• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.755-760
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• 2023

In this paper, the 2.4/5GHz dual band was used to ensure the reliability and stability of the wireless AV surveillance system using the existing 2.4GHz band. The proposed system supports dynamic channel allocation and channel change technology to avoid interference from other signals (Wifi, Bluetooth, etc.), reduces maintenance costs incurred when building wireless CCTV, and can be linked with existing wired CCTV. The service area of the A/V surveillance system used can be expanded.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.8
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• pp.674-682
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• 2014

Radio Frequency Test Set (RFTS) is essential to verify Telemetry, Command & Ranging (TC&R) RF subsystem of both Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellite during Assembly Integration & Test (AI&T). The existing RFTS was specialized for each project and needed to be modified for each new satellite. The new design enables RFTS to be used in various projects. The hardware and software was designed considering this and therefore it could be directly used in other projects within a similar test period without modification or inconvenience. It will be also easily controlled, modified, and managed through the extension in modularization according to each function and the use of COTS (commercial on-the-self) and this will improve system reliability. A more reliable RF test measurement is also provided in this new RFTS by using an accurate reference clock signal.

• 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.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.707-719
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• 2023

In the Fourth Industrial Revolution era, AI technologies, such as Chat GPT, have moved beyond assisting to actively analyzing data and providing solutions. This research assessed Chat GPT's e-service quality's influence on perceived value, innovativeness, and subsequent learning outcomes. Findings revealed that while ease of use and responsiveness weren't significant, safety and reliability were positively related to perceived value and innovativeness. A negative correlation was found between trustworthiness and perceived value. Users who saw Chat GPT as valuable and innovative experienced enhanced learning. The study emphasizes the need for guidelines in deploying Chat GPT academically. Given Chat GPT's recent introduction, further nuanced research is necessary.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.3
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• pp.114-124
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• 2015

The concepts of activity involvement(AI) and place attachment(PA) are useful for explaining the sustainable use of natural resources by humans. Although several studies have investigated the effects of AI and PA on environmental behaviors and found its implications, it has not examined the simultaneous effects of both AI and PA. Thus, the purpose of this study was to develop a typology of both AI and PA. This typology was used to explain the environmentally responsible behaviors of visitors. The study sample surveyed 587 users of the main trail in Namhansanseong Provincial Park The results were analyzed by frequency, reliability, factor analysis, cross-tabulation, T-test, correlation and ANOVA analysis. As a result, the typology identified four subgroups of hikers based on involvement in hiking and attachment to setting. Results also indicate that environmentally responsible behaviors do vary significantly across typology. In detail, general environmental behavior and specific environmental behavior were significantly different between the four groups. These finding suggests that PA seems to play a more powerful role than AI in relation to environmental behavior. While more involved and more attached hikers were more active in environmental behaviors, less involved and less attached hikers had a more passive attitude. In this respect, this study placed emphasis on the fact that the future resource management of tourism and outdoor recreation may be established based on its activity experience in certain place.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Journal of Korea Foundry Society
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• v.22 no.5
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• pp.238-244
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• 2002

Water and oil were completely synthesised with ultrasonic vibration energy irradiation. Pure Pb were added into Al melt during irradiated the ultrasonic vibration energy in 750. And the ultrasonic vibration energy was applied to Al-Pb melt to enhance the miscibility. Microstructural analysis, thermal analysis and X-ray diffraction analysis were carried out to evaluate the effect of the ultrasonic vibration energy on the castability and microstructural reliability. (1) Using the ultrasonic vibration energy irradiation, the complete mixing of water and oil was obtained. (2) The microstructure was refined by the application of ultrasonic vibration energy in Al-Pb alloys. (3) Relatively large Pb particles, $5{\mu}m$ were most distributed alone the grain boundaries with fine Pb particles evenly distributed in the matrix. (4) The solubility of Ph in Al-Pb alloys was increases up to 5% with the application of ultrasonic vibration energy.