• Journal of KIBIM
• /
• v.12 no.1
• /
• pp.42-54
• /
• 2022

BIM facilitates data transparency and consistency through three-dimensional parametric modeling and promotes the accurate managing and sharing of project information. In Korea, however, BIM-based detailed design of architectural interior finishes required during the Construction Documents phase increases the burden on architectural firms due to frequent design changes and manual workload. Therefore, the purpose of this study was to establish rule-based modules using parametric modeling that automates repetitive tasks that occur during the detailed design of interior finishing. Interviews with practitioners were conducted to analyze the major finishing elements. Of these floors, walls, and ceilings, which were the most rudimentary and common items, were selected as the subjects of the study. The modules developed in this study have two functions. One is to create new finish types, and the other is the automatic modeling of new types into rooms. For these functions, parameters that belonged to each finish and room element in a BIM model were analyzed and valid parameters directly used for parametric modeling were derived. Then, based on these parameters, rule-based modules for three elements, I.e., floors, walls, and ceilings were constructed with Revit Dynamo, and the effectiveness of the modules was verified with a pilot test. In conclusion, this study suggested a series of processes for automatic finishing to improve the efficiency of BIM-based architectural detailed design of finishes and to contribute in solving the chronic problems occuring during current design processes.

• Journal of Radiopharmaceuticals and Molecular Probes
• /
• v.8 no.2
• /
• pp.95-101
• /
• 2022

Microfluidic radioimmunoassay (RIA) platform called µ-RIA spends less reagent and shorter reaction time for the analysis compared to the conventional tube-based radioimmunoassay. This study reported the design of µ-RIA chips optimized for the gamma counter which could measure the small samples of radioactive materials automatically. Compared with the previous study, the µ-RIA chips developed in this study were designed to be compatible with conventional RIA test tubes. And, the automatic gamma counter could detect radioactivity from the ¹²⁵I labeled anti-PSA attached to the chips. Effects of the multi-layer microchannels and two-phase flow in the µ-RIA chips were investigated in this study. The measured radioactivity from the ¹²⁵I labeled anti-PSA was linearly proportional to the number of stacked chips, representing that the radioactivity in µ-RIA platform could be amplified by designing the chips with multi-layers. In addition, we designed µ-RIA chip to generate liquid-gas plug flow inside the microfluidic channel. The plug flow can promote binding of the biomolecules onto the microfluidic channel surface with recirculation in the liquid phase. The ratio of liquid slug and air slug length was 1 : 1 when the ¹²⁵I labeled anti-PSA and the air were injected at 1 and 35 µL/min, respectively, exhibiting 1.6 times higher biomolecule attachment compared to the microfluidic chip without the air injection. This experimental result indicated that the biomolecular reaction was improved by generating liquid-gas slugs inside the microfluidic channel. In this study, we presented a novel µ-RIA chips that is compatible with the conventional gamma counter with automated sampler. Therefore, high-throughput radioimmunoassay can be carried out by the automatic measurement of radioactivity with reduced radiowaste generation. We expect the µ-RIA platform can successfully replace conventional tube-based radioimmunoassay in the future.

• Korean Journal of Agricultural Science
• /
• v.48 no.4
• /
• pp.703-718
• /
• 2021

An irrigation monitoring system is an efficient approach to save water and to provide effective irrigation scheduling for rice cultivation in desert soils. This research aimed to design, fabricate, and evaluate the basic performance of an irrigation monitoring system based on information and communication technology (ICT) for rice cultivation under drip and micro-sprinkler irrigation in desert soils using a Raspberry Pi. A data acquisition system was installed and tested inside a rice cultivating net house at the United Arab Emirates University, Al-Foah, Al-Ain. The Raspberry Pi operating system was used to control the irrigation and to monitor the soil water content, ambient temperature, humidity, and light intensity inside the net house. Soil water content sensors were placed in the desert soil at depths of 10, 20, 30, 40, and 50 cm. A sensor-based automatic irrigation logic circuit was used to control the actuators and to manage the crop irrigation operations depending on the soil water content requirements. A developed webserver was used to store the sensor data and update the actuator status by communicating via the Pi-embedded Wi-Fi network. The maximum and minimum average soil water contents, ambient temperatures, humidity levels, and light intensity values were monitored as 33.91 ± 2 to 26.95 ± 1%, 45 ± 3 to 24 ± 3℃, 58 ± 2 to 50 ± 4%, and 7160-90 lx, respectively, during the experimental period. The ICT-based monitoring system ensured precise irrigation scheduling and better performance to provide an adequate water supply and information about the ambient environment.

• Korean Journal of Culture and Social Issue
• /
• v.24 no.3
• /
• pp.451-472
• /
• 2018

The purpose of this study was to examine implicit associations of negative emotion (i.e. anxiety and depression) and self among a college students having experienced posttraumatic stress symptoms. The participants were 61 college students(male 16, female 45). They were classified into two groups, trauma group(n=35) and control group(n=26) according to scores of Korean version of Impact of Events Scale-Revised. Two groups were compared with regard to automatic self-anxious and self-depressive associations measured with the Implicit Association Test using both words and facial expression pictures, respectively. As results, trauma group showed more enhanced self-anxious association in the words conditions, and stronger self-anxious and self-depressive associations in the pictures conditions than control group, whereas there were no significant differences between two groups in explicit cognition and depression. These results suggest that traumatic experiences could influence self-concepts in the automatic process. Limitations of the current study and suggestions for future research were discussed.

• Archives of Plastic Surgery
• /
• v.51 no.1
• /
• pp.30-35
• /
• 2024

Background Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices. Methods S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80-20 training-validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and centimeter-based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t-test and Bland-Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer. Results S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared with physical measurements. However, the nipple to inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively. Conclusion S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.

• Korean Journal of Radiology
• /
• v.21 no.6
• /
• pp.660-669
• /
• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• Korean Circulation Journal
• /
• v.54 no.1
• /
• pp.30-39
• /
• 2024

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions: The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.

• Structural Engineering and Mechanics
• /
• v.91 no.5
• /
• pp.443-457
• /
• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Korean Institute of Interior Design Journal
• /
• no.28
• /
• pp.44-50
• /
• 2001

This study propose the new concept of residence by analyzing the change of residence through the characteristic of popularity and degree of development of technology on home network information appliance for intelligent home. Accordingly, Cyber village represented as home automation and extend to information of society. it encourage need of information and multimedia of home. It expect home information infrastructure for accepting informations, which make smart home to linked home-working home-learning home-treatment. home-shopping and home-banking. The system of intelligent home is the intelligence of human-biology in the side of environmental friendly and multi-function. it distinguish the system of security, controlling system of inside environment, supporting system of house-working, automatic controlling, house working. Future house require to meet demand of young generation, such as small residental space, the multi-functional space, the flexible space, making mood for dual income couple and of single as intelligent home. Accordingly, basic purpose which are pleasantness, the safe and the convenience the mobile multi-function as well as networking with controlling of temperature, security, health-test, home-entertainment, home-office and consider environment together.

• MALSORI
• /
• no.50
• /
• pp.111-122
• /
• 2004

Automatic detection of laryngeal diseases by voice is attractive because of its non-intrusive nature. Cepstrum based approach to detect laryngeal cancer shows reliable performance even when the periodicity of voice signals is severely lost, but it has a drawback that it is not robust to channel mismatch due to different microphone characteristics. In this paper, to deal with mismatched training and test microphone conditions, we investigate channel compensation techniques such as Cepstral Mean Subtraction (CMS) and Pole Filtered CMS (PFCMS). According to our experiments, PFCMS yields better performance than CMS. By using PFCMS, we obtained 12% and 40% error reduction over baseline and CMS, respectively.