• Smart Structures and Systems
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• v.10 no.3
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• pp.193-207
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• 2012

New insights into our previously proposed hybrid-type method for vibration control are highlighted in terms of energy analysis, such as the assessment of energy efficiency and system stability. The hybrid method improves the bang-bang active method by combining it with an energy-recycling approach. Its simple configuration and low energy-consumption property are quite suitable especially for isolated structures whose energy sources are strictly limited. The harmful influence of the external voltage is assessed, as well as its beneficial performance. We show a new chattering prevention approach that both harvests electrical energy from piezoelectric actuators and eliminates the displacement-offset of the equilibrium point of structures. The amount of energy consumption of the hybrid system is assessed qualitatively and is compared with other control systems. Experiments and numerical simulations conducted on a 10-bay truss can provide a thorough energy-efficiency evaluation of the hybrid suppression system having our energy-harvesting system.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.705-713
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• 1999

In this paper, we propose a new motion and force control methodology for constrained robots as an approach of hybrid discrete-continuous dynamical system. The hybrid dynamic system modeling of robotic manipulation tasks with constraints is presented, and the hybrid system control architecture for unconstrained and constrained motion system with parametric uncertainties is synthesized. The optimal reference stiffness of robot manipulator is generated by the hybrid automata as a discrete state system and the control behavior of constrained system which has poor modeling information and time-varying constraint function is improved by the constrained robots as a continuous state system. The performance of the proposed constrained motion control system is successfully evaluated via experimental studies to the constraint tasks.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.531-540
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• 2007

When multiple objectives exist, there are three approaches exist. These are maximin design, compound design, and constrained design. Still, each of three design criteria has its own strength and weakness. In this paper Hybrid approach is suggested when multiple design objectives exist, which is a combination of maximin and constrained design. Sometimes experimenter has several objectives, but he/she has only one or two primary objectives, others less important. A new approach should be useful under this condition. The genetic algorithm is used for few examples. It has been proven to be a very useful technique for this complex situation. Conclusion follows.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.87-88
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• 2012

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.79-89
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• 2017

The Optimization of a Closed-Loop Supply Chain (CLSC) Model Using an Adaptive Hybrid Genetic Algorithm (AHGA) Approach is Considered in this Paper. With Forward and Reverse Logistics as an Integrated Logistics Concept, The CLSC Model is Consisted of Various Facilities Such as Part Supplier, Product Manufacturer, Collection Center, Recovery Center, etc. A Mathematical Model and the AHGA Approach are Used for Representing and Implementing the CLSC Model, Respectively. Several Conventional Approaches Including the AHGA Approach are Used for Comparing their Performances in Numerical Experiment.

• Journal of Chest Surgery
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• v.47 no.1
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• pp.1-5
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• 2014

Background: Recently, a hybrid surgical-electrophysiological (EP) approach for confirming ablation lines in patients with atrial fibrillation (AF) was suggested. The aim of this approach was to overcome the limitations of current surgery- and catheter-based techniques to yield better outcomes. Methods: Ten consecutive patients with AF underwent total thoracoscopic ablation (TTA) following transvenous catheter EP ablation (residual gap and cavotricuspid isthmus [CTI] ablation). Holter monitoring was performed 6 months postoperatively. Results: Ten patients (90% with persistent AF) underwent successful hybrid procedures, and there was no in-hospital mortality. An EP study was performed in 8 patients and showed that successful antral ablation in all pulmonary veins was achieved in 7 of them. The median follow-up duration was 7.63 months (range, 6.7 to 11.6 months). Nine patients underwent Holter monitoring 6 months postoperatively, and the results indicated an underlying sinus rhythm without AF, atrial flutter, or atrial tachycardia lasting more than 30 seconds in all of the patients. There was no recurrence of AF during follow-up. Conclusion: A hybrid approach that consists of TTA followed by transvenous catheter EP ablation (residual gap and CTI ablation) yielded excellent outcomes in our patient population. A hybrid approach should be considered in patients with a high risk of AF recurrence.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1292-1298
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• 2017

In this paper, we represent a nonlinear identification of electronic brake pedal behavior in the brake-by-wire (BBW) system based on hybrid group method of data handling (GMDH) and genetic algorithm (GA). A GMDH is a kind of multi-layer network with a structure that is determined through training and which can express nonlinear dynamics as a mathematical model. The GA is used in the GMDH, enabling each neuron to search for its optimal set of connections with the preceding layer. The results obtained with this hybrid approach were compared with different nonlinear system identification methods. The experimental results showed that the hybrid approach performs better than the other methods in terms of root mean square error (RMSE) and correlation coefficients. The hybrid GMDH/GA approach was effective for modeling and predicting the brake pedal system under random braking conditions.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.38-46
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• 2003

Aim of this article is to propose Micro-Genetic Simulated Annealing (${\mu}GSA$) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithm (${\mu}GAs$) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, ${\mu}GSA$ approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the ${\mu}GSA$, the five well-know benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimism the weight function. From the simulation results, it has been concluded that the proposed ${\mu}GSA$ approach is an effective optimisation tool for soloing continuous nonlinear global optimisation problems in suitable computational time frame.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.65-74
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• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.