Analysis of Critical Factors in Manufacturing by Adopting a Cloud Computing Service

The advantages of a cloud computing service are cost advantages, availability, scalability, flexibility, reduced time to market, and dynamic access to computing resources. Enterprises can improve the successful adoption rate of cloud computing services if they understand the critical factors. To find critical factors, this study first reviewed the literature and established a three-layer hierarchical factor table for adopting a cloud computing service based on the Technology-Organization-Environment framework. Then, a hybrid method that combines two multi-criteria decision-making tools—called the Fuzzy Analytic Network Process method and the concept of VlseKriterijumska Optimizacija I Kompromisno Resenje acceptable advantage—was used to objectively identify critical factors for the adoption of a cloud computing service, replacing the subjective decision of the authors. The results of this study determined five critical factors, namely data access security, information transmission security, senior management support, fallback cloud management, and employee acceptance. Finally, the paper presents the findings and implications of the study.     

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an essential technique in supporting digital map services. It aims to determine a set of location based services (LBS) which cover all client intended activities quantified in the query. But the available web service composition solutions do not consider the complicated spatio-temporal features. For resolving this issue, this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model (F3L-WSCM) in a cloud environment for location awareness. The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking, hotels, car rentals, etc. At the next stage, the firefly algorithm is applied to generate composition plans to minimize the number of composition plans. Followed by, the fuzzy subtractive clustering (FSC) will select the best composition plan from the available composite plans. Besides, the presented F3L-WSCM model involves four input QoS parameters namely service cost, service availability, service response time, and user rating. An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy, execution time, and efficiency.     

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.     

结合隶属度与像素交替引导滤波的鲁棒模糊聚类算法

模糊局部信息C-均值（FLICM）聚类算法是目前应用较广泛的图像分割算法,然而仅适用于处理低噪声图像。FLICM算法与像素引导隶属度滤波的结合在一定程度上提高了噪声抑制能力,但仍无法满足强噪声图像的分割需求。联合引导滤波与基于核度量的加权模糊局部信息C-均值（KWFLICM）聚类算法,提出一种隶属度与像素值交替引导的核模糊聚类算法。将像素引导隶属度滤波模块和隶属度引导像素滤波模块引入KWFLICM算法,构造一种引导滤波约束的多目标核模糊聚类优化模型,采用最小二乘法对该模型进行迭代求解。在迭代过程中,通过像素引导隶属度滤波和隶属度引导像素滤波,分别修正输入图像的隶属度和像素值,进一步提高核模糊聚类算法对含噪图像的鲁棒性。实验结果表明,与同类核模糊聚类算法相比,该算法在莱斯噪声干扰下的误分率、精确度、峰值信噪比、Jaccard相似系数等评价指标上表现突出,具有更好的分割性能和更强的鲁棒性。     

立体停车位液压系统控制策略及节能控制技术研究

针对目前立体停车位均采用阀控液压动力系统,存在系统效率低、能耗大等问题,将伺服电机直驱定量泵的节能技术应用于立体停车位的液压系统中.采用滑模变结构算法与模糊控制算法相结合的模糊滑模控制算法作为立体停车位各个工况切换的控制策略,相比于工业中常用的模糊PID控制算法,模糊滑模变结构控制能够对系统设定流量需求进行快速响应,在多个工况切换时,波动小、稳定速度快.同时对使用节能技术的立体停车位运行时的功率、能耗及系统效率进行测试,结果表明使用伺服电机直驱定量泵的液压节能系统与使用传统阀控液压系统相比,能耗降低了31.2%,系统效率提高了33.2%,具有较好的节能效果.     

Improved robust exponential stability for Takagi–Sugeno fuzzy uncertain systems with time-varying delays

This paper concerns managing the robust exponential stability problem of uncertain Takagi–Sugeno fuzzy systems with time-varying delay by employing a further improved integral inequality matrix approach. Based on the linear matrix inequality (LMI) approach, delay-dependent robust exponential stability criteria have been developed. By taking the relationship among the time-varying delay, its upper bound and their difference into account, some less conservative LMI-based delay-dependent robust exponential stability criteria are obtained without ignoring any useful terms in the derivative of Lyapunov–Krasovskii functionals. Maximum allowable upper bound for time-varying delays is determined. Numerical examples are provided to show that the obtained results significantly improve the allowed upper bounds of delay size over some methods existing in the literature.     

Path integral solution for nonlinear systems under parametric Poissonian white noise input

In this paper the problem of the response evaluation in terms of probability density function of nonlinear systems under parametric Poisson white noise is addressed. Specifically, extension of the Path Integral method to this kind of systems is introduced. Such systems exhibit a jump at each impulse occurrence, whose value is obtained in closed form considering two general classes of nonlinear multiplicative functions. Relying on the obtained closed form relation liking the impulses amplitude distribution and the corresponding jump response of the system, the Path Integral method is extended to deal with systems driven by parametric Poissonian white noise. Several numerical applications are performed to show the accuracy of the results and comparison with pertinent Monte Carlo simulation data assesses the reliability of the proposed procedure.     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

Fast and accurate fuzzy C‐means algorithm for MR brain image segmentation

Fuzzy theory based intelligent techniques are widely preferred for medical applications because of high accuracy. Among the fuzzy based techniques, Fuzzy C‐Means (FCM) algorithm is popular than the other approaches due to the availability of expert knowledge. But, one of the hidden facts is that the computational complexity of the FCM algorithm is significantly high. Since medical applications need to be time effective, suitable modifications must be made in this algorithm for practical feasibility. In this study, necessary changes are included in the FCM approach to make the approach time effective without compromising the segmentation efficiency. An additional data reduction approach is performed in the conventional FCM to minimize the computational complexity and the convergence rate. A comparative analysis with the conventional FCM algorithm and the proposed Fast and Accurate FCM (FAFCM) is also given to show the superior nature of the proposed approach. These techniques are analyzed in terms of segmentation efficiency and convergence rate. Experimental results show promising results for the proposed approach. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 188–195, 2016     

基于粗糙集模糊神经网络的爆破振动预测

以某地下水封洞库爆破工程为研究对象,全面考虑影响爆破振动的各种因素,提出了基于粗糙集理论的模糊神经网络(RS-FNN)预测方法进行质点峰值速度和主频率预测。首先采用粗糙集理论获得最优属性集,然后对实测数据进行模糊处理,建立质点峰值速度和主频率的12-25-1 RS-FNN网络预测模型,并与基于萨道夫斯基经验公式的预测模型进行对比研究。研究结果表明:RS-FNN对质点峰值速度预测要优于经验公式,同时,RS-FNN也首次实现了对主频率的预测,为保障工程爆破安全提供了一定的理论指导。