• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6043-6062
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• 2019

Low-rank matrix decomposition has shown its capability in many applications such as image in-painting, de-noising, background reconstruction and defect detection etc. In this paper, we consider the texture background of rail track images and the sparse foreground of the defects to construct a low-rank matrix decomposition model with block sparsity for defect inspection of rail tracks, which jointly minimizes the nuclear norm and the 2-1 norm. Similar to ADM, an alternative method is proposed in this study to solve the optimization problem. After image decomposition, the defect areas in the resulting low-rank image will form dark stripes that horizontally cross the entire image, indicating the preciselocations of the defects. Finally, a two-stage defect extraction method is proposed to locate the defect areas. The experimental results of the two datasets show that our algorithm achieved better performance compared with other methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.37-45
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• 2015

In modern electronic warfare systems, a demand on the more accurate estimation method based on TDOA and FDOA has been increased. TDOA/FDOA localization consists of two-stage procedures: the extraction of information from signals and the estimation of emitter location. Various algorithms based on CAF(complex ambiguity function), which is known as a basic method, has been presented in the area of extractions. When we extract TDOA and FDOA information using a conventional method based on the CAF algorithm from communication signals, considerably long integration time is required for the accurate position estimation of an unknown emitter far from sensors more than 300 km. Such long integration time yields huge amount of transmission data from sensors to a central processing unit, resulting in heavy computiational complexity. Therefore, we theoretically analyze the integration time for TDOA/FDOA information using CRLB and propose a two-stage global optimization algorithm which can minimize the transmission time and a computational complexity. The proposed method is compared with the conventional CAF-based algorithms in terms of a computational complexity and the CRLB to verify the estimation performance.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.213-220
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• 2023

This paper presents a model simulating the folding process and collision dynamics of "ddakji", a traditional Korean game played using paper tiles (which are also referred to as ddakji). The model uses two A4 sheets as the base materials for ddakji. The folding process involves a series of boundary conditions that transform the wing part of the paper structure into a twisted configuration. A rigid plate boundary condition is also adopted for squeezing, establishing the shape and stress state of the game-ready ddakji through dynamic relaxation analysis. The gaming process analysis involves a forced displacement of the striking ddakji to a predetermined collision position. Collision analysis then follows at a given speed, with the objective of overturning the struck ddakji--a winning condition. A genetic algorithm-based optimization analysis identifies the optimal collision conditions that result in the overturning of the struck ddakji. For efficiency, the collision analysis is divided into two stages, with the second stage carried out only if the first stage predicts a possible overturn. The fitness function for the genetic algorithm during the first stage is the direction cosine of the struck ddakji, whereas in the second stage, it is the inverse of the speed, thus targeting the lowest overall collision speed. Consequently, this analysis provides optimal collision conditions for various compression thicknesses.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.5
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• pp.407-412
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• 2003

An effective methodology is .reported for determining the optimal capacity (lot-size) of batch processing and storage networks which include material recycle or reprocessing streams. We assume that any given storage unit can store one material type which can be purchased from suppliers, be internally produced, internally consumed and/or sold to customers. We further assume that a storage unit is connected to all processing stages that use or produce the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. The objective for optimization is to minimize the total cost composed of raw material procurement, setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory hold-up. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two subproblems. The first yields analytical solutions for determining batch sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks. For the special case in which the number of storage is equal to the number of process stages and raw materials storage units, a complete analytical solution for average flow rates can be derived. The analytical solution for the multistage, strictly sequential batch-storage network case can also be obtained via this approach. The principal contribution of this study is thus the generalization and the extension to non-sequential networks with recycle streams. An illustrative example is presented to demonstrate the results obtainable using this approach.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1859-1864
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• 2004

An effective methodology is reported for the optimal design of multisite batch production/transportation and storage networks under uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, internally consumed, transported to or from other plant sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between plant sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of large-scale supply chain system.

• Geotechnical Engineering
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• v.12 no.4
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• pp.131-144
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• 1996

In order to accelerate the rate of consolidation settlement and to gain a required shear strength for a given soft clay deposit, the preloadina technique combined with a vertical drainage system has been widely applied. Even if a sophisticated numerical analysis technique is applied to solve the consolidation behavior of drainage-installed soft deposits, the actual field behavior is often different from the behavior predicted in the design state due to several uncertainties involved in soil properties, numerical modelling, and measuring system. In this paper, two back-analysis schemes such hs simplex and BFGS methods have been implemented in an a Bisymmetric consolidation program, AXICON which considers the variation of compressibility and permeability during the consolidation process. Utilizing the program, one might be able to appropriately predict the subsequent consolidation behavior from the measured data in an early stage of consolidation of drainage-installed soft deposits.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.537-544
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• 2004

An effective methodology is reported for determining the optimal lot size of batch processing and storage networks which include uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, infernally consumed, transported to or from other sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sires while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of the global supply chain.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.5
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• pp.383-388
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• 2019

It is essential to reduce the Infra-red signature for increasing ship's survivability in ship design stage. However the ship's IR signature is quite sensitive to the maritime and atmosphere. Therefore, it is very important to select the marine meteorological data to be applied to the signature analysis. In this study, we selected the three meteorological sample sets from the population of the Korea Meteorological Administration's marine environment data in 2017. These samples were selected through the two-dimensional stratified sampling method, taking into account the geopolitical threats of the Korean peninsula and the effective area of the buoy. These sample sets were applied to three naval ships classified by their tonnage, and then the IR signature analysis was performed to derive the Contrast Radiant Intensity (CRI) values. Based on the CRI values, the validity of each sample set was determined by comparing Cumulative Distribution Function (CDF), and Probability Density Function (PDF). Also, we checked the degree of scattering in each sample set and determined the efficiency of analysis time and cost according to marine meteorological sample sets to confirm the possibility of a probabilistic method. Through this process, we selected the standard for optimization of marine meteorological sample for ship IR signature analysis. Based on this optimization sample, by applying probabilistic method to the management of IR signature for naval ships, the robust design is possible.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.55-65
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• 2009

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria, up to 90% of electricity is produced by thermal generators (vapor, gas). In order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods have been proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed Parallel GA to solve the multi-objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two subproblems are proposed: the first subproblem is related to the active power planning to minimize the total fuel cost, and the second subproblem is a reactive power planning design based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed was tested on the Algerian 59-bus network test and compared with conventional methods and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at a critical situation and within a reasonable time.

• Radiation Oncology Journal
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• v.9 no.2
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• pp.351-359
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• 1991

The current LINAC technique for radiosurgery utilizes a single isocenter approach with multiple noncoplanar arcs. This approach results in spherical dose distributions in the target. Many arteriovenous malformations and tumors suitable for radiosurgical treatment have non-spherical or irregular shapes. The basic approach presented in this paper is to use two or multiple isocenters with standard arcs to shape irregular target volumes through the use of multiple spherical targets. Selection of reasonable irradiation parameters in the first stage is critical to the success of real-time optimization. A useful guideline for optimum isocenter separation and collimator size is developed to shape the target margin uniformly with an desired isodose surface for an elongated target. The implementation of multiple isocenters with three dimensional dose model and application of multiple isocenters approach to several cases are discussed.