• Journal of Korea Society of Digital Industry and Information Management
• /
• v.9 no.2
• /
• pp.17-22
• /
• 2013

In this paper, we propose a concrete crack detection method using shape properties. It is based on morphology algorithm and crack features. We assume that an input image is contaminated by various noises. Thus, we use a morphology operator and extract patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. Also, it is robust to noisy environment. The proposed algorithm classifies the segmented image into crack and background using shape properties of crack. This method calculates values of properties such as the number of pixels and the maximum length of the segmented region. Also, pixel counts of clusters are considered. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed crack detection method has better results than those by existing detection methods.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.4
• /
• pp.785-796
• /
• 2012

In this paper, we propose an effective Behavior-based detection technique using the frequency of system calls to detect malicious code, when the number of training data is fewer than the number of properties on system calls. In this study, we collect the Native APIs which are Windows kernel data generated by running program code. Then we adopt the normalized freqeuncy of Native APIs as the basic properties. In addition, the basic properties are transformed to new properties by GLDA(Generalized Linear Discriminant Analysis) that is an effective method to discriminate between malicious code and normal code, although the number of training data is fewer than the number of properties. To detect the malicious code, kNN(k-Nearest Neighbor) classification, one of the bayesian classification technique, was used in this paper. We compared the proposed detection method with the other methods on collected Native APIs to verify efficiency of proposed method. It is presented that proposed detection method has a lower false positive rate than other methods on the threshold value when detection rate is 100%.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.9 no.3
• /
• pp.13-20
• /
• 2013

In this paper, we propose a multi-scale crack detection method. This method uses decomposition, composition, and shape properties. It is based on morphology algorithm, crack features. We use a morphology operator which extracts patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. However, morphology methods using only one structure element could detect only fixed width crack. Thus, we use decomposition and composition methods. We use a decimation method for decomposition. After decomposition and morphology operation, we get edge images given by binary values. Our method calculates values of properties such as the number of pixels and the maximum length of the segmented region. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed multi-scale crack detection method has better results than those of existing detection methods.

• Proceedings of the KIEE Conference
• /
• 1996.07c
• /
• pp.1907-1909
• /
• 1996

In this paper, ultrsonic detection properties at a premolde joint utilized in a 23kV cables are studied. In a experiment a artificial defect within a joint and a measuring system are builded for generating discharges, gathering data about a detection properties, respectively. The experiment results show that one point detection is not allowed for monitoring a global status of a joint discharges and a detection sensitivity is less than 100pC. And also the attenuation and wave speed at the material of joint insulator are obtained.

• Korean Journal of Remote Sensing
• /
• v.33 no.4
• /
• pp.359-375
• /
• 2017

We developed fog detection algorithm (KNU_FDA) based on the optical and textural properties of fog using satellite (COMS) and ground observation data. The optical properties are dual channel difference (DCD: BT3.7 - BT11) and albedo, and the textural properties are normalized local standard deviation of IR1 and visible channels. Temperature difference between air temperature and BT11 is applied to discriminate the fog from other clouds. Fog detection is performed according to the solar zenith angle of pixel because of the different availability of satellite data: day, night and dawn/dusk. Post-processing is also performed to increase the probability of detection (POD), in particular, at the edge of main fog area. The fog probability is calculated by the weighted sum of threshold tests. The initial threshold and weighting values are optimized using sensitivity tests for the varying threshold values using receiver operating characteristic analysis. The validation results with ground visibility data for the validation cases showed that the performance of KNU_FDA show relatively consistent detection skills but it clearly depends on the fog types and time of day. The average POD and FAR (False Alarm Ratio) for the training and validation cases are ranged from 0.76 to 0.90 and from 0.41 to 0.63, respectively. In general, the performance is relatively good for the fog without high cloud and strong fog but that is significantly decreased for the weak fog. In order to improve the detection skills and stability, optimization of threshold and weighting values are needed through the various training cases.

• Nuclear Engineering and Technology
• /
• v.52 no.12
• /
• pp.2910-2917
• /
• 2020

Digital light processing three-dimensional (3D) printing technique is a powerful tool to rapidly manufacture plastic scintillators of almost any shape or geometric features. In our previous study, the main properties of light output and transmission were analyzed. However, a more detailed study of the other properties is required to develop 3D printed plastic scintillators with expectable and reproducible properties. The 3D printed plastic scintillator displayed an average decay time constants of 15.6 ns, intrinsic energy resolution of 13.2%, and intrinsic detection efficiency of 6.81% for 477 keV Compton electrons from the ¹³⁷Cs γ-ray source. The 3D printed plastic scintillator showed a similar decay time and intrinsic detection efficiency as that of a commercial plastic scintillator BC408. Furthermore, the presented estimates for the properties showed good agreement with the analyzed data.

• Journal of Sensor Science and Technology
• /
• v.33 no.3
• /
• pp.165-172
• /
• 2024

In this study, we investigated the detection properties of piezoresistive carbon nanotubes/polydimethylsiloxane (CNT/PDMS) devices with porous structures under applied pressure. The device, having dimensions of 10 mm × 10 mm × 5 mm, was fabricated with a porosity of 74.5%. To fabricate piezoresistive CNT/PDMS devices, CNTs were added using two different methods. In the first method, the CNTs were mixed with PDMS before the fabrication of the porous structure, while in the second, the CNTs were coated after the fabrication of the porous structure. Various detection properties of the fabricated devices were examined at different applied pressures. The CNT-coated device exhibited stable outputs with lesser variation than the CNT-mixed device. Moreover, the CNT-coated device exhibited improved reaction properties. The response time of the CNT-coated device was 1 min, which was approximately about 20 times faster than that of the CNT-mixed device. Considering these properties, CNT-coated devices are more suitable for sensing devices. To verify the CNT-coated device as a real sensor, it was applied to the gripping sensor system. A multichannel sensor system was used to measure the pressure distribution of the gripping sensor system. Under various gripping conditions, this system successfully measured the distributed pressures and exhibited stable dynamic responses.

• The Plant Pathology Journal
• /
• v.34 no.2
• /
• pp.85-92
• /
• 2018

Enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), widely used for the detection of plant viruses, are not easily performed, resulting in a demand for an innovative and more efficient diagnostic method. This paper summarizes the characteristics and research trends of biosensors focusing on the physicochemical properties of both interface elements and bioconjugates. In particular, the topological and photophysical properties of quantum dots (QDs) are discussed, along with QD-based biosensors and their practical applications. The QD-based Fluorescence Resonance Energy Transfer (FRET) genosensor, most widely used in the biomolecule detection fields, and QD-based nanosensor for Rev-RRE interaction assay are presented as examples. In recent years, QD-based biosensors have emerged as a new class of sensor and are expected to open opportunities in plant virus detection, but as yet there have been very few practical applications (Table 3). In this article, the details of those cases and their significance for the future of plant virus detection will be discussed.

• Journal of the Korean Statistical Society
• /
• v.13 no.2
• /
• pp.114-120
• /
• 1984

A parallel flats fraction for the $3^n$ design is defined as union of flats ${t}At=c_i(mod 3)}, i=1,2,\cdots, f$ and is symbolically written as At=C where A is rank r. The A matrix partitions the effects into n+1 alias sets where $u=(3^{n-r}-1)/2. For each alias set the f flats produce an ACPM from which a detection matrix is constructed. The set of all possible parallel flats fraction C can be partitioned into equivalence classes. In this paper, we develop some properties of a detection matrix and C.

• Journal of Information Processing Systems
• /
• v.17 no.1
• /
• pp.124-135
• /
• 2021

Object detection is a fundamental yet challenging task in computer vision that plays an important role in object recognition, tracking, scene analysis and understanding. This paper aims to propose a multiproperty fusion algorithm for moving object detection. First, we build a scale-invariant feature transform (SIFT) vector field and analyze vectors in the SIFT vector field to divide vectors in the SIFT vector field into different classes. Second, the distance of each class is calculated by dispersion analysis. Next, the target and contour can be extracted, and then we segment the different images, reversal process and carry on morphological processing, the moving objects can be detected. The experimental results have good stability, accuracy and efficiency.