimal thresholds using optimization techniques namely GA, PSO and hybrid model. Parallel algorithms are also proposed and implemented for these methods to reduce the execution time. From the experimental results, it is inferred that proposed methods take less time for determining the optimal thresholds when compared with existing methods such as Otsu and Kapur methods.     

Cobra: Parallel path following for computing the matrix pseudospectrum

The construction of an accurate approximation of the ε-pseudospectrum of a matrix by means of the standard grid method is a very demanding computational task. In this paper, we describe Cobra, a domain-based method for the computation of pseudospectra that combines predictor corrector path following with a one-dimensional grid. The algorithm offers large and medium grain parallelism and becomes particularly attractive when we seek fine resolution of the pseudospectrum boundary. We implement Cobra using standard LAPACK components and show that it is more robust than the existing path following technique and faster than it and the traditional grid method. Cobra is also combined with a partial SVD algorithm to produce an effective parallel method for computing the matrix pseudospectrum.     

Obstacle-avoiding shortest path derivation in a multicore computing environment

The best obstacle avoiding path in continuous space, referred to as the Euclidean shortest path, is important for spatial analysis, location modeling and wayfinding tasks. This problem has received much attention in the literature given its practical application, and several solution techniques have been proposed. However, existing approaches are limited in their ability to support real time analysis in big data environments. In this research a multicore computing approach is developed that exploits spatial knowledge through the use of geographic information system functionality to efficiently construct an optimal shortest path. The approach utilizes the notion of a convex hull for iteratively evaluating obstacles and constructing pathways. Further, the approach is capable of incrementally improving bounds, made possible through parallel processing. Wayfinding routes that avoid buildings and other obstacles to travel are derived and discussed.     

Optimization of the weld path for overlay coatings

Weld overlay coating is a technique to deposit various alloys onto metallic structures. It may be used, for example, to increase erosion and corrosion resistance. Distortions in the welded structure are likely to develop as a result of the welding. In this paper, a simple approach to find the weld path that minimizes the residual distortions of a structure is developed. Analytical approximations are used for both the thermal and distortional fields. The optimization task of finding the optimal weld path resembles the classic traveling salesperson problem, for which the Method of simulated annealing has proven fairly successful. A variant of this method is implemented for the present optimization. Some preliminary results are presented.     

Opposition versus randomness in soft computing techniques

For many soft computing methods, we need to generate random numbers to use either as initial estimates or during the learning and search process. Recently, results for evolutionary algorithms, reinforcement learning and neural networks have been reported which indicate that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness. This new scheme, called opposition-based learning, has the apparent effect of accelerating soft computing algorithms. This paper mathematically and also experimentally proves this advantage and, as an application, applies that to accelerate differential evolution (DE). By taking advantage of random numbers and their opposites, the optimization, search or learning process in many soft computing techniques can be accelerated when there is no a priori knowledge about the solution. The mathematical proofs and the results of conducted experiments confirm each other.     

Soft computing techniques in ensemble precipitation nowcast

This study presented various soft computing techniques for forecasting the hourly precipitations during tropical cyclones. The purpose of the current study is to present a concise and synthesized documentation of the current level of skill of various models at precipitation forecasts. The techniques involve artificial neural networks (ANN) comprising the multilayer perceptron (MLP) with five training methods (denoted as ANN-1, ANN-2, ANN-3, ANN-4, and ANN-5), and decision trees including classification and regression tree (CART), Chi-squared automatic interaction detector (CHAID), and exhaustive CHAID (E-CHAID). The developed models were applied to the Shihmen Reservoir Watershed in Taiwan. The traditional statistical models including multiple linear regressions (MLR), and climatology average model (CLIM) were selected as the benchmarks and compared with these machine learning. A total of 157 typhoons affecting the watershed were collected. The measures used include numerical statistics and categorical statistics. The RMSE criterion was employed to assess the suitable scenario, while the categorical scores, bias, POD, FAR, HK, and ETS were based on the rain contingency table. Consequently, this study found that ANN and decision trees provide better prediction compared to traditional statistical models according to the various average skill scores.