• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.567-569
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• 2023

GAN은 이미지 생성모델로서 이미지 공간에서 좋은 결과를 보여왔다. 우리는 이러한 GAN의 능력을 더욱 향상하기 위하여 본 연구에서 주파수 영역에서 이미지를 학습하고 생성하는 새로운 방법을 제안한다. 이를 위하여 먼저 학습데이터를 2D FFT로 주파수 영역으로 변환한 후 변환된 학습데이터를 GAN이 학습하게 한다. 학습 후에 GAN은 새로운 이미지를 생성하며 생성된 이미지를 2D IFFT하여 이미지 공간으로 변환한다. 이렇게 주파수 영역에서 이미지를 생성하는 방법은 이미지 공간에서 생성하는 방법보다 다양한 장점이 있다. 생성된 이미지의 품질을 평가하기 위하여 4개 데이터 셋에 4개의 평가지표를 사용하여 평가한 결과 주파수 영역에서 생성한 이미지가 IS, P&R, D&C 측면에서 더 좋은 것으로 평가되었다.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.3
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• pp.311-324
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• 2011

This study was performed to assess the nutritional status of low income elderly women aged ${\geq}$65 years residing in Gyeongnam Masan (n=124). Nutrition intakes, food intake frequency, and health-related behaviors including smoking, drinking, and exercise were investigated. Nutrition intake was calculated by the 24-hour recall method using CAN-pro (ver. 3.0). Average daily intakes of energy were $1,142.3{\pm}39$ kcal (71.4% of EER) in subjects aged 65~74 years and $1,071.0{\pm}41.7$ kcal (66.9% of EER) in subjects aged ${\geq}$75 years and the subjects consumed energy less than both 75% of estimated energy requirement (EER). The proportions of energy derived from protein, fat, and carbohydrate were 15.4:15.5:70.6 (aged 65~74 years), and 15.3:13.4:70.8 (aged ${\geq}$75). Nutrients consumed at less than estimated average requirements (EARs) were Ca (60.4%), P (98.4%), Zn (91%), vitamin E (48% of adequate intake, AI), vitamin $B_1$ (63.3%), vitamin $B_2$ (54%), niacin (87.7%), vitamin C (62.5%), and folate (50.5%). Especially, the intakes of Ca (58%), vitamin E (41% of AI), vitamin $B_1$ (60%), vitamin $B_2$ (50%), folate (46.5%), and vitamin C (54%) were 75% less than the EAR for people aged ${\geq}$75 years. According to the food intake frequency survey, the intakes of calcium, milk, fruits, and vegetables were very poor. In conclusion, this study suggests that a nutritional support program for elderly women of low socioeconomic class must be provided by the government to improve the quality of remaining life.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Proceedings of the KIEE Conference
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• 1996.07c
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• pp.1854-1856
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• 1996

This study describes the minimum ignition limit for LPG-Ai-r mixtures by switching sparks in radio-frequency limits using RF power supply and IEC type ignition spark apparatus. As a result, the minimum ignition limit voltage is increased in proportional to the rate of increasing of frequency in LPG-Air mixed gas. Especially, increment between 10[kHz] and 30[kHz] is typical. It is considered that ignition is caused by one discharge until 10 [kHz] and, beyond 10[kHz] ignition is caused by more than two discharges. The reason is analysed that energy loss is caused by existing pause interval between discharges.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.57-69
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• 2022

During the 10 years from 2011 to 2021, a whopping 2,800 people were killed or injured during port work. Among them, the frequency of occurrence at the port loading and unloading business is high. Container terminal operators must conduct risk assessments and establish reasonable safety measures in accordance with laws and regulations. As a research method, the contents of risk assessment presented in the Industrial Safety and Health Act, the Serious Accident Punishment Act, and the Special Act on Port Safety are presented through literature analysis. In this study, previous studies were analyzed to examine the risk assessment method and risk factors of container terminals. The purpose is to present 'industrial safety AI chatbot technology' that can improve the risk of safety accidents.

• Journal of IKEEE
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• v.26 no.1
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• pp.104-110
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• 2022

In this paper, we propose a new dataset for AI diagnosis and BLDC motor diagnosis in UAV. In the diagnosis of BLDC motor, PVA(Park's Vector Approach) is difficult to apply due to many ripples of frequency components. However, since the components of ripples are the third harmonics, we propose a method to utilize PVA as circle fitting by applying Savitzky-Golay filter which is excellent for the third harmonics. On the other hand, PVA, a technique to convert from three-phase to two-phase, is always based on the origin during the transformation process. This study demonstrates that the error of the origin and the measured center can be detected and diagnosed in the application process of Circle fitting, and that it can be used as a new data set of AI technology.

• Dementia and Neurocognitive Disorders
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• v.23 no.1
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• pp.1-10
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• 2024

Background and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer's disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer's disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions: Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.741-750
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• 2021

School violence has long been recognized as a social problem, and various efforts have been made to prevent it. In this study, we propose a system that can prevent school violence by analyzing data on the frequency of conversations between students, friendship and preference to be in the same group. This data was quantified using a Likert scale questionnaire, and also grouped into the appropriate number of clusters using the K-means algorithm. Additionally, the homeroom teacher observed the frequency and nature of conversations between students, and targeted specific individuals or groups for counseling and intervention, with the aim of reducing school violence. Data analysis revealed that the teachers' qualitative observations were consistent with the quantified data based on student questionnaires, and therefore applicable as quantitative data towards the identification and understanding of student relationships within the classroom. The study has potential limitations. The data used is subjective and based on peer evaluations which can be inconsistent as the students may use different criteria to evaluate one another. It is expected that this study will help homeroom teachers in their efforts to prevent school violence by understanding the relationships between students within the classroom.

• Journal of The Korean Association For Science Education
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• v.43 no.6
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• pp.573-582
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• 2023

This study aimed to explore the potential use of artificial intelligence in science education for gifted students by analyzing the structure of abstracts written by students at a gifted science academy and comparing the performance of various elements extracted using AI. The study involved an analysis of 263 graduation theses from S Science High School over five years (2017-2021), focusing on the frequency and types of background, objectives, methods, results, and discussions included in their abstracts. This was followed by an evaluation of their accuracy using AI classification methods with fine-tuning and prompts. The results revealed that the frequency of elements in the abstracts written by gifted students followed the order of objectives, methods, results, background, and discussions. However, only 57.4% of the abstracts contained all the essential elements, such as objectives, methods, and results. Among these elements, fine-tuned AI classification showed the highest accuracy, with background, objectives, and results demonstrating relatively high performance, while methods and discussions were often inaccurately classified. These findings suggest the need for a more effective use of AI, through providing a better distribution of elements or appropriate datasets for training. Educational implications of these findings were also discussed.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.33-40
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• 2021

Automatic Speech Recognition(ASR) is a technology that analyzes human speech sound into speech signals and then automatically converts them into character strings that can be understandable by human. Speech recognition technology has evolved from the basic level of recognizing a single word to the advanced level of recognizing sentences consisting of multiple words. In real-time voice conversation, the high recognition rate improves the convenience of natural information delivery and expands the scope of voice-based applications. On the other hand, with the active application of speech recognition technology, concerns about related cyber attacks and threats are also increasing. According to the existing studies, researches on the technology development itself, such as the design of the Automatic Speaker Verification(ASV) technique and improvement of accuracy, are being actively conducted. However, there are not many analysis studies of attacks and threats in depth and variety. In this study, we propose a cyber attack model that bypasses voice authentication by simply manipulating voice frequency and voice speed for AI voice recognition service equipped with automated identification technology and analyze cyber threats by conducting extensive experiments on the automated identification system of commercial smartphones. Through this, we intend to inform the seriousness of the related cyber threats and raise interests in research on effective countermeasures.