• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.05a
• /
• pp.117-120
• /
• 2008

In this paper, we developed the EM wave absorber for eliminating false images in collision-avoidance radar. First of all, we fabricated some samples in different composition ratio of $TiO_2$ and CPE. And the relative permittivities of samples are calculated from S-parameter of samples by using 1-port method. We designed and fabricated the EM wave absorber by using the calculated relative permittivity. As a result, the EM wave absorber with composition of $TiO_2$:CPE=70:30 wt% has thickness of 1.85 mm and absorption ability higher than 20 dB in the frequency range 76-77 GHz.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.21 no.12
• /
• pp.1335-1342
• /
• 2010

This paper presents design and measurement result of S-band, $\bigcirc$ kW solid-state transmitter and receiver for active array radar system. Transmitter characteristics show 63 dB gain, 200 usec pulse width(max.), 10 % duty(max.) and 63 dB pulse to pulse stability. Receiver characteristics show 23 dB gain and 3.2 dB noise figure. Receiving mode for pulse network analyzer is used for pulse to pulse stability measurement. Measurement results satisfies all specification.

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

• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.1034-1038
• /
• 2020

In order to effectively process target recognition using radar, accurate signal information for the target is required. However, such a target signal is usually mixed with noise, and this part of the study is continuously carried out. Especially, image processing, target signal processing and target recognition for the target are examples. Since the field of target recognition is important from a military point of view, this paper carried out research on target recognition of aircraft using a tree-structured fuzzy neural networks. Fuzzy neural networks are learned by using reflected signal data for an aircraft to optimize the model, and then test data for the target are used for the optimized model to perform an experiment on target recognition. The effectiveness of the proposed method is verified by the simulation results.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37C no.11
• /
• pp.1084-1093
• /
• 2012

For large multihop networks, large time synchronization (TS) errors can accumulate with conventional methods, such as TPSN, RBS, and FTSP, since they need a large number of hops to cover the network. In this paper, a method combining Concurrent Cooperative Transmission (CCT) and Semi- Cooperative Spectrum Fusion (SCSF) is proposed to reduce the number of hops to cover the large network. In CCT, cooperating nodes transmit the same digitally encoded message in orthogonal channels simultaneously, so receivers can benefit from array and diversity gains. SCSF is an analog cooperative transmission method where different cooperators transmit correlated information simultaneously. The two methods are combined to create a new distributed method of network TS, called the Cooperative Analog and Digital (CANDI) TS protocol, which promises significantly lower network TS errors in multi-hop networks. CANDI and TPSN are compared in simulation for a line network.

• Journal of Korea Water Resources Association
• /
• v.53 no.12
• /
• pp.1159-1172
• /
• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.5
• /
• pp.462-475
• /
• 2022

Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1999.05a
• /
• pp.224-229
• /
• 1999

In distributed object oriented environment based upon wide heterogeneous network, effective cooperation policies between/among distributed objects are needed to resolve a complexity of management of distributed objects because of growing of a large stale of systems. Thus, in this paper, we propose three trading cooperation models between/among traders for supporting a high speed and a wide selection of trader service for clients, by considering three different cooperation models(light weight trader, simple negotiation and federation) depending upon their facilities, goals, and weights of goals.

• Journal of Internet Computing and Services
• /
• v.20 no.4
• /
• pp.39-46
• /
• 2019

In recently years, it has become important to detect underground objects of various marterials including metals, such as detecting the location of sink holes and pipe. For this reason, ground penetrating radar(GPR) technology is attracting attention in the field of underground detection. GPR irradiates the radar wave to find the position of the object buried underground and express the reflected wave from the object as image. However, it is not easy to interpret GPR images because the features reflected from various objects underground are similar to each other in GPR images. Therefore, in order to solve this problem, in this paper, to estimate the piping position in the GRP image according to the threshold value using the CNN (Convolutional Neural Network) model based on deep running, which is widely used in the field of image recognition, As a result of the experiment, it is proved that the pipe position is most reliably detected when the threshold value is 7 or 8.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.4A
• /
• pp.205-213
• /
• 2012

In this paper, we propose a new channel hopping scheme based on IEEE 802.11h as a good countermeasure against jamming attacks in IEEE 802.11 wireless networks. 802.11h Dynamic Frequency Selection (DFS) is a mechanism which enables hopping to a best channel with full channel measurement, not a randomly chosen channel, when the current link quality degradation occurs due to interferers such as military radars. However, under jammer attacks, this needs a time for full channel measurement before a new channel hopping and due to link disconnection during the time network performance degradation is inevitable. In contrast, our proposed schemes make an immediate response right after a jammer detection since every device is aware of next hopping channel in advance. To do this, a next hopping channel is announced by Beacon frames and the channel is selected by full channel measurement within Beacon intervals. Simulation results show that proposed scheme minimizes throughput degradation and keeps the advantages of DFS.