• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.1-11
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• 2022

Recently, research on license plate recognition, which is a core technology of an intelligent transportation system(ITS), is being actively conducted. In this paper, we propose a method to extract numbers and characters from low-quality license plate images by applying the YOLOv4 algorithm. YOLOv4 is a one-stage object detection method using convolution neural network including BACKBONE, NECK, and HEAD parts. It is a method of detecting objects in real time rather than the previous two-stage object detection method such as the faster R-CNN. In this paper, we studied a method to directly extract number and character regions from low-quality license plate images without additional edge detection and image segmentation processes. In order to evaluate the performance of the proposed method we experimented with 500 license plate images. In this experiment, 350 images were used for training and the remaining 150 images were used for the testing process. Computer simulations show that the mean average precision of detecting number and character regions on vehicle license plates was about 93.8%.

• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.1923-1931
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• 2004

Manufacturing of complex surface plates in stern and stem is a major factor in cost of a preliminary ship design by computing process. If these hull plate parts are effectively classified, it helps to compute the processing cost and find the way to cut-down the processing cost. This paper presents a new method to classify surface plates effectively in the preliminary ship design using neural network. A neural-network-based ship hull plate classification program was developed and tested for the automatic classification of ship design. The input variables are regarded as Gaussian curvature distributions on the plate. Various applicable rules of network topology are applied in the ship design. In automation of hull plate classification, two different numbers of input variables are used. By observing the results of the proposed method, the effectiveness of the proposed method is discussed. As a result, high prediction rate was achieved in the ship design. Accordingly, to the initial design stage, the ship hull plate classification program can be used to predict the ship production cost. And the proposed method will contribute to reduce the production cost of ship.

• Korean Journal of Artificial Intelligence
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• v.6 no.2
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• pp.11-15
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• 2018

Breast cancer is one of the leading causes of cancer related death among women. So prediction of overall survival status is important into decided in adjuvant treatment. Deep belief network is a kind of artificial intelligence (AI). We intended to construct prediction model by deep belief network using associated clinicopathologic factors. 103881 cases were found in the Korean Breast Cancer Registry. After preprocessing of data, a total of 15733 cases were enrolled in this study. The median follow-up period was 82.4 months. In univariate analysis for overall survival (OS), the patients with advanced AJCC stage showed relatively high HR (HR=1.216 95% CI: 0.011-289.331, p=0.001). Based on results of univariate and multivariate analysis, input variables for learning model included 17 variables associated with overall survival rate. output was presented in one of two states: event or cencored. Individual sensitivity of training set and test set for predicting overall survival status were 89.6% and 91.2% respectively. And specificity of that were 49.4% and 48.9% respectively. So the accuracy of our study for predicting overall survival status was 82.78%. Prediction model based on Deep belief network appears to be effective in predicting overall survival status and, in particular, is expected to be applicable to decide on adjuvant treatment after surgical treatment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.237-252
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• 2017

In recent years, data centers have become the core infrastructure to deal with big data processing. For these big data applications, network transmission has become one of the most important factors affecting the performance. In order to improve network utilization and reduce job completion time, in this paper, by real-time monitoring from the application layer, we propose job-aware priority scheduling. Our approach takes the correlations of flows in the same job into account, and flows in the same job are assigned the same priority. Therefore, we expect that flows in the same job finish their transmissions at about the same time, avoiding lagging flows. To achieve load balancing, two approaches (Flow-based and Spray) using ECMP (Equal-Cost multi-path routing) are presented. We implemented our scheme using NS-2 simulator. In our evaluations, we emulate real network environment by setting background traffic, scheduling delay and link failures. The experimental results show that our approach can enhance the Hadoop job execution efficiency of the shuffle stage, significantly reduce the network transmission time of the highest priority job.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.81-87
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• 2010

In order to calculate the accurate runoff from a basin, nonlinearity in the relationship between rainfall and runoff has to be considered. Many runoff calculation models assume the linearity in the relationship or are too complicated to be analyzed. Therefore, the storage function method has been used in the prediction of flood because of the simplicity of the model. The storage function method has five parameters with related to the basin and rainfall characteristics which can be estimated by the empirical trial and error method. To optimize these parameters, regression method or optimization techniques such as genetic algorithm have been used, however, it is not easy to optimize them because of the complexity of the method. In this study, the metamodel is proposed to estimate those model parameters. The metamodel is the combination of artificial neural network and genetic algorithm. The model is consisted of two stages. In the first stage, an artificial neural network is constructed using the given rainfall-runoff relationship. In the second stage, the parameters of the storage function method are estimated using genetic algorithm and the trained artificial neural network. The proposed metamodel is applied in the Peong Chang River basin and the results are presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9B
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• pp.939-946
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• 2009

In this paper, we propose a new network ASIP(Application Specific Instruction-set Processor) which was designed for simulation models by a machine descriptions language LISA(Language for Instruction Set Architecture). This network ASIP is aimed for an exclusive engine undertaking packet processing in a router. To achieve the purpose, we added a new necessary instruction set for processing a general ASIP based on MIPS(Microprocessor without Interlock Pipeline Stages) architecture in high speed. The new instructions can be divided into two groups: a classification instruction group and a modification instruction group, and each group is to be processed by its own functional unit in an execution stage. The functional unit was optimized for area and speed through Verilog HDL, and the result after synthesis was compared with the area and operation delay time. Moreownr, it was allocated to the Macro function ana low-level standardized programming language C using CKF(Compiler Known Function). Consequently, we verified performance improvement achieved by analysis and comparison of execution cycles of application programs.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.397-400
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• 1992

Many dynamic control have been proposed; however, most of them are limited within stage of simulation study. The main reason is that the computations required for inverse dynamics are far beyond the ability of the present commercially available microprocessors. In this paper, In order to achieve real-time processing in robot dynamic control, a parallel processing computer for robot dynamic control is implemented using two transputer. Two transputer compute two degree of freedom robot. The transputer is a special purpose MPU for parallel processing. Transputers are used in networks to build a high performance concurrent system. A network of transputers and peripheral controllers is constructed using point-to-point communication. To gain most benifit from the transputer architecture, the whole system is programmed in OCCAM which is a high level language for concurrent applications. This control algorithm is applied to the RHINO SCARA type manipulator. We could taked about 438.6 microseconds to compute robot dynamic with two-processors.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1231-1242
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• 2019

This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2D-ALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.15-36
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• 2015

The purpose of this paper is "A Study on the Establishment of Distribution and Logistics System in the unified Korea". The main conclusion of this paper are as follows : North and South Korea shall conjunctionally foster the exchange and cooperation area and operate the joint pilot project to combine distribution and logistics system. To solve the problems of the maritime affairs, Fisheries, and international logistics, the cooperative agreement between North and South Korea will be needed to protect and develop of shipping, aviation and shipbuilding industry. Unification of two Koreas must be prepared as stage by stage ; ${\cdot}$Stage1(preparation period) : initiation of peace area. ${\cdot}$Stage2(development period) : forming the exchange and cooperation area ${\cdot}$Stage3(settlement period) : establishing peace belt on border area. After the unification, two Koreas must plan and undertake the construction of the distribution and logistics infrastructures, establishment of SCM system through Network and the connection to China through railway and road Network.

• International Journal of Naval Architecture and Ocean Engineering
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• v.2 no.3
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• pp.171-176
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• 2010

This paper proposes the application of sensor technology to assemble ship blocks efficiently. A sensor-based monitoring system is designed and implemented to improve shipbuilding productivity by reducing the labor cost for the adjustment of adequate positioning between ship blocks during pre-erection or erection stage. For the real-time remote monitoring of relative distances between two ship blocks, sensor nodes are applied to measure the distances between corresponding target points on the blocks. Highly precise positioning data can be transferred to a monitoring server via wireless network, and analyzed to support the decision making which needs to determine the next construction process; further adjustment or seam welding between the ship blocks. The developed system is expected to put to practical use, and increase the productivity during ship blocks assembly.