China
Africa
Explore chapters and articles related to this topic
Introduction to mine systems
Published in Amit Kumar Gorai, Snehamoy Chatterjee, Optimization Techniques and their Applications to Mine Systems, 2023
Amit Kumar Gorai, Snehamoy Chatterjee
System analysis is a process of collecting information, identifying problems, and decomposing a system into smaller sub-systems. It is usually done using a standard optimization technique based on the formulated mathematical equations. It should be noted that systems analysis is not simply to solve a mathematical model but requires decision-making for designing a system. The system analysis techniques can be used for solving both descriptive and prescriptive models. A descriptive model explains how a system works; whereas, a prescriptive model offers a solution for optimal operation of the system for achieving the desired objectives.
Description and Specification
Published in Julio Sanchez, Maria P. Canton, Software Solutions for Engineers and Scientists, 2018
Julio Sanchez, Maria P. Canton
An ideal scenario is that the customer, client, or eventual user of a system provides the detailed specification. In this “neat” world all the developer has to do is implement a system according to these specifications thus ensuring that the client’s needs are thoroughly satisfied. In reality the customer/user often has little more than a sketchy idea of the intended system; its specification must be developed through a series of interviews, proposals, models, and revisions. Communications skills and system analysis experience are important factors in the success of this difficult and critical phase.
Development of Wearable Body Area Networks for 5G and Medical Communication Systems
Published in Albert Sabban, Wearable Systems and Antennas Technologies for 5G, IOT and Medical Systems, 2020
The major steps in the system engineering process are: requirements analysis, system analysis control, functional analysis, design synthesis and project management. System engineering development process is presented in Figure 11.7.
Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network
Published in Engineering Applications of Computational Fluid Mechanics, 2018
Pezhman Taherei Ghazvinei, Hossein Hassanpour Darvishi, Amir Mosavi, Khamaruzaman bin Wan Yusof, Meysam Alizamir, Shahaboddin Shamshirband, Kwok-wing Chau
The whole systems analysis is an essential technology that can cope with complexity and variability in delivering benefits to industry and in ensuring research efficiency in a complex operating environment. Central to this approach is the need to advance models for practical applications in industry. Strategic research is required to enhance the knowledge of simulation and optimization models. Delivery research is required to develop implementation strategies for the use of these tools in a participatory manner, to ensure adoption of new options for enhancing whole industry profitability in harmony with other sustainability considerations. The study of water, soil conductivity and sugarcane environment conditions presented in this paper have provided insights to the most effective and practical options for profitable and sustainable sugar production.
A Semantic Model for Enterprise Digital Transformation Analysis
Published in Journal of Computer Information Systems, 2023
Traditionally, systems analysts apply systems analysis and design methods and tools to analyze the system by specifying the requirements and to design the system by detailing the specifications for implementation. During the past decades of enterprise digital transformation, the roles of systems analysts have been shifted from “blueprint making” for systems construction to coordination for systems acquisition.60 Specifically, the major roles of systems analysts for enterprise digital transformation are to coordinate the development activities of all stakeholders involved in the digital transformation process and to assist the decision makers of the organization to monitor the gaps between the current system and the future system during the digital transformation process to ensure the implementation of transformation strategies. To support systems development coordination and supervision, systems analysts have used reference models.61 A reference model is an abstract framework or domain-specific ontology consisting of an interlinked set of clearly defined concepts to encourage clear communication. Our proposed semantic model is a reference model for enterprise digital transformation, and has the following advantages over the traditional systems development “blueprint making” tools such as UML. The semantic model integrates the semantic relationships between all organizational aspects of enterprise digital transformation. The model is able to represent key organizational aspects in enterprise digital transformation, including costs and benefits, system infrastructure, business process and business rules, data architecture, and users’ roles.The proposed enterprise digital transformation model can serve as a devise to integrate managerial requirements and technical specifications.The model is scalable and maintainable through adding, deleting, and modifying the organizational aspects and the relationships between them for a particular enterprise digital transformation.The semantic model can be computerized and readily incorporated into a web portal, as demonstrated in the next section of this paper.The semantic model is easy to understand for all decision makers and system developers in the organization to share knowledge about the enterprise digital transformation.