• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.71-71
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• 2023

국가물관리기본법에 의거하여 통합물관리 정책에 부합하는 농어촌용수 계획 및 관리를 위해 유역 및 용수구역 단위의 농업용수 공급 및 수요 분석이 요구되고 있다. 현재 농업용수는 개수로 방식 용수공급체계 및 수문 직접조작에 의한 용수배분체계로 공급량 대비 사용량의 비율이 48%에 불과하다. 또한, 농경지 상류와 하류의 공급량 차이가 크게 발생하며, 경지면적 감소가 공급 필요량 감소로 연결되지 않는다. 농업용수의 경우 기존 국가유역수자원 모델 (K-WEAP, MODISM)을 통한 물수지 분석시 순물소모량 개념의 회귀수량 산정으로 공급량과 회귀량의 왜곡이 발생하고 있으며, 이에 따른 공급량 왜곡, 유역내 복잡하고 다양한 농업용수 공급체계를 하나의 가상저수지로 단순화함으로서 유역내 들녘별 농업용수 과부족 분석 불가능, 하천과 저수지 공급 우선순위 현장과 불일치 등 농어촌용수구역의 특성 및 실제 현장을 반영하는데 한계가 있다. 본 연구에서는 기존 물수지 분석 모델을 개선하기 위한 농업용수 유역 물수지 분석 모델의 방법론을 제시하고 시범지역 적용을 통한 검증 및 적용성을 평가하고자 한다. 경기 안서 농촌용수구역을 대상으로 농어촌공사 및 지자체 관리 저수지, 양수장, 취입보, 관정 등 총 106개 개별 시설물 자료를 구축하였으며, 39개 지구로 세분화하였다. 한국농어촌공사의 계측 공급량 기반 수요량 및 개별 시설물에 대한 물수지 분석 후 지구 단위, 소유역 단위, 표준유역 단위의 상하류 및 시설물을 연계한 유역 물수지 모델을 제시하고자 한다.

• Journal of Advanced Technology Convergence
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• v.2 no.3
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• pp.49-58
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• 2023

This study tried to verify the context effect in cosmetics advertisements by examining the cosmetics advertisement effect according to whether the brand image and the model image matched, and whether the brand image and the advertisement copy were harmoniously perceived. To this end, data were collected using the brand value type (3) × advertisement copy type (3) factorial design. The results are as follows. First, as a result of confirming the advertising effect according to the matching of the cosmetic brand image and the model image, it was found that both the advertising attitude and purchase intention were significantly high when the model image and the brand image matched. Second, it was confirmed whether there was a difference in the advertisement effect according to whether the cosmetic brand image and copy type matched. As a result, consumers who perceived that the cosmetic brand image and copy type matched had significantly higher advertising attitudes and purchase intentions than consumers who perceived that the copy type did not match. It is expected that it will provide validity as to whether the copy strategy should be established by incorporating the context effect when setting up a copy strategy for cosmetics advertisements in the future.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.71-79
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• 2018

The Internet of Things is a technology that makes it possible for objects made up of hardware and software to exchange information with one another via the Internet, thereby facilitating the servitization of the objects. An IoT service, which is composed of an IoT device and a web service, has recently been applied to the marketing field and is being used as a means to meet customer needs. However, applying appropriate marketing elements to IoT services is not easy. Therefore, analysis tools are needed to properly apply marketing elements to IoT services. This study aims to construct an analysis model for marketing evaluation of IoT services. In this study, the technical elements and marketing elements of IoT were derived through a literature review, and the analysis model for marketing evaluation of IoT services was established by exploring the relationship between the elements based on a service blueprint. We also applied real cases to verify the analytical model. This study is expected to contribute to the development of tools for evaluating IoT services.

• Convergence Security Journal
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• v.23 no.4
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• pp.53-59
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• 2023

Various problems are occurring as teachers and student's parents communicate directly through mobile phones. In this study, a service model was proposed that allows teachers and student's parents to communicate through SNS platforms without knowing each other's mobile phone numbers. In the civil complaint communication service model proposed in this study provides, communication key sets are provided as public data, and a commonly used SNS platform uses the relevant relationship information to implement communication. This model also has expandability that can be applied not only to teachers, but also to ① officers who need to communicate with the parents of soldiers, ② nursing, health and nursing care personnel who frequently contact patient caregivers, and ③ welfare officials.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.31-34
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• 2024

태양광 에너지는 탄소 중립 이행을 위한 주요 방안으로 많은 주목을 받고 있다. 태양광 발전량은 여러 환경적 요인에 따라 크게 달라질 수 있으므로, 정확한 발전량 예측은 전력 네트워크의 안정성과 효율적인 에너지 관리에 근본적으로 중요하다. 대표적인 인공지능 기술인 신경망(Neural Network)은 불안정한 환경 변수와 복잡한 상호작용을 효과적으로 학습할 수 있어 태양광 발전량 예측에서 우수한 성능을 도출하였다. 하지만, 신경망은 모델의 구조나 초매개변수(Hyperparameter)를 최적화하는 것은 복잡하고 시간이 많이 드는 작업이므로, 에너지 분야에서 실제 산업 적용에 한계가 존재한다. 본 논문은 2단계 신경망 최적화를 통한 태양광 발전량 예측 기법을 제안한다. 먼저, 태양광 발전량 데이터 셋을 훈련 집합과 평가 집합으로 분할한다. 훈련 집합에서, 각기 다른 은닉층의 개수로 구성된 여러 신경망 모델을 구성하고, 모델별로 Optuna를 적용하여 최적의 초매개변숫값을 선정한다. 다음으로, 은닉층별 최적화된 신경망 모델을 이용해 훈련과 평가 집합에서는 각각 5겹 교차검증을 적용한 발전량 추정값과 예측값을 출력한다. 마지막으로, 스태킹 앙상블 방식을 채택해 기본 초매개변숫값으로 설정해도 우수한 성능을 도출하는 랜덤 포레스트를 이용하여 추정값을 학습하고, 평가 집합의 예측값을 입력으로 받아 최종 태양광 발전량을 예측한다. 인천 지역으로 실험한 결과, 제안한 방식은 모델링이 간편할 뿐만 아니라 여러 신경망 모델보다 우수한 예측 성능을 도출하였으며, 이를 바탕으로 국내 에너지 산업에 이바지할 수 있을 것으로 기대한다.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.53-59
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• 2004

In case of load forcasting the most important problem is to deal with the load of special days. According this paper presents forecasting method for speaial days peak load by neural networks model. by means of neural networks mothod using the historical past special- days load data, special-days load was directly forecasted, and forecasting % error showed good result as 1∼2% except vacation season in summer Consequently, it is capable of directly special days load, With the models, precision of forecasting was brought satisfactory result. When neural networks was compared with the orthogonal polynomials models at a view of the results of special-days load forecasting, neural networks model which used pattern conversion ratio was more effective on forecasting for special-days load. On the other hand, in case of short special-days load forecasting, both were valid.

• Convergence Security Journal
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• v.21 no.3
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• pp.31-38
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• 2021

Tor, known as Onion Router, guarantees strong anonymity. For this reason, Tor is actively used not only for criminal activities but also for hacking attempts such as rapid port scan and the ex-filtration of stolen credentials. Therefore, fast and accurate detection of Tor traffic is critical to prevent the crime attempts in advance and secure the organization's information system. This paper proposes a novel classification model that can detect Tor traffic and classify the traffic types based on CNN(Convolutional Neural Network). We use UNB Tor 2016 Dataset to evaluate the performance of our model. The experimental results show that the accuracy is 99.98% and 97.27% in binary classification and multiclass classification respectively.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.485-496
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• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.

• Convergence Security Journal
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• v.9 no.2
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• pp.41-48
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• 2009

Distributed denial of service attack detection for more development and research is underway. The method of using statistical techniques, the normal packets and abnormal packets to identify efficient. In this paper several statistical techniques, using a mix of various offers a way to detect the attack. To verify the effectiveness of the proposed technique, it set packet filtering on router and the proposed DDoS attacks detection method on a Linux router. In result, the proposed technique was detect various attacks and provide normal service mostly.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.471-472
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• 2022

손톱 하부 모세혈관(Nailfold Capillary)의 형태와 분포 특징으로부터 다양한 질병을 밝혀내려는 시도가 꾸준히 있어 왔다. 손톱 하부 모세혈관은 그의 대표적인 형태 특징을 따라 몇 가지로 분류할 수 있고, 이 분포와 질병과의 상관관계가 밝혀진 종래 연구들도 다수 존재한다. 현재는 진단하는 과정을 의료 전문가가 직접 촬영된 모세혈관 사진을 보고 주관적인 평가를 하게 되는데, 이러한 분석 방법은 많은 시간과 휴먼 에러가 발생한다는 문제점이 있다. 이를 자동화하기 위하여 본 논문은 손톱 하부 모세혈관의 모세혈관들을 YOLO 객체 인식 모델을 활용하여 모세혈관을 탐지하고 모세혈관의 종류에 따라 분류하는 방법을 제안하고, 그 유효성을 검증하였다.