• Journal of Power Electronics
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• v.16 no.1
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• pp.340-350
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• 2016

A cascaded hybrid multilevel inverter including a reconfiguration technique for low voltage dc distribution applications is proposed in this paper. A PWM generation fault detection and reconfiguration paradigm after an inverter cell fault are developed by using only a single-chip controller. The proposed PWM technique is also modified to reduce switching losses. In addition, the proposed topology can reduce the number of required power switches compared to the conventional cascaded multilevel inverter. The proposed technique is validated by using a 3-kVA prototype. The switching losses of the proposed multilevel inverter are also investigated. The experimental results show that the proposed hybrid inverter can improve system efficiency, reliability and cost effectiveness. The efficiency of proposed system is 97.45% under the tested conditions. The proposed hybrid inverter topology is a promising method for low voltage dc distribution and can be applied for the multiple loads which are required in a data center or telecommunication building.

• Journal of Navigation and Port Research
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• v.37 no.1
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• pp.41-48
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• 2013

A positioning technique using the augmentation system has been researched to improve the accuracy. The network RTK is the precise positioning technique using carrier phase correction data from reference stations and is constantly being researched. The study for the system accuracy has been performed but system integrity research has not been done as much as system accuracy. In this paper, we presented the anomaly detection algorithm by satellite system and the diagnosis algorithm to a basic research in the integrity on network RTK. And the presented algorithms are verified on the DL-V3 dual-frequency receiver and the simulated error scenario using the GSS7700.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.895-902
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• 2015

This paper presents a high-speed sensing and noise cancellation technique for large touch screens, which is called FDCS (Frequency Division Concurrent Sensing). Most conventional touch screen detection methods apply excitation pulses sequentially and analyze the sensing signals sequentially, and so are often unacceptably slow for large touch screens. The proposed technique applies sinusoidal signals of orthogonal frequencies simultaneously to all drive lines, and analyzes the signals from each sense line in frequency domain. Its parallel driving allows high speed detection even for a very large touch screens. It enhances the sensing SNR (Signal to Noise Ratio) by introducing a frequency domain noise filtering scheme. We also propose a pre-distortion equalizer, which compensates the drive signals using the inverse transfer function of touch screen panel to further enhance the sensing SNR. Experimental results with a 23" large touch screen show that the proposed technique enhances the frame scan rate by 273% and an SNR by 43dB compared with a conventional scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.43-46
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• 2015

Recently, full-duplex communications has been considered as one of the most promising techniques for net-generation mobile communication system. In this paper, we propose a compressed sensing based signal detection technique for full-duplex generalized spatial modulation (FD-GSM) systems. In FD-GSM systems, some antennas are used for signal transmission according to input data and the otehrs are used for detecting signals received over the same frequency band. The self-interference (SI) is assumed to be completely removed by help for the recently proposed SI cancellation techniques. The proposed signal detection technique significantly outperforms the conventional ones in terms of symbol error rate (SER). We will investigate the optimal number of used antennas in FD-GSM systems.

• Journal of the Korea Concrete Institute
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• v.17 no.3 s.87
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• pp.361-368
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• 2005

This study proposes an algorithm for detection and analysis of cracks in digital image of concrete surface to automate the measurement process of crack characteristics such as width, length, and orientation based on image processing technique. The special features of algorithm are as follows: (1) application of morphology technique for shading correction, (2) improvement of detection performance based on enhanced binarization and shape analysis, (3) suggestion of calculation algorithms for width, length, and orientation. A MATLAB code was developed for the proposed algorithm, and then test was performed on crack images taken with digital camera to examine validity of the algorithm. Within the limited test in the present study, the proposed algorithm was revealed as accurately detecting and analyzing the cracks when compared to results obtained by a human and classical method.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.9-18
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• 2020

In the field of image processing, a method of detecting and counting passengers as moving objects when getting on and off the bus has been studied. Among these technologies, one of the artificial intelligence techniques, the deep learning technique is used. As another method, a method of detecting an object using a stereo vision camera is also used. However, these techniques require expensive hardware equipment because of the computational complexity of used to detect objects. However, most video equipments have a significant decrease in computational processing power, and thus, in order to detect passengers on the bus, there is a need for an image processing technology suitable for various equipment using a relatively low computational technique. Therefore, in this paper, we propose a technique that can efficiently obtain the number of passengers on the bus by detecting the contour of the object through the background subtraction suitable for low-cost equipment. Experiments have shown that passengers were counted with approximately 70% accuracy on lower-end machines than those equipped with stereo vision camera.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.59-73
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• 2009

Keyword extraction is an important and essential technique for text mining applications such as information retrieval, text categorization, summarization and topic detection. A set of keywords extracted from a large-scale electronic document data are used for significant features for text mining algorithms and they contribute to improve the performance of document browsing, topic detection, and automated text classification. This paper presents a keyword extraction technique that can be used to detect topics for each news domain from a large document collection of internet news portal sites. Basically, we have used six variants of traditional TF-IDF weighting model. On top of the TF-IDF model, we propose a word filtering technique called 'cross-domain comparison filtering'. To prove effectiveness of our method, we have analyzed usefulness of keywords extracted from Korean news articles and have presented changes of the keywords over time of each news domain.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.11
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• pp.101-106
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• 2005

A ground penetrating image radar (GPR) using an ultra wideband (UWB)impulse waveform is developed for non destructive detection of metallic pipelines buried under the ground. Dielectric constant of test field is measured and then a GPR system is designed for better detection up to 1 meter deep. By considering total path loss, volume of complete system, and resolution, upper and lower frequencies are chosen. First, a UWB impulse for the frequency bandwidth of the impulse is chosen with rising time less than 1 ns, and then compact planar UWB dipole antenna suitable for frequency bandwidth of a UWB impulse is designed. Also, to receive reflected signals, a digital storage oscilloscope is used. For measurement, a monostatic technique and a migration technique are used. For visualizing underground targets, simple image processing techniques of A-scan removal and B-scan average removal are applied. The prototype of the system is tested on a test field in wet clay soil and it is shown that the developed system has a good ability in detecting underground metal objects, even small targets of several centimeters.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.4
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• pp.256-266
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• 2016

Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.8 no.2
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• pp.116-125
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• 1997

This paper presents a novel method, entitled Orthogonal Weights Searching(OWS), for the Direction-Of-Arrival(DOA) estimation. Utilizing the modified Conjugate Gradient Method(MCGM), the weight vector which is orthogonal to the signal subspace is directly computed from the signal matrix. The proposed method does not require the computation of the eigenvalues and eigenvectors. In addition, the new technique excludes the procedure for the detection of the number of signals under the assumption that the number of weights in the array is greater than the number of input signals. Since the proposed technique can be performed independently of the detection procedure, it shows a good performance in adverse signal environments in which the detection of the number of array inputs cannot be obtained successfully. The performance of the proposed technique is compared with that of the convectional eigen-decomposition method in terms of angle resolution for a given signal-to-noise ratio(SNR) and a required amount of computations.