• Journal of KIISE
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• v.43 no.4
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• pp.404-410
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• 2016

Automated road map generation poses significant research challenges since GPS-based navigation systems prevail in most general vehicles. This paper proposes an automated detecting method for intersection points using GPS vehicle trajectory data without any background digital map information. The proposed method exploits the fact that the trajectories are generally split into several branches at an intersection point. One problem in previous work on this intersection detecting is that those approaches require stopping points and direction changes for every testing vehicle. However our approach does not require such complex auxiliary information for intersection detecting. Our method is based on partial trajectory matching among trajectories since a set of incoming trajectories split other trajectory cluster branches at the intersection point. We tested our method on a real GPS data set with 1266 vehicles in Gangnam District, Seoul. Our experiment showed that the proposed method works well at some bigger intersection points in Gangnam. Our system scored 75% sensitivity and 78% specificity according to the test data. We believe that more GPS trajectory data would make our system more reliable and applicable in a practice.

• Wind and Structures
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• v.15 no.1
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• pp.27-41
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• 2012

Wind loads on low-rise buildings in general and residential homes in particular can differ significantly depending upon the laboratory in which they were measured. The differences are due in large part to inadequate simulations of the low-frequency content of atmospheric velocity fluctuations in the laboratory and to the small scale of the models used for the measurements. The imperfect spatial coherence of the low frequency velocity fluctuations results in reductions of the overall wind effects with respect to the case of perfectly coherent flows. For large buildings those reductions are significant. However, for buildings with sufficiently small dimensions (e.g., residential homes) the reductions are relatively small. A technique is proposed for simulating the effect of low-frequency flow fluctuations on such buildings more effectively from the point of view of testing accuracy and repeatability than is currently the case. Experimental results are presented that validate the proposed technique. The technique eliminates a major cause of discrepancies among measurements conducted in different laboratories. In addition, the technique allows the use of considerably larger model scales than are possible in conventional testing. This makes it possible to model architectural details, and improves Reynolds number similarity. The technique is applicable to wind tunnels and large scale open jet facilities, and can help to standardize flow simulations for testing residential homes as well as significantly improving testing accuracy and repeatability. The work reported in this paper is a first step in developing the proposed technique. Additional tests are planned to further refine the technique and test the range of its applicability.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Structural Engineering and Mechanics
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• v.57 no.5
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• pp.951-968
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• 2016

Long-span cross-strait bridges extending into deep-sea waters are exposed to complex marine environments. During the construction stage, the flexible freestanding bridge towers are more vulnerable to environmental loads imposed by wind and wave loads. This paper presents an experimental investigation on the dynamic responses of a 389-m-high freestanding bridge tower model in a test facility with a wind tunnel and a wave flume. An elastic bridge model with a geometric scale of 1:150 was designed based on Froude similarity and was tested under wind-only, wave-only and wind-wave combined conditions. The dynamic responses obtained from the tests indicate that large deformation under resonant sea states could be a structural challenge. The dominant role of the wind loads and the wave loads change according to the sea states. The joint wind and wave loads have complex effects on the dynamic responses of the structure, depending on the approaching direction angle and the fluid-induced vibration mechanisms of the waves and wind.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.41-47
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• 2018

The buoy-enabled underwater surveillance system is a device that is installed in a particular sea area and operated for a certain period of tine and moved to another sea area after recovery. In this paper, a mooring method which is applied for a buoy-enabled underwater surveillance system was selected to maintain installation and enure stable operation. Also, the structure of the mooring line was designed. Two-point mooring method was selected considering interference with the communication cable of array-assembly. The composite structure of buoy chain, nylon rope, and anchor chain is designed as the basic component of mooring line. For the verification of design, a numerical simulation and wave tank experiment were performed. Their results were confirmed similarity in test condition. Finally, the mooring lines were designed for the environment of the sea trial location. The mooring line produced by the final design confirmed the stability above the significant wave height considered in the design on the sea trial.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• Geomechanics and Engineering
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• v.25 no.3
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• pp.179-194
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• 2021

In this paper, to study the stability of surrounding rock during roadway excavation in different rock mass structures, the physical model test for roadway excavation process in three types of intact rock mass, layered rock mass and massive rock mass were carried out by using the self-developed two-dimensional simulation testing system of complex underground engineering. Firstly, based on the engineering background of a deep mine in eastern China, the similar materials of the most appropriate ratio in line with the similarity theory were tested, compared and determined. Then, the physical models of four different schemes with 1000 mm (height) × 1000 mm (length) × 250 mm (width) were constructed. Finally, the roadway excavation was carried out after applying boundary conditions to the physical model by the simulation testing system. The results indicate that the supporting effect of rockbolts has a great influence on the shallow surrounding rock, and the rock mass structure can affect the overall stability of the surrounding rock. Furthermore, the failure mechanism and bearing capacity of surrounding rock were further discussed from the comparison of stress evolution characteristics, distribution of stress arch, and failure modes in different schemes.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.23-35
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• 2023

Korea Augmentation Satellite System (KASS) is a satellite-based augmentation system (SBAS) that provides approach procedure with vertical guidance-I (APV-I) level corrections and integrity information to Korea territory. KASS is used to monitor navigation performance in real-time, and this paper introduces the design, implementation, and verification process of mission monitoring (MIMO) in KASS. MIMO was developed in compliance with the Minimum Operational Performance Standards of the Radio Technical Commission for Aeronautics for Global Positioning System (GPS)/SBAS airborne equipment. In this study, the MIMO system was verified by comparing and analyzing the outputs of reference tools. Additionally, the definition and derivation method of accuracy, integrity, continuity, and availability subject to MIMO were examined. The internal and external interfaces and functions were then designed and implemented. The GPS data pre-processing was minimized during the implementation to evaluate the navigation performance experienced by general users. Subsequently, tests and verification methods were used to compare the obtained results based on reference tools. The test was performed using the KASS dataset, which included GPS and SBAS observations. The decoding performance of the developed MIMO was identical to that of the reference tools. Additionally, the navigation performance was verified by confirming the similarity in trends. As MIMO is a component of KASS used for real-time monitoring of the navigation performance of SBAS, the KASS operator can identify whether an abnormality exists in the navigation performance in real-time. Moreover, the preliminary identification of the abnormal point during the post-processing of data can improve operational efficiency.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• Korean Journal of Cognitive Science
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• v.26 no.2
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• pp.147-165
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• 2015

Through short-answer questions, we can reflect the depth of students' understanding and higher-order thinking skills. Scoring for short-answer questions may take long time and may be an issue on consistency of grading. To alleviate such the suffering, automated scoring systems are widely used in Europe and America, but are in the initial stage in research in Korea. In this paper, we propose a semi-automatic scoring system for short Korean free-text responses using semi-supervised learning. First of all, based on the similarity score between students' answers and model answers, the proposed system grades students' answers and the scored answers with high reliability have been included in the model answers through the thorough test. This process repeats until all answers are scored. The proposed system is used experimentally in Korean and social studies in Nationwide Scholastic Achievement Test. We have confirmed that the processing time and the consistency of grades are promisingly improved. Using the system, various assessment methods have got to be developed and comparative studies need to be performed before applying to school fields.