• International Journal of Aeronautical and Space Sciences
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• v.12 no.2
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• pp.175-178
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• 2011

An optical fiber-based beam width measurement technique is presented. The proposed system can be applied to the optical fiber industry in applications such as lensed fiber, optical fiber based laser beam source, and fiber optic sensor. The measurement system is composed of optical fiber, which is used as a transceiver, and a single grating panel which consists of a multi-reflection area with an even non-reflection area. The grating panel is used to vary the reflected light. When the widths of the reflection area and non-reflection area are larger than the optical beam width, the reflected light is varied at the interface between the reflection area and the non-reflection area by the movement of the grating panel. Experiments were conducted in order to verify the feasibility of the proposed technique. Multi-mode fiber combined with a collimator was selected as an emitter and a receiver, and the beam width measurement system was contrived. Subsequently, the proposed method and the system were verified by comparing the experimental results with the results of the conventional charge-coupled device technique.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.65-67
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• 2018

We introduce a system that detects smoking in a specific area. The proposed system is implemented on a wireless ad hoc network consisting of Raspberry Pi boards. It is more economical owing to low-cost device than commercial smoking monitoring system and is scalable than the existing system with single Raspberry Pi. In this paper, the probability density function of carbon monoxide concentration during smoking and non-smoking is approximated as Gauusian distribution, respectively, using data measured from sensors for a long time. Based on this, a maximum likelihood detection technique is adopted to estimate the smoking status by observing the concentration of carbon monoxide. We aim at improving the reliability by estimating the smoking status using the collected values from multiple sensors connected to the ad hoc network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.595-597
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• 2015

In recent years, global Internet of Things is being fostered by strategic industry in major countries (IoT: Internet of Things) technology is the future Internet -based context-aware infrastructure capable of communication between objects and things. Existing technology was only a single device control via P2M (Puman to Machine) method and simple sensor data to someone directly control the devices down command. However, a kind of IoT Machine Socialization of devices, each device uses an internal feature information, and M2M (Machine to Machine) devices in ways that expand the overall scenario collaboration system through the Device Manager. In this paper, an algorithm of the whole system of the Machine Socialization for changing the system of the conventional system of P2M by M2M, and analysis.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.7
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• pp.760-764
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• 2015

Wireless sensors, installed on machinery, and Time Division Multiple Access (TDMA) transmission make an ideal system for monitoring machine conditions in industrial plants because there is no need for electronic wiring. However, there has not yet been a successful field application of such a system, capable of continuously transmitting data at sample rates greater than 100 Hz. In this research, a TDMA network protocol capable of acquiring data from multiple sensors at sample rates greater than 100 Hz was developed for field application. The protocol was implemented in a single cluster-star topology network, and the system was evaluated based on the node number and transmission distance. Network simulator 2 (ns-2) was used for a real field simulation. Non-TDMA and TDMA protocol cases were compared using four sensor nodes. In the cases of 20-s and 40-s transmission times, there was little difference between the reception rates of the non-TDMA and TDMA systems. However, the difference was much greater when using a 60-s transmission time.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.1-9
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• 2012

As we are turnning into the aged society, accidents by falling down are increasing in the aged people's group. In this paper, we design the system with the 3-Axis acceleration sensor which is composed by a single chip. The body activity signal is measured with the signal detector and RF communicator in this proposed system and the and falling by the entering signal pattern analysis with 3-Axis acceleration sensor. For the RF communication, we are using nRF24L01p and 8bits ATmega uC for the processor. The error of energy expenditure estimation between motor driven treadmill and proposed a body activity module was 7.8% respectively. Human activities and falling is monitored according to analyze and judge the critical value of the Signal Vector. as falled down if they don't turn off the alarm after specific period and the aged person's after falling down activities are their position and more.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.11
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• pp.1371-1379
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• 2012

This paper presents a method for estimating the contribution of vibration sources in gasoline direct injection engine parts with a multiple-input system. A partial coherence function was used to identify the cause of the linear dependence indicated by an ordinary coherence function. To apply the partial coherence function to vibration source identification in the powertrain system of a gasoline direct injection engine, a virtual model of a two-input and single-output system is simulated. For the validation of this model, the vibration of the powertrain parts was measured by using triaxial accelerometers attached to the selected vibration sources-a high-pressure pump, fuel rail, injector, and pressure sensor. After calculating the partial coherence between each source based on the virtual model, the vibration contribution of the powertrain system is calculated. This virtual model based on the partial coherence function is implemented to determine the quantitative vibration contribution of each powertrain part.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.895-904
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• 2017

In this paper, we propose an IoT based smart outlet system for one-person household with electric safety, electric energy consumption management and transmission of messages to the reserved contacts by connecting the emergency network. The proposed smart outlet system is implemented by parsing packet data and protocol between server, module, application and WiFi AP receiver. The WiFi AP built for communication can be used not only for the outlet but also for the gas barrier, the door lock and the like. In addition, the proposed method can provide the remote monitoring function by receiving the amount of power of the receptacle received through the AC current sensor (SCT-013) in real time. The smart outlet system is expected to be capable of automatically shutting off the power in case of emergency through automatic power use analysis in addition to the standby power cutoff function.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.224-234
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• 2008

Multi-phase machines can be used in variable speed drives. Their applications include electric ship propulsion, 'more-electric aircraft' and traction applications, electric vehicles, and hybrid electric vehicles. Multi-phase machines enable independent control of a few numbers of machines that are connected in series in a particular manner with their supply being fed from a single voltage source inverter(VSI). The idea was first implemented for a five-phase series-connected two-motor drive system, but is now applicable to any number of phases more than or equal to five-phase. The number of series-connected machines is a function of the phase number of VSI. Theoretical and simulation studies have already been reported for number of multi-phase multi-motor drive configurations of series-connection type. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor, information concerning the rotor speed is extracted from measured stator currents and voltages at motor terminals. Open-loop estimators or closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. This paper analyses operation of an MRAS estimator based sensorless control of a vector controlled series-connected two-motor five-phase drive system with current control in the stationary reference frame. Results, obtained with fixed-voltage, fixed-frequency supply, and hysteresis current control are presented for various operating conditions on the basis of simulation results. The purpose of this paper is to report the first ever simulation results on a sensorless control of a five-phase two-motor series-connected drive system. The operating principle is given followed by a description of the sensorless technique.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1267-1267
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• 2001

The existing fruit sorter has the method of tilting tray and extracting fruits by the action of solenoid or springs. In peaches, the most sort processing is supported by man because the sorter make fatal damage to peaches. In order to sustain commodity and quality of peach non-destructive, non-contact and real time based sorter was needed. This study was performed to develop peach sorter using near-infrared spectroscopy in real time and nondestructively. The prototype was developed to decrease internal and external damage of peach caused by the sorter, which had a way of extracting tray with it. To decrease positioning error of measuring sugar contents in peaches, fiber optic with two direction diverged was developed and attached to the prototype. The program for sorting and operating the prototype was developed using visual basic 6.0 language to measure several quality index such as chlorophyll, some defect, sugar contents. The all sorting result was saved to return farmers for being index of good quality production. Using the prototype, program and MLR(multiple linear regression) model, it was possible to estimate sugar content of peaches with the determination coefficient of 0.71 and SEC of 0.42bx using 16 wavelengths. The developed MLR model had determination coefficient of 0.69, and SEP of 0.49bx, it was better result than single point measurement of 1999's. The peach sweetness grading system based on NIR reflectance method, which consists of photodiode-array sensor, quartz-halogen lamp and fiber optic diverged two bundles for transmitting the light and detecting the reflected light, was developed and evaluated. It was possible to predict the soluble solid contents of peaches in real time and nondestructively using the system which had the accuracy of 91 percentage and the capacity of 7,200 peaches per an hour for grading 2 classes by sugar contents. Draining is one of important factors for production peaches having good qualities. The reason why one farm's product belows others could be estimated for bad draining, over-much nitrogen fertilizer, soil characteristics, etc. After this, the report saved by the peach grading system will have to be good materials to farmers for production high quality peaches. They could share the result or compare with others and diagnose their cultural practice.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.