scholarly journals Decision-Making Optimization of Mine Gas Monitoring Based on Gauss Process Regression and Interval Number Correlation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dingwen Dong
Keyword(s):  
Decision Making ◽  
Correlation Analysis ◽  
Confidence Interval ◽  
Gas Sensor ◽  
Sensor Calibration ◽  
Gas Monitoring ◽  
Interval Numbers ◽  
Monitoring Point ◽  
Correlation Degree ◽  
Gauss Process

For the subjective limitation of gas sensor calibration in coal mines, a decision-making method for gas sensor calibration under monitoring failure was studied based on the Gauss process regression (GPR) and the correlation analysis of interval numbers. Based on the correlation characteristics of gas monitoring data of each monitoring point in the work face area in coal mine, the initial confidence interval of gas concentration in monitoring failure period was obtained by GPR, and then the confidence interval was further optimized by the correlation analysis of interval numbers. According to the correlation characteristics of monitoring data of each monitoring point, its similarity of dynamic variation tendency was measured by using Euclidean distance of interval numbers, and the optimal confidence interval was determined by calculating the correlation degree of interval numbers. The case study shows that making full use of the effective monitoring information of multiple monitoring points ensures the reliability of the initial confidence interval; the dynamic adjustment of model parameters in correlation analysis of interval number avoids the subjectivity defect of similar methods and further obtains the consistency between interval numbers’ reliability and correlation degree, which can ensure the effectiveness of the application of this method.

scholarly journals An Innovative Grey Approach for Group Multi-Criteria Decision Analysis Based on the Median of Ratings by Using Python

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 124
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Predrag S. Stanimirović ◽  
Florentin Smarandache ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Likert Scale ◽  
Decision Makers ◽  
Interval Numbers ◽  
Multi Criteria Decision Analysis

Some decision-making problems, i.e., multi-criteria decision analysis (MCDA) problems, require taking into account the attitudes of a large number of decision-makers and/or respondents. Therefore, an approach to the transformation of crisp ratings, collected from respondents, in grey interval numbers form based on the median of collected scores, i.e., ratings, is considered in this article. In this way, the simplicity of collecting respondents’ attitudes using crisp values, i.e., by applying some form of Likert scale, is combined with the advantages that can be achieved by using grey interval numbers. In this way, a grey extension of MCDA methods is obtained. The application of the proposed approach was considered in the example of evaluating the websites of tourism organizations by using several MCDA methods. Additionally, an analysis of the application of the proposed approach in the case of a large number of respondents, done in Python, is presented. The advantages of the proposed method, as well as its possible limitations, are summarized.

Multi-criteria decision-making algorithm based on aggregation operators under the complex interval-valued q-rung orthopair uncertain linguistic information

2021 ◽  
pp. 1-30
Author(s):  
Harish Garg ◽  
Zeeshan Ali ◽  
Zaoli Yang ◽  
Tahir Mahmood ◽  
Sultan Aljahdali
Keyword(s):  
Decision Making ◽  
Brain Tumors ◽  
Score Function ◽  
Aggregation Operators ◽  
Multi Criteria Decision Making ◽  
Linguistic Features ◽  
Interval Numbers ◽  
Fundamental Properties ◽  
Interval Valued

The paper aims to present a concept of a Complex interval-valued q-rung orthopair uncertain linguistic set (CIVQROULS) and investigated their properties. In the presented set, the membership grades are considered in terms of the interval numbers under the complex domain while the linguistic features are added to address the uncertainties in the data. To further discuss more, we have presented the operation laws and score function for CIVQROULS. In addition to them, we present some averaging and geometric operators to aggregate the different pairs of the CIVQROULS. Some fundamental properties of the proposed operators are stated. Afterward, an algorithm for solving the decision-making problems is addressed based on the proposed operator using the CIVQROULS features. The applicability of the algorithm is demonstrated through a case study related to brain tumors and their effectiveness is compared with the existing studies.

Heterogeneous multi-attribute case retrieval method based on group decision making using incomplete weight information

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhang ◽  
Jing Zheng ◽  
Ying-Ming Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Interval Numbers ◽  
Retrieval Method ◽  
Incomplete Weight ◽  
Incomplete Weight Information ◽  
Evaluation Information

Case-based reasoning (CBR) is one of the most popular methods used in emergency decision making (EDM). Case retrieval plays a key role in EDM processes based on CBR and usually functions by retrieving similar historical cases using similarity measurements. Decision makers (DMs), thus, choose the most appropriate historical cases. Although uncertainty and fuzziness are present in the EDM process, in-depth research on these issues is still lacking. In this study, a heterogeneous multi-attribute case retrieval method based on group decision making (GDM) with incomplete weight information is developed. First, the case similarities between historical and target cases are calculated, and a set of similar historical cases is constructed. Six formats of case attributes are considered, namely crisp numbers, interval numbers, linguistic variables, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (NNs) and interval-valued NNs. Next, the evaluation information from the DMs is expressed using single-valued NNs. Additionally, the evaluation utilities of similar historical cases are obtained by aggregating the evaluation information. The comprehensive utilities of similar historical cases are obtained using case similarities and evaluation utilities. In this process, the weights of incomplete information are determined by constructing optimization models. Furthermore, the most appropriate similar historical case is selected according to the comprehensive utilities. Finally, the proposed method is demonstrated using two examples; its performance is then compared with those of other similar methods to demonstrate its validity and efficacy.

Characterization of SnO2Ceramic Gas Sensor for Exhaust Gas Monitoring of SVE Process

2004 ◽  
Vol 92 (1-3) ◽  
pp. 153-161 ◽  
Author(s):  
Ji-Won Yang ◽  
Hyun-Jeong Cho ◽  
Sang-Hyun Lee ◽  
Jae-Young Lee
Keyword(s):  
Gas Sensor ◽  
Exhaust Gas ◽  
Gas Monitoring

Knowledge-Integration Model for Networked Manufacturing

2011 ◽  
Vol 314-316 ◽  
pp. 2027-2032
Author(s):  
Jiao Jian Liu ◽  
Wen He Liao ◽  
Yu Guo ◽  
Wen Bin Wang
Keyword(s):  
Decision Making ◽  
Correlation Analysis ◽  
Knowledge Sharing ◽  
Manufacturing Process ◽  
Knowledge Integration ◽  
Decision Making Process ◽  
Networked Manufacturing ◽  
Optimum Distribution ◽  
Integration Model ◽  
Key Technologies

In order to maximize knowledge sharing and reuse in networked manufacturing process and improve the rapidity and reliability of decision-making, a knowledge-integration model and its implementation methods are proposed in this paper. First, the requirement for knowledge integration in networked manufacturing is analyzed. On this basis, a knowledge-integration model is built, and then three key technologies are studied, namely knowledge representation and organization based on ontology, knowledge correlation analysis based on complex network and knowledge supply based on decision-making context. This model provides an effective way to realize the optimum distribution of knowledge in networked manufacturing process and to improve the efficiency of decision-making process.

The logical component of thinking as a predictor of decision-making about the presence of corruption risk in a situation

2021 ◽  
Vol 42 (6) ◽  
pp. 25-34
Author(s):  
I. N. Pogozhina ◽  
◽  
M. V. Sergeeva ◽  
Keyword(s):  
Decision Making ◽  
Emotional Intelligence ◽  
Correlation Analysis ◽  
University Students ◽  
Moral Values ◽  
Moral Judgments ◽  
Diagnostic Tools ◽  
Leading Role ◽  
Decision Making System

The links between elements of the decision-making system on the presence of corruption risk (CR) in a situation with the logical component of thinking as a predictor are considered. The hypothesis of the role of logical reasoning component as a predictor of (1) perceptions of corruption, (2) indicators of emotional intelligence and (3) moral judgement was tested on a sample of Moscow university students (N=134; M=35±11 years old). The following diagnostic tools were used: (1) the author's test for recognising CR situations, (2) the method for assessing the content of ideas about corruption (Pogozhina, Pshenichnyuk, Sergeyeva), (3) D. Lucin’s EmIn questionnaire, (4) Molchanov's Justice-Care technique. Correlation analysis and structural modeling were used to process the data. The logical component of thinking was a significant positive predictor of the level of development of perceptions of corruption and understanding one’s own emotions and those of others. Also, the logical component significantly negatively predicted moral judgments based on instrumental individualism, reflexive empathic orientation and unconscious but internalized moral values. The findings suggest that the logical component will play a leading role in the CR decision-making system and should be specifically shaped.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 431-445
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Confidence Interval ◽  
Data Envelopment ◽  
Data Set ◽  
Interval Model ◽  
Time Period ◽  
Proposed Model ◽  
The Future ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

scholarly journals The intertwining of vocational aptitude and interest: A study among university students in Indonesia

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ufi Fatuhrahmah ◽  
Dian Fithriwati Darusmin ◽  
Herlina Siwi Widiana
Keyword(s):  
Decision Making ◽  
Correlation Analysis ◽  
University Students ◽  
Career Decisions ◽  
Career Decision ◽  
Directed Search ◽  
Career Decision Making ◽  
Vocational Interest ◽  
Pearson Product Moment Correlation ◽  
Measurement Tools

Vocational aptitude and interest are the fundamental factors that education and career counselors utilize to provide suggestions to clients. These concepts are often considered as separate constructs. However, aptitude and interest are interrelated and should both be considered when making career decisions. This study involved as many as 343 university students as participants. Two measurement tools were used: Employee Aptitude Survey (EAS) to measure aptitude and Self-Directed Search (SDS) Holland to measure vocational interest. The data were analyzed using canonical and Pearson product-moment correlation analysis. The findings show that there is a correlation between several types of interest and several types of aptitude. Vocational interest that has the strongest correlation with aptitude was the investigative interest, while the numerical aptitude test has the strongest correlation with interest. In the process of career decision-making, particularly for university students, both aptitude and interest must be taken into consideration, especially the investigative interest and numerical aptitude.

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