Journal of Advanced Research in Instrumentation and Control Engineering

2020 ◽  
Keyword(s):  
Control Engineering ◽  
And Control

Control engineering and synthetic biology: working in synergy for the analysis and control of microbial systems

2021 ◽  
Vol 62 ◽  
pp. 68-75
Author(s):  
Giansimone Perrino ◽  
Andreas Hadjimitsis ◽  
Rodrigo Ledesma-Amaro ◽  
Guy-Bart Stan
Keyword(s):  
Synthetic Biology ◽  
Control Engineering ◽  
And Control ◽  
Microbial Systems

Developing on Experiment System of Process Equipment and Control Engineering Based on PC and PLC

2012 ◽  
Vol 490-495 ◽  
pp. 2286-2289
Author(s):  
Jun Han ◽  
Rui Li Chang
Keyword(s):  
Process Control ◽  
Creative Thinking ◽  
Advanced Technology ◽  
Process Equipment ◽  
Control Engineering ◽  
Experiment System ◽  
Engineering Theory ◽  
Control Automation ◽  
And Control ◽  
Equipment Control

Being established in the requirement of teaching and practice, and connecting to the development of process equipment and control engineering specialty, a suit of experiment teaching instrument is designed, which relates to process control, automation, survey and control and correlative specialties. By the instrument, students can taste the actualizing of process equipment control and the application of advanced technology when learning engineering theory. This stimulates their enthusiasm in specialty courses study and enhances their practice capability and creative thinking

Integrated Practice of Metacognitive Strategies And Second Language Writing

2021 ◽  
pp. 118-136
Author(s):  
Mekala Sethuraman ◽  
Geetha Radhakrishnan
Keyword(s):  
Tamil Nadu ◽  
Second Language Writing ◽  
Metacognitive Strategies ◽  
Writing Proficiency ◽  
Control Engineering ◽  
Language Writing ◽  
Teacher Researcher ◽  
Integrated Practice ◽  
And Control

Writing is a cardinal skill for effective communication practised extensively from primary education, but the students are not exhibiting adequate writing proficiency in their higher education and at their workplace. This experimental study focuses on enhancing the students’ writing skills by promoting metacognitive strategies in the classroom. The participants of this study are 51 pre-final year Diploma students belonging to the Department of Instrumentation and Control Engineering of an autonomous polytechnic institute in Tamil Nadu. The teacher-researcher has facilitated students’ cognizance with metacognitive strategies employed in the writing tasks administered during the course. The results have exhibited improvement apropos of coherence and unity in the students’ writing skill. It implies the indispensable role of metacognitive strategies in developing the capacity of the learners’ strategic thinking and guiding them to plan, progress, and process their writing into a coherent text.

Digital Computer and Control Engineering

1961 ◽  
Vol 12 (8) ◽  
pp. 229-229
Author(s):  
C C Ritchie
Keyword(s):  
Digital Computer ◽  
Control Engineering ◽  
And Control

scholarly journals A Nonlinear System Identification Method Based on Adaptive Neural Network

2021 ◽  
Vol 28 (2) ◽  
pp. 111-123
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Generic Model ◽  
Control Engineering ◽  
The Neural Network ◽  
Adaptive Pso ◽  
And Control ◽  
Scientific Engineering

Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.

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