• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.229-239
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• 2008

Most algorithms for detecting incidents have been developed under the premise that congestion must happen whenever an incident occurs. For that reason, the performance of these algorithms could not be guaranteed in cases where congestion did not happen due to traffic operations with low flows despite the occurrence of an incident. The objective of this paper is to develop an automatic incident detection algorithm using a new diagram that can reliably detect the incident under various conditions of traffic operations including a low volume state. Compared with the McMaster Algorithm, the proposed algorithm in this paper was evaluated with three different cases in which the incidents occur in traffic operations with a low volume state, a relatively high volume state, and a recurrent congestion state. It is shown that the new algorithm has a capability to identify the flow characteristics of incidents for all the three cases and is much better than McMaster algorithm in terms of detection rate and false alarm rate.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.277.1-277.1
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• 2013

Molecular diagnostics consists of three processes, which are a sample pretreatment, a nucleic acid amplification, and an amplicon detection. Among three components, sample pretreatment is an important process in that it can increase the limit of detection by purifying nucleic acid in biological sample from contaminants that may interfere with the downstream genetic analysis such as nucleic acid amplification and detection. To achieve point-of-care virus detection system, the sample pretreatment process needs to be simple, rapid, and automatic. However, the commercial RNA extraction kits such as Rneasy (Qiagen) or MagnaPure (Roche) kit are highly labor-intensive and time-consuming due to numerous manual steps, and so it is not adequate for the on-site sample preparation. Herein, we have developed a rotary microfluidic system to extract and purify the RNA without necessity of external mechanical syringe pumps to allow flow control using microfluidic technology. We designed three reservoirs for sample, washing buffer, and elution buffer which were connected with different dimensional microfluidic channels. By controlling RPM, we could dispense a RNA sample solution, a washing buffer, and an elution buffer successively, so that the RNA was captured in the sol-gel solid phase, purified, and eluted in the downstream. Such a novel rotary sample preparation system eliminates some complicated hardwares and human intervention providing the opportunity to construct a fully integrated genetic analysis microsystem.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.449-456
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• 2019

This paper is a study on the construction of a wall-climbing mobile robot using vacuum suction and wheel-type movement, and a comparison of the performance of an automatic wall crack detection algorithm based on machine learning that is suitable for such an embedded environment. In the embedded system environment, we compared performance by applying recently developed learning methods such as YOLO for object learning, and compared performance with existing edge detection algorithms. Finally, in this study, we selected the optimal machine learning method suitable for the embedded environment and good for extracting the crack features, and compared performance with the existing methods and presented its superiority. In addition, intelligent problem - solving function that transmits the image and location information of the detected crack to the manager device is constructed.

• Smart Media Journal
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• v.12 no.10
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• pp.19-28
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• 2023

In the fruit harvesting field, interest in automatic robot harvesting is increasing due to various seasonality and rising harvesting costs. Accurate apple detection is a difficult problem in complex orchard environments with changes in light, vibrations caused by wind, and occlusion of leaves and branches. In this paper, we introduce a dataset and an adaptive heatmap regression model that are advantageous for robot automatic apple harvesting. The apple dataset was labeled with not only the apple location but also the visibility. We propose a method to detect the center point of an apple using an adaptive heatmap regression model that adjusts the Gaussian shape according to visibility. The experimental results showed that the performance of the proposed method was applicable to apple harvesting robots, with MAP@K of 0.9809 and 0.9801 when K=5 and K=10, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3C
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• pp.302-313
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• 2010

In this paper, we propose an enhanced AGC (Automatic Gain Control) structure and P-SCH detection method for initial cell search in 3GPP (3rdGenerationPartnershipProject) LTE (Long Term Evolution) FDD(Frequency Division Duplex) / TDD (Time Division Duplex) dual mode system. Since TDD frame structure consists of uplink subframe and downlink subframe, conventional AGC structure causes P-SCH detection performance degradation by increase of AGC variation due to signal power difference between uplink and downlink subframe. Also, P-SCH detection performance is degraded by distortion of P-SCH correlation characteristic in frequency offset and multipath fading channel environments. Therefore, we propose an AGC structure which can minimize P-SCH detection performance degradation with stable operation in 3GPP LTE TDD mode as well as FDD mode. Also we propose a P-SCH detection method which can reduce distortion of correlation chareteristics in frequency offset and multipath fading environments and obtain good P-SCH detection performance. Simulation results show that the proposed AGC structure and P-SCH detection method have stable AGC operation and excellent P-SCH detection performance for 3GPP LTE TDD / FDD dual mode downlink receiver in various channel environments.

• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.103-113
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• 2016

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.

• Progress in Medical Physics
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• v.15 no.2
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• pp.70-76
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• 2004

The most important work for early detection of liver cancer and decision of its characteristic and location is good segmentation of a liver region from other abdominal organs. This paper proposes a fully automatic liver segmentation algorithm based on the abdominal morphology characteristic as an easy and efficient method. Multi-modal threshold as pre-processing is peformed and a spine is segmented for finding morphological coordinates of an abdomen. Then the liver region is extracted using C-class maximum a posteriori (MAP) decision and morphological filtering. In order to estimate results of the automatic segmented liver region, area error rate (AER) and correlation coefficients of rotational binary region projection matching (RBRPM) are utilized. Experimental results showed automatic liver segmentation obtained by the proposed algorithm provided strong similarity to manual liver segmentation.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.587-595
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• 2011

In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.637-641
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• 2007

Airborne LIDAR (Light Detection and Ranging) technology has reached a degree of the required accuracy in mapping professions, and advanced LIDAR systems are becoming increasingly common in the various fields of application. LiDAR data constitute an excellent source of information for reconstructing the Earth's surface due to capability of rapid and dense 3D spatial data acquisition with high accuracy. However, organizing the LIDAR data and extracting information from the data are difficult tasks because LIDAR data are composed of randomly distributed point clouds and do not provide sufficient semantic information. The main reason for this difficulty in processing LIDAR data is that the data provide only irregularly spaced point coordinates without topological and relational information among the points. This study introduces an efficient and robust method for automatic extraction of building footprints using airborne LIDAR data. The proposed method separates ground and non-ground data based on the histogram analysis and then rearranges the building boundary points using convex hull algorithm to extract building footprints. The method was implemented to LIDAR data of the heavily built-up area. Experimental results showed the feasibility and efficiency of the proposed method for automatic producing building layers of the large scale digital maps and 3D building reconstruction.

• Fire Science and Engineering
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• v.17 no.1
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• pp.1-7
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• 2003

In general, small buildings and residence housings rely on manual fire extinguishers instead of automatic fire suppression systems, which causes bigger disasters during the absence of human-beings or in the presence of children or the olders. For this reason, simple structured and low-cost automatic fire extinguisher using fusible metal detector was newly developed and patented. In this paper, some field tests were carried out to investigate its fire suppression performance. As a result, reaction time of the detection parts varied from 2 min 19s to 7 min 20s depending upon the room size and installation position. It was suggested that to reduce reaction time within 3 minutes, fusible metals with lower melting point should be adopted and the installation location should be moved to near ceiling instead of below 1.5 m.