• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.75-81
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• 2020

Approximately 1.3 million people die from traffic accidents each year, and smartphone usage while driving is one of the main causes of such accidents. Therefore, detection of smartphone usage by drivers has become an important part of distracted driving detection. Previous studies have used single camera-based methods to collect the driver images. However, smartphone usage detection by employing a single camera can be unsuccessful if the driver occludes the phone. In this paper, we present a driver smartphone usage detection system that uses multiple cameras to collect driver images from different perspectives, and then processes these images with ensemble convolutional neural networks. The ensemble method comprises three individual convolutional neural networks with a simple voting system. Each network provides a distinct image perspective and the voting mechanism selects the final classification. Experimental results verified that the proposed method avoided the limitations observed in single camera-based methods, and achieved 98.96% accuracy on our dataset.

• Journal of Photoscience
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• v.11 no.32
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• pp.47-53
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• 2004

A laser scanning fluorescence microscope system has been fabricated for single molecule detection (SMD). Problems associated with the system set-up have been discussed along with proper suggestions. Based on the SMD results obtained by using the apparatus, a statistical method has been suggested to determine the minimum number of required molecules to form a group of uniform average in a selected error range.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.3
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• pp.328-330
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• 2016

A lane departure warning system based on single lane mark detection is presented in this paper. The proposed system is focused on the robust detection of lane departure, even when one of the left or the right lane mark cannot be detected because of poor illumination or abrasion. Experimental results show that the proposed system warns lane departure, even if only a single lane is detected.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4C
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• pp.330-335
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• 2008

This paper proposes a packet detection algorithm for IEEE802.11n system. IEEE802.11n is a multiple input multiple output (MIMO) system and we have to consider several combining techniques which are used in multiple receive antenna system. In this paper, we propose a hybrid packet detection algorithm which combines double sliding window algorithm or delay and correlation algorithm, that is used in single input single output (SISO) system, and multiple receive antenna combining algorithms, and simulated their performances in Iin system environments and shows the results.

• Journal of the Korean Society of Safety
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• v.14 no.1
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• pp.66-72
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• 1999

Local heating transportation pipe has sensor and return lines to detect water-leakage. There are impulse and resistance comparison measurement types for a water-leakage detection. The impulse type shows large detection error within a measurement range. Since the resistance comparison type can find a comparative accurate single water-leakage point in the measurement range of heating pipe, it has been used to detect water-leakages these days. However if the multi water-leakages are happened in the measurement range of transportation pipe. the resistance comparison type shows a detection error point by the parallel resistance between a detection sensor line and ground. But the detection error will be minimized by the divided transportation pipe loops. In this research, it suggests the design of remote controlled detection system which can divide a large pipe loop and a possible single water-leakage measurement process in each divided loops.

• Smart Structures and Systems
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• v.28 no.6
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• pp.811-825
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• 2021

High-voltage isolating switches play a paramount role in ensuring the safety of power supply systems. However, their exposure to outdoor environmental conditions may cause serious physical defects, which may result in great risk to power supply systems and society. Image processing-based methods have been used for anomaly detection. However, their accuracy is affected by numerous uncertainties due to manually extracted features, which makes the anomaly detection of isolating switches still challenging. In this paper, a vision-based anomaly detection method for isolating switches, which uses the rotational angle of the switch system for more accurate and direct anomaly detection with the help of deep learning (DL) and image processing methods (Single Shot Multibox Detector (SSD), improved frame differencing method, and Hough transform), is proposed. The SSD is a deep learning method for object classification and localization. In addition, an improved frame differencing method is introduced for better feature extraction and a hough transform method is adopted for rotational angle calculation. A number of experiments are conducted for anomaly detection of single and multiple switches using video frames. The results of the experiments demonstrate that the SSD outperforms the You-Only-Look-Once network. The effectiveness and robustness of the proposed method have been proven under various conditions, such as different illumination and camera locations using 96 videos from the experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.73.4-73
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• 2001

We suggest a system to detection whether material picked up is double or single when the robot feeds them in automation line. This system measures thickness of metal plate and alarms when they are double sheet measuring boundary lines of metal sheet. This system calculates the minimum distance of straight boundary lines by eliminating noise in the edge regions. In the experimental system, double or single is determined by calculating implemented the thickness in the image where the distance of pixels are retrieved by the hash table. In this paper, the algorithm of histogram is adapted to experiment in the implement, thus proved as double sheet detection system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.11
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• pp.587-595
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• 2015

In this study, fault symptoms were simulated and analyzed for a single-effect absorption chiller. The fault patterns of fault detection parameters were tabulated using the fault symptom simulation results. Fault detection and diagnosis by a process history-based method were performed for the in-situ experiment of a single-effect absorption chiller. Simulated fault modes for the in-situ experimental study are the decreases in cooling water and chilled water mass flow rates. Five no-fault reference models for fault detection of a single-effect absorption chiller were developed using fault-free steady-state data. A sensitivity analysis of fault detection using the normalized distance method was carried out with respect to fault progress. When mass flow rates of the cooling and chilled water decrease by more than 19.3% and 17.8%, respectively, the fault can be detected using the normalized distance method, and COP reductions are 6.8% and 4.7%, respectively, compared with normal operation performance. The pattern recognition method for fault diagnosis of a single-effect absorption chiller was found to indicate each failure mode accurately.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1037-1043
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• 2004

In this paper, a new fault detection and isolation algorithm fur inertial sensor system is proposed. To identify the inertial sensor fault, single antenna GPS receiver is used as an effective redundancy source. To use GPS receiver as redundancy for the inertial sensors, the algorithm to estimate the attitude and acceleration using single antenna GPS receiver is adopted. By using Doppler shift of carrier phase signal and kinetic characteristics of aircraft, attitude information of aircraft can be obtained at the coordinated flight condition. Based on this idea, fault diagnosis algorithm for inertial sensors using single antenna GPS based attitude is proposed. For more effective FDI, decision variables considering the aircraft maneuver are proposed. The effectiveness of the proposed algorithm is verified through the numerical simulations.