• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.61-74
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• 2016

The purpose of this paper is to analyze the relationship between the location of the epicenter of a medium-sized earthquake(magnitude 4.8) that occurred on January 20, 2007 in the Odaesan area with lineament features using a shaded relief map(1/25,000 scale) and satellite images from LANDSAT-8 and KOMPSAT-2. Previous studies have analyzed lineament features in tectonic settings primarily by examining two-dimensional satellite images and shaded relief maps. These methods, however, limit the application of the visual interpretation of relief features long considered as the major component of lineament extraction. To overcome some existing limitations of two-dimensional images, this study examined three-dimensional images, produced from a Digital Elevation Model and drainage network map, for lineament extraction. This approach reduces mapping errors introduced by visual interpretation. In addition, spline interpolation was conducted to produce density maps of lineament frequency, intersection, and length required to estimate the density of lineament at the epicenter of the earthquake. An algorithm was developed to compute the Value of the Relative Density(VRD) representing the relative density of lineament from the map. The VRD is the lineament density of each map grid divided by the maximum density value from the map. As such, it is a quantified value that indicates the concentration level of the lineament density across the area impacted by the earthquake. Using this algorithm, the VRD calculated at the earthquake epicenter using the lineament's frequency, intersection, and length density maps ranged from approximately 0.60(min) to 0.90(max). However, because there were differences in mapped images such as those for solar altitude and azimuth, the mean of VRD was used rather than those categorized by the images. The results show that the average frequency of VRD was approximately 0.85, which was 21% higher than the intersection and length of VRD, demonstrating the close relationship that exists between lineament and the epicenter. Therefore, it is concluded that the density map analysis described in this study, based on lineament extraction, is valid and can be used as a primary data analysis tool for earthquake research in the future.

• Journal of the Korea Concrete Institute
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• v.20 no.1
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• pp.13-22
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• 2008

The initiation and growth processes of cyclic ice body in porous systems are affected by the thermo-physical and mass transport properties, as well as gradients of temperature and chemical potentials. Furthermore, the diffusivity of deicing chemicals shows significantly higher value under cyclic freeze-thaw conditions. Consequently, the disintegration of concrete structures is aggravated at marine environments, higher altitudes, and northern areas. However, the properties of cyclic freeze-thaw with crack growth and the deterioration by the accumulated damages are hard to identify in tests. In order to predict the accumulated damages by cyclic freeze-thaw, a regression analysis by the response surface method (RSM) is used. The important parameters for cyclic freeze-thawdeterioration of concrete structures, such as water to cement ratio, entrained air pores, and the number of cycles of freezing and thawing, are used to compose the limit state function. The regression equation fitted to the important deterioration criteria, such as accumulated plastic deformation, relative dynamic modulus, or equivalent plastic deformations, were used as the probabilistic evaluations of performance for the degraded structural resistance. The predicted results of relative dynamic modulus and residual strains after 300 cycles of freeze-thaw show very good agreements with the experimental results. The RSM result can be used to predict the probability of occurrence for designer specified critical values. Therefore, it is possible to evaluate the life cycle management of concrete structures considering the accumulated damages due to the cyclic freeze-thaw using the proposed prediction method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.31-40
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• 2016

In vehicular ad-hoc networks (VANETs), vehicles sense information on emergency incidents (e.g., accidents, unexpected road conditions, etc.) and propagate this information to following vehicles and a server to share the information. However, this process of emergency message propagation is based on multiple broadcast messages and can lead to broadcast storms. To address this issue, in this work, we use a novel approach to detect the vehicles that are farthest away but within communication range of the transmitting vehicle. Specifically, we discuss a signal-to-noise ratio (SNR)-based linear back-off (SLB) scheme where vehicles implicitly detect their relative locations to the transmitter with respect to the SNR of the received packets. Once the relative locations are detected, nodes that are farther away will set a relatively shorter back-off to prioritize its forwarding process so that other vehicles can suppress their transmissions based on packet overhearing. We evaluate SLB using a realistic simulation environment which consists of a NS-3 VANET simulation environment, a software-based WiFi-IP gateway, and an ITS server operating on a separate machine. Comparisons with other broadcasting-based schemes indicate that SLB successfully propagates emergency messages with latencies and hop counts that is close to the experimental optimal while reducing the number of transmissions by as much as 1/20.

• Tunnel and Underground Space
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• v.9 no.4
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• pp.306-314
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• 1999

In underground spaces including nuclear waste repository, prediction of air quantity, temperature/humidity and pollutant concentration is utmost important for space construction and management during the normal state as well as for determining the measures in emergency cases such as underground fires. This study aims at developing a model for underground space environment which has capabilities to take into account the effects of autocompression for the natural ventilation head calculation, to find the optimal location and size of fans and regulators, to predict the temperature and humidity by calculating the convective heat transfer coefficient and the sensible and latent heat transfer rates, and to estimate the pollutant levels throughout the network. The temperature/humidity prediction model was applied to a military storage underground space and the relative differences of dry and wet temperatures were 1.5 ~ 2.9% and 0.6 ~ 6.1%, respectively. The convection-based pollutant transport model was applied to two different vehicle tunnels. Coefficients of turbulent diffusion due to the atmospheric turbulence were found to be 9.78 and 17.35$m^2$/s, but measurements of smoke and CO concentrations in a tunnel with high traffic density and under operation of ventilation equipment showed relative differences of 5.88 and 6.62% compared with estimates from the convection-based model. These findings indicate convection is the governing mechanism for pollutant diffusion in most of the tunnel-type spaces.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.2
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• pp.220-226
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• 2013

Recently, with the rapid development of factory automation and logistics system, a few workers were able to manage the broad workplace such as large vessels and warehouse. To estimate the exact location of these workers in the conventional wireless indoor localization systems, three or more fixed nodes are generally used to recognize the location of a mobile node consisting of a single node. However, these methods are inefficient in terms of node deployment because the broad workplace requires a lot of fixed nodes compared to workers(mobile nodes). Therefore, to efficiently deploy fixed nodes in these environments that need a few workers, this paper presents a novel estimation algorithm which can reduce the number of fixed nodes by efficiently recognizing the relative coordinates of two fixed nodes through a mobile node composed of three nodes. Also, to minimize the distance errors between mobile node and fixed node, rounding estimation(RE) technique is proposed. Experimental results show that the error rate of localization is improved, by using proposed RE technique, 90.9% compared to conventional trilateration in the free space. In addition, despite the number of fixed nodes can be reduced by up to 50% in the indoor free space, the proposed estimation algorithm recognizes precise location which has average error of 0.15m.

• Journal of the Korea Concrete Institute
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• v.20 no.2
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• pp.131-138
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• 2008

Humidity and strain were estimated for understanding the relation between humidity change by self-desiccation and shrinkage in high-performance concrete with low water binder ratio. Internal humidity change and shrinkage strain were about 10%, 4% and $320\times10^{-6}$, $120\times10^{-6}$ respectively on concrete with water binder ratio 0.3, 0.4 and from the results, humidity change and shrinkage represented the strong linear relation regardless of mixture. For specifying the relation on internal humidity change and autogenous shrinkage strain, shrinkage model was established which is driven by capillary pressure in pore water and surface energy in hydrates on the assumption of a single network and extended meniscus in pore system of concrete. This model and experimental results had a similar tendency so it would be concluded that the internal humidity change by self-desiccation in HPC originated in small pores less than 20 nm, therefore controlling plan on autogenous shrinkage might be focused on surface tension of water and degree of saturation in small pore.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.47-58
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• 2024

This study were conduced experimentally to analyze the heat-generating performance, flexural strength, and microstructure of conductive mortar mixed with micro steel fiber and multi-wall carbon nanotube (MWCNT). In the conductive mortar heat-generating performance and flexural strength tests, the mixing concentration of MWCNT was selected as 0.0wt%, 0.5wt%, and 1.0wt% relative to the weight of cement, and micro steel fibers were mixed at 2.0vol% relative to the volume. The performance experiments were conducted with various applied voltages (DC 10V, 30V, 60V) and different electrode spacings (40 mm, 120 mm) as parameters, and the flexural strength was measured at the curing age of 28 days and compared and analyzed with the normal mortar. Furthermore, the surface shape and microstructure of conductive mortar were analyzed using a field emission scanning electron microscope (FE-SEM). The results showed that the heat-generating performance improved as the mixing concentration of MWCNT and the applied voltage increased, and it further improved as the electrode spacing became narrower. However, even if the mixing concentration of MWCNT was added up to 1.0 wt%, the heat-generating performance was not significantly improved. As a result of the flexural strength test, the average flexural strength of all specimens except the PM specimen and the MWCNT mixed specimens was 4.5 MPa or more, showing high flexural strength due to the incorporation of micro steel fibers. Through FE-SEM image analysis, Through FE-SEM image analysis, it was confirmed that a conductive network was formed between micro steel fibers and MWCNT particles in the cement matrix.

• International Journal of Quality Innovation
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• v.8 no.3
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• pp.156-172
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• 2007

The high-tech industry is the economic lifeline for Taiwan. Its characteristics are short product life cycle, rapid changes in the market, and a high obsolescence rate for new products. Under globalization, the high-tech industry has adopted Information Technology (IT) to shorten the manufacturing process, reduce costs and conduct product research and development (R&D) to increase the core competence of enterprises and achieve the goal of sustainable operations. Enterprises should actively strengthen their integration with internal and external resources and lead in R&D management to increase industrial operating performance. Effectively managing operations and R&D management evaluation in Taiwan's High-tech Industry has become a critical subject. This study adopted 4 major Balanced Scorecard (BSC) perspectives to establish the Total Performance Evaluation Indicators for the R&D management department in Taiwan's High-tech Industry. The Analytic Network Process (ANP) was applied to evaluate the overall performance of the R&D management department. The research framework is divided into 2 phases. The first phase is combined with the 4 major perspectives, Financial, Customer, Internal Business Process and Learning and Growth, as the related indicators for each measurement perspective. The Key Performance Indicators (KPI) were selected using Factor Analysis to identify the key factor from the complicated indicators. The relationship between the characteristics of each BSC's evaluation perspective is dependence and feedback. This study applied ANP to conduct the calculation and adjustment of correlation between each KPI, and determine on their relative weights for the objective KPI. The "Financial Perspective" for R&D management department in Taiwan's High-tech Industry focused on the budget achievement rate of R&D management. The weight indicator value is (0.05863). The "Customer Perspective" focused on problem-solving satisfaction. The weight value of this indicator is (0.17549). The "Internal Business Process Perspective" focused on the quantity and quality of R&D. The weight value of this indicator is (0.13506). The "Learning and Growth Perspective" focused on improving competence in the research personnel's professional techniques. The weight value of this indicator is (0.02789). From the total weighting indicators, the order of the Performance Indicators for the R&D management department in Taiwan's High-tech Industry is: (1) Customer Perspective; (2) Internal Business Process Perspective; (3) Financial Perspective; and (4) Learning and Growth Perspective.