• Journal of Korean Tunnelling and Underground Space Association
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• 제20권6호
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• pp.1049-1060
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• 2018

Soil conditioning is one of the key factors for successfull tunnel excavations utilizing the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) by increasing the tunnel face stability and extraction efficiency of excavated soils. In this study, conditioning agents are mixed with the weathered granite soils which are abundant in the Korean peninsula and the workability of the resulting mixture is evaluated through the slump tests to derive and propose the most suitable conditioning agent as well as the most appropriate agent mix ratios. However, since it is cumbersome to perform the slump tests all the time either in the laboratory or in-situ, a simpler test may be needed instead of the slump test; the fall cone test was proposed as a substitute. In this paper, the correlation between the slump value obtained from the slump test and the cone penetration depth obtained from the proposed fall cone test was obtained. Test results showed that a very good correlation between two was observed; it means that the simpler fall cone test can be used to assess the suitability of the conditioned soils instead of the more cumbersome slump test.

• Korean Journal of Remote Sensing
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• 제34권6_3호
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• pp.1399-1414
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• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

• The Journal of Society for e-Business Studies
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• 제24권3호
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• pp.19-33
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• 2019

Recently, manufacturers have been struggling to efficiently use production equipment as their production methods become more sophisticated and complex. Typical factors hindering the efficiency of the manufacturing process include setup cost due to job change. Especially, in the process of using expensive production equipment such as semiconductor / LCD process, efficient use of equipment is very important. Balancing the tradeoff between meeting the deadline and minimizing setup cost incurred by changes of work type is crucial planning task. In this study, we developed a scheduling model to achieve the goal of minimizing the duedate and setup costs by using reinforcement learning in parallel machines with duedate and work preparation costs. The proposed model is a Deep Q-Network (DQN) scheduling model and is a reinforcement learning-based model. To validate the effectiveness of our proposed model, we compared it against the heuristic model and DNN(deep neural network) based model. It was confirmed that our proposed DQN method causes less due date violation and setup costs than the benchmark methods.

• The Journal of Society for e-Business Studies
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• 제24권1호
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Journal of the korean academy of Pediatric Dentistry
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• 제46권2호
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• pp.165-172
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• 2019

The purpose of this study was to evaluate the effect of application time and phosphoric acid etching of 8th generation adhesives containing functional monomer on adhesive performance in primary teeth. 80 extracted non-carious human primary teeth were selected and divided into 8 groups based on 3 factors: (1) adhesive: G-Premio bond and Single bond universal; (2) application time: shortened time and manufacture's instruction; (3) acid etching mode: self-etching and total-etching. Shear bond strength was measured using a universal testing machine, and fractured surface were observed under scanning electron microscope. Microleakage was evaluated by dye penetration depth. G-Premio bond were not significant different in shear bond strength and microleakage depending on application time of adhesive and acid etching mode. In Single bond universal, shear bond strength of short application time was significantly lower than that of long adhesive application time (p = 0.014). Clinically applicable shear bond strength values (> 17 MPa) were identified in all groups. These results suggested that G-Premio bond be used clinically for a short application time without phosphoric acid etching.

• Journal of Korean Tunnelling and Underground Space Association
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• 제21권2호
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• pp.227-242
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• 2019

This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제22권1호
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• pp.511-519
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• 2021

Slewing bearing is applied to the transmission of rotational power of the body and turret in a special vehicle for anti-aircraft weapons that overcomes the enemy flight system approaching at low altitudes with rapid response fire. When the turret load and impact load generated when shooting are combined in performing the combat mission of a special vehicle, structural stability must be secured to achieve a successful function. Among the components of the slewing bearing, the stability of the components against the complex loads acting by the turret drive and shooting was evaluated by considering the shape and material characteristics of the ring-gear, roller, and wire-race. As a research method for stability evaluation, based on engineering theory, the strength characteristics of the components were examined by numerical calculations. Finite element analysis was performed on components using the ANSYS analysis program. The results of theoretical analysis and the results of finite element analysis were very similar. A structural stability evaluation for the slewing bearing, which was performed mainly on the analysis, confirmed that the design strength of the slewing bearing determined in the preliminary design in the early stage of localization development was sufficient.

• The Journal of Korean Institute of Next Generation Computing
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• 제14권6호
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• Journal of the Korea Institute of Information Security & Cryptology
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• 제21권6호
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• The Korean Journal of Applied Statistics
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• 제34권3호
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.