• Journal of Digital Contents Society
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• v.16 no.1
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• pp.97-104
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• 2015

The conventional 1 Hall sensor-type water gauge has some defects that it can not detect counterflow and low-speed flow of water, and it also generates power consumption during even sleep mode. In this paper, a low-power consumption wireless digital remote water gauge with a counterflow detection capability is proposed. The proposed water gauge detects the direction and amount of water flow by using the three Hall sensors placed at $120^{\circ}$ intervals with 8-year national standard life durability. The water gauge with three Hall sensors works without error regardless of water speed does not generate power dissipation during sleep mode by presented reading algorithm for bew water gauge. The proposed water gauge is designed to send its ID, current time and counting value to repeater or central control center with specified frequency by RF Module.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.391-398
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• 2017

In this paper, we compared the performance of "Network Intrusion Detection System based on attack feature selection using fuzzy control language"[1] and "Intelligent Intrusion Detection System Model for attack classification using RNN"[2]. In this paper, we compare the intrusion detection performance of two techniques using KDD CUP 99 dataset. The KDD 99 dataset contains data sets for training and test data sets that can detect existing intrusions through training. There are also data that can test whether training data and the types of intrusions that are not present in the test data can be detected. We compared two papers showing good intrusion detection performance in training and test data. In the comparative paper, there is a lack of performance to detect intrusions that exist but have no existing intrusion detection capability. Among the attack types, DoS, Probe, and R2L have high detection rate using fuzzy and U2L has a high detection rate using RNN.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.2
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• pp.90-95
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• 2006

For single engine application like T-50, advanced supersonic jet trainer, airstart capability is very important. This paper presents the results of airstart tests performed to verify T-50 airstart capability for various flight condition. The tests include spooldown, APU assisted and auto-relight airstart tests. Except for the auto-relight tests T-50 engine was successfully restarted for all airstart tests. After modifying FADEC flameout detection schedule, auto-relight tests also were successfully demonstrated. Through T-50 engine airstart tests excellent T-50 airstart capability was validated.

• Journal of the Korea Safety Management & Science
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• v.23 no.3
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• pp.27-33
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• 2021

The purpose of this study is to present a novel indicator for analyzing machine failure based on its idle time and productivity. Existing machine repair plan was limited to machine experts from its manufacturing industries. This study evaluates the repair status of machines and extracts machines that need improvement. In this study, F-RPN was calculated using the etching process data provided by the 2018 PHM Data Challenge. Each S(S: Severity), O(O: Occurence), D(D: Detection) is divided into the idle time of the machine, the number of fault data, and the failure rate, respectively. The repair status of machine is quantified through the F-RPN calculated by multiplying S, O, and D. This study conducts a case study of machine in a semiconductor etching process. The process capability index has the disadvantage of not being able to divide the values outside the range. The performance of this index declines when the manufacturing process is under control, hereby introducing F-RPN to evaluate machine status that are difficult to distinguish by process capability index.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.255-262
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• 2007

This paper proposes an enhanced skin color-based detection method to find a region of human face in color images. The proposed detection method combines three color spaces, RGB, $YC_bC_r$, YIQ and builds color union histograms of luminance and chrominance components respectively. Combined color union histograms are then fed in to the back-propagation neural network for training and Levenberg-Marquadt algorithm is applied to the iteration process of training. Proposed method with Levenberg-Marquadt algorithm applied to training process of neural network contributes to solve a local minimum problem of back-propagation neural network, one of common methods of training for face detection, and lead to make lower a detection error rate. Further, proposed color-based detection method using combined color union histograms which give emphasis to chrominance components divided from luminance components inputs more confident values at the neural network and shows higher detection accuracy in comparison to the histogram of single color space. The experiments show that these approaches perform a good capability for face region detection, and these are robust to illumination conditions.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.549-549
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• 2012

Early detection of cancer biomarkers in the blood is of vital importance for reducing the mortality and morbidity in a number of cancers. From this point of view, immunosensors based on nanowire (NW) and carbon nanotube (CNT) field-effect transistors (FETs) that allow the ultra-sensitive, highly specific, and label-free electrical detection of biomarkers received much attention. Nevertheless 1D nano-FET biosensors showed high performance, several challenges remain to be resolved for the uncomplicated, reproducible, low-cost and high-throughput nanofabrication. Recently, two-dimensional (2D) graphene and reduced GO (RGO) nanosheets or films find widespread applications such as clean energy storage and conversion devices, optical detector, field-effect transistors, electromechanical resonators, and chemical & biological sensors. In particular, the graphene- and RGO-FETs devices are very promising for sensing applications because of advantages including large detection area, low noise level in solution, ease of fabrication, and the high sensitivity to ions and biomolecules comparable to 1D nano-FETs. Even though a limited number of biosensor applications including chemical vapor deposition (CVD) grown graphene film for DNA detection, single-layer graphene for protein detection and single-layer graphene or solution-processed RGO film for cell monitoring have been reported, development of facile fabrication methods and full understanding of sensing mechanism are still lacking. Furthermore, there have been no reports on demonstration of ultrasensitive electrical detection of a cancer biomarker using the graphene- or RGO-FET. Here we describe scalable and facile fabrication of reduced graphene oxide FET (RGO-FET) with the capability of label-free, ultrasensitive electrical detection of a cancer biomarker, prostate specific antigen/${\alpha}$ 1-antichymotrypsin (PSA-ACT) complex, in which the ultrathin RGO channel was formed by a uniform self-assembly of two-dimensional RGO nanosheets, and also we will discuss about the immunosensing mechanism.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.282-288
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• 2005

In this paper, we propose a new intrusion detection model, which keeps advantages of existing misuse detection model and anomaly detection model and resolves their problems. This new intrusion detection system, named to MMIDS, was designed to satisfy all the following requirements : 1) Fast detection of new types of attack unknown to the system; 2) Provision of detail information about the detected types of attack; 3) cost-effective maintenance due to fast and efficient learning and update; 4) incrementality and scalability of system. The fast and efficient training and updating faculties of proposed novel multi-class SVM which is a core component of MMIDS provide cost-effective maintenance of intrusion detection system. According to the experimental results, our method can provide superior performance in separating similar patterns and detailed separation capability of MMIDS is relatively good.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3965-3982
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• 2015

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.

• Journal of Biosystems Engineering
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• v.24 no.6
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• pp.539-546
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• 1999

Some stress for a plant could be detected to a certain degree by plant physiological measuring technique of the state of the art. The capability of early detection of my measuring system depends on kind of plant and kind and level of stress. The objectives of this study were to evaluate the capability of several fast and intact type plant stress detection systems to detect nitrogen deficiency of cucumber in the field. A series of experiment was carried out with four kinds of intact type measuring devices - a chlorophyll content meter, a chlorophyll fluorescence measurement system, an infrared thermometer and an optical spectrometer. The experiments resulted that the chlorophyll content meter could detect the stress of N deficiency at a confidence level higher than 95% on 3rd day for the earliest case and the detection of high precision was possible from 7th day after the stress was applied. The chlorophyll fluorescence measurement system detected the stress at a confidence level higher than 95% on 3rd day for the earliest case but the detection was not as much precise as the chlorophyll content meter. Leaf temperature measurement noted very poor results to detect the stress. Using the spectrometer, sensitive wavelength regions to detect the stress were searched and found out as 562∼564 nm, 700∼724 nm and 1,886∼1,894 nm. With the spectrometer using any of wavelength within the sensitive wavelength region, detection of the stress at a confidence level higher than 95% was possible from 3rd or 4th day after the stress was applied.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1570-1575
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• 1991

The feasibility of using an adaptive resonance network (ART2) with unsupervised learning capability for too] wear detection in turning operations is investigated. Specifically, acoustic emission (AE) and cutting force signals were measured during machining, the multichannel AR coefficients of the two signals were calculated and then presented to the network to make a decision on tool wear. If the presented features are significantly different from previously learned patterns associated with a fresh tool, the network will recognize the difference and form a new category m worn tool. The experimental results show that tool wear can be effectively detected with or without minimum prior training using the self-organization property of the ART2 network.