• International Journal of Aerospace System Engineering
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• v.5 no.2
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• pp.1-7
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• 2018

This study examines the history of Korean aviation industry and presents the investment strategy based on the evidence and the 4th industrial revolution. Looking at the evolution of the Korean aviation industry and its technological development will be a great help to support industrial and technological innovation in the future. The modern aviation industry is divided into stages of development, focusing on maintenance of equipment introduced in advanced countries, localization through license assembly, production of products based on technology, and international joint development. The development of aeronautics technology has been progressing towards a general improvement of economic efficiency, aircraft safety efficiency through environmental-friendliness, unmanned operation, and downsizing. The Korea Aerospace Research Institute has secured key technologies through development of several aircrafts such as Experimental Aircraft Kachi, EXPO Unmanned Airship, Twin-engine Composite Aircraft, Canard Aircraft, Multi-Purpose Stratosphere unmanned-airship, Medium Aerostats, Smart UAV, Surion, EAV-2H, KC-100, and OPV. The development strategy is discussed at the level of the evidence-based investment strategy that is currently being discussed, and so the investment priorities in aircraft is high. Current drone usage and development direction are not only producing parts using 3D printer, but also autonomous flight, communication (IoT, 5G), information processing (big data, machine learning). Therefore, the aviation industry is expected to lead the fourth industrial revolution.

• Smart Media Journal
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• v.3 no.1
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• pp.16-21
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• 2014

The demand for food rises proportionally as population grows. To be able to achieve sustainable supply of livestock products, efficient farm management is a necessity. With the advancement in technology it also brought innovations that could be harness in order to achieve better productivity in animal production and agriculture. Precision Livestock Farming (PLF) is a budding concept of making use of smart sensors or available devices to automatically and continuously monitor and manage livestock production. With this concept, this paper introduces a swine management system that integrates image processing technique for weight monitoring. This system captures pig images using camera, evaluate and estimate the weight base on the captured image. It is comprised of Pig Module, Breeding Module, Health and Medication Module, Weighr Module, Data Analysis Module and Report Module to help swine farm administrators better understand the performance and situation of the swine farm. This paper aims to improve the management in both small and big livestock raisers.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.137-142
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• 2021

The Internet of Things (IoT) paradigm is at the forefront of present and future research activities. The huge amount of sensing data from IoT devices needing to be processed is increasing dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing jobs. The fog computing technology is a model that is used to support cloud computing by implementing pre-processing jobs close to the end-user for realizing low latency, less power consumption in the cloud side, and high scalability. However, it may be that some resources in fog computing networks are not suitable for some kind of jobs, or the number of requests increases outside capacity. So, it is more efficient to decrease sending jobs to the cloud. Hence some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud server. Obviously, this issue affects the performance of the fog environment when dealing with big data applications or applications that are sensitive to time processing. This research aims to build a fog topology job scheduling (FTJS) to schedule the incoming jobs which are generated from the IoT devices and discover all available fog nodes with their capabilities. Also, the fog topology job placement algorithm is introduced to deploy jobs into appropriate resources in the network effectively. Finally, by comparing our result with the state-of-art first come first serve (FCFS) scheduling technique, the overall execution time is reduced significantly by approximately 20%, the energy consumption in the cloud side is reduced by 18%.

• Knowledge Management Research
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• v.17 no.2
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• pp.27-50
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• 2016

Korean manufacturing industry have recently faced the catch-up of China in the mass commodity product, such as automotive, display, and smart phone in terms of market as well as technology. Accordingly, discussion on the importance of achieving catch-up in complex product systems (CoPS) has been increasing as a new innovation engine for the industry. In order to achieve successful catch-up of CoPS, we explored emerging technologies of CoPS, which are featured by the characteristics of radical novelty, relatively fast growth and self-sustaining, through the study of emerging technologies of gas turbine for power generation. We found that emerging technologies of the gas turbine are technologies for combustion nozzle and composition of electrical machine for increasing power efficiency, washing technology for particulate matter, cast and material processing technology for enhancing durability from fatigue, cooling technologies from extremely high temperature, interconnection operation technology between renewable energy and the gas turbine for flexibility in power generation, and big data technology for remote monitoring and diagnosis of the gas turbine. We also found that those emerging technologies resulted in technological progress of the gas turbine by converging with other conventional technologies in the gas turbine. It indicates that emerging technologies in CoPS can be appeared on various technological knowledge fields and have complementary relationship with conventional technologies for technology progress of CoPS. It also implies that latecomers need to pursue integrated learning that includes emerging technologies as well as conventional technologies rather than independent learning related to emerging technologies for successful catch-up of CoPS. Our findings provide an important initial theoretical ground for investigating the emerging technologies and their characteristics in CoPS as well as recognizing knowledge management strategy for successful catch-up of latecomers. Our findings also contribute to the policy development of the CoPS from the perspective of innovation strategy and knowledge management.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.87-94
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• 2015

Recently, management based on statistical data has become a big issue and the importance of the statistics has been emphasized for the management innovation in the defense area. However, the Military Management based on the statistics is hard to expect because of the shortage of the statistics in the military. There are many military information systems having great many data created in real time. Since the infrastructure for gathering data form the many systems and making statistics by using gathered data is not equipped, the usage of the statistics is poor in the military. The Analytical Defense Statistics System is designed to improve effectively the defense management in this study. The new system having the sub-systems of Data Management, Analysis and Service can gather the operational data from interlocked other Defense Operational Systems and produce Defense Statistics by using the gathered data beside providing statistics services. Additionally, the special function for the user oriented statistics production is added to make new statistics by handling many statistics and data. The Data Warehouse is considered to manage the data and Online Analytical Processing tool is used to enhance the efficiency of the data handling. The main functions of the R, which is a well-known analysis program, are considered for the statistical analysis. The Quality Management Technique is applied to find the fault from the data of the regular and irregular type. The new Statistics System will be the essence of the new technology like as Data Warehouse, Business Intelligence, Data Standardization and Statistics Analysis and will be helpful to improve the efficiency of the Military Management.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.1
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• pp.15-20
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• 2024

In the field of ship inspection in South Korea, due to outdated workflow processes, there is a possibility of tampering with inspection results. Accordingly, research is being conducted to prevent tampering with inspection results by introducing blockchain technology and cloud-based systems that allow real-time tracking and sharing of data, and to establish a transparent and efficient communication system. In this study, unit and integrated processes for overall data management and inspection execution related to ship inspection were implemented to automatically collect, manage, and track various inspection results occurring during the ship inspection process. Through this, it aimed to increase the efficiency of the ship inspection process overall, inducing growth in the ship inspection industry as a whole. The implemented web portal reached a level where trend analysis and comparative analysis with other ships based on inspection results are possible, and subsequent research aims to demonstrate the excellence of the system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.61-67
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• 2024

In this paper, we presented a strengthening plan to prevent personal information leakage incidents by securing legal compliance for personal information impact assessment and suggesting measures to strengthen privacy during personal information impact assessment. Recently, as various services based on big data have been created, efforts are being made to protect personal information, focusing on the EU's GDPR and Korea's Personal Information Protection Act. In this society, companies entrust processing of personal information to provide customized services based on the latest technology, but at this time, the problem of personal information leakage through consignees is seriously occurring. Therefore, the use of personal information by trustees.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.195-198
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• 2015

최근 TV 서비스의 가입자 및 TV 프로그램 콘텐츠의 급격한 증가에 따라 빅데이터 처리에 적합한 추천 시스템의 필요성이 증가하고 있다. 본 논문은 사용자들의 간접 평가 데이터 기반의 추천 시스템 디자인 시, 누적된 사용자의 과거 이용내역 데이터를 저장하지 않고 새로 생성된 사용자 이용내역 데이터를 학습하는 효율적인 알고리즘이면서, 시간 흐름에 따라 사용자들의 선호도 변화 및 TV 프로그램 스케줄 변화의 추적이 가능한 토픽 모델링 기반의 알고리즘을 제안한다. 빅데이터 처리를 위해서는 분산처리 형태의 알고리즘을 피할 수 없는데, 기존의 연구들 중 토픽 모델링 기반의 추론 알고리즘의 병렬분산처리 과정 중에 핵심이 되는 부분은 많은 데이터를 여러 대의 기계에 나누어 병렬분산 학습하면서 전역변수 데이터를 동기화하는 부분이다. 그런데, 이러한 전역데이터 동기화 기술에 있어, 여러 대의 컴퓨터를 병렬분산처리하기위한 하둡 기반의 시스템 및 서버-클라이언트간의 중재, 고장 감내 시스템 등을 모두 고려한 알고리즘들이 제안되어 왔으나, 네트워크 대역폭 한계로 인해 데이터 증가에 따른 동기화 시간 지연은 피할 수 없는 부분이다. 이에, 본 논문에서는 빅데이터 처리를 위해 사용자들을 클러스터링하고, 클러스터별 제안 알고리즘으로 전역데이터 동기화를 수행한 것과 지역 데이터를 활용하여 추론 연산한 결과, 클러스터별 지역별 TV프로그램 시청 토큰 별 은닉토픽 할당 테이블을 유지할 때 추천 성능이 더욱 향상되어 나오는 결과를 확인하여, 제안된 구조의 추천 시스템 디자인의 효율성과 합리성을 확인할 수 있었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.201-206
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• 2018

As the artificial intelligence and big data technology has been developed recently, the importance of GPGPU, which is a general purpose graphics processing unit, is emphasized. In addition, by the demand for mining equipment to obtain bit coins, which is a block chain application technology, the price of GPGPU has increased sharply with scarcity. If a GPGPU can be precisely simulated, it is possible to conduct experiments on various GPGPU types and analyze performance without purchasing expensive ones. In this paper, we investigate the configuration of a GPGPU simulator and measure the performance of various benchmark programs using GPGPU-Sim.