• 품질경영학회지
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• 제46권3호
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• pp.677-694
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• 2018

Purpose: The purpose of this study is to investigate various prior studies on artificial intelligence and to examine the concept and characteristics of various prior studies of existing service quality. And this paper is to study the concept and characteristics of artificial intelligence services and propose suitable quality measurement items. Methods: The research method of this paper is to examine previous research related to existing artificial intelligence and to analyze characteristics related to service quality. Results: This paper examines the concept and characteristics of artificial intelligence service in a new era by examining previous studies related to artificial intelligence and derives quality measurement items. Conclusion: In the future, it is necessary to verify the validity of the quality measurement items of artificial intelligence service. Therefore, it is necessary to elicit and verify the main quality measurement items through the investigation of the expert group.

• 품질경영학회지
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• 제47권4호
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• pp.837-846
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• 2019

Purpose: The purpose of this study is to define various concepts, features, and scopes by examining various previous studies on AI services that are completely different from existing services. It also examines the limitations of existing service quality evaluation methods and studies the characteristics by combining them with various cases of new AI services. And this is to derive and propose quality evaluation attributes of AI service. Methods: The concept and characteristics of artificial intelligence were derived through research and analysis of various previous studies related to artificial intelligence. The key characteristics and quality evaluation items were derived through the KJ method and matching based on the keywords and characteristics derived from previous studies and various cases. Results: Based on the review of various previous studies on the quality of artificial intelligence services, this study presents the main characteristics and quality evaluation items of new artificial intelligence services, which are completely different from existing service quality evaluations. Conclusion: The quality measurement model of AI service is very useful when planning and developing AI-based new products or services because it can accurately evaluate the requirements of consumers using the services of the new AI era. In addition, consumers can be recommended a customized service according to the situation or taste, and can be provided with a customized service based on this.

• 디지털융복합연구
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• 제17권7호
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• pp.73-87
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• 2019

본 연구는 최근 빠르게 확산되는 인공지능의 지속적인수용에 관하여 탐색하고자 온라인 구인정보 플랫폼에 적용된 인공지능의 품질을 정의하고 인공지능의 선호, 지속사용의도 간의 구조적인 관계를 규명하였다. 인공지능 사용자를 대상으로 설문조사를 시행하였고 184개를 회수하였다. 실증분석결과 인공지능의 품질과 선호가 만족에 긍정적인 영향을 미치며, 인공지능의 만족이 지속사용의도에 통계적으로 유의한 수준에서 긍정적인 영향을 미치는 것으로 나타났다. 그러나 예상과는 달리 인공지능의 품질은 지속사용의도에 유의한 영향을 미치지 않는 것으로 나타났다. 이와 같은 결과는 향후 인공지능 기술을 제품이나 서비스에 적용하는데 있어 이론적, 실무적인 차원의 유용한 가이드라인을 제시할 수 있을 것으로 기대한다.

• 반도체디스플레이기술학회지
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• 제21권2호
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• pp.133-137
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• 2022

In the smart industry where data plays an important role, cloud computing is being used in a complex and advanced way as a convergence technology because it has and fits well with its strengths. Accordingly, in order to utilize artificial intelligence rather than human beings for quality management of cloud computing services, a consistent standardization method of data collected from various nodes in various areas is required. Therefore, this study analyzed technologies and cases for incorporating artificial intelligence into specific services through previous studies, suggested a plan to use artificial intelligence to comprehensively standardize data in quality management of cloud computing services, and then verified it through case studies. It can also be applied to the artificial intelligence learning model that analyzes the risks arising from the data formalization method presented in this study and predicts the quality risks that are likely to occur. However, there is also a limitation that separate policy development for service quality management needs to be supplemented.

• 산업과 과학
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• 1권1호
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• pp.28-34
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• 2022

소프트웨어 품질평가에 대해서는 많은 연구가 진행되어왔다. 최근에 인공지능 관련 소프트웨어들이 많이 개발되어지면서 기존 소프트웨어에 인공지능 기능을 평가하기 위한 방안에 대한 연구가 진행되어지고 있다. 소프트웨어 평가는 기능적합성(Functional suitability), 신뢰성(Reliability), 사용성(Usability), 유지보수성(Maintainability), 효율성(Performance efficiency), 이식성(Portability), 상호운영성(Compatibility), 보안성(Security)이란 8가지 품질 특성을 기반으로 평가 되어왔으나 인공지능 기능을 가지고 있는 소프트웨어의 경우는 8가지 품질 특성뿐만 아니라 인공지능 부분의 기능에 대해서 평가를 통해서 확인해야 하는 부분에 대한 연구가 진행되고 있다. 본 연구는 이 부분에서 평가 방안에 대한 내용을 소개하려 한다. 기존에 소프트웨어 품질 평가 방안과 인공지능 부분에서 고려해야 하는 부분에 대한 제시를 통해서 인공지능 소프트웨어의 품질 평가 방안을 제시하려 한다.

• 상하수도학회지
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• 제38권1호
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• pp.1-15
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• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• 한국IT서비스학회지
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• 제22권4호
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• pp.53-80
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• 2023

Expectations for artificial intelligence technology are increasing, and its utility value is growing, leading to active use in the public sector. The use of artificial intelligence technology in the public sector has a positive impact on aspects such as improving public work efficiency and service quality, enhancing transparency and reliability, and contributing to the development of technology and industries. For these reasons, major countries including Korea are actively developing and using artificial intelligence in the public sector. However, artificial intelligence also presents issues such as bias, inequality, and infringement of individuals' right to self-determination, which are evident even in its utilization in the public sector. Especially the use of artificial intelligence technology in the public sector has significant societal implications, as well as direct implications on limiting and infringing upon the rights of citizens. Therefore, careful consideration is necessary in the introduction and utilization of such technology. This paper comprehensively examines the legal issues that require consideration regarding the introduction of artificial intelligence in the public sector. Methodological discussions that can minimize the risks that may arise from artificial intelligence and maximize the utility of technology were proposed in each process and step of introduction.

• 전자통신동향분석
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• 제38권6호
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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.

• 대한의용생체공학회:의공학회지
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• 제44권3호
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• pp.182-189
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• 2023

CT is a medical device that acquires medical images based on Attenuation coefficient of human organs related to X-rays. In addition, using this theory, it can acquire sagittal and coronal planes and 3D images of the human body. Then, CT is essential device for universal diagnostic test. But Exposure of CT scan is so high that it is regulated and managed with special medical equipment. As the special medical equipment, CT must implement quality control. In detail of quality control, Spatial resolution of existing phantom imaging tests, Contrast resolution and clinical image evaluation are qualitative tests. These tests are not objective, so the reliability of the CT undermine trust. Therefore, by applying an artificial intelligence classification model, we wanted to confirm the possibility of quantitative evaluation of the qualitative evaluation part of the phantom test. We used intelligence classification models (VGG19, DenseNet201, EfficientNet B2, inception_resnet_v2, ResNet50V2, and Xception). And the fine-tuning process used for learning was additionally performed. As a result, in all classification models, the accuracy of spatial resolution was 0.9562 or higher, the precision was 0.9535, the recall was 1, the loss value was 0.1774, and the learning time was from a maximum of 14 minutes to a minimum of 8 minutes and 10 seconds. Through the experimental results, it was concluded that the artificial intelligence model can be applied to CT implements quality control in spatial resolution and contrast resolution.

• 대한방사선기술학회지:방사선기술과학
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• 제43권6호
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• pp.461-467
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• 2020

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.