• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.4
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• pp.357-374
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• 2012

Considerable uncertainties are included in ground properties used for tunnel designs due to the limited investigation and tests. In this study, a back analysis was performed to find optimal ground properties based on artificial neural network using both face mapping data and convergence measurement data. First of all, the rock class of a study tunnel is determined from face mapping data. Then the possible ranges of ground properties were selected for each rock class through a literature review on the previous studies and utilized to establish more precise learning data. To find an optimal training model, a sensitivity analysis was also conducted by varying the number of hidden layers and the number of nodes more minutely than the previous study. As a result of this study, more accurate ground properties could be obtained. Therefore it was confirmed that the accuracy of the results could be increased by making use of not only convergence measurement data but also face mapping data in tunnel back analyses using artificial neural network. In future, it is expected that the methodology suggested in this study can be used to estimate ground properties more precisely.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.131-139
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• 2013

To overcome the limitations in performance and power consumption of traditional electrical interconnection based network-on-chips (NoCs), a hybrid optical network-on-chip (HONoC) architecture using optical interconnects is emerging. However, the HONoC architecture should use circuit-switching scheme owing to the overhead by optical devices, which worsens the latency unfairness problem caused by frequent path collisions. This resultingly exert a bad influence in overall performance of the system. In this paper, we propose a new task mapping algorithm for optimizing latency by reducing path collisions. The proposed algorithm allocates a task to a certain processing element (PE) for the purpose of minimizing path collisions and worst case latencies. Compared to the random mapping technique and the bandwidth-constrained mapping technique, simulation results show the reduction in latency by 43% and 61% in average for each $4{\times}4$ and $8{\times}8$ mesh topology, respectively.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.768-773
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• 2011

The prediction method of future events using the time histories of velocity or pressure, etc., is a useful way for controlling various air vehicles. For example, the sensors of velocity or pressure can be used to extract the time mode coefficients of eigenmode of flow field, and then the result is applied to suppress wake or drag. The velocity information is mapped to the entire flow field, so this mapping function can be used to predict the future events based on the current information. The mapping function is composed of the huge amount of weight parameters, so the efficient way of finding these parameters is needed. Here, the neural network algorithm is studied to draw a mapping function using the number and location of velocity sensors.

• 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

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.75-80
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• 2017

In this paper, we describe the effect of the regularization method and the network with identity mapping on the performance of the activation functions in deep convolutional neural networks. The activation functions act as nonlinear transformation. In early convolutional neural networks, a sigmoid function was used. To overcome the problem of the existing activation functions such as gradient vanishing, various activation functions were developed such as ReLU, Leaky ReLU, parametric ReLU, and ELU. To solve the overfitting problem, regularization methods such as dropout and batch normalization were developed on the sidelines of the activation functions. Additionally, data augmentation is usually applied to deep learning to avoid overfitting. The activation functions mentioned above have different characteristics, but the new regularization method and the network with identity mapping were validated only using ReLU. Therefore, we have experimentally shown the effect of the regularization method and the network with identity mapping on the performance of the activation functions. Through this analysis, we have presented the tendency of the performance of activation functions according to regularization and identity mapping. These results will reduce the number of training trials to find the best activation function.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.5
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• pp.70-75
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• 1999

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.4
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• pp.117-123
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• 2008

In this paper, we propose a novel ant colony optimization(ACO)-based application core ma ins method for implementing network-on-chip(NoC)-based systems-on-chip(SoCs). The proposed method efficiently put application cores to a mesh-type NoC satisfying a given design objective, the network latency. Experimental results using a functional circuit including 12 cores show that the proposed algorithm can produce near optimal mapping results within a second.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.1
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• pp.15-23
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• 2010

This paper presents a method of constructing a large interlinked ontology network for the Web of Data through the mapping among typical ontologies. When an ontology is open to the public, and more easily shared and used by people, its value is increased more and more. By linking CoreOnto, an IT core ontology constructed in Korea, to the worldwide ontology network, CoreOnto can be open to abroad and enhanced its usability. YAGO is an ontology constructed by combining category information of Wikipedia and taxonomy of WordNet, and used as the backbone of DBpedia, an ontology constructed by analyzing Wikipedia structure. So a mapping method is suggested by linking CoreOnto to YAGO and DBpedia through the synset of WordNet.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.718-731
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• 2003

SAN(Storage Area Network) was developed in response to the requirements of high availability of data, scalable growth, and system performance. In order to use the SAN more efficiently, most of the SAN operating software supports storage virtualization concepts that allow users to view physical storage devices of the SAN as a large volume. A logical volume manager plays a key role in storage virtualization. It realizes the storage virtualization by mapping logical addresses to physical addresses. In this paper, we design and implement an efficient and flexible mapping method for the logical volume manager. Additionally we also design and implement a free space management method for flexible mapping. Our mapping method supports a snapshot that preserves a volume image at certain time and on-line reorganization to allow users to add or remove storage devices to and from the SAN even while the system is running. To justify our mapping method, we compare it with the mapping method of the GFS (Global File System) through various experiments.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.157-166
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• 1995

The Electroencephalogram (EEG) and evoked potential (EP) t;ave widely been used for study of brain functions. The EEG and EP signals acquired from multi-channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG). Since these artifact-affected EEG signals degrade EEG mapping, the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. In this paper a neural-network based classification is proposed to replace or to reduce human expert's efforts and time. From experiments, the neural-network based classification performs as good as human experts : variation of decisions between the neural network and human expert appears even smaller than that between human experts.