• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.469-472
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• 2008

In this paper, we discuss the recent trends on the passive UHF RFID tag chip design techniques and several important system parameters. We also summarize link budget studies on both conventional and modem UHF RFID communications. The paper highlights the reverse link limited case, which has known to be the minor concern if reader continuous wave (CW) can reach the tag in sufficient level. This makes sense when the tag sensitivity is rather high (over 10-12${\mu}W$); however, since the tag chip fabrication technologies have been developed by time, the tag chip threshold levels are now less-dominant in determining link margin. If the tag limitation can be alleviated, the forward link limited case can be resolved; thus, we rather focus on the path-loss problem. Since the path-losses are still exist in both forward and reverse links, and it can be doubled while CW travels the reader-tag-reader path because forward link and reverse link are on the same distance. Consider if reader receiver sensitivity is very high in the worst case. In this case, weaken tag response (i.e., backscatters) cannot reach the level that reader receiver can process tag data; bit-error rate can be higher. Overall, backscatter levels should be high enough so that reader receiver can correctly function. After discussing link budget, we carried out practical measurements on fading effects between two circularly polarized UHF RFID antennas in a small scale area.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.179-190
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• 2022

In this study, we investigated the characteristic impedance and bandwidth of CPW3DCS (coplanar waveguide employing periodic 3D coupling structures), and examined its potential for the development of a marine radio communication FISoC (fully-integrated system on chip) semiconductor device. To extract bandwidth and characteristic impedance of the CPW3DC, we induced a measurement-based equation reflecting measured insertion loss, and compared the measured results of the propagation constant β and characteristic impedance with the measured ones. According to the results of the comparison, the calculated results show a good agreement with the measured ones. Concretely, the propagation constant β and characteristic impedance exhibited an maximum error of 3.9% and 6.4%, respectively. According to the results of this study, in a range of LT = 30 ~ 150 ㎛ for the length of periodic structures, the CPW3DC exhibited a passband characteristic of 121 GHz, and a very small dependency of characteristic impedance on frequency. We could realize a low impedance transmission line with a characteristic impedance lower than 20 Ω by using CPW3DCS with a line width of 20 ㎛, which was highly reduced, compared with a 3mm line width of conventional transmission line with the same impedance. The characteristic impedance was easily adjusted by changing LT. The above results indicate that the CPW3DC can be usefully used for the development of a wireless communication FISoC (fully-integrated system on chip) semiconductor device. This is the first report of a study on the bandwidth of the CPW3DC.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.807-821
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• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.355-364
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• 2023

The arbitrary Lagrangian-Eulerian (ALE) method has been extensively researched owing to its capability to accurately predict the propagation of blast shock waves. Although the use of the ALE method for dynamic analysis can produce unreliable results depending on the mesh size of the finite element, few studies have explored the relationship between the mesh size for the air domain and the accuracy of numerical analysis. In this study, we propose a procedure to calculate the optimal mesh size based on the mean squared error between the maximum blast pressure values obtained from numerical simulations and experiments. Furthermore, we analyze the relationship between the weight of explosive material (TNT) and the optimal mesh size of the air domain. The findings from this study can contribute to estimating the optimal mesh size in blast simulations with various explosion weights and promote the development of advanced blast numerical analysis models.

• Journal of Navigation and Port Research
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• v.34 no.3
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• pp.175-180
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• 2010

In the almost application parts, GNSS being used the primary navigation system on world-widely. However, some of nations attempt or deliberate to enhance current Loran system, as a backup to satellite navigation system because of the vulnerability to the disturbance signal. Loran interests in supplemental navigation system by the development and enhancement, which is called eLoran, and that consists of advancement of receiver and transmitter and of differential Loran in order to increase the accuracy of current Loran-C. A significant factor limiting the ranging accuracy of the eLoran signal is the ASF in the TOAs observed by the receiver. The ASF is mostly due to the fact that the ground-wave signal is likely to propagate over paths of varying conductivity and topography. This paper presents comparison results between the predicted ASF and the measured ASF in a southern east region of Korea. For predicting ASF, the Monteath model is used. Actual ASF is measured from the legacy Loran signal transmitted Pohang station in the GRI 9930 chain. The test results showed the repeatability of the measured ASF and the consistent characteristics between the predicted and the measured ASF values.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11B
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• pp.1501-1509
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• 2001

Recently, the use of wireless communication systems has been rapidly increasing, which results in a difficult problem in efficient control of limited frequency resources. As a way of solving this problem, the ultra wideband time hopping impulse radio system attracts much attention. The impulse radio system communicates pulse position modulated data using Gaussian monocycle pulses of very short duration less than 1 nsec. Thus the transmitted signal has very low power spectral density and ultra wide bandwidth from near D.C. to a few GHz. It is blown that it hardly interferes with the existing communication systems because of its very low power spectral density. The purpose of this paper is to characterize multipath propagation of the impulse radio signal and to evaluate the performance of the correlator-based receiver for the multipath environments. In this paper, we consider the deterministic two-path model and the statistical indoor multipath model of Saleh and Valenzuela. For the two-path model the output of the correlator with the ideal reference waveform varies according to the relative difference between the indirect path delay and the time interval of PPM, and to the indirect path gains. In addition, the characteristics of bit error rates is measured for the two models through computer simulation. The simulation results indicate that the performance of the impulse radio system depends both on the relative difference between the indirect path delay and the time interval of PPM, and on the indirect path gains. Furthermore, it is observed that the reference signal designed for the AWGN channel can not be applied to the multipath channels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.957-960
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• 2006

In this paper, we studied the location estimation algorithm of a spatial 3 dimension which extend the location estimation algorithm of a plane 2 dimension in 2.45GHz band RTLS(Real time location system). We used TDOA scheme which need not a time of transmission information of the tag and estimated 3 dimension coordinates. Also, estimated intersection of hyperbolic curve to X, Y coordinate of the tag at 2D coordinates searching area, $300m\times300m$ and LOS propagation environments. And, we estimated Z coordinate ultimately using X, Y coordinate. The location estimation algorithm of a spatial 3 dimension satisfies the RTLS specification requirement, 3m radius accuracy. From the result, we confirm that the location of tag which similar to actual coordinate in the case to an ideal received offset. However, we verified that the location of tag which escapes from a radius 3m within error range when received offset increased. Therefore, as the future work we are consider enhanced location accuracy of a spatial 3 dimension in RTLS system which using the decrease scheme of reader offset or the discriminate scheme of the estimation location.