• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.737-742
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• 2011

Navigation problems include the task of determining the control input under various constraints for systems such as mobile robots subject to uncertain disturbance. Such tasks can be modeled as constrained stochastic control problems. In order to solve these control problems, one may try to utilize the dynamic programming(DP) methods which rely on the concept of optimal value function. However, in most real-world problems, this trial would give us many difficulties; for examples, the exact system model may not be known; the computation of the optimal control policy may be impossible; and/or a huge amount of computing resource may be in need. As a strategy to overcome the difficulties of DP, one can utilize ADP(approximate dynamic programming) methods, which find suboptimal control policies resorting to approximate value functions. In this paper, we apply recently proposed ADP methods to a class of navigation problems having complex constraints, and observe the resultant performance characteristics.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.4
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• pp.113-118
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• 2017

As the demand for industrial robots and Automated Guided Vehicles (AGVs) increases, higher performance is also required from them. Fuzzy controllers, as part of an intelligent control system, are a direct control method that leverages human knowledge and experience to easily control highly nonlinear, uncertain, and complex systems. This paper uses a $LabVIEW^{(R)}-based$ fuzzy controller with gain scheduling to demonstrate better performance than one could obtain with a fuzzy controller alone. First, the work area was set based on forward kinematics and inverse kinematics programs. Next, $LabVIEW^{(R)}$ was used to configure the fuzzy controller and perform the gain scheduling. Finally, the proposed fuzzy gain scheduling controller was compared with to controllers without gain scheduling.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.176-179
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• 2007

This paper proposes a fuzzy linear programming based solution approach fur the long-term generation mix with multi-stages (years) considering air pollution constraints on $CO_2$ emissions, under uncertain circumstances as like as ambiguities of budget and reliability criterion level. This paper approaches to generation mix problem for 2030 year in Korea eventually. The proposed approach may give more flexible solution rather than too robust plan. The effectiveness of the proposed approach is demonstrated by applying it to solve the multi-years best generation mix problem on the Korea power system which contains nuclear, coal, LNG, oil and pumped-storage hydro plants.

• Journal of Communications and Networks
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• v.16 no.2
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• pp.146-154
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• 2014

This paper proposes a bandwidth-aware localized-routing algorithm that is capable of sensing the available spectrum bands within a two-hop neighboring for choosing the highly opportunistic routes. A mixed-integer linear programming (MILP) is utilized to formulate the optimization problem. Then, the proposed algorithm is used to determine the maximum bandwidth possible of link pairs via a bandwidth approximation process of relaxed variables. Thereby, the proposed algorithm can allow selected routes corresponding to maximum bandwidth possible between cognitive radio (CR) users through link pairs in cognitive radio networks. By comparing the solution values to previous works, simulation results demonstrate that the proposed algorithm can offer a closed-optimal solution for routing performance in cognitive radio networks. The contribution of this paper is achieved through approximately 50% throughput utilized in the network.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.149-151
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• 2005

A new approach considering CO2 air pollution constraints in the long-term generation mix is proposed under uncertain circumstances. A characteristic feature of the presented approach in this paper is what effects give the air pollution constraints in long term best generation mix. Best generation mix problem is formulated by linear programming with fuel and construction cost minimization with load growth, reliability (reserve margin rate) and air pollutionconstraints. The proposed method accommodates the operation of pumped-storage generator. It was assumed in this study that the construction planning of the hydro power plants is given separately from the other generation plans. The effectiveness of the proposed approach is demonstrated by applying to the best generation mix problem of KEPCO-system, which contains nuclear, coal, LNG, oil and pumped-storage hydro plant multi-years.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.984-987
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• 1996

We consider a feedback control system including interval plant where uncertain parameters expressed in the hyperrectangular box X. Here we define the maximum value of the integral of the error(ISE) as the worst performance index(WPI) due to the plant parameter uncertainty. Suppose that the closed loop system retains robust stability and it belongs to type I. Then we show that the WPI occurs only on the exposed edges of Q. In particular, it is also shown that if ISE is a convex function relative to X, the WPI is attained at one of vertices of X. Some examples are given.

• Management Science and Financial Engineering
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• v.18 no.2
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• pp.23-27
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• 2012

The problem of jointly determining a robust optimal bundle of price and order quantity for a retailer in a single-retailer, single supplier, single-product supply chain is considered. Demand is modeled as a decreasing power function of product price, and unit purchasing cost is modeled as a decreasing power function of order quantity and demand. Parameters defining the two power functions are uncertain but their possible values are characterized by ellipsoids. We extend a previous study in two ways; the purchasing cost function is generalized to take into account the economies of scale realized by higher product demand in addition to larger order quantity, and an exact transformation into an equivalent convex optimization program is developed instead of a geometric programming approximation scheme proposed in the previous study.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.3
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• pp.183-191
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• 2013

We address the power generation mix problem that considers not only nuclear and fossil fuels such as oil, coal and LNG but also renewable energy technologies. Unlike nuclear or other generation technologies, the expansion plan of renewable energy is highly uncertain because of its dependency on the government policy and uncertainty associated with technology improvements. To address this issue, we conduct a delphi survey and forecast the capacity of renewable energy. We further propose a stochastic mixed integer programming model that determines an optimal capacity expansion and the amount of power generation using each generation technology. Using the proposed model, we test eight generation mix scenarios and particularly evaluate how much the expansion of renewable energy contributes to the total costs for power generation in Korea. The evaluation results show that the use of renewable energy incurs additional costs.

• Korean Chemical Engineering Research
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• v.50 no.3
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• pp.471-478
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• 2012

A carbon capture and storage (CCS) plays a very important role to reduce $CO_2$ dramatically in $CO_2$ emission sources which are distributed throughout various areas. Numerous research works have been undertaken to analyze the techno-economic feasibility of planning the CCS infrastructure. However, uncertainties such as $CO_2$ emissions, $CO_2$ reduction costs, and carbon taxes may exist in various impact factors of the CCS infrastructure. However, few research works have adopted these uncertainties in designing the CCS infrastructure. In this study, a two-stage stochastic programming model is developed for planning the CCS infrastructure under uncertain operating costs and carbon taxes. It can help determine where and how much $CO_2$ to capture, store or transport for the purpose of minimizing the total annual $CO_2$ reduction cost in handling the uncertainties while meeting the $CO_2$ mitigation target. The capability of the proposed model to provide correct decisions despite changing the operating costs and carbon taxes is tested by applying it to a real case study based on Korea. The results will help to determine planning of a CCS infrastructure under uncertain environments.

• Journal of Information Technology and Architecture
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• v.11 no.2
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• pp.187-199
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• 2014

The DoD of United State, the MoD of United Kingdom and NATO have developed and implemented not only the concepts of joint capability but also management system for the joint capability to respond the future uncertain operations effectively. The resources of the DoD in United State have been efficiently planning, programming, budgeting, execution and evaluation using the PPBEE system which developed based on the concept of joint capability. The joint capability of the DoD has been managing through the JCAMS (Joint Capability Area Management System) which constructed based on JCA, UJTL and so on. Recently the MND of Korea eager to try introducing concept of the JCMS and developing the JCMS like the JCAMS of DoD for the future joint operation in the uncertain environment. We propose the holistic mechanism to develop the JCMS (Joint Capability Management System) of MND which can implement during the constructing the JCMS of Korea efficiently and effectively.