• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.901-908
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• 2006

With the development of wireless communication techniques and mobile environment we are able to transmit data between mobile systems without restriction of time and space. Recently, researches on the data communication between mobile systems have focused on a small amount of sending out or receiving data and data synchronization at a fixed server and mobile clients in mobile environment. However, two more servers should be able to move mutual independently, information is shared with other systems, and data is synchronized in the special environment like a battlefield situation. Therefore, we propose a data synchronization method between systems moving mutual independently in mobile environment. The proposed method is an optimization solution to data propagation path between servers that considers limited bandwidth and process of data for disconnection communication. In addition, we propose a data reduction method that considers importance and sharing of information in order to reduce data transmission between huge servers. We verified the accuracy of data after accomplishing our data synchronization method by applying it in the real world environment. Additionally, we showed that our method could accomplish data synchronization normally within an allowance tolerance when we considered data propagating delay time by server extension.

• Journal of KIISE
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• v.43 no.3
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• pp.389-397
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• 2016

In this paper, we propose a highway safety system that comprises a small number of central servers and smart mobile devices. To implement this system, we constructed a central server that collects GPS location information on cars, whose update messages are decreased via the car location estimation algorithm. The in-car mobile devices use the accelerometer sensors to detect hazardous situations; this information is updated to the central server that relays the information to the corresponding endangered cars via location-based unicast using LTE communication. To evaluate the proposed algorithm, we equipped a mobile device app on a real car and conducted real experiments in various environments such as city streets, rural areas, and highway roads. Furthermore, we conducted simulations to evaluate the propagation of danger information. Finally, we conducted simulated experiments to detect car collisions as well as exceptions, such as falling of the mobile device from the cradle.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2172-2177
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• 2015

The DF-MZEP-CME (decision feedback - maximum zero-error probability for constant modulus errors) algorithm that makes the probability for constant modulus error (CME) close to zero and employs decision feedback (DF) structures shows more improved performance in channel distortion compensation. However the DF-MZEP-CME algorithm has a computational complexity proportional to a sample size for probability estimation and this property plays a role of an obstacle in practical implementation. In this paper, the gradient of DF-MZEP-CME is proposed to be estimated recursively and shown to solve the computational problem by making the algorithm independent of the sample size. For a sample size N, the conventional method has 10N multiplications but the proposed has only 20 regardless of N. Also the recursive gradient estimation for weight update is kept in continuity from the initial state to the steady state without any error propagation.

• Journal of Korea Spatial Information System Society
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• v.5 no.2 s.10
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• pp.69-82
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• 2003

Mobile transactions updating spatial objects are long transactions that update local objects of mobile clients during disconnection. Since a recovered transaction cannot read the write sets of other transactions committed before the recovery due to disconnection, the recovered transaction may conflicts with them. However, aborting of the recovered long transaction leads to the cancellation of all updates including the recovered updates. It is definitely unsuitable to cancel the recovered updates due to the conflicts. In this paper, we propose the recovery scheme to retrieve foreign conflictive objects from the write sets of other transactions for reducing aborting of a recovered transaction. The foreign conflictive objects are part of the data committed by other transactions and may conflict with the objects updated by the recovered transaction. In the scheme, since the recovered transaction can read both the foreign conflictive objects and the recently checkpoint read set, it is possible to reupdate properly the potentially conflicted objects.

• The Journal of Engineering Research
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• v.2 no.1
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• pp.23-30
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• 1997

For a effective management and easy tool integration of CAD database, Hierarchy interface System(HIS) was designed and GROCO(Graph Representation fOr Complex Objects) Model was presented in another my paper[10]. Hierarchy Interface System which is composed of two subsystems of a configurator and a converter is designed for the interface between a conventional database management system and CAD tools. In this paper, Version Management System is presented for supporting effective operations of HIS using GROCO model. Version Management System supports efficiently CAD database charaters having a hierarchical structure of composite objects. In Version Management System, A design evolves in discrete states through mutation and derivation for going phases of design giving rise to multiple versions. Operations and rules are provided transition between their different states. and for controlling update propagation and preventing version proliferation. Version Modeling Graph is proposed for dealing with versioning at the instance and type levels.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.7
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• pp.32-38
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• 2012

In this paper, to eliminate intersymbol interference of the received signal due to multipath propagation, a parallel equalization algorithm using two error estimation functions is proposed. In the proposed algorithm, multilevel two-dimensional signals are considered as equivalent binary signals, then error signals are estimated using the sigmoid nonlinearity effective at the initial phase equalization and threshold nonlinearity with high steady-state performance. The two errors are scaled by a weight depending on the relative accuracy of the two error estimations, then two filters are updated differentially. As a result, the combined output of two filters was to be the optimum value, fast convergence at initial stage of equalization and low steady-state error level were achieved at the same time thanks to the combining effect of two operation modes smoothly. Usefulness of the proposed algorithm was verified and compared with the conventional method through computer simulations.

• Steel and Composite Structures
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• v.37 no.6
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• pp.663-678
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• 2020

This paper proposes a novel topology optimization method generating multiple materials for external linear plane crack structures based on the combination of IsoGeometric Analysis (IGA) and eXtended Finite Element Method (X-FEM). A so-called eXtended IsoGeometric Analysis (X-IGA) is derived for a mechanical description of a strong discontinuity state's continuous boundaries through the inherited special properties of X-FEM. In X-IGA, control points and patches play the same role with nodes and sub-domains in the finite element method. While being similar to X-FEM, enrichment functions are added to finite element approximation without any mesh generation. The geometry of structures based on basic functions of Non-Uniform Rational B-Splines (NURBS) provides accurate and reliable results. Moreover, the basis function to define the geometry becomes a systematic p-refinement to control the field approximation order without altering the geometry or its parameterization. The accuracy of analytical solutions of X-IGA for the crack problem, which is superior to a conventional X-FEM, guarantees the reliability of the optimal multi-material retrofitting against external cracks through using topology optimization. Topology optimization is applied to the minimal compliance design of two-dimensional plane linear cracked structures retrofitted by multiple distinct materials to prevent the propagation of the present crack pattern. The alternating active-phase algorithm with optimality criteria-based algorithms is employed to update design variables of element densities. Numerical results under different lengths, positions, and angles of given cracks verify the proposed method's efficiency and feasibility in using X-IGA compared to a conventional X-FEM.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.629-635
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• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.

• 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.