• Journal of Convergence for Information Technology
• /
• v.10 no.6
• /
• pp.41-48
• /
• 2020

The sensor nodes in underwater IoT networks have practical limitations in power supply. Thus, the reduction of power consumption is one of the most important issues in underwater environments. In this regard, AMC(Adaptive Modulation and Coding) techniques are used by using the relation between SNR and BER. However, according to our hands-on experience, we observed that the relation between SNR and BER is not that tight in underwater environments. Therefore, we propose a deep learning based MLP classification model to reflect multiple underwater channel parameters at the same time. It correctly predicts BER with a high accuracy of 85.2%. The proposed model can choose the best parameters to have the highest throughput. Simulation results show that the throughput can be enhanced by 4.4 times higher than the conventionally measured results.

• The KIPS Transactions:PartD
• /
• v.13D no.6 s.109
• /
• pp.755-764
• /
• 2006

In recent, data streams are generated in various application fields such as a ubiquitous computing and a sensor network, and various algorithms are actively proposed for processing data streams efficiently. They mainly focus on the restriction of their memory usage and minimization of their processing time per data element. However, in the algorithms, if data elements of a data stream are generated in a rapid rate for a time unit, some of the data elements cannot be processed in real time. Therefore, an efficient load shedding technique is required to process data streams effcientlv. For this purpose, a load shedding technique over a data stream is proposed in this paper, which is based on the predicting technique of the frequency of data element considering its current frequency. In the proposed technique, considering the change of the data stream, its threshold for tuple alive is controlled adaptively. It can help to prevent unnecessary load shedding.

• Journal of the Society of Disaster Information
• /
• v.10 no.3
• /
• pp.374-387
• /
• 2014

Since the sexual violence crime has a high probability of repeated crime, the electronic monitoring system has been introduced as a measure to it. And this system allows the police to know the location of former criminal around the clock through the electronic device, the former criminal has the psychological/mental oppression which can restrain the intention of crime to a degree. However, there is a limit in blocking criminals with strong will from repeated crime. The next-generation intelligent electronic anklet currently under study collects and analyzes the change bio-data in real time through the location information of electronic monitoring target and attached sensor. This study is aimed to predict the symptom of crime occurrence in advance based on this and block the crime intention in advance or stop the ongoing crime before it is expanded.

• Journal of Sensor Science and Technology
• /
• v.1 no.2
• /
• pp.155-163
• /
• 1992

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three-dimensional adaptive tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track update illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver detector. A detailed simulation is conducted to test the validity of our tracking algorithm for encounter geometries under various conditions of maneuver.

• Proceedings of the KSRS Conference
• /
• 1998.09a
• /
• pp.43-48
• /
• 1998

Prior to launch, simulated radiances of the Ocean Scanning Multispectral Imager (OSMI) will be very useful to guess the real imagery of OSMI and to check the data processing system for OSMI. The data processing system for OSMI which is one sensor of Korea Mult i - Purpose Satellite (KOMPSAT) scheduled for launch in 1999 is being developed based on the SeaWiFS Data Analysis System (SeaDAS). Such a simulation should include the spectral bands, orbital and scanning characteristics of the OSMI and KOMPSAT spacecraft. The simulation is also very helpful for finding and preparing for problem areas before launch. This paper describes a method to create simulated radiances of the OSMI over the oceans. Our method for constructing a simulated OSMI imagery is to propagate a KOMPSAT orbit over a field of Coastal Zone Color Scanner (CZCS) pigment values and to use the values and atmospheric components to calculate total radiances. A modified Brouwer - Lyddane model with drag was used for the realistic orbit prediction, the CZCS pigment data were used to compute water - leaving radiances, and a variety of radiative transfer models were used to calculate atmospheric contributions to total radiances detected by OSMI. Imagery of the simulated OSMI total radiances for 6 nominal bands was obtained. As expected, water - leaving radiances were only a small fraction of total radiances and sun glint contaminations were observed near the solar declination. Therefore, atmospheric correction is very important in the calculation of pigment concentration from total radiances. Because the imagery near the sun's glitter pattern is virtually useless and must be discarded, more advanced mission planning will be required.

• Journal of Biosystems Engineering
• /
• v.42 no.4
• /
• pp.301-313
• /
• 2017

Purpose: This study was conducted to discriminate between normal hulled barley and Fusarium (Fusarium asiaticum and Fusarium graminearum) infected hulled barley by using the near-infrared spectroscopy (NIRS) technique. Methods: Fusarium asiaticum and Fusarium graminearum were artificially inoculated in hulled barley and the reflectance spectrum of the barley spike was obtained by using a near-infrared spectral sensor with wavelength band in the range 1,175-2,170 nm. After obtaining the spectrum of the specimen, the hulled barley was cultivated in a greenhouse and visually inspected for infections. Results: From a partial least squares discriminant analysis (PLS-DA) prediction model developed from the raw spectrum data of the hulled barley, the discrimination accuracy for the normal and infected hulled barley was 99.82% (563/564) and 100% (672/672), respectively. Conclusions: NIRS is effective as a quick and nondestructive method to detect whether hulled barley has been infected with Fusarium. Further, it expected that NIRS will be able to detect Fusarium infections in other grains as well.

• Journal of Energy Engineering
• /
• v.20 no.4
• /
• pp.292-297
• /
• 2011

Gas pipeline safety management and risk prediction are recognized as a very important issue. And the effort to prevent accidents is essential. So, in this study, it was studied through correlation of pressure changes for leak point detection in real-time. It experimented by installing the five leakage valves in the pipe of 378 m and compared the actual leak points with simulation results. The results showed that experimental leak points and the actual leak points have differences within the 6 m. And this technology has to be commercialized by the demonstration in dangerous zone.

• The Journal of Engineering Geology
• /
• v.25 no.3
• /
• pp.331-337
• /
• 2015

A new technique is presented to detect unstable slope behavior, based on Hotelling's T² analysis of pore pressure and water content obtained during flume tests using granitic and gneissic weathered soils. Three sets of pore pressure-water content values were simultaneously obtained during each test, and T² statistics at the 90.0% and 95.0% confidence levels were calculated based on the correlations between values. The results show that unsuccessful detection of some local failures of the flume slope depended on the sensor position. In the case of global slope failures, anomalous behavior was detected between several hundred and several thousand seconds before the event as T² statistics exceeded the confidence interval 90%. Hotelling's T² analysis provides a single control criterion because it enables correlations between diverse measured values within the same slope; the criterion also includes stepwise criteria for a forecasting and warning system based on confidence levels.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.49 no.2
• /
• pp.1-6
• /
• 2012

In recent years global climate change of hurricanes and torrential rains are going to significantly, that increase damages to property and human life. The disasters have been several claimed in every field. In future, climate changes blowing are keen to strike released to the world like in several movies. Reducing the damage of long-term weather phenomena are emerging with predicting changes in weather. In this study, it is shown how to predict the red tide phenomenon with multiple linear regression analysis and artificial neural network techniques. The red tide phenomenon causing risk could be reduced by filtering sensor data which are transmitted and forecasted in real time. It could be ubiquitous driven custom marine information service system, and forecasting techniques to use throughout the meteorological disasters to minimize damage.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.6
• /
• pp.1403-1412
• /
• 2019

Effect of damage and loss cost for downtime is huge, if physical devices such as turbines, pipe, and storage tanks are in the abnormal state originated from not only aging, but also cyber attacks on the control and monitoring system like PLC (Programmable Logic Controller). To improve availability and dependability of the physical devices, we design and implement an indirect health monitoring system which sense temperature, acceleration, current, etc. indirectly, and put sensor data into Influx DB in real-time. Then, the actual performance of detecting abnormal state is shown using the indirect health monitoring system. Analyzing data are acquired using the real-time indirect health monitoring system, abnormal state and security threats can be double-monitored and lower maintenance cost utilizing prognostics and health management.