• Journal of the Korean Statistical Society
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• v.21 no.1
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• pp.35-46
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• 1992

On Box-Cox transformation, one or few responses are influential on transformation parameter estimator. To detect influential observatins, several diagnostics (Cook and Wang 1983, Hinkley and Wang 1988, Lawrance 1988, Tsai and Wu 1990) have been suggested. We compare these diagnostics and denote the necessity of multiple cases deletion which is important especially when the masking effect is present. Also, analytic expression of Tsai and Wu's diagnostic is given. We suggest a computationally feasible and useful algorithm based on the basic building blocks, and present descriptive examples using artificial data.

• Journal of the Korean Society of Safety
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• v.32 no.1
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• pp.53-59
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• 2017

Safety inspection activities of the facilities including the electricity, gas, building, and firefighting, etc. are individual checks by the separate law of each government department, comprehensive inspections for the specific managed facilities or during a weak season, and national safety overall diagnostics for the disaster prevention. Thus, types of the inspections are various and they have been carried out repeatedly as well as duplicately. That can make the people or institutes to take such inspections feel great burdens. Therefore, the investigation on the current situation of the individual inspection by the separate law for the electricity, gas, building, and firefighting, etc. as well as of various others by the government needs to be carried out and according to the results the repetitive and duplicated inspections should be reduced and converged to one comprehensive one. In this regard, we proposed solutions to improve the government safety inspection system, function, and role.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.485-492
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• 2020

Information on the energy consumption of buildings that can be obtained through conventional methods is limited. Therefore, this study aims to develop a model that can support decision making about building facility management through digital transformation technologies. Through the IoT sensor, the building's energy data and indoor air quality data are collected, and the monitored data is visualized through the ELK Stack and produced as a dashboard. In addition, the target building is photographed with a 360-degree camera and maps using a tool to create a 360-degree tour. Using such digital transformation technologies, users of buildings can obtain various information in real time without visiting buildings directly. This can lead to changes in actions or actions for building management, supporting facility management decisions, and consequently reducing building energy consumption.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.82-86
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• 2019

We aim to use public data different from the remote BEMS energy diagnostics technology and already established and then switch the conventional operation environment to a big-data-based integrated management environment to operate and build a building energy management environment of maximized efficiency. In Step 1, various network management environments of the system integrated with a big data platform and the BEMS management system are used to collect logs created in various types of data by means of the big data platform. In Step 2, the collected data are stored in the HDFS (Hadoop Distributed File System) to manage the data in real time about internal and external changes on the basis of integration analysis, for example, relations and interrelation for automatic efficient management.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.199-212
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• 2002

This article introduces transfer function model (TFM) with conditional heteroscedasticity where ARCH concept is built into the traditional TFM of Box and Jenkins (1976). Model building strategies such as identification, estimation and diagnostics of the model are discussed and are illustrated via empirical study including simulated data and real data as well. Comparisons with the classical TFM are also made.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.401-404
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• 1989

Some magnetic probes for theta-pinch experiment have been constructed. Methods of building the coil, conecting it to the oscilloscope, shielding and enclosing the probe are discribed. Such a probe has been used to measure the magnetic flux penetrating it within theta - pinched plasmas.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.279-286
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• 2013

This paper introduces a new method for identification of building energy performance problems. The presented method is based on automated analysis and visualization of deviations between actual and expected energy performance of the building using EPAR (Energy Performance Augmented Reality) models. For generating EPAR models, during building inspections, energy auditors collect a large number of digital and thermal imagery using a consumer-level single thermal camera that has a built-in digital lens. Based on a pipeline of image-based 3D reconstruction algorithms built on GPU and multi-core CPU architecture, 3D geometrical and thermal point cloud models of the building under inspection are automatically generated and integrated. Then, the resulting actual 3D spatio-thermal model and the expected energy performance model simulated using computational fluid dynamics (CFD) analysis are superimposed within an augmented reality environment. Based on the resulting EPAR models which jointly visualize the actual and expected energy performance of the building under inspection, two new algorithms are introduced for quick and reliable identification of potential performance problems: 1) 3D thermal mesh modeling using k-d trees and nearest neighbor searching to automate calculation of temperature deviations; and 2) automated visualization of performance deviations using a metaphor based on traffic light colors. The proposed EPAR v2.0 modeling method is validated on several interior locations of a residential building and an instructional facility. Our empirical observations show that the automated energy performance analysis using EPAR models enables performance deviations to be rapidly and accurately identified. The visualization of performance deviations in 3D enables auditors to easily identify potential building performance problems. Rather than manually analyzing thermal imagery, auditors can focus on other important tasks such as evaluating possible remedial alternatives.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.313-314
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• 2023

This paper summarized the safety diagnosis method in the field of durability among the structural safety suggested in the safety diagnosis manual of the reconstruction project and analyzed the safety diagnosis case. It is expected to be a reference material for safety diagnosis in the field of durability by pointing out cases that are wrong during existing safety diagnosis and cases where diagnostics are likely to make errors.

• Structural Engineering and Mechanics
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• v.58 no.5
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• pp.757-781
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• 2016

The results of diagnostics and analysis of an industrial hall located on the premises of a thermal power plant severely damaged by fire are presented in the paper. The comprehensive failure-related diagnostics, non-destructive and destructive tests of steel and concrete materials, geodetic surveying of selected structural members, numerical modelling, static analysis and reliability assessment were focused on two basic goals: The determination of the current technical condition of the load bearing structure and the assessment of its post fire resistance as well as assessing the degree of damage and subsequent design of reconstruction measures and arrangements which would enable the safe and reliable use of the building. The current mechanical properties of the steel material obtained from the tests and measured geometric characteristics of the structural members with imperfections were employed in finite element models to study the post-fire behaviour of the structure. In order to compare the behaviour of the numerically modelled steel roof truss, subjected to the effects of fire, with the real post-fire response of the damaged structure theoretically obtained resistance, critical temperature and the time at which the structure no longer meets the required reliability criteria under its given loading are compared with real values. A very good agreement between the simulated results and real characteristics of the structure after the fire was observed.