Model of Machining Error for the Circular External Plunge Grinding Taking into Account the Dynamic Features of Process

2022 ◽  
pp. 129-136
Author(s):  
A. D. Almawash ◽  
P. P. Pereverzev
Keyword(s):  
Dynamic Features ◽  
Machining Error ◽  
Plunge Grinding

A Dynamic View of Global Integration and Local Responsiveness Framework

2018 ◽  
Vol 6 (8) ◽  
pp. 58
Author(s):  
Uyen-Minh Le ◽  
Tung-Shan Liao
Keyword(s):  
International Business ◽  
Business Management ◽  
External Environment ◽  
Dynamic Capability ◽  
International Strategy ◽  
Global Integration ◽  
Dynamic Features ◽  
Dynamic View ◽  
Concept Building ◽  
Strategy Types

Global-Integration and Local-Responsiveness (IR) framework with four pairs of external environment and appropriate international strategy types has contributed significantly to international business management. Nevertheless, the framework is still incomplete and lacks dynamic features. To deal with such limitations and enhance the theory, this paper, therefore, brings dynamic features regarding both environment and strategy into the IR grid. Under a dynamic capability angle with three steps of sensing, seizing and transforming [30], the dynamic global integration and local responsiveness framework – a new concept building for international business – would be explicated.

Cylindrical plunge grinding process of sialon.

1988 ◽  
Vol 54 (7) ◽  
pp. 1298-1303
Author(s):  
Toshikatsu NAKAJIMA ◽  
Yoshiyuki UNO ◽  
Takanori FUJIWARA ◽  
Atsunori IKEJIRI ◽  
Kazuhito OHASHI
Keyword(s):  
Grinding Process ◽  
Plunge Grinding

Experimental analysis of work regenerative chatter in plunge grinding.

1985 ◽  
Vol 51 (12) ◽  
pp. 2296-2301
Author(s):  
Tomio MATSUBARA ◽  
Hiroshi MIZUMOTO ◽  
Hisataka YAMAMOTO ◽  
Motoharu SATO
Keyword(s):  
Experimental Analysis ◽  
Regenerative Chatter ◽  
Plunge Grinding

STRUCTURAL AND DYNAMIC FEATURES OF EAR-LY DISABILITY DUE TO MENTAL DISEASES

2020 ◽  
pp. 62-67
Author(s):  
Shmakova O.P.
Keyword(s):  
People With Disabilities ◽  
Child And Adolescent Psychiatry ◽  
Child Psychiatrist ◽  
Childhood And Adolescence ◽  
Disabled Children ◽  
Dynamic Features ◽  
Factors Affecting ◽  
Number Of Children ◽  
Number Of Patients

Prevention of disability is one of the most significant tasks of child and adolescent psychiatry. Obtaining data on the dynamics of the number of people with disabilities and the factors affecting this indicator seems to be one of the relevant aspects. Aim: to trace the dynamics of the number of children with disabili-ties and to assess the change in the structure of early disability over the past decades. Materials and Meth-ods. A comparative analysis of two cohorts of patients was carried out: 1st - patients born in 1990-1992. (1203 patients (men - 914, 76%; women - 289, 24%)) who applied to the district neuropsychiatric dispensa-ry for outpatient care in childhood and adolescence; II - children and adolescents born in 2005 - 2018 (602 patients (male - 410, 68%; female - 192, 32%), ob-served at the time of the study by a child psychiatrist in the neuropsychiatric dispensary. Research methods: clinical and psychopathological; follow-up; statisti-cal. Results. Comparison of the number and nosologi-cal distribution of disabled children in two cohorts showed that over the 15th year there has been a shift towards an increase in the proportion of disabled children among patients observed by child and ado-lescent psychiatrists. The increase in the number of children with disabilities was due to those suffering from childhood autism and other disorders of general development. There were no statistically significant differences in the number of people with disabilities who received benefits before the age of 7, as well as differences in gender ratios among disabled people in the two cohorts. Conclusion. Early disability is a mul-tifactorial phenomenon, prevalence, dynamics, the structure of which depends not only on clinical, but also on socio-administrative realities. Children with autism require increased attention, since there has been a multiple increase in the number of patients with this diagnosis.

ANALYSIS OF THE ACCURACY OF ELECTROMECHANICAL MICROMACHINING

2020 ◽  
pp. 60-64
Author(s):  
Yu.A. Morgunov ◽  
B.P. Saushkin ◽  
N.V. Homyakova
Keyword(s):  
Electrochemical Performance ◽  
Electrochemical Machining ◽  
Machining Error ◽  
Processing Mode ◽  
Primary Error ◽  
Flow Through ◽  
Relative Errors ◽  
Size Tolerance

The achieved accuracy in the electrochemical performance of understatement with a depth of 18 mcm with a tolerance of 4.5 mcm in a flow-through interelectrode channel is studied. The primary error of the size. The allowed absolute and relative errors of processing mode parameters are set. Keywords: UNDERSTATEMENT, ELECTROCHEMICAL MACHINING, ERROR, PRECISION SIZE, TOLERANCE, PROCESSING MODE. [email protected]

DEVELOPMENT OF THE BASIC CRITERIA FOR RUSSIAN COASTS TYPIFICATION

2017 ◽  
Author(s):  
Рубен Косян ◽  
Ruben Kosyan ◽  
Viacheslav Krylenko ◽  
Viacheslav Krylenko
Keyword(s):  
Coastal Zone ◽  
Coastal Area ◽  
Economic Activity ◽  
Basic Information ◽  
Dynamic Features ◽  
Static State ◽  
Evolutionary Features ◽  
Coastal Features ◽  
The Stability

There are many types of coasts classifications that indicate main coastal features. As a rule, the "static" state of the coasts is considered regardless of their evolutionary features and ways to further transformation. Since the most part of the coastal zone studies aimed at ensuring of economic activity, it is clear that the classification of coast types should indicate total information required by the users. Accordingly, the coast classification should include the criterion, characterizing as dynamic features of the coast and the conditions and opportunities of economic activity. The coast classification, of course, should be based on geomorphological coast typification. Similar typification has been developed by leading scientists from Russia and can be used with minimal modifications. The authors propose to add to basic information (geomorphological type of coast) the evaluative part for each coast sector. It will include the estimation of the coast changes probability and the complexity of the coast stabilization for economic activity. This method will allow to assess the dynamics of specific coastal sections and the processes intensity and, as a result – the stability of the coastal area.

scholarly journals Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

2021 ◽  
Vol 11 (4) ◽  
pp. 1880
Author(s):  
Roberta Fusco ◽  
Adele Piccirillo ◽  
Mario Sansone ◽  
Vincenza Granata ◽  
Paolo Vallone ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Feature Selection ◽  
Resonance Imaging ◽  
Dynamic Features ◽  
Surgical Specimen ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Malignant Lesions

Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were analyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions.

scholarly journals A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

2021 ◽  
Vol 14 (7) ◽  
pp. 308
Author(s):  
Usha Rekha Chinthapalli
Keyword(s):  
Investment Decision ◽  
Theoretical Work ◽  
Financial Asset ◽  
Dynamic Features ◽  
Asset Return ◽  
Financial Variables ◽  
Ann Models ◽  
Hidden Layer ◽  
Association Structure ◽  
The Impact

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

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