• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.342-348
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• 2016

We propose an obstacle detection algorithm for unmanned ground vehicle on uneven terrain. The key ideas of the proposed algorithm are the use of two-layer laser range data to calculate the gradient of a target, which is characterized as either ground or obstacles. The proposed obstacle detection algorithm includes 4-steps: 1) Obtain the distance data for each angle from multiple lidars or a multi-layer scan lidar. 2) Calcualate the gradient for each angle of the uneven terrain. 3) Determine ground or obstacle for each angle on the basis of reference gradient. 4) Generate a new distance data for each angle for a virtual laser scanner. The proposed algorithm is verified by various experiments.

• The Plant Pathology Journal
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• v.36 no.6
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• pp.651-657
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• 2020

Lotus is one of the most important aquatic vegetables in China. Previously, we detected sweet potato latent virus from lotus (SPLV-lotus) and found that it has highly significant sequence diversity with SPLV-sweet potato isolates (SPLV-sp). Here, we developed serological methods for the detection of SPLV-lotus in Chinese lotus cultivation areas. Based on the high sensitivity of SPLV-lotus coat protein antiserum, rapid, sensitive and large-scale diagnosis methods of enzyme-linked immunosorbent assay (ELISA) and dot blot in lotus planting area were developed. The established ELISA and dot blot diagnostic methods can be used to detect SPLV-lotus from samples successfully. And our results also showed that the SPLV-lotus and sweet potato isolates appeared clearly distinction in serology. Our study provides a high-throughput, sensitive, and rapid diagnostic method based on serology that can detect SPLV on lotus, which is suggested to be included in viral disease management approach due to its good detection level.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.78-81
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• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.155-162
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• 2022

Over the last 10 years, there has been rapid growth in the use of Machine Learning (ML) techniques to automate the process of intrusion threat detection at a scale never imagined before. This has prompted researchers, software engineers, and network specialists to rethink the applications of machine ML techniques particularly in the area of cybersecurity. As a result there exists numerous research documentations on the use ML techniques to detect and block cyber-attacks. This article is a systematic review involving the identification of published scholarly articles as found on IEEE Explore and Scopus databases. The articles exclusively related to the use of machine learning in Intrusion Detection Systems (IDS). Methods, concepts, results, and conclusions as found in the texts are analyzed. A description on the process taken in the identification of the research articles included: First, an introduction to the topic which is followed by a methodology section. A table is used to list identified research articles in the form of title, authors, methodology, and key findings.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Clinical Endoscopy
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• v.55 no.3
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• pp.332-338
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• 2022

Continuous measurement of quality indicators (QIs) should be a routine part of colonoscopy, as a wide variation still exists in the performance and quality levels of colonoscopy in Korea. Among the many QIs of colonoscopy, the adenoma detection rate, average withdrawal time, bowel preparation adequacy, and cecal intubation rate should be monitored in daily clinical practice to improve the quality of the procedure. The adenoma detection rate is the best indicator of the quality of colonoscopy; however, it has many limitations for universal use in daily practice. With the development of natural language processing, the adenoma detection rate is expected to become more effective and useful. It is important that colonoscopists do not strictly and mechanically maintain an average withdrawal time of 6 minutes but instead perform careful colonoscopy to maximally expose the colonic mucosa with a withdrawal time of at least 6 minutes. To achieve adequate bowel preparation, documentation of bowel preparation with the Boston Bowel Preparation Scale (BBPS) should be a routine part of colonoscopy. When colonoscopists routinely followed the bowel preparation protocols, ≥85% of outpatient screening colonoscopies had a BBPS score of ≥6. In addition, the cecal intubation rate should be ≥95% of all screening colonoscopies. The first step in improving colonoscopy quality in Korea is to apply these key performance measurements in clinical practice.

• Advances in concrete construction
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• v.17 no.2
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• pp.53-66
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• 2024

Domain Name Systems (DNS) provide critical performance in directing Internet traffic. It is a significant duty of DNS service providers to protect DNS servers from bandwidth attacks. Data mining techniques may identify different trends in detecting anomalies, but these approaches are insufficient to provide adequate methods for querying traffic data in significant network environments. The patterns can enable the providers of DNS services to find anomalies. Accordingly, this research has used a new approach to find the anomalies using the Neural Network (NN) because intrusion detection techniques or conventional rule-based anomaly are insufficient to detect general DNS anomalies using multi-enterprise network traffic data obtained from network traffic data (from different organizations). NN was developed, and its results were measured to determine the best performance in anomaly detection using DNS query data. Going through the R² results, it was found that NN could satisfactorily perform the DNS anomaly detection process. Based on the results, the security weaknesses and problems related to unpredictable matters could be practically distinguished, and many could be avoided in advance. Based on the R² results, the NN could perform remarkably well in general DNS anomaly detection processing in this study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.237-243
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• 2017

In this paper, the detection performance of the electro-optical telescopes which observes and surveils space objects including artificial satellites, is analyzed. To perform the Modeling & Simulation(M&S) based analysis, satellite orbit model, telescope model, and the atmospheric model are constructed and a detection scenario observing the satellite is organized. Based on the organized scenario, pointing accuracy is analyzed according to the Field of View(FOV), which is one of the key factors of the telescope, considering pointing angle command error. In accordance with the preceding result, detection possibility according to the pixel-count of the detector and the FOV of the telescope is analyzed by discerning detection by Signal-to-Noise Ratio(SNR). The result shows that pointing accuracy increases with larger FOV, whereas the detection probability increases with smaller FOV and higher pixel-count. Therefore, major specification of the telescope such as FOV and pixel-count should be determined considering the result of M&S based analysis performed in this paper and the operational circumstances.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.158-164
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• 2015

Lane detection is one of the key parts among autonomous vehicle technologies because lane keeping and path planning are based on lane detection. Camera is used for lane detection but there are severe limitations such as narrow field of view and effect of illumination. On the other hands, Lidar sensor has the merits of having large field of view and being little influenced by illumination because it uses intensity information. Existing researches that use methods such as Hough transform, histogram hardly handle multiple lanes in the co-occuring situation of lanes and road marking. In this paper, we propose a method based on RANSAC and regularization which provides a stable and precise detection result in the co-occuring situation of lanes and road marking in highway scenarios. This is performed by precise lane point extraction using circular model RANSAC and regularization aided least square fitting. Through quantitative evaluation, we verify that the proposed algorithm is capable of multi lane detection with high accuracy in real-time on our own acquired road data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8A
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• pp.639-647
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• 2012

Vision-based on-road vehicle detection is one of the key techniques in automotive driver assistance systems. However, due to the huge within-class variability in vehicle appearance and environmental changes, it remains a challenging task to develop an accurate and reliable detection system. In general, a vehicle detection system consists of two steps. The candidate locations of vehicles are found in the Hypothesis Generation (HG) step, and the detected locations in the HG step are verified in the Hypothesis Verification (HV) step. Since the final decision is made in the HV step, the HV step is crucial for accurate detection. In this paper, we propose using a reduced multivariate polynomial pattern classifier (RM) for the HV step. Our experimental results show that the RM classifier outperforms the well-known Support Vector Machine (SVM) classifier, particularly in terms of the fast decision speed, which is suitable for real-time implementation.