• Journal of the Korean GEO-environmental Society
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• v.12 no.9
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• pp.71-78
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• 2011

Cement treating technique, such as deep mixing method, has been used widely to stabilize the dredged clayey soil for many years. Despite of its effectiveness in treating soil by cement, several efforts have also been made to try to reduce the side effect of the cement that used to stabilize the dredged clay. However, authors considered that more detailed study on the physical and mechanical properties of lean-mixed soil-cement has been required to establish the design procedure to apply the practical problems. Therefore, in this study, the curing time and mixing ratio was used as key parameters to estimate the physical and mechanical properties including long-term behavior. The unconfined strength of lean-mixed soil-cement increase continuously during curing period, 270 days, while increasing rate becomes low in ordinary cement-treated dredged clay. We also concluded that cement-treated dredging clay shows apparent quasi overconsolidation behavior even in low cement proportion. By this study, fundamental approach was carried out for effective reuse of very soft dredged clayey soil both in mechanical and environmental aspect. It can be also expected that this study can propose a basic design data to use the lean-mixed soil cement.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.19-30
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• 2018

Visual information understanding is known as one of the most difficult and challenging problems in the realization of machine intelligence. This paper proposes deriving visual verb and construction of ActionNet database as a video database for video semantic understanding. Even though development AI (artificial intelligence) algorithms have contributed to the large part of modern advances in AI technologies, huge amount of database for algorithm development and test plays a great role as well. As the performance of object recognition algorithms in still images are surpassing human's ability, research interests shifting to semantic understanding of video contents. This paper proposes candidates of visual verb requiring in the construction of ActionNet as a learning and test database for video understanding. In order to this, we first investigate verb taxonomy in linguistics, and then propose candidates of visual verb from video description database and frequency of verbs. Based on the derived visual verb candidates, we have defined and constructed ActionNet schema and database. According to expanding usability of ActionNet database on open environment, we expect to contribute in the development of video understanding technologies.

• Korean Journal of Veterinary Service
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• v.43 no.4
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• pp.201-209
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• 2020

In Korea, bovine tuberculosis (bTB) is a representative zoonotic disease that causes considerable economic loss. In determining the positive bTB, the ELISA method for examining the amount of interferon-gamma (IFN-γ) is included in Korea's diagnostic standard method. Recently, commercially available BIONOTE TB-Feron ELISA Plus (TB-Feron Plus) that detects IFN-γ has been introduced. However, since the scientific basis for the performance is limited, we evaluated performance by comparing it with the results of another IFN-γ ELISA assay kit (BOVIGAM®) certified by Office International des Epizooties. In our research, 42 positive blood samples preliminarily tested with a tuberculin skin test and/or BOVIGAM® and 54 negative blood samples collected from three bTB free farms were subjected to IFN-γ assay using the TB-Feron Plus and the BOVIGAM®, respectively. The result shows that the sensitivity, specificity and accuracy were 81.0% (34/42), 100% (54/54), 91.7% (88/96) in TB-Feron Plus kit and 78.6% (33/42), 100% (54/54), 90.6% (87/96) in BOVIGAM® kit, respectively. Moreover, the overall accordance percentage of the two kits was 99.0% (95/96) and there was almost perfect agreement between two assays (Kappa=0.977, P<0.0001). Furthermore, additional studies confirmed that elevated lymphocyte numbers in blood did not interfere with the results of the TB-Feron Plus kit. And, delayed time from sampling to culture decreased the optical density (OD) value. Therefore, we concluded that the TB-Feron Plus kit was not inferior to BOVIGAM® in performance. High lymphocyte numbers in blood did not impact on TB-Feron Plus results, while delayed time before culture interfered with OD value.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.35-43
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• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Journal of IKEEE
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• v.25 no.2
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• pp.273-279
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• 2021

In this paper, we propose an algorithm that shortens the learning time by performing individual learning using partitioning the deep learning structure. The proposed algorithm consists of four processes: network classification origin setting process, feature vector extraction process, feature noise removal process, and class classification process. First, in the process of setting the network classification starting point, the division starting point of the network structure for effective feature vector extraction is set. Second, in the feature vector extraction process, feature vectors are extracted without additional learning using the weights previously learned. Third, in the feature noise removal process, the extracted feature vector is received and the output value of each class is learned to remove noise from the data. Fourth, in the class classification process, the noise-removed feature vector is input to the multi-layer perceptron structure, and the result is output and learned. To evaluate the performance of the proposed algorithm, we experimented with the Extended Yale B face database. As a result of the experiment, in the case of the time required for one-time learning, the proposed algorithm reduced 40.7% based on the existing algorithm. In addition, the number of learning up to the target recognition rate was shortened compared with the existing algorithm. Through the experimental results, it was confirmed that the one-time learning time and the total learning time were reduced and improved over the existing algorithm.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.99-108
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• 2021

There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.81-90
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• 2020

This paper describes a method of building Korean conversational speech data in the emergency medical domain and proposes an annotation method for the collected data in order to improve speech recognition performance. To suggest future research directions, baseline speech recognition experiments were conducted by using partial data that were collected and annotated. All voices were recorded at 16-bit resolution at 16 kHz sampling rate. A total of 166 conversations were collected, amounting to 8 hours and 35 minutes. Various information was manually transcribed such as orthography, pronunciation, dialect, noise, and medical information using Praat. Baseline speech recognition experiments were used to depict problems related to speech recognition in the emergency medical domain. The Korean conversational speech data presented in this paper are first-stage data in the emergency medical domain and are expected to be used as training data for developing conversational systems for emergency medical applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.296-301
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• 2021

GPR is used for non-destructive investigations, ground investigations, and underground facilities exploration at construction sites. In this study, the applicability to GPR exploration of water pipes linked to Network RTK was presented. Data on the water supply pipes in the study site were acquired using GPR, and the location and depth of buried water pipes could be measured. The accuracy was evaluated from the GNSS observation performance and showed a deviation of -0.16m ~ 0.15m. This satisfied the equipment performance of the public survey work regulation, suggesting that the exploration of water pipes using GPR is possible. Because GPR does not require grounding installation, as in conventional metal pipe detectors, it will increase the efficiency of work for underground facility exploration. Exploration using GPR can acquire the location and depth of metallic and non-metallic underground facilities, so it can be utilized in the construction of a GIS system. If a comparison of the exploration characteristics is carried out, it will be possible to present various uses of underground facility exploration using GPR.