• Journal of Information Processing Systems
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• v.9 no.3
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• pp.477-488
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• 2013

Wireless Sensor Networks (WSNs) are comprised of sensor nodes that forward data in the shape of packets inside a network. Proficient packet forwarding is a prerequisite in sensor networks since many tasks in the network, together with redundancy evaluation and localization, depend upon the methods of packet forwarding. With the motivation to develop a fault tolerant packet forwarding scheme a Self-Localized Packet Forwarding Algorithm (SLPFA) to control redundancy in WSNs is proposed in this paper. The proposed algorithm infuses the aspects of the gossip protocol for forwarding packets and the end to end performance of the proposed algorithm is evaluated for different values of node densities in the same deployment area by means of simulations.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.119-124
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• 1998

Block Matching Motion Estimation(BMME) Algorithm is demands a very large amount of computing power and have been proposed many fast algorithms. These algorithms are many problem that larger size of VLSI scale due to non-localized search block data and problem of non-reuse of input data for each processing step. In this paper, we designed systolic arry of high processing capacity, constraints input output pin size and reuse of input data for small VLSI size. The proposed systolic array is optimized memory access time because of iterative reuse of input data on search block and become independent of problem size due to increase of algorithm's parallelism and total processing elements connection is localized spatial and temporal. The designed systolic array is reduced O(N6) time complexity to O(N3) on moving vector and has O(N) input/output pin size.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• The Journal of the Acoustical Society of Korea
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• v.20 no.1E
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• pp.31-37
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• 2001

Matched Field Processing (MFP) is a successive process of correcting mismatches between true and assumed parameters by matching the measured acoustic field data with numerically simulated data which we call replica. The MFP is widely used both in geo-acoustic parameter inversions and in source localizations. Whether a certain parameter can be inverted effectively or whether a source can be localized correctly depends on the amount of the influence that a parameter has on the acoustic field during the matching process. Sensitive parameters can be better estimated than the less sensitive ones in MFP. On the contrary, the sensitive parameters affect adversely on the source localization results when they have uncertainties. In this paper, a sensitivity index is defined based upon the field variation resulting from the perturbed parameters. Numerical test results show that the index behaves in accordance with the results of source localization under a mismatched environment and also with the inversion solutions.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09b
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• pp.738-739
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• 2006

This paper will describe a powder and processing method that facilitates single press-single sintered densities approaching $7.5g/cm^3$. At this sintered density, mechanical properties of the powder metal (P/M) component are significantly improved over current P/M technologies and begin to approach the performance of wrought steels. High performance gears have the added requirement of rolling contact fatigue durability that is dependent upon localized density and thermal processing. Combining high density processing of engineered P/M materials with selective surface densification enables powder metal components to achieve rolling contact fatigue durability and mechanical property performance that satisfy the performance requirements of many high strength automotive transmission gears. Data will be presented that document P/M part performance in comparison to conventional wrought steel grades.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8A
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• pp.777-784
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• 2010

Recently, the necessity of underwater communication and demand for transmitting and receiving various data such as voice or high resolution image data are increasing as well. The performance of underwater acoustic communication system is influenced by characteristics of the underwater communication channels. Especially, ISI(inter symbol interference) occurs because of delay spread according to multi-path and communication performance is degraded. In this paper, we study the OFDM technique to overcome the delay spread in underwater channel and by using CP, we compensate for delay spread. But PAPR which OFDM system has problem is very high. Therefore, we use DFT-spread OFDM method to avoid nonlinear distortion by high PAPR and to improve efficiency of amplifier. DFT-spread OFDM technique obtains high PAPR reduction effect because of each parallel data loads to all subcarrier by DFT spread processing before IFFT. In this paper, we show performance about delay spread through OFDM system and verify method that DFT spread OFDM is more suitable than OFDM for underwater communication. And we analyze performance according to two subcarrier mapping methods(Interleaved, Localized). Through the simulation results, performance of DFT spread OFDM is better about 5~6dB at $10^{-4}$ than OFDM. When compared to BER according to subcarrier mapping, Interleaved method is better about 3.5dB at $10^{-4}$ than Localized method.

• Economic and Environmental Geology
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• v.48 no.1
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• pp.51-63
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• 2015

I produced a secular variation model of geomagnetic field by using the magnetic component data from four geomagnetic observatories located in Northeast Asia during the years between 1997 and 2011. The Earth's magnetic field varies with time and location due to the dynamics of fluid outer core and the magnetic observatories on the surface measure in time series. To adequately represent the magnetic field or secular variations of the Earth, a spatio-temporal model is required. In making a global model, satellite observations as well as limited observatory data are necessary to cover the regions and time intervals. However, you need a considerable work and time to process a huge amount of the dataset with complicated signal separation procedures. When you update the model, the same amount of chores is demanded. Besides, the global model might be affected by the measurement errors of each observatory that are biased and the processing errors in satellite data so that the accuracy of the model would be degraded. In this study, as considered these problems, I introduced a localized method in modeling secular variation of the Earth's magnetic field over Northeast Asia region. Secular variation data from three Japanese observatories and one Chinese observatory that are all in the INTERMAGNET are implemented in the model valid between 1997 to 2011 with the interval of 6 months. With the resulting model, I compared with the global model called CHAOS-4, which includes the main, secular variation and secular acceleration models between 1997 to 2013 by using the three satellites' databases and INTERMAGNET observatory data. Also, the geomagnetic 'jerk' which is known as a sudden change in the time derivatives of the main field of the Earth, was discussed from the localized secular acceleration coefficients derived from spline models.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1037-1040
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• 2006

This paper presents an efficient overlay multicast routing protocol (ROMP) and its architecture. ROMP using GPS and region map stands on the basis of scalable 2-tiered multicast architecture and maintains a global overlay multicast tree through reactive fashion. With this approach, the coarse-grain location-awareness for an efficient overlay multicasting and the determination of hot-spot area for efficient data delivery are feasible. The simulation results show that the localized control packets of ROMP prevent the overlay control packets from squandering the physical network resources.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.1091-1092
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• 2007

Wireless sensor networks (WSN) can be used in military surveillance, in which highly confidential data needs to be transmitted. In effect, security becomes a very important aspect in such networks. We present an efficient key management scheme for WSN. Our scheme is an improvement over SHELL [1] and mostly relies on communication within a cluster of nodes.

• Smart Structures and Systems
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• v.14 no.2
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.