• Journal of Korean Society of Forest Science
• /
• v.100 no.3
• /
• pp.315-326
• /
• 2011

In recent years, domestic and international interests focus on climate change, and importance of forest as carbon sink have been also increased. Particularly REDD+ mechanism expanded from REDD (Reduced Emissions from Deforestation and Degradation) is expected to perform a new mechanism for reducing greenhouse gas in post 2012. To conduct this mechanism, countries which try to get a carbon credit have to certify effectiveness of their activities by MRV (Measuring, Reporting and Verification) system. This study analyzed the approaches for detecting land cover change and estimating carbon stock by remote sensing technology which is considered as the effective method to develop MRV system. The most appropriate remote sensing for detection of land cover change is optical medium resolution sensors and satellite SAR (Synthetic Aperture Radar) according to cost efficiency and uncertainty assessment. In case of estimating carbon stock, integration of low uncertainty techniques, airborne LiDAR (Light Detection and Ranging), SAR, and cost efficient techniques, optical medium resolution sensors and satellite SAR, could be more appropriate. However, due to absence of certificate authority, guideline, and standard of uncertainty, we should pay continuously our attention on international information flow and establish appropriate methods. Moreover, to apply monitoring system to developing countries, close collaboration and monitoring method reflected characteristics of each countries should be considered.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.4
• /
• pp.483-491
• /
• 2021

The identification of regional collision risks in water areas is significant for the safety of navigation. This paper introduces a new method of collision risk assessment that incorporates a clustering method based on the distance factor - hierarchical clustering - and uses real-time data in case of several surrounding vessels, group methodology and preliminary assessment to classify vessels and evaluate the basis of collision risk evaluation (called HCAAP processing). The vessels are clustered using the hierarchical program to obtain clusters of encounter vessels and are combined with the preliminary assessment to filter relatively safe vessels. Subsequently, the distance at the closest point of approach (DCPA) and time to the closest point of approach (TCPA) between encounter vessels within each cluster are calculated to obtain the relation and comparison with the collision risk index (CRI). The mathematical relationship of CRI for each cluster of encounter vessels with DCPA and TCPA is constructed using a negative exponential function. Operators can easily evaluate the safety of all vessels navigating in the defined area using the calculated CRI. Therefore, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of life and property. To illustrate the effectiveness of the framework proposed, an experimental case study was conducted within the coastal waters of Mokpo, Korea. The results demonstrated that the framework was effective and efficient in detecting and ranking collision risk indexes between encounter vessels within each cluster, which allowed an automatic risk prioritization of encounter vessels for further investigation by operators.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.3
• /
• pp.99-108
• /
• 2021

There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.3
• /
• pp.227-237
• /
• 2022

The most critical factors for detecting changes in very high-resolution satellite images are building positional inconsistencies and relief displacements caused by satellite side-view. To resolve the above problems, additional processing using a digital elevation model and deep learning approach have been proposed. Unfortunately, these approaches are not sufficiently effective in solving these problems. This study proposed a change detection method that considers both positional and topology information of buildings. Mask R-CNN (Region-based Convolutional Neural Network) was trained on a SpaceNet building detection v2 dataset, and the central points of each building were extracted as building nodes. Then, triangulated irregular network graphs were created on building nodes from temporal images. To extract the area, where there is a structural difference between two graphs, a change index reflecting the similarity of the graphs and differences in the location of building nodes was proposed. Finally, newly changed or deleted buildings were detected by comparing the two graphs. Three pairs of test sites were selected to evaluate the proposed method's effectiveness, and the results showed that changed buildings were detected in the case of side-view satellite images with building positional inconsistencies.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.36 no.4
• /
• pp.221-232
• /
• 2023

This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.

• Geophysics and Geophysical Exploration
• /
• v.26 no.4
• /
• pp.211-228
• /
• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Phonetics and Speech Sciences
• /
• v.15 no.4
• /
• pp.71-80
• /
• 2023

This study primarily aimed to develop an automated stuttering identification and classification method using artificial intelligence technology. In particular, this study aimed to develop a deep learning-based identification model utilizing the convolutional neural networks (CNNs) algorithm for Korean speakers who stutter. To this aim, speech data were collected from 9 adults who stutter and 9 normally-fluent speakers. The data were automatically segmented at the phrasal level using Google Cloud speech-to-text (STT), and labels such as 'fluent', 'blockage', prolongation', and 'repetition' were assigned to them. Mel frequency cepstral coefficients (MFCCs) and the CNN-based classifier were also used for detecting and classifying each type of the stuttered disfluency. However, in the case of prolongation, five results were found and, therefore, excluded from the classifier model. Results showed that the accuracy of the CNN classifier was 0.96, and the F1-score for classification performance was as follows: 'fluent' 1.00, 'blockage' 0.67, and 'repetition' 0.74. Although the effectiveness of the automatic classification identifier was validated using CNNs to detect the stuttered disfluencies, the performance was found to be inadequate especially for the blockage and prolongation types. Consequently, the establishment of a big speech database for collecting data based on the types of stuttered disfluencies was identified as a necessary foundation for improving classification performance.

• Food Science and Preservation
• /
• v.30 no.2
• /
• pp.262-271
• /
• 2023

The objective of this study is to compare and assess the effectiveness of real-time polymerase chain reaction (RT-PCR), loop-mediated isothermal amplification (LAMP), and the selective agar plate method for the detection of Salmonella spp. and Listeria monocytogenes in ready-to-eat (RTE) foods. In RTE foods, the detection performance of the three methods (RT-PCR [SureTect^TM kit and PowerChek^TM kit], LAMP [3M MDS], selective agar) were similar at 0-10, 10-50, 50-100, and 100- CFU/mL of Salmonella spp. and L. monocytogenes. We found that with RT-PCR, the Ct value of salad was significantly higher (p<0.05) than other RTE foods, indicating that fiber plays a critical role as an obstacle to the rapid detection of Salmonella spp. However, the Ct value displayed a mixed pattern according to the inoculation level of L. monocytogenes. The use of rapid detection kits and machines mostly depends on the user's choice, with accuracy, ease of use, and economy being the primary considerations. As an RT-PCR kit, SureTect^TM and PowerChek^TM showed high accuracy in detecting Salmonella spp. and L. monocytogenes in RTE foods, showing that they can replace the existing RT-PCR kits available. Additionally, LAMP also showed excellent detection performance, suggesting that it has the potential to be used as a food safety management tool.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.2
• /
• pp.225-233
• /
• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• Food Science of Animal Resources
• /
• v.29 no.4
• /
• pp.506-512
• /
• 2009

Salmonellosis is an important worldwide foodborne infectious disease that is transmitted by many food vehicles including raw and processed animal products and fresh produce. In this study, the effectiveness of automated ELISA ($VIDAS^{(R)}$) and realtime PCR in the detection of Salmonella spp. in steamed pork and raw broccoli sprouts was evaluated by comparing their results with those of a conventional culture method. Bulk samples (500 g) of steamed pork and raw broccoli sprouts were inoculated with various levels of Salmonella and divided into 20 samples (25 g each). All the samples, including the controls, were analyzed using a conventional culture method, $VIDAS^{(R)}$, and real-time PCR to detect the presence of Salmonella. In addition, the levels of background flora in the steamed pork and the raw broccoli sprouts were determined. In the steamed pork that contained less than 100 CFU/g of aerobic bacteria, all three methods detected low levels of Salmonella without a statistical difference in their performance. In the broccoli sprouts with high quantities of background flora (ca. $6.7{\times}10^7$ CFU/g), however, all three methods were unable to detect low levels of Salmonella, and real-time PCR and $VIDAS^{(R)}$ more sensitively detected Salmonella than the culture method, with significant statistical differences. In conclusion, $VIDAS^{(R)}$ and real-time PCR could be superior to conventional culture methods in detecting Salmonella in food with high levels of background flora.