• Journal of the Korean Society for Precision Engineering
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• v.22 no.2
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• pp.98-104
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• 2005

A glass transfer robot is used for handling LCDs in the production line of flat panel displays under clean environments. During glass transfer operations of the robot, chattering phenomenon occurs at the robot hand. This deteriorates the accuracy and repeatability of the end-effector of the robot. In this paper, we present the kinematic solution of the robot and then analyze the cause of this chattering phenomenon in view of the mechanism and servo control and propose a practical solution that can reduce the chattering significantly at the robot hand of the glass transfer robot.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.72-73
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• 2018

The accuracy of contingency estimation plays an important role for dealing with the uncertainty of the financial success of construction project. Its' estimation may be used for various purposes such as schedule control, emergency resolve, and quality expense, etc. This paper presents a contingency estimation method which is schedule control specific. The method 1) implements stochastic EVMS, 2) detects a specific timing for schedule compression, 3) identifies an optimal strategy for shortening planned schedule, 4) finds a probability density function (PDF) of project cost overrun, and 5) estimates the optimal contingency cost based on the level of confidence. The method facilitates expeditious decisions involved in project budgeting. The validity of the method is confirmed by performing test case.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.39-46
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• 2015

In this paper, we propose a performance-aware workload model for efficient implementation of control systems. When implementing a control algorithm as an embedded computer system, the control code executes periodically. For such systems, its control performance depends on not only the accuracy of the control algorithm itself but also temporal parameters such as control period and sensing to actuation delay. In this regard, this paper studies the relation between control period and delay by measuring and analyzing the control performance of LKAS (Lane Keeping Assist System) with varying period and delay combinations. Through this experimental study, this paper shows that the two timing parameters, i.e.,control period and delay, has a tradeoff relation in terms of control performance.

• Korean Journal of Geomatics
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• v.4 no.2
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• pp.47-52
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• 2004

We can process and compute the position, velocity and time by satellite signals of GPS. The signals are used to compute positioning of three dimensions and timing offset of the receiver clock when we can track the tour satellite signals at least. One of the specified aims is to use less expensive single frequency code/carrier phase GPS receivers, which are typically around half the price of dual frequency receivers. In the study, the author analyzed the accuracy and applicability of frequence linear combination using triangulation points evaluated distance limitation.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.4
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• pp.200-207
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• 2004

This paper presents a simple and fast delay metric RC-class interconnects under saturated ramp inputs. The RC delay metric under saturated ramp inputs, called FDM(Fast Delay Metric), can estimate delay times at an arbitrary node using a simple closed-form expression and is extended from delay metric under step input easily As compared with similar techniques proposed in previous researches, it is shown that the FDM technique complexity for a similar accuracy. As the number of circuit nodes increases, there will be a significant difference in estimation times of RC delay between the previous techniques based on two circuit moments and the FDM which do not depend on circuit moments.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.3
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• pp.28-34
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• 2012

The recent transition to Performance Based Navigation in aviation enhances the accuracy of aircraft position, safety and efficiency in air traffic operations by using satellite-based navigation system such as GNSS. However, intentional interferences with GNSS signal as well as ones coming from natural phenomena such as solar storm increase. GNSS have very low power and therefore their signals are more susceptible to interferences than ground-based navigation signals. This paper introduces requirements of alternative positioning, navigation and timing(APNT) system and relevant technologies when the GNSS signals are not valid.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.2
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• pp.89-104
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• 2015

In order to effectively manage forested areas in South Korea on a national scale, using remotely sensed data is considered most suitable. In this study, utilizing Land coverage maps and Forest type maps of national geographic information instead of collecting field data was tested for conducting supervised classification on SPOT-5 and KOMPSAT-2 imagery focusing on forested areas. Supervised classification were conducted in two ways: analysing a whole area around the study site and/or only forested areas around the study site, using Support Vector Machine. The overall accuracy for the classification on the whole area ranged from 54.9% to 68.9% with kappa coefficients of over 0.4, which meant the supervised classification was in general considered moderate because of sub-classifying forested areas into three categories (i.e. hardwood, conifer, mixed forests). Compared to this, the overall accuracy for forested areas were better for sub-classification of forested areas probably due to less distraction in the classification. To further improve the overall accuracy, it is needed to gain individual imagery rather than mosaic imagery to use more spetral bands and select more suitable conditions such as seasonal timing. It is also necessary to obtain precise and accurate training data for sub-classifying forested areas. This new approach can be considered as a basis of developing an excellent analysis manner for understanding and managing forest landscape.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.118-129
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• 2021

The sediment transportation caused by soil erosion due to rainfall-discharge in the large watershed scale plays critical role in human society. The relationship between rainfall-discharge-sediment transportation is depending on the start time of rainfall and end of rainfall but, the studies related with rainfall characteristics are insufficient. In this study, The Soil and Water Assession Tool (SWAT) model was used to study the relationship between rainfall-discharge-sediment transportation at the Sook river watershed which is monitored by the Ministry of Environment. To do this, first of all, the sensitivity analysis about model attributes was performed using monitored data. The accuracy analysis of SWAT model was conducted using the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and the coefficient of determination (R²). After that, it was studied what results could be obtained according to changes in rainfall timing and end points. In the result of discharge simulation, the modified rainfall values (sum of total rainfall starting time and end time) showed more high accuracy values (R²:0.90, NSE: 0.8) than original rainfall values (R²:0.76, NSE: 0.72). In the result of sediment transportation simulation, during calibration had more resonable results(R²:0.87, NSE: 0.86) than compared with original rainfall values (R²:0.44, NSE: 0.41). However, validation results of sediment transportation simulation showed low accuracy values compared with calibration results. This results maybe cause monitoring periods of sediment flow compared with discharge monitoring periods. Nevertheless, since rainfall characteristic plays critical rule in model results, continuous research on rainfall characteristic is needed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.57-64
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• 2020

Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.