• Journal of the Korean Geotechnical Society
• /
• v.26 no.7
• /
• pp.93-106
• /
• 2010

In performing a series of in-situ thermal response tests, the effective thermal conductivities of six vertical closed-loop ground heat exchangers were experimentally evaluated and compared one another, which were constructed in a test bed in Wonju. To compare thermal efficiency of the ground heat exchangers in field, the six boreholes were constructed with different construction conditions: grouting materials (cement vs. bentonite), different additives (silica sand vs. graphite) and the shape of pipe-sections (general U-loop type vs. 3 pipe-type). From the test results, it can be concluded that cement grouting has a higher effective thermal conductivity than bentonite grouting, and the efficiency of graphite better performs than silica sand as a thermally-enhancing addictive. In addition, a new 3 pipe-type heat exchanger provides less thermal interference between the inlet and outlet pipe than the conventional U-loop type heat exchanger, which results in superior thermal performance. Based on the results from the in-situ thermal response tests, a series of economic analyses have been made to show the applicability of the new addictives and 3 pipe-type heat exchanger.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.1D
• /
• pp.17-23
• /
• 2009

Due to the rapid increase of vehicles on the street, Korean society is facing worsening traffic congestions and air pollutions. Also, the oil price pickup has led to increasing need for the use of public transportation. In particular, transfering among public transportation may be a main factor for riders who are commuting for a long distance journey. In order to ensure such connectivity, transfer stations have been actively built in Korea. However, it would be necessary to shift those vehicles, from cars to public transportations by enhancing the users' satisfaction with public transportation through strategies for minimizing the users' waiting cost by building an efficient connective system between transportation modes as well as the preparation of aforementioned transfer stations. Therefore, this study aimed to develop an algorithm for minimizing transferring passengers' waiting costs based on service intervals of linked buses within the transfer facilities. In order to adjust the service interval, we calculated the total costs, involving the wait cost of transfer passengers and bus operation costs, and produced an allocation interval, that would minimize the costs. We selected a KTX departing from Seoul station, and a No. 6014 bus route in Gwangmyeong city where it starts from the Gwangmyeong station in order to for verifying the model. Then, the transfer passengers' total waitting cost was reduced equivalent to the maximum of 212 minutes, and it revealed that the model performed very effectively.

• Convergence Security Journal
• /
• v.22 no.5
• /
• pp.69-80
• /
• 2022

Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.1D
• /
• pp.115-123
• /
• 2006

Recently, construction claims have been increased for protecting the rights of construction participants and effectively adjusting the changes under the contract. Thus, the importance of claim management has been emphasized in the construction industry. In domestic construction industry, some claim issues involved in construction activities are often being developed into disputes and even litigations because of the absence of methods or systems for the dispute resolution, and the lack of judicial precedents which can be provided as the references for resolving a particular dispute. In general, the judicial precedents related to the disputes and litigations occurred among construction participants would be extremely valuable in evaluating and analyzing current claims issues. However, such useful information has not been effectively accumulated and utilized in resolving the similar or sometimes identical types of disputes, thus requiring a large amount of additional costs, time and efforts. The primary objective of this study is to propose a web-based decision support system for construction claim management, which enables contractual participants to easily access and use the information of the judicial precedents related to the current construction claims. The decision support system is composed of 'prevention' and 'settlement' modules for avoiding and systematically resolving the construction claims.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.4D
• /
• pp.351-360
• /
• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.37 no.1
• /
• pp.36-42
• /
• 2024

Roll-to-roll processing holds an integral position within the manufacturing landscape, and its significance reverberates across numerous industries. This versatile technology platform encompasses a diverse array of process methods and accommodates a wide spectrum of material categories, making it a cornerstone of modern production. Within this expansive domain, two commonly employed coating techniques, namely the slot die and gravure coating methods, have earned their prominence for their precision and efficiency in delivering flawless coatings. Additionally, the realm of drying processes relies heavily on thermal drying, infrared (IR) drying, and ultraviolet (UV) drying methods to expedite the transformation of materials from their liquid or semi-liquid states to solid, ready-to-use products. The undeniable importance of roll-to-roll processing lies in its ability to streamline manufacturing processes, reduce costs, and enhance product quality. This article embarks on a comprehensive journey to fathom the depth of this importance by delving into the intricacies of these common roll-to-roll process methods. Through rigorous research and meticulous data collection, we aim to shed light on the pivotal role these techniques play in shaping various industries and advancing the world of manufacturing. By understanding their significance, we can harness the full potential of roll-to-roll processing and pave the way for innovation and excellence in production.

• The Journal of Bigdata
• /
• v.8 no.2
• /
• pp.125-131
• /
• 2023

Globally, drug side effects rank among the top causes of death. To effectively respond to these adverse drug reactions, a shift towards an active real-time monitoring system, along with the standardization and quality improvement of data, is necessary. Integrating individual institutional data and utilizing large-scale data to enhance the accuracy of drug side effect predictions is critical. However, data sharing between institutions poses privacy concerns and involves varying data standards. To address this issue, our research adopts a federated learning approach, where data is not shared directly in compliance with privacy regulations, but rather the results of the model's learning are shared. We employ the Common Data Model (CDM) to standardize different data formats, ensuring accuracy and consistency of data. Additionally, we propose a drug monitoring system that enhances security and scalability management through a cloud-based federated learning environment. This system allows for effective monitoring and prediction of drug side effects while protecting the privacy of data shared between hospitals. The goal is to reduce mortality due to drug side effects and cut medical costs, exploring various technical approaches and methodologies to achieve this.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.27 no.3
• /
• pp.76-86
• /
• 2024

This study sought to introduce smart urban regeneration to solve the problem of aging and substandard housing in large cities that occurred during the rapid industrialization and urbanization of local cities in Korea. Accordingly, this study aims to activative the old downtown through the convergence of the existing urban regeneration project and smart city project and to improve the physical, social, and economic aspects. As a research method, the literature related to smart cities and urban regeneration was systematically reviewed, and the possibility of introducing smart city services in the Samho-dong district of Yangsan City was explored through domestic and foreign case analysis. As a result of the research, the necessity of smart urban regeneration was highlighted, and the conclusion was reached that it is important to improve the efficiency of urban regeneration projects by using information and communication technology and strengthen sustainability by urban regeneration. This study is expected to contribute to the activative the old downtown and the improvement of the quality of life of citizens, and it is necessary to strengthen the interaction between smart city and urban regeneration in the future, and the introduction of smart city services suitable for local characteristics is judged to play an important role in sustainable urban development through local community and citizen participation.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.9
• /
• pp.1-7
• /
• 2024

In this paper, we proposes a Convolutional Neural Networks(CNN) equipped with Batch Normalization(BN) for handwritten digit recognition training the MNIST dataset. Aiming to surpass the performance of LeNet-5 by LeCun et al., a 6-layer neural network was designed. The proposed model processes 28×28 pixel images through convolution, Max Pooling, and Fully connected layers, with the batch normalization to improve learning stability and performance. The experiment utilized 60,000 training images and 10,000 test images, applying the Momentum optimization algorithm. The model configuration used 30 filters with a 5×5 filter size, padding 0, stride 1, and ReLU as activation function. The training process was set with a mini-batch size of 100, 20 epochs in total, and a learning rate of 0.1. As a result, the proposed model achieved a test accuracy of 99.22%, surpassing LeNet-5's 99.05%, and recorded an F1-score of 0.9919, demonstrating the model's performance. Moreover, the 6-layer model proposed in this paper emphasizes model efficiency with a simpler structure compared to LeCun et al.'s LeNet-5 (7-layer model) and the model proposed by Ji, Chun and Kim (10-layer model). The results of this study show potential for application in real industrial applications such as AI vision inspection systems. It is expected to be effectively applied in smart factories, particularly in determining the defective status of parts.

• Phonetics and Speech Sciences
• /
• v.16 no.3
• /
• pp.79-86
• /
• 2024

Transformer models have shown remarkable performance in extracting meaningful information from sequential input data such as text and images, and are gaining attention as end-to-end models for speech recognition. This study compared the performances of the Transformer speech recognition model and its enhanced versions, the Conformer and E-Branchformer, when applied to Korean speech recognition. Using Korean speech data from AIHub, we prepared a training set of approximately 7,500 hours and evaluated the models using the ESPnet toolkit. Additionally, we compared syllables and subwords as recognition units and analyzed the performance differences with changes in the number of tokens using Byte Pair Encoding. The results showed that the E-Branchformer achieved the best performance in Korean speech recognition and Conformer outperformed Transformer but degraded in performance for long utterances owing to cross-attention alignment errors. We aimed to determine the optimal settings by analyzing the performance changes with subword token adjustments. This study comprehensively evaluated model accuracy and processing speed to maximize the efficiency of Korean speech recognition. This is expected to contribute to the training of large-scale Korean speech recognition models and improve Conformer recognition errors. Future research should include additional experiments with diverse Korean speech datasets and enhance the recognition performance through structural improvements in the Conformer.