Artificial intelligence (AI) continues to be a transformative field, offering significant contributions to data science by supporting optimal decision-making processes. One notable application of AI is in digital forensics, particularly in spam email classification. This paper presents a two-step approach to differentiate between regular and spam emails. In the first step, emails are evaluated for vulnerabilities based on three key criteria: varying time intervals between Mail Transfer Agents (MTA), the presence of binary attachments, and inconsistencies in IP addresses associated with the same user. In the second step, a comparative study is conducted between Machine Learning (ML) and Deep Learning (DL) algorithms to identify the most effective method for achieving accurate classification results. The findings demonstrate that the Support Vector Machine (SVM) algorithm from ML outperforms the Recurrent Neural Network (RNN) algorithm from DL, achieving an accuracy rate of 96 % compared to 90 %. A notable conclusion from this research is that manual pre-processing leads to more accurate results and better interpretability compared to automatic pre-processing. This highlights the importance of human intervention in certain stages of AI-driven processes, even when using advanced algorithms. The results suggest that a combination of strategic criteria evaluation and algorithm selection is essential for enhancing the precision of spam classification in digital forensics.

To ensure grid stability and efficient power conversion, advanced inverter technologies play a crucial role in integrating photovoltaic (PV) systems into the electrical grid. The article suggests a photovoltaic system which is connected to the grid utilizing a nine-level Packed U-Cell (PUC9) topology, which incorporates a PV panel coupled with a DC-DC boost converter. This setup is managed by a Maximum Power Point Tracking (MPPT) algorithm, specifically using the Perturb and Observe (P&O) method, to optimize energy output by adjusting to varying irradiance and temperature conditions. The PUC9 inverter is designed with four pairs of complementary switches, one DC source, and two flying capacitors, and connects to the grid via a filtering inductor. This topology generates nine distinct voltage levels with fewer active and passive components than traditional multilevel inverters, leading to improved output quality and reduced total harmonic distortion (THD). The study assesses the performance of an advanced Finite Control Set Model Predictive Control (FCS-MPC) approach, comparing it to a traditional Proportional-Integral (PI) Pulse-width Modulation (PWM) method. While PI-PWM is recognized for its straightforward design and simplicity in application, it often struggles to maintain consistent performance under variable conditions due to its dependency on fixed control parameters. In contrast, the FCS-MPC approach offers dynamic response and better adaptability, which are essential for effective power conversion and system stability. Simulations conducted in MATLAB/SimulinkTM prove that the suggested MPC method provides improved tracking accuracy for maximum power points under changing irradiance conditions while maintaining efficient integration of power into the grid. The findings underline the potential of the PUC9 topology combined with the FCS-MPC strategy to provide high-quality output with improved robustness and resilience, making them viable solutions for contemporary renewable energy applications.

The paper refers to the study, design and some trials and tests of a 1 kW high-speed three-phase asynchronous electric machine that is achieved in inverted construction. This electric machine is in inverted construction that is with the rotor on the outside. The rotor of the electric machine is in a copper cage, to increase efficiency and space saving. Electrical tests were performed in motor and electric generator mode. Finite element modelling of the magnetic field was done in FEMM. Dynamic balancing tests were also performed. Mechanical stresses were analysed, and thermal stress calculations were performed. The control of the inverted high-speed asynchronous machine at variable speed was done with a frequency converter. The practical and theoretical results obtained prove a solid construction of the electric machine at variable frequencies, from o to 100 Hz. The three-phase asynchronous electric machines in inverted construction have several advantages, such as high efficiency and space saving. This advantages that make them viable for important specific applications, such as aircraft and electric vehicles.

This study presents the control methods applied to the wind power generating system that is linked to a dual fed induction generator. The main objective of this work is to evaluate the efficacy of a maximum power point tracking technique based on fuzzy logic concepts. The suggested methodology is utilized to ascertain the doubly fed induction generator’s electromagnetic torque, hence enabling the computation of the optimal mechanical turbine speed. The paper further explores the use of the indirect field-oriented control method to dynamically regulate both active and reactive power derived from the rotor voltage-source converter. This selection is not arbitrary; instead, it is grounded in the fact that the machine is typically connected to a robust network characterized by constant voltage and frequency, resulting in a stable field in the generator's stator. The control law of indirect field-oriented is formulated using proportional and integral power and current controller in the rotor side converter. To assess the efficacy of the suggested approach, a simulation is performed in Matlab/Simulink environment to implement vector control for the dual fed induction generator that is coupled to a wind turbine model. The simulation results illustrate the effectiveness of the fuzzy logic control-based maximum power point tracking technique, indicating favorable system performances.

As human civilization become modernized day after day, requirement of human being increases along with the utilization of electrical power. The way of procurement of electrical power should be more lucrative and customer should able to choose the beneficial deals to procure the same. Previously we found that the electrical market is monopoly in nature and customers/consumers are often uncomfortable to procure electrical power. In respect to enjoy the benefit to all suppliers and consumers, a deregulated competitive open market is required. The competitive market can be constructed by restructuring it by incorporating competitive and profit maximization format. This market can be operated by an independent regulating body which is responsible to maintain the financial and administrative constrains of the market. In every nodal point of an electrical network, Locational marginal price (LMP) is an important parameter which can be estimated by Nodal congestion price (NCP). On the other hand, IMO pay is another crucial criterion in open market power bidding system. This paper introduces a method for forecasting Nodal congestion price (NCP), IMO Pay using ML. The performance of Ridge is outperformed by comparing prediction results with Gradient boosting Regression, Gradient Boosting, Decision tree and LR.

Multilevel inverters have a vast range of research and development opportunities that remain growing in depth and breadth. They also present an amazing amount of problems. Furthermore, to reduce the total harmonic distortion, improve efficiency, reliability, simplicity and reduce the cost of multilevel inverters. The primary motivation of this research is to establish an efficient MLI with reduced active switching elements to more levels in the output in order to get improved waveform quality by reducing THD and also reduce TSV per unit and cost function per level. NLC techniques is used in these inverters to control the switching schemes. Thirty-three level new reduced switch count MLI is recommended with only 17 switches and 4 unsymmetrical voltage sources. The performance of recommended system is verified for R and RL loads. TSV per unit calculated and it is 6.09. The total harmonic distortion is 3.11 %, the cost function per level of the recommended inverter is 4.24 for weight co-efficient 0.5 and 4.61 for weight co-efficient 1. The performance of the recommended inverters is reliable, less complexity, TSV per level is low and cost function per level is less. The THD produced by these 33 level inverters meet the standards of IEEE.

Gear pumps often bear unbalanced radial hydraulic pressure, which is easy to cause serious wear of bearings, and the improvement of working pressure will be limited by many aspects. The built-in double gear pump of hydraulic mechanical automatic transmission is designed, and the structural composition and characteristics are analyzed; the basic parameters of gear pump are designed, and the working process and instantaneous flow characteristics are analyzed; the radial force generated by the hydraulic pressure along the gear circumference, the radial force generated by the gear meshing transmission torque and the synthetic force are analyzed; the structural parameters of gear pump are designed. According to the analysis results, the gear strength is checked by using the finite element model. Based on the gear pump test bench, the characteristics of the gear pump are analyzed under two working conditions: fixed speed (1,500 rpm), regulated pressure (1 MPa - 5 MPa), fixed pressure (2 MPa) and regulated speed (600 rpm – 1,800 rpm) to test the reliability of the design. The results show that the radial force generated by the liquid pressure of the center wheel of the double gear pump is balanced, and the meshing force of the center wheel is also balanced. The maximum stress on the center gear of gear pump occurs at the tooth root, which is 98.453 MPa, far less than the yield limit of gear material. When the pump speed is constant, the total efficiency, mechanical efficiency and volumetric efficiency of the pump show a decreasing trend with the increase of pressure, but the range is small. When the pressure remains unchanged, the flow of the pump increases gradually with the increase of rotating speed. The torque increase shows a downward trend, but the range is small. The mechanical efficiency shows an increasing trend, but the range is small. The volumetric efficiency increases first and then decrease. When the rotating speed is 1,000 rpm ~ 1,500 rpm, the volumetric efficiency of the pump is high. The analysis contents and results provide a reference for the design of this kind of gear pump.

The study explores the development and implementation of an intelligent lift service system aimed at enhancing energy efficiency and reducing power consumption. The system leverages advanced technologies such as facial detection and data forecasting to optimize lift operations by predicting user destinations based on historical usage patterns. By minimizing unnecessary lift movements and efficiently managing the power used for lifting and lowering the cabin, the intelligent lift system ensures significant energy savings. The approach involves the integration of an automated alert system for lift lockouts, which enhances service reliability and contributes to cost-effective maintenance. A key feature of the system is its ability to intelligently schedule and route lifts, reducing idle times and optimizing energy usage. The facial detection technology identifies and authenticates users, enabling personalized service and security. Data forecasting allows the system to anticipate peak usage times and adjust operations, accordingly, ensuring smooth and efficient service during high-demand periods. The implementation of the proposed intelligent software solutions highlights their potential for broad applications across various building types, from commercial to residential environments. The scalable nature of the technology allows for easy adaptation to different infrastructure sizes and requirements, making it a versatile solution for modernizing lift systems.

The cyclic motion of engineering vehicles is of great significance for hybrid power design, which can effectively improve the fuel economy of vehicles. Based on the characteristics of the cyclic operation of the studied vehicle, the engine and generator are connected in series to drive the vehicle, and supercapacitors are used for energy storage and release to achieve the structural design of the driving system; The control strategy of the system is designed using dynamic control to achieve dynamic control of each energy unit throughout the entire cycle, achieving energy control balance of the system, and the entire vehicle operates in the optimal mode. Based on the hybrid power test bench, the system control strategy is verified, mainly through the matching control of the series drive system and supercapacitor under various working conditions, to verify the effectiveness of the control system and strategy. The results show that in three working cycles, the hybrid drive system can adjust its working mode according to the control strategy and achieve smooth switching between various working modes, with all system parameters within the normal range. The control strategy is effective and provides reference for the design of such systems.

Although the Dead-Beat Model Predictive Control (DB-MPC) for induction motor (IM) control has many advantages, there are still that require further study, where DB-MPC is highly dependent on the accuracy of the motor parameters. To improve control robustness, a model-free predictive control method that combines Ultra-Local Model (ULM) and Integrated Sliding Mode Observer (ISMO) with some optimization methods are proposed in this paper. The ULM is used because it does not depend on the uncertain parameters of the all system. Enhanced ISMO design is proposed to reduce the chattering phenomenon. Moreover, the careful calculation of factors related to the design of this strategy affects system performance and ensures system convergence. Furthermore, in this paper, these factors are obtained off-line using a Cuckoo Search Algorithm (CSA). The performance of the proposed control is evaluated under various operating conditions through simulation tests, and the results are compared with the conventional DB-MPC.