Quadratic Search Research Articles

Identification Algorithm for Stability Improvement of Welding Robot End-Effector

Aiming to solve the problem that the significant error between the actual joint torque and the calculated joint torque of a welding robot leads to the vibration of the end-effector, which in turn affects the stability of the end-effector, this paper proposes a identification algorithm based on the Weighted Least Squares Genetic Algorithm (WLS-GA) to construct and solve the dynamical model to obtain the accurate dynamical parameters. Firstly, a linear model of welding robot dynamics is derived. The fifth-order optimal Fourier series excitation trajectory is designed to collect experimental data such as joint torque. Then, a rough solution of the parameters to be recognized is obtained by solving the dynamics model through the Weighted Least Squares (WLS) method, the search space is determined based on the rough solution, and the optimal solution is obtained by using the Genetic Algorithm (GA) to perform a quadratic search in the search space. Finally, the identification data obtained from the algorithm is analyzed and compared with the experimental data. The results show that the error between the identification data obtained using the WLS-GA identification algorithm and the experimental data is relatively small. The results show that the identification data obtained using the WLS-GA identification algorithm have less error than the experimental data, taking the Root Mean Square (RMS) value of the joint torque error obtained using the weighted least squares algorithm as a criterion. The accuracy of the WLS-GA identification algorithm can be improved by up to 66.85% compared with that of the weighted least squares algorithm and by up to 78.0% compared with that of the Ordinary Least Squares (OLS) algorithm. In summary, the WLS-GA identification algorithm can accurately identify the dynamic parameters of the welding robot and more accurately construct a dynamic model to solve the effect of joint torque error on the control characteristics of the welding robot. It can improve the stability of the end-effector of the welding robot to ensure the quality of the automobile body and beam welding and welding speed.

SDTS: Security Driven Task Scheduling Algorithm for Real-Time Applications Using Fog Computing

Nowadays, security and scheduling have become more critical issues of rapidly increasing applications running in real-time systems. In this work, we tried to solve these intricate issues and develop a security-driven task scheduling algorithm for real-time applications using Fog computing, which is abbreviated as SDTS. It works for the enhancement of both the security and scheduling services of real-time applications. SDTS is the integration of various proposed algorithms that are modified quadratic search algorithm, Min_Heap algorithm, and modified Earliest Deadline First (m-EDF). In which, modified quadratic search, Min Heap algorithm, and m-EDF algorithms are used for evaluating the average security value of tasks, optimal nodes using Fog computing, and task selection according to their absolute deadlines, respectively. The quantitative results analysis shows that the proposed model outperforms the existing models in terms of guarantee ratio (rate of success), average security value, and overall performance, i.e. 7.4% for success ratio, 7.69% for average security value, and 1.02% for overall performance in comparison to traditional methods.

Enhanced quadratic approximation integrated with butterfly optimization: a new search algorithm tested on structural and mathematical problems

The Butterfly Optimization Algorithm (BOA) is a swarm based technique, inspired from mating and food searching process of butterflies, developed in last year. Experiments indicate that BOA provides substantial exploration capability on conventional non-constrained benchmark problems, however for the cases with more complex and noisy domains the algorithm can easily be trapped into local minima due to its restricted exploitation behavior. To tackle this issue, current study deals with introducing an alternative search strategy to explore the region of the search domain with high certainty. Such that, firstly a weighted agent is defined and then a quadratic search is performed in the vicinity of this pre-defined agent. This alternative search strategy is named as Enhanced Quadratic Approximation (EQA) and it is combined with BOA method to improve its exploitation behavior and provide an efficient search algorithm. Thus, obtained new method is named as Enhanced Quadratic Approximation Integrated with Butterfly Optimization (EQB) algorithm. Different properties of proposed EQB are tested on mathematical and structural benchmark problems. Acquired results show that the introduced algorithm, in comparison with its parent method and some other well-stablished reported algorithms in the literature, provides a competitive performance in terms of stability, accuracy and convergence rate.

OpenPepXL: An Open-Source Tool for Sensitive Identification of Cross-Linked Peptides in XL-MS

Cross-linking MS (XL-MS) has been recognized as an effective source of information about protein structures and interactions. In contrast to regular peptide identification, XL-MS has to deal with a quadratic search space, where peptides from every protein could potentially be cross-linked to any other protein. To cope with this search space, most tools apply different heuristics for search space reduction. We introduce a new open-source XL-MS database search algorithm, OpenPepXL, which offers increased sensitivity compared with other tools. OpenPepXL searches the full search space of an XL-MS experiment without using heuristics to reduce it. Because of efficient data structures and built-in parallelization OpenPepXL achieves excellent runtimes and can also be deployed on large compute clusters and cloud services while maintaining a slim memory footprint. We compared OpenPepXL to several other commonly used tools for identification of noncleavable labeled and label-free cross-linkers on a diverse set of XL-MS experiments. In our first comparison, we used a data set from a fraction of a cell lysate with a protein database of 128 targets and 128 decoys. At 5% FDR, OpenPepXL finds from 7% to over 50% more unique residue pairs (URPs) than other tools. On data sets with available high-resolution structures for cross-link validation OpenPepXL reports from 7% to over 40% more structurally validated URPs than other tools. Additionally, we used a synthetic peptide data set that allows objective validation of cross-links without relying on structural information and found that OpenPepXL reports at least 12% more validated URPs than other tools. It has been built as part of the OpenMS suite of tools and supports Windows, macOS, and Linux operating systems. OpenPepXL also supports the MzIdentML 1.2 format for XL-MS identification results. It is freely available under a three-clause BSD license at https://openms.org/openpepxl.

Adaptive Powell’s Identification of Elastic Constants of Composite Glass Girder with Layered Shell Element Theory

For the composite glass box girder, the generalized Bayesian objective function of elastic constants of the structure was derived based on layered shell element theory. Mechanical performances of the composite glass box girder were solved by layered shell element method. Combined with quadratic parabolic interpolation search scheme of optimized step length, the adaptive Powell’s optimization theory was taken to complete the stochastic identification of elastic constants of composite glass box girder. Then the adaptive Powell’s identification steps of elastic constants of the structure were presented in detail and the adaptive Powell’s identification procedure was accomplished. From some classic examples, it is finally achieved that the adaptive Powell’s identification of elastic constants of composite glass box girder has perfect convergence and numerical stability, which testifies that the adaptive Powell’s identification theory of elastic constants of composite glass box girder is correct and reliable. The stochastic characteristics of systematic responses and elastic constants are well deliberated in generalized Bayesian objective function. And in iterative processes, the adaptive Powell’s identification is irrelevant with the complicated partial differentiation of the systematic responses from the layered shell element model to the elastic constants, which proves high computation efficiency.

The Minimum Wave Resistance of Hull Form Design Method Based on CFD Method

To reduce the resistance of the ship in actual navigation, a hull form optimization design method for minimum wave resistance is proposed. A numerical wave tank of regular waves is established based on the CFD (Computational Fluid Dynamics) theory. Overlapping grid technology is adopted to mesh the hull form and free surface. The new hull offsets are generated by using the parameters of modified function as design variables. The geometric expression and reconstruction of optimized hull form is determined through the three-dimensional CAD (Computer Aided Design) modeling technology. The displacement limit was used as the basic constraint, and other additional constraints were considered; nonlinear programming quadratic line search algorithm was used to set up a mathematical model of hull form optimization on the ISIGHT platform. The optimization calculations of Wigley hull form and surface ship DTMB5415 are carried out. Compared with the parent hull, the wave resistance of the optimized hull form is significantly reduced, and the feasibility of this method is confirmed. The research results can provide technical support for the development of "green ship"design.

Efficiency Analysis of Binary Search and Quadratic Search in Big and Small Data

When doing a searching process, Binary Search is one of the classic algorithm used in sorted data. The characteristic of this algorithm is to make a comparison of the keywords you want to find with the start, middle, and end values of a data series. Keyword search is done by reducing the range of start and end points to finally find the keyword you want to search. The time complexity of the binary search algorithm is O(log2n) while the memory capacity needed is O(1) for iterative implementation and O(log2n) for recursive implementation. This research will develop a level of comparison in binary search in order to get optimal performance in accordance with the amount of data available.

An Improved Network Traffic Classification Model Based on a Support Vector Machine

Network traffic classification based on machine learning is an important branch of pattern recognition in computer science. It is a key technology for dynamic intelligent network management and enhanced network controllability. However, the traffic classification methods still facing severe challenges: The optimal set of features is difficult to determine. The classification method is highly dependent on the effective characteristic combination. Meanwhile, it is also important to balance the experience risk and generalization ability of the classifier. In this paper, an improved network traffic classification model based on a support vector machine is proposed. First, a filter-wrapper hybrid feature selection method is proposed to solve the false deletion of combined features caused by a traditional feature selection method. Second, to balance the empirical risk and generalization ability of support vector machine (SVM) traffic classification model, an improved parameter optimization algorithm is proposed. The algorithm can dynamically adjust the quadratic search area, reduce the density of quadratic mesh generation, improve the search efficiency of the algorithm, and prevent the over-fitting while optimizing the parameters. The experiments show that the improved traffic classification model achieves higher classification accuracy, lower dimension and shorter elapsed time and performs significantly better than traditional SVM and the other three typical supervised ML algorithms.

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.

The Balance Condition in Search‐and‐Matching Models

Most of the literature that studies frictional search‐and‐matching models with heterogeneous agents and random search investigates steady state equilibria. Steady state equilibrium requires, in particular, that the flows of agents into and out of the population of unmatched agents balance. We investigate the structure of this balance condition, taking agents' matching behavior as given. Building on the “fundamental matching lemma” for quadratic search technologies in Shimer and Smith (2000), we establish existence, uniqueness, and comparative statics properties of the solution to the balance condition for any search technology satisfying minimal regularity conditions. Implications for the existence and structure of steady state equilibria in the Shimer–Smith model and extensions thereof are noted. These reinforce the point that much of the structure of search‐and‐matching models with quadratic search technologies carries over to more general search technologies.

Analysis of variant working vacation queue with reneging under a multi-server environment

ABSTRACTThis paper analyses an M/M/c queueing system with variant working vacations. The service times during regular busy periods, working vacation periods and vacation times are exponentially distributed and are mutually independent. During working vacation, customers may renege due to impatience and the impatient timer follows an exponential distribution. We derive the probability generating function of the steady-state probabilities and obtain some performance measures. The cost function associated with the model is also constructed and its optimization is investigated using quadratic fit search method. We have also presented the numerical illustrations of the effect of some parameters on the performance measures using graphs.

Steady States in Search-and-Matching Models

Most of the literature that studies frictional search-and-matching models with heterogeneous agents and random search investigates steady-state equilibria. Steady-state equilibrium requires, in particular, that the flows of agents into and out of the population of unmatched agents balance. We investigate the structure of this balance condition, taking agents' matching behavior as given. Building on the fundamental matching lemma for quadratic search technologies in Shimer and Smith (2000), we establish existence, uniqueness, and comparative-static properties of the solution to the balance condition for any search technology satisfying minimal regularity conditions. Implications for the existence and structure of steady-state equilibria are noted.

Accuracy and Performance of Functional Parameter Estimation Using a Novel Numerical Optimization Approach for GPU-Based Kinetic Compartmental Modeling.

Quantitative kinetic parameters derived from dynamic contrast-enhanced (DCE) data are dependent on signal measurement quality and choice of pharmacokinetic model. However, the fundamental optimization analysis method is equally important and its impact on pharmacokinetic parameters has been mostly overlooked. We examine the effects of those choices on accuracy and performance of parameter estimation using both computer processing unit and graphical processing unit (GPU) numerical optimization implementations and evaluate the improvements offered by a novel optimization approach. A test framework was developed where experimentally derived population-average arterial input function and randomly sampled parameter sets {Ktrans, Kep, Vb, τ} were used to generate known tissue curves. Five numerical optimization algorithms were evaluated: sequential quadratic programming, downhill simplex (Nelder–Mead), pattern search, simulated annealing, and differential evolution. This was combined with various objective function implementation details: delay approximation, discretization and varying sampling rates. Then, impact of noise and CPU/GPU implementation was tested for speed and accuracy. Finally, the optimal method was compared to conventional implementation as applied to clinical DCE computed tomography. Nelder–Mead, differential evolution and sequential quadratic programming produced good results on clean and noisy input data outperforming simulated annealing and pattern search in terms of speed and accuracy in the respective order of 10−8%, 10−7%, and ×10−6%). A novel approach for DCE numerical optimization (infinite impulse response with fractional delay approximation) was implemented on GPU for speed increase of at least 2 orders of magnitude. Applied to clinical data, the magnitude of overall parameter error was <10%.

Optimizing the Performance of Quadratic Search using Memorization

Time is the most important feature that counts for an executing process. Nowadays, applications are more focused to reduce the computational time and thus to increase the speed. Therefore, there is a need to redesign the fundamental algorithms especially for searching methods to achieve this objective. Targeting this, approach of memorization is proposed in this paper to optimize the time of searching during execution. It is an optimization technique used to eliminate re-execution time for pure function. A novel approach of memoized Quadratic Search Algorithm is presented in this paper. Results show remarkable improvement in the performance in terms of time leading to achieve the said objective. This will help to optimize the time in other respective areas of computation processes. Keywords: Binary Search, Quadratic Search, Optimization, Memoization, Execution Time.

Obstacle Avoidance for Redundant Manipulators Utilizing a Backward Quadratic Search Algorithm

Obstacle avoidance can be achieved as a secondary task by appropriate inverse kinematics (IK) resolution of redundant manipulators. Most prior literature requires the time-consuming determination of the closest point to the obstacle for every calculation step. Aiming at the relief of computational burden, this paper develops what is termed a backward quadratic search algorithm (BQSA) as another option for solving IK problems in obstacle avoidance. The BQSA detects possible collisions based on the root property of a category of quadratic functions, which are derived from ellipse-enveloped obstacles and the positions of each link's end-points. The algorithm executes a backward search for possible obstacle collisions, from the end-effector to the base, and avoids obstacles by utilizing a hybrid IK scheme, incorporating the damped least-squares method, the weighted least-norm method and the gradient projection method. Some details of the hybrid IK scheme, such as values of the damped factor, weights and the clamping velocity, are discussed, along with a comparison of computational load between previous methods and BQSA. Simulations of a planar seven-link manipulator and a PUMA 560 robot verify the effectiveness of BQSA.

Maximizing the sum of a generalized Rayleigh quotient and another Rayleigh quotient on the unit sphere via semidefinite programming

The problem is a type of "sum-of-ratios" fractional programming and is known to be NP-hard. Due to many local maxima, finding the global maximizer is in general difficult. The best attempt so far is a critical point approach based on a necessary optimality condition. The problem therefore has not been completely solved. Our novel idea is to replace the generalized Rayleigh quotient by a parameter $$\mu $$μ and generate a family of quadratic subproblems $$(\hbox {P}_{\mu })'s$$(Pμ)?s subject to two quadratic constraints. Each $$(\hbox {P}_{\mu })$$(Pμ), if the problem dimension $$n\ge 3$$n?3, can be solved in polynomial time by incorporating a version of S-lemma; a tight SDP relaxation; and a matrix rank-one decomposition procedure. Then, the difficulty of the problem is largely reduced to become a one-dimensional maximization problem over an interval of parameters $$[\underline{\mu },\bar{\mu }]$$[μ?,μ¯]. We propose a two-stage scheme incorporating the quadratic fit line search algorithm to find $$\mu ^*$$μ? numerically. Computational experiments show that our method solves the problem correctly and efficiently.

Ergodesk-desktop Ergonomics Using the New Quadratic Search Algorithm

Ergonomics is nothing but the rules governing the workplace. ErgoDesk is the ultimate drug-free way to look after your spine and health. This research provides an interesting and realistic solution towards achieving the goal maintaining good health. Physical stress at the work environment can reduce efficiency of the individuals at work. Ergonomics is described as the rules to be adapted by one, at work environment. The main focus of ergonomics is to reduce the physical stress caused by factors like improper body mechanics, repetitive motor movements, static positions, vibrations, lighting and impact or contact with objects. Henceforth, through this paper, we present a distinct tool called the “ERGODESK”, which could be useful for monitoring a computer user’s posture and activities. In this study, we present a real time feedback system for detecting people and their postures and generating summaries of postures and activities over a specified period of time. The system runs reliably on different people and under any lighting. The fundamental challenge, to detect the change in user’s posture most accurately in the least time, has been analysed and a solution in form of new Quadratic Search Algorithm has been proposed. The system captures an image of the user at regular intervals of time, carries out certain pre-processing steps and then checks for a change in user’s posture by comparing it with a reference image acquired previously in series of steps as per the New Quadratic Search algorithm. Then the user is notified about the change of his ergonomic posture.

Discrete-time renewal input state dependent queue with working vacations and change over time

This paper considers a discrete-time renewal input state dependent queue with multiple working vacations and change over time. Using the supplementary variable and recursive techniques, the steady state system length distributions at different time epochs have been derived. Based on the system length distributions, some performance measures of the model have been discussed. The optimum values of service rates have been found through quadratic fit search method under a given cost structure.

A Cuckoo Search based WDM Channel Allocation Algorithm

More and more modern metaheuristics nature–inspired algorithms are emerging and they become increasingly popular. This paper formulates an algorithm for solving the channel–allocation problem in optical wavelength division multiplexing (WDM) systems to suppress four–wave mixing crosstalk (FWM) based on a novel nature–inspired algorithm, called Cuckoo Search algorithm by using the concept of Optimal Golomb ruler (OGR) sequences. Simulation results conclude the significance performance improvement, without the requirement of increased total optical channel bandwidth, unlike two existing classical channel–allocation algorithms i.e. Extended Quadratic Congruence (EQC) and Search Algorithm (SA) and one of the existing nature–inspired algorithm i.e. Genetic Algorithm (GA). General Terms Classical computing, Four–wave mixing, Metaheuristics, Optimization.

Analysis of a working vacations queue with impatient customers operating under a triadic policy

The objective of this paper is to analyze a finite buffer M/M/2 working vacations queue with balking and reneging wherein the servers operate under a triadic (0,Q,N,M) policy. As soon as the system becomes empty, both the servers leave for working vacations with only one of the two servers being active during the vacation. The service times during the working vacation and the vacation times are assumed to be exponentially distributed. Various performance measures are discussed and cost analysis is carried out using the quadratic fit search method. Finally, using some numerical results we present the parameter effect on the performance measures of the model.