• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.673-680
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• 2000

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, a back-propagation neural network model for estimating a consolidation behavior of clay from soil parameter, site investigation data and the first settlement curve is proposed. The training and testing of the network were based on a database of 63 settlement curve from two different sites. Five different network models were used to study the ability of the neural network to predict the desired output to increasing degree of accuracy. The study showed that the neural network model predicted a consolidation behavior of clay reasonably well.

• Journal of Ocean Engineering and Technology
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• v.12 no.3 s.29
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• pp.96-102
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• 1998

This paper presents to create a SAC(Sectional Area Curve) using an ANFIS(Adaptive-Network-based Fuzzy Inference System). First, it defines SACs of parent ships by using a B-spline approximation and a genetic algorithm and accumulates a database about SAC's control points. Second, it learns an ANFIS from parent ship data, which are related with principal dimensions and SAC's control points. This process is to model an ANFIS for SAC inferreice. When an ANFIS modeling is completed, we can determine a SAC through an ANFIS inferring.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.60-70
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• 2021

In this paper we study the problem of implementation of security issues of blue tooth, especially secure simple pairing, with the help of an efficient four user authenticated key (4UAK) for an elliptic curve cryptography (ECC). This paper also deals with the design, implement and performance evaluation of secure simple pairing (SSP) using an elliptic curve cryptography, such as Diffie Hellman protocol when four users are involved. Here, we also compute the best, worst and average case step counts (time complexities). This work puts forth an efficient way of providing security in blue tooth. The time complexity of O(n⁴) is achieved using Rabin Miller Primality methodology. The method also reduces the calculation price and light communication loads.

• Tunnel and Underground Space
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• v.28 no.3
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• pp.277-291
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• 2018

LSTM (Long Short-Term Memory) algorithm which is a kind of recurrent neural network was used to establish a model to predict the stress-strain curve of an material under uniaxial compression. The model was established from the stress-strain data from uniaxial compression tests of silica-gypsum specimens. After training the model, it can predict the behavior of the material up to the failure state by using an early stage of stress-strain curve whose stress is very low. Because the LSTM neural network predict a value by using the previous state of data and proceed forward step by step, a higher error was found at the prediction of higher stress state due to the accumulation of error. However, this model generally predict the stress-strain curve with high accuracy. The accuracy of both LSTM and tangential prediction models increased with increased length of input data, while a difference in performance between them decreased as the amount of input data increased. LSTM model showed relatively superior performance to the tangential prediction when only few input data was given, which enhanced the necessity for application of the model.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1071-1075
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• 2010

Because a sensor node in wireless sensor networks(WSNs) has limited resources, such as battery capacity and memory, data aggregation techniques have been studied to manage the limited resources efficiently. Because sensor network uses wireless communication, a data can be disclosed by attacker. Thus, the study on data protection schemes for data aggregation is essential in WSNs. But the existing data aggregation methods require both a large number of computation and communication, in case of network construction and data aggregation processing. To solve the problem, we propose a data protection scheme based on Hilbert-curve for data aggregation. Our scheme can minimizes communications among neighboring sensor nodes by using tree-based routing. Moreover, it can protect the data from attacker by doing encryption through a Hilbert-curve technique based on a private seed, Finally, we show that our scheme outperforms the existing methods in terms of message transmission and average sensor node lifetime.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.133-138
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• 2001

This paper describes a system that can used to recognize an unknown material regardless of the change of ambient tem-perature using temperature response curve fitting and fuzzy neural network(FNN). There are some problems to realize the recogni-tion system using temperature response. It requires too many memories to store the vast temperature response data and it has to be filtered to remove noise which occurs in experiment. And the temperature response is influenced by the change of ambient tempera-ture. So, this paper proposes a practical method using curve fitting the remove above problems of memories and nose. And FNN is propose to overcome the problem caused by the change of ambient temperature. Using the FNN which is learned by temperature responses on fixed ambient temperature and known thermal conductivity, the thermal conductivity of the material can be inferred on various ambient temperature. So the material can be recognized by the thermal conductivity.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.169-176
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• 2023

The vehicular ad hoc network (VANET) is currently an important approach to improve personal safety and driving comfort. ANEL is a MAC-based authentication scheme that offers all the advantages of MAC-based authentication schemes and overcomes all their limitations at the same time. In addition, the given scheme, ANEL, can achieve the security objectives such as authentication, privacy preservation, non-repudiation, etc. In addition, our scheme provides effective bio-password login, system key update, bio-password update, and other security services. Additionally, in the proposed scheme, the Trusted Authority (TA) can disclose the source driver and vehicle of each malicious message. The heavy traffic congestion increases the number of messages transmitted, some of which need to be secretly transmitted between vehicles. Therefore, ANEL requires lightweight mechanisms to overcome security challenges. To ensure security in our ANEL scheme we can use cryptographic techniques such as elliptic curve technique, session key technique, shared key technique and message authentication code technique. This article proposes a new efficient and light authentication scheme (ANEL) which consists in the protection of texts transmitted between vehicles in order not to allow a third party to know the context of the information. A detail of the mapping from text passing to elliptic curve cryptography (ECC) to the inverse mapping operation is covered in detail. Finally, an example of application of the proposed steps with an illustration

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.264-269
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• 2008

In this paper, two neural networks as a material model, which are based on the state-space method, have been proposed. One outputs the rates of inelastic strain and material internal variables whereas the outputs of the other are the next state of the inelastic strain and material internal variables. Both the neural networks were trained using input-output data generated from Chaboche's model and successfully converged. The former neural network could reproduce the original stress-strain curve. The neural network also demonstrated its ability of interpolation by generating untrained curve. It was also found that the neural network can extrapolate in close proximity to the training data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.747-754
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• 2016

DHCP(Dynamic Host Configuration Protocol) is a protocol for efficiency and convenience of the IP address management. DHCP automatically assigns an IP address and configuration information needed to run the TCP/IP communication to individual host in the network. However, existing DHCP is vulnerable for network attack such as DHCP spoofing, release attack because there is no mutual authentication systems between server and client. To solve this problem, we have designed a new DHCP protocol supporting the following features: First, ECDH(Elliptic Curve Diffie-Hellman) is used to create session key and ECDSA(Elliptic Curve Digital Signature Algorithm) is used for mutual authentication between server and client. Also this protocol ensures integrity of message by adding a HMAC(Hash-based Message Authentication Code) on the message. And replay attacks can be prevented by using a Nonce. As a result, The receiver can prevent the network attack by discarding the received message from unauthorized host.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.1-6
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• 2008

Biological and chemical sensors are the two most frequently used sensors to monitor the water resource. Chemical sensor is very accurate to pick up the types and to measure the concentration of the chemical substance. Drawback is that it works for just one type of chemical substance. Therefore a lot of expensive monitoring system needs to be installed to determine the safeness of the water, which costs too much expense. Biological sensor, on the contrary, can judge the degree of pollution of the water with just one monitoring system. However, it is not easy to figure out the type of contaminant with a biological sensor. In this study, an endeavor is made to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve (FIC) from a biological monitoring system. Wem-tox values are calculated from the amount of flourescence of contaminated and reference water. Curve fitting is executed to find the representative curve of the raw data of Wem-tox values. Then the curves are digitalized at the same interval to train the neural network model. Taguchi method is used to optimize the neural network model parameters. The optimized model shows a good capacity to figure out the toxicant from FIC.