Effect of atmospheric stability on air pollutant concentration and its generalization for real and idealized urban block models based on field observation data and wind tunnel experiments

2020 ◽  
Vol 207 ◽  
pp. 104380
Author(s):  
Tingting Hu ◽  
Ryuichiro Yoshie
Keyword(s):  
Wind Tunnel ◽  
Field Observation ◽  
Atmospheric Stability ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Observation Data ◽  
Wind Tunnel Experiments ◽  
Field Observation Data ◽  
Block Models

scholarly journals Developing a Reliability Model of CNC System under Limited Sample Data Based on Multisource Information Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chong Peng ◽  
Yuzhen Cai ◽  
Guangpeng Liu ◽  
T. W. Liao
Keyword(s):  
Information Fusion ◽  
Field Observation ◽  
Prior Information ◽  
Small Sample ◽  
Numerical Control ◽  
Observation Data ◽  
Reliability Model ◽  
Cnc System ◽  
Reliability Models ◽  
Field Observation Data

The reliability of the computer numerical control (CNC) system affects its processing performance and is a major concern in the manufacturing industry today. However, existing reliability models to assess the reliability of the CNC system often exhibit relatively large errors due to inadequate treatment of small samples. In order to get around the constraint of limited lifetime failure data and take full advantage of existing reliability parameters in traditional reliability models, a multisource information fusion-based reliability model grounded on Bayesian inference is proposed to deal with the small sample size. The prior distributions are derived by using the probability encoding method and conjugate distribution based on the idea of multisource information fusion. Then, using the Jensen–Shannon divergence (JSD) to measure the similarity between prior information and field observation data, a constrained optimization problem is established to determine the respective weight of prior information and field observation data. Finally, by conducting the reliability analysis of repairable CNC systems, the validity of the proposed model and its prior distribution derivation method are verified.

scholarly journals Soil Mapping in Chongwe, Zambia by Digital Analysis of Landsat Data

2016 ◽  
Vol 8 (9) ◽  
pp. 152
Author(s):  
Chizumba Shepande ◽  
Marvin Bauer ◽  
Jay Bell ◽  
Victor Shitumbanuma
Keyword(s):  
Field Observation ◽  
Landsat Imagery ◽  
Soil Types ◽  
Observation Data ◽  
Digital Analysis ◽  
Landsat Data ◽  
Reasonable Accuracy ◽  
Landsat 7 ◽  
Field Observation Data ◽  
Semi Arid Region

<p>Designing a methodology for mapping and studying soils in a quick and inexpensive way is critical especially in developing countries like Zambia, which lack detailed soil surveys. Therefore, this study was conducted to determine the potential of Landsat 7 ETM+ data (Enhanced Thematic Mapper plus) in mapping soils in Chongwe, a semi-arid region in Zambia. In addition, the study attempted to establish how accurate spectral soil maps produced by digital analysis of Landsat data can be and how such maps compared with field observation data. Also, in situations where there was poor agreement between Landsat data and field observation data, possible causes of such discrepancies where determined.</p><p>A soil inventory of the Chongwe region of Zambia was prepared using computer-aided digital analysis of two Landsat 7 ETM+ satellite images acquired in the dry and rainy seasons to investigate the hypothesis that there is a relationship between Landsat spectral reflectance and certain soil types and that this relationship can be used to map soils with reasonable accuracy.</p><p>The study revealed that digital analysis of Landsat 7 ETM images has the capacity to map and delineate soil patterns with reasonable accuracy, especially when acquired during the dry season when there are long periods of cloud free skies, low soil moisture and minimal vegetation cover. The overall agreement between the Landsat classification and reference data was 72%, indicating a definite relationship between Landsat imagery and soil types.</p><p>In terms of soilscape boundary delineation, the Landsat derived map was had a higher level of agreement with field observations than the conventional soil map. In addition, the study showed that overall, upland areas have a better agreement with Landsat spectral data compared to lowland areas, probably due to the diverse origin of sediments and low spatial extent of most landforms in lowland areas.</p>

scholarly journals Impacts of the 2011 East Japan tsunami in the Papua region, Indonesia: field observation data and numerical analyses

2013 ◽  
Vol 194 (3) ◽  
pp. 1625-1639 ◽  
Author(s):  
Subandono Diposaptono ◽  
Abdul Muhari ◽  
Fumihiko Imamura ◽  
Shunichi Koshimura ◽  
Hideaki Yanagisawa
Keyword(s):  
Field Observation ◽  
Numerical Analyses ◽  
Observation Data ◽  
Field Observation Data

scholarly journals STUDY ON OFFSHORE WIND CHARACTERISTICS IN JAPANESE COASTAL REGION BASED ON FIELD OBSERVATION DATA

2011 ◽  
Vol 67 (2) ◽  
pp. I_961-I_966
Author(s):  
Toshihiko NAGAI ◽  
Koji KAWAGUCHI ◽  
YUTAKA YOSHIMURA ◽  
Kaiin MEIYO ◽  
Ryoichi TANIKAWA ◽  
...  
Keyword(s):  
Field Observation ◽  
Coastal Region ◽  
Offshore Wind ◽  
Observation Data ◽  
Field Observation Data ◽  
Wind Characteristics

Bicycle Safety Analysis at Intersections from Crowdsourced Data

2019 ◽  
Vol 2673 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Moatz Saad ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee ◽  
Qing Cai
Keyword(s):  
Field Observation ◽  
Negative Binomial ◽  
Population Distribution ◽  
Safety Performance ◽  
Observation Data ◽  
Major Step ◽  
Bicycle Safety ◽  
Safety Performance Functions ◽  
Crowdsourced Data ◽  
Field Observation Data

Cycling is encouraged in countries around the world as an economic, energy efficient, and sustainable mode of transportation. Although there are many studies focusing on analyzing bicycle safety, they have limitations because of the shortage of bicycle exposure data. This study represents a major step forward in estimating safety performance functions for bicycle crashes at intersections by using crowdsourced data from STRAVA. Several adjustments in respect of the population distribution and field observations were made to overcome the disproportionate representation of the STRAVA data. The adjusted STRAVA data which include bicycle exposure information were used as input to develop safety performance functions. The functions are negative binomial models aimed at predicting frequencies of bicycle crashes at intersections. The developed model was compared with three counterparts: the model using the unadjusted STRAVA data, the model using the STRAVA data with field observation data adjustments only, and the model using the STRAVA data with adjusted population. The results revealed that the case of STRAVA data with both population and field observation data adjustments had the best performance in bicycle crash modeling. The results also addressed several key factors (e.g., signal control system, intersection size, bike lanes) which are associated with bicycle safety at intersections. Additionally, the safety-in-numbers effect was acknowledged when bicycle crash rates decreased as bicycle activities increased. The study concluded that crowdsourced data are a reliable source for exploring bicycle safety after the appropriate adjustments.

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