Most recent studies have focused on using modern intelligent techniques spatially, such as those
developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern
artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the
ability to learn and recognize what they had learned. The importance of developing such systems came after
the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This
would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder
detection module depending on Multi-Connect Architecture Associative Memory (MCA) has been proposed.
Via using the MCA associative memory as a new trend, the proposed module goes through two phases: the
first is the training phase (which is executed once during the module installation process) and the second is
the analysis phase. Both phases will be developed through the use of MCA, each according to its process.
The training phase will take place through the learning phase of MCA, while the analysis phase will take
place through the convergence phase of MCA. The use of MCA increases the efficiency of the training
process for the proposed system by using a minimum number of training images that do not exceed 10
training images of the total number of frames in JPG format. The proposed module has been evaluated using
11,825 images that have been extracted from 11 tested videos. As a result, the module can detect the intruder
with an accuracy ratio in the range of 97%–100%. The average training process time for the training videos
was in the range of 10.2 s to 23.2 s.
Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b
In this study, a genetic algorithm (GA) is used to detect damage in curved beam model, stiffness as well as mass matrices of the curved beam elements is formulated using Hamilton's principle. Each node of the curved beam element possesses seven degrees of freedom including the warping degree of freedom. The curved beam element had been derived based on the Kang and Yoo’s thin-walled curved beam theory. The identification of damage is formulated as an optimization problem, binary and continuous genetic algorithms
(BGA, CGA) are used to detect and locate the damage using two objective functions (change in natural frequencies, Modal Assurance Criterion MAC). The results show the objective function based on change in natural frequency i
The study included the collection of samples of raw cow milk to isolate Leuconostoc bacteria, samples were sub cultured on De-Man Rogosa Sharpe-Vancomycin medium, the pure colonies were selected and subjected to the cultural and microscopically tests, according to that 25 cocci bacterial isolates were obtained, then isolates were subjected to biochemical tests. Result of tests showed that 12 isolates belong to the genus Leuconostoc out of 25 cocci bacterial isolates, Vitek2 system was used as a supplementary step. Results of final identification showed that 3 sub species were obtained included Leuconostoc mesenteroides ssp. cremoris 9 out of 12 isolates, while it was 2 isolates of Leuconostoc mesenteroides ssp. mesenteroides and one isol
... Show MoreIn this paper, a simulation of the electrical performance for Pentacene-based top-contact bottom-gate (TCBG) Organic Field-Effect Transistors (OFET) model with Polymethyl methacrylate (PMMA) and silicon nitride (Si3N4) as gate dielectrics was studied. The effects of gate dielectrics thickness on the device performance were investigated. The thickness of the two gate dielectric materials was in the range of 100-200nm to maintain a large current density and stable performance. MATLAB simulation demonstrated for model simulation results in terms of output and transfer characteristics for drain current and the transconductance. The layer thickness of 200nm may result in gate leakage current points to the requirement of optimizing the t
... Show MoreIn this paper, a miniaturized 2 × 2 electro-optic plasmonic Mach– Zehnder switch (MZS) based on metal–polymer–silicon hybrid waveguide is presented. Adiabatic tapers are designed to couple the light between the plasmonic phase shifter, implemented in each of the MZS arms, and the 3-dB input/output directional couplers. For 6 µm-long hybrid plasmonic waveguide supported by JRD1 polymer (r33= 390 pm/V), a π-phase shift voltage of 2 V is obtained. The switch is designed for 1550 nm operation wavelength using COMSOL software and characterizes by 2.3 dB insertion loss, 9.9 fJ/bit power consumption, and 640 GHz operation bandwidth
A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
... Show MoreThe translation of phraseological units is among the most challenging tasks in literary translation, as fixed expressions convey denotative, connotative, and cultural meanings that resist full equivalence. This study classifies the strategies employed in the Russian translation of Viola Ardone’s The Train of Children, drawing on the frameworks of Vinay and Darbelnet, Newmark, and Koller. Analysis of 37 idioms reveals five strategies, with phraseological equivalence prevailing, while compensation—though less frequent—emerges as stylistically most significant.
Dam operation and management have become more complex recently because of the need for considering hydraulic structure sustainability and environmental protect on. An Earthfill dam that includes a powerhouse system is considered as a significant multipurpose hydraulic structure. Understanding the effects of running hydropower plant turbines on the dam body is one of the major safety concerns for earthfill dams. In this research, dynamic analysis of earthfill dam, integrated with a hydropower plant system containing six vertical Kaplan turbines (i.e., Haditha dam), is investigated. In the first stage of the study, ANSYS-CFX was used to represent one vertical Kaplan turbine unit by designing a three-dimensional (3-D) finite element (F
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