• 한국정보교육학회:학술대회논문집
• /
• 2021.08a
• /
• pp.369-376
• /
• 2021

With the development of artificial intelligence (AI) technology, discussions on the use of artificial intelligence are actively taking place in many fields, and various policies for nurturing artificial intelligence talents are being promoted in the field of education. In this study, we propose a robot programming framework using artificial intelligence technology, and based on this, we use Python, which is used frequently in the machine learning field, and an educational robot that is highly utilized in the field of education to provide artificial intelligence. (AI) education program was proposed. The level of autonomous driving (levels 0-5) suggested by the International Society of Automotive Engineers (SAE) is simplified to four levels, and based on this, the camera attached to the robot recognizes and detects lines (objects). The goal was to make a line detector that can move by itself. The developed program is not a standardized form of solving a given problem by simply using a specific programming language, but has the experience of defining complex and unstructured problems in life autonomously and solving them based on artificial intelligence (AI) technology. It is meaningful.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.7
• /
• pp.870-874
• /
• 2018

In the worst maritime accidents, people should abandon ship, but ship structures are narrow and complex and operation takes place on rough seas, so escape is not easy. In particular, passengers on cruise ships are untrained and varied, making evacuation prospects worse. In such a case, the evacuation management of the crew plays a very important role. If a rescuer enters a ship at distress and conducts rescue activities, which zones represent the most effective entry should be examined. Generally, crew and rescuers take the shortest route, but if an accident occurs along the shortest route, it is necessary to select the second-best alternative. To solve this situation, this study aims to calculate evacuation routes using Q-Learning of Reinforcement Learning, which is a machine learning technique. Reinforcement learning is one of the most important functions of artificial intelligence and is currently used in many fields. Most evacuation analysis programs developed so far use the shortest path search method. For this reason, this study explored optimal paths using reinforcement learning. In the future, machine learning techniques will be applicable to various marine-related industries for such purposes as the selection of optimal routes for autonomous vessels and risk avoidance.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.7
• /
• pp.1216-1221
• /
• 2022

Ships tend to be larger to increase the efficiency of cargo transportation. Larger ships lead to increased travel time for ship workers, increased work intensity, and reduced work efficiency. Problems such as increased work intensity are reducing the influx of young people into labor, along with the phenomenon of avoidance of high intensity labor by the younger generation. In addition, the rapid aging of the population and decrease in the young labor force aggravate the labor shortage problem in the maritime industry. To overcome this, the maritime industry has recently introduced technologies such as an intelligent production design platform and a smart production operation management system, and a smart autonomous logistics system in one of these technologies. The smart autonomous logistics system is a technology that delivers various goods using intelligent mobile robots, and enables the robot to drive itself by using sensors such as lidar and camera. Therefore, in this paper, it was checked whether the mobile robot could autonomously drive to the stop sign by detecting the passage way of the ship deck. The autonomous driving was performed by detecting the passage way of the ship deck through the camera mounted on the mobile robot based on the data learned through Nvidia's End-to-end learning. The mobile robot was stopped by checking the stop sign using SSD MobileNetV2. The experiment was repeated five times in which the mobile robot autonomously drives to the stop sign without deviation from the ship deck passage way at a distance of about 70m. As a result of the experiment, it was confirmed that the mobile robot was driven without deviation from passage way. If the smart autonomous logistics system to which this result is applied is used in the marine industry, it is thought that the stability, reduction of labor force, and work efficiency will be improved when workers work.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.5
• /
• pp.712-717
• /
• 2008

In swarm robot systems, each robot must act by itself according to the its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. By distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.16 no.2
• /
• pp.44-49
• /
• 2007

An agent is an abstract unit for problem solving in the field of distributed artificial intelligence, and an agent-based system is designed and implemented based on the definition of agent as its central concept. Agent modeling is advantageous to abstraction, disintegration and structuring for describing complex system, so its application is increased in various areas including air traffic control, power transmission, e-commerce and medicine. There is no agreed definition of agent but agents have common points as follows: autonomy, reactivity, pro-activeness and cooperation. An agent-oriented modeling is an approach of a concept different form existing object-oriented modeling. This study proposed the agent application for E-Beam manufacturing system. To evaluate the performance of the proposed process design, we used the JADE library. The JADE toolkit provides a FIPA-compliant agent platform and a package to develp Java agents. It provides a basic set of functionalities that are regarded as essential for an autonomous agent architecture.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.4 no.2
• /
• pp.397-406
• /
• 2000

Genetic algorithm is used as a means of search, optimization md machine learning, its structure is simple but it is applied to various areas. And it is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an action base system evolving by itself is also being considered. There is to have a problem that depended entirely on heuristic knowledge of expert forming membership function and control rule for fuzzy controller design. In this paper, for forming the fuzzy control to perform self-organization, we tuned the membership function to the most optimal using a genetic algorithm(GA) and improved the control efficiency by the self-correction and generation of control rules.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.56 no.4
• /
• pp.384-394
• /
• 2020

This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

• 한국IT서비스학회:학술대회논문집
• /
• 2008.11a
• /
• pp.543-546
• /
• 2008

국내 50인 이상, 매출 100억이상 규모의 중소제조업을 위한 생산정보화는 그간 정부의 정보화 지원사업의 지원으로 중소제조업에서 평균 생산성 22%이상의 성과를 달성하였다. 그러나, 50인이하, 매출 100억 미만의 국내제조업은 약 20만개 이상이 되고 있으나, 정부에서 지원하고 있는 정보화지원사업의 혜택을 받지 못하고 있다. 그간 ERP, SCM 등과 같은 기업정보화의 경우 ASP (Application Service Provider)모델을 개발하여 적용을 시도하여 보았으나 활성화되지 못했고 생산자원 4M (Man, Machine Material & Method)정보를 실시간 처리가 요구되는 생산정보화의 경우에는 기술적 문제로 인하여 적용 시도조차 할 수 없었다. 최근 RFID/USN, 개방형 임베디드, 모바일, 차세대 인터넷 및 SOA (Service Oriented Architecture)기술의 급속한 발전으로 전세계 산재되어 있는 복수 공장의 생산자원을 중앙에서 서비스기반으로 중앙관리가 가능하게 되었다. 본 논문에서는 50인 이하, 매출 100억 미만의 군소제조업의 생산자원 4M을 유무선 센서 기술을 통하여 자율 재구성 능력 (Autonomous Reconfigurable) 기반의 원격관리가 가능한 핵심기능의 소개와 복수 공장의 다양한 생산정보화 애플리케이션을 단일 표준 플랫폼으로 구현할 수 있는 적용 모델에 대하여 설명한다.

• Electronics and Telecommunications Trends
• /
• v.33 no.5
• /
• pp.121-134
• /
• 2018

The Von Neumann based architecture of the modern computer has dominated the computing industry for the past 50 years, sparking the digital revolution and propelling us into today's information age. Recent research focus and market trends have shown significant effort toward the advancement and application of artificial intelligence technologies. Although artificial intelligence has been studied for decades since the Turing machine was first introduced, the field has recently emerged into the spotlight thanks to remarkable milestones such as AlexNet-CNN and Alpha-Go, whose neural-network based deep learning methods have achieved a ground-breaking performance superior to existing recognition, classification, and decision algorithms. Unprecedented results in a wide variety of applications (drones, autonomous driving, robots, stock markets, computer vision, voice, and so on) have signaled the beginning of a golden age for artificial intelligence after 40 years of relative dormancy. Algorithmic research continues to progress at a breath-taking pace as evidenced by the rate of new neural networks being announced. However, traditional Von Neumann based architectures have proven to be inadequate in terms of computation power, and inherently inefficient in their processing of vastly parallel computations, which is a characteristic of deep neural networks. Consequently, global conglomerates such as Intel, Huawei, and Google, as well as large domestic corporations and fabless companies are developing dedicated semiconductor chips customized for artificial intelligence computations. The AI Processor Research Laboratory at ETRI is focusing on the research and development of super low-power AI processor chips. In this article, we present the current trends in computation platform, parallel processing, AI processor, and super-threaded AI processor research being conducted at ETRI.

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