• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.85-92
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• 2021

Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

• 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.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.121-130
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• 2023

In the school field, AI convergence education is recommended, which utilizes AI in education to change the paradigm of society. This study was conducted to define the terms of AI and AI convergence education to minimize the confusion of terms and to analyze the educational needs of teachers from the perspective of conducting AI convergence education. To achieve the purpose, 19 experts' opinions were collected, and a self-administered questionnaire was administered to 125 secondary school teachers enrolled in the AI convergence major at the Graduate School of Education. As a result of the analysis, the experts defined AI convergence education as a methodology for problem solving, not AI-based or utilization education. In the analysis of teachers' educational needs, "AI and big data" was ranked first, followed by "AI convergence education methodology" and "learning practice using AI". The significance of this study is that it defined the terminology by collecting the opinions of experts amidst the confusion of various terms related to AI, and presented the educational direction of AI convergence education for in-service teachers.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.129-139
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• 2018

The crowdsourcing software development (CSD) is growing rapidly in the open call format in a competitive environment. In CSD, tasks are posted on a web-based CSD platform for CSD workers to compete for the task and win rewards. Task searching and assigning are very important aspects of the CSD environment because tasks posted on different platforms are in hundreds. To search and evaluate a thousand submissions on the platform are very difficult and time-consuming process for both the developer and platform. However, there are many other problems that are affecting CSD quality and reliability of CSD workers to assign the task which include the required knowledge, large participation, time complexity and incentive motivations. In order to attract the right person for the right task, the execution of action plans will help the CSD platform as well the CSD worker for the best matching with their tasks. This study formalized the task assignment method by utilizing different situations in a CSD competition-based environment in artificial intelligence (AI) planning. The results from this study suggested that assigning the task has many challenges whenever there are undefined conditions, especially in a competitive environment. Our main focus is to evaluate the AI automated planning to provide the best possible solution to matching the CSD worker with their personality type.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2668-2717
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• 2021

Demands for high-speed wireless data services grow rapidly. It is a big challenge to increasing the network capacity operating on licensed spectrum resources. Unlicensed spectrum cellular networks have been proposed as a solution in response to severe spectrum shortage. Licensed Assisted Access (LAA) was standardized by 3GPP, aiming to deliver data services through unlicensed 5 GHz spectrum. Furthermore, the 3GPP proposed 5G New Radio-Unlicensed (NR-U) study item. On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR). To tackle the challenges of unlicensed spectrum networks in 4G/5G/B5G systems, a lot of works have been done, focusing on using Machine Learning (ML) to support resource allocation in LTE-LAA/NR-U and Wi-Fi coexistence environments. Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems. In this paper, we aim to conduct a comprehensive survey on the recent research efforts related to unlicensed cellular networks and IR technologies, which work jointly to implement 5G and beyond wireless networks. Furthermore, we introduce a positioning assisted LTE-LAA system based on the difference in received signal strength (DRSS) to allocate resources among UEs. We will also discuss some open issues and challenges for future research on the IR applications in unlicensed cellular networks.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.309-316
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• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• Journal of Industrial Convergence
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• v.20 no.4
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• pp.23-30
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• 2022

Recently, with the development of artificial intelligence (AI) and deep learning, the importance of conversational artificial intelligence chatbots is being highlighted. In addition, chatbot research is being conducted in various fields. To build a chatbot, it is developed using an open source platform or a commercial platform for ease of development. These chatbot platforms mainly use RNN and application algorithms. The RNN algorithm has the advantages of fast learning speed, ease of monitoring and verification, and good inference performance. In this paper, a method for improving the inference performance of RNNs and applied algorithms was studied. The proposed method used the word group expansion learning technique of key words for each sentence when RNN and applied algorithm were applied. As a result of this study, the RNN, GRU, and LSTM three algorithms with a cyclic structure achieved a minimum of 0.37% and a maximum of 1.25% inference performance improvement. The research results obtained through this study can accelerate the adoption of artificial intelligence chatbots in related industries. In addition, it can contribute to utilizing various RNN application algorithms. In future research, it will be necessary to study the effect of various activation functions on the performance improvement of artificial neural network algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1872-1879
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• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.