• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.120-128
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• 2015

Quality function deployment (QFD) is a useful method in product design and development to maximize customer satisfaction. In the QFD, the technical attributes (TAs) affecting the product performance are identified, and product performance is improved to optimize customer requirements (CRs). For product development, determining the optimal levels of TAs is crucial during QFD optimization. Many optimization methods have been proposed to obtain the optimal levels of TAs in QFD. In these studies, the levels of TAs are assumed to be continuous while they are often taken as discrete in real world application. Another assumption in QFD optimization is that the requirements of the heterogeneous customers can be generalized and hence only one house of quality (HoQ) is used to connect with CRs. However, customers often have various requirements and preferences on a product. Therefore, a product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. To overcome these problems, this paper proposes an optimization approach to find the optimal set of TAs under multi-segment market. Dynamic Programming (DP) methodology is developed to maximize the overall customer satisfaction for the market considering the weights of importance of different segments. Finally, a case study is provided for illustrating the proposed optimization approach.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2074-2079
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• 2018

Global state space's optimal policy is used for offline controller in the form of table by using Dynamic Programming. If an optimal policy table has a large amount of control data, it is difficult to use the system in a low capacity system. To resolve these problem, controller using the compressed optimal policy table is proposed in this paper. A DCT is used for compression method and the cosine function is used as a basis. The size of cosine function decreased as the frequency increased. In other words, an essential information which is used for restoration is concentrated in the low frequency band and a value of small size that belong to a high frequency band could be discarded by quantization because high frequency's information doesn't have a big effect on restoration. Therefore, memory could be largely reduced by removing the information. The compressed output is stored in memory of embedded system in offline and optimal control input which correspond to state of plant is computed by interpolation with Inverse DCT in online. To verify the performance of the proposed controller, computer simulation was accomplished with a one link inverted pendulum.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.97-114
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• 2022

Interest is increasing in electrocardiogram (ECG) signal analysis for embedded devices, creating the need to develop an algorithm suitable for a low-power, low-memory embedded device. Linear approximation of the ECG signal facilitates the detection of fiducial points by expressing the signal as a small number of vertices. However, dynamic programming, a global optimization method used for linear approximation, has the disadvantage of high complexity using memoization. In this paper, the calculation area and memory usage are improved using a linear approximated template. The proposed algorithm reduces the calculation area required for dynamic programming through local optimization around the vertices of the template. In addition, it minimizes the storage space required by expressing the time information using the error from the vertices of the template, which is more compact than the time difference between vertices. When the length of the signal is L, the number of vertices is N, and the margin tolerance is M, the spatial complexity improves from O(NL) to O(NM). In our experiment, the linear approximation processing time was 12.45 times faster, from 18.18 ms to 1.46 ms on average, for each beat. The quality distribution of the percentage root mean square difference confirms that the proposed algorithm is a stable approximation.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.171-171
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• 2020

Decision making strategies should consider both adaptiveness and robustness in order to deal with two main characteristics of climate change: non-stationarity and deep uncertainty. Especially, robust strategies are different from traditional optimal strategies in the sense that they are satisfactory over a wider range of uncertainty and may act as a key when confronting climate change. In this study, a new framework named Robust Stochastic Dynamic Programming (R-SDP) is proposed, which couples previously developed robust optimization (RO) into the objective function and constraint of SDP. Two main approaches of RO, feasibility robustness and solution robustness, are considered in the optimization algorithm and consequently, three models to be tested are developed: conventional-SDP (CSDP), R-SDP-Feasibility (RSDP-F), and R-SDP-Solution (RSDP-S). The developed models were used to derive optimal monthly release rules in a single reservoir, and multiple simulations of the derived monthly policy under inflow scenarios with varying mean and standard deviations are undergone. Simulation results were then evaluated with a wide range of evaluation metrics from reliability, resiliency, vulnerability to additional robustness measures. Evaluation results were finally visualized with advanced visualization tools that are used in multi-objective robust decision making (MORDM) framework. As a result, RSDP-F and RSDP-S models yielded more risk averse, or conservative, results than the CSDP model, and a trade-off relationship between traditional and robustness metrics was discovered.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.113.2-113
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• 2001

In this paper, we present a robot off-Line programming system with collision detection. The collision detection is a very important factor of robot oft-line programming system for collision avoidance, path planning, and so on. The System developed in this paper, basically using an algorithm for the minimum distance calculation between general polyhedra. The proposed system shows an exact and interactive result in static and dynamic environments.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.370-377
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• 2010

An Intelligent service robot performs various complex tasks in dynamic environment, providing useful intelligent services for human users. The robot needs to continuously monitor dynamically changing environment and reactively choose the best behavior for the changing context. The selected behaviors may include nondeterministic or parallel actions. In this paper, we present a structured reactive robot programming language, SPRIT that is based on Structured Circuit Semantics (SCS). SPRIT is fully implemented as a task executor and tested for reactive robot tasks in dynamic environment to show that it can be used to explicitly represent and effectively implement the complex reactive behaviors of intelligent robot systems.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.39-49
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• 2004

When there is a lack of detailed kinetic information, dFBA(dynamic flux balance analysis) has correctly predicted cellular behavior under given environmental conditions with FBA and different ial equations. However, until now, dFBA has centered on substrate concentration, cell growth, and gene on/off, but a detailed hierarchical structure of a regulatory network has not been taken into account. For this reason, the dFBA has limited the represen tation of interactions between specific regulatory proteins and genes and the whole transcriptional regulation mechanism with environmental change. Moreover, to calculate optimal metabolic flux distribution which maximizes the growth flux and predict the b ehavior of cell system, linear programming package(LINDO) and spreadsheet package(EXCEL) have been used simultaneously. thses two software package have limited in the visual representation of simulation results and it can be difficult for a user to look at the effects of changing inputs to the models. Here, we descirbes the construction of hierarchical regulatory network with defined symbolsand the development of an integrated system that can predict the total control mechanism of regulatory elements (opero ns, genes, effectors, etc.), substrate concentration, growth rate, and optimal flux distribution with time. All programming procedures were accoplished in a visual programming environment (LabVIEW).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.100-105
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• 2008

Cost reduction, time to market, and quality improvement of software product are critical issues to the software companies which try to survive in recent competitive market environments. Software Product Line Engineering (SPLE) is one of the approaches to address these issues. The goal of software product line is to maximize the software reuse and achieve the best productivity with the minimum cost. In software product line, software components are classified into the common and variable modules for composition work. In this paper, we proposed a dynamic composition process based on aspect-oriented programming methodology in which software requirements are classified into the core-concerns and cross-cutting concerns, and then assembled into the final software product. It enables developers to concentrate on the core logics of given problem, not the side-issues of software product such as transactions and logging. We also proposed useful composition patterns based on aspect oriented programming paradigm. Finally, we implemented a prototype of the proposed process using Java and Aspect to show the proposed approach's feasibility. The scenario of the prototype is based on the embedded analysis software of telecommunication devices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.2
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• pp.87-95
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• 2003

Dynamic Programming Matching (DPM) is a mathematical optimization technique for sequentially structured problems, which has, over the years, played a major role in providing primary algorithms in pattern recognition fields. Most practical applications of this method in signature verification have been based on the practical implementational version proposed by Sakoe and Chiba [9], and il usually applied as a case of slope constraint p = 0. We found, in this case, a modified version of DPM by applying a heuristic (forward seeking) implementation is more efficient, offering significantly reduced processing complexity as well as slightly improved verification performance.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.118-128
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• 2017

Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.