• Journal of KIBIM
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• v.10 no.2
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• pp.1-11
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• 2020

BIM(Building Information Modeling) is a salient technology for influential innovation in the construction industry. The patent network analysis is useful for suggesting the direction of technology development and exploring the research and development field. Therefore, the purpose of this study is to analyze the BIM technology structure and core technologies according to the convergence of BIM technology and market expansion. In this study, social network analysis was conducted by establishing a co-classification IPC network for the United States BIM patent. In particular, the characteristics of the major technical areas in the BIM technology network were identified through centrality analysis. G06F017/00, digital computing or data processing method, is a core technology field in the BIM network. Arrangements, apparatus or systems for transmission of digital information, H04L029/00 is an influential technology across the network. B25J009/00 for program controlled manipulators is an intermediary technology field and G06T019/00, manipulating 3D models or images for computer graphics, is an important field for technological development competitiveness.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.1-9
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• 2021

Deep learning in computer vision has made accelerated improvement over a short period but large-scale learning data and computing power are still essential that required time-consuming trial and error tasks are involved to derive an optimal network model. In this study, we propose a similar image classification performance improvement method based on CR (Confusion Rate) that considers only the characteristics of the data itself regardless of network optimization or data reinforcement. The proposed method is a technique that improves the performance of the deep learning model by calculating the CRs for images in a dataset with similar characteristics and reflecting it in the weight of the Loss Function. Also, the CR-based recognition method is advantageous for image identification with high similarity because it enables image recognition in consideration of similarity between classes. As a result of applying the proposed method to the Resnet18 model, it showed a performance improvement of 0.22% in HanDB and 3.38% in Animal-10N. The proposed method is expected to be the basis for artificial intelligence research using noisy labeled data accompanying large-scale learning data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1A
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• pp.17-24
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• 2012

In this paper, we described a received signal model in terms of the starting point of FFT window and derive a post-detection SINR for the receiver with MMSE filter and the corresponding filter coefficients in order to mitigate the effects of interferences caused by time and frequency selective fading channels and time difference of arrival (TDoA) in OFDMA-based Mesh Networks. In addition, we proposed an MMSE Bidirectional Successive Detection (BSD) technique which can suppress the effects of interferences among multi-nodes without any redundant FFT operations. It was shown by simulation that the proposed interference suppression technique has not an error floor at higher average SNR than 30dB in terms of 64QAM uncoded BER contrary to the conventional techniques.

• Journal of Satellite, Information and Communications
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• v.1 no.1
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• pp.12-19
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• 2006

In this paper, we propose the bandwidth borrowing scheme which improves the performance of the cal admission control of the integrated GEO satellite networks. In general, target transmission rates of communications and streaming services are fixed, but data services do not have the target transmission rates. Therefore, we can control the transmission rates for data services flexibly according to the system loading situation. When the available bandwidth of the system is insufficient, the bandwidth borrowing scheme gives the bandwidth to request real time services by the transmission rates control of data services through packet scheduler. We make the queueing model for our system model and demonstrate the results through simulations. The simulation results show that there is a 8.7-35.2 dB gain at the total blocking probability according to the use of bandwidth borrowing scheme.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.55-61
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• 2020

With the development of deep learning technology and advances in computing power, video-based research is now gaining more and more attention. Video data contains a large amount of temporal and spatial information, which is the biggest difference compared with image data. It has a larger amount of data. It has attracted intense attention in computer vision. Among them, motion recognition is one of the research focuses. However, the action recognition of human in the video is extremely complex and challenging subject. Based on many research in human beings, we have found that artificial intelligence-like attention mechanisms are an efficient model for cognition. This efficient model is ideal for processing image information and complex continuous video information. We introduce this attention mechanism into video action recognition, paying attention to human actions in video and effectively improving recognition efficiency. In this paper, we propose a new 3D residual attention network using convolutional neural network based on two attention models to identify human action behavior in the video. An evaluation result of our model showed up to 90.7% accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.842-845
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• 2020

본 연구는 시각-관성 기반의 딥러닝 학습으로 자유분방하게 움직이는 드론의 주행기록을 정확하게 추정하는 것을 목표로 한다. 드론의 비행주행은 드론의 온보드 센서와 조정값을 이용하는 것이 일반적이다. 본 연구에서는 이 온보드 센서 데이터를 학습에 사용하여 비행주행의 위치추정을 실험하였다. 선행연구로써 DeepVO[1]룰 구현하여 KITTI[3] 데이터와 Midair[4] 데이터를 비교, 분석하였다. 3D 좌표면에서의 위치 추정에 선행연구 모델의 한계가 있음을 확인하고 IMU를 Feature로써 사용하였다. 본 모델은 FlowNet[2]을 모방한 CNN 네트워크로부터 Optical Flow Feature에 IMU 데이터를 더해 RNN으로 학습을 진행하였다. 본 연구를 통해 주행기록 예측을 다소 정확히 했다고 할 수 없지만, IMU Feature를 통해 주행기록의 예측이 가능함을 볼 수 있었다. 본 연구를 통해 시각-관성 분야에서 사람의 지식이나 조정이 들어가는 센서를 융합하는 기존의 방식에서 사람의 제어가 들어가지 않는 End-to-End 방식으로 인공지능을 학습했다. 또한, 시각과 관성 데이터를 통해 주행기록을 추정할 수 있었고 시각적으로 그래프를 그려 정답과 얼마나 차이 있는지 확인해보았다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.193-196
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• 2021

홀로그램(Hologram)은 3차원 물체에서 나오는 빛의 정보를 제어하는 기술이다. 현재는 컴퓨터 생성 홀로그램(CGH)으로 생성한 디지털 홀로그램에 관한 연구, 특히 물체에서 나오는 빛의 정보를 최대한 기록하고 재현하여 디지털 홀로그램의 해상도를 향상 시키려는 연구가 활발히 진행되고 있다. 이에 본 논문에서는 고해상도 홀로그램 영상을 얻기 위해 딥러닝 기반 초해상도(Super Resolution) 네트워크를 훈련 및 최적화하여, 저해상도 위상 홀로그램 영상으로부터 높은 화질의 홀로그램 영상을 재현하는 고해상도 위상 홀로그램 영상을 생성하는 것을 목표로 한다. 이때 위상 홀로그램 영상의 특성을 이용한 순환 손실 함수(Circular loss function)를 새롭게 제안하며, 기존의 이미지 초해상도 신경망 모델을 학습시킬 때 자주 사용하는 L1 손실 함수와 비교했을 때 약 0.13dB 정도의 성능 향상이 있었다.

• Korean Chemical Engineering Research
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• v.44 no.1
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• pp.97-105
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• 2006

In order to investigate the effect of inhibitors on L-threonine biosynthesis in Escherichia coli, we have constructed a metabolic network model of amino acid biosynthesis from L-aspartate to L-threonine by using available informations from literatures and databases. In the model, the effects of inhibitors on the biosynthesis of L-threonine was included as an appropriate mathematical form. For simulation study, we used initial values as L-aspartate 5 mM, ATP 5 mM, NADPH 2 mM, and observed the concentration changes of intermediate metabolites over concentration changes of respective inhibitors. As a result, we found that concentrations of intermediate metabolites were not significantly changed over concentration changes of L-lysine, L-methionine, and L-glutamate. But, there were considerable changes of intermediates over concentration changes of L-serine, L-cysteine, and L-threonine, which can be considered as essential effectors on L-threonine synthesis. Contrary, the synthesis of L-threonine seems to be not related to the amounts of L-aspartate, and inversely proportional to the accumulated amount of D,L-aspartic ${\beta}$-semialdehyde.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1B
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• pp.101-110
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• 2011

With the deployment of broadband networking technology, many clients are enabled to receive various Video on Demand (VoD) services. To support many clients, the network should be designed by considering the following factors: viewer's waiting time, buffer requirement at each client, number of channel required for video delivery, and video segmentation complexity. Among the currently available VoD service approaches, the Polyharmonic and Staircase broadcasting approaches show best performance with respect to each viewer's waiting time and buffer requirement, respectively. However, these approaches have the problem of dividing a video into too many segments, which causes very many channels to be managed and used at a time. To overcome this problem, we propose Polyharmonic-Staircase-Staggered (PSS) broadcasting approach that uses the Polyharmonic and Staircase approaches for the head part transmission and the Staggered approach for the tail part transmission. It is simple and bandwidth efficient. The numerical results demonstrate that our approach shows viewer's waiting time is comparable to that in the Harmonic approach with a slight increase in the bandwidth requirement, and saves the buffer requirement by about 60\% compared to the Harmonic broadcasting approach by simply adjusting the video partitioning coefficient factor. More importantly, our approach shows the best performance in terms of the number of segments and the number of channels managed and used simultaneously, which is a critical factor in real operation of VoD services. Lastly, we present how to configure the system adaptively according to the video partitioning coefficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2317-2323
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• 2015

In this paper, we analyze the performance of dual-hop amplify-and-forward (AF) relaying in cooperative networks with partial relay selection. An AF relay gain considered in this paper includes channel-noise-assisted relay gain. Leveraging a received signal-to-noise ratio (SNR) model, we derive exact closed-form expressions for the probability density function (pdf) and cumulative distribution function (cdf) of the end-to-end SNR. Moreover, an exact closed-form expression of the ergodic capacity for dual-hop AF relaying with channel-noise-assisted relay gain and partial relay selection is investigated. The analytical results shown in this paper are confirmed by Monte-Carlo simulations.