• Journal of the Korean Magnetics Society
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• v.27 no.3
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• pp.104-113
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• 2017

K. Suwab, a chairman of the World Economic Forum (WEF), reported the emerging of the $4^{th}$ industrial revolution by the convergence of artificial intelligence and robot as well as the fusion of true and virtual reality in the WEF 2016. He suggested the $4^{th}$ industrial revolution would change greatly the paradigm of industry and society in the future, and damage the security and quality of the human job severely. In this review the argument of terminology, the impacts on economics, industry technologies and jobs by the $4^{th}$ industrial revolution have been discussed. And the role of magnetics on the 2-4th industrial revolutions was reviewed briefly.

• The Korean Journal of Food & Health Convergence
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• v.7 no.5
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• pp.1-7
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• 2021

The aging of the population has a fundamental impact on the national economy, including decline in productive population, atrophy of available funds, slowdown of technological innovation, slowdown of economic growth, and decrease in vitality of society as a whole. Increase of elderly population would lead to increase in elderly welfare consumers, which would also lead to increase the demand for elderly welfare services. However, due to the continuation of the low birth rate, there is a great shortage of human resources who can handle this. In such a situation, the main goal of the elderly welfare system in the future should aim to actively try to design effective policies, prepare systems, and implement services for the problems of the aged society, and to find ways to expand the finances, manpower, methods, and facilities necessary for the welfare of the elderly. Elderly welfare services in Korea have been changed and developed in accordance with socioeconomic changes such as industrialization and urbanization. This study examines the changes in elderly welfare services in Korea by the flow of times and presents a method which utilizes artificial intelligence and Internet of Things in services for the elderly welfare consumers to improve both quality and efficiency.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.99-102
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• 2021

The article considers one of the areas of development of artificial intelligence where there is the development of computer intelligent systems capable of performing functions traditionally considered intelligent - language comprehension, inference, use of accumulated knowledge, learning, pattern recognition, as well as learn and explain their decisions. It is found that informational intellectual systems are promising in their development. The article is devoted to intelligent information systems and technologies in educational activities, ie issues of organization, design, development and application of systems designed for information processing, which are based on the use of artificial intelligence methods.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.9-14
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• 2020

Artificial intelligence (AI) has been applied to most industries by enhancing automation and contributing greatly to efficient processes and high-quality production. This research analyzes the applications of AI-based automobile accident prevention systems. It deals with AI-based collision prevention systems that learn information from various sensors attached to cars and AI-based accident detection systems that automatically report accidents to the control center in the event of a collision. Based on the literature review, technological and institutional changes are taking place at the national levels, which recognize the effectiveness of the systems. In addition, start-ups at home and abroad as well as major car manufacturers are in the process of commercializing auto parts equipped with AI-based collision prevention technology.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.650-664
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• 2022

Mobile applications can be easily downloaded and installed via markets. However, malware and malicious applications containing unwanted advertisements exist in these application markets. Therefore, smartphone users install applications with reference to the application review to avoid such malicious applications. An application review typically comprises contents for evaluation; however, a false review with a specific purpose can be included. Such false reviews are known as fake reviews, and they can be generated using artificial intelligence (AI)-based text-generating models. Recently, AI-based text-generating models have been developed rapidly and demonstrate high-quality generated texts. Herein, we analyze the features of fake reviews generated from Generative Pre-Training-2 (GPT-2), an AI-based text-generating model and create a model to detect those fake reviews. First, we collect a real human-written application review from Kaggle. Subsequently, we identify features of the fake review using natural language processing and statistical analysis. Next, we generate fake review detection models using five types of machine-learning models trained using identified features. In terms of the performances of the fake review detection models, we achieved average F1-scores of 0.738, 0.723, and 0.730 for the fake review, real review, and overall classifications, respectively.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.301-306
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• 2022

With the recent widespread implementation of Internet of Things (IoT) technology driven by Industry 4.0, self-powered sensors for wearable and implantable systems are increasingly gaining attention. Piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs), which convert biomechanical energy into electrical energy, can be considered as efficient self-powered sensor platforms. These are energy harvesters that are used as low-power energy sources. However, they can also be used as sensors when an output signal is used to sense any mechanical stimuli. For sensors, collecting high-quality data is important. However, the accuracy of sensing for practical applications is equally important. This paper provides a brief review of the performance advanced by the materials and structures of the latest PENG/TENG-based wearable sensors and intelligent applications applied using artificial intelligence (AI)

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Fourth Industrial Review
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• v.2 no.1
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• pp.19-32
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• 2022

Purpose - As new technologies that have led the 4th industrial revolution spread after the COVID-19 pandemic, the business crisis of existing financial institutions and the threat of employee jobs are growing, especially in the financial sector. The purpose of this study is to propose a human-technology convergence curriculum for creating high value-added in financial institutions and upskilling financial manpower. Research design, data, and methodology - In this study, a curriculum was designed to strengthen job competency for Private Bankers, high-quality employees of a bank dealing with high-net-worth owners. The focus of the design is that learners acquire skills to use robo-advisors as a tool and supplement artificial intelligence ethics. Result - The curriculum is organized into a total of 16 classes, and the main contents are changes in the financial environment and financial consumers, the core technology of robo-advisors and AI ethics, and establishment and evaluation of hyper-personalized asset management strategies using robo-advisors. To achieve the educational goal, two evaluations are performed to derive individual tasks and team project results. Conclusion - Human-centered upskilling convergence education will contribute to improving employee value and expanding corporate high value-added business areas by utilizing new technologies as tools. It is expected that the development and application of convergence curriculum in various fields will continue to be advanced in the future.