• Korean Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.121-126
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• 1998

It is one of important issues to find intersection curve? in representation of complex surfaces on a computer. Three typical methods, i.e. the tracing method, the subdivision method, and hybrid method, are often applied to find intersection curves between sculptured surfaces. In this paper two topics are dealt with for efficiency and robustness of the hybrid method. One tropic is about how to determine step sizes variably during tracing, the ethel is about how to find tangential points between surfaces. Tracing by variable step size finds intersections rapidly and requires less memory size. Some illustrations show tangential points between surfaces.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2004.04a
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• pp.127-130
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• 2004

In this paper, active shape control of composite structure actuated by shape memory alloy (SMA) wires is presented. Hybrid composite structure was established by attaching SMA actuators on the surfaces of graphite/epoxy composite beam using bolt-joint connectors. SMA actuators were activated by phase transformation, which induced by temperature rising over austenite finish temperatures. In this paper, electrical resistive heating was applied to the hybrid composite structures to activate the SMA actuators. For faster and more accurate shape or deflection control of the hybrid composite structure, PID feedback controller was designed from numerical simulations and experimentally applied to the SMA actuators.

• ETRI Journal
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• v.40 no.4
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• pp.421-434
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• 2018

The application of deep neural networks (DNNs) to connect the world with cyber physical systems (CPSs) has attracted much attention. However, DNNs require a large amount of memory and computational cost, which hinders their use in the relatively low-end smart devices that are widely used in CPSs. In this paper, we aim to determine whether DNNs can be efficiently deployed and operated in low-end smart devices. To do this, we develop a method to reduce the memory requirement of DNNs and increase the inference speed, while maintaining the performance (for example, accuracy) close to the original level. The parameters of DNNs are decomposed using a hybrid of canonical polyadic-singular value decomposition, approximated using a tensor power method, and fine-tuned by performing iterative one-shot hybrid fine-tuning to recover from a decreased accuracy. In this study, we evaluate our method on frequently used networks. We also present results from extensive experiments on the effects of several fine-tuning methods, the importance of iterative fine-tuning, and decomposition techniques. We demonstrate the effectiveness of the proposed method by deploying compressed networks in smartphones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1944-1956
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• 2016

The Digital Video Broadcasting-Common Scrambling Algorithm is an ETSI-designated algorithm designed for protecting MPEG-2 signal streams, and it is universally used. Its structure is a typical hybrid symmetric cipher which contains stream part and block part within a symmetric cipher, although the entropy is 64 bits, there haven't any effective cryptanalytic results up to now. This paper studies the security level of CSA against impossible differential cryptanalysis, a 20-round impossible differential for the block cipher part is proposed and a flaw in the cipher structure is revealed. When we attack the block cipher part alone, to recover 16 bits of the initial key, the data complexity of the attack is O(2^44.5), computational complexity is O(2^22.7) and memory complexity is O(2^10.5) when we attack CSA-BC reduced to 21 rounds. According to the structure flaw, an attack on CSA with block cipher part reduced to 21 rounds is proposed, the computational complexity is O(2^21.7), data complexity is O(2^43.5) and memory complexity is O(2^10.5), we can recover 8 bits of the key accordingly. Taking both the block cipher part and stream cipher part of CSA into consideration, it is currently the best result on CSA which is accessible as far as we know.

• The KIPS Transactions:PartA
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• v.13A no.2 s.99
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• pp.157-162
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• 2006

Recent High Performance Computing (HPC) platforms can be classified as Shared-Memory Multiprocessors (SMP), Massively Parallel Processors (MPP), and Clusters of computing nodes. These platforms are deployed in many scientific and engineering applications which require very high demand on computing power. In order to realize an optimal performance for these applications, it is crucial to find and use the suitable computing platforms and programming paradigms. In this paper, we use SPEC HPC 2002 benchmark suite developed in various parallel programming models (MPI, OpenMP, and hybrid of MPI/OpenMP) to find an optimal computing environments and programming paradigms for them through their performance analyses.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.11a
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• pp.453-456
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• 2009

Impact resistance of shape memory alloy hybrid composite(SMAHC) plates were experimentally investigated. Shape memory alloy(SMA) have large failure strain and failure stress and can absorb large strain energies through phase transformation. SMA wires were embedded in composite plates to improve their weak impact resistance. Tensile tests of SMA wires were performed at various temperature to investigate their thermo-mechanical properties. Low-Velocity impact tests of several types of composite plates with SMA/Al/Fe were performed. Embedding SMA wires was most effective to improve impact resistance of composite plates. The effects of SMA position on impact resistance were also investigated.

• Elastomers and Composites
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• v.45 no.3
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• pp.188-198
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• 2010

The term 'shape memory polymers (SMPs)' describes a class of polymers which can remember the original shape and recover from deformed to its original shape by the applied stimuli, e.g., heat, electricity, magnetic field, light, etc. SMPs are classified as one of the 'smart polymers' and have great potentials as high-value-added materials. Especially, low thermal, electrical, and mechanical properties of SMPs can be improved by incorporating the various fillers. This paper aims to review the SMPs and their basic principles, and the trends of the development of SMPs nanocomposites.