Extracting Horizontal Curvature Data from GIS Maps: Clustering Method

2019 ◽  
Vol 2673 (11) ◽  
pp. 264-275
Author(s):  
Bekir Bartin ◽  
Kaan Ozbay ◽  
Chuan Xu
Keyword(s):  
Extraction Method ◽  
Satellite Images ◽  
Data Extraction ◽  
Clustering Method ◽  
Gps Measurements ◽  
Mobile Access ◽  
Average Percent Difference ◽  
Crash Modification Factors ◽  
Data Points ◽  
Horizontal Curvature

This paper presents the use of a clustering method for automatically estimating horizontal curvature data and crash modification factors (CMFs) using Geographic Information System (GIS) roadway shapefiles. The clustering method identifies distinct sections on a roadway, either curved or tangent, based on the proximity of the approximated curvature values of data points from GIS roadway centerline shapefiles, and calculates horizontal curvature data and the corresponding CMFs. The results of the clustering method are compared with two other methods: (1) the mobile access vehicle method based on field GPS measurements and (2) the manual data extraction method based on satellite images. The comparison was conducted on a total of 24.7 mi of four NJ rural two-lane roads. The results showed that the CMFs estimated by the clustering method were within 12.2 and 15.5% of the ones produced by the mobile asset vehicle and the manual data extraction method, respectively. In addition, the sensitivity of the manually extracted horizontal curvature data was examined by conducting three additional independent trials. The average percent difference in the calculated CMFs between trials was 15.5%. This study therefore concludes that the clustering method can produce CMF estimates as accurate as the two other methods method much more efficiently in relation to time and money.

scholarly journals Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids

ELKHA ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 54
Author(s):  
Eska Rizqi Naufal ◽  
Gigih Priyandoko ◽  
Fachrudin Hunaini
Keyword(s):  
Induction Motor ◽  
Extraction Method ◽  
Data Extraction ◽  
Working Environment ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Group Data ◽  
Low Pass

The 3 phase induction motor is a reliable and strong motor also has cheap price. However induction motor are also vulnerable, from the result of survey conducted by Electric Power Research Institute (EPRI), there are 41% cases of damage occur in the bearing caused by working environment condition, bearing age, and several other factors. Bearing fault is not easily to identified, with applying the data extraction method using the Discrete Wavelet Transform (DWT) and the K-Medoids clustering method will facilitate the identification process. The extraction method will pass the data in the form of current signals into the digital filter (Low Pass Filter and High Pass Filter) to be mapped into the region of frequency and time simultaneously, and clustering method will group data based on certain characteristics. Based on the clustering tests that have been done on the 3 phase induction motor current signal data with 3 bearing conditions, the Discrete Wavelet Transformation with mother wavelet bior1.1 decomposition level 2 and K-Medoids produce an accuracy rate of 86.8%.

Data Extraction Method from Printed Images with Different Formats

2017 ◽  
Vol E100.A (11) ◽  
pp. 2355-2357
Author(s):  
Mitsuji MUNEYASU ◽  
Nayuta JINDA ◽  
Yuuya MORITANI ◽  
Soh YOSHIDA
Keyword(s):  
Extraction Method ◽  
Data Extraction

scholarly journals A simple clustering technique to extract subsets of data for function approximation

2015 ◽  
Vol 17 (5) ◽  
pp. 719-732
Author(s):  
Dulakshi Santhusitha Kumari Karunasingha ◽  
Shie-Yui Liong
Keyword(s):  
Function Approximation ◽  
Prediction Models ◽  
Data Extraction ◽  
Single Parameter ◽  
Subtractive Clustering ◽  
Data Sets ◽  
Clustering Methods ◽  
Clustering Method ◽  
Data Set ◽  
Functional Relationships

A simple clustering method is proposed for extracting representative subsets from lengthy data sets. The main purpose of the extracted subset of data is to use it to build prediction models (of the form of approximating functional relationships) instead of using the entire large data set. Such smaller subsets of data are often required in exploratory analysis stages of studies that involve resource consuming investigations. A few recent studies have used a subtractive clustering method (SCM) for such data extraction, in the absence of clustering methods for function approximation. SCM, however, requires several parameters to be specified. This study proposes a clustering method, which requires only a single parameter to be specified, yet it is shown to be as effective as the SCM. A method to find suitable values for the parameter is also proposed. Due to having only a single parameter, using the proposed clustering method is shown to be orders of magnitudes more efficient than using SCM. The effectiveness of the proposed method is demonstrated on phase space prediction of three univariate time series and prediction of two multivariate data sets. Some drawbacks of SCM when applied for data extraction are identified, and the proposed method is shown to be a solution for them.

Applying Supervised Clustering to Landsat MSS Images into GIS-Application

2013 ◽  
Vol 4 (3) ◽  
pp. 114-122
Author(s):  
Miguel Torres ◽  
Marco Moreno-Ibarra ◽  
Rolando Quintero ◽  
Giovanni Guzmán
Keyword(s):  
Supervised Classification ◽  
Satellite Images ◽  
Training Data ◽  
Clustering Method ◽  
Maximum Likelihood Classification ◽  
Gis Application ◽  
Supervised Clustering ◽  
Gis Applications ◽  
Geographic Environment ◽  
Decision Making Model

In this paper, the authors describe and implement an algorithm to perform a supervised classification into Landsat MSS satellite images. The Maximum Likelihood Classification method is used to generate raster digital thematic maps by means of a supervised clustering. The clustering method has been proved in Landsat MSS images of different regions of Mexico to detect several training data related to the geographic environment. The algorithm has been integrated into Spatial Analyzer Module to improve the decision making model and the spatial analysis into GIS-applications.

Validation Study of Assay for Neomycin in Feed

1994 ◽  
Vol 77 (6) ◽  
pp. 1403-1410
Author(s):  
Gerald L Stahl ◽  
Terry J Gilbertson ◽  
D Dal Kratzer ◽  
James J Blondia ◽  
Michael R Stoline
Keyword(s):  
Infinite Number ◽  
Extraction Method ◽  
Confidence Limits ◽  
Type B ◽  
Slight Improvement ◽  
Statistical Estimates ◽  
Data Points ◽  
Type C ◽  
Cattle Feeds ◽  
Feed Samples

Abstract The neomycin microbiological cylinder plate assay using an ionic extraction method was validated for 2 swine feeds with neomycin base concentrations of 140.0 and 280.0 g/ton, 2 cattle feeds with neomycin base concentrations of 140.0 and 280.0 g/ton, and 2 premixes with neomycin base concentrations of 1.4 and 7.0 g/lb. The accuracy, precision, and reproducibility were evaluated by assaying the 6 feed samples in duplicate on 6 different days by an Upjohn laboratory (Laboratory 1) and 2 outside laboratories (Laboratories 2 and 3) for a total of 72 data points per laboratory. Three plates were used for each sample assayed. The recovery over all complete (type C) feed samples and (type B) premixes tested, ranged from 82 to 109% for Laboratory 1,85 to 114% for Laboratory 2, and 78 to 134% for Laboratory 3. Mean percent recoveries for all samples tested were 98.0,98.9, and 103.7% for Laboratories 1,2, and 3, respectively. Day-to-day performance yielded 95% confidence limits of ±11.9, ±16.9, and ±25% for Laboratories 1,2, and 3, respectively. Of 215 assay values, 206 (96%) fell within 20% of the actual targeted recovery (100%). Plate-to-plate variation of each of the 3 laboratories also was assessed. Statistical estimates for the use of 1,5, and an infinite number of plates showed that when 2 plates were used by Laboratories 1,2, and 3, than 99,99, and 95%, respectively, of the assay results fell within the acceptable assay compliance limits (70–125% recovery) for complete feeds and premixes. Slight improvement was found only for Laboratory 3, when more than 2 plates were used.

The use of IT to improve practice quality within a radiation oncology program.

2014 ◽  
Vol 32 (30_suppl) ◽  
pp. 170-170
Author(s):  
James Varghese ◽  
Julia Giovinazzo ◽  
Tessa Larsen ◽  
Gaylene Medlam ◽  
Natasha Moran ◽  
...  
Keyword(s):  
Information System ◽  
Data Extraction ◽  
High Rate ◽  
Department Heads ◽  
Naming Convention ◽  
Continuous Quality ◽  
Data Points ◽  
Treatment Plans ◽  
Operational Decision Making ◽  
Program Engagement

170 Background: Our centre has used ARIA as its oncology information system since 2005. Anecdotally, since 2008, dosimetrists have identified a high rate of replanned treatment plans. In 2010, a 2% replan rate was estimated however, it was felt that the results did not capture the true rework impact. A definition for replan was created to distinguish it from plan revisions and an attempt to standardize replan naming conventions was initiated. The replan workload was again raised as an issue in 2013 and an interdisciplinary review workgroup was formed with the objective to develop a process to capture workload more accurately. Methods: Rework was further categorized into replan, rework and revision. The nomenclature for the ARIA plan name data field was standardized to identify replans and plan revisions. In addition, rework task activities were created to identify the discipline initiating the rework. Embedding these changes into our process allowed for efficient and accurate workload data extraction. Results of monthly data analysis are shared with the dosimetrists and actionable items are sent to department heads for program engagement. Results: In March 2014, we achieved 100% compliance to the plan naming convention. Data showed 6% replan and 6.5% revision rates. Table 1 shows the rework numbers initiated by discipline. Conclusions: A paperless environment lends itself well to an organization’s ability to capture data. Our experience shows that if processes are designed to support and be supported by the information system, the input of data points can be embedded seamlessly and practically in the clinician’s workflow. This results in improved data capture where data becomes more meaningful, and can then be used for continuous quality improvement, operational decision-making and research projects, all of which can lead to practice improvements. [Table: see text]

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