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The Laboratory Use of Computers
Published in Grinberg Nelu, Rodriguez Sonia, Ewing’s Analytical Instrumentation Handbook, Fourth Edition, 2019
The interface used to connect the hard drives to the system affects the number of drives and data transfer rates of the drives. The first hard drives were connected to the system via a hard drive controller that was connected to the bus. Each drive was connected separately to the motherboard via a controller and the operating system addressed each drive individually. Later, the controller was actually integrated onto the drive, integrated drive electronics (IDE), instead of being a separate card. Originally the CPU was responsible for handling the data transfer process but that has been shifted to the bus master in peripheral component interconnect (PCI)-based systems. Currently, the serial advanced technology attachment (SATA) storage interface boasts data transfer rates of up to 16 Gbit/s with an advanced host controller interface (AHCI). The interface is limited to one hard drive, but multiple interfaces can be installed (or built into a motherboard).
Storage Access Methods
Published in Al Kovalick, Video Systems in an IT Environment, 2013
In general, ATA (started life as IDE) connectivity is the alternative to SCSI-based connectivity. ATA drives have been used in PCs for many years. Because they offer lower performance than SCSI drives, ATA rules at the low end and SCSI at the high end. The commands, protocols, and physical connectors are completely different between SCSI-P and ATA-P. SATA is the serial equivalent of ATA-P just as SAS is the serial equivalent to SCSI-P. Fortunately, due to excellent cooperation among industry groups, there is now only one backplane connector type for both SATA and SAS drives. This allows storage array manufacturers to build one array enclosure, and it can be populated with either ATA or SCSI drives or a mix (if supported). Finally, SCSI and ATA are converging along parallel lines.
The PC
Published in Mike Tooley, PC Based Instrumentation and Control, 2013
Serial ATA drives offer several advantages over IDE drives, not the least of which is speed. The maximum data transfer rate (or burst rate) for most parallel drives is between 100 and 133 MB/s whilst drives using the first generation of the SATA interface can often reach 150 MBps. SATA drive speeds are expected to increase significantly over the next few years.
An Optimal Reinforced Deep Belief Network for Detection of Malicious Network Traffic
Published in IETE Journal of Research, 2023
The proposed model was implemented in Matlab on a PC equipped with an Intel Core i7 Processor 2.80 GHz processor, 8GB DDR3 RAM, and 1TB SATA Hard Disk Drive. The experiments are conducted by comparing the proposed model with different state-of-art techniques such as deep transfer learning (DTL), Bayesian data fusion (BDF), density-based mean shift clustering with deep packet inspection classification (DMSC-DPIC), and network-in-network (NIN) in terms of different performance metrics.
Feedback passivation plus tracking-error-based multivariable control for a class of free-radical polymerisation reactors
Published in International Journal of Control, 2019
T. Sang Nguyen, N. Ha Hoang, M. Azlan Hussain
Free-radical polymerisation (FRP) reactors are used and widely operated in industry to synthesise many different types of plastic such as polystyrene, polymethylmethacrylate and polyvinyl chloride (Meyer & Keurentjes, 2005). Together with the experimental researches (George & Hayes, 1975; Lederle & Hübner, 2017; Sammaljärvi et al., 2016), the theoretical studies on mathematical modelling, optimal operation and control of the FRP processes play a key role, and therefore, attract much attention of practitioners and researchers over the years. This is due to the fact that such system exhibits highly nonlinear characteristics because of reaction kinetics and constitutive equations (such as transport phenomena, etc.). As a consequence, this provides abnormal dynamical behaviours of the system, see e.g. stable/unstable multiple steady states, limit cycle and bifurcation behaviour, etc. (Freitas Filho, Biscaia, & Pinto, 1994; Jaisinghani & Ray, 1977; Russo & Bequette, 1998; Van Dootingh, Viel, Rakotopara, Gauthier, & Hobbes, 1992). From both theoretical and practical viewpoints, these abnormal properties make the FRP processes fairly difficult to operate and stabilise at a desired equilibrium point with some affordable performance. To circumvent the challenge issue, an appropriate feedback control scheme is vitally necessary because this allows to remove the inherent nonlinearities (possibly together with parameter uncertainty) and to solve the soft constraint requirements imposed by practical operation (for example, input constraints). Over the years, many control strategies have been designed such as the model predictive control (Bustos, Ferramosca, Godoy, & González, 2016; Hidalgo & Brosilow, 1990; Prasad, Schley, Russo, & Bequette, 2002), fuzzy logic (Ghasem, Sata, & Hussain, 2007; Hosen, Hussain, & Mjalli, 2011; Wei, Hussain, & Wahab, 2007), the artificial neural network (Hosen, Hussain, & Mjalli, 2011; Hussain & Kershenbaum, 2000), the input-output linearisation technique without/with input constrains (Assala, Viel, & Gauthier, 1997; Viel, Busvelle, & Gauthier, 1995), the backstepping control (Biswas & Samanta, 2013) and the multivariable feedforward-feedback nonlinear controller (Alvarez & González, 2007; Alvarez, Suárez, & Sánchez, 1990). These control methodologies are of great interest, yet for instance paid by heavily mathematical calculations due to the complexity of the system at hand in general. In other words, the energy's aspects of the system have not been taken into consideration in the design yet.