• 제어로봇시스템학회논문지
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• 제19권2호
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• pp.140-145
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• 2013

This paper proposes an efficient integration method for GPS (Global Positioning System) and INS (Inertial Navigation System). To obtain accuracy and computational conveniency at the same time with low cost global positioning system receivers and micro mechanical inertial sensors, a new mechanization method and a new filter architecture are proposed. The proposed mechanization method simplifies velocity and attitude computation by eliminating the need to compute complex transport rate related to the locally-level frame which continuously changes due to unpredictable vehicle motions. The proposed filter architecture adopts two heterogeneous filters, i.e. position-domain Hatch filter and velocity-aided Kalman filter. Due to distict characteristics of the two filters and the distribution of computation into the two hetegrogeneous filters, it eliminates the cascaded filter problem of the conventional loosly-coupled integration method and mitigates the computational burden of the conventional tightly-coupled integration method. An experiment result with field-collected measurements verifies the feasibility of the proposed method.

• Smart Structures and Systems
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• 제20권1호
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• pp.99-114
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• 2017

In cognitive science, it is illustrated how the collective opinions of a group of individuals answers to questions involving quantity estimation. One example of this approach is introduced in this article as Star Graph (SG) algorithm. This graph describes the details of communication among individuals to share their information and make a new decision. A new labyrinthine network of neighbors is defined in the decision-making process of the algorithm. In order to prevent getting trapped in local optima, the neighboring networks are regenerated in each iteration of the algorithm. In this algorithm, the normal distribution is utilized for a group of agents with the best results (guidance group) to replace the existing infeasible solutions. Here, some new functions are introduced to provide a high convergence for the method. These functions not only increase the local and global search capabilities but also require less computational effort. Various benchmark functions and engineering problems are examined and the results are compared with those of some other algorithms to show the capability and performance of the presented method.

• Structural Engineering and Mechanics
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• 제65권6호
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• pp.707-719
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• 2018

In this article, a new method is introduced to improve the local search capability of meta-heuristic algorithms using the projection of the path on the border of constraints. In a mathematical point of view, the Gradient Projection Method is applied through a new approach, while the imposed limitations are removed. Accordingly, the gradient vector is replaced with a new meta-heuristic based vector. Besides, the active constraint identification algorithm, and the projection method are changed into less complex approaches. As a result, if a constraint is violated by an agent, a new path will be suggested to correct the direction of the agent's movement. The presented procedure includes three main steps: (1) the identification of the active constraint, (2) the neighboring point determination, and (3) the new direction and step length. Moreover, this method can be applied to some meta-heuristic algorithms. It increases the chance of convergence in the final phase of the search process, especially when the number of the violations of the constraints increases. The method is applied jointly with the authors' newly developed meta-heuristic algorithm, entitled Star Graph. The capability of the resulted hybrid method is examined using the optimal design of truss and frame structures. Eventually, the comparison of the results with other meta-heuristics of the literature shows that the hybrid method is successful in the global as well as local search.

• Structural Engineering and Mechanics
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• 제36권1호
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• pp.37-55
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• 2010

For large-scale structures, the calculation of the eigensolution and the eigensensitivity is usually very time-consuming. This paper develops the Kron's substructuring method to compute the first-order derivatives of the eigenvalues and eigenvectors with respect to the structural parameters. The global structure is divided into several substructures. The eigensensitivity of the substructures are calculated via the conventional manner, and then assembled into the eigensensitivity of the global structure by performing some constraints on the derivative matrices of the substructures. With the proposed substructuring method, the eigenvalue and eigenvector derivatives with respect to an elemental parameter are computed within the substructure solely which contains the element, while the derivative matrices of all other substructures with respect to the parameter are zero. Consequently this can reduce the computation cost significantly. The proposed substructuring method is applied to the GARTEUR AG-11 frame and a highway bridge, which is proved to be computationally efficient and accurate for calculation of the eigensensitivity. The influence of the master modes and the division formations are also discussed.

• 품질경영학회지
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• 제37권1호
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• pp.1-9
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• 2009

Data envelopment analysis(DEA) has proved to be powerful for benchmarking and has been widely used in a variety of settings since the advent of it. DEA can be used in identifying the best performing units to be benchmarked against as well as in providing actionable measure for improvement of a organization's performance. However, the selection of performance benchmarks is a matter of both technical production possibilities and organizational policy considerations, managerial preferences and external restrictions. In that regards, DEA has a limited value in benchmarking because it focuses on only technical production Possibilities. This research proposes a new perspective in using DEA and a frame-work for benchmarking to select benchmarks that are both feasible and desirable in terms of organizational context. To do this, the concept of local and global efficiency is newly proposed. To show how useful the suggested concept and framework are, a case study is addressed.

• Acta Via Serica
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• 제2권2호
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• pp.133-162
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• 2017

The Bering Strait crossing would link the entirety of Eurasia to the entirety of the Americas, and it can be seen as a natural extension of the historical Silk Road. There are some immense geopolitical benefits to such a project. It would bring about a profound and lasting change to the global economic and political outlook. The most valued function of the Bering Strait crossing and the extension of the associated railroad network would be to release the massive natural resources trapped underneath the tundra and permafrost for the benefit of Russia and the world. Moreover, the railroad project(s) would also build development corridors in those underdeveloped parts of the Russian Federation. The development of the resources and their rapid transportation to the global markets would contribute not only to the overall development of the region but also would be valuable for the resource-poor countries of Northeast Asia such as Japan, Korea, and China (relative to its economic size). This paper will explore the possible impact(s) of the Bering Strait crossing as a formidable infrastructure project for the economic development of the Russian Far East (RFE) from the Russian perspective under the frame of geopolitics. Furthermore, it will equally scrutinize the implications for the adjacent countries in the region.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제18권2호
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• pp.181-192
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• 2014

In this paper, we propose a robust real-time myocardial border tracking algorithm for echocardiography. Commonly, after an initial contour of LV border is traced at one or two frame from the entire cardiac cycle, LV contour tracking is performed over the remaining frames. Among a variety of tracking techniques, optical flow method is the most widely used for motion estimation of moving objects. However, when echocardiography data is heavily corrupted in some local regions, the errors bring the tracking point out of the endocardial border, resulting in distorted LV contours. This shape distortion often occurs in practice since the data acquisition is affected by ultrasound artifacts, dropout or shadowing phenomena of cardiac walls. The proposed method deals with this shape distortion problem and reflects the motion realistic LV shape by applying global deformation modeled as affine transform partitively to the contour. We partition the tracking points on the contour into a few groups and determine each affine transform governing the motion of the partitioned contour points. To compute the coefficients of each affine transform, we use the least squares method with equality constraints that are given by the relationship between the coefficients and a few contour points showing good tracking results. Many real experiments show that the proposed method supports better performance than existing methods.

• 전기학회논문지
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• 제68권1호
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• pp.172-181
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• 2019

This paper deals with the problem of generating the energy optimal trajectory which is intended to enhance the target tracking performance of a passive homing missile. Noticing that the essence of passive target tracking is the range estimation problem, the target information gathered by passive measurements can be readily analyzed by introducing the range estimator designed in line-of-sight(LOS) frame. Moreover, for the linear filter structure of the suggested range estimator, the cost function associated with the target information is clearly expressed as a function of the line-of-sight rate. Based on this idea, the optimal missile trajectory maximizing the target information is obtained by solving the saddle point problem for an indefinite quadratic cost which consists of the target information and the energy. It is shown that, different from the previous heuristic approaches, the guidance command producing the optimal passive homing trajectory is produced by the modified proportional navigation guidance law whose navigation constant is determined by the weighting coefficient for target information cost.

• 한국기후변화학회지
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• 제5권3호
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• pp.267-275
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• 2014

A key driver for climate change caused by global average temperature rise is greenhouse gas cumulative emissions that stay for long term in the atmosphere. Although at the moment there is no GHG emission, global warming will continue owing to GHG cumulative emission. In this study, scenarios are developed based on two types of optimistic and conservative diffusion goal. There were a total of 6 alternatives scenarios. The objective of this study are to compare scenarios in terms of GHG cumulative emissions and alternative fuels. An object of analysis is the residential buildings and time frame of scenarios is set up by 2030. And this study uses the LEAP model that is a bottom-up energy model. In conclusion, It is important to set specific diffusion pathway for mitigating climate change virtually.

• 전기전자학회논문지
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• 제24권4호
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.