• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.179-183
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• 2020

말뭉치는 기계학습 및 심층학습을 위한 필수 자원이다. 한국어 개체명의 경우 학습에 사용할 잘 정제된 개체명 부착 말뭉치가 충분하지 않다. 말뭉치 정제 작업은 시간적, 경제적으로 많은 비용이 소모된다. 따라서 본 논문에서는 적은 양의 말뭉치를 이용하여 말뭉치를 자동적으로 확장하는 방법을 제안한다. 특별히 소규모 말뭉치에 속하는 문장의 단어에 대한 형제어들을 선정하여 형제어의 확률추출을 기반으로 대체함으로써 새로운 문장을 생성함으로써 말뭉치 확장하는 방법이다. 본 논문에서는 확장된 말뭉치를 이용해서 대부분의 시스템에서 성능이 향상됨을 확인할 수 있었다. 앞으로 단어의 삭제 및 삽입 등 다양한 방법으로 좀 더 다양한 문장을 생성할 수 있을 것으로 생각합니다.

• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.192-200
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• 2022

Nitrogen is the most essential macronutrient for the growth of fruit trees and is important factor determining the fruit yield. In order to produce high-quality fruits, it is necessary to supply the appropriate nitrogen fertilizer at the right time. For this, it is a prerequisite to accurately diagnose the nitrogen status of fruit trees. The fastest and most accurate way to determine the nitrogen deficiency of fruit trees is to measure the nitrogen concentration in leaves. However, it is not easy for citrus growers to measure nitrogen concentration through leaf analysis. In this study, several machine learning models were developed to classify the nitrogen deficiency based on the concentration measurement of mineral nutrients in the leaves of tangor Shiranuhi (Citrus unshiu × C. sinensis). The data analyzed from the leaves were increased to about 1,000 training dataset through the bootstrapping method and used to train the models. As a result of testing each model, gradient boosting model showed the best classification performance with an accuracy of 0.971.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.171-184
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• 2023

Machine learning (ML)-based cavity detection using a large amount of survey data obtained from vehicle-mounted ground penetrating radar (GPR) has been actively studied to identify underground cavities. However, only simple image processing techniques have been used for preprocessing the ML input, and many conventional seismic and GPR data processing techniques, which have been used for decades, have not been fully exploited. In this study, based on the idea that a cavity can be identified using diffraction, we applied ML-based diffraction separation to GPR data to increase the accuracy of cavity detection using the YOLO v5 model. The original ML-based seismic diffraction separation technique was modified, and the separated diffraction image was used as the input to train the cavity detection model. The performance of the proposed method was verified using public GPR data released by the Seoul Metropolitan Government. Underground cavities and objects were more accurately detected using separated diffraction images. In the future, the proposed method can be useful in various fields in which GPR surveys are used.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.84-93
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• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.517-524
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• 2003

현재 전 세계 각국은 지식 경쟁력 확보에 혈안이 되어 있으며, 우리나라는 풍부한 인터넷 인프라를 구비하여 지식 강국으로 발돋움하기 위한 충분한 토대를 마련하고 있다. 특히, 인력은 인터넷 시대의 핵심적 지식 자원으로서, 전문가 두뇌 연계 망(네트워크)의 구축과 운영을 통해 해당 분야 전문가간 협력 및 교류가 진행되면, 지식 정보의 동시 생성, 공유, 활용 체제의 확립이 가능하다 전문가 커뮤니티의 구성원은 정보의 공유와 확산에 자발적으로 기여하는 지식의 선 순환 구조를 이루게 될 것이다. 본 논문에서는 국내외 과학기술 전문가로 구성된 한민족과학기술자 네트워크(KOSEN, www.kosen21.org)를 사례로 전문가 네트워크의 역할과 특징을 살펴보고, 지식 기반 사회에서 전문가 네트워크의 발전 방향을 제안하고자 한다. KOSEN은 지식의 생성, 공유, 활용 등의 지식관리 프로세스를 지원하는 과학기술 전문가 커뮤니티이다. 향후 인적 자원 및 정보 자원의 적절한 연계를 통해 지식의 활용 측면을 더욱 확대하여 본격적인 지식 정보 활용의 장으로 거듭나야 한다. 컨텐트 가치증대를 통한 전문가 참여 확대, 전문가들간 상호 연계의 확대를 통한 소 공동체 형성, 전문가들간 상호 학습, 정보 거래 메커니즘 구축 등의 다양한 방안을 통해 보완될 것으로 기대한다.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.287-291
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• 2020

본 연구에서는 한국어 의미 표상 자원 구축과 의미 파싱 성능 향상을 위한 데이터 자동 증강 방법을 제안하고 수동 구축 결과 대비 자동 변환 정확도를 보인다. 지도 학습 기반의 AMR 파싱 모델이 유의미한 성능에 도달하려면 대량의 주석 데이터가 반드시 필요하다. 본 연구에서는 기성 언어 분석 기술 또는 기존에 구축된 말뭉치의 주석 정보를 바탕으로 Semi-AMR 데이터를 변환해내는 알고리즘을 제시하며, 자동 변환 결과는 Gold-standard 데이터에 대해 Smatch F1 0.46의 일치도를 보였다. 일정 수준 이상의 정확도를 보이는 자동 증강 데이터는 주석 프로젝트에 소요되는 비용을 경감시키는 데에 활용될 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5594-5600
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• 2011

Even though many studies have showed that competence is positively related to organizational performance, few studies have attempted to find out the process of competency - performance. This study focuses on the organizational factors to explore their effect on the competence of customer relationship. Based on the data collected by KRIVET and the Ministry of employment and labor, strategic role of HR department and information systems are examined. As well human resource competency is investigated as a mediating variable. This study used surveys targeting department managers and executive members in firms and sample size was 1086 after cleaning the dataset by deleting all the cases with many missing values. The findings show that strategic role of HR department and information system has an influence on human resource competency, significantly. In addition, the human resource competency affect customer relationship competency, positively. Implications and directions for future research are discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.47-52
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• 2023

With the growing data size and the increased computing load in machine learning, energy-efficient resource planning in IoT systems is becoming increasingly important. In this paper, we suggest a new resource planning policy for real-time workloads that can be fluctuated over time in IoT systems. To handle such situations, we categorize real-time tasks into fixed tasks and variable tasks, and optimize the resource planning for various workload conditions. Based on this, we initiate the IoT system with the configuration for the fixed tasks, and when variable tasks are activated, we update the resource planning promptly for the situation. Simulation experiments show that the proposed policy saves the processor and memory energy significantly.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.721-732
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• 2024

Species identification of zooplankton is the most basic process in understanding the marine ecosystem and studying global warming. In this study, we propose an convolutional neural network model that can classify females and males of three zooplankton at the species level. First, training data including morphological features is constructed based on microscopic images acquired by researchers. In constructing training data, a data argumentation method that preserves morphological feature information of the target species is applied. Next, we propose a convolutional neural network model in which features can be learned from the constructed learning data. The proposed model minimized the information loss of training image in consideration of high resolution and minimized the number of learning parameters by using the global average polling layer instead of the fully connected layer. In addition, in order to present the generality of the proposed model, the performance was presented based on newly acquired data. Finally, through the visualization of the features extracted from the model, the key features of the classification model were presented.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.256-264
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• 2020

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.