Earticle

This study uses a characteristic function to explain correlations between the objective function and design variables. For the use, structural analysis and buckling analysis are carried out. the dimensional change of an original overhead crane is made based on the table of orthogonal array. For two functions or more, the effectiveness of design change can be evaluated in accordance with change in design parameters. Also, the overhead crane's weight is reduced by up to 10.55 percent while its structural stability maintained.

Robust design technology has been applied to versatile engineering problems to ensure consistency in product performance. Since 1980s, the concept of robust design has been introduced to numerical optimization field, which is called the robust optimization. The robustness in the robust optimization is determined by a measure of insensitiveness with respect to the variation of a response. However, there are significant difficulties associated with the calculation of variations represented as its mean and variance. To overcome the current limitation, this research presents an implementation of the approximate statistical moment method based on kriging metamodel. Two sampling methods are simultaneously utilized to obtain the sequential surrogate model of a response. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method. Then, the simulated annealing algorithm of global optimization methods is adopted to find the global robust optimum. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.

This research introduces an innovative approach in implementing Know Your Customer (KYC) on blockchain technology as a means of using data, hybrid robust m-estimation, and the iteratively reweighted less squares (IRLS) method to optimize CLV data. This approach aims to improve the accuracy and reliability of CLV predictions by ensuring the security and reliability of customer data. This tool can help companies manage and increase CLV more effectively, meeting data security and compliance standards. The R-squared validation test results are close to 1, so the model can explain data variations well. RMSE and MSE have small values, ??so the model has good performance in predicting the target value. With these achievements, this approach contributes to the development of better marketing strategies and business decisions in an increasingly complex and rapidly changing digital environment.

Since randomness and uncertainties of design parameters are inherent, the robust design has gained an ever increasing importance in mechanical engineering. The robustness is assessed by the measure of performance variability around mean value, which is called as standard deviation. Hence, constraints in robust optimization problem can be approached as probability constraints in reliability based optimization. Then, the FOSM (first order second moment) method or the AFOSM (advanced first order second moment) method can be used to calculate the mean values and the standard deviations of functions describing constraints and object. Among two methods, AFOSM method has some advantage over FOSM method in evaluation of probability. Nevertheless, it is difficult to obtain the mean value and the standard deviation of objective function using AFOSM method, because it requires that the mean value of function is always positive. This paper presented a special technique to overcome this weakness of AFOSM method. The mean value and the standard deviation of objective function by the proposed method are reliable as shown in examples compared with results by FOSM method.

The different issues in the wireless channel, such as multipath fading, shadowing, and hidden terminal problems, limit the sensing abilities of the single user in a cognitive radio network (CRN). In this connection, it is more suitable to follow cooperative spectrum sensing (CSS). However, the various categories of malicious users (MUs) pose threats to the performance of CSS. These MUs are eager to report false sensing data to the fusion center (FC) to deceive its decision. This work considered the CSS performance in the presence of always yes MU (AYMU), opposite MU (OMU), and always no MU (ANMU). Moreover, the considered particle swarm optimization (PSO) algorithm takes out reports of the MUs using one to many sensing distances (OTMSD) and z-score outlier detection techniques before the final decision. Simulation results validate the effectiveness of the proposed scheme in the presence of MUs.

Taguchi's robust parameter design is an approach to reduce the performance variation of quality characteristics in products and processes. In robust design, the signal-to-noise ratio (SN ratio) was used to find the optimum condition to minimize the variation of quality characteristics as much as possible and bring the average of quality characteristics closer to the target value. In this paper, we propose a simultaneous optimization method based on a linear model of the SN ratio as a method to find the optimal condition of the control factor in case of multi-characteristics. In addition, the proposed method and the existing method were compared and studied by taking actual cases.

Robust design is an approach to reduce the performance variation of mutiple responses in products and processes. In fact, in many experimental designs require the simultaneous optimization of multiple responses. In this paper, we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

The correlation between the object function and the design parameter is shown on this paper by using the characteristic function for the mixed result of the structural analysis, the buckling analysis and the table of orthogonal array according to the original overhead crane's dimensional change. About the above two functions, the effectiveness of design change according to the change of design parameters could be estimated. Also, the overhead crane's weight is reduced up to 10.55 percent maintaining the structural stability according to the thickness of plate.

본 논문에서는 컴퓨터 비전 분야의 대표적인 도전 과제 중의 하나인 강인한 객체 추적을 위한 새로운 접근을 제안하 고 있다. 중첩과 같이 객체의 추적을 어렵게 하는 문제를 해결하기 위해 색상과 깊이를 함께 사용하는 복합 특징 공 간에서의 스웜 최적화를 기반으로 하는 강인한 객체 추적 모델을 제안한다. 추적하고자 하는 대상에 대한 색상 특징 과 함께 깊이 특징을 복합적으로 사용함으로써 추적 대상의 중첩과 재등장 상황에서도 추적 성능을 제고할 수 있다. 먼저, 입력 영상을 여러 개의 패치로 구분하고 각 패치로부터 색상 특징과 깊이 특징의 동기화 및 통합을 수행하여 색상-깊이 복합 특징 공간에서 특징을 추출한다. 다음으로 스웜 최적화를 통해 파티클의 위치를 업데이트하며 추적 대상 객체를 추적한다. 이 과정에서 추적 대상의 중첩 및 크기 변화에 대응하여 강인한 객체 추적을 가능하게 한다. 제안하는 접근법의 검증을 위해 색상-깊이 공개 데이터셋인 프린스턴 트래킹 벤치마크(PTB; Princeton Tracking Benchmark)를 사용한다. 제안하는 방법에 의한 실험 결과 기존 방법에 비해 추적 성능의 개선을 가져온다는 것을 확인할 수 있다.

This paper proposes a robust object tracking scheme that is one of the most representative challenge in computer vision research area. In order to solve the problems in object tracking such as occlusion, this paper employs swarm optimization in complex feature space using color and depth features. This complex feature space can increase performance of target tracking in occlusion and reappearance situation. First, the input image is divided into several patches, and the color and depth features are synchronized and integrated from each patch to extract features from the color-depth composite feature space. Then, the target object is tracked by updating the position of particles in swarm optimization. During this process, it enables to track target object robustly even in occlusion and size changes of the target. For performance evaluation, this paper uses Princeton Tracking Benchmark (PTB), a color-depth public dataset. The experimental results using the proposed method, it can be confirmed that tracking performance is improved compared to the existing methods.

본 논문에서는 기존 파티클 스웜 최적화를 기반으로 추적 대상 객체의 이동 궤적을 이용하는 객체 추적기에서 시간 정보 활용의 문제점을 개선한 강인한 객체 추적 알고리즘을 제안한다. 제안하는 알고리즘은 추적 대상 객체와 유사 한 특징을 가지는 변위들의 집합에 대한 위치들의 온라인 업데이트와 추적을 가능하게 한다. 객체들의 중첩을 검출 하고 추적 대상의 위치를 결정하기 위해 궤적 정보와 변위들의 집합을 기반으로 적응적 파라미터를 사용하는 규칙 기반 접근을 사용한다. 기존 알고리즘들과 비교해보면 제안하는 접근법은 가용한 정보를 복합적으로 사용함으로써 각종 임계값에 대한 적응적 조정을 가능하게 한다. 또한, 파티클 스웜 최적화에서 발산에 의한 손실과 불완전한 수 렴의 문제를 해결하기 위해 효율적인 가중치 조절 함수를 제안하고 있다. 제안하는 가중치 조절 함수는 파티클들이 최적의 해에 수렴하기 이전에 전체 프레임 영역에서 탐색할 수 있도록 한다. 유사한 특징 조합을 가지는 다중 객체 가 존재하는 환경에서 제안 알고리즘을 테스트한 결과, 기존 스웜 최적화 기반의 객체 추적기들에 비해 기존 유사 변위들에 대한 잘못된 추적을 현저히 줄이는 것을 확인할 수 있었다.

This paper proposes an enhanced object tracking algorithm to compensate the lack of temporal information in existing particle swarm optimization based object trackers using the trajectory of the target object. The proposed scheme also enables the tracking and documentation of the location of an online updated set of distractions. Based on the trajectories information and the distraction set, a rule based approach with adaptive parameters is utilized for occlusion detection and determination of the target position. Compare to existing algorithms, the proposed approach provides more comprehensive use of available information and does not require manual adjustment of threshold values. Moreover, an effective weight adjustment function is proposed to alleviate the diversity loss and pre-mature convergence problem in particle swarm optimization. The proposed weight function ensures particles to search thoroughly in the frame before convergence to an optimum solution. In the existence of multiple objects with similar feature composition, this algorithm is tested to significantly reduce convergence to nearby distractions compared to the other existing swarm intelligence based object trackers.

The frequency and magnitude of droughts and floods are increasing due to climate change, necessitating comprehensive water resource management of estuary reservoirs considering both water supply and flood control. Therefore, the purpose of this study is to optimize the seasonal managed water levels of estuary reservoirs using a Robust Decision-Making (RDM) approach. Sustainability indices were used to establish evaluation indicators that consider both water supply and flood control. Climate change data were selected from six Global Climate Models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6), designated as S1 to S6 in order of their precipitation levels, utilizing SSP (Shared Socioeconomic Pathway)1, 2, 3, and 5 scenarios. The Hydrological Simulation Program-Fortran (HSPF) was used to simulate changes in inflow due to climate change, and changes in reservoir water levels were simulated according to sluice gate operations. The evaluation indicators calculated under current managed water level conditions showed very poor performance especially in terms of water supply. The optimized managed water level based on RDM using regret was WL0 to WL5 at 0.0, 0.0, -0.3, -1.1, -1.7, and -2.2 EL. m, respectively. Applying the optimal alternative showed improvements in terms of water supply.

Catering Service Supply Chain (CSSC) is a new supply chain, of which Catering Service Integrator (CSI) is in the center accompany with Functional Catering Service Provider (FCSP). The core objective of CSSC is to provide different catering enterprises with supply chain services. Instability, non-standardization, specialty and diversity characteristics of the Chinese catering industry inevitably lead to demand uncertainty of CSSC, directly resulting in the uncertainty of operation and supply side. The uncertainty of supply chain system is the basis of robust optimization study, under the influence of uncertainty, robustness is extremely important to ensure stable income and continuous operation of supply chain. This research classifies various uncertainty scenarios, based on robust optimization method. Two strategies, service coordination with widely knowledge sharing, and synchronous decision with intelligent inducement, are designed to induce and integrate personalized demand to get the scale effect and the coordination of the entire supply chain. From a point of quantitative view, this research builds up robust optimization model to improve robustness of CSSC, deposing the target into two coordinated parts. The coordination is of great sense, including two objectives simultaneously, the first one aims to maximize the delivery rate of order demand, the second one aims to optimize profit of CSI and FCSP. According to data examination, the coordinative strategies and robust optimization model can effectively control the uncertainty of the whole supply chain, and then improve the robustness of CSSC.

A dynamic robust optimization model is established based on the objective of profit maximization to deal with the uncertainty of the problem through formulating a robust linear programming model. An equivalent robust optimization model using duality theory and auxiliary problem principle is obtained which allows the solutions to be derived more efficiently. Following the numerical simulation and sensitivity analysis is presented. The numerical simulation shows that the robust optimization mode can reduce the risk and uncertainty of reverse logistics operation process and the environment of demand uncertainty, which verifies the effectiveness of the approach.

The focus of this research is on the development, modeling and high precision robust control of an electro-mechanical continuum robot manipulator that serves as a sensing and motion system for hybrid testing. In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty controller based on sliding mode theory. This design resulted in strongly non-linear and coupled dynamics as well as an inertial moving platform that attracted model-based control strategies. A novel non-linear control technique based on sliding mode Lyapunov based was selected to meet the multiple simultaneous specification control of nonlinear, uncertain and asymptotic tracking. Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for continuum robot manipulator, because this controller is robust and stable in presence of partly uncertainties. Sliding mode controller was used to achieve a stable tracking, while the parallel fuzzy-logic optimization added intelligence to the control system through an automatic tuning of the sliding mode methodology uncertainties. Simulation results demonstrated the validity of the Mamdani parallel fuzzy-optimization control with asymptotic and stable tracking at different position inputs. This compensation demonstrated a well synchronized control signal at different excitation conditions.

This paper proposes a novel tuning approach for robust proportional-integral-derivative (PID) controller based on H∞ loop shaping synthesis in combination with gap metric and Particle Swarm Optimization (PSO) algorithm. Different from the traditional research, the controller is designed through the search region constrained by H∞ loop shaping synthesis and gap metric theorem. PSO algorithm is used for tuning the robust PID controller parameters based on the underlying constrained optimization problems without resolving complex arithmetical calculations. The control technique is applied for the robust controller design so as to get a low order structured controller and achieve robust performance and the ability of restraining disturbance. The simulation shows that the proposed method can character the set of all values of the controller parameters that guarantees the robust stability with any supposed accuracy and achieves favorable control performance for uncertain systems.

Based on real situations of supply chain network among enterprise alliances, this paper takes certain factors in every round of supply chain into consideration to establish robust model of supply chain network design. Before that, the strategy “decomposition- coordination” is adopted to get solutions optimizing supply chain system, putting focus on the analysis of supplier selection cost and decision-making cost of supply chain facilities, which is integrated and coordinated through activity-based costing. After the model establishment, examples are used to illustrate practicability and effectiveness of the model and algorithm.

In electric machine design, there is a large computation cost for finite element analyses (FEA) when analyzing nonlinear characteristics in the machine Therefore, for the optimal design of an electric machine, designers commonly use an optimization algorithm capable of excellent convergence performance. However, robustness consideration, as this factor can guarantee machine performances capabilities within design uncertainties such as the manufacturing tolerance or external perturbations, is essential during the machine design process. Moreover, additional FEA is required to search robust optimum. To address this issue, this paper proposes a computationally efficient robust optimization algorithm. To reduce the computational burden of the FEA, the proposed algorithm employs a useful technique which termed static analysis assisted technique (SAAT). The proposed method is verified via the effective robust optimal design of electric machine to reduce cogging torque at a reasonable computational cost.

This paper gives a brief review of the theory and applications of the robust optimization. Major issues including tractability, relations with relevant optimization frameworks, and dynamic robust optimization are addressed and future research directions are presented.

Uncertainties in loads, materials and manufacturing quality must be considered during electromagnetic devices design. This paper presents an effective methodology for robust optimization design based on the variance decomposition in order to keep higher accuracy of the robustness prediction. Sobol' theory is employed to estimate the response variance under some specific tolerance in design variables. Then, an optimal design is obtained by adding a criterion of response variance upon typical optimization problems as a constraint of the optimization. The main contribution of this paper is that the proposed method applies the variance decomposition to obtain a more accurate variance of the response, as well save the computational cost. The performance and robustness of the proposed algorithms are investigated through a numerical experiment with both an analytic function and the TEAM 22 problem.