Search Step Research Articles

Fuzzy Logic Control for Doppler Search in DSSS Systems

The study on the Doppler frequency search strategy in direct sequence spread spectrum (DSSS) systems has been addressed in this article. To reduce the estimation error and alleviate the possible false detection, a novel Doppler search algorithm that combines the serial-based search and the fuzzy-logic (FL)-based search is presented. At first, the serial-based search is used until the decision variable exceeds a preset threshold; the FL-based search is then performed for a second search and the final decision is made. In the second search stage, Doppler search step size is adaptively adjusted by an FL controller. To design a robust FL controller, the characteristics of the spectrum distribution and several possible abnormal conditions are analyzed, input and output fuzzy membership functions are derived, and a set of fuzzy rules is formulated. Finally, the effectiveness of the proposed approach is evaluated through Monte Carlo simulations. The obtained results show that a smaller estimation error is achieved when the proposed algorithm is used; further, reasonable mean acquisition times are attained compared with other existing search techniques. Furthermore, the proposed algorithm can alleviate possible false detection in the abnormal conditions.

Design and research on optimization of single point mooring system

In this paper, we establish a statics model to analyze the force balance of the buoys, four steel pipes and steel buckets, and we derive the relationship between the inclination angle of steel pipes and steel buckets and the draught depth and wind speed, which is solved by moment in mechanics. Next, by applying the catenary curve’s equation to per unit of anchor chain, we can obtain the vertical height of anchor chain, thus the total depth of sea water can be obtained. Finally, the variable step search algorithm is used to search the draught depth, and we can obtain each parameter of the optimization result.

Transmissivity Identification by Combination of CVFEM and Genetic Algorithm: Application to the Coastal Aquifer

The solution of inverse problems in groundwater flow has been massively invested by several researchers around the world. This type of problem has been formulated by a constrained optimization problem and this constraint is none other than the direct problem (DP) itself. Thus, solving algorithms are developed that simultaneously solve the direct problem (Darcy’s equation) and the associated optimization problem. Several papers have been published in the literature using optimization methods based on computation of the objective function gradients. This type of method suffers from the inability to provide a global optimum. Similarly, they also have the disadvantage of not being applicable to objective functions of discontinuous derivatives. This paper is proposed to avoid these disadvantages. Indeed, for the optimization phase, we use random search-based methods that do not use derivative computations, but based on a search step followed only by evaluation of the objective function as many times as necessary to the convergence towards the global optimum. Among the different algorithms of this type of methods, we adopted the genetic algorithm (GA). On the other hand, the numerical solution of the direct problem is accomplished by the CVFEM discretization method (Control Volume Finite Element Method) which ensures the mass conservation in a natural way by its mathematical formulation. The resulting computation code HySubF-CVFEM (Hydrodynamic of Subsurface Flow by Control Volume Finite Element Method) solves the Darcy equation in a heterogeneous porous medium. Thus, this paper describes the description of the integrated optimization algorithm called HySubF-CVFEM/GA that has been successfully implemented and validated successfully compared to a schematic flow case offering analytical solutions. The results of this comparison are qualified of excellent accuracy. To identify the transmissivity field of the realistic study area, the code HySubF-CVFEM/GA was applied to the coastal “Chaouia” groundwater located in Western of Morocco. This aquifer of high heterogeneity is essential for water resources for the Casablanca region. Results analysis of this study has shown that the developed code is capable of providing high accuracy transmissivity fields, thus representing the heterogeneity observed in situ. However, in comparison with gradient method optimization the HySubF-CVFEM/GA code converges too slowly to the optimal solution (large CPU-time consuming). Despite this disadvantage, and given the high accuracy of the obtained results, the HySubF-CVFEM/GA code can be recommended to solve in an efficient and effective manner the identification parameters problems in hydrogeology.

Catalyst: Can Systems Chemistry Unravel the Mysteries of the Chemical Origins of Life?

Daniela Kroiss obtained her MSc degree in biotechnology at BOKU and is currently a PhD student in the biochemistry program at CUNY, where she works on a joint project between the Nanoscience Initiative and the University of Strathclyde. Gonen Ashkenasy is head of the Laboratory for Systems Chemistry at the Ben-Gurion University Chemistry Department. His research focuses on the design and analysis of synthetic replication networks. Adam Braunschweig earned his PhD in organic chemistry at the University of California and is an associate professor in the CUNY Nanoscience Initiative. His research focuses on the interface of organic chemistry with biology and material science. Tell Tuttle obtained his PhD in theoretical chemistry at Goteborg University and is a professor of theoretical chemistry at the University of Strathclyde. His research focuses on the directed discovery of peptide-based materials. Rein Ulijn gained his PhD in physical chemistry at the University of Strathclyde and is founding director of the Nanoscience Initiative at the CUNY Advanced Science Research Center. His research focuses on nanotechnology inspired by living systems.

Mean acquisition time analysis for GNSS parallel and hybrid search strategies

Mean acquisition time (MAT) is one of the key performance indicators for the acquisition of global navigation satellite system (GNSS) signals. Usually, there are two stages in GNSS signal acquisition, called coarse acquisition and fine acquisition. A receiver starts the coarse acquisition with relatively large code-phase and frequency steps to obtain rough code-phase and Doppler estimates. Then, it follows a fine acquisition, in which the search steps are much smaller, to obtain the more accurate code-phase and Doppler estimates to meet the tracking loop requirements, and the search space is dramatically reduced. The existing MAT models only consider the coarse acquisition stage. We present the MAT expressions in consideration of both the coarse and fine acquisition stages. Also, our models are extended versions of the existing ones. The MAT expressions for three common GNSS signal acquisition schemes are provided, including parallel code-phase search (PCS), parallel frequency search, and hybrid search. The analytical expressions are verified by Monte Carlo simulations of GPS L1 C/A signals. We show how to optimize the acquisition parameters, including detection threshold, coherent integration time, and non-coherent integration time with the metric of MAT. The models facilitate not only MAT performance evaluation but also design parameter optimization of GNSS receivers.

Bi-objective integer programming analysis based on the characteristic equation

In this paper, a bi-objective integer programming problem is analysed using the characteristic equation that was developed to solve a single-objective pure integer program. This equation can also provides other ranked solutions i.e. 2nd, 3rd,... best solutions. These solutions are potential non-dominated points for a bi-objective integer program, which is being investigated in this paper. A “C” code is developed to solve the characteristic equation, a tool which is not available in the IBM ILOG CPLEX library. Two versions of this algorithm are developed to identify the non-dominated points for the bi-objective integer programming problem. The second version improves on the first by reducing the number of search steps. Computational experiments are carried out with respect to the two algorithms developed in this paper and comparisons have also been carried out with one of the recently developed method, the balanced box method. These computational experiments indicate that the second version of the algorithm developed in this paper performed significantly better than the first version and out performed the balanced box method with respect to both CPU time and the number of iterations.

A Novel Modified Electromagnetism-like Algorithm for Solving Constrained Optimization Problem

The original Electromagnetism-like mechanism (EM) is a meta-heuristic algorithm utilizing an attraction-repulsion mechanism (called as force F) to move sample points towards optimality in global optimization problems. To solve the potential problem of stagnation happening in the original algorithm, new searching procedures were proposed in modified algorithm to determine the next search direction and step length of points in the paper. Compared to the original algorithm, tri-factors of the best historical visited positions of each point, best point and total force F were considered in the improved searching procedures. Moreover, the feasibility and dominance (FAD) rules were incorporated to extend the proposed algorithm to solve constrained optimization problem. Finally, Preliminary experiments verified the effectiveness of the proposed algorithm.

Planar Motion Measurement of a Compliant Micro Stage: An Enhanced Microscopic Vision Approach

This paper presents an enhanced microscopic vision-based approach supporting fast and high-precision measurement on planar motions of a multi-degree of freedom (DOF) compliant stage with sub-micrometer motion accuracy. The proposed method is based on an improved particle swarm optimization (PSO) algorithm, including two phases. First, an inverted pyramid structure is employed to divide the solution space into a top-level solution space, such that the expected solution can be quickly located into a smaller range from bottom to top. Then, the spacial three search step (S-TSS) algorithm is applied to achieve an accurate result. The effectiveness of the proposed method is evaluated on a three-DOF micro stage with translational and rotational motions, where comparative studies with existing results are also conducted. The experimental results demonstrate significant improvement on algorithm efficiency without sacrificing the measurement accuracy, which indicates good potential for visual servo control of such micro stages with the proposed approach.

Optimization of a frame structure using the Coulomb force search strategy-based dragonfly algorithm

ABSTRACTThis article proposes an adaptive dragonfly algorithm (DA) for the structural optimization of frame structures. This algorithm, named the Coulomb force search strategy-based dragonfly algorithm (CFSSDA), combines the Coulomb force search strategy (CFSS) with the DA method. The CFSS is applied to automatically determine an adaptive search step in each iteration, thus enhancing the exploration capability of the CFSSDA. The performance of the CFSSDA is investigated on the basis of three widely used benchmark numerical examples. The results indicate that the proposed CFSSDA achieves a markedly improved convergence rate while maintaining a high accuracy. Furthermore, the CFSSDA is utilized to find the optimal parameters for an engineering frame structure problem with discrete variables under stress constraints. These results also show that the CFSSDA can rapidly generate an optimal structure with a weight that is reduced by 27.97% compared with the initial design.

Multi-Objective Optimal Operations Based on Improved NSGA-II for Hanjiang to Wei River Water Diversion Project, China

It is important to investigate the laws of reservoir multi-objective optimization operations, because it can obtain the best benefits from inter-basin water transfer projects to mitigate water shortage in intake areas. Given the multifaceted demands of the Hanjiang to Wei River Water Diversion Project, China (referred hereafter as “the Project”), an easy-to-operate multi-objective optimal model based on simulation is built and applied to search the multi-objective optimization operation rules between power generation and energy consumption. The Project includes two reservoirs connected by a water transfer tunnel. One is Huangjinxia, located in the mainstream of Hanjiang with abundant inflow but no regulation ability, and the other is Sanhekou, located in the tributary of Hanjiang with multi-year regulation ability but less water. The layout of the Project increases the difficulty of reservoir joint optimization operations. Therefore, an improved Non-dominated Sorting Genetic Algorithm-II (I-NSGA-II) with a feasible search space is proposed to solve the model based on long-term series data. The results show that: (1) The validated simulation model is helpful to obtain Pareto front curves to reveal the rules between power generation and energy consumption. (2) Choosing a reasonable search step size to build a feasible search space based on simulation results for the I-NSGA-II can help find more optimized solutions. Considering the influence of the initial populations of the algorithm and limited computing ability of computers, the qualified rate of Pareto points solved by I-NSGA-II are superior to NSGA-II. (3) According to the characteristics of the Project, water transfer ratio threshold value of two reservoirs are quantified for maximize economic benefits. Moreover, the flood season is a critical operation period for the Project, in which both reservoirs should supply more water to intake areas to ensure the energy balanced of the entire system. The findings provide an easy-to-operate multi-objective operation model with the I-NSGA-II that can easily be applied in optimal management of inter-basin water transfer projects by relevant authorities.

Antenna Array Design by Artificial Bee Colony Algorithm With Similarity Induced Search Method

Antenna array has been extensively applied to wireless communication systems. Optimal design of antenna array is very useful to attain desired spatial beam and reduce signal degradation. Potential search directions in artificial bee colony (ABC) algorithm are mixed with both large step size and small step size. This could lead to the malfunction of employed bee stage and onlooker bee stage of ABC algorithm. This paper proposes to improve the effectiveness of ABC by a similarity induced search method. This method adaptively adjusts search step size. Employed bees perform large step size search, while onlooker bees do small step size search. Scout bees could probe potential high-quality solutions in neighborhoods. The proposed algorithm is tested on antenna array design problem. Compared with four other algorithms, the results verify the effectiveness of the proposed algorithm. Furthermore, the scalability of the proposed algorithm is also analyzed in terms of array element and computational time. Scalability analysis also shows that the proposed algorithm is suitable for solving antenna array design problems.

Derivative-Free Feasible Backtracking Search Methods for Nonlinear Multiobjective Optimization with Simple Boundary Constraint

In this paper, a derivative-free linear feasible direction models with backtracking search technique is considered for solving nonlinear multiobjective optimization problems subject to simple boundary constraint. The algorithm is designed to build linear interpolation models for each function of problem [Formula: see text]. We build the linear programming subproblem using linear interpolation function without the second-order derivative information. The new backtracking search step size function is given in our algorithm which guarantees both the monotone descent property of each function and the feasibility of the iterative point. Under reasonable assumptions, we prove that the algorithm converges to a weakly Pareto critical point of problem. The results of numerical experiments are reported to show the effectiveness of the proposed algorithm.

ОПТИМІЗАЦІЯ ПРУЖНО-ДЕМПФЕРНИХ ПІДВІСОК ОБЛАДНАННЯ СИСТЕМИ КОНДИЦІОНУВАННЯ ПОВІТРЯ ЛІТАКА

The design of equipment overload protection in an aircraft air-conditioning unit requires the use of sufficiently effective methods for analyzing the vibration characteristics of the suspension, as well as the development of an appropriate synthesis method for the specific task. To solve the problem applied the adaptive hybrid optimization method. The object of optimization is the design of the suspension of the air conditioning unit with an asymmetrical distribution of masses and arrangement of joints. Joints are the attachment points in the form of shock absorbers and bellows for joining with pipes. Block is considered solid. Links will be highlighted as optimized elements. The aim of the work is to improve the method of optimizing the parameters of shock absorbers, which provide protection from vibration and operating shock loads. The proposed optimization method uses a number of methods that are called hybridients. Sets the criterion by which the most effective hybridients are selected. This criterion includes information that characterizes a changing situation; namely, signs of the structure and metric parameters of the space where the search is carried out; the prehistory of the computational process by which the possible continuation is established; the nature of the system of functions that determine the problem being solved. Adaptive control is introduced, which carries out the getting of decision vectors, search directions and search steps, respectively, the changing situation. Since it is possible to effectively minimize mass only with joint minimization of overload which is the parameter, to the boundary of which the solution search process goes, it is natural to use multi-criteria optimization. In this case, it is at the same time optimizing mass and overload. Restrictions are imposed on the structural dimensions and strength of the shock absorbers. As a result of the optimization, the weight of the suspension elements and the overload acting on the suspended block are reduced. The limits to which mass and overload can be reduced are determined.

Numerical Solution for Classical Optimal Control Problem Governing by Hyperbolic Partial Differential Equation via Galerkin Finite Element-Implicit method with Gradient Projection Method

This paper deals with the numerical solution of the discrete classical optimal control problem (DCOCP) governing by linear hyperbolic boundary value problem (LHBVP). The method which is used here consists of: the GFEIM " the Galerkin finite element method in space variable with the implicit finite difference method in time variable" to find the solution of the discrete state equation (DSE) and the solution of its corresponding discrete adjoint equation, where a discrete classical control (DCC) is given. The gradient projection method with either the Armijo method (GPARM) or with the optimal method (GPOSM) is used to solve the minimization problem which is obtained from the necessary condition for optimality of the DCOCP to find the DCC.An algorithm is given and a computer program is coded using the above methods to find the numerical solution of the DCOCP with step length of space variable , and step length of time variable . Illustration examples are given to explain the efficiency of these methods. The results show the methods which are used here are better than those obtained when we used the Gradient method (GM) or Frank Wolfe method (FWM) with Armijo step search method to solve the minimization problem.

Hop-Count Quantization Ranging and Hybrid Cuckoo Search Optimized for DV-HOP in WSNs

Localization technology occupies a very important position in wireless sensor network (WSN). Distance vector-hop (DV-HOP) algorithm is range-free localization algorithm and has advantages of low overhead and can handle the case where a normal node has less than three neighbor anchors. However, considering the problem of high localization error of DV-HOP algorithm in WSN. An hybrid DV-HOP localization algorithm based on hop-count quantization anchor and modified cuckoo search (HMCS-D) is proposed. The algorithm using the correction factor to correct the number of hops that can reduce the error caused by the recording inaccurate minimum hops. The nodes of the hop neighbor nodes in the network are divided into three disjoint subsets and the distance between the nodes is estimated according geometric method. Then according to the weight of each anchor node to select average jump distance of unknown nodes. Finally, introduced hybrid cuckoo search which can dynamically adjust the search step size and using the search algorithm to calculate the node coordinates instead of the maximum likelihood estimation method. Simulation results show that compare with DV-HOP and cuckoo search DV-HOP algorithm, the average positioning error of HMCS-D algorithm decrease 39.7%, 10.6% respectively. Prove that the HMCS-D algorithm effectively improve the node localization accuracy, reduce the positioning error and without affecting the hardware cost.

Developing Theory-Driven, Evidence-Based Serious Games for Health: Framework Based on Research Community Insights.

BackgroundThe idea of using serious games to effectuate better outcomes in health care has gained significant traction among a growing community of researchers, developers, and health care professionals. Many now recognize the importance of creating evidence-based games that are purposefully designed to address physical and mental health challenges faced by end users. To date, no regulatory resources have been established to guide the development of serious games for health (SGH). Developers must therefore look elsewhere for guidance. Although a more robust level of evidence exists in the research literature, it is neither structured nor is there any clear consensus. Developers currently use a variety of approaches and methodologies. The establishment of a well-defined framework that represents the consensus views of the SGH research community would help developers improve the efficiency of internal development processes, as well as chances of success. A consensus framework would also enhance the credibility of SGH and help provide quality evidence of their effectiveness.ObjectiveThis research aimed to (1) identify and evaluate the requirements, recommendations, and guidelines proposed by the SGH community in the research literature, and; (2) develop a consensus framework to guide developers, designers, researchers, and health care professionals in the development of evidence-based SGH.MethodsA critical review of the literature was performed in October to November 2018. A 3-step search strategy and a predefined set of inclusion criteria were used to identify relevant articles in PubMed, ScienceDirect, Institute of Electrical and Electronics Engineers Xplore, CiteSeerX, and Google Scholar. A supplemental search of publications from regulatory authorities was conducted to capture their specific requirements. Three researchers independently evaluated the identified articles. The evidence was coded and categorized for analysis.ResultsThis review identified 5 categories of high-level requirements and 20 low-level requirements suggested by the SGH community. These advocate a methodological approach that is multidisciplinary, iterative, and participatory. On the basis of the requirements identified, we propose a framework for developing theory-driven, evidence-based SGH. It comprises 5 stages that are informed by various stakeholders. It focuses on building strong scientific and design foundations that guide the creative and technical development. It includes quantitative trials to evaluate whether the SGH achieve the intended outcomes, as well as efforts to disseminate trial findings and follow-up monitoring after the SGH are rolled out for use.ConclusionsThis review resulted in the formulation of a framework for developing theory-driven, evidence-based SGH that represents many of the requirements set out by SGH stakeholders in the literature. It covers all aspects of the development process (scientific, technological, and design) and is transparently described in sufficient detail to allow SGH stakeholders to implement it in a wide variety of projects, irrespective of discipline, health care segments, or focus.

SHVC performance enhancement using superior step search algorithm

Abstract The SHVC (Scalable High Efficiency Video Coding) standard developed as an extension to H.265 HEVC (High Efficiency Video Coding) that focus on data transmission over heterogeneous network with wide scalability. In this paper a Superior Step Search (SSS) algorithm is proposed to enhance the PSNR performance along with coding efficiency without much increase in the computational complexity. The proposed SSS algorithm performs dynamic search to predict the best matching block using a fine search pattern. Simulation results show 0.5% average increased performance in PSNR and 32% coding efficiency in Bit rate when compared with the SHM reference software.

Low-Complexity CTU Partition Structure Decision and Fast Intra Mode Decision for Versatile Video Coding

Quadtree with nested multi-type tree (QTMT) partition structure is an efficient improvement in versatile video coding (VVC) over the quadtree (QT) structure in the advanced high-efficiency video coding (HEVC) standard. With the exception of the recursive QT partition structure, recursive multi-type tree partition is applied to each leaf node, which generates more flexible block sizes. Besides, intra prediction modes are extended from 35 to 67 so as to satisfy various texture patterns. These newly developed techniques achieve high coding efficiency but also result in very high computational complexity. To tackle this problem, we propose a fast intra-coding algorithm consisting of low-complexity coding tree units (CTU) structure decision and fast intra mode decision in this paper. The contributions of the proposed algorithm lie in the following aspects: 1) the new block size and coding mode distribution features are first explored for a reasonable fast coding scheme; 2) a novel fast QTMT partition decision framework is developed, which can determine the partition decision on both QT and multi-type tree with a novel cascade decision structure; and 3) fast intra mode decision with gradient descent search is introduced, while the best initial search point and search step are also investigated in this paper. The simulation results show that the complexity reduction of the proposed algorithm is up to 70% compared to VVC reference software (VTM), and averagely 63% encoding time saving is achieved with 1.93% BDBR increasing. Such results demonstrate that our method yields a superior performance in terms of computational complexity and compression quality compared to the state-of-the-art methods.

Algoritmo simulated annealing modificado para diseño óptimo de estructuras de acero

Structural optimization aims to design structures under certain constraints to achieve better behavior and have a proper manufacturing cost. This type of optimization corresponds to highly non-linear and non-convex problems including several local optima. Therefore, to solve such problems effectively, designers need to use adequate optimization methods which can make a good balance between the computational cost and the quality of solutions. In this paper the modified simulated annealing algorithm (MSAA) is employed to solve optimal design of steel structures. MSSA is a newly improved version of the simulated annealing (SA) algorithm with three modifications: preliminary exploration, search step and a new probability of acceptance. The performance, robustness and applicability of the MSAA are demonstrated through six structural optimization problems. Obtained results in all considered examples indicate that the MSAA is superior to several other methods in existing literature in terms of the quality of solution and convergence speed.

A tight linear time $$\frac{13}{12}$$ 13 12 -approximation algorithm for the $$P2 || C_{\max }$$ P 2 | | C max problem

We consider problem $$P2 || C_{\max }$$ where the goal is to schedule n jobs on two identical parallel machines to minimize the makespan. We focus on constant factor approximation algorithms with complexity independent from the required accuracy. We exploit the famous Longest Processing Time (LPT) rule that requires to sort jobs in non-ascending order of processing times and then to assign one job at a time to the machine whose load is smallest so far. We propose an approximation algorithm that applies LPT to a subset of 2k jobs, then considers a single step of local search on the obtained subschedule and finally applies list scheduling to the remaining jobs. This algorithm, when applied for $$k=5$$ , reaches a tight $$\frac{13}{12}$$ -approximation ratio improving the ratio of $$\frac{12}{11}$$ proposed by He et al. (Nav Res Logist 47:593–601, 2000). We use Mathematical Programming to analyze the approximation ratio of our approach. As a byproduct, we also show how to improve the FPTAS of Sahni for any $$n > 1/\epsilon $$ so as to reach an approximation ratio $$(1 + \epsilon )$$ with time complexity $$O(n + \frac{1}{\epsilon ^3})$$ .