• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• 인터넷정보학회논문지
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• 제23권3호
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• pp.111-123
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• 2022

지능정보화 사회에서 가장 핵심으로 발전하고 있는 인공지능은 인간에게 편의성과 긍정적인 삶의 변화를 가져오고 있다. 하지만 인공지능 발전과 함께 인간의 인격권과 재산이 위협받고, 윤리적인 문제가 발생하는 사례도 증가하고 있기 때문에 그에 따른 대안이 필요하다. 본 연구에서는 인공지능의 역기능에서 가장 쟁점화되고 있는 인공지능 윤리(Artificial Intelligence Ethics) 문제를 인공지능 윤리원칙과 구성요소 기반 하에 우선적으로 인간의 인격권과 재산을 보호할 수 있도록 인공지능 윤리 측정지표를 연구, 개발하는 데 목표를 두었다. 인공지능 윤리 측정지표를 연구, 개발하기 위해 다양한 관련 문헌과 전문가 심층 면접(FGI), 델파이 설문조사를 실시하여 43개 항목의 윤리 측정지표를 도출하였다. 설문조사와 통계분석에 의하여 윤리 측정지표에 대한 기술통계량 분석, 신뢰도 분석, 상관관계 분석으로 40개 항목의 인공지능 윤리 측정지표를 확정하여 제안하였다. 제안된 인공지능 윤리 측정지표는 인공지능 설계, 개발, 교육, 인증, 운영, 표준화 등에 활용될 수 있으며, 안전하고 신뢰할 수 있는 인공지능 발전에 기여할 수 있을 것이다.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.31-44
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• 2024

People have been using more energy in the last years. Several research studies were conducted to develop sustainable energy sources that can produce clean energy to fulfill our energy requirements. Using renewable energy sources helps to decrease the harm to the environment caused by conventional power plants. Choosing the right location and capacity for DG-RESs can greatly impact the performance of Radial Distribution Systems. It is beneficial to have a good and stable electrical power supply with low energy waste and high effectiveness because it improves the performance and reliability of the system. This research investigates the ideal location and size for solar and wind power systems, which are popular methods for producing clean electricity. A new artificial intelligent algorithm called Nutcracker Optimization Algorithm (NOA) is used to find the best solution in two common electrical systems named IEEE 33 and 69 bus systems to examine the improvement in the efficiency & reliability of power system network by reducing power losses, making voltage deviation smaller, and improving voltage stability. Finally, the NOA method is compared with another method called PSO and developed Hybrid Algorithm (NOA+PSO) to validate the proposed algorithm effectiveness and enhancement of both efficiency and reliability aspects.

• 한국정보시스템학회지:정보시스템연구
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• 제28권2호
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• pp.25-52
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• 2019

Purpose Despite the recent increase in the use of selection tools using artificial intelligence (AI), far less is known about the effectiveness of them in recruitment and selection research. Design/methodology/approach This paper tests the impact of AI-based initial screening and interview on intentions to apply. We also examine the moderating role of individual difference (i.e., reliability on technology) in the relationship. Findings Using policy-capturing with undergraduate students at a large university in South Korea, this study showed that AI-based interview has a negative effect on intentions to apply, where AI-based initial screening has no effect. These results suggest that applicants may have a negative feeling of AI-based interview, but they may not AI-based initial screening. In other words, AI-based interview can reduce application rates, but AI-based screening not. Results also indicated that the relationship between AI-based initial screening and intentions to apply is moderated by the level of applicant's reliability on technology. Specifically, respondents with high levels of reliability are more likely than those with low levels of reliability to apply for firms using AI-based initial screening. However, the moderating role of reliability was not significant in the relationship between the AI interview and the applying intention. Employing uncertainty reduction theory, this study indicated that the relationship between AI-based selection tools and intentions to apply is dynamic, suggesting that organizations should carefully manage their AI-based selection techniques throughout the recruitment and selection process.

• 정보화정책
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• 제29권1호
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• pp.60-83
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• 2022

본 연구는 인공지능에 관한 개인의 인식과 디지털 정보를 이해하고 활용할 수 있는 디지털 리터러시(Digital Literacy) 수준이 인공지능 기반 공공서비스의 수용에 미치는 영향을 실증적으로 분석하는 것을 목적으로 한다. 실증적 분석을 위해 2017년에 수행된 설문조사 자료를 바탕으로 기술수용모형과 계획된 행동이론에 근거하여 연구모형을 설정하고 구조방정식을 통해 분석하였다. 분석 결과를 요약하면, 첫째, 인공지능 기술에 대한 개인의 긍정적 인식은 인공지능 기술이 도입된 공공민원서비스에 대한 혜택에 대한 태도를 강화하고, 우려는 감소시키는 역할을 한다. 둘째, 디지털 리터러시 수준은 인공지능 기술에 대한 혜택과 우려를 모두 강화하지만, 인공지능 기술에 대한 프라이버시 염려보다는 개인이 인식하는 인공지능 기술의 혜택을 통해 공공민원서비스를 이용할 의도를 강화하는 것으로 나타났다. 셋째, 인공지능 기술에 대한 개인의 지각된 혜택은 공공민원서비스 이용의도를 강화하고, 프라이버시 염려는 이용의도에 부정적 영향이 확인되었다. 특히, 프라이버시 염려보다는 지각된 이용 편의성과 유용성의 영향이 이용의도를 더욱 강화하는 것으로 확인되었다. 이러한 분석 결과는 인공지능 기술을 통해 제공되는 정보의 정확성과 신뢰성에 관한 시민의 긍정적 인식 강화, 인공지능 기술로 인한 오류에 대한 책임 소재에 대한 제도적 보완, 프라이버시 보호와 관련된 기술적 문제 해결의 필요성을 제기한다.

• 한국전문물리치료학회지
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• 제28권3호
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• pp.186-193
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• 2021

Background: While the formal test has been used to provide a quantitative measurement of core stability, studies have reported inconsistent results regarding its test-retest and intraobserver reliabilities. Furthermore, the validity of the formal test has never been established. Objects: This study aimed to establish the concurrent validity and test-retest reliability of the formal test. Methods: Twenty-two young adults with and without core instability (23.1 ± 2.0 years) were recruited. Concurrent validity was determined by comparing the muscle thickness changes of the external oblique, internal oblique, and transverse abdominal muscle to changes in core stability pressure during the formal test using ultrasound (US) imaging and pressure biofeedback, respectively. For the test-retest reliability, muscle thickness and pressure changes were repeatedly measured approximately 24 hours apart. Electromyography (EMG) was used to monitor trunk muscle activity during the formal test. Results: The Pearson's correlation analysis showed an excellent correlation between transverse abdominal thickness and pressure biofeedback unit (PBU) pressure as well as internal oblique thickness and PBU pressure, ranging from r = 0.856-0.980, p < 0.05. The test-retest reliability was good, intraclass correlation coefficient (ICC1,2) = 0.876 for the core stability pressure measure and ICC1,2 = 0.939 to 0.989 for the abdominal muscle thickness measure. Conclusion: Our results provide clinical evidence that the formal test is valid and reliable, when concurrently incorporated into EMG and US measurements.

• 인터넷정보학회논문지
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• 제25권2호
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.

• 한국인공지능학회지
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• 제12권3호
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• pp.9-15
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• 2024

This paper addresses the critical need for quadcopters in GPS-denied indoor environments by proposing a novel attitude control mechanism that enables autonomous navigation without external guidance. Utilizing AR marker detection integrated with a dual PID controller algorithm, this system ensures accurate maneuvering and positioning of the quadcopter by compensating for the absence of GPS, a common limitation in indoor settings. This capability is paramount in environments where traditional navigation aids are ineffective, necessitating the use of quadcopters equipped with advanced sensors and control systems. The actual position and location of the quadcopter is achieved by AR marker detection technique with the image processing system. Moreover, in order to enhance the reliability of the attitude PID control, the dual closed loop control feedback PID control with dual update periods is suggested. With AR marker detection technique and autonomous attitude control, the proposed quadcopter system decreases the need of additional sensor and manual manipulation. The experimental results are demonstrated that the quadrotor's autonomous attitude control and operation with the dual closed loop control feedback PID controller with hierarchical (inner-loop and outer-loop) command update period is successfully performed under the non-GPS aided indoor environment and it enhanced the reliability of the attitude and the position PID controllers within 17 seconds. Therefore, it is concluded that the proposed attitude control mechanism is very suitable to GPS-denied indoor environments, which enables a quadcopter to autonomously navigate and hover without external guidance or control.

• International Journal of Internet, Broadcasting and Communication
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• 제14권1호
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• pp.142-151
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• 2022

In this paper, a model combined with explanatory artificial intelligence (xAI) models was presented to secure the reliability of machine learning-based sentiment analysis and prediction. The applicability of the proposed model was tested and described using the IMDB dataset. This approach has an advantage in that it can explain how the data affects the prediction results of the model from various perspectives. In various applications of sentiment analysis such as recommendation system, emotion analysis through facial expression recognition, and opinion analysis, it is possible to gain trust from users of the system by presenting more specific and evidence-based analysis results to users.

• 한국간호교육학회지
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• 제28권4호
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• pp.357-367
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• 2022

Purpose: The aim of the study was to verify the validity and reliability of the Korean version of the General Attitudes towards Artificial Intelligence Scale (GAAIS-K) for nursing students. Methods: Data from 235 participants were collected from April 12 to April 26, 2022. A total of 230 participants' data were analyzed. The data were analyzed for content, discriminant, known-groups, and construct validity using content validity index, correlation coefficient, and confirmatory factor analyses. The reliability of the GAAIS-K was examined using internal consistency and test-retest analyses. Results: The expert-rated content validity index was ≥.80. The sub-scales of the GAAIS-K were moderately correlated with attitude toward accepting technology, indicative of its discriminant validity. The male students' positive attitude score was significantly higher than that of the female students, satisfying the known-groups validity. Cronbach's α for the scale was .86 (positive) and .74 (negative), and the intra-class correlation coefficient for the two-week test-retest reliability was .86 (positive) and .60 (negative). The scores for positive and negative attitudes were 3.68±0.46 and 3.05±0.55. Conclusion: This study shows that the GAAIS-K is a valid and reliable instrument for assessing nursing students. Additional research is recommended to continue the evaluation of the GAAIS-K with a focus on healthcare settings.