• Restorative Dentistry and Endodontics
• /
• v.40 no.1
• /
• pp.58-67
• /
• 2015

Objectives: This study aimed to compare the accuracy of conventional intraoral (CI) radiography, photostimulable phosphor (PSP) radiography, cone beam computed tomography (CBCT) and multidetector computed tomography (MDCT) for detection of strip and root perforations in endodontically treated teeth. Materials and Methods: Mesial and distal roots of 72 recently extracted molar were endodontically prepared. Perforations were created in 0.2, 0.3, or 0.4 mm diameter around the furcation of 48 roots (strip perforation) and at the external surface of 48 roots (root perforation); 48 roots were not perforated (control group). After root obturation, intraoral radiography, CBCT and MDCT were taken. Discontinuity in the root structure was interpreted as perforation. Two observers examined the images. Data were analyzed using Stata software and Chi-square test. Results: The sensitivity and specificity of CI, PSP, CBCT and MDCT in detection of strip perforations were 81.25% and 93.75%, 85.42% and 91.67%, 97.92% and 85.42%, and 72.92% and 87.50%, respectively. For diagnosis of root perforation, the sensitivity and specificity were 87.50% and 93.75%, 89.58% and 91.67%, 97.92% and 85.42%, and 81.25% and 87.50%, respectively. For detection of strip perforation, the difference between CBCT and all other methods including CI, PSP and MDCT was significant (p < 0.05). For detection of root perforation, only the difference between CBCT and MDCT was significant, and for all the other methods no statistically significant difference was observed. Conclusions: If it is not possible to diagnose the root perforations by periapical radiographs, CBCT is the best radiographic technique while MDCT is not recommended.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.160-160
• /
• 2019

Water is a limited source that needs to be properly managed and distributed to the ever-growing population of the world. Rapid urbanization and development have increased the overall water demand of the world drastically. However, there is loss of billions of liters of water every year due to leakages in water distribution systems. Such water loss means significant financial loss for the utilities as well. World bank estimates a loss of $14 billion annually from wasted water. To address these issues and for the development of efficient and reliable leakage management techniques, high efforts have been made by the researchers and engineers. Over the past decade, various techniques and technologies have been developed for leakage management and leak detection. These include ideas such as pressure management in water distribution networks, use of Advanced Metering Infrastructure, use of machine learning algorithms, etc. For leakage detection, techniques such as acoustic technique, and in recent yeats transient test-based techniques have become popular. Smart Water Grid uses two-way real time network monitoring by utilizing sensors and devices in the water distribution system. Hence, valuable real time data of the water distribution network can be collected. Best results and outcomes may be produced by proper utilization of the collected data in unison with advanced detection and management techniques. Long term reduction in Non Revenue Water can be achieved by detecting, localizing and repairing leakages as quickly and as efficiently as possible. However, there are still numerous challenges to be met and future research works to be conducted in this field.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.39 no.5
• /
• pp.631-636
• /
• 2019

Over the last decade, many researchers have investigated a number of vision-based construction object detection algorithms for the purpose of construction site monitoring. However, previous methods require the ground truth labeling, which is a process of manually marking types and locations of target objects from training image data, and thus a large amount of time and effort is being wasted. To address this drawback, this paper proposes a vision-based construction object detection framework that employs an active learning technique while reducing manual labeling efforts. For the validation, the research team performed experiments using an open construction benchmark dataset. The results showed that the method was able to successfully detect construction objects that have various visual characteristics, and also indicated that it is possible to develop the high performance of an object detection model using smaller amount of training data and less iterative training steps compared to the previous approaches. The findings of this study can be used to reduce the manual labeling processes and minimize the time and costs required to build a training database.

• Nano
• /
• v.13 no.11
• /
• pp.1850134.1-1850134.10
• /
• 2018

Increasing active sites and enhancing electric conductivity are critical factors to improve sensing performance toward phenol. Herein, Ni nanoparticle was successfully anchored on acidified multiwalled carbon nanotube (a-MWCNT) surface by electroless plating technique to avoid Ni nanoparticle agglomeration and guarantee high conductivity. The crystal structure, phase composition and surface morphology were characterized by XRD, SEM and TEM measurement. The as-prepared Ni/a-MWCNT nanohybrid was immobilized onto glassy carbon electrode (GCE) surface for constructing phenol sensor. The phenol sensing performance indicated that Ni/a-MWCNT/GCE exhibited an amazing detection performance with rapid response time of 4 s, a relatively wide detection range from 0.01 mM to 0.48 mM, a detection limit of $7.07{\mu}M$ and high sensitivity of $566.2{\mu}A\;mM^{-1}\;cm^{-2}$. The superior selectivity, reproducibility, stability and applicability in real sample of Ni/a-MWCNT/GCE endowed it with potential application in discharged wastewater.

• Geophysics and Geophysical Exploration
• /
• v.22 no.2
• /
• pp.72-79
• /
• 2019

Underwater object detection method adopting electrical resistivity technique was proposed recently, and the need of advanced data processing algorithm development counteracting various marine environmental conditions was required. In this paper, we present an improved water tank experiment system and its operation results, which can provide efficient test and verification. The main features of the system are as follows: 1) All the processes enabling real time process for not only simultaneous gathering of object images but also the electrical field measurement and visualization are carried out at 5 Hz refresh rates. 2) Data acquisition and processing for two detection lines are performed in real time to distinguish the moving direction of a target object. 3) Playback and retest functions for the saved data are equipped. 4) Through the monitoring screen, the movement of the target object and the measurement status of two detection lines can be intuitively identified. We confirmed that the enhanced physical modeling system works properly and facilitates efficient experiments.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.1
• /
• pp.73-83
• /
• 2019

In this paper, we propose the augmented reality(AR) based driving navigation based on robust lane detection method to dynamic environment changes. The proposed technique uses the detected lane position as a marker which is a key element for enhancing driving information. We propose Symmetrical Local Threshold(SLT) algorithm which is able to robustly detect lane to dynamic illumination environment change such as shadows. In addition, by using Morphology operation and Connected Component Analysis(CCA) algorithm, it is possible to minimize noises in the image, and Region Of Interest(ROI) is defined through region division using a straight line passing through several vanishing points We also propose the augmented reality aided visualization method for Interchange(IC) and driving navigation using reference point detection based on the detected lane coordinates inside and outside the ROI. Validation experiments were carried out to assess the accuracy and robustness of the proposed system in vairous environment changes. The average accuracy of the proposed system in daytime, nighttime, rainy day, and cloudy day is 79.3% on 4600 images. The results of the proposed system for AR based IC and driving navigation were also presented. We are hopeful that the proposed research will open a new discussion on AR based driving navigation platforms, and thus, that such efforts will enrich the autonomous vehicle services in the near future.

• Asian-Australasian Journal of Animal Sciences
• /
• v.32 no.6
• /
• pp.891-895
• /
• 2019

Objective: Aim of present study was the set up of a fast and reliable protocol using species-specific markers for the quali-quantitative analysis of DNA and the detection of ruminant biological components in dairy products. For this purpose, the promoter of the gene coding for the ${\alpha}$-lactoalbumin (LALBA) was chosen as possible candidate for the presence of short interspersed nuclear elements (SINEs). Methods: DNA was isolated from somatic cells of 120 individual milk samples of cattle (30), Mediterranean river buffalo (30), goat (30), and sheep (30) and the gene promoter region (about 600/700 bp) of LALBA (from about 600 bp upstream of exon 1) has been sequenced. For the development of a single polymerase chain reaction (PCR) protocol that allows the simultaneous identification of DNA from the four species of ruminants, the following internal primers pair were used: 5'-CACTGATCTTAAAGCTCAGGTT-3' (forward) and 5'-TCAGA GTAGGCCACAGAAG-3' (reverse). Results: Sequencing results of LALBA gene promoter region confirmed the presence of SINEs as monomorphic "within" and variable in size "among" the selected species. Amplicon lengths were 582 bp in cattle, 592 bp in buffalo, 655 in goat and 729 bp in sheep. PCR specificity was demonstrated by the detection of trace amounts of species-specific DNA from mixed sources ($0.25ng/{\mu}L$). Conclusion: We developed a rapid PCR protocol for the quali-quantitative analysis of DNA and the traceability of dairy products using a species-specific marker with only one pair of primers. Our results validate the proposed technique as a suitable tool for a simple and inexpensive (economic) detection of animal origin components in foodstuffs.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.39 no.3
• /
• pp.399-407
• /
• 2019

In Korea, the construction industry has been known to have the highest risk of safety accidents compared to other industries. Therefore, in order to improve safety in the construction industry, several researches have been carried out from the past. This study aims at improving safety of labors in construction site by constructing an effective automatic safety helmet detection system using object detection algorithm based on image data of construction field. Deep learning was conducted using Region-based Fully Convolutional Network (R-FCN) which is one of the object detection algorithms based on Convolutional Neural Network (CNN) with Transfer Learning technique. Learning was conducted with 1089 images including human and safety helmet collected from ImageNet and the mean Average Precision (mAP) of the human and the safety helmet was measured as 0.86 and 0.83, respectively.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.4
• /
• pp.374-381
• /
• 2021

Raman spectrometers are studied and developed for the military purposes because of their nondestructive inspection capability to capture unique spectral features induced by molecular structures of colorless and odorless chemical warfare agents(CWAs) in any phase. Raman spectrometers often suffer from random noise caused by their detector inherent noise, background signal, etc. Thus, reducing the random noise in a measured Raman spectrum can help detection algorithms to find spectral features of CWAs and effectively detect them. In this paper, we propose a denoising autoencoder for Raman spectra with a loss function for sample efficient learning using noisy dataset. We conduct experiments to compare its effect on the measured spectra and detection performance with several existing noise reduction algorithms. The experimental results show that the denoising autoencoder is the most effective noise reduction algorithm among existing noise reduction algorithms for Raman spectrum based standoff detection of CWAs.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.16 no.3
• /
• pp.17-28
• /
• 2006

The appearance of variant malicious codes using obfuscation techniques is accelerating the spread of malicious codes around the detection by a vaccine. n a system does not patch detection patterns for vulnerabilities and worms to the vaccine, it can be infected by the worms and malicious codes can be spreaded rapidly to other systems and networks in a few minute. Moreover, It is limited to the conventional pattern based detection and treatment for variants or new malicious codes. In this paper, we propose a method of behavior based detection by the static analysis, the dynamic analysis and the dynamic monitoring to detect a malicious code using obfuscation techniques with the PE compression. Also we show that dynamic monitoring can detect worms with the PE compression which accesses to important resources such as a registry, a cpu, a memory and files with the proposed method for similarity.