• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제39권6호
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• pp.599-607
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• 2021

Street-view images, which are omnidirectional scenes centered on a specific location on the road, can provide various obstacle information for the pedestrians. Pedestrian network data for the navigation services should reflect the up-to-date obstacle information to ensure the mobility of pedestrians, including people with disabilities. In this study, the object detection model was trained for the bollard as a major obstacle in Seoul using street-view images and a deep learning algorithm. Also, a process for updating information about the presence and number of bollards as obstacle properties for the crosswalk node through spatial matching between the detected bollards and the pedestrian nodes was proposed. The missing crosswalk information can also be updated concurrently by the proposed process. The proposed approach is appropriate for crowdsourcing data as the model trained using the street-view images can be applied to photos taken with a smartphone while walking. Through additional training with various obstacles captured in the street-view images, it is expected to enable efficient information update about obstacles on the road.

• Journal of Convergence for Information Technology
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• 제11권5호
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• pp.1-8
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• 2021

In this paper, we propose the construction of an IOT that monitors foresters' physical abnormalities in real time, performs emergency measures, and provides alarms for natural disasters or heatstroke such as a nearby forest fire or landslide. Nodes provided to foresters include 6-axis sensors, temperature sensors, GPS, and LoRa, and transmit the measured data to the network server through the gateway using LoRa communication. The network server uses 6-axis sensor data to determine whether or not a forester has any signs of abnormal body, and performs emergency measures by tracking GPS location. After analyzing the temperature data, it provides an alarm when there is a possibility of heat stroke or when a forest fire or landslide occurs in the vicinity. In this paper, it was confirmed that the real-time detection of physical abnormalities of foresters and the establishment of disaster early warning IOT is possible by analyzing the data obtained by constructing a node and a gateway and constructing a network server.

• Smart Structures and Systems
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• 제30권6호
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• Journal of the Korean Society of Clothing and Textiles
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• 제46권5호
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• pp.760-771
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• 2022

This study sought to develop clothing for a companion animal that can provide stable ECG measurements. A pattern for the smart clothing of a companion dog was manufactured using the replica method to select a location and method that best suited the stable measurement of ECG and improved the clothing's fitness. The smart clothing was developed as the following three types: strap type, top type, and combined top and vest type with a detachable wireless ECG monitor. The detection abilities of these were observed using the PQRST rate taken after ECG measurements while the three companion dogs were tested while resting and moving. The results revealed that apart from using an electrode, applying a gel pad is the most effective way to achieve stable ECG measurements, and the central chest region is more reliable than the left armpit for providing steady readings. The combined top and vest type showed the highest average ECG PQRST detection number, meaning that the ECG signal measurement was steady. These results may contribute to the measurement of ECG in smartwear for U-Healthcare to measure other biometric data of a companion dog.

• Journal of the Korea Society of Computer and Information
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• 제27권9호
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• pp.33-40
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• 2022

In this paper, we propose a system that analyzes drone photographic images of panel-type factory roofs and conducts abnormal detection of bolts. Currently, inspectors directly climb onto the roof to carry out the inspection. However, safety accidents caused by working conditions at high places are continuously occurring, and new alternatives are needed. In response, the results of drone photography, which has recently emerged as an alternative to the dangerous environment inspection plan, will be easily inspected by finding the location of abnormal bolts using deep learning. The system proposed in this study proceeds with scanning the captured drone image using a sample image for the situation where the bolt cap is released. Furthermore, the scanned position is discriminated by using AI, and the presence/absence of the bolt abnormality is accurately discriminated. The AI used in this study showed 99% accuracy in test results based on VGGNet.

• Imaging Science in Dentistry
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• 제53권3호
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• pp.177-191
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• 2023

Purpose: This systematic review examined the detection of the posterior superior alveolar artery, along with various anatomic characteristics, on cone-beam computed tomography images. Materials and Methods: Studies were identified electronically through the Web of Science, MEDLINE, Scopus, and Embase databases. The quality of the included studies was evaluated using a 5-item binary scale. The detection rate, location, and classified diameter of the posterior superior alveolar artery were estimated as prevalence values. The diameter of this artery, as well as the distances from the artery to the alveolar crest and sinus floor, were estimated as means with associated 95% confidence intervals. Results: Thirty-seven studies were enrolled, with 34 of these included in the meta-analysis. The mean detection rate was 79% (range: 72%-84%), and the mean diameter was 1.06±0.05 mm (range: 0.96-1.16 mm). The posterior superior alveolar artery was located intraosseously in 64% of cases. The mean distance of the artery from the alveolar crest was 16.71±0.49 mm (range: 15.75-17.68 mm), while the mean distance from the artery to the sinus floor was 8.85±0.4 mm (range: 8.05-9.64 mm). Conclusion: According to the findings of this meta-analysis regarding various anatomic characteristics of the posterior superior alveolar artery, severe hemorrhage after damage to this artery during sinus augmentation procedures is not a substantial clinical problem.

• Smart Media Journal
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• 제11권11호
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• pp.92-98
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• 2022

Identifying defects in textiles is a key procedure for quality control. This study attempted to create a model that detects defects by analyzing the images of the fabrics. The models used in the study were deep learning-based VGGNet and ResNet, and the defect detection performance of the two models was compared and evaluated. The accuracy of the VGGNet and the ResNet model was 0.859 and 0.893, respectively, which showed the higher accuracy of the ResNet. In addition, the region of attention of the model was derived by using the Grad-CAM algorithm, an eXplainable Artificial Intelligence (XAI) technique, to find out the location of the region that the deep learning model recognized as a defect in the fabric image. As a result, it was confirmed that the region recognized by the deep learning model as a defect in the fabric was actually defective even with the naked eyes. The results of this study are expected to reduce the time and cost incurred in the fabric production process by utilizing deep learning-based artificial intelligence in the defect detection of the textile industry.

• Journal of Korea Water Resources Association
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• 제56권5호
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• pp.347-356
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• 2023

In Water Distribution Systems (WDSs), can abnormal hydraulic and water quality conditions such as red-water phenomenon and leakage occur. To restore them, data is generated through various meters data to predict and detect. However, in the case of leakage if difficult to detect unless direct exploration is performed. Among them, unreported leakage, are not seen visually and account for the most considerable volumes of leakage, which leads to economic loss. Bur direct exploration is limited through on site conditions such as securing professional manpower. In this paper, leakage volumes and location were randomly generated for the WDS, which was assumed to be calibrated, and it was detected through a deep learning model. For abnormal data generation, the leakage was simulated using the emitter coefficient, and leakage detection was successfully performed through the generated abnormal data and normal data.

• Journal of the Korea institute for structural maintenance and inspection
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• 제10권2호
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• pp.153-165
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• 2006

A damage in a structure changes its dynamic characteristics such as natural frequencies, damping ratios, and the mode shapes. In this paper the effort has been spent in obtaining the characteristics of the reduction ratio in natural frequencies and the damage detection is performed using the reduction ratios. Most of the emphasis has been on using the artificial neural network to determine the location and the extent of the damage as well as the existence of the damage. The data for learning and verifying neural network were obtained from the analytical analysis. The data have no errors. Considering the real measurements the data including errors which are difference this study between other studies also were used for neural network. The position and extent of the damage can be detected using the neural network trained by reduction ratios of natural frequencies.

• KIPS Transactions on Software and Data Engineering
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• 제10권7호
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.