• 한국인공지능학회지
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• 제11권3호
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• Fisheries and Aquatic Sciences
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• 제27권5호
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• pp.276-282
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• 2024

The present study was conducted to investigate the behavior and distribution characteristics of fishes near an artificial reef close to the waters of Uljin marine ranch. A 200-kHz, dual-beam frequency transducer was attached to the side of a ship for acoustic measurements. The fish formed small groups in the bottom layer near the artificial reef around the afternoon-sunset period; at night, the fish did not form groups and remained individually scattered. During dawn-sunrise and morning, the fish formed groups again and were found near the upper and middle layers of the artificial reef. High density of fish occurred near the middle of the nautical zone during morning, afternoon-sunset, and dawn-sunrise, periods; at nighttime, the distribution was uniform across the entire zone. Moreover, the mean Nautical Area Scattering Coefficients (NASC, m²/nmi²) value was highest during dawn-sunrise at 400.2 m²/nmi², similar during night and morning (100.5 m²/nmi²), and lowest during afternoon-sunset (20.1 m²/nmi²). The present study is expected to provide the background for understanding the behavioral characteristics of fish living near artificial reefs and estimating the density and biomass of fish.

• 수산해양기술연구
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• 제45권4호
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• pp.234-242
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• 2009

To develop the artificial spawning seaweeds of the sandfish, Arctoscopus japonicus, the effects by the material type of artificial spawning seaweeds were investigated at Dongsan port in Gangwon-do from December 2006 to March 2007. Sargassum fulvellum, S. horneri, rope and net were used as materials for artificial spawning seaweeds, and the most effective thing among them was S. fulvellum. A. japonicus began to attach the egg mass to artificial spawning seaweeds when sea temperature dropped below 10${^{\circ}C}$ in December, spawned heavily when it was around 8${^{\circ}C}$ in January, and completed the behavior when it started to increase over 10${^{\circ}C}$ in February. The hatching period of eggs was estimated to be about 60 days. The middle position in artificial spawning seaweed had the highest number of egg masses and the diameter of the egg mass ranged from 25mm to 62mm. Based on the result for the effects, the artificial spawning seaweeds of A. japonicus were developed and it is possible to use them to form seaweed forests or spawning grounds of other species.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.369-376
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• 2021

인공지능(AI) 기술의 발전에 따라 많은 분야에서 인공지능 활용 방안에 대한 논의가 활발하게 일어나고 있으며 교육 분야에서도 인공지능 인재 양성을 위한 각종 정책이 추진되고 있다. 본 연구에서는 인공지능 기술을 활용한 로봇 프로그래밍 프레임워크를 제안하고 이를 기반으로 머신러닝(Machine Learning) 분야에서 높은 빈도로 활용되는 파이썬(Python)과 교육 현장의 활용도가 높은 교육용 로봇을 활용하여 인공지능(AI) 교육 프로그램을 제안하였다. 국제자동차공학회(SAE)에서 제시하는 자율주행자동차 수준(0~5단계)을 4단계로 단순화하고 이를 기반으로 로봇에 부착된 카메라가 선(객체)을 인지(Perception)하고 검출(Object detection)하여 스스로 움직일 수 있는 라인 디텍터(Line Detector)를 만드는 것을 목표로 하였다. 개발된 프로그램은 단순히 특정 프로그래밍 언어를 활용하여 주어진 문제를 해결하는 정형화된 형태가 아니라 생활 속의 복잡하고 비구조화된 문제를 자기주도적으로 정의하고 인공지능(AI) 기술을 기반으로 해결하는 경험을 가지는데 그 의의가 있다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2005년도 춘계 학술기술논문발표대회 논문집
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• pp.119-124
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• 2005

It is necessary for prediction of recycled aggregate concrete(RAC) strength at the early stage that facilitate concrete form removal and scheduling for construction. However, to predict RAC strength is difficult because of being influenced by complicated many factors. Therefore, this research suggest optimized estimation method that can reflect many factors. One way is Case-Based Reasoning(CBR) that solved new problems by adapting solutions to similar problems solved in the past, which are solved in the case library. Other way is Artificial Neural Networks(ANN) that solved new problems by training using a set of data, which is representative of problem domain. This study is to propose comparing accuracy of the estimating the compressive strength of recycled aggregate concrete using Case-Based Reasoning(CBR) and Artificial Neural Networks(ANN).

• 한국지능시스템학회논문지
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• 제22권4호
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• pp.476-480
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• 2012

본 논문에서는 선도 로봇을 추종하는 군집 로봇의 대형 제어를 인공 포텐셜 장을 사용하여 제안한다. 또한, 인공 포텐셜 장은 물리적으로 해석하기 쉬운 전기장을 모델링하여 구성하고, 장애물을 더욱 효과적으로 모델링하기 위해서, 장애물의 모양에 따라 전기장의식을 달리한다. 제안하는 방법은 선도 로봇의 경로를 인공 포텐셜 장을 통해 계획한 뒤, 선도 로봇을 추종 로봇이 뒤따라가는 형태로 구성된다. 마지막으로 시뮬레이션 예제를 통해 제안하는 기법의 타당성을 검증한다.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.180-188
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• 1999

The objective of this paper is, based upon the principles of artificial life, to induce emergent behaviors of multiple autonomous mobile robots which complex global intelligence form from simple local interactions. Here, we propose an architecture of neural network learning with reinforcement signals which perceives the neighborhood information and decides the direction and the velocity of movement as mobile robots navigate in a group. As the results of the simulations, the optimum weight is obtained in real time, which not only prevent the collisions between agents and obstacles in the dynamic environment, but also have the mobile robots move and keep in various patterns.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1998년도 춘계학술대회 논문집
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• pp.88-93
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• 1998

This paper presents a new solution approach to moving obstacle avoidance problem for a mobile robot. A new concept avoidability measure(AVM) is defined to describe the state of a pair of a robot and an obstacle regarding the collision between them. As an AVM, virtual distance function(VDF) is derived as a function of the distance from the obstacle to the robot and outward speed of the obstacle relative to the robot. By keeping the virtual distance above some positive limit value, the robot avoids the obstacle. In terns of the VDF, an artificial potential field is constructed to repel the robot away from the obstacle and to attract the robot toward a goal location. At every sampling time, the artificial potential field is updated and the force driving the robot is derived form the gradient of the artificial potential field. The suggested algorithm drives the robot to avoid moving obstacles in real time. Since the algorithm considers the mobility of the obstacle as well as the distance, it is effective for moving obstacle avoidance. Some simulation studies show the effectiveness of the proposed approach.

• Journal of Mechanical Science and Technology
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• 제19권spc1호
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• pp.255-264
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• 2005

In this paper, a practical bushing model is proposed to improve the accuracy of the vehicle dynamic analysis. The results of the rubber bushing are used to develop an empirical bushing model with an artificial neural network. A back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model. Then, a full car dynamic model with artificial neural network bushings is simulated to show the feasibility of the proposed bushing model.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2005년도 총회 및 춘계학술발표회
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• pp.348-351
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• 2005

In this study, solute breakthrough curves through the unsaturated zone were predicted using artificial neural network (ANN) by numerical tests and laboratory experiments. In the numerical tests, applicability of ANN model to prediction of breakthrough curves was evaluated using synthetic data generated by HYDRUS-2D. An appropriate strategy of ANN application and input data form were recommended. The ANN model was validated by laboratory experiments comparing with HYDRUS-2D simulations. The results show that the ANN model can be an effective method for forecasting solute breakthrough curves through the unsaturated zone when hydraulic data are available.