• Advanced Industrial SCIence
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• v.1 no.1
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• pp.28-34
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• 2022

Many studies have been conducted on software quality evaluation. Recently, as artificial intelligence-related software has been developed a lot, research on methods for evaluating artificial intelligence functions in existing software is being conducted. Software evaluation has been based on eight quality characteristics: functional suitability, reliability, usability, maintainability, performance efficiency, portability, compatibility, and security. Research on the part that needs to be confirmed through evaluation of the function of the intelligence part is in progress. This study intends to introduce the contents of the evaluation method in this part. We are going to propose a quality evaluation method for artificial intelligence software by presenting the existing software quality evaluation method and the part to be considered in the AI part.

• Convergence Security Journal
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• v.23 no.2
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• pp.55-66
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• 2023

Recently, there is an increasing movement to increase the value of AI learning data and to secure high-quality data based on previous research on AI learning data in all areas of society. Therefore, quality management is very important in construction projects to secure high-quality data. In this paper, quality management to secure high-quality data when building AI learning data and improvement plans for each construction process are presented. In particular, more than 80% of the data quality of unstructured data built for AI learning is determined during the construction process. In this paper, we performed quality inspection of image/video data. In addition, we identified inspection procedures and problem elements that occurred in the construction phases of acquisition, data cleaning, labeling, and models, and suggested ways to secure high-quality data by solving them. Through this, it is expected that it will be an alternative to overcome the quality deviation of data for research groups and operators participating in the construction of AI learning data.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.73-87
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• 2019

The purpose of this study is to clarify the continuous usage intention of artificial intelligence products and services. In this study, we try to define the artificial intelligence quality and preference on the online job information platform and investigate the effect of artificial intelligence on continues usage intention. A survey of artificial intelligence users was conducted and recalled 184. The empirical analysis shows that the artificial intelligence quality and preference have a positive effect on satisfaction, and that the satisfaction has significant effect on the intention of continuing use. but the artificial intelligence quality does not significantly affect the intention of continuing use. These results are expected to provide useful guidelines for artificial intelligence technology products or services in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.167-177
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• 2022

Unlike previous studies on AI chatbot preference that focused mostly on satisfaction, this study considered both satisfaction and dissatisfaction. This study established that (1) AI chatbot preference is driven by attractive, must-be, and one-dimensional qualities, (2) AI chatbot need to develop service strategies by taking into account users' satisfaction and dissatisfaction in accordance with preference drivers, and (3) users view interaction as a requisite and thus, if they are not satisfied with services of a AI chatbot, they don't tend to appeal their opinion and leave the service with AI chatbot. This study emphasizes that a AI chatbot that desires to be a dominant market player must provide differentiated services according to the preference drivers and must continuously encourage user participation in order to improve service quality.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.145-147
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• 2023

요즘은 모든 산업에서 인공지능(Artificial Intelligence : AI) 채택을 빠르게 진행하고 있으며, 디지털 기술과 산업 기술이 융합된 인공지능 분야가 강화되고 여러 서비스 사업 혁신이 이루어지면서 여러 산업의 시장 성장을 견인하는 것으로 나타났다. 특히 게임 산업과 관련한 게임업계에서는 인공지능 관련 전문 지식을 확보하기 위한 투자가 활발하게 이어짐에 따라 발전과 경쟁력 확보를 위한 움직임들이 지속될 것으로 전망된다. 본 논문에서는 게임개발 기술에 인공지능(AI) 기술 접목이 집중되고 있는 상황에서 개발하고 있는 게임에 대한 품질을 보증하고 관리하기 위한 AI 기반의 게임 QA(Quality Assurance) 기술 적용을 위한 방법들에 대해서 제시하고자 한다.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.177-183
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• 2024

The study investigates the impact of data quality on the performance of artificial intelligence (AI). To this end, the impact of labeling error levels on the performance of artificial intelligence was compared and analyzed through simulation, taking into account the similarity of data features and the imbalance of class composition. As a result, data with high similarity between characteristic variables were found to be more sensitive to labeling accuracy than data with low similarity between characteristic variables. It was observed that artificial intelligence accuracy tended to decrease rapidly as class imbalance increased. This will serve as the fundamental data for evaluating the quality criteria and conducting related research on artificial intelligence learning data.

• 전기의세계
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• v.37 no.4
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• pp.47-57
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• 1988

본 고는 인공지능 기술이 특히 산업 공정 제어 분야에 응용되는 것에 대하여 기술한다. 그 내용은 크게 두 부분으로 나뉘어져 있으며, 처음 부분은 인공지능에 대하여 개괄적으로 기술하고 특히 산업 공정 제어 분야와 관련되는 인공지능의 기술에 대하여 설명한다. 다음 부분은 산업 공정 제어라는 응용분야의 특수적 상황을 설명하고 인공지능 기술이 실제로 어떻게 이 특수적 상황에 응용될 수 있는가에 대하여 기술한다. 단 여기서는 인공지능이 생산성 및 품질의 향상을 위하여 산업 공정 제어분야에 어떻게 응용되는가에 대하여 간략히 소개하며, 결코 기술적으로 상세한 내용을 기술하는 의도가 아님을 밝혀두고자 한다.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.66-68
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• 2008

본 논문은, 전력계통 내의 순시 파형으로부터 전력품질 자동진단을 위한 인공지능형 단상전력품질 진단기를 제안한다. 진단하는 전력품질은 전압강하(Sag), 전압상승(Swell), 과도현상(Transient) 및 전고조파함유율(THD) 이다. 인공지능 구현을 위해서 인공신경망 이론을 이용하였으며, 시뮬레이션 및 TI DSP 320C6713 사용하여 하드웨어를 구현하였다. 인공신경망의 학습을 위하여, 00변전소에서 일년(2007년)동안 측정한 데이터 중에서 Sag, Swell, Transient 특성이 명확히 관측된 150주기의 파형과 정상상태의 50주기 파형으로 구성된 총 200주기의 데이터를 사용하였다. 측정된 파형을 1/60[sec.]마다 256번 샘플링하여, FFT 및 웨이블렛 변환을 시행하여 얻어진 값을 인공신경망 학습에 사용하였다. 상용프로그램 PSIM을 이용하여 인공신경망 학습을 시뮬레이션하였으며, DSP 프로세서를 이용하여 하드웨어로 구현하여 검증하였다.

• Information Systems Review
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• v.26 no.1
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• pp.19-56
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• 2024

This study is empirical research to enhance understanding of AI (artificial intelligence) training data project in South Korea. It primarily focuses on the various concerns regarding data quality from policy-executing institutions, data construction companies, and organizations utilizing AI training data to develop the most reliable algorithm for society. For academic contribution, this study suggests a theoretical foundation and research model for understanding AI training data quality and its antecedents, as well as the unique data and ethical aspects of AI. For this purpose, this study proposes a research model with important antecedents related to AI training data quality, such as data attribute factors, data building environmental factors, and data type-related factors. The study collects 393 sample data from actual practitioners and personnel from companies building artificial intelligence training data and companies developing artificial intelligence services. Data analysis was conducted through Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Artificial Neural Network analysis (ANN), presenting academic and practical implications related to the quality of AI training data.

• 발명특허
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• v.16 no.10 s.188
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• pp.52-57
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• 1991

인간이 지닌 지적인 능력을 규명하여 컴퓨터로 하여금 지능이 필요로 하는 일을 수행할 수 있도록 하는 인공지능(Artificial Intelligence, 이하 AI)기술에 관한 관심이 높아지고 있는 가운데 선진각국에서는 철강, 자동차, 산업기계 등 다양한 분야에서 제품의 라이프 사이클 단축, 다품종 소량 생산, 효율적인 조업, 고도의 품질제어 요구에 유연하게 대처하기 위하여 인공지능 개발 프로젝트를 활발히 진행중이다. 본고에서는 산업기계분야에서 인공지능 개발에 필요한 기반환경에 대하여 살펴보고 선진국의 주요 개발동향 및 우리나라의 개발실태를 살표보고자 한다.