• Journal of Information Processing Systems
• /
• v.13 no.2
• /
• pp.204-214
• /
• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• KNOM Review
• /
• v.24 no.1
• /
• pp.13-19
• /
• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.871-884
• /
• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Journal of Korea Artificial Intelligence Association
• /
• v.2 no.1
• /
• pp.25-30
• /
• 2024

Kodály hand signs are symbols that intuitively represent pitch and note names based on the shape and height of the hand. They are an excellent tool that can be easily expressed using the human body, making them highly engaging for children who are new to music. Traditional hand signs help beginners easily understand pitch and significantly aid in music learning and performance. However, Kodály hand signs have distinctive features, such as the ability to indicate key changes or chords using both hands and to clearly represent accidentals. These features enable the effective use of Kodály hand signs. In this paper, we aim to investigate the changes in recognition rates according to the complexity of scales by creating a device for learning Kodály hand signs, teaching simple Do-Re-Mi scales, and then gradually increasing the complexity of the scales and teaching complex scales and children's songs (such as "May Had A Little Lamb"). The learning device utilizes accelerometer and bending sensors. The accelerometer detects the tilt of the hand, while the bending sensor detects the degree of bending in the fingers. The utilized accelerometer is a 6-axis accelerometer that can also measure angular velocity, ensuring accurate data collection. The learning and performance evaluation of the Kodály learning device were conducted using Python.

• Informatization Policy
• /
• v.27 no.4
• /
• pp.47-66
• /
• 2020

The rapid development of artificial intelligence technologies such as machine learning and deep learning is expanding its impact in the public administrative and public policy sphere. This paper is an exploratory study on policy decision-making in the age of artificial intelligence to design automated configuration and operation through data analysis and algorithm development. The theoretical framework was composed of the types of policy problems according to the degree of problem structuring, and the success and failure cases were classified and analyzed to derive implications. In other words, when the problem structuring is more difficult than others, the greater the possibility of failure or side effects of decision-making using artificial intelligence. Also, concerns about the neutrality of the algorithm were presented. As a policy suggestion, a subcommittee was proposed in which experts in technical and social aspects play a professional role in establishing the AI promotion system in Korea. Although the subcommittee works independently, it suggests that it is necessary to establish governance in which the results of activities can be synthesized and integrated.

• ETRI Journal
• /
• v.44 no.4
• /
• pp.613-623
• /
• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.4
• /
• pp.19-27
• /
• 2020

In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.

• ETRI Journal
• /
• v.43 no.4
• /
• pp.630-639
• /
• 2021

Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.

• Korean Journal of Artificial Intelligence
• /
• v.8 no.2
• /
• pp.7-10
• /
• 2020

In this paper, a study was conducted to find main factorsto Pima Indians Diabetes based on machine learning. Diabetes is a type of metabolic disease such as insufficient secretion of insulin or inability to function normally and is characterized by a high blood glucose concentration. According to a situation report from WHO(World Health Organization), Diabetes is a chronic, metabolic disease characterized by elevated levels of blood glucose (or blood sugar), which leads over time to serious damage to the heart, blood vessels, eyes, kidneys and nerves. And also about 422 million people worldwide have diabetes, the majority living in low-and middle-income countries, and 1.6 million deaths are directly attributed to diabetes each year. Both the number of cases and the prevalence of diabetes have been steadily increasing over the past few decades. Therefore, in this study, we used Support Vector Machine (SVM), Decision Tree, and correlation analysisto discover three important factorsthat predict Pima Indians diabetes with 70% accuracy. Applying the results suggested in this paper, doctors can quickly diagnose potential Pima Indians diabetics and prevent Pima Indians diabetes.

• Journal of The Korean Association of Information Education
• /
• v.24 no.4
• /
• pp.313-325
• /
• 2020

There is a growing interest and need for the educational use of artificial intelligence as artificial intelligence technologies such as machine learning and deep learning, the core technologies of the intelligent information society, owing to the recent innovative technological advances. Consequently, the Ministry of Education announced the First Information Education Comprehensive Plan for introducing artificial intelligence competence enhancing education into the education field in preparation for the intelligent information society based on artificial intelligence technologies. Therefore, this study collected 416 overseas papers related to the educational use of artificial intelligence from the Web of Science (WoS) in order to explore the potential for using artificial intelligence educationally. This study analyzed the research status and research topic by country, citation counts, network analysis on keywords of the collected data by using the bibliometrix package of R program. Through this, it was possible to identify the research trend on the educational use of artificial intelligence, currently being conducted in foreign countries. It is believed that it will be possible to obtain implications for the topics and directions to be studied in the information education for strengthening artificial intelligence education based on the results of this study.