• Annual Conference of KIPS
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• 2023.11a
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• pp.1084-1085
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• 2023

현재 국내 항만에서의 작업은 대부분 수작업으로 진행되기 때문에 다양한 안전사고 발생과 시간 및 비용 등의 손실이 우려된다. 이를 해소하고자 최적경로 알고리즘을 이용한 AGV 차량 및 자동화 크레인으로 무인 스마트 항만을 제안한다. RFID 인식으로 컨테이너의 정보를 확인하고, 각 경로의 노드 정보가 담긴 QR 코드 인식을 통해 최적으로 목적지에 달성하는 것이 핵심이다. 본 논문은 이러한 기능으로 시간 및 비용 절감, 효율 상승과 인명피해 및 안전사고 예방을 목표로 한다.

• Annual Conference of KIPS
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• 2023.05a
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• pp.496-498
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• 2023

Brain tumor segmentation problem has challenges in the tumor diversity of location, imbalance, and morphology. Attention mechanisms have recently been used widely to tackle medical segmentation problems efficiently by focusing on essential regions. In contrast, the fusion approaches enhance performance by merging mutual benefits from many models. In this study, we proposed a 3D dual fusion attention network to combine the advantages of fusion approaches and attention mechanisms by residual self-attention and local blocks. Compared to fusion approaches and related works, our proposed method has shown promising results on the BraTS 2018 dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2688-2703
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• 2022

Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1480-1493
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• 2018

Recently, computational intelligence has received a lot of attention from researchers due to its potential applications to artificial intelligence. In computer science, computational intelligence refers to a machine's ability to learn how to compete various tasks, such as making observations or carrying out experiments. We adopted a computational intelligence solution to monitoring residual resources in cloud computing environments. The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so that the post migration performance of a virtual machine is as consistent with the pre-migration performance as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of service and service level agreement during the migration. We carried out a number of experimental evaluations to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that the proposed scheme intelligently measures the similarities between virtual machines in cloud computing environments without causing performance degradation, whilst preserving the quality of service and service level agreement.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.179-195
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• 2016

Popular business magazines continue to highlight the employee's emotional intelligence in the organization. The purpose of this study is to explore a mediation mechanism of the relationship between emotional intelligence and job satisfaction in China IT corporation. For this, this study focused on examining the influential relationship between employee's emotional intelligence, Awareness of Self-Emotion, Awareness of other-Emotion, Emotion management, Emotion Control and job satisfaction and the mediating effect of organization culture awareness upon the relationship. Study findings are as follows : First, China IT corporation employee emotional intelligence has a significant positive(+) effect on job satisfaction and is partially supported organization culture awareness. Second, organization culture awareness has partially mediated effect on relationship between emotional intelligence and job satisfaction. In this study, the more employee's emotional intelligence have a positive impact on job satisfaction and organizational culture. We can designing a support program that emotional intelligence and organization culture awareness for China IT corporation employees will have direct influence upon job satisfaction. Employee's great talent to make good use of elements of emotional intelligence that promote the work environment and their's condition and the relationship between employee and employee.

• Communications of Mathematical Education
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• v.32 no.1
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• pp.23-36
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• 2018

Recently, we are working to utilize it in various fields with the expectation of the potential of artificial intelligence. There is also interest in applying to the field of education. In the field of education, machine learning and deep learning, which are used in artificial intelligence technology, are deeply interested in how to learn on their own. We are interested in how artificial intelligence and artificial intelligence technologies can be used in education and we have an interest in how artificial intelligence can be applied to mathematics education. The purpose of this study is to investigate the direction of mathematics education as the change of education paradigm and the development of artificial intelligence according to the development of information and communication technology. Furthermore, we examined how artificial intelligence can be applied to mathematics education.

• 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.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.87-95
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• 2018

Recently, with the technological development of artificial intelligence, related market is expanding rapidly. In the artificial intelligence technology field, which is still in the early stage but still expanding, it is important to reduce uncertainty about research direction and investment field. Therefore, this study examined technology trends using text mining and topic modeling among big data analysis methods and suggested trends of core technology and future growth potential. We hope that the results of this study will provide researchers with an understanding of artificial intelligence technology trends and new implications for future research directions.

• Knowledge Management Research
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• v.19 no.1
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.