• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.134-139
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• 2009

Various modules are generally combined with one another in order to perform rotorcraft preliminary design and its optimization. At the stage of the preliminary design, analysis fidelity is less important than the rapid assessment of a design is. Most of the previous researchers attempted to implement sophisticated applications in order to increase the fidelity of analysis, but the present paper focuses on a rapid assessment while keeping the similar level of fidelity. Each small-sized module will be controlled by an externally-operated global optimization module. Results from each module are automatically handled from one discipline to another which reduces the amount of computational effort and time greatly when compared with manual execution. Automatically handled process decreases computational cycle and time by factor of approximately two. Previous researchers and the rotorcraft industries developed their own integrated analysis for rotorcraft design task, such as HESCOMP, VASCOMP, and RWSIZE. When a specific mission profile is given to these programs, those will estimate the aircraft size, performance, rotor performance, component weight, and other aspects. Such results can become good sources for the supplemental analysis in terms of stability, handling qualities, and cost. If the results do not satisfy the stability criteria or other constraints, additional sizing processes may be used to re-evaluate rotorcraft size based on the result from stability analysis. Trade-off study can be conducted by connecting disciplines, and it is an important advantage in a preliminary design study. In this paper among the existing rotorcraft design programs, an adequate program is selected for a baseline of the design framework, and modularization strategy will be applied and further improvements for each module be pursued.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2626-2646
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• 2014

This study presents a power and Physical Resource Blocks (PRBs) joint allocation algorithm to coordinate uplink (UL) interference in the device-to-device (D2D) underlaying Long Term Evolution-Advanced (LTE-A) networks. The objective is to find a mechanism to mitigate the UL interference between the two subsystems and maximize the weighted sum throughput as well. This optimization problem is formulated as a mixed integer nonlinear programming (MINLP) which is further decomposed into PRBs assignment and transmission power allocation. Specifically, the scenario of applying imperfect channel state information (CSI) is also taken into account in our study. Analysis reveals that the proposed PRBs allocation strategy is energy efficient and it suppresses the interference not only suffered by the LTE-A system but also to the D2D users. In another side, a low-complexity technique is proposed to obtain the optimal power allocation which resides in one of at most three feasible power vectors. Simulations show that the optimal power allocation combined with the proposed PRBs assignment achieves a higher weighted sum throughput as compared to traditional algorithms even when imperfect CSI is utilized.

• Journal of the Korean Ceramic Society
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• v.47 no.1
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• pp.92-96
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• 2010

Based on the differential electrode equilibria approach, potentiometric YSZ sensors with semiconducting oxide electrodes for CO detection are developed. To improve the selectivity, sensitivity and response-time of the sensor, our strategy includes (a) selection of an oxide with a semiconducting response to CO, (b) addition of other semiconducting materials, (c) addition of a catalyst (Pd), (d) utilization of combined p- and n-type electrodes in one sensor configuration, and (e) optimization of operating temperatures. Excellent sensing performance is obtained by a novel device structure incorporating $La_2CuO_4$ electrodes on one side and $TiO_2$-based electrodes on opposite substrate faces with Pt contacts. The resulting response produces additive effects for the individual $La_2CuO_4$ and $TiO_2$-based electrodes voltages, thereby realizing an even higher CO sensitivity. The device also is highly selective to CO versus NO with minor sensitivity for NO concentration, compared to a notably large CO sensitivity.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.12
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• pp.608-616
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• 2006

Recently, a switched reluctance motor is widely used in various industries because it has many advantages such as a simple structure, robustness, less maintenance, high torque/weight ratio, and easy speed control over other types of motors. However, a switched reluctance motor inherently produces acoustic noise and vibration caused by torque ripple. Applications of these motors where silent operation is desirable have thus been limited. In this paper, a new stator pole face having a non-uniform air-gap and a pole shoe attached to the lateral face of the rotor pole are suggested in order to minimize torque ripple. The effects of each design parameter are validated using a time-stepping finite element method. The parameters are optimized by utilizing response surface method (RSM) combined with (1+1) evolution strategy. The result shows that the optimized shape gives higher average torque and drastically reduced torque ripple.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.6
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• pp.711-721
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• 2013

Optimal operation of a combined sewer network with distributed storage facilities aims to use the whole retention capacity of all reservoirs efficiently before overflows take place somewhere in the considered network system. An efficient real-time network control (RTNC) strategy has been emerging as an attractive approach for reducing substantially the overflows from a sewer network compared to the conventional fixed or manually adjusted gate setting method, but the related concrete framework for RTC development has not been throughly introduced so far. The main goal of this study is to give a detailed description of the RTNC systems via reviewing several guidelines published abroad, and finally to suggest methods for the proper application of RTNC on distributed storage facilities. Especially, this study is focused on emphasizing the importance of hierarchical structure of RTNC system that consists of three control layers (management, global control and local control). Further, with regard to the global control layer which is responsible for the central overall network control, the wide-ranging details of two components (adaption and optimization layers) are also presented. This study can provide the valuable basis for the RTNC implementation in the particular sewer network with distributed multiple storage facilities.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.641-654
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• 2014

A new computer code, named CORONA (Core Reliable Optimization and thermo-fluid Network Analysis), was developed for the core thermo-fluid analysis of a prismatic gas cooled reactor. The CORONA code is targeted for whole-core thermo-fluid analysis of a prismatic gas cooled reactor, with fast computation and reasonable accuracy. In order to achieve this target, the development of CORONA focused on (1) an efficient numerical method, (2) efficient grid generation, and (3) parallel computation. The key idea for the efficient numerical method of CORONA is to solve a three-dimensional solid heat conduction equation combined with one-dimensional fluid flow network equations. The typical difficulties in generating computational grids for a whole core analysis were overcome by using a basic unit cell concept. A fast calculation was finally achieved by a block-wise parallel computation method. The objective of the present paper is to summarize the motivation and strategy, numerical approaches, verification and validation, parallel computation, and perspective of the CORONA code.

• Advances in concrete construction
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• v.11 no.1
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.260-267
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• 2017

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.