scholarly journals Selection of Optimal Number of Francis Runner Blades for a Sediment Laden Micro Hydropower Plant in Nepal

2015 ◽  
Vol 8 (4) ◽  
pp. 294-303 ◽  
Author(s):  
Binaya Baidar ◽  
Sailesh Chitrakar ◽  
Ravi Koirala ◽  
Hari Prasad Neopane
Keyword(s):  
Optimal Number ◽  
Hydropower Plant ◽  
Selection Of

scholarly journals An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3311
Author(s):  
Riccardo Ballarini ◽  
Marco Ghislieri ◽  
Marco Knaflitz ◽  
Valentina Agostini
Keyword(s):  
Motor Task ◽  
Optimal Number ◽  
The Other ◽  
Simulated Dataset ◽  
Muscle Synergies ◽  
Mean Square ◽  
Real Dataset ◽  
Traditional Approaches ◽  
Selection Of ◽  
Semg Signals

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds.

scholarly journals Likert scale and change in range of response categories vs. the factors extraction in EFA model

2015 ◽  
Vol 1 (311) ◽  
Author(s):  
Piotr Tarka
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Rating Scale ◽  
Likert Scale ◽  
Optimal Number ◽  
Number Of Factors ◽  
Scale Experiment ◽  
Methodological Aspects ◽  
The Impact ◽  
Selection Of

Abstract: The objective article is the comparative analysis of Likert rating scale based on the following range of response categories, i.e. 5, 7, 9 and 11 in context of the appropriate process of factors extraction in exploratory factor analysis (EFA). The problem which is being addressed in article relates primarily to the methodological aspects, both in selection of the optimal number of response categories of the measured items (constituting the Likert scale) and identification of possible changes, differences or similarities associated (as a result of the impact of four types of scales) with extraction and determination the appropriate number of factors in EFA model.Keywords: Exploratory factor analysis, Likert scale, experiment research, marketing

Investigating cluster validation metrics for optimal number of clusters determination

2021 ◽  
pp. 1-16
Author(s):  
Aikaterini Karanikola ◽  
Charalampos M. Liapis ◽  
Sotiris Kotsiantis
Keyword(s):  
Real World ◽  
Optimal Number ◽  
Cluster Validation ◽  
Clustering Methods ◽  
Number Of Clusters ◽  
Validity Indices ◽  
Selection Of ◽  
Specific Distance ◽  
Optimal Number Of Clusters

In short, clustering is the process of partitioning a given set of objects into groups containing highly related instances. This relation is determined by a specific distance metric with which the intra-cluster similarity is estimated. Finding an optimal number of such partitions is usually the key step in the entire process, yet a rather difficult one. Selecting an unsuitable number of clusters might lead to incorrect conclusions and, consequently, to wrong decisions: the term “optimal” is quite ambiguous. Furthermore, various inherent characteristics of the datasets, such as clusters that overlap or clusters containing subclusters, will most often increase the level of difficulty of the task. Thus, the methods used to detect similarities and the parameter selection of the partition algorithm have a major impact on the quality of the groups and the identification of their optimal number. Given that each dataset constitutes a rather distinct case, validity indices are indicators introduced to address the problem of selecting such an optimal number of clusters. In this work, an extensive set of well-known validity indices, based on the approach of the so-called relative criteria, are examined comparatively. A total of 26 cluster validation measures were investigated in two distinct case studies: one in real-world and one in artificially generated data. To ensure a certain degree of difficulty, both real-world and generated data were selected to exhibit variations and inhomogeneity. Each of the indices is being deployed under the schemes of 9 different clustering methods, which incorporate 5 different distance metrics. All results are presented in various explanatory forms.

scholarly journals Tecnologies of defining the indicators of call centre

2020 ◽  
pp. 58-67
Author(s):  
Ana Kobiashvili ◽  
◽  
Ketevan Kutateladze ◽  
Nodar Darchiashvili ◽  
◽  
...  
Keyword(s):  
Service Level ◽  
Optimal Number ◽  
Call Centre ◽  
Treatment Service ◽  
Level Indicator ◽  
Call Centres ◽  
Operational Level ◽  
Criteria For Selection ◽  
Selection Of

A great number of calls enter call centres daily. It is difficult to determine the state of the call cenre without evaluation of the indicators of the call centre operational level. In order to control all significant indicators it is necessary to have software, which will allow a real-time monitoring of various data. The paper describes all significant indicators of operational level of the call centre, such as the duration of waiting for the answer; volume of calls; the duration of call treatment; service level indicator; the percentage of those calls, which helped to fix the problem; the quality of conducted services. The assignments of each of them are discussed, definatory formulae and examples of some indicators are given, criteria for selection of an optimal number of call centre operators are analyzed, theoretical and practical assessments of various indicators of calls are conducted and necessary recommendations for improving the performance of a call cenre are formed as well.

scholarly journals Performance of Trichograma spp in small tomato borer (Neoleucinodes elegantalis Guenée): Pest management with egg parasitoid

2020 ◽  
Vol 42 ◽  
pp. e46915
Author(s):  
José Romário de Carvalho ◽  
Luis Moreira de Araujo Junior ◽  
Dirceu Pratissoli ◽  
Débora Fragoso ◽  
Amanda Túler
Keyword(s):  
Technological Development ◽  
Physicochemical Characteristics ◽  
Egg Parasitoid ◽  
Optimal Number ◽  
Small Fruit ◽  
Fruit Borer ◽  
Alternative Techniques ◽  
Tomato Borer ◽  
Number Of Individuals ◽  
Selection Of

The tomato is a crop of great importance for Brazilian agriculture. Among the most damaging pests, the small tomato borer, Neoleucinodes elegantalis Guenée (Lepidoptera: Crambiadae) has caused great losses, since they directly reach the fruits to be commercialized, being used for its handling a large volume of insecticides. In this way, the use of alternative techniques that help in the management of this pest becomes of great importance. Among them, the use of the egg parasitoid Trichogramma spp. has been promising. Thus, the present study aimed to evaluate the performance of Trichogramma species and/or strains in N. elegantalis by selection of strains. The selection was made based on four lineages maintained in the Nucleus of Scientific and Technological Development in Phytosanitary Management of Pests (NUDEMAFI), being three strains of the species T. pretiosum and one of T. galloi species. The parameters evaluated were percentage of parasitized eggs, egg viability, number of individuals per eggs, sex ratio and number of Trichogramma spp. to be released. The T. galloi (T. g1) showed the best parameters for selection of the strain. Estimating the optimal number of T. g1 in eggs of small-fruit-borer was 82 individuals per egg parasitoid. Therefore, this strain was selected for the management of the small tomato-borer, whose eggs presented favorable physicochemical characteristics for the development of the parasitoid.

scholarly journals Automated Fitting Process Using Robust Reliable Weighted Average on Near Infrared Spectral Data Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2099
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Weighted Average ◽  
Optimal Number ◽  
Optimum Number ◽  
Plsr Model ◽  
Pls Method ◽  
Selection Of

With the complexity of Near Infrared (NIR) spectral data, the selection of the optimal number of Partial Least Squares (PLS) components in the fitted Partial Least Squares Regression (PLSR) model is very important. Selecting a small number of PLS components leads to under fitting, whereas selecting a large number of PLS components results in over fitting. Several methods exist in the selection procedure, and each yields a different result. However, so far no one has been able to determine the more superior method. In addition, the current methods are susceptible to the presence of outliers and High Leverage Points (HLP) in a dataset. In this study, a new automated fitting process method on PLSR model is introduced. The method is called the Robust Reliable Weighted Average—PLS (RRWA-PLS), and it is less sensitive to the optimum number of PLS components. The RRWA-PLS uses the weighted average strategy from multiple PLSR models generated by the different complexities of the PLS components. The method assigns robust procedures in the weighing schemes as an improvement to the existing Weighted Average—PLS (WA-PLS) method. The weighing schemes in the proposed method are resistant to outliers and HLP and thus, preserve the contribution of the most relevant variables in the fitted model. The evaluation was done by utilizing artificial data with the Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp. Based on the results, the method claims to have shown its superiority in the improvement of the weight and variable selection procedures in the WA-PLS. It is also resistant to the influence of outliers and HLP in the dataset. The RRWA-PLS method provides a promising robust solution for the automated fitting process in the PLSR model as unlike the classical PLS, it does not require the selection of an optimal number of PLS components.

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