scholarly journals Modification K-Means Model With Local Deviation Method To Improve The Accuracy In Forming Clusters

2019 ◽  
Author(s):  
M Martiano ◽  
Muhammad Zarlis ◽  
Sutarman Wage
Keyword(s):  
Local Deviation ◽  
Deviation Method

State Policy Control and Local Deviation: The Case of Immigration

2021 ◽  
pp. 0160323X2110008
Author(s):  
Brittany Blizzard ◽  
Jocelyn M. Johnston
Keyword(s):  
Local Government ◽  
Significant Influence ◽  
Mixed Method ◽  
State Policy ◽  
Special Interests ◽  
Policy Control ◽  
Local Support ◽  
Local Deviation ◽  
Immigration And Customs Enforcement ◽  
Method Approach

State preemption of local government discretion is examined through the lenses of county cooperation with federal Immigration and Customs Enforcement (ICE) and “immigration federalism.” Through a mixed-method approach, we examine why counties collaborate with ICE as well as how and why they deviate from state preemptions on local support for immigration. Analysis of a sample restricted to Georgia and Texas, states with especially robust preemptive anti-immigrant laws, suggests that special interests—those related to immigrant-dependent industries important to county economies—have significant influence over county decisions to minimize cooperation with ICE.

scholarly journals Application of Convolutional Neural Network for Spatiotemporal Bias Correction of Daily Satellite-Based Precipitation

2020 ◽  
Vol 12 (17) ◽  
pp. 2731
Author(s):  
Xuan-Hien Le ◽  
Giha Lee ◽  
Kwansue Jung ◽  
Hyun-uk An ◽  
Seungsoo Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Standard Deviation ◽  
Convolutional Neural Network ◽  
Bias Correction ◽  
Temporal Correlation ◽  
Precipitation Data ◽  
Climate Data ◽  
Extreme Rainfall Events ◽  
Essential Components ◽  
Deviation Method

Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data.

A method for shock train leading edge detection based on differential pressure

2016 ◽  
Vol 231 (1) ◽  
pp. 61-71 ◽  
Author(s):  
B Xiong ◽  
Z-G Wang ◽  
X-Q Fan ◽  
Y Wang
Keyword(s):  
Standard Deviation ◽  
Edge Detection ◽  
Detection Method ◽  
Pressure Ratio ◽  
Leading Edge ◽  
Differential Pressure ◽  
Shock Train ◽  
Pressure Method ◽  
Operational Application ◽  
Deviation Method

In order to make the shock train leading edge detection method more possible for operational application, a new detection method based on differential pressure signals is introduced in this paper. Firstly, three previous detection methods, including the pressure ratio method, the pressure increase method, and the standard deviation method, have been examined whether they are also applicable for shock train moving at different speeds. Accordingly, three experimental cases of back-pressure changing at different rates were conducted in this paper. The results show that the pressure ratio and the pressure increase method both have acceptable detection accuracy for shock train moving rapidly and slowly, and the standard deviation method is not applicable for rapid shock train movement due to its running time window. Considering the operational application, the differential pressure method is raised and tested in this paper. This detection method has sufficient temporal resolution for rapidly and slowly shock train moving, and can make a real-time detection. In the end, the improvements brought by the differential pressure method have been discussed.

scholarly journals Algorithm for Probabilistic Dual Hesitant Fuzzy Multi-Criteria Decision-Making Based on Aggregation Operators With New Distance Measures

Mathematics ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 280 ◽  
Author(s):  
Harish Garg ◽  
Gagandeep Kaur
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Weight Vector ◽  
Aggregation Operators ◽  
Distance Measures ◽  
Weighted Averaging ◽  
Hesitant Fuzzy Set ◽  
Maximum Deviation ◽  
Dual Hesitant Fuzzy Set ◽  
Deviation Method

Probabilistic dual hesitant fuzzy set (PDHFS) is an enhanced version of a dual hesitant fuzzy set (DHFS) in which each membership and non-membership hesitant value is considered along with its occurrence probability. These assigned probabilities give more details about the level of agreeness or disagreeness. By emphasizing the advantages of the PDHFS and the aggregation operators, in this manuscript, we have proposed several weighted and ordered weighted averaging and geometric aggregation operators by using Einstein norm operations, where the preferences related to each object is taken in terms of probabilistic dual hesitant fuzzy elements. Several desirable properties and relations are also investigated in details. Also, we have proposed two distance measures and its based maximum deviation method to compute the weight vector of the different criteria. Finally, a multi-criteria group decision-making approach is constructed based on proposed operators and the presented algorithm is explained with the help of the numerical example. The reliability of the presented decision-making method is explored with the help of testing criteria and by comparing the results of the example with several prevailing studies.

scholarly journals Analisis Penerapan Standar Deviasi Dalam Penentuan Persediaan Pengaman Pada Ud Mirama Kota Gorontalo.

2018 ◽  
Vol 1 (1) ◽  
pp. 103
Author(s):  
Sasmita R. Setiawan
Keyword(s):  
Standard Deviation ◽  
International Markets ◽  
Global Market ◽  
Safety Stock ◽  
Trading Company ◽  
Service Production ◽  
Supply Problem ◽  
Additional Costs ◽  
Local Companies ◽  
Deviation Method

Currently all companies are looking for opportunities in the openness of international markets or global markets. Companies that can enter this global market are companies that have comparative and competitive advantages. It is necessary to make an important decision concerning the level or quantity of production for the manufacturing company, the determination of the amount or stock of merchandise for the trading company as well as the level of service production for the service company. One important aspect that needs proper management is the supply problem. One of the local companies that became the focus or object of research in this writing is UD. Mirama located in Gorontalo Province, especially the city of Gorontalo which sells AC electronic goods and TV. just like any other trading company the company is also having trouble determining the amount of merchandise inventory. Because often companies have difficulty in the amount of safety stock that must be prepared at the time of booking so that companies can anticipate the number of fluctuating demand. With this problem it is necessary to apply a method used as corporate guidance to overcome the problem of inventory. The method that will try to use is standard deviation method in determining the amount of safety stock. This method is applied to see how the most optimal security inventory, which is the most economical, in the sense that not too much which means waste or additional costs that are not necessary or not too little which means there is still danger of running out of inventory. By using model of qualitative and quantitative analysis, the result of research analysis from the research object is the application of standard deviation method can be determined how much the safety stock will be held by the company and can cover the occurrence of stock out when demand increase or fluctuation in demand.

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