• Tuberculosis and Respiratory Diseases
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• 제86권4호
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• pp.251-263
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• 2023

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

• 한국인공지능학회지
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• 제10권1호
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• pp.21-25
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• 2022

Korea has relatively less crime than other countries. However, the crime rate is steadily increasing. Many people think the crime rate is decreasing, but the crime arrest rate has increased. The goal is to check the relationship between CCTV and the crime rate as a way to lower the crime rate, and to identify the correlation between areas without CCTV and areas without CCTV. If you see a crime that can happen at any time, I think you should use a random forest algorithm. We also plan to use machine learning random forest algorithms to reduce the risk of overfitting, reduce the required training time, and verify high-level accuracy. The goal is to identify the relationship between CCTV and crime occurrence by creating a crime prevention algorithm using machine learning random forest techniques. Assuming that no crime occurs without CCTV, it compares the crime rate between the areas where the most crimes occur and the areas where there are no crimes, and predicts areas where there are many crimes. The impact of CCTV on crime prevention and arrest can be interpreted as a comprehensive effect in part, and the purpose isto identify areas and frequency of frequent crimes by comparing the time and time without CCTV.

• 한국인공지능학회지
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• 제10권1호
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• pp.9-14
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• 2022

The demand for public bicycles operated by the Seoul Metropolitan Government is increasing every year. The size of the Seoul public bicycle project, which first started with about 5,600 units, increased to 3,7500 units as of September 2021, and the number of members is also increasing every year. However, as the size of the project grows, excessive budget spending and deficit problems are emerging for public bicycle projects, and new bicycles, rental office costs, and bicycle maintenance costs are blamed for the deficit. In this paper, the Azure Machine Learning Studio program and the Boosted Decision Tree Regression technique are used to predict the number of public bicycle rental over environmental factors and time. Predicted results it was confirmed that the demand for public bicycles was high in the season except for winter, and the demand for public bicycles was the highest at 6 p.m. In addition, in this paper compare four additional regression algorithms in addition to the Boosted Decision Tree Regression algorithm to measure algorithm performance. The results showed high accuracy in the order of the First Boosted Decision Tree Regression Algorithm (0.878802), second Decision Forest Regression (0.838232), third Poison Regression (0.62699), and fourth Linear Regression (0.618773). Based on these predictions, it is expected that more public bicycles will be placed at rental stations near public transportation to meet the growing demand for commuting hours and that more bicycles will be placed in rental stations in summer than winter and the life of bicycles can be extended in winter.

• 해양환경안전학회지
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• 제24권7호
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• pp.870-874
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• 2018

선박은 해양사고 발생 시 최악의 경우 퇴선을 해야 하나 특성상 협소하고 복잡하며 해상에서 운항하므로 퇴선이 쉽지 않다. 특히, 여객선의 경우 해상에서의 안전훈련을 이수하지 않은 불특정 다수의 승객들로 인해 더욱 퇴선이 어려운 상황이 된다. 이런 경우 승무원들의 피난 유도가 상당히 중요한 역할을 하게 된다. 그리고 구조자가 사고 선박에 진입하여 구조 활동을 하는 경우 어느 구역으로 진입해야 가장 효과적인지에 대한 검토가 필요하다. 일반적으로 승무원 및 구조자는 최단경로를 택하여 이동하는 것이 일반적이나 최단 경로에 사고 상황 등이 발생했을 경우 제2의 최적 경로 선택이 필요하다. 이러한 상황을 해결하기 위해 이 연구에서는 머신러닝(Machine learning)의 기법 중에 하나인 강화학습(Reinforcement Learning)의 Q-Learning 이용하여 퇴선 경로를 산출하고자 한다. 강화학습은 인공지능(Artificial Intelligence)의 가장 핵심적인 기능으로 현재 여러 분야에 사용되고 있다. 현재까지 개발된 대부분의 피난분석 프로그램은 최단 경로를 탐색하는 기법을 사용하고 있다. 이 연구에서는 최단경로가 아닌 최적경로를 분석하기 위해 머신러닝의 강화학습 기법을 이용하였다. 향후 AI기법인 머신러닝은 자율운항선박의 최적항로 선정 및 위험요소 회피 등 다양한 해양관련 산업에 적용 가능할 것이다.

• 터널과지하공간
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• 제31권1호
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• pp.25-40
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• 2021

4차 산업혁명 시대의 도래에 따라 암반공학분야에서도 인공지능을 활용한 연구가 점차 증가하고 있다. 본 논문에서는 인공지능에 대한 이해와 그 활용도를 더욱 증진시키기 위하여, 암반공학기술의 주된 적용대상인 터널, 발파, 광산과 관련된 최근의 국내외 연구 중 인공지능이 활용된 논문들에서 그 알고리즘의 종류와 적용방법을 분석하였다. 터널에서는 암반분류, TBM굴진율 및 막장전방 지질 예측, 발파에서는 암반의 파쇄도 및 비산거리, 광산에서는 폐광의 침하가능성 예측을 위해 주로 활용되고 있으며, 기계학습의 다양한 알고리즘 중 인공신경망이 압도적으로 많이 활용되고 있는 것으로 나타났다. 연구결과의 정확도와 신뢰성 제고를 위해 사용하고자 하는 인공지능 알고리즘에 대한 정확하고 상세한 이해가 필수적이며, 현재는 접근이나 분석이 난해한 암반공학 분야의 다양한 문제해결을 위해 기계학습뿐 아니라 CNN 또는 RNN과 같은 딥러닝을 활용한 연구 아이디어들이 점차 증가될 것으로 기대된다.

• 한국인공지능학회지
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• 제12권2호
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• pp.17-23
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• 2024

Various methods have been developed to predict the flight path of an air-launched weapon to intercept a fast-moving target in the air. However, it is also getting more challenging to predict the optimal firing zone and provide it to a pilot in real-time during engagements for advanced weapons having new complicated guidance and thrust control. In this study, a method is proposed to develop an optimized weapon engagement zone by the SCG (Scaled Conjugate Gradient) algorithm to achieve both accurate and fast estimates and provide an optimized launch display to a pilot during combat engagement. SCG algorithm is fully automated, includes no critical user-dependent parameters, and avoids an exhaustive search used repeatedly to determine the appropriate stage and size of machine learning. Compared with real data, this study showed that the development of a machine learning-based weapon aiming algorithm can provide proper output for optimum weapon launch zones that can be used for operational fighters. This study also established a process to develop one of the critical aircraft-weapon integration software, which can be commonly used for aircraft integration of air-launched weapons.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.334-336
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• 2021

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.45-52
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• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

• 인터넷정보학회논문지
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• 제23권2호
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.