• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.57-64
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• 2021

In the last decade, the effect of digitalization was the most cited issue in economic discourse, especially since technological advances, automation, and artificial intelligence are the key to the future discussions. Unemployment is one of the most important and continuous debates, especially in times of crisis due to the COVID-19 pandemic. Therefore, this study aims to analyze the influence of leadership style in innovation organizations to deal with the crisis. In this study, a non-probability purposive sampling method was used. A total of 377 respondents were from LinkedIn social media in Indonesia, with the criteria of employees who have worked for at least 6 years. The structural equation model was analyzed with Amos 25.0. The results show that virtual, servant, and transformational leadership influence employee creativity. Moreover, employee creativity strongly influences organizational innovation; therefore, a new model was found to meet the challenges during the COVID-19 pandemic crisis, which is leadership. Therefore, these results are useful for managers to overcome challenges during the COVID-19 pandemic crisis to manage employee creativity for a better innovative organization and make science a reference for finding solutions to the global wave of unemployment in the revolution 5.0 era.

• The Journal of Bigdata
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• v.6 no.1
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• pp.115-125
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• 2021

Due to various ICT Technology innovations and Digital Transformation, the Internet of Things(IoT) environment is increasingly requiring intelligence, decentralization, and automated service, especially an advanced and stable smart service environment in the Industrial Internet of Things(IIoT) where communication network(5G), data analysis and artificial intelligence(AI), and digital twin technology are combined. In this study, we propose IIoT Edge middleware systems for flexible interface with heterogeneous devices such as facilities and sensors at various industrial sites and for quick and stable data collection and processing.

• Korean Journal of Clinical Pharmacy
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• v.30 no.4
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• pp.217-225
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• 2020

The fourth industrial revolution, with its characteristics of "hyper-connectivity", "hyper-intelligence" and "automation", is a hot topic worldwide. It will fundamentally change industry, economy, and business models through technological innovations, such as big data, cloud computing, Internet of Things (IoT), artificial intelligence (AI), and 3D printing. In particular, the development of highly advanced information technology (IT) and AI is expected to replace human roles, thereby changing employment and occupation prospects in the future. Based on this, some predict that the profession of the pharmacist will soon disappear. To counter this, pharmacists' attention and efforts are required to seek innovative transformations in their functions by responding sensitively and promptly to changes of the fourth industrial revolution. It is also necessary to recognize the new roles of pharmacists and to develop the competencies to perform them. The fourth industrial revolution is an inevitable change of the times. At this time, we should take comprehensive and open perspectives on how the future society will change economically, culturally, and socially, and use it as an opportunity to shape the new future of pharmacists.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.409-415
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• 2022

In computer vision, an image of a measurement target is acquired using a camera. And feature values, vectors, and regions are detected by applying algorithms and library functions. The detected data is calculated and analyzed in various forms depending on the purpose of use. Computer vision is being used in various places, especially in the field of automatically recognizing automobile parts or measuring the quality. Computer vision is being used as the term machine vision in the industrial field, and it is connected with artificial intelligence to judge product quality or predict results. In this study, a vision system for judging the quality of automobile parts was built, and the results were compared by applying five machine learning classification models to the produced data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3355-3372
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• 2022

Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment. Algorithmic composition aims to develop algorithms for music composition. Recently, algorithmic composition using artificial intelligence technologies received considerable attention. In particular, computational intelligence is widely used and achieves promising results in the creation of music. This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm. First, transform the MIDI music format files to digital data. Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern. Except for time series, the converted data also includes duration, pitch, and velocity. Second, using MC simulation to deal with them summed up their distribution law respectively. The two main control parameters are the value of discrete sampling and standard deviation. Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.222-224
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• 2022

Korea has more than 2,000 fires and more than 2,000 casualties every year. This study takes measures to facilitate the incorporation of 119 fire trucks by judging vehicles or standing signs using real-time image reading YOLO5 before the fire trucks arrive at the fire site. It is possible to shorten the time to extinguish a fire by photographing a fire site, transmitting the situation of the site, and analyzing the components of smoke to determine the type of fire. As a result, it is expected that it will be able to minimize casualties by keeping the golden time.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.1-11
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• 2023

Large language models seem promising for handling reasoning problems, but their underlying solving mechanisms remain unclear. Large language models will establish a new paradigm in artificial intelligence and the society as a whole. However, a major challenge of large language models is the massive resources required for training and operation. To address this issue, researchers are actively exploring compact large language models that retain the capabilities of large language models while notably reducing the model size. These research efforts are mainly focused on improving pretraining, instruction tuning, and alignment. On the other hand, chain-of-thought prompting is a technique aimed at enhancing the reasoning ability of large language models. It provides an answer through a series of intermediate reasoning steps when given a problem. By guiding the model through a multistep problem-solving process, chain-of-thought prompting may improve the model reasoning skills. Mathematical reasoning, which is a fundamental aspect of human intelligence, has played a crucial role in advancing large language models toward human-level performance. As a result, mathematical reasoning is being widely explored in the context of large language models. This type of research extends to various domains such as geometry problem solving, tabular mathematical reasoning, visual question answering, and other areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.77-82
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• 2023

Intelligence Home and Home Automation, which attracted attention before Smart Home, caused inconvenience to users by focusing on applying cutting-edge technologies to homes, and failed to popularize them due to lack of unemployment efficiency. However, with the 4th Industrial Revolution, various services using technologies related to big data, artificial intelligence, and the Internet of Things are increasing, and the rate of smart home services that operate, manage, and automate products at home is gradually increasing. In line with this trend, this paper implements a program app that can connect, manipulate, and manage products using MQTT server, Django web framework, and WIFI communication module.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.485-496
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• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1666-1689
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• 2021

The red line of Permanent Basic Farmland is the most important part in the "three-line" demarcation of China's national territorial development plan. The scientific and reasonable delineation of the red line is a major strategic measure being taken by China to improve its ability to safeguard the practical interests of farmers and guarantee national food security. The delineation of Permanent Basic Farmland zoning (DPBFZ) is essentially a multi-objective optimization problem. However, the traditional method of demarcation does not take into account the synergistic development goals of conservation of cultivated land utilization, ecological conservation, or urban expansion. Therefore, this research introduces the idea of artificial immune optimization and proposes a multi-objective model of DPBFZ red line delineation based on a clone selection algorithm. This research proposes an objective functional system consisting of these three sub-objectives: optimal quality of cropland, spatially concentrated distribution, and stability of cropland. It also takes into consideration constraints such as the red line of ecological protection, topography, and space for major development projects. The mathematical formal expressions for the objectives and constraints are given in the paper, and a multi-objective optimal decision model with multiple constraints for the DPBFZ problem is constructed based on the clone selection algorithm. An antibody coding scheme was designed according to the spatial pattern of DPBFZ zoning. In addition, the antibody-antigen affinity function, the clone mechanism, and mutation strategy were constructed and improved to solve the DPBFZ problem with a spatial optimization feature. Finally, Tongxu County in Henan province was selected as the study area, and a controlled experiment was set up according to different target preferences. The results show that the model proposed in this paper is operational in the work of delineating DPBFZ. It not only avoids the adverse effects of subjective factors in the delineation process but also provides multiple scenarios DPBFZ layouts for decision makers by adjusting the weighting of the objective function.