• 한국지반공학회논문집
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• 제39권10호
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• pp.41-48
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• 2023

도로 하부에 발생된 이상구간은 사용자의 안전을 위협하고 보수하기 위해서도 많은 사회적 비용이 동반된다. 본 연구에서는 적외선 카메라를 사용하여 이상구간 매질에 따른 온도 분포를 실험적으로 평가하고 이를 머신러닝 기법으로 분석하고자 하였다. 대상 현장은 가로와 세로 및 깊이가 모두 50cm인 정육면체 형태로 설정하였고, 이상구간은 물과 공기로 결정하였다. 실험부지의 상부는 포장층을 모사하기 위해 콘크리트 블록을 설치하였으며, 오후 4시부터 다음날 오후 3시까지 총 23시간 동안 포장층의 온도 분포를 측정하였다. 측정된 값은 이미지 형태로 도출되었으며, 이미지 중간부분에서 측정 온도의 수치를 추출하였다. 최대온도와 최저온도의 차이는 물, 공기, 그리고 원 지반에서 각각 34.8℃, 34.2℃ 그리고 28.6℃로 나타났으며, 이미지 분석 기법인 convolution neural network(CNN) 방법을 활용하여 각 측정 이미지에 해당하는 조건을 분류하였다. 분류를 수행하기 위해서는 res net 101과 squeeze net 네트워크가 이용되었다. res net 101의 분류 정확도는 물, 공기 그리고 원 지반에서 각각 70%, 50% 그리고 80%로 나타났고, squeeze net의 분류 정확도는 60%, 30% 그리고 70%로 나타났다. 해당 연구 결과는 수치데이터로 특징 판단이 어려울 경우 이미지 기반의 CNN 알고리즘을 활용하면 매질 특성 분석이 가능하고 지반내 상태도 예측할 수 있는 방법론을 보여준다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.226-227
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• 2020

근래의 초해상화 (super-resolution, SR) 연구는 네트워크를 깊고, 넓게 만들어 성능을 높이는데 주를 이뤘다. 그러나 동시에 높은 연산량과 메모리 소비량이 증가하는 문제가 발생하기 때문에 이를 실제로 하드웨어로 구현하기에는 어려운 문제가 존재한다. 그렇기에 우리는 네트워크 최적화를 통해 성능 감소를 최소화하면서 파라미터 수를 줄이는 네트워크 SqueezeSR을 설계하였다. 또한 지식 증류(Knowledge Distillation, KD)를 이용해 추가적인 파라미터 수 증가 없이 성능을 높일 수 있는 학습 방법을 제안한다. 또한 KD 시 teacher network의 성능이 보다 student network에 잘 전달되도록 feature map 간의 비교를 통해 학습 효율을 높일 수 있었다. 결과적으로 우리는 KD 기법을 통해 추가적인 파라미터 수 증가 없이 성능을 높여 다른 SR네트워크 대비 더 빠르고 성능 감소를 최소화한 네트워크를 제안한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 추계학술발표대회
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• pp.631-633
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• 2018

최근 영상처리 분야에서 딥러닝(Deep learning)을 이용한 기술이 좋은 성능을 보이면서 이에 대한 관심과 연구가 증가하고 있다. 본 연구에서는 최근 딥러닝 네트워크 중 적은 파라미터 수로 AlexNet수준의 성능을 보인 SquezeNet을 영상 분할(Image segmentation)의 특징 추출(feature extraction)영역으로 사용하고, CUDA C기반으로 코드를 작성하여 정확도를 유지하면서 계산 속도 면에서도 좋은 성능을 얻을 수 있었다.

• 한국주조공학회지
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• 제18권4호
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• pp.373-382
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• 1998

Particulate reinforced aluminum alloys produced by indirect squeeze casting are difficult to shape by cutting or milling. Therefore near net shape forming of complex shapes is of high economic and technical interest. The complex shape products of $SiC_p/6061$ Al composites are fabricated by the melt-stirring and indirect squeeze casting process. The mold temperatures are $200^{\circ}C$ and $300^{\circ}C$ and applied pressures are 70, 100, and 130 MPa. The volume fractions of the reinforcements are in the range of 5 vol% to 15 vol%. The reinforcement dispersion state are observed using on optical microscope. By employing observed results systematically a correlation is demonstrated among the microstructure, particles behavior, mechanical properties and processing parameters for an optimum melt-stirring(compocasting) and indirect squeeze casting process of MMCs. A procedure to establish the optimum squeeze casting of Al-MMCs is proposed.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2021년도 추계학술대회
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• pp.154-156
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• 2021

Depth-wise separable convolution 은 컴퓨터 자원이 제한된 환경에서 기존의 standard convolution을 대체하는데 강력하고, 효과적인 대안으로 잘 알려져 있다.[1] MobileNetV2 에서는 Inverted residual block을 소개한다. 이는 depth-wise separable convolution으로 인해 생기는 손실, 즉 channel 간의 데이터를 조합해 새로운 feature를 만들어낼 기회를 잃어버릴 때, 이를 depth-wise separable convolution 양단에 point-wise convolution(1×1 convolution)을 사용함으로써 극복해낸 block이다.[1] 하지만 1×1 convolution은 채널 수에 의존적(dependent)인 특징을 갖고 있고, 따라서 결국 네트워크가 깊어지면 깊어질수록 효율적이고(efficient) 가벼운(light weight) 네트워크를 만드는데 병목 현상(bottleneck)을 일으키고 만다. 이 논문에서는 channel-wise squeeze and excitation block(CSE)을 통해 1×1 convolution을 부분적으로 대체하는 방법을 통해 이 병목 현상을 해결한다.

• 대한기계학회논문집
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• 제17권7호
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• pp.1751-1760
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• 1993

The squeeze casting process is considered as an attractive way to form the primary product of near net shape metal matrix composites for wide use in automobile industry. To understand for infiltration limit in squeeze casting processes, the SAFFIL short fiber preform of volume fraction $10%{\sim}23%$ were fabricated by vaccum pumping and speed control press, and the optimal condition for fiber preform fabrication had been experimentally obtained. The composite materials were fabricated by forced infiltration of molten metals such as Al6061, Al7075, pure Al, AC8A, and Al2024. The infiltration distance and deformation of fiber preform are observed, and tensile strength were measured from at the room temperature.

• Korean Journal of Radiology
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• 제20권8호
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• pp.1275-1284
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• 2019

Objective: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. Materials and Methods: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6-10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50-100, > 100), time intervals to DWI, and DWI protocols. Results: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). Conclusion: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.

• ETRI Journal
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• 제45권2호
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• pp.318-328
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• 2023

Recently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep-learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep-learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep-learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator-scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type-based operator-scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling-based operator-scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1431-1445
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• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• 한국주조공학회지
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• 제28권3호
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• pp.136-140
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• 2008

A new concept of salt core, a melting temperature of which is lower than the solidus temperature of cast alloy, was introduced to produced an integrated casting part having a complicated inner shape or requiring under-cut in high pressure die casting or squeeze casting process. The main goal of this study is to develop a new integrated net-shape forming technology using fusible core of lower melting temperature than that of a casting alloy. This integrated net-shape forming technology would be very successful and cost-effective for producing the integrated products having a complicated inner shape or requiring under-cut. The technology for measuring and evaluating a various property of fusible core such as a thermal conductivity and thermal expansion coefficient, melting temperature was established. Also, the work space can be cleaned without a pollution inducing products.