Spatial correlation-based WRF observation-nudging approach in simulating regional wind field

Hehe Ren,Shujin Laima,Wen-Li Chen,Anxin Guo,Hui Li 한국풍공학회 2019 Wind and Structures, An International Journal (WAS Vol.28 No.2

Accurately simulating the wind field of large-scale region, for instant urban areas, the locations of large span bridges, wind farms and so on, is very difficult, due to the complicated terrains or land surfaces. Currently, the regional wind field can be simulated through the combination of observation data and numerical model using observation-nudging in the Weather Research and Forecasting model (WRF). However, the main drawback of original observation-nudging method in WRF is the effects of observation on the surrounding field is fully mathematical express in terms of temporal and spatial, and it ignores the effects of terrain, wind direction and atmospheric circulation, while these are physically unreasonable for the turbulence. For these reasons, a spatial correlation-based observation-nudging method, which can take account the influence of complicated terrain, is proposed in the paper. The validation and comparation results show that proposed method can obtain more reasonable and accurate result than original observation-nudging method. Finally, the discussion of wind field along bridge span obtained from the simulation with spatial correlation-based observation-nudging method was carried out.

딥러닝을 이용한 공간예측

최승배(Seung Bae Choi),강창완(Chang Wan Kang),윤상후(Sanghoo Yoon) 한국자료분석학회 2021 Journal of the Korean Data Analysis Society Vol.23 No.1

공간상에서 얻어지는 데이터는 일반통계학과 다르게 얻어지는 관측값들은 서로 상관되어 있다는 전제하에서 분석이 행해진다. 공간통계학에서 공간데이터는 (1) 경험 베리오그램 추정, (2) 추정된 경험베리오그램을 이용한 이론베리오그램 적합, 그리고 (3) 이론베리오그램을 이용하여 기지(旣知)의 위치에서 측정된 관측값을 이용하여 미지(味知)의 위치에서의 관측값을 예측하는 크리깅의 과정을 거쳐 분석된다. 최근 이슈화되고 있는 인공지능 기법의 하나인 딥러닝이 역시 예측의 한 방법으로 널리 적용되고 있다. 전기한 두 방법 모두 예측을 한다는 측면에서는 유사 하지만, 공간통계학의 분석과정을 거쳐 예측하는 방법은 분석자의 주관이 개입될 수 있을 뿐만 아니라 분석과정은 그리 간단하지 않다. 그리고 공간데이터 분석의 가정을 만족하지 못하는 경우도 있다. 그러나 딥러닝은 이론적으로는 복잡할 수 있으나 공간통계학에서 행해지는 분석방법 보다 훨씬 사용하기가 간단하다는 장점이 있다. 본 연구에서는 시뮬레이션을 이용하여 두 방법을 사용하여 분석을 수행하고, 어느 방법이 더 예측적인 측면에서 우월한가에 대해서 알아보고, 실제 적용 예를 통해서 본 연구의 타당성을 알아본다. 예측력의 기준으로 RMSE를 사용하였고, 분석결과 기존의 공간통계학 예측방법과 딥러닝 기법에 따른 예측력은 비슷한 결과를 보였다. 따라서 공간데이터를 분석함에 있어 공간예측의 단점을 보완할 수 있는 딥러닝을 적용한 공간예측 분석을 제안한다. Unlike general statistics, spatial data is analyzed on the premise that they are spatially correlated. In general, spatial data is analyzed by kriging method as follow stages: (1) Estimating an empirical variogram, (2) Fitting a theoretical variogram using the estimated empirical variogram, (3) Predicting the value in the unknown location using the observations from the known location with the fitted theoretical variogram. Deep learning, one of the artificial intelligence techniques that have recently become an issue, is also widely applied as a way of prediction. Although both methods are similar in terms of making predictions, not only can the subject of the analyst be involved in the method of predicting through the analysis process of kriging method, but the analysis process is not very simple. However, deep learning can be complex to design, but it has the advantage of being much simpler to use than analytical methods conducted in kriging method. In this work, we use both methods to conduct our analysis and to find out which methods are superior in terms of more spatial predictive aspects. The RMSE was used as the criteria for prediction performance. The result of the prediction comparison showed that kriging method and deep learning have similar power for spatial prediction. Therefore, we propose to perform spatial analysis by applying deep learning that can compensate for the disadvantages of spatial prediction by triple stages of kriging method.

Spatial Correlation-based Resource Sharing in Cognitive Radio SWIPT Networks

Mei Rong,Zhonghua Liang 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.9

Cognitive radio-simultaneous wireless information and power transfer (CR-SWIPT) has attracted much interest since it can improve both the spectrum and energy efficiency of wireless networks. This paper focuses on the resource sharing between a point-to-point primary system (PRS) and a multiuser multi-antenna cellular cognitive radio system (CRS) containing a large number of cognitive users (CUs). The resource sharing optimization problem is formulated by jointly scheduling CUs and adjusting the transmit power at the cognitive base station (CBS). The effect of accessing CUs’ spatial channel correlation on the possible transmit power of the CBS is investigated. Accordingly, we provide a low-complexity suboptimal approach termed the semi-correlated semi-orthogonal user selection (SC-SOUS) algorithm to enhance the spectrum efficiency. In the proposed algorithm, CUs that are highly correlated to the information decoding primary receiver (IPR) and mutually near orthogonal are selected for simultaneous transmission to reduce the interference to the IPR and increase the sum rate of the CRS. We further develop a spatial correlation-based resource sharing (SC-RS) strategy to improve energy sharing performance. CUs nearly orthogonal to the energy harvesting primary receiver (EPR) are chosen as candidates for user selection. Therefore, the EPR can harvest more energy from the CBS so that the energy utilization of the network can improve. Besides, zero-forcing precoding and power control are adopted to eliminate interference within the CRS and meet the transmit power constraints. Simulation results and analysis show that, compared with the existing CU selection methods, the proposed low-complex strategy can enhance both the achievable sum rate of the CRS and the energy sharing capability of the network.

도시 열환경 분석을 위한 공간정보 빅데이터 구축

이준호,윤성환 대한건축학회 2020 대한건축학회논문집 Vol.36 No.5

The purpose of this study is to build a database of Spatial information Bigdata of cities using satellite images and spatial information, and toexamine the correlations with the surface temperature. Using architectural structure and usage in building information, DEM and Slopetopographical information for constructed with 300 x 300 mesh grids for Busan. The satellite image is used to prepare the NormalizedDifference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BI), and Land Surface Temperature(LST). In addition, the building area in the grid was calculated and the building ratio was constructed to build the urban environment DB. Inarchitectural structure, positive correlation was found in masonry and concrete structures. On the terrain, negative correlations were observedbetween DEM and slope. NDBI and BI were positively correlated, and NDVI was negatively correlated. The higher the Building ratio, thehigher the surface temperature. It was found that the urban environment DB could be used as a basic data for urban environment analysis,and it was possible to quantitatively grasp the impact on the architecture and urban environment by adding local meteorological factors. Thisresult is expected to be used as basic data for future urban environment planning and disaster prevention data construction.

Hybrid Diversity-Beamforming Technique for Outage Probability Minimization in Spatially Correlated Channels

Kwon, Ho-Joong,Lee, Byeong-Gi The Korea Institute of Information and Commucation 2007 Journal of communications and networks Vol.9 No.3

In this paper, we present a hybrid multi-antenna technique that can minimize the outage probability by combining the diversity and beamforming techniques. The hybrid technique clusters the transmission antennas into multiple groups and exploit diversity among different groups and beamforming within each group. We analyze the performance of the resulting hybrid technique for an arbitrary correlation among the transmission antennas. Through the performance analysis, we derive a closed-form expression of the outage probability for the hybrid technique. This enables to optimize the antenna grouping for the given spatial correlation. We show through numerical results that the hybrid technique can balance the trade-offs between diversity and beamforming according to the spatial correlation and that the optimally designed hybrid technique yields a much lower outage probability than the diversity or beamforming technique does in partially correlated fading channels.

Error influence of radar rainfall estimate on rainfall runoff

Taewoong Park,Taesam Lee,Sora Ahn,Dongyul Lee,Jungchan Kim,Dasang Ko 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-

Radars have been widely employed to detect precipitation and to predict rainfall. However, the radar-based estimate of rainfall is affected by uncertainties or errors such as mis-calibration, beam blockage, anomalous propagation, and ground cutter. Even though these uncertainties of radar rainfall estimate (RRE) have been studied, their effect on a runoff simulation especially to the peak discharge and peak time have not been much focused. Therefore, the objective of current study is to analyze the effect of the RRE uncertainties or errors based on synthetic simulation of RRE and its effect on peak discharge. First of all, mean of modeled radar rainfall is fixed (e.g., 100mm) and its error variance was set as ±10mm, ±20mm, ±40mm, and ±50mm independent to each grid cell. This independent simulation is based on white-noise process. The second simulation included a spatial-correlation between grid cells in simulating the error variance. The relationship between the distances of rain gauges and the corresponding correlations was modeled with the power law function. The parameters of the function were estimated through meta-heuristic method (specifically harmony search). Moreover, in order to find the correlation of observed data, the whole data from 27 rain gauges in the basin and the corresponding RRE from the dual polarization radar on Mt. Bisl in Korea were employed. The results of the former simulation (independent errors to each grid cell) show that the bias of the peak discharge is increased along with the variance increased, which is caused by influence of zero values. In the latter simulation (spatially correlated errors between grid cells), the results show that the peak discharge variance from the latter presents much larger than that of the former. Furthermore, the spatial distribution pattern of the modeled radar rainfall exhibited very similar to that of the real rainfall. Finally, we concluded that the error variance of RRE on runoff simulation leading bias and high uncertainty.

Spatial-Temporal Correlation-Based Low-Latency Compressed Sensing in WSNs

Jun Wang,Shuqiang Ji,Yong Cheng 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.3

Wireless Sensor Networks (WSNs) have characteristics of large size, limited resources, large amount of transmission data, and so on. In order to reduce the redundancy of sensed data and decrease network data traffic. We applied CS to clustered structure, proposed Low-Latency Compressed Sensing model (LLCS) which is based on the spatial-temporal correlation of sensed data, the model is also capable of processing sparse abnormal events which is a crucial feature in WSNs. We analyzed the relationship between compression ratio and sampling rounds and verified the abnormal event processing method. The results of simulation experiments using the real data show that LLCS could reduce data transfer volume significantly and process abnormal readings effectively.

Event-aware Hierarchical Routing with Differential Compression to Extend WSN Lifetime

You-Chiun Wang,Bo-Chun Pan 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09

Wireless sensor networks are popularly used in IoT applications. It is essential to save energy of sensors on reporting data. Though many routing methods have been developed, how to well integrate packet routing with data compression is not much discussed. Hence, we propose an event-aware hierarchical routing with differential compression (EHR-DC) scheme. It groups sensors and selects a cluster head (CH) in each group to manage routing and compression. In normal times, sensors transmit data to their CHs, which are condensed by exploiting spatial correlation. When events appear, sensors adaptively forward data to nearby CHs to raise the efficiency of compression. Through simulations, we show that EHR-DC outperforms other methods in terms of network lifetime and the amount of sensing data retrieved by the sink.

공기력에 미치는 어드미턴스 효과를 고려한 공간상 변동풍속 생성

이정화,윤제성,김호경 한국풍공학회 2013 한국풍공학회지 Vol.17 No.3

이 논문에서는 제 2 진도대교를 대상으로 교량 거더에 작용하는 변동풍속을 생성하였다. 변동풍속 시간이력 생성 시 주어진 풍속 스펙트럼, 평균풍속, 난류길이와 난류강도를 목표 값으로 설정하고, ARMA 기법을 이용하여 변동풍속을 생성하였다. 생성된 변동풍속의 초기 조건, 코히어런스, 자기/상호상관함수를 목표 값과 비교하여 변동풍속의 사용 적정성을 판단하고, 기대한 만큼의 공간/시간상관도가 고려되고 있는지 확인하였다. 검토 결과 변동풍속 시간이력 샘플의 난류 특성과 상관도 모두 목표치를 만족하고 있어 적합함을 보였다. 또한 변동풍속 생성 시 변동풍속 성분에 의한 동적 공기력 변화 효과를 고려하기 위해 공력 어드미턴스 함수를 도입하여 주파수에 의존적인 어드미턴스 효과를 고려한 시간이력 샘플을 생성하였다. 공력 어드미턴스 함수를 도입하여 생성된 변동풍속을 통해 시간영역 버페팅 해석으로도 주파수별 난류의 공기력 저감 효과가 고려된 풍응답을 산정할 수 있을 것으로 기대된다. This study is aiming to simulate the wind velocity fluctuations along the girder of the 2nd Jindo Bridge. Time series of wind velocity fluctuations were simulated based on given spectra, mean wind velocity, turbulence length and intensity using ARMA method. Simulated wind velocity fluctuations were examined by comparison with the target properties such as the spectral density functions and the spatial/temporal correlation. Both parameters showed a good agreement. The aerodynamic admittance function was also introduced in order to consider the reduction of aerodynamic forces due to the fluctuation components in wind velocity. The use of the generated wind velocity samples is expected to enable the consideration of frequency dependent aerodynamic admittance effect in a time-domain buffeting analysis.

An Improved Spatial-based Fuzzy Logic Event Detecting Algorithm for Wireless Sensor Networks

Baowei Wang,Aoran Zhuang,Huiyu Sun,Tao Li,Xingming Sun 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.4