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Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent
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The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is suitable for multi-objective optimisation based on cooperative multi-agent systems (CMAS). The framework of DCLTML is used greedy iterative training to get an optimal set of weights and tabulated as a layer for each clustering structure. Such layers can deal with the challenges of large space and its massive data. Then the layer weights of each cluster are tuned by the Quasi-Newton (QN) algorithm to make the action sequence of CMAS optimal. Such a policy of CMAS effectively manipulates the inputs of the AHU, where the agents of the AHU activate the natural ventilation and set chillers into an idle state when the outdoor temperature crosses the recommended value. So, it is reasonable to assess the impact potential of thermal mass and hybrid ventilation strategy in reducing cooling energy; accordingly, the assigning results of the proposed DCLTML show that its main cooling coil saves >40% compared to the conventional benchmarks. Besides significant energy savings and improving environmental comfort, the DCLTML exhibits superior high-speed response and robustness performance and eliminates fatigue and wear due to shuttering valves. The results show that the DCLTML algorithm is a promising new approach for controlling HVAC systems. It is more robust to environmental variations than traditional controllers, and it can learn to control the HVAC system in a way that minimises energy consumption. The DCLTML algorithm is still under development, but it can potentially revolutionise how HVAC systems are controlled.

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Publication Date
Thu Jan 23 2020
Journal Name
Iraqi Journal Of Laser
Effect of High Energy Nd:Glass Laser on the Drilled in the 5052 AlMg Alloy
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This study presents the effect of laser energy on burning loss of magnesium from the holes' drilled in aluminum alloy 5052. High energy free running pulsed Nd:Glass laser of 300 µs pulse duration has been used to perform the experiments. The laser energy was varied from 1.0 to 8.0 Joules, The drilling processes have been carried out under atmospheric pressure and vacuum inside a specially designed chamber. Microhardness of the blind drilled holes has been investigated .The results indicated that the magnesium loss could be manipulated by adjusting the focusing conditions of the laser beam. Almost, the obtained holes were free of cracks with low taper and low sputter deposition. .The holes performed under atmospheric conditions have high

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Publication Date
Fri Jul 01 2022
Journal Name
Health Science Reports
Immunohistochemical expression of angiotensin‐converting enzyme 2 in superficial and deep maxillofacial tissues: A cross‐sectional study
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Abstract<sec><title>Background and Aims

The involvement of maxillofacial tissues in SARS‐CoV‐2 infections ranges from mild dysgeusia to life‐threatening tissue necrosis, as seen in SARS‐CoV‐2‐associated mucormycosis. Angiotensin‐converting enzyme 2 (ACE2) which functions as a receptor for SARS‐CoV‐2 was reported in the epithelial surfaces of the oral and nasal cavities; however, a complete understanding of the expression patterns in deep oral and maxillofacial tissues is still lacking.

Methods

The immunohistochemical expression of ACE2 was analyzed in 95 specimens from maxillofacial tissues and 10 specimens o

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Publication Date
Thu Jun 20 2019
Journal Name
Baghdad Science Journal
Using Backpropagation to Predict Drought Factor in Keetch-Byram Drought Index
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Forest fires continue to rise during the dry season and they are difficult to stop. In this case, high temperatures in the dry season can cause an increase in drought index that could potentially burn the forest every time. Thus, the government should conduct surveillance throughout the dry season. Continuous surveillance without the focus on a particular time becomes ineffective and inefficient because of preventive measures carried out without the knowledge of potential fire risk. Based on the Keetch-Byram Drought Index (KBDI), formulation of Drought Factor is used just for calculating the drought today based on current weather conditions, and yesterday's drought index. However, to find out the factors of drought a day after, the data

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Publication Date
Mon May 01 2023
Journal Name
Journal Of Economics And Administrative Sciences (jeas)
Using Statistical Methods to Increase the Contrast Level in Digital Images
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This research deals with the use of a number of statistical methods, such as the kernel method, watershed, histogram and cubic spline, to improve the contrast of digital images. The results obtained according to the RSME and NCC standards have proven that the spline method is the most accurate in the results compared to other statistical methods

Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Economics And Administrative Sciences
Using Statistical Methods to Increase the Contrast Level in Digital Images
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This research deals with the use of a number of statistical methods, such as the kernel method, watershed, histogram, and cubic spline, to improve the contrast of digital images. The results obtained according to the RSME and NCC standards have proven that the spline method is the most accurate in the results compared to other statistical methods.

 

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Publication Date
Tue Jun 23 2020
Journal Name
Baghdad Science Journal
Anomaly Detection Approach Based on Deep Neural Network and Dropout
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   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Advance Science And Technology
MR Images Classification of Alzheimer's Disease Based on Deep Belief Network Method
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Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

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Publication Date
Fri Feb 17 2012
Journal Name
Smart Materials And Structures
Frequency tuning of piezoelectric energy harvesters by magnetic force
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Publication Date
Thu Aug 31 2017
Journal Name
Journal Of Engineering
Energy Analysis of Solar Collector With perforated Absorber Plate
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The thermal performance of three solar collectors with 3, 6 mm and without perforation absorber plate was assessed experimentally. The experimental tests were implemented in Baghdad during the January and February 2017. Five values of airflow rates range between 0.01 – 0.1 m3/s were used through the test with a constant airflow rate during the test day. The variation of the following parameters air temperature difference, useful energy, absorber plate temperature, and collector efficiency was recorded every 15 minutes. The experimental data reports that the increases  the number of absorber plate perforations with a small diameter is more efficient rather than increasing the hole diameter of the absorber plate with decr

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Publication Date
Wed Mar 01 2023
Journal Name
Baghdad Science Journal
Quotient Energy of Zero Divisor Graphs And Identity Graphs
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Consider the (p,q) simple connected graph . The sum absolute values of the spectrum of quotient matrix of a graph  make up the graph's quotient energy. The objective of this study is to examine the quotient energy of identity graphs and zero-divisor graphs  of commutative rings using group theory, graph theory, and applications. In this study, the identity graphs  derived from the group  and a few classes of zero-divisor graphs  of the commutative ring R are examined.

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