Search

A comprehensive loop parameters designing method and an auxiliary pulling module for an optical phase-locked loop system were discussed. The experiment successfully demonstrated a pull-in frequency range as wide as 2.4GHz and a frequency accuracy of 2MHz.

Exoskeleton robots have been developed to enhance human mobility and reduce the muscle fatigue caused by heavy loads. Now that the exoskeleton should reach transparency with the wearer, the human-machine interaction based control strategy will significantly enhance collaboration between the exoskeleton and the user. This paper proposes a novel method called Hybrid Phase Control (HPC) based on different dynamic models in stance and swing phase of a gait cycle. The dynamic models are derived by using Euler-Lagrange Equation. The proposed method is applied in a lower extremity exoskeleton to track a desired trajectory. In order to improve the adaptive capacity of the controller, we introduce the Sliding Mode Control (SMC) and further eliminate the chattering phenomenon of traditional SMC by using fuzzy logic system (FLS) to adjust SMC switch-gain. The proposed control strategy was proved stable with Lyapunov analysis and used for trajectory tracking in comparison with a conventional sliding mode control. Results with Matlab simulation shows that the proposed controller can achieve better tracking capability and the chattering effect is greatly reduced.

Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP) represents a widely-applied manufacturing pattern, which is formed by two relatively independent stages, i.e., processing stage and assembly stage. In this paper, we introduce the Just-in-Time (JIT) constraint between the two stages of the conventional DAPFSP to form the Just-in-Time DAPFSP (JIT-DAPFSP), with respect to minimizing the maximum weighted earliness/tardiness cost. A variable neighborhood search based Memetic Algorithm (VNS-MA) is proposed by incorporating several novel neighborhoods manipulating the factory assignment. Computational tests are carried out on 10 small-scale and 5 large-scale benchmark problems to compare the results of different operators and algorithms. To the best of our knowledge, this is the first report to model JIT constraints and objective in DAPFSP.

Digital IIR filter design problem is a crucial research issue in control and signal processing. From the viewpoint of optimization, IIR filter designing problems can be formulated as a multi-modal optimization problem with multiple decision variables. This study investigate the feasibility of applying a newly proposed optimization method labeled as water wave optimization (WWO) algorithm to identify the unknown parameters for IIR filter. To enhance its fine (local) searching performance of WWO, Nelder-Mead simplex algorithm based local improvement is incorporated into WWO so as to continually search for the global optima through the reflection, expansion, contraction, and shrink operators. By working on well-known IIR designing benchmark problems, we evaluate the effectiveness and efficacy of the proposed improved WWO algorithm. Experimental results as well as the comparisons with some state-of-the-art algorithm illustrate merit of the hybrid algorithm in which the local search and global search are well balanced.

I Ching (Chinese characters: $$$) philosophy, stemming from ancient Chinese culture, is Chinese view of empiricism, world outlook and dialectics. Over thousands of years of evolving and interpretation, its cosmological wisdom has broad and profound influences not only on Chinese prevailing philosophy, but also on westerns. I Ching concentrates on the virtue of being moderate and appropriate, representing the balance by which it is contended that power/destiny can be well generated/controlled. Among the whole 64 hexagrams in I Ching, is the QIAN hexagram which is ranked in the first place, standing for the strong action and the everlasting desire for success. Behind I Ching philosophy lie the thinking and action models of optimization, specifically, identification from changing states, embracing vision, and optimal action obeying the balance of moderateness and appropriateness. I Ching philosophy could act as the driving force to establish and develop new optimization scheme. In this regard, enlightened by I Ching philosophy, particularly the QIAN hexagram, we propose I Ching philosophy inspired optimization, labeled as ICO for continuous optimization problems. Characterized by population-based, stochastic, iterative, and empiricism inspired features, the ICO evolves the population of solutions by multiple searching operators derived from underlying mechanism of the QIAN hexagram, as expected that the balance between global exploration and local exploitation can be well achieved. Specifically, the whole population is divided into four subpopulations, each of which corresponds to one solid line (state in the QIAN hexagram), then five search operators are developed to perform the learning, emerging, controlling, chasing and stabilizing operations on each subpopulation. The performances of the proposed ICO are investigated on well-known testing benchmarks and digital IIR filter designing problem which is crucial in control field. Comparisons with several state-of-the-art algorithms show the efficacy of the proposed ICO algorithm.

Distributed permutation flowshop scheduling problem (DPFSP) is a typical NP-hard combinatorial optimization problem and represents an important area in multiple distributed production systems. In this study, both machine blocking and job sequence dependent setup time constraints are considered in DPFSP, which make the conventional model more suitable to the realistic situation. The combination with new constraints means a substantial increase in the complexity of the problem and the volatility of landscape, which sharply increase the solving difficulty. Probabilistic memetic framework (PrMF) is a novel MA framework which balances the exploration and exploitation by controlling the learning intensity of each individual. In this paper, an EDA-based PrMF algorithm, called EDAPrMF, is proposed to solve the DBPFSP with SDST, in which PrMF is modified and extended for distributed scheduling problems with two-layer encoding. Specifically, a novel solution matrix based distance matrix is defined for DBPFSP with SDST, which serves as a suitable measure between two feasible solutions and can also be connected with the probability matrix of EDA, the global search meme in PrMF. Meta-Lamarckian learning strategy is also equipped in PrMF to guide the local search direction. The experimental results and comparisons with existing algorithms show the efficiency of the proposed EDAPrMF in solving both small-scale and large-scale DBPFSP with SDSP and the effectiveness of PrMF in improve the search ability.

During the past two decades, developing and improving intelligent optimization algorithms (IOAs) has been one of the hottest research topics in evolutionary computing. Most of the IOAs are inspired by natural phenomena, and shown to be highly effective in solving complex optimization problems. In this study, we propose a new population based metaheuristic algorithm named Team Effectiveness Based Optimization (TEBO), which is inspired from human society rather than natural world. The high-intelligence of human beings together with the research achievements in the field of team effectiveness motivate the development of this novel optimization algorithm. In this paper, we present how the members learn and cooperate in a team environment in order to improve the overall team performance. We investigate the proposed TEBO on a comprehensive set of 30 benchmark problems in CEC 2014 competition on Single Objective Real-Parameter Numerical Optimization. The results confirm the competitiveness of the proposed algorithm comparing to other state-of-the-art intelligent optimization algorithms, including invasive weed optimization (IWO), biogeography-based optimization (BBO), gravitational search algorithm (GSA), hunting search (HuS), bat algorithm (BA) and water wave optimization (WWO).

The distributed assembly permutation flowshop scheduling problem (DAPFSP) is a kind of typical NP-hard combinatorial optimization problem, and represents important area in operational research. This paper presents a new type of DAPFSP with four realistic extensions, i.e., random nature of processing times and assembly times, stochastic sequence-dependent setup times (SDST) on processing machines, stochastic job release times, as well as the no-wait constraint in the processing stage. To address the above mentioned stochastic version of DAPFSP with complex constraints, our previously proposed optimization framework, labeled as PSOSAHT which is characterized by PSO-based exploration, SA-based local search and HT method for evaluating and comparing the stochastic makespan, is adapted and investigated. Computational tests are conducted on benchmark problems, demonstrating the effectiveness and efficiency of the PSOSAHT method. To the best of knowledge, it is the first attempt to study the DAPFSP with job release time and sequence-dependent setup time both of which are stochastic.

In this paper, a new type of distributed permutation flow-shop scheduling problem (DPFSP) is proposed, in which different transport timetable and different loading capacity for each factory are considered. Under this model generalization, we assume that there are a total of F different factories and then a sequence has to be calculated for the jobs assigned to each factory. We also assume that the distances to different factories are not equal, transport timetables and loading capacities are also considered. Even though the assumption in this study is necessary for today's economy, there are seldom studies in literature dealing with such constraints. To the best of our knowledge, it is the first report on incorporating vehicle timetables and vehicle capacities to DPFSP. Standard DPFSP is a typical NP-hard problem and this problem is even harder. A simulated annealing based local search with multiple different neighborhoods is used to solve the problem with the objective to minimize the maximum completion time. Three different neighborhood searching methods are also proposed. A comprehensive computational and statistical analysis is conducted in order to analyze the performance of the proposed method.

This paper studies a new extension of distributed permutation flow shop scheduling problem (DPFSP) referred to as the DPFSP with flowline eligibility. Under this extension, the problem contains two stages. At the first assignment stage, a set N of n jobs is distributed among F factories with flowline eligibility constraint, i.e., not all the factories are available for every job. Then each factory can be regarded as a regular permutation flow shop scheduling problem (PFSP) in which jobs assigned to each factory have to be processed on m machines according to the given permutation. The objective is to minimize the maximum completion time or makespan of all the factories. The flowline eligibility constraint adds asymmetry and complexity to the regular DPFSP. In the proposed hybrid estimation of distribution algorithm (hybrid EDA), a heuristic of Framinan and Leisten (FLH) based solution-generate method (FLHSGM), two novel factory-based operators are proposed, and an effective local search based on variable neighborhood search (VNS) is employed and incorporated into EDA. Numerical simulations with comprehensive computational and statistical analysis are carried out. The experimental results and comparisons with existing algorithms show that the FLHSGM and the VNS-based local search enhances the search ability of EDA and the effectiveness of the hybrid EDA in solving both small-scale and large-scale DPFSP with flowline eligibility.

Despite the global fast coarse search capability of Teaching-Learning Based Optimization (TLBO), analysis in literature on the performance of TLBO reveals it often risks getting prematurely stuck in local optima for numerical optimization problems. In this study, Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is incorporated into the conventional TLBO to enhance its local searching performance through local search operators. The proposed TLBO-BFGS would enrich the searching modes and behaviors, and balance the global exploration and local exploitation as well. Simulation results on six well-known benchmark problems and comparisons with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard TLBO show that our proposed TLBO-BFGS can effectively enhance the searching efficiency and greatly improve the searching quality.

In this paper, a tabu-based variable neighborhood local search (TBVLS) is proposed to solve the n-vehicle exploration problem (NVEP), in which a fleet of n vehicles' trip sequence is determined so as to ensure one of the vehicles visits the farthest distance. In TBVLS, tabu search is employed to conduct iteratively local search around the solution space, which is incorporated with three variable neighborhood local search operators, including swap, insert, and inverse. It is expected via this hybrid algorithm that the ability of searching promising region could be enhanced by the variable neighborhood local search, and the global search around the neighborhood of current solution could be diversified by the three different operators. Numerical results about 14 benchmark instances are provided, and the comparisons suggest that TBVLS could achieve better performances in the 14 instances, which demonstrates the effectiveness of the proposed TBVLS in sloving NVEP.

Tagging systems enable users to organise their online entities with distinct tags. Exploiting these user generated content and underlying bilingual information have become more and more important in recommendation system. Probabilistic topic model has been widely used in document management and social network mining. In this paper, we propose a new method to do tag-based recommendation with topic model. Some existing methods are based on mining association rules and similarity measures. In these cases, tags serve as the essential intermediates for statistical computation, but they have the drawbacks that results are sensitive to parameter setup. Even though they are popular in some real application situations, they are simply lack of scalability as the computational procedure differs over distinguished platforms. It's natural to take tags as words, from which topics can be effectively extracted by using topic model. Under the assumption of the generating process in topic model, user's topic distribution parameter implies his or her topic preference. Recommendation results are obtained according to the final probability calculated by summing over topics. Our experiments show that the proposed model is effective to do both tags and items recommendation on two sparse datasets.

Humidity controlling blocks of biological and inorganic materials were studied and made to substitute brick for greenhouse wall which utilizes the capacity of the humidity-controlling materials to raise and lower the humidity to solves the present problems of humidity controlling measures in greenhouse This paper describes the preparation of the raw materials of the humidity-controlling bricks, the proportion of additive in the raw materials and its effect on relative humidity. The manufacturing process of the composite humidity-controlling blocks are also introcuced in this paper. The proportions of all the raw materials of the bricks for optimal humidity were obtained through tests of the capacity of the composite humidity-controlling blocks of different proportions of raw materials. The composite blocks have the great advantage of energy-saving, environmental protection, heat preservation and simple operation in the process of adjusting the humidity in greenhouses, which provides a broad prospect of application.

This study analyzed the bra designed for WearerSelf-caused Comfort, physical beauty Degree and Bra itself in the form of beauty and give their own psychological feelings of cultural symbols. To use the human body Ergonomic related theories, Explore the design process in bra man and man and the environment, And “Formal” and “ ergonomics ” of the harmony and unity, so that bra design with every step of the human body as the basis. Also designed an artistic component and bra wearer to best match the actual performance of the state. Thus Discusses the design of science in the bra to be expressed in the form of art, designed to achieve a bra harmony of science and art.