• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.374-386
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• 2016

Provenance means the meta data that represents the history or lineage of a data in collaboration storage environments. Therefore, as provenance has been accruing over time, it takes several ten times as large as the original data. The schemes for effciently compressing huge amounts of provenance are required. In this paper, we propose a provenance compression scheme considering the RDF graph patterns. The proposed scheme represents provenance based on a standard PROV model and encodes provenance in numeric data through the text encoding. We compress provenance and RDF data using the graph patterns. Unlike conventional provenance compression techniques, we compress provenance by considering RDF documents on the semantic web. In order to show the superiority of the proposed scheme, we compare it with the existing scheme in terms of compression ratio and the processing time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.101-111
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• 2021

Two different types of shear-friction tests were classified by external loadings and referred to as a push-off and a pull-off test. In a pull-off test, a tension force is applied in the transverse direction of the test specimen to produce a shear stress at the shear plane. This paper presents a method to evaluate shear transfer strengths of uncracked pull-off specimens. The method is based on the compression field theory and different constitutive laws are applied in some ways to gain accurate shear strengths considering softening effects of concrete struts based on Modified Compression Field Theory (MCFT) and Softened Truss Model (STM). The validity of the proposed method is examined by applying to some selected test specimens in literatures and results are compared with the predicted values. A general agreement is observed between predicted and measured values at ultimate loading stages in initially uncracked pull-off test specimens. A shear strength evaluation formula considering the effective compressive strength of a concrete strut was proposed, and the applicability of the proposed formula was verified by comparing with the experimental results in the literature.

• The KIPS Transactions:PartC
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• v.8C no.3
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• pp.311-318
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• 2001

본 논문에서는 연결수락 제어시 사용자가 전송하는 트래픽 파라메타(샐 개수의 분산값과 평균값)를 압축하여 망에 신고하는 방법을 제안하고, 압축방법에 의한 연결수락제어의 성능을 분석 비교한다. 트래픽 파라메타 압축방법은 K-means, CL(Competitive Learning), Fuzzy ISODATA,FNC(Fuzzy Neural Clustering)를 사용한다. 제안한 트래픽 파라메타의 압축에 의한 연결수락제어는 퍼지 매핑함수(Fuzzy Mapping Funciton)fp 의해 신고한 트래픽 패턴을 추정하고, 전방향 구조의 신경망을 사용하여 연결의 수락/거절을 결정한다. ON-OFF 트래픽 모델 환경에서 컴퓨터 실험을 통하여 여러 가지 압축방법들을 사용한 연결수락제어의 성능을 Fuzziness 값에 따라 비교하였고, 그 결과 FNC 방법이 우수함을 알 수 있었다. EH한 연결수락제어의 성능을 높히기 위해서 관측 프레임의 셀 분산값이 크면 Fuzziness 값을 작게 선정하고, 작으면 상대적으로 크게 선정해야 함을 알 수 있었다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.6
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• pp.533-542
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• 2014

In this study TBM disc cutter prediction models including Gehring, CSM and NTNU models were investigated and the characteristics of the models were examined. The influence of penetration, uniaxial compressive strength and abrasiveness index on the models was analyzed. The life of disc cutter linearly increases with penetration per revolution and decreases with increasing uniaxial compressive strength of rocks. As the abrasiveness index, CAI, increases, the life of disc cutter in Gehring and CSM model decreases. On the contrary, the life of disc cutter life in NTNU model decreases with increasing CLI. Also, comparisons of predicted disc life were made between models using actual job site data.

• Magazine of the Korea Concrete Institute
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• v.7 no.5
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• pp.145-154
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• 1995

Concrete has two different failure mechanisms : compressive crushing and tensile cracking. Concrete models should use the two different failure criteria to analyze the inelastic behavior of concrete including multiaxial crushing and tensile cracking. Concrete models used in this study are based on plasticity with multiple failure criteria of compressive crushing and tensile cracking. For tensile cracking behavior, two different plasticity models are investigated. The* ,e are rotating-crack and fixed-crack plasticity models, classified according to idealization of crack 0rientat:ions. The material models simplify inelastic behavior of concrete for plane stress problenls. The material models are used for the finite element anlaysis. Analytical results are compared with several experiments of reinforced concrete member. The advantages and disadva.ntages of rotating-crack and fixed -crack plasticity models are discussed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1191-1194
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• 2022

인터넷의 발전으로 수많은 이미지와 비디오를 손쉽게 이용할 수 있게 되었다. 이미지와 비디오 데이터의 양이 기하급수적으로 증가함에 따라, JPEG, HEVC, VVC 등 이미지와 비디오를 효율적으로 저장하기 위한 부호화 기술들이 등장했다. 최근에는 인공신경망을 활용한 학습 기반 모델이 발전함에 따라, 이를 활용한 이미지 및 비디오 압축 기술에 관한 연구가 빠르게 진행되고 있다. NNIC (Neural Network based Image Coding)는 이러한 학습 가능한 인공신경망 기반 이미지 부호화 기술을 의미한다. 본 논문에서는 NNIC 모델과 인공신경망 기반의 초해상화(Super Resolution) 모델을 합동훈련하여 기존 NNIC 모델보다 더 높은 성능을 보일 수 있는 방법을 제시한다. 먼저 NNIC 인코더(Encoder)에 이미지를 입력하기 전 다운 스케일링(Down Scaling)으로 쌍삼차보간법을 사용하여 이미지의 화소를 줄인 후 부호화(Encoding)한다. NNIC 디코더(Decoder)를 통해 부호화된 이미지를 복호화(Decoding)하고 업 스케일링으로 초해상화를 통해 복호화된 이미지를 원본 이미지로 복원한다. 이때 NNIC 모델과 초해상화 모델을 합동훈련한다. 결과적으로 낮은 비트량에서 더 높은 성능을 볼 수 있는 가능성을 보았다. 또한 합동훈련을 함으로써 전체 성능의 향상을 보아 학습 시간을 늘리고, 압축 잡음을 위한 초해상화 모델을 사용한다면 기존의 NNIC 보다 나은 성능을 보일 수 있는 가능성을 시사한다.

• Transactions on Semiconductor Engineering
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• v.1 no.1
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• pp.23-30
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• 2023

With the recent development of Generative AI technology by IT giants, the size of the transformer model is increasing exponentially over trillion won. In order to continuously enable these AI services, it is essential to reduce the weight of the model. In this paper, we find a hardware-friendly structured pruning pattern and propose a lightweight method of the transformer model. Since compression proceeds by utilizing the characteristics of the model algorithm, the size of the model can be reduced and performance can be maintained as much as possible. Experiments show that the structured pruning proposed when pruning GPT-2 and BERT language models shows almost similar performance to fine-grained pruning even in highly sparse regions. This approach reduces model parameters by 80% and allows hardware acceleration in structured form with 0.003% accuracy loss compared to fine-tuned pruning.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.125-131
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• 2021

In recent years, Convolutional Neural Networks (CNNs) have achieved outstanding performance in the fields of computer vision such as image classification, object detection, visual quality enhancement, etc. However, as huge amount of computation and memory are required in CNN models, there is a limitation in the application of CNN to low-power environments such as mobile or IoT devices. Therefore, the need for neural network compression to reduce the model size while keeping the task performance as much as possible has been emerging. In this paper, we propose a method to compress CNN models by combining matrix decomposition methods of LR (Low-Rank) approximation and CP (Canonical Polyadic) decomposition. Unlike conventional methods that apply one matrix decomposition method to CNN models, we selectively apply two decomposition methods depending on the layer types of CNN to enhance the compression performance. To evaluate the performance of the proposed method, we use the models for image classification such as VGG-16, RestNet50 and MobileNetV2 models. The experimental results show that the proposed method gives improved classification performance at the same range of 1.5 to 12.1 times compression ratio than the existing method that applies only the LR approximation.

• Geotechnical Engineering
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• v.8 no.2
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• pp.45-58
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• 1992

An elasto-plastic constitutive model for geological materials, which satisfies the flezibility and stability at the same time, can be used in a number of geotechnical problems. Using the spacing ratio of critical state, a flexible model is proposed based on the stability of modified Camflay model. The spacing ratio of critical state can be simply evaluated, and practically used in describing the undrained shearing behavior of clay. The proposed model has precisely predicted the stress paths and stress -strain relationships, compared with the modified Camflay model, with respect to undrained triaxial test results. Besides, the effects of strain rate, creep, and relaxation can also be considered. Using the quasi-state boundary surface, the constitutive relations are well predicted. Therefore, it is found that the assumption of associative flow rule is well posed for undrained behavior of normally consolidated clay.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.602-607
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• 2002

In this paper, we proposed an approach to efficiently compress and transmit multiresoltion 3D lariat models for multimedia and very low bit rate applications. A personal facial model is obtained by a 3D laser digitizer, and further re-quantized at several resolutions according to different scope of applications, such as animation, video game, and video conference. By deforming 2D templates to match and re-quantize a 3D digitized facial model, we obtain its compressed model. In the present study, we create hierarchical 2D lariat wireframe templates are adapted according to facial feature points and the proposed piecewise chainlet affined transformation(PACT) method. The 3D digitized model after requantization are reduced significantly without perceptual loss. Moreover the proposed multiresoulation lariat models possessed of hierarchial data structure are apt to be progressively transmitted and displayed across internet.