• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1581-1588
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• 2017

Recently, circulating ransomware is becoming intelligent and sophisticated through a spreading new viruses and variants, targeted spreading using social engineering attack, malvertising that circulate a large quantity of ransomware by hacking advertising server, or RaaS(Ransomware-as-a- Service), from the existing attack way that encrypt the files and demand money. In particular, it makes it difficult to track down attackers by bypassing security solutions, disabling parameter checking via file encryption, and attacking target-based ransomware with APT(Advanced Persistent Threat) attacks. For remove the threat of ransomware, various detection techniques are developed, but, it is very hard to respond to new and varietal ransomware. Accordingly, in this paper, find out a making Signature-based Detection Patterns and problems, and present a pattern automation model of ransomware detecting for responding to ransomware more actively. This study is expected to be applicable to various forms in enterprise or public security control center.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.219-240
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• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• The Korean Nurse
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• v.20 no.4 s.112
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• pp.39-49
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• 1981

기존 간호 영역 내 간호는 질적으로, 양적으로 급격히 팽창 확대되어 가고 있다. 많은 나라에서 건강관리체계가 부적절하게 분배되어 있으며 따라서 많은 사람들이 적절한 건강관리를 제공받지 못하고 있어 수준 높은 양질의 건강관리를 전체적으로 확대시키는 것이 시급하다. 혹 건강관리의 혜택을 받는다고 해도 이들 역시 보다 더 양질의 인간적인 간호를 요하고 있는 실정이다. 간호는 또한 간호영역 자체 내에서도 급격히 확대되어가고 있다. 예를들면, 미국같은 선진국가의 건강간호사(Nurse practitioner)는 간호전문직의 새로운 직종으로 건강관리체계에서 독자적인 실무자로 그 두각을 나타내고 있다. 의사의 심한 부족난으로 고심하는 발전도상에 있는 나라들에서는 간호원들에게 전통적인 간호기능 뿐 아니라 건강관리체계에서 보다 많은 역할을 수행하도록 기대하며 일선지방의 건강센터(Health center) 직종에 많은 간호원을 투입하고 있다. 가령 우리 한국정부에서 최근에 시도한 무의촌지역에서 졸업간호원들이 건강관리를 제공할 수 있도록 한 법적 조치는 이러한 구체적인 예라고 할 수 있다. 기존 간호영역내외의 이런 급격한 변화는 Melvin Toffler가 말한 대로 ''미래의 충격''을 초래하게 되었다. 따라서 이러한 역동적인 변화는 간호전문직에 대하여 몇가지 질문을 던져준다. 첫째, 미래사회에서 간호영역의 특성은 무엇인가? 둘째, 이러한 새로운 영역에서 요구되는 간호원을 길러내기 위해 간호교육자는 어떤 역할을 수행해야 하는가? 셋째 내일의 간호원을 양성하는 간호교육자를 준비시키기 위한 실질적이면서도 현실적인 전략은 무엇인가 등이다. 1. 미래사회에서 간호영역의 특성은 무엇인가? 미래의 간호원은 다음에 열거하는 여러가지 요인으로 인하여 지금까지의 것과는 판이한 환경에서 일하게 될 것이다. 1) 건강관리를 제공하는 과정에서 컴퓨터화되고 자동화된 기계 및 기구 등 새로운 기술을 많이 사용할 것이다. 2) 1차건강관리가 대부분 간호원에 의해 제공될 것이다. 3) 내일의 건강관리는 소비자 주축의 것이 될 것이다. 4) 간호영역내에 많은 새로운 전문분야들이 생길 것이다. 5) 미래의 건강관리체계는 사회적인 변화와 이의 요구에 더 민감한 반응을 하게 될 것이다. 6) 건강관리체계의 강조점이 의료진료에서 건강관리로 바뀔 것이다. 7) 건강관리체계에서의 간호원의 역할은 의료적인 진단과 치료계획의 기능에서 크게 탈피하여 병원내외에서 보다 더 독특한 실무형태로 발전될 것이다. 이러한 변화와 더불어 미래 간호영역에서 보다 효과적인 간호를 수행하기 위해 미래 간호원들은 지금까지의 간호원보다 더 광범위하고 깊은 교육과 훈련을 받아야 한다. 보다 발전된 기술환경에서 전인적인 접근을 하기위해 신체과학이나 의학뿐 아니라 행동과학 $\cdot$ 경영과학 등에 이르기까지 다양한 훈련을 받아야 할 필요가 있다. 또한 행동양상면에서 전문직인 답게 보다 진취적이고 표현적이며 자동적이고 응용과학적인 역할을 수행하도록 훈련을 받아야 한다. 그리하여 간호원은 효과적인 의사결정자$\cdot$문제해결자$\cdot$능숙한 실무자일 뿐 아니라 소비자의 건강요구를 예리하게 관찰하고 이 요구에 효과적인 존재를 발전시켜 나가는 연구자가 되어야 한다. 2. 미래의 간호교육자는 어떤 역할을 수행해야 하는가? 간호교육은 전문직으로서의 실무를 제공하기 위한 기초석이다. 이는 간호교육자야말로 미래사회에서 국민의 건강요구를 충족시키기는 능력있는 간호원을 공급하는 일에 전무해야 함을 시사해준다. 그러면 이러한 일을 달성하기 위해 간호교육자는 무엇을 해야 하는가? 우선 간호교육자는 두가지 측면에서 이 일을 수정해야 된다고 본다. 그 하나는 간호교육기관에서의 측면이고 다른 하나는 간호교육자 개인적인 측면엣서이다. 우선 간호교육기관에서 간호교육자는 1) 미래사회에서 요구되는 간호원을 교육시키기 위한 프로그램을 제공해야 한다. 2) 효과적인 교과과정의 발전과 수정보완을 계속적으로 진행시켜야 한다. 3) 잘된 교과과정에 따라 적절한 훈련을 철저히 시켜야 한다. 4) 간호교육자 자신이 미래의 예측된 현상을 오늘의 교육과정에 포함시킬 수 있는 자신감과 창의력을 가지고 모델이 되어야 한다. 5) 연구 및 학생들의 학습에 영향을 미치는 중요한 의사결정에 학생들을 참여시키도록 해야한다. 간호교육자 개인적인 측면에서는 교육자 자신들이 능력있고 신빙성있으며 간호의 이론$\cdot$실무$\cdot$연구면에 걸친 권위와 자동성$\cdot$독창성, 그리고 인간을 진정으로 이해하려는 자질을 갖추도록 계속 노력해야 한다. 3. 미래의 간호원을 양성하는 능력있는 간호교육자를 준비시키기 위한 실질적이면서도 현실적인 전략은 무엇인가? 내일의 도전을 충족시킬 수 있는 능력있는 간호교육자를 준비시키기 위한 실질적이고 현실적인 전략을 논함에 있어 우리나라의 실정을 참조하겠다. 전문직 간호교육자를 준비하는데 세가지 방법을 통해 할 수 있다고 생각한다. 첫째는 간호원 훈련수준을 전문직 실무를 수행할 수 있는 단계로 면허를 높이는 것이고, 둘째는 훈련수준을 더 향상시키기 위하여 학사 및 석사간호교육과정을 발전시키고 확대하는 것이며, 셋째는 현존하는 간호교육 프로그램의 질을 높이는 것이다. 첫째와 둘째방법은 정부의 관할이 직접 개입되는 방법이기 때문에 여기서는 생략하고 현존하는 교과과정을 발전시키고 그 질을 향상시키는 것에 대해서만 언급하고자 한다. 미래의 여러가지 도전에 부응할 수 있는 교육자를 준비시키는 교육과정의 발전을 두가지 면에서 추진시킬 수 있다고 본다. 첫째는 국제간의 교류를 통하여 idea 및 경험을 나눔으로서 교육과정의 질을 높일 수 있다. 서로 다른 나라의 간호교육자들이 정기적으로 모여 생각과 경험을 교환하고 연구하므로서 보다 체계적이고 효과적인 발전체인(chain)이 형성되는 것이다. ICN같은 국제적인 조직에 의해 이러한 모임을 시도하는 것인 가치있는 기회라고 생각한다. 국가간 또는 국제적인 간호교육자 훈련을 위한 교육과정의 교환은 한 나라안에서 그 idea를 확산시키는데 효과적인 영향을 미칠 수 있다. 충분한 간호교육전문가를 갖춘 간호교육기관이 새로운 교육과정을 개발하여 그렇지 못한 기관과의 연차적인 conference를 가지므로 확산시킬 수도 있으며 이런 방법은 경제적인 면에서도 효과적일 뿐만 아니라 그 나라 그 문화상황에 적합한 교과과정 개발에도 효과적일 수 있다. 간호교육자를 준비시키는 둘째전략은 현존간호교육자들이 간호이론과 실무$\cdot$연구를 통합하고 발전시키는데 있어서 당면하는 여러가지 요인-전인적인 간호에 적절한 과목을 이수하지 못하고 임상실무경험의 부족등-을 보충하는 방법이다. 이런 실제적인 문제를 잠정적으로 해결하기 위하여 1) 몇몇 대학에서 방학중에 계속교육 프로그램을 개발하여 현직 간호교육자들에게 필요하고 적절한 과목을 이수하도록 한다. 따라서 임상실무교육도 이때 실시할 수 있다. 2) 대학원과정 간호교육프로그램의 입학자의 자격에 2$\~$3년의 실무경험을 포함시키도록 한다. 결론적으로 교수와 학생간의 진정한 동반자관계는 자격을 구비한 능력있는 교수의 실천적인 모델을 통하여서 가능하게 이루어 질수 있다고 믿는 바이다.

• Smart Media Journal
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• v.12 no.11
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• pp.57-66
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• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.

• Journal of the Korean Institute of Gas
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• v.21 no.4
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• pp.47-61
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• 2017

The preconditions of the usefulness of an operator safety training model in large plants are the versatility and accuracy of operational procedures, obtained by detailed analysis of the various types of risks associated with the operation, and the systematic representation of knowledge. In this study, we consider the artificial intelligence planning method for the generation of operation procedures; classify them into general actions, actions and technical terms of the operator; and take into account the sharing and reuse of knowledge, defining a knowledge expression ontology. In order to expand and extend the general operations of the operation, we apply a Hierarchical Task Network (HTN). Actual boiler plant case studies are classified according to operating conditions, states and operating objectives between the units, and general emergency shutdown procedures are created to confirm the applicability of the proposed method. These results based on systematic knowledge representation can be easily applied to general plant operation procedures and operator safety training scenarios and will be used for automatic generation of safety training scenarios.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.777-780
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• 2015

Currently, our country has promoted sustainable growth in agriculture field by expanding the growth engine which is going to creat new value through agrifood industry & ICT convergence, the deployment of computerization in rural areas and the efficiency increase of agricultural administration system. Since the level of domestic agriculture-ICT convergence technology focusing on production areas is at early stage, it is necessary to deploy the successful models through the systematic development of technology and standardization including production, distribution and consumption phase. In addition, because the management and control systems of large glass greenhouse are mostly foreign products with no standardization and related small domestic companies, there is a limit to agri-food & ICT convergence activation led by the agri-food private sector. Also, it is vital to increase productivity & efficiency and improve quality throughout the entire agricultural process including production, distribution and consumption by the fusion of information technology, automatic control technology and unique ICT on existing agricultural technology, Therefore, in this paper we propose the agricultural-ICT convergence technology fields in which our country can lead technology and the standardization plans through analyzing the development, policy and standardization trends on agricultural-ICT convergence technology.

• Economic and Environmental Geology
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• v.24 no.4
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• pp.421-434
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• 1991

Ground magnetic surveys were conducted at four areas where the Yangsan fault, the most prominent lineament in the Kyeongsang basin, appears to be passed through. For data processing, IGRF correction, upward continuation and reduction-to-the-pole were performed. The automatic inversion by using a matrix computation method, which takes the depth to bottom layer of the horizontal two layer structure as the model parameter, has been attempted to delineate the subsurface structure. Upward continuation of the surface magnetic map to the same level of the aeromagnetic survey (KIER, 1989) resulted in very similiar patterns to those of aeromagnetic data. Subsurface modeling of eight profile data show that the strike and dip of the Yangsan fault in study areas are $N6^{\circ}-15^{\circ}E$, and near vertical to somewhat eastward, repectively, despite of the local lithological contrast of each study area. It seems that the magnetic effect of faulting in the study area 1, which locates in the most northern part of the survey areas, is disturbed by that of igneous intrusion. At study area 2, the possibility of volcanic or igneous intrusion, which is 200-300 meters wide along the fault plane was presented. At study area 3, unlike other study areas, distinct fracture zone of 500-700 meters in width was revealed along the surface fault line. The andesitic rocks of the study area 4 have very high susceptibilities and the fault line on surface of this area was shifted about 500 meter eastward, as compared with the inferred fault line by the previous study.