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.
Well-dispersed Cu2FeSnSe4 (CFTSe) nanoparticles were first synthesized using the hot-injection method. The structure and phase purity of as-synthesized CFTSe nanoparticles were examined by X-ray diffraction (XRD) and Raman spectroscopy. Their morphological properties were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The average particle sizes of the nanoparticles were about 7-10 nm. The band gap of the as-synthesized CFTS nanoparticles was determined to be about 1.15 eV by ultraviolet-visible (UV-Vis) spectrophotometry. Photoelectrochemical characteristics of CFTSe nanoparticles were also studied, which indicated their potential application in solar energy water splitting.
Construction and operation of (2 m) parabolic solar dish for hot water application were illustrated. The heater was designed to supply hot water up to 100 oC using the clean solar thermal energy. The system includes the design and construction of solar tracking unit in order to increase system performance. Experimental test results, which obtained from clear and sunny day, refer to highly energy-conversion efficiency and promising a well-performed water heating system.
The research abstract included introduction and the importance of the research, also included display of the problem represented by weakness for the players when performing some of the basic skills in badminton and the shuttle not reaching to the back corners of the court which gives the player the opportunity to win through applying the pressure on the opponent and make him away from the control center(T) which definitely required level of a collection muscular strength contributed in performance perhaps this related to a number of reasons related with weakness in physical changes especially explosive and characterized by speed forces for the badminton players and be acquainted with them and knowing the extent of their effect in performanc
... Show MoreIn this study, the Halder-Wagner method was used for an analysisX-ray lines of Tio2 nanoparticles. Where the software was used to calculate the FWHM and integral breath (β) to calculate the area under the curve for each of the lines of diffraction. After that, the general equation of the halder- Wagner method is applied to calculate the volume (D), strain (ε), stress (σ), and energy per unit(u). Volume (β). Where the value of the crystal volume was equal to (0.16149870 nm) and the strain was equal to (1.044126), stress (181.678 N / m2), and energy per unit volume (94.8474 J m-3).The results obtained from these methods were then compared with those obtained from each of the new paradigm of the HalderWagner method, the Shearer developm
... Show MoreChurning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date. A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s
... Show MoreStatistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c
... Show MoreThe purpose of this study is to evaluate the hydraulic performance and efficiency of using direction diverting blocks, DDBs, fixed on the surface on an Ogee spillway in reducing the acceleration and dissipating the energy of the incoming supercritical flow. Fifteen types of DDB models were made from wood with a triangulate shape and different sizes were used. Investigation tests on pressure distribution at the DDBs boundaries were curried out to insure there is no negative pressures is developed that cause cavitation. In these tests, thirty six test runs were accomplished by using six types of blocks with the same size but differ in apex angle. Results of these test showed no negative pressures developed at the boundarie
... Show MoreFingerprints are commonly utilized as a key technique and for personal recognition and in identification systems for personal security affairs. The most widely used fingerprint systems utilizing the distribution of minutiae points for fingerprint matching and representation. These techniques become unsuccessful when partial fingerprint images are capture, or the finger ridges suffer from lot of cuts or injuries or skin sickness. This paper suggests a fingerprint recognition technique which utilizes the local features for fingerprint representation and matching. The adopted local features have determined using Haar wavelet subbands. The system was tested experimentally using FVC2004 databases, which consists of four datasets, each set holds
... Show More