scholarly journals Enterprise Management Network Architecture: The Organization Layer

1990 ◽  
Author(s):  
Michel Roboam ◽  
Mark S. Fox ◽  
Katia Sycara
Keyword(s):  
Network Architecture ◽  
Enterprise Management

Network Architecture Based on 3-D Digital Design and Manufacture for Scaled Enterprise Ally

2008 ◽  
Vol 392-394 ◽  
pp. 848-854
Author(s):  
Li Zhi Gu ◽  
Y. Gao ◽  
Q. Zhang
Keyword(s):  
Network Architecture ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Manufacturing System ◽  
Low Cost ◽  
Digital Design ◽  
Enterprise Management ◽  
Digital Manufacturing ◽  
Organization And Management ◽  
Design And Manufacturing

With the features of rapid response, premium quality, low cost, flexibility, technology of digital design and manufacturing is becoming the principal boost that enhances the manufacturing industry in the 21st century. There are about several hundreds of thousands of scaled manufacturing enterprises that face the upgrading challenge of production technology, organization, and management transformation with the core alteration from 2D design- conventional manufacturing to 3D digital design and digital manufacturing. Netting of three-D digital design and manufacturing system is the only way of upgrading the manufacturing industry orientating to future. An idea of scaled enterprise ally is presented, and based on the scaled enterprise ally, netting frame is put forward and constructed of the digital design and manufacturing system, expounding the functions and features of shared data platform, those of linkages of digital design, digital manufacturing, cell enterprise management, and CSG modeling technique, creation procedure of CAPP, virtual testing technique in the digital design and manufacturing systems, explaining the key considerations including interrelationships among the enterprises , resources share, information security, network security.

MODERN PROBLEMS OF ACCOUNTING AND ANALYTICAL ENSURING FINANCIAL RESULTS MANAGEMENT

2020 ◽  
Author(s):  
Yuliia Peniak ◽  
◽  
Nataliia Horokhovatska ◽  
Keyword(s):  
Performance Management ◽  
Enterprise Management ◽  
Goods And Services ◽  
Financial Condition ◽  
General Evaluation ◽  
Net Profit ◽  
Business Entities ◽  
Results Management ◽  
Main Components ◽  
The Impact

The main purpose of any enterprise in the market economy is to obtain high financial results. One of the main conditions for the effective functioning of the enterprise is ability to generate profit in the amount that will create the financial basis for further development and expansion of the enterprise, comply with social and material needs, ensure competitiveness in the market of goods and services. The need for accounting and analytical management of financial results stems from needs of owners, the state and employees in information that will enable them to identify patterns and trends in financial results, identify and assess the main factors influencing the process of their creation, distribution and usage, identify reserves and thus increase the level of profitability. Despite the significant scientific contribution in the field of research of financial results of the enterprises, the issue of improvement aims to the accounting and analytical maintenance of management of financial results of the enterprise remains actual. That is why the purpose of the study is to substantiate the theoretical and practical aspects and develop approaches to improving the mechanism of formation of accounting and analytical support for the management of financial results of the enterprise. Accounting and analytical management of financial results of the enterprise is a set of interconnected elements of production and management system, activities carried out by the subject of management, creation of a certain structure, as well as collection, accumulation, storage and analysis of information necessary for effective operation of the enterprise. The main components of the study of accounting and analytical support of financial performance management are the formation of methods of analysis, control and forecasting of financial results, which requires specification of the components of the analytical and controlled process within the organizational and information model. Namely, the formation of reliable information about the financial condition of the enterprise, the analysis of economic indicators of the enterprise is of great importance in the system of general evaluation of business entities. Their research makes it possible to assess the dynamics of the structure of income and expenses, to determine the impact of factors on the company's profit from various activities, as well as to find reserves to increase the net profit of enterprises. Thus, the improvement of accounting and analytical support of enterprise management is based on the use of modern forms, methods and principles that place new demands on the formation of unbiased, complete, timely, clear and useful accounting and analytical information about the enterprise and its financial results.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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