• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.93-99
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• 2019

Small electric vehicles, such as electric bicycles or electric kickboards, operate with the power charged in a battery mounted in the vehicle, and some of these users use emergency power sockets installed in apartments or public facilities without getting permission. For this reason, the necessity for a simple method to approve the use of power with instant payment system rises for the building managers and small vehicle users as well. In this paper, we propose a technique to charge batteries for small electric vehicles with less than 1 [kW] through a power supply control device installed on the existing 15 [A]. sockets on the common residential properties or public buildings. It also describes the power user authorization algorithm and how to charge fees for the power used. As a result of this research, this paper shows how the user authentication power supply system with the effect of preventing power theft can be realized by creating an environment in which a battery in a small electric vehicle can be easily charged.

• Korean Institute of Interior Design Journal
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• v.21 no.3
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• pp.50-57
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• 2012

In Ubiquitous era. architecture is not the old building adapting ubiquitous technology. In order to take the role as new architectural paradigm in space, environment and technology, it has to develop technology continuously and experimental architecture at the same time. it must have co-evolution of architectural field and others through organic network. by that, the evolution of space will be in the way that combines space which is responded to human emotion and user-centric human-friendly. It will be the new paradigm of Ubiquitous digital space. Digital technology resulted in a change to a society as well as to the life of human and its way of thinking. Due to those changes, new terms or concepts come out and a new meaning is added to the conventional concepts. This aims to examine type of spatial contexts for interaction design experience. This study is performed through Literature research for theory by interactive space and case studies for construction elements to design. The range of case study is limited to interaction space in addition of interactive elements and user interface. And analysis conclusion is show the many type, First, Interactive space has special purpose for make a interaction by intelligent elements.(sensor, program, algorithm, New-technology) Second, Interactive space was cooperation with various professional for space purpose. Third. Interactive space is self-develop by algorithm, program, sensor network and that is harmonize with user. finally. Interaction space is show the temper elements about allness, metastatic, activeness, liquidity, relationship. That was written by ecological theory.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.1
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• pp.103-112
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• 2007

Recently structures become longer and higher because of the developments of new materials and construction techniques. However, such modern structures are susceptible to excessive structural vibrations, which may induce problems of serviceability and structural damages. In this paper we attempt to control structural vibration using the probabilistic neural network(PNN) and the artificial neural network(ANN) based on the training pattern that consist of only the structural state vector and the control force. The state vectors of the structure and control forces made by linear quadratic regulator(LQR) algorithm are used for training pattern of PNN and ANN. The proposed algorithm is applied for the vibration control of the three story shear building under Northridge earthquake. Control results by the proposed PNN and ANN are compared with each other.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.45-55
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• 2022

BIM models allow building spaces to be instantiated and recognized as unique objects independently of model elements. These instantiated spaces provide the required semantics that can be leveraged for building code checking, energy analysis, and evacuation route analysis. However, theses spaces or rooms need to be designated manually, which in practice, lead to errors and omissions. Thus, most BIM models today does not guarantee the semantic integrity of space designations, limiting their potential applicability. Recent studies have explored ways to automate space allocation in BIM models using artificial intelligence algorithms, but they are limited in their scope and relatively low classification accuracy. This study explored the use of Graph Convolutional Networks, an algorithm exclusively tailored for graph data structures. The goal was to utilize not only geometry information but also the semantic relational data between spaces and elements in the BIM model. Results of the study confirmed that the accuracy was improved by about 8% compared to algorithms that only used geometric distinctions of the individual spaces.

• Land and Housing Review
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• v.14 no.3
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• pp.145-155
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• 2023

Recently, with the widespread adoption of Building Information Modeling (BIM) technology in the construction industry, various object detection algorithms have been used to verify errors between 3D models and actual construction elements. Since the characteristics of objects vary depending on the type of construction facility, such as buildings, bridges, and tunnels, appropriate methods for object detection technology need to be employed. Additionally, for object detection, initial object images are required, and to obtain these, various methods, such as drones and smartphones, can be used for image acquisition. The study uses a 360° camera optimized for internal tunnel imaging to capture initial images of the tunnel structures of railway and road facilities. Various object detection methodologies including the YOLO, SSD, and R-CNN algorithms are applied to detect actual objects from the captured images. And the Faster R-CNN algorithm had a higher recognition rate and mAP value than the SSD and YOLO v5 algorithms, and the difference between the minimum and maximum values of the recognition rates was small, showing equal detection ability. Considering the increasing adoption of BIM in current railway and road construction projects, this research highlights the potential utilization of 360° cameras and object detection methodologies for tunnel facility sections, aiming to expand their application in maintenance.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.859-872
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• 2003

Class hierarchies often constitute the backbone of object-oriented software. Their quality is therefore quite crucial. Building class hierarchies with good qualify is a very important and common tasks on the object oriented software development, but such hierarchies are not so easy to build. Moreover, the class hierarchy structure under construction is frequently restructured and refined until it becomes suitable for the requirement on the iterative and incremental development lifecycle. Therefore, there has been renewal of interest in all methodologies and tools to assist the object oriented developers in this task. In this paper, we define a set of quantitative metrics which provide a wav of capturing features of a rough estimation of complexity of class hierarchy structure. In addition to, we suggest a set of algorithms that transform a original class hierarchy structure into reorganized one based on the proposed metrics for class hierarchy structure. Furthermore, we also prove that each algorithm is "object-preserving". That is, we prove that the set of objects are never changed before and after applying the algorithm on a class hierarchy. The technique presented in this paper can be used as a guidelines of the construction, restructuring and refinement of class hierarchies. Moreover, the proposed set of algorithms based on metrics can be helpful for developers as an useful instrument for the object-oriented software development.velopment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.669-678
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• 2018

This study developed information technology infrastructures for building a driving environment analysis platform using various big data, such as vehicle sensing data, public data, etc. First, a small platform server with a parallel structure for big data distribution processing was developed with H/W technology. Next, programs for big data collection/storage, processing/analysis, and information visualization were developed with S/W technology. The collection S/W was developed as a collection interface using Kafka, Flume, and Sqoop. The storage S/W was developed to be divided into a Hadoop distributed file system and Cassandra DB according to the utilization of data. Processing S/W was developed for spatial unit matching and time interval interpolation/aggregation of the collected data by applying the grid index method. An analysis S/W was developed as an analytical tool based on the Zeppelin notebook for the application and evaluation of a development algorithm. Finally, Information Visualization S/W was developed as a Web GIS engine program for providing various driving environment information and visualization. As a result of the performance evaluation, the number of executors, the optimal memory capacity, and number of cores for the development server were derived, and the computation performance was superior to that of the other cloud computing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.84-93
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• 2011

In order to provide the real time and proper information to the pedestrian who is using the transport connection and transfer center through data collecting and processing process, the design of the test-bed (Gimpo airport)'s communication construction and the technology of the pedestrian location tracking has been researched. The design of the communication construction should make sure that it can provide believable data to the user of the transfer center. At the same time, the location tracking should also be considered, so that the require of the communication efficiency and the location tracking efficiency can be met together. In order to make the efficient location tracking technology, the problems related to the commercial technology based real time location identification will be resolved and the new approach method was proposed and be applied and analysed to the test-bed. The wireless access points can be located in the most real-world situation which has added the characteristics of the real building to the electronic map, and through the analysis of theirs location, they can be set as the mainly necessary points for the communication construction design and the location tracking and the method to locate that points has been proposed. How to set, how to apply it to the test-bed and the examination result will be introduced in this paper.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.