• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.1
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• pp.1-9
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• 2022

In this study, the selection action on the mesh in the net pot for whelk (Buccinum opisthoplectum) is experimentally considered, and the selectivity was compared by the SELECT model and the Nashimoto's method with the probability model according to the contact shape of the mesh and the whelk. The experiments of the mesh size selectivity was conducted for two mesh sizes: 70 mm (inner stretched size 65.4 mm) and 44 mm (inner stretched size 39.5 mm). Selectivity experiments were conducted three times in total for each mesh size used 264 whelks. In addition, Nashimoto's method analyzed the retention probability using probability model for whether the mesh passed or not based on the carapace width of the whelk. As a result of the selectivity analysis, the 50% selection carapace width for the mesh size of 70 mm was similar to 43.62 mm in the SELECT model and 42.64 mm in the Nashimoto's method. However, the 44 mm mesh with relatively small mesh size showed differences of 40.01 mm and 26.80 mm, respectively. As for the mesh size selectivity of whelk, it was found that the smaller the mesh size, the lower the selectivity. In addition, in the selectivity study on the mesh size of whelk, an evaluation method that closely considers the contact shape between the mesh and the target species is required.

• International Journal of Highway Engineering
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• v.9 no.4
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• pp.227-236
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• 2007

The design number of gyration is required in the process of asphalt mix design using gyratory compactor. The purpose of this study is to select the design number of gyration for asphalt mix design in the laboratory. Three types of methods were used to select the design number of gyration. The first method is to select the gyration number which gives the same density with the mixtures compacted with 75 blows of Marshall Compaction. The second method is to select the gyration number which gives the same deformation strength with the mixtures compacted with 75 blows of Marshall Compactor. The third method is to select the gyration number which meet the 4% air voids. Ten mixtures, one type of aggregate(granite), one type of asphalt binder(pen. 60-80), and 10 types of gradation, were prepared for the laboratory tests. As a result, 100 number of gyration was selected for the design number of gyration of the asphalt mix design. This result shows a similar trend with the design number of gyration used in the foreign countries. Thus, the design number of gyration selected in this study can be used for the asphalt mix design using the gyratory compactors.

• Tunnel and Underground Space
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• v.14 no.4
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• pp.275-286
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• 2004

It is studied to select the reinforcement method of an abandoned tunnel which are intersected under the new roadway line. In the various decision makings, the reasonability for the reinforcement method of an abandoned tunnel was estimated using the pair-wise comparison and the fuzzy approximate reasoning to simplify the process of survey research. And there is reflected all the qualitative and quantitative characterizations by investigation items. In order to select the reinforcement method of an abandoned tunnel, 4 characteristic factors of construction, economical efficiency, safety and maintenance were used. Using the simple survey research and pair-wise comparison matrix, the weight of 4 factors was decided. The fuzzy approximate reasoning was used to calculate the quantitative value of each factor And then reflecting each weight to these results, the final reinforcement method of an abandoned tunnel could be determined.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.1-6
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• 2018

In this paper, we propose the method to eliminate repetitive processes for key-point detection on multi-scale image space. The proposed method detects key-points from the original image, and select a good key-points using the cluster filters, and create the key-point clusters. And it select reference objects by using direction angles of the key-point clusters, predict the scale of the original image by using the distributed distance ratio. It transform the scale of the reference image, and apply the detection of key-points to the transformed reference image. In the results of the experiment, the proposed method can be found to improve the key-points detection time by 75 % and 71 % compared to SIFT method and scaled ORB method using the multi-scale images.

• Journal of the Optical Society of Korea
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• v.13 no.1
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• pp.116-122
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• 2009

In this paper, a robust method is developed to locate the irises of both eyes. The method doesn't put any restrictions on the background. The method is based on the AdaBoost algorithm for face and eye candidate points detection. Candidate points are tuned such that two candidate points are exactly in the centers of the irises. Mean crossing function and convolution template are proposed to filter out candidate points and select the iris pair. The advantage of using this kind of hybrid method is that AdaBoost is robust to different illumination conditions and backgrounds. The tuning step improves the precision of iris localization while the convolution filter and mean crossing function reliably filter out candidate points and select the iris pair. The proposed structure is evaluated on three public databases, Bern, Yale and BioID. Extensive experimental results verified the robustness and accuracy of the proposed method. Using the Bern database, the performance of the proposed algorithm is also compared with some of the existing methods.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.41-45
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• 2023

Currently, the method of loading items in the warehouse, the worker directly decides the loading location, and the most used method is to load the product at the location closest to the entrance. This can be effective when there is no difference in the required amount for goods, but when there is a difference in the required amount for goods, it is inefficient because items with a small required amount are loaded near the entrance and occupy the corresponding space for a long time. Therefore, in order to minimize the release time of goods, it is essential to select an appropriate location when loading goods. In this study, a method for determining the loading location by predicting the required amount of goods was studied to select the optimal loading location. Deep learning based bidirectional long-term memory networks (Bi-LSTM) was used to predict the required amount for goods. This study compares and analyzes the release time of goods in the conventional method of loading close to the entrance and in the loading method using the required amount for goods using the Bi-LSTM model.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1589-1592
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• 1997

Among the prerequisite abilities (perception of environment, path planning and position estimation) of an autonomous mobile robot, position estimation has been seldom studied by mobile robot researchers. In most cases, conventional positioin estimation has been performed by placing landmarks or giving the entrire environmental information in advance. Unlikely to the conventional ones, the study addresses a new method that the robot itself can select distinctive features in the environment and save them as landmarks without any a priori knowledge, which can maximize the autonomous behavior of the robot. First, an orjentaion probaility model is applied to construct a lcoal map of robot's surrounding. The feature of the object in the map is then extracted and the map is saved as landmark. Also, presented is the position estimation method that utilizes the correspondence between landmarks and current local map. In dong this, the uncertainty of the robot's current positioin is estimated in order to select the corresponding landmark stored in the previous steps. The usefulness of all these approaches are illustrated with the results porduced by a real robot equipped with ultrasonic sensors.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1951-1958
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• 2011

The objective of this study is to analyze the performance of digital low-pass differentiators(LPD) and then to provide a method to select effective LPD filter, for detecting spikes of surface motor unit action potentials(MUAP). The successful spike detection of MUAPs is a first important step for EMG signal decomposition. The performances of simple and weighted LPD(SLPD and WLPD) filters are analyzed based on different filter lengths and varying MUAPs from simulated surface EMG signals. The SNR improving coefficient and effective MUAP duration range from the analysis results can be used to select proper LPD filters under the varying conditions of surface EMG.