• International journal of advanced smart convergence
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• v.10 no.3
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• pp.59-65
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• 2021

In this paper, we propose the AI certification standardization activities for systematic research and planning for the standardization of trustworthy artificial intelligence (AI). The activities will be in two-fold. In the stage 1, we investigate the scope and possibility of standardization through AI reliability technology research targeting international standards organizations. And we establish the AI reliability technology standard and AI reliability verification for the feasibility of the AI reliability technology/certification standards. In the stage 2, based on the standard technical specifications established in the previous stage, we establish AI reliability certification program for verification of products, systems and services. Along with the establishment of the AI reliability certification system, a global InterOp (Interoperability test) event, an AI reliability certification international standard meetings and seminars are to be held for the spread of AI reliability certification. Finally, TAIPP (Trustworthy AI Partnership Project) will be established through the participation of relevant standards organizations and industries to overall maintain and develop standards and certification programs to ensure the governance of AI reliability certification standards.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1271-1287
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• 2022

When abnormal operating conditions occur in nuclear power plants, operators must identify the occurrence cause and implement the necessary mitigation measures. Accordingly, the operator must rapidly and accurately analyze the symptom requirements of more than 200 abnormal scenarios from the trends of many variables to perform diagnostic tasks and implement mitigation actions rapidly. However, the probability of human error increases owing to the characteristics of the diagnostic tasks performed by the operator. Researches regarding diagnostic tasks based on Artificial Intelligence (AI) have been conducted recently to reduce the likelihood of human errors; however, reliability issues due to the black box characteristics of AI have been pointed out. Hence, the application of eXplainable Artificial Intelligence (XAI), which can provide AI diagnostic evidence for operators, is considered. In conclusion, the XAI to solve the reliability problem of AI is included in the AI-based diagnostic algorithm. A reliable intelligent diagnostic assistant based on a merged diagnostic algorithm, in the form of an operator support system, is developed, and includes an interface to efficiently inform operators.

• Journal of Information Technology Services
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• v.22 no.4
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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.

• 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.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.29-37
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.1
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• pp.30-41
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• 2023

With the aggressive down-scaling of advanced integrated circuits (ICs), electronic packages have become the bottleneck of both reliability and performance of whole electronic systems. In order to resolve the reliability issues, Institute of Electrical and Electronics Engineers (IEEE) laid down a roadmap on fault detection and diagnosis (FDD), thrusting the digital twin: a combination of reliability physics and artificial intelligence (AI). In this paper, we especially review research works regarding the signal-based FDD approaches on the electronic packages. We also discuss the research trend of FDD utilizing AI techniques.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.699-705
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• 2022

Purpose: Sequence control is widely used by being applied to manufacturing, distribution, construction, and automation in the medical industry. With the development of the fourth industry, artificial intelligence convergence technology in the control field is becoming an important factor in the industry. In particular, it is required to evaluate the safety and innovation of facilities where microprocessors and artificial intelligence are fused to existing systems and develop reliable equipment, so it is intended to develop equipment for educational purposes and drive the development of the field. Method: The self-developed all-in-one artificial intelligence controller module is a device that combines artificial intelligence capabilities with existing sequence and PLC control circuits. As the performance evaluation items of this equipment, the recognition ability of motion, voice, text, color, etc. and the stability and reliability of the circuit were evaluated. Conclusion: After designing the sequence and PLC circuit, the performance evaluation items of the integrated integrated artificial intelligence controller module were all satisfied, and there was no problem in the safety and reliability of the circuit.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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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.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.111-123
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• 2022

Artificial intelligence, which is developing as the core of an intelligent information society, is bringing convenience and positive life changes to humans. However, with the development of artificial intelligence, human rights and property are threatened, and ethical problems are increasing, so alternatives are needed accordingly. In this study, the most controversial artificial intelligence ethics problem in the dysfunction of artificial intelligence was aimed at researching and developing artificial intelligence ethical measurement indicators to protect human personality rights and property first under artificial intelligence ethical principles and components. In order to research and develop artificial intelligence ethics measurement indicators, various related literature, focus group interview(FGI), and Delphi surveys were conducted to derive 43 items of ethics measurement indicators. By survey and statistical analysis, 40 items of artificial intelligence ethics measurement indicators were confirmed and proposed through descriptive statistics analysis, reliability analysis, and correlation analysis for ethical measurement indicators. The proposed artificial intelligence ethics measurement indicators can be used for artificial intelligence design, development, education, authentication, operation, and standardization, and can contribute to the development of safe and reliable artificial intelligence.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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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.