• Restorative Dentistry and Endodontics
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• 제44권1호
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• pp.10.1-10.11
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• 2019

Objectives: The objective of this study was to assess coronal discoloration induced by the following intracanal medicaments: calcium hydroxide (CH), a mixture of CH paste and chlorhexidine gel (CH/CHX), and triple antibiotic paste (3Mix). Materials and Methods: Seventy extracted single-canal teeth were selected. Access cavities were prepared and each canal was instrumented with a rotary ProTaper system. The specimens were randomly assigned to CH, CH/CHX, and 3Mix paste experimental groups (n = 20 each) or a control group (n = 10). Each experimental group was randomly divided into 2 subgroups (A and B). In subgroup A, medicaments were only applied to the root canals, while in subgroup B, the root canals were completely filled with medicaments and a cotton pellet dipped in medicament was also placed in the pulp chamber. Spectrophotometric readings were obtained from the mid-buccal surface of the tooth crowns immediately after placing the medicaments (T1) and at 1 week (T2), 1 month (T3), and 3 months (T4) after filling. The ${\Delta}E$ was then calculated. Data were analyzed using 2-way analysis of variance (ANOVA), 3-way ANOVA, and the $Scheff{\acute{e}}$ post hoc test. Results: The greatest color change (${\Delta}E$) was observed at 3 months (p < 0.0001) and in 3Mix subgroup B (p = 0.0057). No significant color change occurred in the CH (p = 0.7865) or CH/CHX (p = 0.1367) groups over time, but the 3Mix group showed a significant ${\Delta}E$ (p = 0.0164). Conclusion: Intracanal medicaments may induce tooth discoloration. Use of 3Mix must be short and it must be carefully applied only to the root canals; the access cavity should be thoroughly cleaned afterwards.

• 대기
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• 제31권1호
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• pp.85-100
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• 2021

The necessity of accurate high-resolution meteorological forecasts becomes increasing in socio-economical applications and disaster risk management. The Korea Meteorological Administration Post-Processing (KMAPP) system has been operated to provide high-resolution meteorological forecasts of 100 m over the South Korea region. This study evaluates and improves the KMAPP performance in simulating wind speeds over complex terrain areas using the ICE-POP 2018 field campaign measurements. The mountainous measurements give a unique opportunity to evaluate the operational wind speed forecasts over the complex terrain area. The one-month wintertime forecasts revealed that the operational Local Data Assimilation and Prediction System (LDAPS) has systematic errors over the complex mountainous area, especially in deep valley areas, due to the orographic smoothing effect. The KMAPP reproduced the orographic height variation over the complex terrain area but failed to reduce the wind speed forecast errors of the LDAPS model. It even showed unreasonable values (~0.1 m s-1) for deep valley sites due to topographic overcorrection. The model's static parameters have been revised and applied to the KMAPP-Wind system, developed newly in this study, to represent the local topographic characteristics better over the region. Besides, sensitivity tests were conducted to investigate the effects of the model's physical correction methods. The KMAPP-Wind system showed better performance in predicting near-surface wind speed during the ICE-POP period than the original KMAPP version, reducing the forecast error by 21.2%. It suggests that a realistic representation of the topographic parameters is a prerequisite for the physical downscaling of near-ground wind speed over complex terrain areas.

• 한국정보통신학회논문지
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• 제25권8호
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• pp.1013-1018
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• 2021

최근 자율주행 자동차가 일으킨 사고 때문에 인공지능의 윤리적 측면에 대한 논의가 활발히 진행되고 있다. 본 논문은 인공지능이 윤리적 요소와 필연적으로 결부되어 있음을 로봇-인공지능 윤리 관련 개념과 공학기술로부터 확인하고 윤리적 측면이 사후적으로 발생하는 것이 아니라 내장되어 있음을 논한다. 또한, 자율주행 자동차와 관련된 윤리적 문제의 실마리가 될 수 있는 트롤리 딜레마에 대한 해결방법을 고안한다. 우선적으로 베이지안 네트워크를 작성하고 전처리 과정을 거쳐 중요하고 영향력 있는 데이터만 남도록 하며, 네트워크의 정확한 수치를 계산하기 위해 크라우드 소싱과 외삽법을 이용한다. 이러한 과정을 통해 알고리즘 및 모델을 구현할 때에 인간의 주관이 필연적으로 포함될 수밖에 없음을 주장하고 인공지능 시스템에 관한 왜곡과 편향을 방지하기 위해 전공 교육과 구분되는 공학 교양 교육, 특히 윤리 교육의 필요성과 방향에 대해 논한다.

• 대기
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• 제31권5호
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• pp.489-510
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• 2021

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2131-2153
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• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• 대기
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• 제31권5호
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• pp.473-488
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• 2021

A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model's physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.

• 한국컴퓨터정보학회논문지
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• 제28권1호
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• pp.1-8
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• 2023

점점 더 복잡해지고 다양해지는 무기체계와 급격하게 변화하는 전장정보에 따라서, 인공지능을 사용한 전장 상황 분석 연구의 필요성이 대두되고 있다. 본 논문에서는 전장 상황을 분석하여 현재 상황에 적합한 가설을 추천해주는 분석결과 추천 학습모델의 학습 및 설계 방안을 제안한다. 학습 모델은 두 가설을 비교하여 결정되는 선호 여부를 레이블 데이터로 활용하여, 어떠한 가설이 현재 전장상황을 잘 분석하고 있는지 학습한다. 또한 후처리 랭킹 알고리즘을 통하여 각각의 가설에 대한 종합점수를 부여하고, 점수가 높은 상위 가설들을 지휘관에게 추천할 수 있음을 확인한다.

• 한국재난정보학회 논문집
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• 제18권4호
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• pp.786-794
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• 2022

건설현장에서는 사후적 안전관리를 벗어나 실효성 있는 예방적 안전관리가 요구되고 있다. 연구목적: 본 연구는 자율적이고 예방적 안전관리를 위한 안전관리 활동, 안전문화 핵심요소, 안전, 불안전 행동관리, 안전성과의 관계모형을 제시하고 그 관계를 분석하는 것이 목적이다. 연구방법:설문조사 데이터를 구조방정식에 적용하여 관계를 분석하고, 검증된 가설이 시사하는 부분을 해석하여 외생변수로부터 안전성과에 도달하는 경로를 탐색하고 주요 논점을 제시하였다. 연구결과: 예비모형과 경로모형을 분석한 결과, 적합한 모형적합도를 확인하였고 외생변수가 내생변수에 미치는 유의한 결과를 확인하였다. 결론: 안전관리 활동, 안전, 불안전 행동관리, 안전문화 핵심요소관리로 안전사고 발생 전에 지속적인 안전성과 향상에 효과가 있을 것으로 판단된다.

• Smart Structures and Systems
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• 제31권4호
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• Computers and Concrete
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• 제31권5호
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• pp.457-468
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• 2023

Buildings, bridges, and dams are examples of civil infrastructure that play an important role in public life. These structures are prone to structural variations over time as a result of external forces that might disrupt the operation of the structures, cause structural integrity issues, and raise safety concerns for the occupants. Therefore, monitoring the state of a structure, also known as structural health monitoring (SHM), is essential. Owing to the emergence of the fourth industrial revolution, next-generation sensors, such as wireless sensors, UAVs, and video cameras, have recently been utilized to improve the quality and efficiency of building forensics. This study presents a method that uses a target-based system to estimate the dynamic displacement and its corresponding dynamic properties of structures using UAV-based video. A laboratory experiment was performed to verify the tracking technique using a shaking table to excite an SDOF specimen and comparing the results between a laser distance sensor, accelerometer, and fixed camera. Then a field test was conducted to validate the proposed framework. One target marker is placed on the specimen, and another marker is attached to the ground, which serves as a stationary reference to account for the undesired UAV movement. The results from the UAV and stationary camera displayed a root mean square (RMS) error of 2.02% for the displacement, and after post-processing the displacement data using an OMA method, the identified natural frequency and damping ratio showed significant accuracy and similarities. The findings illustrate the capabilities and reliabilities of the methodology using UAV to evaluate the dynamic properties of structures.