Pattern Search Method Research Articles

Crack detections in fatigue loaded structures by means of low-cost Thermoelastic Stress Analysis setups

The work presents the results of an experimental evaluation of the thermoelastic signal on fatigue loaded single-edge-notched and tensile samples made of stainless steel. The thermographic signal is acquired by a micro-bolometer sensor Flir A655sc. The evaluation is performed varying the loading frequency and sampling frequency. The thermoelastic signal is evaluated by a Discrete Fourier Transform algorithm accounting for the influence of spectral leakage. Two different crack tip search algorithms are applied and compared: a grid method with pixel and sub-pixel accuracy and a sub-pixel pattern search method, both based on least square fitting of the Williams’ Series stress function. The two algorithms are also evaluated in terms of accuracy, automation level and computation timing. The SIF evaluated by the Micro-Bolometer is corrected by a specific camera dependent calibration procedure, and the influence of the number of points used in the least square fitting on the final value of the SIF is investigated in order to obtain the fastest and reliable setup conditions for an automatic full fatigue crack growth characterisation with a non-contact and low cost Infrared camera system.

Data clustering using hybrid water cycle algorithm and a local pattern search method

Cluster analysis is a valuable data analysis and data mining technique. Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems including data clustering. In this paper, a recently proposed algorithm called the water cycle algorithm, based on the evaporation rate is used in conjunction with a local search method namely Hookes and Jeeves method to perform data clustering. Statistical analyses were carried out which show that the hybrid optimization method, in general, performs superior to the methods reported in the literature in terms of solution quality as well as computational performance. The proposed hybrid algorithm is tested on some selected standard datasets obtained from the UCI machine-learning repository. The objective function is based on the Euclidean distance as well as the DB index. The experimental results were compared with the data clustering results reported in published literature. The simulation results confirm the superiority of the proposed hybrid method as an efficient and reliable algorithm to solve clustering problems.

Trihedral lattice towers geometry optimization

The problem of trihedral lattice towers geometry optimization, the width of which varies linearly with height, has been considered. The variable parameters were the support width at the base and top point, as well as the cross-sectional areas of the lattice and chords. A restriction was introduced on the mass constancy. The objective function was the potential strain energy, the maximum displacement and the first frequency of natural vibrations. In the first and second cases the optimum corresponds to the minimum of the objective function, and in the third - to the maximum. The solution was performed by the finite element method in combination with four nonlinear optimization methods: the interior point method, surrogate optimization method, genetic algorithm and pattern search method. The efficiency of each of the listed optimization methods has been compared by the authors.

Performance and Optimization of Outage Probability in CBTC Systems

The performance of two Access Points (APs) Communication-Based Train-Control (CBTC) systems subjected to an imperfect channel state information (CSI) scenario is investigated. More precisely, we first model the realistic small-scale fading distribution for which the receiver only has the outdated CSI due to the mobility of the train. We then derive the Outage Probability (OP) of the train and the ergodic capacity of the system in the closed-form expression based on the considered channel model. Additionally, we formulate an optimization problem that minimizes the maximal OP, which corresponds to the location of the train. For such, the signal-to-noise-ratio (SNR) is minimized as a function of the position of two APs. The pattern search method is utilized to solve the considered minimization problem. Numerical results confirm that by the optimal placement of the position of APs, the OP can be significantly ameliorated compared to the other placement methods. Furthermore, the numerical results also illustrate that the proposed solution completely coincides with the exhaustive search but with much less computation time. Finally, the mathematical framework is verified extensively by Monte Carlo simulations.

Techno-Economic Assessment of Hydrogen Usage in a Smart Grid, Employing a Staggered Optimization Algorithm

A sustainable energy economy implies high shares of volatile renewable energy sources and requires the use of energy storage technologies. Hydrogen is a very flexible energy carrier and can be employed as a large-scale energy storage in electric grids. This paper focuses on the integration of hydrogen production, conversion and storage options in a smart grid environment. A process current source (PCS) that functions as a rectifier for an alkaline electrolysis system and is compared to a conventional rectifier structure that is not specifically designed for dynamic operation. All components of the smart grid are scaled using a staggered algorithm that combines a pattern search algorithm and a genetic algorithm. This smart optimization tool shows high flexibility, accuracy and low computing times. The required computing time has been reduced by 56% in contrast to genetic algorithms without the pattern search method. Furthermore, the energy system optimization reduced the alkaline electrolysis below 30% of the initial scale in order to yield lower costs. Therefore, the difference in rectifier performance was reduced to a minor contribution.

Метод и однородное устройство матричного поиска простого и составного образцов

The research consists in the development of hardware pattern search methods that use the principles of shared access and parallel data processing inherent in associative memory. Reducing the search time is achieved through the formation and parallel processing of a binary (characteristic) matrix of com parisons of pattern symbols and text. A composite pattern has been introduced, it allows flexible description of search terms. The characteristic matrix is in the form of a parallelogram; it consists of rows shifted to the right, starting from the first row. This form allows you to conduct a parallel search for simple and composite patterns on the diagonal elements of the matrix. The developed method supports hardware search in the characteristic matrix. It is distinguished by the simultaneous consideration of local and distributed relationships between the elements of the diagonals and rows of the matrix. Separate calculation of starting values along the diagonals of the matrix allows you to independently search for two types of samples by calculating in the cells of the diagonals of the characteristic matrix of two output search functions. The method has linear time and quadratic hardware complexity. The clock pulse duration is determined by the sum of the delays of the comparison circuit for a pair of symbols, a D-flip-flop and a two-input element I. The homogeneous structure of the matrix search device, the use of standard operations allow the device to be implemented on a promising FPGA element base, which determines its use in high-performance systems for processing and transmitting heterogeneous information.

Multivariable Unconstrained Pattern Search Method for Optimizing Digital PID Controllers Applied to Isolated Forward Converter

Most of the traditional PID tuning methods are heuristic in nature. The heuristic approach-based tuned PID controllers show only nominal performance. In addition, in the case of a digital redesign approach, mapping of the heuristically-designed continuous-time PID controllers into discrete-time PID controllers and in case of the direct digital design approach, mapping of the continuous-time plant (forward converter) into the discrete-time plant, results in frequency distortion (or warping). Besides this, nonlinear elements such as ADC and DAC, and delay in the digital control loop deteriorate the control performance. There is a need to tune conventionally-designed digital controllers to enhance performance. This paper proposes optimized discrete-time PID controllers for a forward DC–DC converter operating in continuous conduction mode (CCM). The considered conventional digital PID controllers designed on the basis of the digital redesign and direct digital approaches are tuned by one of the multivariable unconstrained pattern search methods named Hooke–Jeeves (H–J) search method to ensure excellent output voltage regulation performance against the changes in input voltage and load current. Numerical results show that the H–J-based optimized PID compensated forward converter system shows tremendous improvement in performance compared to its unoptimized counterpart and simulated annealing (SA)-based compensated system, thus justifying the applicability of the H–J method for enhancing the performance.

Kernel-based curve-fitting method with spatial regularization for generation of parametric images in dynamic PET

Due to high levels of noise in pixel-wise time-activity curves, the indirect method that generates kinetic parametric images from dynamic positron emission tomography (PET) images often results in poor parametric image quality. We have demonstrated that the quality of parametric images can be improved by denoising dynamic PET images, using gradient-free curve-fitting and applying a kernel-based post-filtering to parametric images. However, many gradient-free curve-fitting methods are time-consuming. Moreover, some parameter estimates (e.g. k2 and k3) have large variability. To provide high-quality PET parametric images with low computational cost, we propose a curve-fitting method that incorporates the kernel-based denoising method and the highly constrained backprojection technique into the Levenberg-Marquardt (LM) algorithm. We conducted a simulation study to evaluate the performance of the proposed curve-fitting method. Dynamic PET images were reconstructed using the expectation-maximization (EM) algorithm and were denoised before parameter estimation. Compared to the LM algorithm with and without the kernel-based post-filtering, the proposed method achieved superior performance, offering a decrease in both bias and coefficient of variation (CV) on all parametric images. Overall, the proposed method exhibited lower bias and slightly higher CV than the gradient-free pattern search method with the kernel-based post-filtering (PatS-K). Moreover, the computation time of the proposed method was about 18 times lower than that of the PatS-K method. Finally, we show that the proposed method can further improve the quality of parametric images when dynamic PET images are reconstructed using the kernel-based EM algorithm.

Optimal Design of Outer-Rotor Surface Mounted Permanent Magnet Synchronous Motor for Cogging Torque Reduction Using Territory Particle Swarm Optimization

In this paper, territory particle swarm optimization (TPSO) is proposed for the optimal design of surface mounted permanent magnet synchronous motor (SPMSM) for high altitude long endurance (HALE) unmanned aerial vehicle (UAV). The proposed algorithm exploits the concept of a territory to the particle swarm optimization (PSO) to solve the problem that the conventional PSO cannot find the multimodal solutions. Also, by combining the stochastic method, PSO, with the deterministic method, pattern search method, it is possible to find optimal solutions quickly and accurately. The superiority of the proposed algorithm is verified through comparison in the test functions with niching genetic algorithm, which is well known multimodal optimization algorithm. The TPSO is applied to the optimization design of outer-rotor SPM for HALE UAV, and the optimal design with reduced cogging torque has been derived.

Chattering Free Adaptive Sliding Mode Controller for Photovoltaic Panels with Maximum Power Point Tracking

Photovoltaic system is utilized to generate energy that relies upon the ecological conditions, for example, temperature, irradiance, and the load associated with it. Considering the non-linear component of photovoltaic (PV) array and the issue of low effectiveness because of the variable natural conditions, the Maximum Power Point Tracking (MPPT) method is required to extract the maximum power from the PV system. The adopted control is executed utilizing an Adaptive Sliding Mode Controller (ASMC) and the enhancement is actualized utilizing an Improved Pattern Search Method (IPSM). This work employs IPSM based optimization approach in order to command the underlying ASMC controller. The upper level decision determines the sliding surface for the adaptive controller. As a non-linear strategy, the stability of the adaptive controller is guaranteed by conducting a Liapunov analysis. On the practical side, MATLAB/Simulink is used as simulator for the controller implementation and coupling with PSIM in order to connect it with the PV system object of control. The simulation results validate that the proposed controller effectively improves the voltage tracking, system power with reduced chattering effect and steady-state error. The performance of the proposed control architectures is validated by comparing the proposals with that of the well-known and widely used Proportional Integral Derivative (PID) controller. That operated as a lower level controller for a Perturb & Observe (P&O) and Particle Swarm Optimization (PSO).

Identification of Dynamic Characteristics Using Vibration Measurement Data of Saemangeum Mangyeong Offshore Observation Tower and Numerical Model Updating by Pattern Search Method

해상에 설치된 관측타워에 미치는 환경 하중 변화에 따른 구조물의 동특성 íŒŒì• ì€ 구조물의 ì•ˆì „ì„± 평가에 중요한 인자로 í™œìš©ë˜ê³ ìžˆë‹¤. 이 연구에서는 새만금 ë°©ì¡°ì œ 인근에 위치한 만경해상관측타워(이하 만경타워) 구조물에 대한 현장계측실험을 통하여 동특성을 ë¶„ì„í•˜ê³ ìˆ˜ì¹˜í•´ì„ëª¨ë¸ì„ 구성하였다. 계측실험 ê²°ê³¼, 조위가 í•˜ê°•í• ìˆ˜ë¡ ê³ ìœ ì£¼íŒŒìˆ˜ëŠ” 증가하는 추세를 보였다. 또한 동일한 모드가 2개의 주파수를 갖는 것을 확인하였으며, 이는 세굴에 의하여 파일과 지반이 ì ‘ì´‰ 시에 ê³ ìœ ì£¼íŒŒìˆ˜ê°€ 일부 증가하는 현상으로 판단되었다. 수치해석을 위하여 구조물의 상부 질량, ê°€ìƒê³ ì •ì , 세굴 깊이 및 ìœ ì²´ 영향을 ê³ ë ¤í•œ 부가 질량 등을 만경타워의 êµ¬ì¡°ì  특성으로 반영하였으며, ì¶”ì •ëœ ê³ ìœ ì£¼íŒŒìˆ˜ 및 패턴서치 ì•Œê³ ë¦¬ì¦˜ìœ¼ë¡œë¶€í„° 수치해석모델에 대한 모델 ê°œì„ ìž‘ì— ì„ 수행하였다. ê°œì„ ëœ 해석모델로부터 추후 만경타워에 대한 ì•ˆì •ì„± ê²€í† ì¸¡ë©´ì—ì„œì˜ 연구에 ì ìš©ë ìˆ˜ 있을 것으로 기대된다. 핵심용어: 만경타워, ê³ ìœ ì£¼íŒŒìˆ˜, ê°€ìƒê³ ì •ì , 세굴 깊이, 모델 개ì„

Search and comparison of (epi)genomic feature patterns in multiple genome browser tracks

BackgroundGenome browsers are widely used for locating interesting genomic regions, but their interactive use is obviously limited to inspecting short genomic portions. An ideal interaction is to provide patterns of regions on the browser, and then extract other genomic regions over the whole genome where such patterns occur, ranked by similarity.ResultsWe developed SimSearch, an optimized pattern-search method and an open source plugin for the Integrated Genome Browser (IGB), to find genomic region sets that are similar to a given region pattern. It provides efficient visual genome-wide analytics computation in large datasets; the plugin supports intuitive user interactions for selecting an interesting pattern on IGB tracks and visualizing the computed occurrences of similar patterns along the entire genome. SimSearch also includes functions for the annotation and enrichment of results, and is enhanced with a Quickload repository including numerous epigenomic feature datasets from ENCODE and Roadmap Epigenomics. The paper also includes some use cases to show multiple genome-wide analyses of biological interest, which can be easily performed by taking advantage of the presented approach.ConclusionsThe novel SimSearch method provides innovative support for effective genome-wide pattern search and visualization; its relevance and practical usefulness is demonstrated through a number of significant use cases of biological interest. The SimSearch IGB plugin, documentation, and code are freely available at https://deib-geco.github.io/simsearch-app/ and https://github.com/DEIB-GECO/simsearch-app/.

Optimization of multi-electrode implant configurations and programming for the delivery of non-ablative electric fields in intratumoral modulation therapy.

Application of low intensity electric fields to interfere with tumor growth is being increasingly recognized as a promising new cancer treatment modality. Intratumoral modulation therapy (IMT) is a developing technology that uses multiple electrodes implanted within or adjacent tumor regions to deliver electric fields to treat cancer. In this study, the determination of optimal IMT parameters was cast as a mathematical optimization problem, and electrode configurations, programming, optimization, and maximum treatable tumor size were evaluated in the simplest and easiest to understand spherical tumor model. The establishment of electrode placement and programming rules to maximize electric field tumor coverage designed specifically for IMT is the first step in developing an effective IMT treatment planning system. Finite element method electric field computer simulations for tumor models with 2 to 7 implanted electrodes were performed to quantify the electric field over time with various parameters, including number of electrodes (2 to 7), number of contacts per electrode (1 to 3), location within tumor volume, and input waveform with relative phase shift between 0 and 2π radians. Homogeneous tissue specific conductivity and dielectric values were assigned to the spherical tumor and surrounding tissue volume. In order to achieve the goal of covering the tumor volume with a uniform threshold of 1V/cm electric field, a custom least square objective function was used to maximize the tumor volume covered by 1V/cm time averaged field, while maximizing the electric field in voxels receiving less than this threshold. An additional term in the objective function was investigated with a weighted tissue sparing term, to minimize the field to surrounding tissues. The positions of the electrodes were also optimized to maximize target coverage with the fewest number of electrodes. The complexity of this optimization problem including its non-convexity, the presence of many local minima, and the computational load associated with these stochastic based optimizations led to the use of a custom pattern search algorithm. Optimization parameters were bounded between 0 and 2π radians for phase shift, and anywhere within the tumor volume for location. The robustness of the pattern search method was then evaluated with 50 random initial parameter values. The optimization algorithm was successfully implemented, and for 2 to 4 electrodes, equally spaced relative phase shifts and electrodes placed equidistant from each other was optimal. For 5 electrodes, up to 2.5cm diameter tumors with 2.0V, and 4.1cm with 4.0V could be treated with the optimal configuration of a centrally placed electrode and 4 surrounding electrodes. The use of 7 electrodes allow for 3.4cm diameter coverage at 2.0V and 5.5cm at 4.0V. The evaluation of the optimization method using 50 random initial parameter values found the method to be robust in finding the optimal solution. This study has established a robust optimization method for temporally optimizing electric field tumor coverage for IMT, with the adaptability to optimize a variety of parameters including geometrical and relative phase shift configurations.

Gear Ratio Optimization along with a Novel Gearshift Scheduling Strategy for a Two-Speed Transmission System in Electric Vehicle

A novel gearshift scheduling strategy has been framed for a two-speed transmission system in electric vehicles that can save energy during hilly driving and frequently changing driving conditions through efficient electric motor operation. Unlike the traditional approach, the proposed gearshift strategy is based on the preferred vehicle speed range, vehicle acceleration, and road grade to ensure desired vehicle performances with minimum energy consumption. Meanwhile, the vehicle speed range is chosen around the electric motor rated speed, and two gearshift schedules in relation to vehicle acceleration and road grade are developed based on the motor torque generating capacity and efficiency. Appropriate gear is selected through a combined assessment of the required vehicle speed, acceleration, and road grade information. A guideline is developed and explained for the primary gearshift schedule. Next, the gear ratios and gearshift schedules are optimized combinedly in a Simulink environment using the gradient descent method and pattern search method on three driving cycles separately. Depending on the driving scenarios, around 4% to 7.5% energy saving has been experienced through optimization, while the gear ratios and gearshift schedule in relation to the road grade are found to be major contributors to the vehicle economic driving compared to that with the gearshift schedule for vehicle acceleration.

Structural model updating of the Gageocho Ocean Research Station using mass reallocation method

To study oceanic and meteorological problems related to climate change, Korea has been operating several ocean research stations (ORSs). In 2011, the Gageocho ORS was attacked by Typhoon Muifa, and its structural members and several observation devices were severely damaged. After this event, the Gageocho ORS was rehabilitated with 5 m height to account for 100-yr extreme wave height, and the vibration measurement system was equipped to monitor the structural vibrational characteristics including natural frequencies and modal damping ratios. In this study, a mass reallocation method is presented for structural model updating of the Gageocho ORS based on the experimentally identified natural frequencies. A preliminary finite element (FE) model was constructed based on design drawings, and several of the candidate baseline FE models were manually built, taking into account the different structural conditions such as corroded thickness. Among these candidate baseline FE models, the most reasonable baseline FE model was selected by comparing the differences between the identified and calculated natural frequencies; the most suitable baseline FE model was updated based on the identified modal properties, and by using the pattern search method, which is one of direct search optimization methods. The mass reallocation method is newly proposed as a means to determine the equivalent mass quantities along the height and in a floor. It was found that the natural frequencies calculated based on the updated FE model was very close to the identified natural frequencies. In conclusion, it is expected that these results, which were obtained by updating a baseline FE model, can be useful for establishing the reference database for jacket-type offshore structures, and assessing the structural integrity of the Gageocho ORS.

Motion Parameter Identification and Motion Compensation for Shipborne HFSWR by Using the Reference RF Signal Generated at the Shore

The shipborne high-frequency surface wave radar (HFSWR) platform produces six degrees of freedom (DOF) motion at sea, which affects the performance of radar target detection and remote sensing of ocean surface dynamics parameters. Motion compensation can mitigate the effect of six-DOF motion, but motion parameters (including amplitude and angular frequency) need to be known. Motion parameters obtained by using high precision sensors are affected by the precision error and time delay, thus affecting the effect of motion compensation. To obtain the motion parameters accurately and in real time, a method of identifying the motion parameters by using an artificially transmitted reference radio frequency (RF) signal generated at the shore is proposed. Based on the results of the parameter identification, the reference RF signal and the first-order radar cross-sections (RCSs) modulated by six-DOF motion of the shipborne HFSWR platform can be compensated. The identification of angular frequency is divided into two steps: (1) Preliminary identification results are obtained by using the reference RF signal; (2) the pattern search method is used to further improve the identification accuracy of angular frequency. The amplitude of translation (including surge and sway) can be identified accurately through the reference RF signal. Due to the small amplitude of rotation (including roll, pitch, and yaw), it needs to be identified by the reference RF signal and pattern search method. After identifying the motion parameters, division in the time domain is used for motion compensation. Through the simulation results, both translation and rotation have good motion compensation effects. In addition, the method of using high precision sensors to obtain motion parameters and compensation is compared with the method in this paper, the simulation results of motion compensation show that the latter is better.

Identifying the center of innovation industrial clusters to provide the federal district's evolution foresight

Subject. This article deals with the issues of placing and locating the information and logistics center of the Federal District's clusters. Objectives. The article aims to create a technology to identify the center of innovative industrial clusters, which would ensure their effective economic, financial, information, and logistics cooperation. Methods. To solve the problem of location of the information and logistics center of clusters in order to anticipate the evolution of the Federal District, we used simulation modeling algorithms, genetic algorithm, and the simulated annealing and pattern search methods. Results. These methods have been tested for the Volga (Privolzhsky) Federal District of the Russian Federation. As a result of analysis, modeling and calculations, it turns out that the information and logistics center of the Volga (Privolzhsky) Federal District should be the City of Kazan, the Republic of Tatarstan. Conclusions and Relevance. The deployment of the information and logistics center for the Volga (Privolzhsky) Federal District in Kazan can significantly reduce the transaction costs associated with the regulation of information and transportation flows within the Federal District under study. This will reduce the financial costs in the District and increase the synergistic effect of a large innovation system uniting innovation and industrial clusters in a large area of the entire Federal District. The additional synergies will help the governmental structures and their experts conduct a better policy of economic and financial foresight of the evolution of the Volga (Privolzhsky) Federal District.

A method to circumvent the combinatorial difficulties in the optimal design of distillation columns

A new method is presented for the optimal design of distillation columns with structural and operational decisions. This has been a challenging issue because of difficulties such as complexity of problem formulation, numerical instabilities and lengthy computational times. The new approach presented here solves combinatorial optimization problems by a pattern search method. In the method, the best combination of integer values, each representing the number of stages placed in a column section of a distillation unit, is identified by comparing the objective values evaluated by the embedded NLP solver, while derivatives are not used explicitly. The reliability of the implemented algorithm is numerically confirmed by solving DWC (dividing wall column) problems. This reliability is due to the use of globally convergent pattern search in combination with a full-space Hessian SQP algorithm where global convergence in large and highly nonlinear flowsheeting problems is ensured (Ishii & Otto, 2018).

Non-interactive approach to solve multi-objective optimal power flow problem

The purpose of this research work is to solve the multi-objective optimal power flow (MO-OPF) problem using non-interactive approach. In this approach, the decision maker (DM) is not involved; however, the prior preference information is available to the DM. A satisficing function is offered to take care of the conflict between non-commensurable objectives, and the multi-objective problem is reformulated as a scalar optimization problem. This approach reduces the computation work involved for generating the Pareto front and for selecting the best satisficing solution. To attain the satisficing solutions, a hybrid optimization technique is applied, which integrates invasive weed optimization (IWO) with Powell’s pattern search (PPS) method. The IWO algorithm, utilized as the stochastic search technique, takes inspiration from the ability of weeds to adopt the environmental changes. Being a conjugate-based local search technique, the PPS method exhibits admirable exploitation search capability that further improves the solution provided by the IWO technique. The effectiveness of the proposed solution approach is confirmed by applying it to the three standard test systems, and the comparison is carried out with the well-established algorithms. Further, t test confirms the robustness of the proposed solution approach.

Hybrid particle swarm optimization and pattern search algorithm

Particle swarm optimization (PSO) is one of the most commonly used stochastic optimization algorithms for many researchers and scientists of the last two decades, and the pattern search (PS) method is one of the most important local optimization algorithms. In this paper, we test three methods of hybridizing PSO and PS to improve the global minima and robustness. All methods let PSO run first followed by PS. The first method lets PSO use a large number of particles for a limited number of iterations. The second method lets PSO run normally until tolerance is reached. The third method lets PSO run normally until the average particle distance from the global best location is within a threshold. Numerical results using non-differentiable test functions reveal that all three methods improve the global minima and robustness versus PSO. The third hybrid method was also applied to a basin network optimization problem and outperformed PSO with filter method and genetic algorithm with implicit filtering.