Метод трикритеріального евклідового ранжування та його використання для багатокритеріального порівняння альтернатив

In this paper, a new combination method for sound field prediction is proposed. An optimization approach based on the genetic algorithm is employed for optimizing the transition frequency of the combined sound field prediction method in classrooms. The selected optimization approach can identify the optimal transition frequency so that the combined sound field prediction can obtain more efficient and accurate prediction results. The proposed combined sound field prediction method consists of a wave-based method and geometric acoustic methods that are separated by the transition frequency. In low frequency domain (below the transition frequency), the sound field is calculated by the finite element method (FEM), while a hybrid geometric acoustic method is employed in the high frequency domain (above the transition frequency). The proposed combined prediction models are validated by comparing them with previous results and experimental measurements. The optimization approach is illustrated by several examples and compared with traditional combination results. Compared to existed sound field prediction simulations in classrooms, the proposed combination methods take the sound field in low frequencies into account. The results demonstrate the effectiveness of the proposed model. Practical applications: This study proposes a combined sound field prediction method separated by transition frequency. A genetic algorithm optimization method is employed for searching the optimal transition frequency. The outcomes of this paper are essential for acoustical designs and acoustical environmental assessments.

There are several non‐functional‐form‐based interaction tests for testing additivity in unreplicated two‐way layouts. The distinctness of the various tests in this setting is somewhat unfortunate, as no single test can detect all patterns of possible interaction, and the tests are sensitive to a particular pattern of interaction. Therefore, it is reasonable to combine multiple interaction tests for testing additivity. The main problem is how we can combine multiple dependent tests into a single test such that it has an acceptable performance in terms of controlling the type I error rate and maximizing the power. In this paper, we review six methods for combining interaction tests which utilize six different non‐functional‐form‐based interaction tests. We also propose two novel combination methods based on the Jacobi polynomial expansion and the Gaussian copula. We first compare these eight combination methods in terms of controlling the type I error rate via a simulation study. We then compare the six interaction tests and four combination methods in terms of power. The simulation results show that our proposed combination method that is based on the Jacobi polynomial expansion has the best performance in terms of controlling the type I error rate and maximizing the power. Therefore, we recommend this combination method as a test procedure for testing additivity. The major contribution of this work is to provide researchers with a testing approach which leverages many existing methods to detect disparate forms of non‐additivity. This alleviates the need for researchers to naively choose among highly specialized methods by instead allowing for a general approach that captures a number of interaction effects with reasonable power.

One of the most important parts of all applications trying to discriminate between a person's different mental tasks using their recorded EEG data is the process of feature construction. A common practice for this is to exploit an apriori knowledge about the nature of the mental processes of interest and their impact on the EEG signals. However, the use of features constructed in this way is restricted to applications concerning the corresponding mental processes. We present here a novel method for EEG data classification which is very general as it makes no assumptions about the nature of the EEG signals. It is based on the construction of a characteristic signal for each class which remains as invariant as possible over the trials belonging to that class. We use the proposed method in combination with a novel method for channel selection in an oddball experiment to predict a person's quick or late response.

Using color information in object tracking is a prudent choice, but the vast variety of choices and difficulties of obtaining a desirable stable result, unnerves many scholars. Color histograms, as a compact and robust representation is the center of attention while it suffers from lack of spatial information about colors. Besides, comparison and updating such histograms in a meaningful and efficient manner is challenging. In this paper, we proposed the idea of gridding for color histogram, which grants specific statistical property to the histogram through a decomposition phase followed by a recombination stage. Additionally, a thorough comparison of the modern similarity functions and model update techniques in RGB colorspace is presented. This comparison reveals that our proposed method in combination with established similarity measures, enhances the tracking performance.

Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latentfingerprint reconstruction model based on conditional generative adversarial networks $(cGANs)$.Two modifications are applied to the cGANto adapt itfor the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information are considered in the generation process, and prevent the model from filling large missing areas and generating erroneous minutiae. Second, a perceptual IDpreservation approach is developed to force the generator to preserve the IDinformation during the reconstruction process. Using a synthetically generated database of latent fingerprints, the deep network learns to predict missing information from the input latent samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms on several publicly available latent fingerprint datasets. Weachieved rank-10 accuracy of 88.02% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50accuracy of 70.89% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching. Experimental results of matching reconstructed samples in both latent-to-sensor and latent-to-latent frameworks indicate that the proposed method significantly increases the matching accuracy of the fingerprint recognition systems for the latent samples.

Extension of VIKOR method for decision making problem based on hesitant fuzzy set

Spectral unmixing amounts at estimating the abundance of pure spectral signatures (called endmembers) in each mixed pixel of a hyperspectral image, where mixed pixels arise due to insufficient spatial resolution and other phenomena. A challenging problem is how to automatically identify endmembers, as the presence of mixed pixels generally prevents the localization of pure spectral signatures in transition areas between different land-cover classes. A possible strategy to address this problem is to guide the endmember identification process to spatially homogeneous areas, expected to contain the purest signatures available in the scene. For this purpose, several spatial preprocessing methods have been used prior to endmember identification. However, the preprocessing methods developed thus far only exploit the spatial information and relegate the use of spectral information to the subsequent endmember identification stage. In this paper, we develop a new spatial-spectral preprocessing (SSPP) module which can be used prior to endmember identification and spectral unmixing. The method first derives a spatial homogeneity index for each pixel in the hyperspectral image. This index is relatively insensitive to the noise present in the data. At the same time, it performs unsupervised clustering to identify a set of clusters in spectral space. Finally, it fuses spatial and spectral information by selecting a subset of spatially homogeneous and spectrally pure pixels from each cluster. These pixels constitute the new set of candidates for endmember identification. An innovative contribution of this paper is the combination of spatial and spectral information at the preprocessing stage. Another contribution is the combination, for the first time in the literature, of preprocessing techniques with endmember identification algorithms that do not assume the presence of pure signatures in the scene. An experimental comparison of the proposed method in combination with different endmember identification techniques is conducted using both synthetic and real hyperspectral data collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experiments indicate that endmember identification techniques (with and without the pure signature assumption) can greatly benefit from the proposed preprocessing approach, which considers both spatial and spectral information.

Durability evaluation and lifetime prediction of recycled coarse aggregate self-compacting concrete after freeze-thaw and sulfate erosion coupling

Article history: Received June 25, 2015 Received in revised format: October 12, 2015 Accepted November 15, 2015 Available online November 16 2015 The objective of this paper is to optimize the process parameters by combined approach of VIKOR and Entropy weight measurement method during Electrical discharge machining (EDM) process of Al-18wt.%SiCp metal matrix composite (MMC). The central composite design (CCD) method is considered to evaluate the effect of three process parameters; namely pulse on time (Ton), peak current (Ip) and flushing pressure (Fp) on the responses like material removal rate (MRR), tool wear rate (TWR), Radial over cut (ROC) and surface roughness (Ra). The Entropy weight measurement method evaluates the individual weights of each response and, using VIKOR method, the multi-objective responses are optimized to get a single numerical index known as VIKOR Index. Then the Analysis of Variance (ANOVA) technique is used to determine the significance of the process parameters on the VIKOR Index. Finally, the result of the VIKOR Indexed is validated by conformation test using the liner mathematical model equation develop by responses surface methodology to identify the effectiveness of the proposed method. Growing Science Ltd. All rights reserved. 6 © 201

In this communication, we propose a generalized fuzzy knowledge measure and prove its efficiency by comparing it with some existing entropies. We also propose a generalized fuzzy accuracy measure and show some of its properties. This accuracy measure may serve as a compatibility measure between two fuzzy sets and helpful in some specific situations. We introduce a generalized fuzzy knowledge and accuracy measure-based TOPSIS for multiple-attribute decision-making problems and presents its comparison with MOORA method, VIKOR method, and a compromise-type variable weight decision-making method. The application of the proposed TOPSIS approach in multiple-attribute decision-making (MADM) is demonstrated using a numerical example. We also investigate the application and efficiency of the generalized fuzzy accuracy in pattern recognition problems.

Green supplier selection is an integral part of Supply Chain Management in recent times. With increasing concern on environment protection and ecological preservation use of green factors in supplier selection is vital for every organization. The general concept behind green supplier selection is to evaluate suppliers based on certain environmental and cost effective criteria and select the one with best ranking. Hence green supplier selection is a crucial process. Present work proposes the application of a hybrid MCDM method, Grey relational analysis with fuzzy logic for green supplier selection. An example problem is solved by this method and a comparison with the outcome obtained using MOORA and COPRAS method for same problem is presented to verify the proposed method. Results revealed that ranking orders for the first supplier and second supplier were interchanged with grey relational analysis when compared with COPRAS and MOORA methods. But after using fuzzy logics GFRG values of both the supplier obtained are same. This indicates that both the suppliers are equally competitive and there is flexibility to choose any one of them. It indicates that when vagueness is involved, grey-fuzzy logic algorithm can be effectively applied for solving multi-criteria supplier selection problems.

With the rapid development of science and technology, new technology is starting to be used in various fields including education. Education has a very important progress in a nation, because it can optimize individual potential. The educational process in higher education has a big influence on a student's future. However, there are many students who experience difficulties in choosing a college major after graduating from vocational high school (SMK) or senior high school (SMA), because of the complexity in identifying the suitability between interests and study programs. Decision making is a problem that everyone faces, especially in choosing an appropriate college major. There are many factors to consider, and with many options available, the decision-making process can be difficult. To overcome this problem, this research aims to create a decision support system (DSS) using the AHP (Analytical Hierarchy Process) and TOPSIS (Technique For Order By Similarity To Ideal Solution) methods. The AHP method is used to solve complex problems by structuring a hierarchy of criteria and developing weights or priorities. However, when the AHP method is used with many criteria and alternatives, other methods are needed for more effective results. Therefore, a combined method of AHP and TOPSIS was chosen, where AHP focuses on pairwise comparison matrices and consistency analysis, while TOPSIS can measure relative performance and alternative decision making simply and efficiently. The results of research on decision support systems for selecting majors using the AHP and TOPSIS methods show that the application of the combined AHP-TOPSIS method can be used to determine the best alternative in selecting majors by comparing alternatives based on predetermined criteria. In addition, system testing results show that the system functions as expected and meets user needs. With a decision support system for selecting college majors implemented on the website, it can simplify the decision-making process for students and BK teachers in choosing college majors. The results of the calculation of the percentage of system feasibility in blackbox testing show a value of 95.71%, which indicates that this major selection decision support system website can be used.

Survey academic performance by educational data mining is one of the most important issues in the field of educational management and researchers focus on it. The purpose of this study is to present an experimental method for appropriate algorithm selection in predicting students' academic status in two and three classes. Two-class database predicts the admission or rejection of students in the course, while the database of the three classes, in addition to admission or rejection, identifies students who are prone and elite. Using the previous articles in the field of educational data mining and experts' opinions, factors that effect on academic performance of students were identified and database was compiled based on them. After optimization of parameters and implementation of different algorithms, the performance scores of the algorithms were calculated using paired t-test based on three indexes include of accuracy, f-measure, and ROC, algorithms were compared by TOPSIS and VIKOR methods. In the two-class mode, Support Vector Machine algorithm in TOPSIS with value of 0.999115 and VIKOR with value of zero has shown the best performance. In the multi-class mode, the Logistic Regression algorithm in TOPSIS and VIKOR in turns with values 0.9986044 and 0.0009798 performances better than other algorithms. The proposed method can be used as a tool for selecting algorithm that has the best pergormance in educational data mining. Because choosing the algorithm to achieve accurate and exact results is very effective and can be taken into account in the process of counseling and preventing students' academic failure

One or more isothermal heating process was introduced to modify single and regenerative Brayton cycles by some scholars, which effectively improved the thermal efficiency and significantly reduced the emissions. To analyze and optimize the performance of this type of Brayton cycle, a regenerative modified Brayton cycle with an isothermal heating process is established in this paper based on finite time thermodynamics. The isothermal pressure drop ratio is variable. The irreversibilities of the compressor, turbine and all heat exchangers are considered in the cycle, and the heat reservoirs are variable-temperature ones. The function expressions of four performance indexes; that is, dimensionless power output, thermal efficiency, dimensionless power density and dimensionless ecological function are obtained. With the dimensionless power density as the optimization objective, the heat conductance distributions among all heat exchangers and the thermal capacitance rate matching among the working fluid and heat reservoir are optimized. Based on the NSGA-II algorithm, the cycle’s double-, triple- and quadruple-objective optimization are conducted with the total pressure ratio and the heat conductance distributions among heat exchangers as design variables. The optimal value is chosen from the Pareto frontier by applying the LINMAP, TOPSIS and Shannon entropy methods. The results show that when the pressure ratio in the compressor is less than 12.0, it is beneficial to add the regenerator to improve the cycle performance; when the pressure ratio is greater than 12.0, adding the regenerator will reduce the cycle performance. For single-objective optimization, the four performance indexes could be maximized under the optimal pressure ratios, respectively. When the pressure ratio is greater than 9.2, the cycle is simplified to a closed irreversible simple modified Brayton cycle with one isothermal heating process and coupled to variable-temperature heat reservoirs. Therefore, when the regenerator is used, the range of pressure ratio is limited, and a suitable pressure ratio should be selected. The triple objective (dimensionless power output, dimensionless power density and dimensionless ecological function) optimization’ deviation index gained by LINMAP or TOPSIS method is the smallest. The optimization results gained in this paper could offer some new pointers for the regenerative Brayton cycles’ optimal designs.

The Youth and Sports Office of Bengkulu City is a selection committee for Paskibraka candidates which is carried out every year. The assessment of post-baraka candidates consists of 8 aspects, namely Parade, PBB, Psychotest, Samapta/Physical, Health and Fitness, Interview, Regional Arts, and General Knowledge (data attached). The results of the assessment will be recapitulated to determine which participants succeeded in passing and which participants failed to pass, so that from this recapitulation it can be announced at the end of the selection activity. The analysis of the sensitivity test for the selection of paskibraka admissions using the TOPSIS method and the Promethee method at the Bengkulu City Youth and Sports Office was carried out by comparing the final results obtained from the two methods based on the assessment data of each paskribraka candidate. The analysis is done by looking at the number of criteria used, the processing time and also the ranking results. In helping the process of comparative analysis of the two methods, an application was built using the Visual Basic .Net programming language. where the application will show the results of the comparison of the TOPSIS and Promethee methods. Based on the tests that have been carried out, the results show that the application functionality can run well and is able to display the results of the assessment on each prospective paskibraka according to the stages of the TOPSIS and Promethee methods, in terms of processing time the TOPSIS method is faster than the Promethee method.