• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.44 no.1
• /
• pp.24-31
• /
• 2007

DVB-T (Digital Video Broadcasting-Terrestrial) is an important multimedia broadcasting technology capable of high data-rate transmission and adopted by Europe. OFDM (Orthogonal Frequency Division Multiplexing) is the backbone technique employed in DVB-T to support multimedia services that have various bandwidths. Unfortunately, an OFDM signal has a large PAPR (Peak-to-Average Power Ratio). In this paper, we investigate the performance of a simple PAPR reduction scheme for the DVB-T system, which requires no change of a receiver structure or no additional information transmission. The approach we employed is clipping in the time and frequency domains. The time-domain clipping is carried out with a predetermined clipping level while the frequency-domain clipping is done within EVM (Error Vector Magnitude). This approach is suboptimal with lower computational complexity compared to the optimal method. The simulation results demonstrate that the proposed one is getting more effective at lower modulation levels and with more allowed constellation error.

• Ocean and Polar Research
• /
• v.44 no.4
• /
• pp.355-364
• /
• 2022

When the 60-ton-class patrol boat '72' of the Korea Coast Guard (KCG) was on duty and she accidentally collided with another patrol boat ('207', 200-ton-class) and sank. A month-long search found a small amount of lost items, but neither the crew nor the ship was found. For the first time in 39 years since the accident, the Korea Institute of Ocean Science and Technology (KIOST) searched the boat 72 using the latest integrated geophysical techniques. A number of sonar images presumed to be of a sunken ship was acquired using a combined system of side scan sonar and marine magnetometer, operated at an altitude of approximately 30 m from the seabed. At the same time, a strong magnetic anomaly (100 nT) was detected in one place, indicating the presence of an iron ship. A video survey using a remotely operated underwater vehicle (ROV) confirmed the presence of a shielding part of a personal firearm at the stern of the sunken vessel. Based on these comprehensive data, the sunken vessel discovered in this exploration was assumed to be '72'. This result is meaningful in terms of future ocean exploration and underwater archaeology, as the integrated system of various geophysical methods is an efficient means of identifying objects present in the water.

• Geophysics and Geophysical Exploration
• /
• v.24 no.3
• /
• pp.73-77
• /
• 2021

Induced polarization (IP) is useful for mineral exploration and hydrogeological studies by visualizing the electrochemical reactions at the interface between polarized minerals and groundwater. Frequency-domain IP (FDIP) is not actively applied to field surveys because it takes longer to acquire data, despite its higher data quality than conventional time-domain IP. However, data quality is more important in current mineral exploration as the targets gradually shift to deep or low-grade ore bodies. In addition, the measurement time reduced by automated instrumentation increases the potential for FDIP field applications. Therefore, we demonstrate that FDIP can detect mineral exploration targets by performing geophysical logging in the boreholes of a skarn deposit, in South Korea. Alternating current (AC) resistivity, percent frequency effect (PFE) and metal factor (MF) were calculated from impedance values obtained at two different frequencies. Skarn zones containing magnetite or pyrite showed relatively low AC resistivity, high PFE, and high MF compared to other zones. Therefore, FDIP surveys are considered to be useful for mineral exploration.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.7
• /
• pp.633-640
• /
• 2001

In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2008.10a
• /
• pp.51-56
• /
• 2008

The waveform inversion for isotropic media has ever been studied since the 1980s, but there has been few studies for anisotropic media. We present a seismic waveform inversion algorithm for 2-D heterogeneous transversely isotropic structures. A cell-based finite difference algorithm for anisotropic media in time domain is adopted. The steepest descent during the non-linear iterative inversion approach is obtained by backpropagating residual errors using a reverse time migration technique. For scaling the gradient of a misfit function, we use the pseudo Hessian matrix which is assumed to neglect the zero-lag auto-correlation terms of impulse responses in the approximate Hessian matrix of the Gauss-Newton method. We demonstrate the use of these waveform inversion algorithm by applying them to a two layer model and the anisotropic Marmousi model data. With numerical examples, we show that it's difficult to converge to the true model when we assumed that anisotropic media are isotropic. Therefore, it is expected that our waveform inversion algorithm for anisotropic media is adequate to interpret real seismic exploration data.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.12B
• /
• pp.1498-1508
• /
• 2011

A cloud service customer has no other way but to wait for his lost data to be recovered by the cloud service provider when the data was lost or not accessible for a while due to the provider's system failure, cracking attempt, malfunction, or outage. We consider a solution to address this problem that can be implemented in the cloud client's domain, rather than in the cloud service provider's domain. We propose a high level architecture and scheme for successfully retrieving data units even when several cloud storages are not accessible at the same time. The scheme is based on a clever way of partitioning and partial overlapping of data for being stored on multiple cloud storages. In addition to providing a high level of data availability, the scheme makes it possible to re-encrypt data units with new keys in a user transparent way, and can produce the complete log of every user's data units accessed, for assessing data disclosure, if needed.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2001.04a
• /
• pp.58-65
• /
• 2001

It is essential to know exactly what the response of the seismograph is inclusive of characteristic of the seismic sensors before using it for detailed seismic study. This is because the recorded earthquake data can be more or less affected by the overall system and need to be corrected properly to the analysis`s best to obtain the right results. In this respect, two basic earthquake data processing techniques are introduced and applied, for validation purpose, to real data from KEPRI seismic monitoring system which were established for determining the site-specific characteristics of the earthquakes around the Nuclear Power Plants. One is conventional instrumental correction technique for velocity data and the other is for removing acausal ringing originate from using linear phase FIR filter. These techniques are all implemented in the time domain using digital filtering process and shows the desired results when applied to real earthquake data.

• Journal of Information Processing Systems
• /
• v.7 no.4
• /
• pp.717-732
• /
• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.4
• /
• pp.111-119
• /
• 2012

Time Stretched Pulse (TSP) is used for transmitting and analyzing the impulse signal over the designated spatial place. However, if transfer functions of transmitter and receiver are unknown, performance investigation of free field in temporal domain is barely possible due to the overlap between the direct and indirect signal from the space. Generally, the free field or hemi-free field is evaluated by the Annex A of ISO 3745 in which utilizing the inverse square law with one-third octave band signals. In this paper, the author performs analysis of free field via applying TSP with inverse square law and the results are compared with the one-third octave band signals. According to the analysis of deviation between the corresponding signal and inverse square law model, the proposed TSP method provides the comparable performance index to the one-third octave band signal with reduced measuring time. Provided that the pre-whitening can be implementable by employing the speaker and microphone transfer function, further analyses from TSP compression are able to be performed such as multipath separation from time domain data. The anechoic chamber used in this experiment is verified conformance with ISO 3745 for free field and hemi-free field condition for limited frequency of the signal.

• Journal of Bio-Environment Control
• /
• v.29 no.3
• /
• pp.277-284
• /
• 2020

To increase the utilization of the intelligent methodology of smart farm management, estimation modeling techniques are required to assess prior examination of crops and environment changes in realtime. A mandatory environmental factor such as CO₂ is challenging to establish a reliable estimation model in time domain accounted for indoor agricultural facilities where various correlated variables are highly coupled. Thus, this study was conducted to develop an artificial neural network for reducing time complexity by using environmental information distributed in adjacent areas from a time perspective as input and output variables as CO₂. The environmental factors in the smart farm were continuously measured using measuring devices that integrated sensors through experiments. Modeling 1 predicted by the mean data of the experiment period and modeling 2 predicted by the day-to-day data were constructed to predict the correlation of CO₂. Modeling 2 predicted by the previous day's data learning performed better than Modeling 1 predicted by the 60-day average value. Until 30 days, most of them showed a coefficient of determination between 0.70 and 0.88, and Model 2 was about 0.05 higher. However, after 30 days, the modeling coefficients of both models showed low values below 0.50. According to the modeling approach, comparing and analyzing the values of the determinants showed that data from adjacent time zones were relatively high performance at points requiring prediction rather than a fixed neural network model.