• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.85-90
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• 2013

Currently, the electric fire prediction system uses PIC(Peripheral Interface Controller) for controller microprocessor. PIC has a slower computing speed than DSP does, so its real-time computing ability is inadequate. So with the basic characteristics waveform during arc generation as the standard reference, the comparison to this reference is used to predict and alarm electric fire from arc. While such alarm can be detected and taken care of from a remote central server, that prediction error rate is high and remote control in mobile environment is not available. In this article, the arc detection of time domain and frequency domain and wavelet-based adaptation algorithm executing the adaptation algorithm in conversion domain were applied to develop an electric fire prediction system loaded with new real-time arc detection algorithm using DSP. Also, remote control was made available through iPhone environment-based app development which enabled remote monitoring for arc's electric signal and power quality, and its utility was verified.

• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.732-742
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• 2001

The concept of an agent has become important in computer science and has been applied to the number of application domains such electronic commerce and information retrieval. But, no one has proposed yet in software test. The test agent system applied the concept of an agent to software test is new test tool. It consists of the User Interface Agent. the Test Case Selection & Testing Agent and the Regression Test Agent. Each of these agents, with their intelligent rules, carry out the tests autonomously by empolying the object-oriented test processes. This system has 2 advantages. Firstly since the tests are carried our autonomously, it minimizes tester interference and secondly, since redundant-free and consistent effective test cases are intellectually selected, the testing time is reduced while the fault detection effectiveness improves. In this paper, by actually showing the testing process being carried out autonomously by the 3 agents that form the TAS, we show that the TAS minimizes tester interference. By also carrying out the 4 different types of experiments on the RE-Rule, CTS-Rule, overall TAS experiment, and the fault-detection effectiveness experiment on the RE-Rule, we show the cut-down on the testing time and improvement in the fault detection effectivity.

• Korean Journal of Optics and Photonics
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• v.24 no.2
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• pp.86-91
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• 2013

In this study, we have investigated the correlation of far-field detection sensitivity of surface plasmon resonance (SPR) biosensors with optical signatures associated with the near-field overlap of biomolecules. The results confirm a direct relation between the far-field and near-field parameters, particularly for optical signatures defined in terms of lateral electric field components that are tangential to the interface and thus continuous across the interface. The overall correlation between near-field optical signatures and far-field resonance shift exceeded 97%. The results can be highly useful to evaluate detection sensitivity of SPR biosensors that take advantage of complex structures for localization of surface waves.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.837-850
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• 2002

In this paper, we propose an EEG-based response time prediction method during a yes/no cognitive decision task. In the experimental task, a subject goes through responding of visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining CT (cut time), ST (selection time), and RP (repeated period). Based on the assumption between ST and RT in the mental model, we predict subjects' response time by detection of selection time. To recognize the subjects' selection time ST, we extract 3 types of feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, ${\gamma}$ waves in 4 electrode pairs combined by spatial relationships. From the extracted features, we construct specific rules for each subject and meta rules including common factors in all subjects. Applying the ST detection rules to 8 subjects gives 83% success rates and also shows that the subjects will hit a key in 0.73 seconds after ST detected. To validate the detection rules and parameters, we test the rules for 2 subjects among 8 and discuss about the experimental results. We expect that the proposed detection method can be a basic technology for brain-computer-interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1089-1098
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• 2009

Recently, many researches of making more comfortable input device based on gaze detection technology have been performed in human computer interface. Previous researches were performed on the computer environment with a large sized monitor. With recent increase of using mobile device, the necessities of interfacing by gaze detection on mobile environment were also increased. In this paper, we research about the gaze detection method by using UMPC (Ultra-Mobile PC) and an embedded camera of UMPC based on face and facial feature detection by AAM (Active Appearance Model). This paper has following three originalities. First, different from previous research, we propose a method for tracking user's gaze position in mobile device which has a small sized screen. Second, in order to detect facial feature points, we use AAM. Third, gaze detection accuracy is not degraded according to Z distance based on the normalization of input features by using the features which are obtained in an initial user calibration stage. Experimental results showed that gaze detection error was 1.77 degrees and it was reduced by mouse dragging based on the additional facial movement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1129-1135
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• 2007

Human face detection technique plays an important role in computer vision area. It has lots of applications such as face recognition, video surveillance, human computer interface, face image database management, and querying image databases. In this paper, a fast face detection approach using Principal Component Analysis (PCA) and Support Vector Machines (SVM) is proposed based on the previous study on face detection technique. In the proposed detection system, firstly it filter the face potential area using statistical feature which is generated by analyzing the local histogram distribution the detection process is speeded up by eliminating most of the non-face area in this step. In the next step, PCA feature vectors are generated, and then detect whether there are faces present in the test image using SVM classifier. Finally, store the detection results and output the results on the test image. The test images in this paper are from CMU face database. The face and non-face samples are selected from the MIT data set. The experimental results indicate the proposed method has good performance for face detection.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.5
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• pp.45-50
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• 2013

Recently, as the market for SMART TV expands, the camera is embedded for providing various user experience. However, this leads to occurrence of camera failure due to TV power up sequence problem, which are usually not detectable in conventional test equipments. Although the failure-detection can be possible by re-generating control signals for audio interface with new equipment, it is expensive and also requires much time to test. In this paper, for SMART TV, FPGA(Field Programmable Gate Array)-based failure-detection system is proposed which can lead to reduction of both cost and time for test.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• Smart Structures and Systems
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• v.12 no.6
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• pp.619-640
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• 2013

It is highly desirable to explore efficient algorithms for detecting structural damage of large size structural systems with limited input and output measurements. In this paper, a new structural damage detection algorithm based on substructure approach is proposed for large size structural systems with limited input and output measurements. Inter-connection effect between adjacent substructures is treated as 'additional unknown inputs' to substructures. Extended state vector of each substructure and its unknown excitations are estimated by sequential extended Kalman estimator and least-squares estimation, respectively. It is shown that the 'additional unknown inputs' can be estimated by the algorithm without the measurements on the substructure interface DOFs, which is superior to previous substructural identification approaches. Also, structural parameters and unknown excitation are estimated in a sequential manner, which simplifies the identification problem compared with other existing work. Structural damage can be detected from the degradation of the identified substructural element stiffness values. The performances of the proposed algorithm are demonstrated by several numerical examples and a lab experiment. Measurement noise effect is considered. Both the simulation results and experimental data validate that the proposed algorithm is viable for structural damage detection of large size structural systems with limited input and output measurements.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.5
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• pp.15-21
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• 2009

Deformity detection in a Light Emitting Diode (LED) is an important aspect for improving its quality. These LED deformities can be checked through several methods. This paper details the automatic deformity detection inspection system of a LED using the LabVIEW Builder 3.6 software. This software has a graphical user interface which makes it easy to observe and modify the behavior of its element. The LabVIEWs essential elements are also presented and explained aside from its image acquisition system. Details on how to build an inspection system and how to implement vision inspection algorithm which mainly consists of edge detection, geometry point location, and distance measurement are included in this paper.