• 로봇학회논문지
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• 제17권4호
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• pp.425-430
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• 2022

As robots are considered one of the mainstream digital transformations, robots with machine vision becomes a main area of study providing the ability to check what robots watch and make decisions based on it. However, it is difficult to find a small object in the image mainly due to the flaw of the most of visual recognition networks. Because visual recognition networks are mostly convolution neural network which usually consider local features. So, we make a model considering not only local feature, but also global feature. In this paper, we propose a detection method of a small marker on the object using deep learning and an algorithm that considers global features by combining Transformer's self-attention technique with a convolutional neural network. We suggest a self-attention model with new definition of Query, Key and Value for model to learn global feature and simplified equation by getting rid of position vector and classification token which cause the model to be heavy and slow. Finally, we show that our model achieves higher mAP than state of the art model YOLOr.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 하계학술대회
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• pp.22-25
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• 2020

본 논문에서는 큰 크기의 심층 신경망을 압축하기위해 네트워크 수준의 가중치 공유방법인 Global Weight 패러다임을 최초로 제시한다. 기존의 가중치 공유방법은 계층별로 가중치를 공유하는 것이 대부분이었다. Global Weight 는 기존 방법과 달리 전체 네트워크에서 가중치를 공유하는 효율적인 방법이다. 우리는 Global Weight 를 사용하여 학습되는 새로운 컨볼루션 연산인 Global Weight Convolution(GWConv)연산과 GWConv를 적용한 Global Weight Networks(GWNet)을 제안한다. CIFAR10 데이터셋에서 실험한 결과 2.18 배 압축에서 85.64%, 3.41 배 압축에서 85.46%의 정확도를 보였다. Global Weight 패러다임은 가중치 공유가 궁극적으로 풀고자 했던 중복되는 가중치를 최소화하는 획기적인 방법이며, 추후 심도 있는 연구가 수행될 수 있음을 시사한다.

• 한국콘텐츠학회논문지
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• 제6권3호
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• pp.28-37
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• 2006

인터넷 기술은 지난 30여 년간 비약적으로 발전하여 모든 산업분야를 변혁시키고 개인과 기업의 필수도 구로서 국가의 중요한 기반시설로 자리 잡았다. 1990년대 중반부터 선진국들은 인터넷을 과학 및 교육 분야의 발전에 가장 중요한 기반시설의 하나로 인식하고 국가 연구 교육망을 구축하고 이를 새로운 망 기술과 과학 기술 개발을 위한 도구로 제공하고 있다. 이 논문에서 우리는 선진국의 연구 교육망 발전 현황을 종합적으로 살펴보고, 국내 첨단망 활동을 선진국과 비교하여 문제점 분석하고, 이를 토대로 한국 첨단망의 발전 방안을 제시하였다.

• 한국멀티미디어학회논문지
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• 제25권11호
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• pp.1643-1652
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• 2022

Most of the existing machine reading research has used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms as networks. Among them, RNN was slow in training, and Question Answering Network (QANet) was announced to improve training speed. QANet is a model composed of CNN and self-attention. CNN extracts semantic and syntactic information well from the local corpus, but there is a limit to extracting the corresponding information from the global corpus. Graph Convolutional Networks (GCN) extracts semantic and syntactic information relatively well from the global corpus. In this paper, to take advantage of this strength of GCN, we propose I-QANet, which changed the CNN of QANet to GCN. The proposed model performed 1.2 times faster than the baseline in the Stanford Question Answering Dataset (SQuAD) dataset and showed 0.2% higher performance in Exact Match (EM) and 0.7% higher in F1. Furthermore, in the Korean Question Answering Dataset (KorQuAD) dataset consisting only of Korean, the learning time was 1.1 times faster than the baseline, and the EM and F1 performance were also 0.9% and 0.7% higher, respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권3호
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• pp.179-183
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• 2006

ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.

• Journal of applied mathematics & informatics
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• 제30권5_6호
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• pp.1051-1065
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• 2012

In this paper, the global stability and almost periodicity are investigated for generalized Hopfield neural networks with time-varying neutral delays. Some sufficient conditions are obtained for the existence and globally exponential stability of almost periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper are new and complement previously known results. Finally, an example is given to demonstrate the effectiveness of our results.

• Journal of Computing Science and Engineering
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• 제12권1호
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• pp.24-35
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• 2018

Deep convolutional neural network (DCNN) is an advanced technology in image recognition. Because of extreme computing resource requirements, DCNN implementation with software alone cannot achieve real-time requirement. Therefore, the need to implement DCNN accelerator hardware is increasing. In this paper, we present a field programmable gate array (FPGA)-based hardware accelerator design of DCNN targeting handwritten Hangul character recognition application. Also, we present design optimization techniques in SDAccel environments for searching the optimal FPGA design space. The techniques we used include memory access optimization and computing unit parallelism, and data conversion. We achieved about 11.19 ms recognition time per character with Xilinx FPGA accelerator. Our design optimization was performed with Xilinx HLS and SDAccel environment targeting Kintex XCKU115 FPGA from Xilinx. Our design outperforms CPU in terms of energy efficiency (the number of samples per unit energy) by 5.88 times, and GPGPU in terms of energy efficiency by 5 times. We expect the research results will be an alternative to GPGPU solution for real-time applications, especially in data centers or server farms where energy consumption is a critical problem.

• International Journal of Knowledge Content Development & Technology
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• 제10권4호
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• pp.29-46
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• 2020

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

• 산업클러스터
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• 제2권1호
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• pp.14-27
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• 2008

마이클 포터에 의해 소개된 클러스터는 지난 십여 년간 많은 학자들의 관심과 분석의 대상이었다. 그러나 최근 연구들은 포터가 소개했던 클러스터 개념 이 수정되어야 할 필요성을 제시해 주고 있다. 특히 클러스터가 외부 세계와는 재화와 용역의 수출입만으로 연결된 지리적으로 국한된 지역이라는 개념은 의심의 여지가 충분하다. 지역발전에 있어 관계 중심적 접근은 클러스터 외부의 중요 협력파트너들과의 네트워크를 통해 이루어지는 지식과 정보의 교환이 가지는 중요성을 강조한다. 기업들은 이들 협력파트너들과 다양한 형태의 관계를 형성하는 것이다. 본 논문은 글로벌 네트워크를 중시하는 최근의 추세와 서유럽의 구산업지역인 스코틀랜드의 경험을 바탕으로 기존의 클러스터 개념의 재해석을 시도한다. 스코틀랜드 지역의 클러스터 경험을 평가함에 있어 본 논문은 석유와 가스, 전기 클러스터를 분석한다. 마지막으로 본 논문은 클러스터 정책은 해당 지역이 주요산업 및 클러스터에 대한 오너십과 콘트롤을 충분히 가지고 있을 경우에만 지속적인 효과를 거둘 수 있다고 결론짓는다.

• 천문학회보
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• 제27권1호
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• pp.61-61
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• 2002