• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.11-19
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• 2006

In this study ship collision risk analysis is performed to determine the design vessel for collision impact analysis of suspension bridge. Method II in AASHTO LRFD bridge design specifications which is a more complicated probability based analysis procedure is used to select the design vessel for collision impact. From the assessment of ship collision risk for each bridge pier exposed to ship collision, the design impact lateral strength of bridge pier is determined. The analysis procedure is an iterative process in which a trial impact resistance is selected for a bridge component and a computed annual frequency of collapse(AF) is compared to the acceptance criterion, and revisions to the analysis variables are made as necessary to achieve compliance. The acceptance criterion is allocated to each pier using allocation weights based on the previous predictions. This AF allocation method is compared to the pylon concentration allocation method to obtain safety and economy in results. This method seems to be more reasonable than the pylon concentration allocation method because AF allocation by weights takes the design parameter characteristics quantitatively into consideration although the pylon concentration allocation method brings more economical results when the overestimated design collision strength of piers compared to the strength of pylon is moderately modified. The design vessel for each pier corresponding with the design impact lateral strength obtained from the ship collision risk assessment is then selected. The design impact lateral strength can vary greatly among the components of the same bridge, depending upon the waterway geometry, available water depth, bridge geometry, and vessel traffic characteristics. Therefore more researches on the allocation model of AF and the selection of design vessel are required.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2C
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• pp.85-93
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• 2010

The shear stiffness has become an important design parameter to understand the soil behavior. In particular, the elastic moduli and void ratio has been considered as important parameters for the design of the geotechnical structures. The objective of this paper is the development of the penetration type Field Velocity and Resistivity Probe (FVRP) which is able to assess the elastic moduli and void ratio based on the elastic wave velocities and electrical resistivity. The elastic waves including the compressional and shear wave are measured by piezo disk elements and bender elements. And the electrical resistivity is measured by the resistivity probe, which is manufactured and installed at the tip of the FVRP. The penetration tests are carried out in calibration chamber and field. In the laboratory calibration chamber test, after the sand-clay slurry mixtures are prepared and consolidated. The FVRP is progressively penetrated and the data are measured at each 1 cm. The field experiment is also carried out in the southern part of Korea Peninsular. Data gathering is performed in the depth of 6~20 m at each 10 cm. The elastic moduli and void ratio are estimated based on the analytical and empirical solutions by using the elastic wave velocities and electrical resistivity measured in the chamber and field. The void ratios based on the elastic wave velocities and the electrical resistivity are similar to the volume based void ratio. This study suggests that the FVRP, which evaluates the elastic wave velocities and the electrical resistivity, may be a useful instrument for assessing the elastic moduli and void ratio in soft soils.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.219-231
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• 2010

The Reference Evapotranspiration Calculator Software (REF-ET) supports computational guidelines for the reference evapotranspiration using seventeen FAO Penman-Monteith (PM) equations simultaneously such as the ASCE and FAO standardized forms. The REF-ET can conveniently consider missing data predictions and regional site characterizations, when reference ET is computed on monthly, daily, and hourly time steps. The applicability of the REF-ET was estimated to simulate the reference ET using hourly weather data from Seoul weather station for 29 years. The result found that the FAO24-Rd and 1957-Makk equations closely concerned with solar radiation parameter which were the most highly correlated to reference ET computed by pan coefficient. In addition, the 1957-Makk equation was identified as the most correct computational method for reference ET by analysis of bias and root mean square error. The 1957-Makk equation could predict the reference ET within the error of less than 1.06 mm/day, though all the other equations tended toward overestimation of predicting the reference ET in comparison with refecence ET of pan. The results of this study suggest that the REF-ET will be applicable to support reference ET estimation for a variety of field condition and time-scale.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3913-3923
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• 2023

Todays, medium energy resolution detectors are preferably used in radioisotope identification devices(RID) in nuclear and radioactive material categorization. However, there is still a need to develop or enhance « automated identifiers » for the useful RID algorithms. To decide whether any material is SNM or NORM, a key parameter is the better energy resolution of the detector. Although masking, shielding and gain shift/stabilization and other affecting parameters on site are also important for successful operations, the suitability of the RID algorithm is also a critical point to enhance the identification reliability while extracting the features from the spectral analysis. In this study, a RID algorithm based on Bayesian statistical method has been modified for medium energy resolution detectors and applied to the uranium gamma-ray spectra taken by a LaBr3:Ce detector. The present Bayesian RID algorithm covers up to 2000 keV energy range. It uses the peak centroids, the peak areas from the measured gamma-ray spectra. The extraction features are derived from the peak-based Bayesian classifiers to estimate a posterior probability for each isotope in the ANSI library. The program operations were tested under a MATLAB platform. The present peak based Bayesian RID algorithm was validated by using single isotopes(²⁴¹Am, ⁵⁷Co, ¹³⁷Cs, ⁵⁴Mn, ⁶⁰Co), and then applied to five standard nuclear materials(0.32-4.51% at.²³⁵U), as well as natural U- and Th-ores. The ID performance of the RID algorithm was quantified in terms of F-score for each isotope. The posterior probability is calculated to be 54.5-74.4% for ²³⁸U and 4.7-10.5% for ²³⁵U in EC-NRM171 uranium materials. For the case of the more complex gamma-ray spectra from CRMs, the total scoring (ST) method was preferred for its ID performance evaluation. It was shown that the present peak based Bayesian RID algorithm can be applied to identify ²³⁵U and ²³⁸U isotopes in LEU or natural U-Th samples if a medium energy resolution detector is was in the measurements.

• The Journal of Advanced Prosthodontics
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• v.15 no.5
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• pp.238-247
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• 2023

PURPOSE. The purpose of this study was to assess the effect of resin cement shade on the color of different novel ultratranslucent monolithic zirconia and lithium disilicate veneer materials. MATERIALS AND METHODS. For a total of 40 specimens, flat cylindrical discs with a 9-mm diameter and 0.5-mm thickness were created using CAD/CAM technology. The specimens were divided into five groups according to their material (n = 8) (e.max, Prettau, Aidite, Shofu and Dima) using A1 shade. Resin discs with the same diameter and shade as the specimens served as tooth-colored substructures. Three shades (neutral, light and warm) of resin cement try-in pastes (Variolink Esthetic LC) were used as the luting cement material. The color of each material group was measured before and after cementation using the three cement shades, and the CIE L^*a^*b^* coordinates were obtained with a spectrophotometer. Values for the translucency parameter (TP) and color change delta E (E) before (baseline) and after cementation of each specimen were determined. To compare differences among the material groups within each shade of cement and among various shades of cement within each material, the data were analyzed using one-way ANOVA and post hoc testing. RESULTS. Color coordinates L^*, a^* and b^* significantly changed after the application of try-in pastes relative to baseline values, with a noticeable decrease in lightness (L^*) (P < .05). A significant color change (ΔE) was observed in all tested materials after cementation, with ΔE values exceeding 3.3 (P < .05). Although TP changed after cementation for most materials tested, these changes were not statistically significant (P > .05). Shofu and Dima ceramics showed the lowest TP values, while Aidite and Prettau showed the highest TP values. For e.max, translucency decreased after cementation with neutral and warm shades, and it significantly increased after cementation with a light shade. CONCLUSION. The shade of cement significantly altered the final color of the ceramic veneer material to a level above the threshold at which the clinical perception of color change occurred (> 3.3). The TP was not influenced by the cement shade. The translucency levels of the novel ultratranslucent multilayer monolithic zirconia ceramics Aidite and Prettau were higher than that of the lithium disilicate e.max material.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.30-42
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• 2023

Various studies have been conducted using microtraffic simulation (VISSIM) to analyze the safety of traffic flow when introducing autonomous vehicles. However, no studies have analyzed traffic safety in mixed traffic while considering the driving behavior of general vehicles as a parameter in VISSIM. Therefore, the aim of this study was to optimize the input variables of VISSIM for non-autonomous vehicles through genetic algorithms to obtain realistic behavior. A traffic safety analysis was then performed according to the penetration rate of autonomous vehicles. In a 640 meter section of US highway I-101, the number of conflicts was analyzed when the trailing vehicle was a non-autonomous vehicle. The total number of conflicts increased until the proportion of autonomous vehicles exceeded 20%, and the number of conflicts decreased continuously after exceeding 20%. The number of conflicts between non-autonomous vehicles and autonomous vehicles increased with proportions of autonomous vehicles of up to 60%. However, there was a limitation in that the driving behavior of autonomous vehicles was based on the results of the literature and did not represent actual driving behavior. Therefore, for a more accurate analysis, future studies should reflect the actual driving behavior of autonomous vehicles.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.55-55
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• 2020

This study investigated the growth and chlorophyll fluorescence reactions of three Ardisia genus grown under various indoor light intensity conditions with the aim of evaluating their suitability as indoor plants. Young seedlings of A. crispa (Thunb.) A.DC., A. pusilla DC., and A. japonica (Thunb.) Blume were used in the experiment. The plants were cultivated indoors for 10 weeks under different light intensities: 10, 50, 100, and 200 PPFD (μmol·m^-2·s^-1), and their growth was compared with that of plants cultivated in a greenhouse during the same period (mean value 236.8±20.4 PPFD at noon). Also, chlorophyll fluorescence analysis was investigated with a portable PAM fluorometer. The indoor plants were maintained at 12/12 h photoperiod, temperature at 25±1℃, and humidity at 55±3%. Irrigation frequency (once every three days) was the same for the indoors and the greenhouse. The results of growth in three Ardisia plants showed that almost all parameters except leaf number and chlorophyll content had similar levels regardless of light intensity. A. crispa and A. pusilla plants grown in 200 PPFD were investigated to have low chlorophyll contents. Meanwhile, chlorophyll fluorescence parameters differed based on light levels. In A. crispa, the Fv/Fm (0.77), DIo/RC (0.47) and Fm/Fo (4.77) parameters tended to be poor at 200 PPFD compared to those at other light intensities. Similarly, the DIo/RC, Fm/Fo, and Pi_Abs parameters of A. pusilla plant (200 PPFD) are 0.45, 4.48 and 2.42, respectively, which can be considered stress. The analysis of fluorescence in A. japonica showed that all parameters except ETo/RC had similar levels regardless of light intensity. The ETo/RC parameter was 0.49 and 0.72 in the control plants and plants 200 PPFD, respectively, which was lower than those in plants at other light intensities. Therefore, it seems that the relatively high light intensity acted as a stressor for Ardisia plants.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.