Design of swing arm type actuator and suspension for micro optical disk drive

One of the trends on information storage device is focused on the development of micro-optical devices with optical flying head (OFH). Many different types of sliders for OFH and optical component systems have been introduced in the literature. However, the important issues on the mechanical system, which consists of suspension, swing arm and voice coil motor (VCM) part has not been discussed up to date. In this research, a swing arm type actuator with suspension for micro optical disk drive is designed and developed, basically focused on the mechanical issues, which should be solved for real application.     

Design of suspension and swing arm for improved shock resistance in the micro optical disk drives

One of the trends on information storage device is focused on the development of micro optical disk with optical flying head (OFH). The OFH in the small form factor ODD consists of optical slider, suspension, swing arm, and so on. In order for a slider with OFH to be successfully implemented in the system, the shock performance of the head-gimbal assembly should be guaranteed for mobile applications. We design development model of suspension and swing arm for improved shock resistance using topology optimization and shape optimization. The objective of the suspension design is to decrease the equivalent mass with increasing first torsional frequency. The objective of the swing arm design is mass reduction for reducing power consumption and fast access time. In this paper, we suggest a design method for the improvement of the dynamic performance of suspension and swing arm for the OFH considering shock characteristics.     

A schedule of join operations to reduce I/O cost in spatial database systems

In this paper, we propose a graph-based cluster-sequencing method to minimize the I/O cost in spatial join processing. We first define the maximum overlapping (MO) order in a graph, proving that the problem of finding an MO order in a graph is NP-complete. Then, we propose an algorithm to find an approximation to MO order in a graph. We also prove that the approximation to MO order obtained from our method is close to the optimal result. Simulations have been conducted to demonstrate the saving of I/O cost in spatial join by using our method.     

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

This paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO‐GVNS) metaheuristic, named adapted MO‐GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO‐GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA‐II), and NSGA‐III, and another MO‐GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem.     

Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons

Evolutionary techniques for multi-objective(MO) optimization are currently gainingsignificant attention from researchers invarious fields due to their effectiveness androbustness in searching for a set of trade-offsolutions. Unlike conventional methods thataggregate multiple attributes to form acomposite scalar objective function,evolutionary algorithms with modifiedreproduction schemes for MO optimization arecapable of treating each objective componentseparately and lead the search in discoveringthe global Pareto-optimal front. The rapidadvances of multi-objective evolutionaryalgorithms, however, poses the difficulty ofkeeping track of the developments in this fieldas well as selecting an existing approach thatbest suits the optimization problem in-hand.This paper thus provides a survey on variousevolutionary methods for MO optimization. Manywell-known multi-objective evolutionaryalgorithms have been experimented with andcompared extensively on four benchmark problemswith different MO optimization difficulties.Besides considering the usual performancemeasures in MO optimization, e.g., the spreadacross the Pareto-optimal front and the abilityto attain the global trade-offs, the paper alsopresents a few metrics to examinethe strength and weakness of each evolutionaryapproach both quantitatively and qualitatively.Simulation results for the comparisons areanalyzed, summarized and commented.     

一种新型的高性能计算机存储系统的研究与实现

文中提出了一种改善计算机存储系统写请求特别是小写请求性能的新的存储结构。采用有着高存储速度、高可靠性的固态盘和廉介的硬盘空间共同和为磁光盘的写高速缓存,并结合顺序文件存取技术,从存储体系结构角度出发,研究并实现了一种以较低成本实现快速、可靠、大容量的存储系统。试验表明新型存储系统不必修改现有文件系统,即能大幅提高存储系统性能。中用于要求高可靠性的军用环境以及高可靠性和快速大容量要求的民用系统。     

On convergence of the multi-objective particle swarm optimizers

Several variants of the particle swarm optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization (MO) problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that govern the convergence behavior of the algorithm to the optimal Pareto front in the objective function space. Computer simulations over benchmark MO problems have also been provided to substantiate the theoretical derivations.     

Multiobjective optimization of complex antenna structures using response surface models

A very efficient multiobjective (MO) design technique for complex antenna structures involving a large number of design parameters is presented. This design technique, multiobjective‐fractional factorial design (MO‐FFD), is very different from conventional Pareto‐based MO algorithms, which take a great deal of effort to balance the trade‐off between all the design specifications. By performing one single combination of simulations, all the response surface models of design goals are simultaneously built, and Derringer's desirability functions are readily applied to these models so that the optimum structure is obtained. Compared to classical MO algorithms such as Strength Pareto Evolutionary Algorithm 2, nondominated sorting particle swarm optimizer, and cultural MO particle swarm optimization, MO‐FFD yields more desirable performances yet the required number of simulations is reduced by 97%. This article thoroughly illustrates the mathematical development of MO‐FFD, deriving a novel application of ultrawideband (UWB) antennas because of its MO optimization capability. More explicitly, MO‐FFD overcomes all the design challenges of dual band‐notched UWB antennas including desired impedance characteristics, enhanced fidelity factors, and uniform peak gains over the passband, which are what conventional Pareto‐based algorithms cannot attain. The measured results show that all the performance criteria are met; especially, the time‐domain signal distortion is minimized. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:62–71, 2016.     

电影本体的构建与一致性分析

针对移动网络对电影服务系统提出的更高要求和电影领域知识描述的不足,阐述构建电影领域本体(MO)的必要性和可行性：首先,概述电影本体的对象和组成,提出构建电影领域本体模型的原则和方法,利用OWL和Protege 4.1构建模型;其次,详细说明电影本体的类、属性、实例、公理和推理规则的具体表述;最后,分析电影本体的一致性,包括类间关系的一致性分析和基于公理的一致性分析。     

Evolutionary multiobjective industrial design: the case of a racing car tire-suspension system

When dealing with multiobjective optimization (MO) of the tire-suspension system of a racing car, a large number of design variables and a large number of objectives have to be taken into account. Two different models have been used, both validated on data coming from an instrumented car, a differential equation-based physical model, and a neural network purely numerical model. Up to 23 objective functions have been defined, at least 14 of which are in strict conflict of each other. The equivalent scalar function based and the objective-as-constraint formulations are intentionally avoided due to their well-known limitations. A fuzzy definition of optima, being a generalization of Pareto optimality, is applied to the problem. The result of such an approach is that subsets of Pareto optimal solutions (on such a problem, a big portion of the entire search space) can be properly selected as a consequence of input from the designer. The obtained optimal solutions are compared with the reference vehicle and with the optima previously obtained with design of experiment techniques and different MO optimization strategies. The proposed strategy improves both the reference (actual) car and previously obtained optima (scalar preference function) in the majority of objectives with technically significant improvements. Moreover, the strategy offers an univoque criterion for the choice among tradeoff solutions in the 14-dimensional objective space. The problem is used as a test of a proposed optimal design strategy for industrial problems, integrating differential equation and neural networks modeling, design of experiments, MO, and fuzzy optimal-based decision making. Such a linked approach gives also a unified view of where to concentrate the computational effort.     

A multi-objective GRASP for partial classification

Metaheuristic algorithms have been used successfully in a number of data mining contexts and specifically in the production of classification rules. Classification rules describe a class of interest or a subset of this class, and as such may also be used as an aid in prediction. The production and selection of classification rules for a particular class of the database is often referred to as partial classification. Since partial classification rules are often evaluated according to a number of conflicting objectives, the generation of such rules is a task that is well suited to a multi-objective (MO) metaheuristic approach. In this paper we discuss how to adapt well known MO algorithms for the task of partial classification. Additionally, we introduce a new MO algorithm for this task based on a greedy randomized adaptive search procedure (GRASP). GRASP has been applied to a number of problems in combinatorial optimization, but it has very seldom been used in a MO setting, and generally only through repeated optimization of single objective problems, using either linear combinations of the objectives or additional constraints. The approach presented takes advantage of some specific characteristics of the data mining problem being solved, allowing for the very effective construction of a set of solutions that form the starting point for the local search phase of the GRASP. The resulting algorithm is guided solely by the concepts of dominance and Pareto-optimality. We present experimental results for our partial classification GRASP and other MO metaheuristics. These show that such algorithms are generally very well suited to this data mining task and furthermore, the GRASP brings additional efficiency to the search for partial classification rules.     

Taguchi's method for multi‐objective optimization problems

Taguchi's method is a quality design technique whose applications in numerical single‐objective optimization have been recently exploited. In this article, a novel multi‐objective (MO) algorithm based on Taguchi's technique is illustrated and its performances assessed. Validation is performed through a comparison between the presented algorithm and a MO genetic algorithm (GA) based optimization, first on different sets of test functions and then on a practical antenna array synthesis problem. Results indicate a generally better behavior of the proposed algorithm in terms of convergence and spreading over the Pareto front with respect to the GA benchmark. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2013.     

A new PID controller tuning method based on multiple integrations

The magnitude optimum (MO) technique provides a non-oscillatory closed-loop response for a large class of process models. However, this technique is based on a transfer function model that requires precise process identification and extensive computations. In the present paper, it is shown that a close relation exists between multiple integrations of the process step response and the MO criterion. Due to this relation, the MO criterion can be more simply achieved. Some practical guidelines for performing multiple integrations and for re-tuning controller parameters are also given.     

插烯系硫靛发色体电子光谱的含时密度泛函理论研究

在密度泛函理论的B3LYP/6-31 G~*水平上对硫靛染料发色体及其插烯系化合物的几何构型进行优化计算;在获得基态稳定结构的基础上,应用含时密度泛函理论在相同水平下计算其电子吸收光谱,探讨了发色体的延伸对电子吸收光谱的影响,得到了与实验值基本一致的变化规律。结果表明,发色体的纵向延伸使光谱产生轻微的红移。通过对前线轨道组成进行自然布居分析,揭示了硫靛染料的发光均源自分子中HOMO-LUMO(π→π~(?))电子跃迁。     

One-dimensional and multi-dimensional substring selectivity estimation

With the increasing importance of XML, LDAP directories, and text-based information sources on the Internet, there is an ever-greater need to evaluate queries involving (sub)string matching. In many cases, matches need to be on multiple attributes/dimensions, with correlations between the multiple dimensions. Effective query optimization in this context requires good selectivity estimates. In this paper, we use pruned count-suffix trees (PSTs) as the basic data structure for substring selectivity estimation. For the 1-D problem, we present a novel technique called MO (Maximal Overlap). We then develop and analyze two 1-D estimation algorithms, MOC and MOLC, based on MO and a constraint-based characterization of all possible completions of a given PST. For the k-D problem, we first generalize PSTs to multiple dimensions and develop a space- and time-efficient probabilistic algorithm to construct k-D PSTs directly. We then show how to extend MO to multiple dimensions. Finally, we demonstrate, both analytically and experimentally, that MO is both practical and substantially superior to competing algorithms. Received April 28, 2000 / Accepted July 11, 2000     

A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization

This paper reviews a number of popular distribution preservation mechanisms and examines their characteristics and effectiveness in evolutionary multi-objective (MO) optimization. A conceptual framework consisting of solution assessment and elitism is presented to better understand the search guidance in evolutionary MO optimization. Simulation studies among different distribution preservation techniques are performed over fifteen representative distribution samples and the performances are compared based upon two distribution metrics proposed in this paper. The results and findings reported in this paper are valuable for better understanding of the working principle and characteristics of distribution preservation mechanisms, which are very useful for incorporating different distribution preservation features into MO evolutionary algorithms in a modular fashion or improving the effectiveness of existing preservation approaches.     

Δ3-1,2,4—噻二唑类化合物的量子化学及构效关系研究

Δ3-1,2,4—噻二唑类化合物具有抑制黄瓜霜霉病毒的杀菌活性。本文用半经验PM3方法对25个该类化合物进行了量子化学计算,并依据原子的净电荷、前沿轨道能级、轨道主要成分及原子在前沿轨道中所占比重等计算结果和多重回归分析讨论了该类化合物的活性部位,为该类化合物结构与活性的研究奠定了理论基础。     

Δ~3-1,2,4—噻二唑类化合物的量子化学及构效关系研究

Δ3 1 ,2 ,4—噻二唑类化合物具有抑制黄瓜霜霉病毒的杀菌活性。本文用半经验PM3方法对 2 5个该类化合物进行了量子化学计算 ,并依据原子的净电荷、前沿轨道能级、轨道主要成分及原子在前沿轨道中所占比重等计算结果和多重回归分析讨论了该类化合物的活性部位 ,为该类化合物结构与活性的研究奠定了理论基础。