• International Journal of Fluid Machinery and Systems
• /
• v.8 no.4
• /
• pp.264-273
• /
• 2015

This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and two Francis model test turbines all at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that two to three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.

• Journal of Sensor Science and Technology
• /
• v.19 no.6
• /
• pp.456-461
• /
• 2010

The detection of C-reactive protein(CRP) using self-assembled monolayer(SAM) was investigated by a portable surface plasmon resonance(SPR) sensor system. The CRP is a biomarker for the possible cardiovascular disease. The SAM was formed on gold(Au) surface to anchor the monoclonal antibody of CRP(anti-CRP) for detection of CRP. Sequence injection of the anti-CRP and bovine serum albumin(BSA) into the sensor system has been carried out immobilize the antibody and to prevent non-specific binding. The portable SPR system has two flow channels: one for the sample measurements and the other for the reference. The output SPR signal was increased with the injection of the anti-CRP, BSA and CRP due to binding of the proteins on the sensor chip. The valid output SPR signals was linearly related to the critical range of the CRP concentration. The experimental results showed the feasibility of the portable SPR system with newly developed SAM to diagnose a risk of the future cardiovascular events.

• 한국정보통신설비학회:학술대회논문집
• /
• 2008.08a
• /
• pp.262-267
• /
• 2008

Recently, landslides frequently occur on natural slope and/or man-made cut slope during periods of intense rainfall. With a rapidly increasing population on or near steep terrain, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide monitoring systems have been developed throughout the world. In this paper, a simple landslide detection system that enables people to escape the endangered area is introduced. The system is focused on the debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of wireless sensor nodes, gateway, and remote server system. Wireless sensor nodes and gateway are deployed by commercially available Microstrain G-Link products. Five wireless sensor nodes and one gateway are installed at the test slope for detecting ground movement. The acceleration and inclination data of test slope can be obtained, which provides a potential to detect landslide. In addition, thresholds to determine whether the test slope is stable or not are suggested by a series of numerical simulations, using geotechnical analysis software package. It is obtained that the alarm should be issued if the x-direction displacement of sensor node is greater than 20mili-meters and the inclination of sensor node is greater than 3 degrees. It is expected that the landslide detection method using wireless senor network can provide early warning where landslides are prone to occur.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.12 no.1
• /
• pp.9-18
• /
• 2007

This paper proposes the modeling of sensorless drive system using 120 degree conduction method for IPM (Interior Permanent Magnet) BLDC motors and analyzes characteristics of the terminal voltage that is used to detect the rotor position. This paper shows that the ZCP (Zero-Crossing Point) of the measured terminal voltage used In sensorless control is ahead of that of the back EMF of IPM motors because they have a saliency. This research also analyzes that the amount of position detection error is related to saliency, rotor speed, and load condition. In addition, this paper shows that motors have bigger advance angles than we have expected because the ZCP of terminal voltage precedes the actual ZCP, and under operation conditions such as heavy load and high speed it may generate abnormal currents that flow toward opposite direction after phase current becomes zero.

• Journal of Korean Neurosurgical Society
• /
• v.29 no.3
• /
• pp.345-352
• /
• 2000

Objective : The purpose of this study was to compare computed tomographic angiography(CTA) with conventional cerebral angiography(CCA) and to assess usefulness of CTA in detection and anatomic definition of intracranial aneurysms of the circle of Willis in subarachnoid hemorrhage. Patients and Methods : Fifty consecutive patients with known or suspected intracranial saccular aneurysms underwent CTA with preoperative CCA from 1997 to 1999. Using surface shaded display post-processing technique, CTA was interpreted for the presence, location of aneurysms and anatomic features. The image obtained with CTA was then compared with CCA image. Results : In 47 patients, CCA revealed 57 cerebral aneurysms and CTA revealed 54 aneurysms. Two of the 57 cerebral aneurysms were located outside of the imaging volume of CTA and one case was misdiagnosed. The sensitivity of CTA was 94.7% and the specificity was 100%. The results obtained with CTA were, compared with the results obtained with CCA, equal in determining dome shape, direction and lobularity. However, CTA provided a 3-dimensional representation of aneurysmal lesion very useful for surgical planning. Moreover, CTA was useful for rapid and relatively noninvasive detection of aneurysms in the circle of Willis. Conclusion : CTA can be a diagnostic tool for the patients with acute subarachnoid hemorrhage due to a ruptured aneurysm of the circle of Willis and provides adequate anatomic detail for surgical planning, especially to complex cerebral aneurysms. However, we think CCA is necessary because of CTA limitations including its difficulty in detecting unusually located aneurysms(including those in cavernous sinus or distal artery) and combined vascular lesion (including arteriovenous malformation) and acquiring dynamic flow information.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.1
• /
• pp.105-115
• /
• 2019

Recently, intelligent Android malicious codes have become difficult to detect malicious behavior by static analysis alone. Malicious code with SO file, dynamic loading, and string obfuscation are difficult to extract information about original code even with various tools for static analysis. There are many dynamic analysis methods to solve this problem, but dynamic analysis requires rooting or emulator environment. However, in the case of dynamic analysis, malicious code performs the rooting and the emulator detection to bypass the analysis environment. To solve this problem, this paper investigates a variety of root detection schemes and builds an environment for bypassing the rooting detection in real devices. In addition, SDK code hooking module for Android malicious code analysis is designed using Xposed, and intent tracking for code flow, dynamic loading file information, and various API information extraction are implemented. This work will contribute to the analysis of obfuscated information and behavior of Android Malware.

• Analytical Science and Technology
• /
• v.32 no.4
• /
• pp.139-146
• /
• 2019

A simple and reliable analytical method based on high-performance liquid chromatography-ultraviolet detection was established for the analysis of the flowers of Chrysanthemum morifolium (CM). Luteolin-7-O-glucoside (LU7G) was chosen as a target analyte considering its content, availability, and ease of analysis. Chromatographic separation of LU7G was achieved using a Phenomenex Gemini $C_{18}$ column ($250{\times}4.6mm$, $5{\mu}m$) run with a mobile phase consisting of 0.5 % acetic acid in water and 0.5 % acetic acid in acetonitrile at a flow rate of $1.0mL\;min^{-1}$. The detection wavelength and column temperature were set at 350 nm and $40^{\circ}C$, respectively. Method validation was performed according to the AOAC guidelines and the method was specific, linear ($R^2=0.9991$ for $50-300{\mu}g\;mL^{-1}$), precise (${\leq}3.91%$RSD), and accurate (100.1-105.7 %). The limits of detection and quantification were 3.62 and $10.96{\mu}g\;mL^{-1}$, respectively. The established method was successfully applied to determine the contents of LU7G in various batches of bulk CM extracts and labscale CM extract. The developed method is a readily applicable method for the quality assessment of CM and its related products.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3729-3749
• /
• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2168-2187
• /
• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• Korean Journal of Veterinary Service
• /
• v.43 no.4
• /
• pp.261-265
• /
• 2020

Each commercial cyromazine insecticide has different HPLC conditions. The aim of this study was to establish a standardized chromatographic method for the determination of cyromazine in commercial insecticides. The separation was achieved on two C18 columns - Waters® Bondapak C (4×300 nm i.d., 10 ㎛) and X bridge (4.6×250 nm i.d., 5 ㎛) using a mobile phase composed of water/methanol/ethanolamine (76:24:0.1, v/v), with UV detection at wavelengths 230 nm and 254 nm. A total of six commercial cyromazine insecticides were analyzed. In this study, the optimal high-performance liquid chromatography conditions for the analysis of cyromazine were as follows: a mobile phase of water/methanol/ethanolamine (76:24:0.1, v/v) at a flow rate of 1.0 mL/min and a detection wavelength of 230 nm using a X bridge C18 column (4.6×250 nm i.d., 5 ㎛) at a column temperature of 25℃. The calibration curve was linear in the concentration range of 5~50 ㎍/mL, with a correlation coefficient of 0.99995. The cyromazine detection limit was 0.2 ㎍/mL, and the limit of quantification was 0.59 ㎍/mL. The percentage recovery ranged from 99.8% to 101.0% for cyromazine, and the relative standard deviation was not over 2.0%. The cyromazine concentration ranged from 92.7% to 109.4% and was within the acceptable range (90~120%) for the percent of the labeled amount. This method was found to be suitable for determining cyromazine in commercial insecticides.