• Convergence Security Journal
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• v.24 no.2
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• pp.63-70
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• 2024

With the development of artificial intelligence technology, our lives are changing in innovative ways, but at the same time, new ethical issues are emerging. In particular, issues of discrimination due to algorithm and data bias, deep fakes, and personal information leakage issues are judged to be social priorities that must be resolved as artificial intelligence services expand. To this end, this paper examines the concept of artificial intelligence and ethical issues from the perspective of artificial intelligence ethics, and includes each country's ethical guidelines, laws, artificial intelligence impact assessment system, artificial intelligence certification system, and the current status of technologies related to artificial intelligence algorithm transparency to prevent this. We would like to examine and suggest institutional improvement measures to strengthen artificial intelligence ethics.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.167-175
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• 2023

Chest PA is the basic examination of radiographic imaging. Moreover, Chest PA's demands are constantly increasing because of the Increase in respiratory diseases. However, it is not meeting the demand due to problems such as a shortage of radiological technologist, sexual shame caused by patient contact, and the spread of infectious diseases. There have been many cases of using artificial intelligence to solve this problem. Therefore, the purpose of this research is to build an artificial intelligence dataset of Chest PA and to find a posture evaluation method. To construct the posture dataset, the posture image is acquired during actual and simulated examination and classified correct and incorrect posture of the patient. And to evaluate the artificial intelligence posture method, a posture estimation algorithm is used to preprocess the dataset and an artificial intelligence classification algorithm is applied. As a result, Chest PA posture dataset is validated with in over 95% accuracy in all artificial intelligence classification and the accuracy is improved through the Top-Down posture estimation algorithm AlphaPose and the classification InceptionV3 algorithm. Based on this, it will be possible to build a non-face-to-face automatic Chest PA examination system using artificial intelligence.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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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).

• Journal of Korean Elementary Science Education
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• v.37 no.2
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• pp.126-146
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• 2018

The current study explores students' views about artificial intelligence (AI) through analyses of their drawings and perceptions. The data were gathered from a total of 177 elementary school students. The constant comparative analysis was used as the data analysis method. Based on the result, the current study found that students' views about artificial intelligence were constructed into two distinct dimensions: form and relationship. The form dimension, students' views about artificial intelligence were categorized into human, household goods, machine, smart computer, electronic chip/algorithm, or the hybridized form related to the game of go such as AlphaGo. On the relationship dimension, students' views about artificial intelligence were categorized into servants, friends or enemy. Given the combination of two dimensions, the current study found two noted patterns. The first, students who viewed artificial intelligence as human form perceived artificial intelligence as a friend or an enemy. However, those who viewed artificial intelligence as non-human form perceived artificial intelligence as a servant or an enemy. Based on these results, educational implications related to the preparation of artificial intelligence era for elementary science education are discussed.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.803-812
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• 2021

This study explored the concept of artificial intelligence and the problem-solving process that can be explained through literature research. Through this study, the educational meaning and application plan of artificial intelligence that can be explained were presented. XAI education is a human-centered artificial intelligence education that deals with human-related artificial intelligence problems, and students can cultivate problem-solving skills. In addition, through algorithmic education, it is possible to understand the principles of artificial intelligence, explain artificial intelligence models related to real-life problem situations, and expand to the field of application of artificial intelligence. In order for such XAI education to be applied in elementary schools, examples related to real world must be used, and it is recommended to utilize those that the algorithm itself has interpretability. In addition, various teaching and learning methods and tools should be used for understanding to move toward explanation. Ahead of the introduction of artificial intelligence in the revised curriculum in 2022, we hope that this study will be meaningfully used as the basis for actual classes.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.18-28
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• ETRI Journal
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• v.42 no.6
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• pp.872-885
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• 2020

Distributed consensus requires the consent of more than half of the congress to produce irreversible results, and the performance of the consensus algorithm deteriorates with the increase in the number of nodes. This problem can be addressed by delegating the agreement to a few selected nodes. Since the selected nodes must comply with the Byzantine node ratio criteria required by the algorithm, the result selected by any decentralized node cannot be trusted. However, some trusted nodes monopolize the consensus node selection process, thereby breaking decentralization and causing a trilemma. Therefore, a consensus node selection algorithm is required that can construct a congress that can withstand Byzantine faults with the decentralized method. In this paper, an algorithm based on the Byzantine agreement among decentralized agents to facilitate agreement between decentralization nodes is proposed. It selects a group of random consensus nodes per block by applying the proposed proof of nonce algorithm. By controlling the percentage of Byzantine included in the selected nodes, it solves the trilemma when an arbitrary node selects the consensus nodes.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.22-30
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• 2023

Recently, artificial intelligence has been in the spotlight across various fields. Artificial intelligence uses massive amounts of data to train machine learning models and performs various tasks using the trained models. For model training, large, high-quality data sets are essential, and database systems have provided such data. Driven by advances in artificial intelligence, attempts are being made to improve various components of database systems using artificial intelligence. Replacing traditional complex algorithm-based database components with their artificial-intelligence-based counterparts can lead to substantial savings of resources and computation time, thereby improving the system performance and efficiency. We analyze trends in the application of artificial intelligence to database systems.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1271-1287
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• 2022

When abnormal operating conditions occur in nuclear power plants, operators must identify the occurrence cause and implement the necessary mitigation measures. Accordingly, the operator must rapidly and accurately analyze the symptom requirements of more than 200 abnormal scenarios from the trends of many variables to perform diagnostic tasks and implement mitigation actions rapidly. However, the probability of human error increases owing to the characteristics of the diagnostic tasks performed by the operator. Researches regarding diagnostic tasks based on Artificial Intelligence (AI) have been conducted recently to reduce the likelihood of human errors; however, reliability issues due to the black box characteristics of AI have been pointed out. Hence, the application of eXplainable Artificial Intelligence (XAI), which can provide AI diagnostic evidence for operators, is considered. In conclusion, the XAI to solve the reliability problem of AI is included in the AI-based diagnostic algorithm. A reliable intelligent diagnostic assistant based on a merged diagnostic algorithm, in the form of an operator support system, is developed, and includes an interface to efficiently inform operators.

• Journal of Korean Society for Quality Management
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• v.19 no.1
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• pp.151-162
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• 1991

The purpose of this study is to propose a facility layout design algorithm based on artificial intelligence concept, and then to develop a computer program which is more practical than any other conventional facility layout design systems. The algorithm is composed of five step layout procedures; knowledge and data input, knowledge interpretation, priority determination, inference of layout design, and evaluation, In the step of priority determination, the algorithm is divided into single row and multi row layout problem. In the step of inference of layout design, alternatives are generated by constraints-directed reasoning and depth first search method based on artificial intelligence concept. Alternatives are evaluated by the moving cost and relationship value by interactive man-machine interface in the step of evaluation. As a case study, analytical considerations over conventional programs such as CRAFT and CORELAP was investigated and compared with algorithm propsed in this study. The proposed algorithm in this study will give useful practical tool for layout planner. The computer progran was written in C language for IBM PC-AT.