• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.703-704
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• 2018

An artificial neural network design that traces the pupil for the disables suffering from Lou Gehrig disease is introduced. It grasps the position of the pupil required for the communication system. Tensorflow is used for generating and learning the neural network, and the pupil position is determined through the learned neural network. Convolution neural network(CNN) which consists of 2 stages of convolution layer and 2 layers of complete connection layer is implemented for the system.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.92-92
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• 2023

본 연구에서는 대한민국 도시 유역에 대하여 딥러닝 네트워크 기반의 분산형 수문 모형을 개발하였다. 개발된 모형은 완전연결계층(Fully Connected Layer)으로 연결된 여러 개의 장단기 메모리(LSTM-Long Short-Term Memory) 은닉 유닛(Hidden Unit)으로 구성되었다. 개발된 모형을 사용하여 연구 지역인 중랑천 유역을 분석하기 위해 1km² 해상도의 239개 모델 격자 셀에서 10분 단위 레이더-지상 합성 강수량과 10분 단위 기온의 시계열을 입력으로 사용하여 10분 단위 하도 유량을 모의하였다. 모형은 보정과(2013~2016년)과 검증 기간(2017~2019년)에 대한 NSE 계수는각각 0.99와 0.67로 높은 정확도를 보였다. 본 연구는 모형을 추가적으로 심층 분석하여 다음과 같은 결론을 도출하였다: (1) 모형을 기반으로 생성된 유출-강수 비율 지도는 토지 피복 데이터에서 얻은 연구 지역의 불투수율 지도와 유사하며, 이는 모형이 수문학에 대한 선험적 정보에 의존하지 않고 입력 및 출력 데이터만으로 강우-유출 분할과정을 성공적으로 학습하였음을 의미한다. (2) 모형은 연속 수문 모형의 필수 전제 조건인 토양 수분 의존 유출 프로세스를 성공적으로 재현하였다; (3) 각 LSTM 은닉 유닛은 강수 자극에 대한 시간적 민감도가 다르며, 응답이 빠른 LSTM 은닉 유닛은 유역 출구 근처에서 더 큰 출력 가중치 계수를 가졌는데, 이는 모형이 강수 입력에 대한 직접 유출과 지하수가 주도하는 기저 흐름과 같이 응답 시간의 차이가 뚜렷한 수문순환의 구성 요소를 별도로 고려하는 메커니즘을 가지고 있음을 의미한다.

• The Journal of Information Technology
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• v.5 no.1
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• pp.69-77
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• 2002

The Bluetooth is expected to be one of the most popular wireless telecommunication technology in the near future, and the protocol stack is essential to providing the various services with the Bluetooth-embedding systems or devices. The Bluetooth specification is an open, global specification defining the complete system, however, the protocol stack is usually implemented partly in hardware and partly as software running on its system, with different implementations partitioning the functionality between hardware and software in different ways. I investigate how to design and implement the Bluetooth protocol stack according to its specification. I focus on the HCI and the lower layer of the software protocol stack as a basic step for the development of our own protocol stack. As a result, paper provides how to partitioning the role of HCI layer, and how to implement the relationship between HCI packets, it's functionality and the flow control. Experiments show the discovering other Bluetooth devices and their connection. Furthermore experiments demonstrate the proper operation of data communication between the Bluetooth modules.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.938-949
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• 2015

In this paper, we propose a robust speed-limit sign recognition system which is durable to any sign changes caused by exterior damage or color contrast due to light direction. For recognition of speed-limit sign, we apply CNN which is showing an outstanding performance in pattern recognition field. However, original CNN uses multiple hidden layers to extract features and uses fully-connected method with MLP(Multi-layer perceptron) on the result. Therefore, the major demerit of conventional CNN is to require a long time for training and testing. In this paper, we apply randomly-connected classifier instead of fully-connected classifier by combining random forest with output of 2 layers of CNN. We prove that the recognition results of CNN with random forest show best performance than recognition results of CNN with SVM (Support Vector Machine) or MLP classifier when we use eight speed-limit signs of GTSRB (German Traffic Sign Recognition Benchmark).

• Journal of Digital Convergence
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• v.17 no.1
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• pp.123-130
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• 2019

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1153-1160
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• 2019

In this paper, we experimented to find out how the number of convolution layers, the size, and the number of kernels affect the CNN. In addition, the general CNN was also tested for analysis and compared with the CNN used in the experiment. The neural networks used for the analysis are based on CNN, and each experimental model is experimented with the number of layers, the size, and the number of kernels at a constant value. All experiments were conducted using two layers of fully connected layers as a fixed. All other variables were tested with the same value. As the result of the analysis, when the number of layers is small, the data variance value is small regardless of the size and number of kernels, showing a solid accuracy. As the number of layers increases, the accuracy increases, but from above a certain number, the accuracy decreases, and the variance value also increases, resulting in a large accuracy deviation. The number of kernels had a greater effect on learning speed than other variables.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1889-1896
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• 2011

There has been a lot of researches to develop techniques that provide redundant paths, there by making Multistage Interconnection Networks(MINs) fault tolerant. So far, the redundant paths in MINs have been realized by adding additional hardware such as extra stages or duplicated data links. This paper presents a new MIN topology called Hierarchical MIN. The proposed MIN is constructed with 2.5N-4 switching elements, which are much fewer than that of the classical MINs. Even though there are fewer hardware than the classical MINs, the HMIN possesses the property of full access and also provides alternative paths for the fault tolerant. Furthermore, since there is the short cut in HMIN for the localized communication, it takes advantage of exploiting the locality of reference in multiprocessor systems. Its performance under varying degrees of localized communication is analysed and simulated.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2002.11a
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• pp.67-67
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• 2002

패키지 기술의 개발은 저비용, 고성능, 높은 패키징 효율의 추세로 가고 있다. 이러한 추세에 따라 기판재료의 개발 및 구조의 변형이 요구된다. 패키지의 한 형태인 MCM(Multi-Chip Module)에 연성기판을 사용할 경우 fine pattern이 가능하고 부피가 작기 때문에 패키지의 효율이 좋고 또한 reel to reel process에 적용이 가능하기 때문에 대량생산의 이점을 가지고 있다. 연성기판은 좋은 전기적 특성을 가진 polyimide와 구리 층으로 구성된다. 그러나 polyimide와 구리 계층 사이에 약한 접착력과 구리로의 polyamic acid의 diffusion, 다층 기판의 제조의 어려움 등의 문제점을 남겨두고 있다. 본 연구는 일반적인 polyimide/copper가 구조가 가지고 있는 문제점을 해결하고 구리 패턴을 제작하기 위해 에칭을 쓰는 것을 배제함으로 fine pattern을 이루어 내었으며 전기도금으로 완전하게 채워진 pluged via을 사용함으로 각층간의 연결에 신뢰성을 부여하였다. 또한, 연성기판의 구조적인 문제점인 해결하여 다층 연성기판을 제조하려고 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.651-658
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• 2021

Because bolts with anti-loosening coatings are used mainly for joining safety-related components in automobiles, accurate automatic screening of these coatings is essential to detect defects efficiently. The performance of the convolutional neural network (CNN) used in a previous study [Identification of bolt coating defects using CNN and Grad-CAM] increased with increasing number of data for the analysis of image patterns and characteristics. On the other hand, obtaining the necessary amount of data for coated bolts is difficult, making training time-consuming. In this paper, resorting to the same VGG16 model as in a previous study, transfer learning was applied to decrease the training time and achieve the same or better accuracy with fewer data. The classifier was trained, considering the number of training data for this study and its similarity with ImageNet data. In conjunction with the fully connected layer, the highest accuracy was achieved (95%). To enhance the performance further, the last convolution layer and the classifier were fine-tuned, which resulted in a 2% increase in accuracy (97%). This shows that the learning time can be reduced by transfer learning and fine-tuning while maintaining a high screening accuracy.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.