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Integration in Systems Engineering Context
Published in Gary O. Langford, Engineering Systems Integration, 2016
Architecture describes what the system does and generally how it does it. It reflects the optimizations and trade-offs that support the key operations. It identifies the processes to be performed by the subsystems and components, defines the flows of information and interfaces between the elements, and signifies the priorities. Architecture is explicitly concerned with the views of what and how things are done in the context of the domains. The domain of a relation is the set that contains all parameters that identify the members of a relation. The domain is defined as the sphere of activity that includes the physical entities, functions, processes through their relations, and context. Domain analysis is defined as determining the (1) operations, (2) unit modularity of data and associated processing (data), (3) properties and abstractions, and (4) appropriate partitioning. Domain analysis provides a representation of the requirements of the domain. The domain model identifies and describes the structure of data, flow of information, functions, constraints, and controls within the domain.
An Approach for Designing of Domain Models in Smart Health Informatics Systems Considering Their Cognitive Characteristics
Published in Abdel-Badeeh M. Salem, Innovative Smart Healthcare and Bio-Medical Systems, 2020
Olena Chebanyuk, Olexandr Palahin
Domain analysis: This involves the process of identifying, collecting, organizing, and representing the relevant information in a domain based on the study of existing systems and their development histories, knowledge captured from domain experts, underlying theory, and emerging technology within the domain.
Deep adversarial transfer neural network for fault diagnosis of wind turbine gearbox
Published in International Journal of Green Energy, 2023
Yuanchi Ma, Yongqian Liu, Zhiling Yang, Ming Cheng, Hang Meng
The fault diagnosis of wind turbine is a challenging problem. At present, domestic and overseas scholars have carried out a lot of work in relevant directions and have made some meaningful achievements. According to the classification of diagnosis methods, the fault diagnosis methods of wind turbine gearbox are divided into the physical model and data-driven model. According to the classification of signal classification, including vibration signal, acoustic signal, electrical signal, temperature and oil composition. The vibration analysis is the most commonly applied condition monitoring technology for rotating machinery and is the most effective method for fault diagnosis of wind turbine drive trains (Isham et al. 2019). Time domain analysis, frequency domain analysis and the time frequency domain analysis are the main methods of traditional vibration. R.Uma Maheswari et al. (Maheswari and Umamaheswari 2017) concluded the feature extraction and fault classification of non-linear and non-stationary signals in variable speed drive such as wind turbines drive chain to improve the fault diagnosis accuracy. However, considering of the limitations of existing methods in reality, the fault identification of wind turbine gearbox still depends on the expert’s experience to make the final judgment, which is subjective, and is difficult to describe it clearly in a formalized way, which means that these methods will not be versatile and generalizable in wind farms with multitude turbines of different models and operating conditions.
PCA-Aided FCM Identifies Stressful Events of Sleep EEG Under Hot Environment
Published in IETE Journal of Research, 2022
Prabhat Kumar Upadhyay, Chetna Nagpal
Literature has been reviewed on automatic sleep stage classification methods, proposed by researchers, which are based on feature extraction methods and classification algorithms [21–24]. A set of desired feature vectors are extracted from the feature space to serve as the inputs to the classifier in order to detect different stages of sleep [11,25]. Feature extraction methods for identification of sleep stages consist of time-frequency domain analysis [26–28], time-domain analysis [29,30] and frequency-domain analysis [3,31,32]. Since sleep is a non-homogeneous state containing highly complex and non-linear waves, researchers have successfully investigated sleep EEG by using non-linear parameters and complexity measures [33,34]. In many applications, authors have applied dimensionality reduction technique to get low-dimensional feature space without losing important features [9,35,36]. Applications of soft-computing tools to classify sleep stages, which have been a part of machine learning approach, include Fuzzy Logic [26,37,38], ANN [4,6,14,15], Support Vector Machine (SVM) [39–41], Linear Discriminant Analysis [34,41,42], K-Nearest Neighbour (KNN) [25] etc.
AMPL: aspect multiple product lines
Published in International Journal of Computers and Applications, 2022
Amina Guendouz, Djamal Bennouar
Domain engineering encompasses the three main processes: domain analysis, domain design and domain implementation with the principal outputs being the identification of SPL members, the extraction of similarities and variability between them and core assets construction. A core asset is a reusable artefact or resource that is used in the production of more than one product in a SPL (including architecture, components, requirements specification …) [8]. Application engineering process aims to derive an SPL application by exploiting the SPL commonality and variability that had been established earlier in domain engineering [1]. As for product derivation [9], this refers to the process of building a product from the selection, composition and customization of the SPL core assets addressing a specific SPL product. During a particular application derivation, the predefined SPL variability will be bounded according to the applications’ specific needs.