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In recent years, in order to reduce the damage from the blackout accident, the demand of the power system for its basic resistance ability has gradually increased. Therefore, this paper proposes a double-layer optimization method for the prevention of cascading failure of the power system that takes into account the transient stability constraint for cascading trips that directly lead to the accident. In terms of models, the paper reconstructs an outer model for optimizing the safety margin based on the inner model that solves the safety margin of cascading failure prevention. In terms of algorithms, the paper uses the Extended Particle Swarm Optimization (EPSO) algorithm with higher precision and accuracy to solve the inner model and innovatively introduced the Practical Dynamic Security Region (PDSR), which uses PSASP software for offline calculation and online application, as a dynamic analysis method to quickly realize transient stability constraint to solve the outer model. Three evaluation indexes of the preventive optimization effect can be obtained by this method; dispatchers can plan more flexible and reliable optimal dispatching schemes through these indexes, to comprehensively improve the safety margin of cascading failure prevention in the current operating state and enhance the risk resistance ability of the power grid. This paper has used MATLAB and PSASP to perform simulation analysis using the IEEE39 node system as a test example to verify the effectiveness and superiority of the proposed model and algorithm.

Lighting has emerged as a central concern in the domain of city planning and design in recent decades. Better lighting does more than just make cities safer and more secure; it also makes them more aesthetically pleasing and easier to live in. A single type of optical sensor is no longer sufficient to meet the needs of intelligent lighting for urban roads, and as such, there is a growing demand for cutting-edge control systems that can adapt to the dynamic lighting needs in urban environments. This paper’s goal is to create an intelligent urban lighting control system by integrating optical multisensor technology and the gray model (GM model). Programmable logic controller (PLC) serves as the system’s central processing unit, with light intensity sensors and color sensor-detecting devices placed strategically throughout each city and linked directly to the controller. Each road streetlight is equipped with a motion sensor detection device that is tasked with identifying the presence of vehicles and pedestrians within its field of view. Data fusion technology is utilized to process the environmental data gathered by optical multisensors, the collected data are then used to control and predict outcomes using the robust prediction capability of the GM model, and the result is a lighting control strategy that is both efficient and intelligent. In the end, the strategy presented in this paper is applied to improving the management of an industrial park lighting system’s energy consumption. The results of the evaluations show that the fresh method is successful in dimming, prediction, and control. This conclusively demonstrates the efficacy of the paper’s proposed design solution, which integrates optical multisensor technology with sophisticated control algorithms and data analysis to improve the quality of life in urban areas by boosting the efficiency and sustainability of the urban lighting system.

In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process.

Distribution network security situation awareness refers to the process of perception, understanding, and state projection of systems, elements, and environmental factors within a certain space-time volume. Security situation awareness is an important part of the security assessment of the distribution network. In response to the rapid and accurate distribution network security situation awareness requirements, this paper proposes a distribution network security situation awareness method based the distribution network topology layered model. First, a hierarchical model of the distribution network topology under the premise of optimizing the location of the synchronous phasor measuring device is constructed. This model can quickly capture the system security situation elements. Then, a support vector data description algorithm fused with information entropy is used to realize the identification and understanding of abnormal information in the security situation elements of the distribution network. The long- and short-term memory network is then used to predict the operation trend of the distribution network under normal operation and fault disturbance. Finally, a simulation is established, and the IEEE-33 distribution network model is used to verify the effectiveness of the method proposed in this paper. The results show that the method of this paper improves the speed and accuracy of obtaining the security situation elements of the distribution network, shortens the identification time of the security situation elements, and realizes the security situation awareness of the nodes of the distribution network.

Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect.

This paper addresses the synchronization and operation of a 140 kVA inverter system connected to the main grid as part of a decentralized microgeneration system. The considerations for the supply of electrical energy stored in battery banks, mostly of photovoltaic origin, involve a study of the details of a rigid nonlinear system, which parallels the generation and distribution standards typical of hydroelectric and thermoelectric plants. Considering aspects related to power electronics operation, this paper presents both the modeling and the controlling aspects necessary to synchronize and ensure a stable operation of the microgeneration systems when connected to the main grid. Statistical processing was developed to guarantee synchronization between the systems without presenting electric shocks by simulating the magnetic link in asynchronous generators to meet this aim. The proposed model simulates the increase in power by a phase shift by maintaining a constant frequency based on a Chirp wave generator. The proposed process considers a generation power baseband operation. A Mamdani-type fuzzy proportional-integral controller is used to determine the power setpoint, which sets the Chirp generator phase shift setpoint, which includes a Mamdani fuzzy proportional-type controller. Both controllers are connected in a cascade. The applied correlational technique to achieve the synthesis of the sinusoid and the synchronization presented optimal performance when using 17 samples per signal period. The design of the transformer primarily, guaranteed a phase shift of −4.3018°, allowed for a THD below 2.75%.

The rise of artificial intelligence technology has promoted the development of human-computer interaction and other fields. In human-computer interaction, in order to enable the machine to accurately perceive and understand the user’s emotion in real time, thereby improving the service quality of the machine, user emotion recognition has been widely studied. In real life, because voice output not only is convenient, but also contains rich emotional information, human-computer interaction is mainly carried out in the form of voice. Speech carries a wealth of linguistic, paralinguistic, and nonlinguistic information that is essential for human-computer interaction. Understanding language information alone will not allow a computer to fully comprehend the speaker’s purpose. For computers to behave like humans, speech recognition systems must be able to process nonverbal information, such as the emotional state of the speaker. As a result, developing machine understanding of human emotions requires speech-based emotion recognition. This paper proposes an improved long short-term memory network (ILSTM) for emotion recognition. Because the initial LSTM only analyzes the preceding moment’s input, it will miss out on a lot of information for the full context scene. In this way, all the features in the speech segment can be extracted. In order to be able to select the feature that can express emotion the most among the many features, this paper also introduces the attention mechanism. Experiments are carried out on public datasets, and the experimental results show that the ILSTM used in this paper is very effective in classifying speech emotion data and the classification accuracy can reach more than 0.6. This fully shows that this research can be applied to actual products and has certain feasibility and reference value.

In order to improve the automatic test effect of the electronic controller, this paper combines the intelligent control algorithm to construct the automatic test system of the electronic controller. Moreover, the paper makes a detailed theoretical analysis of several basic problems relating to signal integrity and gives a general method to solve the main SI problems. In addition, this paper analyzes the principle of the microstrip transmission line parameter model and its relationship with the signal integrity and constructs an automated test system for an intelligent electronic controller. The simulation test results show that the electronic controller automated test system based on the intelligent control algorithm proposed in this paper can play an important role in the electronic controller automated test.

In order to improve the monitoring effect of building indoor environment, this paper carries out indoor environment simulation through 3D modeling, uses image processing technology to collect environmental monitoring data, and proposes a routing algorithm applied in this environment. Moreover, this paper introduces the energy consumption model of sensor nodes and the multi-attribute decision-making method and uses the multi-attribute decision-making to propose an adaptive energy balance routing algorithm based on the indoor environment of the building. In addition, this paper establishes a simulation analysis, compares it with several other routing algorithms, and verifies that the proposed algorithm has better balance in network energy consumption and longer network life cycle. Finally, this paper combines three-dimensional modeling and image processing technology to construct the building indoor environment monitoring system. It can be seen from the simulation experiments that the proposed building indoor environment monitoring system based on 3D modeling and image processing can meet the actual needs of intelligent building indoor monitoring.

This paper proposes a study on the potential of waste-to-energy (WTE) possibility in the Arab Republic of Egypt. WTE is a viable option for municipal solid waste (MSW) management and a renewable energy source. The issue of waste spread is a chronic environmental challenge in the Arab Republic of Egypt. The MSW practices in Egypt are simply done by collecting the waste and dumping it in open landfill sites. This research aims to assess the potential contribution of the WTE facility to meet electricity demand in Cairo City, which is Egypt’s capital and the biggest city in population as a sample, and then apply the results to all of Egypt. The paper introduced a step-by-step calculation for the electrical power that can be generated from Cairo MSW. Four scenarios for WTE utilization were developed: Mass Burn without any waste processing, Mass Burn with 25% recycling, Mass Burn excluding 50% of organic material, and Mass Burn excluding 50% of organic material with 25% recycling. The Mass Burn scenario implies full MSW stream incineration; the Mass Burn with recycling scenario considers a partial separation of reusable materials and the waste leftover for incineration; the Mass Burn with excluding 50% of organic material scenario considers a partial separation of organic materials and the waste leftover for incineration; the Mass Burn with excluding 50% of organic material with 25% recycling scenario considers a partial separation of organic materials and partial separation of reusable materials and the waste leftover for incineration. The analyses were done for the period 2011–2031 for Cairo with a total population of about 10 million in Nov 2021. The results show the huge potential of WTE conversion as a source of renewable energy, which is a key factor in Egypt’s sustainable development strategy 2030. Available data have been theoretically processed to show the decision-makers in Egypt the amount of electrical power that can be generated by using the WTE options to manage the MSW in Egypt.

With the increasing improvement of people’s living standards, more and more tourists choose to travel independently, which puts forward higher requirements for the existing tourist guide system of scenic spots. Augmented reality (AR) is a technology that integrates computer-generated virtual information into real scenes. It provides more interactive modes, allowing visitors to have a stronger sense of immersion in the real world. The interactive navigation system is an analysis and processing system that recommends algorithms for tourists through the back-end server by collecting the user’s registration information data. It provides tourists with reasonable travel itineraries. This can not only allow users to avoid the peak flow of people and save tourists’ time, but also reduce the hidden dangers of scenic spots and improve the turnover rate of passenger flow. This paper aims to study an interactive guide system combined with augmented reality technology to provide tourism services for tourists in tourist attractions based on geographic locations. This paper proposes an improved probabilistic algorithm model to accurately locate tourists. (Probabilistic algorithms are also called randomization algorithms. Probabilistic algorithms allow the random choice of the next computational step during execution. In many cases, when the algorithm is faced with a choice during the execution process, the random choice is time-saving compared to the optimal one. Therefore, the probabilistic algorithm can greatly reduce the complexity of the algorithm.) At the same time, the paper designs an AR scenic spot tour guide system. It tests the performance of the system. The results show that the initial response speed of the improved probability algorithm is 0.69 s, the average response time is 4 s when the number of concurrent users is finally increased to 600, and the growth rate is about 0.006 s. The improved algorithm can obviously enhance the response speed.

In order to improve the cost control effect of the supply chain of intelligent manufacturing enterprises, this paper improves the IQC algorithm. In order to overcome the problem of multitype data processing in the supply chain, this paper describes the high-frequency NCS as a delta arithmetic system with input time-varying delay, high-frequency constraints, and actuator saturation. Moreover, through a double closed-loop feedback configuration, this paper combines the loop structure of the supply chain to analyze the stability of the actuator saturated high-frequency NCS and combines the experiments to verify the reliability of the improved algorithm. In addition, on the basis of the improved algorithm, this paper constructs an intelligent supply chain cost control system, evaluates its functional modules, sets up the system architecture, and analyzes the performance of the system in combination with experimental research. The experimental research results show that the supply chain cost control method of intelligent manufacturing enterprises based on the improved IQC algorithm proposed in this paper has good results and meets the operational needs of intelligent manufacturing enterprises.

At present, the electric power system is getting bigger and more complex, and its loading is also increasing. As a consequence, planning, operation, and control of the power system also become more complicated. It is known that system planning and operation are mostly based on the steady-state condition of the power system, and the system steady-state condition can only be determined from the load flow study. Thus, the development of a reliable and efficient method to solve the load flow problem is necessary so that the system steady-state condition can properly be evaluated. Since the characteristics of the electric distribution system are different from those of the transmission system, special treatments are usually required in the distribution system load flow (DSLF) analysis. In this context, several interesting techniques have been proposed in the analysis. In this paper, the application and extension of the trust-region method to solve the three-phase DSLF problem are proposed and investigated. Case studies using 19-node, 25-node, and 123-node distribution systems are also given in this paper. Results of the studies show that the output values obtained by the proposed method are in excellent agreement with those obtained by previously published methods. These results confirm the validity of the proposed method. Case study results also indicate that the proposed method has better computational performances than the forward/backward sweeping (FBS) method.

Audit evidence is the proof material on which the auditors issue audit opinions and make relevant audit conclusions, and audit evidence in the era of big data presents new characteristics in terms of adequacy, relevance, and reliability. This paper combines the improved association algorithm to construct the audit data mining system to improve the data processing effect of the audit process. Moreover, this paper proposes a new dynamic threshold method, gives the calculation method of some important parameters in the algorithm, and presents the improved cell-based association algorithm flow. In addition, this paper discusses how the outlier algorithm is applied to the acquisition of audit evidence and the application scenarios in the audit system. The experimental research results show that the audit data mining system based on the improved association algorithm proposed in this paper has a good effect in the audit of accounting and financial data.

At present, there is no obvious development of virtual reality technology in the garden industry. In order to improve the application of virtual reality technology in the sustainable development garden landscape design, this paper combines data mining technology and virtual reality technology to construct the sustainable development garden landscape design system. Moreover, this paper proposes a method of using the maximum likelihood method to solve the optimal hyperparameters of the covariance function and analyzes the elite-preserving genetic algorithm and the rough steps of its optimization. In addition, this paper analyzes the digital design process of garden landscape and improves the algorithm. Finally, on the basis of virtual reality technology and data mining technology, an intelligent and sustainable development garden landscape design system is constructed. The experimental analysis results show that the sustainable development garden landscape design system based on data mining and virtual reality proposed in this paper has a good effect.

The installation of automotive electrical switches is a complex three-dimensional space assembly project which has high requirements for installation accuracy. In order to improve the installation effect of automotive electrical switches, this paper applies the PSO-BP neural network algorithm to automotive electrical switches and integrates PSO and ELM algorithms. The training speed of the ELM model is fast, the model generalizes the data well, and the noise data have little effect on the model. Moreover, this article combines simulation research to evaluate the effect of this algorithm. After confirming the performance of the effect, this paper uses a case study to study the effect of the application of the PSO-BP neural network algorithm to the automotive electrical switch. The research results show that the CAD-assisted 3D assembly system of automobile electrical switch considering PSO-BP neural network algorithm has a good effect.

Most of the existing resource allocation algorithms do not take into account the restriction conditions such as the delay limitation of different services and the service volume requirements of different STAs. Moreover, the surge in the number of smart terminal devices has brought great challenges to the access effect of WLAN systems. Therefore, 6G technology will surely replace the current technology as the main means of future communication technology. This paper combines 6G technology to study the smart system resource coordination algorithm and analyzes the calculation process and actual effect of the algorithm. Moreover, this paper analyzes the coordination of smart system resources and verifies the effect of 6G technology in the coordination of smart system resources through experimental research. The results of experimental research show that the smart system resource coordination algorithm based on 6G technology can effectively improve the resource coordination effect of smart systems and has certain anti-interference, which will promote the construction of smart systems in the future.

The interelement spacing of a coprime array breaks through the half-wavelength limitation, so that a larger array aperture can be obtained with a fixed number of array elements. In this paper, the symmetry of the noncircular signal is used to virtually expand the L-shaped array into an orthogonal cross array. Furthermore, the virtual received signal of the augmented array is obtained by the second-order statistic of the received data. Decoupling and dimension reduction of elevation and azimuth are realized by a z-axis subarray and y-axis subarray. Finally, the sparse reconstruction of the signal is realized by the minimum absolute convergence and selection operator method. This method can enlarge the aperture and freedom of array and has higher accuracy and resolution of DOA estimation. It has the advantages of automatic parameter pairing without additional pairing operation and is effective for coherent and incoherent signals. The final numerical simulation results prove the effectiveness of the method in this paper.

Cognitive radio is an innovative technology in the field of wireless communication systems, aimed at significantly improving the use of the radio spectrum while allowing secondary users to access the spectral band opportunistically. Spectrum management mechanism ensures the transmission of data by controlling the efficiency of operation between the primary and secondary networks. The main task of spectrum management is to ensure that secondary users benefit from the spectrum without interfering with primary users. This paper deals with some of the important characteristics of spectrum mobility in the cognitive radio networks. The new management approaches of the mobility and the connection are designed to reduce the latency and loss of information during spectrum handoff, a list of channel safeguard is maintained in this effect, but the maintenance and update are a challenge. In this paper, we describe the reasons and mechanisms of spectrum handoff. Protocols have been developed to illustrate this handoff mechanism. We also make a comparison between the different methods of spectrum handoff. The simulation results obtained confirm that the protocols developed and the proposed method performed better than the pure reactive handoff method.

To maintain the security of transformer differential protection, it is essential to restrain its response to oversetting differential current caused by the inrush current or other switching conditions. This paper presents a new proposed method to discriminate the transformer’s internal fault from the inrush current; the discrimination process is based on a convolutional neural network (CNN) with a combination of the higher order spectral estimations that perform a deep learning classification with high accuracy. This research succeeded in proposing two robust and efficient CNN models; the first one is the 1D CNN, which takes the sole signal without any transformation, while the second model is the 2D CNN, which takes the short-time Fourier transform of the signal. Both developed models are light and have the minimum number of layers that can achieve a very high performance. The performance of the proposed models is tested using a dataset prepared according to laboratory-measured transformer parameters. The results are compared with other well-known methods, and the achievement of high numerical performance evaluation validates the consistency of the proposed methods.

Switched reluctance motors’ (SRMs) implementation in electric vehicle drives are widely increasing due to several merits, such as a wide speed range, high torque density, and rare earth-free characteristics, but it also has some drawbacks, such as more torque ripples, acoustic noise, and vibration, that hinder its usage in electrical vehicle applications. This paper introduces a novel geometry of rotor teeth of SRM, where two slots are inserted per each tooth of rotor to create rotor-slotted tooth SRM (RST-SRM). The novel topology aims to enhance the performance of SRMs, especially at high speeds. The overall performance of the RST-SRM was carried out in addition to comparing it with performance of both stator-slotted tooth SRM (SST-SRM) and conventional SRM. The design parameters of the three models are the same and carried out by finite element tools (FETs) to obtain the magnetic characteristics, and the static analysis of the three models is compared. Dynamic performance was achieved by implementing the MATLAB Simulink package. The steady-state and start-up performance of the three models were obtained and compared. The performance parameters are estimated at various slot dimensions of the novel technique, in addition to studying the effect of conduction angles for three models. Finally, the efficiency, iron losses, and torque ripple maps of the three models are compared to simulate the scope of the work of electric vehicles.

The complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low-frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low-frequency oscillation phenomena. To address matter that the traditional S-transform time-frequency window function has a fixed scale and cannot be applied to the specific local characteristics of different signals, an adaptive generalized S-transform algorithm based on a bat algorithm is proposed in this paper. It uses adjustment parameters to control the generalized Gaussian window function. The parameters are automatically adjusted by a bat algorithm adaptive optimization to find the best time-frequency characterization. Secondly, the PMU data waveform with implicit low-frequency oscillation information is converted into a two-dimensional time-frequency figure including the onset moment, frequency, and amplitude. The system enables identification and visual monitoring of low-frequency oscillations. After that, simulation experiments of New England system are conducted. The superiority of the proposed method is verified, which can greatly improve the time-frequency resolution of PMU active power data signal and has effective noise immunity.

This paper shows the design and implementation of a multimode optical source in the VPIphotonics software. Considering all the powerful features this software provides, the absence of a device such as a multimode source in its library constitutes a limitation for particular simulations. The use of single-mode optical sources available in the library allows for obtaining a multimode spectrum. The mathematical relationship that describes a multimode optical source is numerically evaluated in MATLAB. Afterward, this graphical result is compared with the obtained in the VPIphotonics software. The importance of using this type of optical source resides in its applications for modern optical communications systems, microwave photonic filters (MPFs), opto-electronic oscillators (OEOs), and sensors. This implementation aims to drive virtual systems where required, and by setting its parameters similar to the real ones, it allows good studying and understanding of system responses. Even more, it predicts or ensures the results of experimental systems by having the possibility to vary different parameters. A practical application of the obtained multimode optical spectrum is shown with its use in the implementation of an MPF. This last simulation is experimentally corroborated. This work presents a technique for designing a multimode optical source in this particular software and provides also academics and students, with a methodology for the use of this software for this kind of photonic design.

The paper proposes a mathematical model for long-term load forecast (LTLF) based on parametric and time series statistical techniques. The flowchart of the proposed algorithm was also presented. The multiple linear regression (MLR) as well as the autoregressive integrated moving average (ARIMA) model with different model orders are employed in load forecasting. Historical data from 1990 to 2022 were utilized in the model implementation and validation. The input data imply gross domestic product (GDP), oil prices, population, and the energy from renewable energy projects as independent variables, and the annual peak load is the dependent variable. The results obtained by MLR show that population and GDP have a positive impact on electricity demand, whereas the oil price and the penetration of the renewable energy have a negative impact on electricity demand. In ARIMA, the load forecast is estimated based on error (residual) estimation that is determined based on the time lag operator and autoregressive and moving average coefficients. The ARIMA (p, d, and q) model with six different model orders is investigated. The Bayesian information criteria (BIC) and Akaike information criteria (AIC) indices are used as a measure in the selection of the appropriate model for prediction. The comparison between the six model’s scenarios shows that ARIMA (1, 1, 1) is the best model that fits the time series with minimum error and with the lowest BIC and AIC. The RMSE, MAPE, MSE, and MAE provided by ARIMA (1, 1, 1) are 3.57%, 2.55%, 0.13%, and 54.36, respectively, whereas the AIC and BIC are 386.8 and 394.0, respectively. A comparison between ARIMA and MLR shows that ARIMA is better than MLR in terms of time series fitting and error level. The LTLF forecast period covering the period 2024–2035 considers three scenarios of future development in the country: the normal (medium forecast), the optimistic (high forecast), and the pessimistic (low forecast). The forecast is made in the three directions (i.e., medium, high, and low) to avoid the ambiguity and uncertainty that may occur in the data input used in forecasting the electricity demand. A comparison between the MLR and ARIMA in the last year 2035 shows a small deviation between the two methods with the forecasted values in the range between 5480 MW and 5520 MW, respectively.

This paper presents new hierarchical image-based time-frequency convolutional neural network (HTFICNN) for sorted bearing fault detection in brushless DC (BLDC) motors. The HTFICNN combines three different time-frequency visualisation methods: scalogram, spectrogram, and Hilbert spectrum for the transformation of current and vibration signals into time-frequency information (TFI). We use three different convolutional layers to extract features from three generated TFIs. We detect the targets using three patterns generated by the convolutional layers, utilizing three deep network structures, each containing a softmax classifier that identifies one pattern out of three. Using a three-pattern approach, the hierarchical fusion classification method then realizes the final target decision. We tested the proposed HTFICNN with data from BLDC motors and found that it works better than other deep learning models to detect bearing faults. Moreover, this work contributes to the advancement of fault diagnosis and predictive maintenance for BLDC motors, with potential applications in other fault detection scenarios.

This paper discusses amplify-and-forward relay-assisted air-to-ground unmanned aerial vehicle communication over a recently proposed single-shadowing-based composite fading scenario. Included are the results derived from the Fluctuating Beckmann faded co-channel interference at the receiver node and relay. Maximal ratio combining based diversity scheme is considered. The power splitting at the receiver for energy harvesting has been assimilated. The utilization of characteristic function leads to the derivation of outage, success probabilities, and capacity with outage expressions. These expressions represent the impact of path-loss, aerial vehicle-relay, relay-receiver distances, and channel characteristics of desired and interference signals. Utilizing these expressions, numerical outcomes are presented and evaluated.

Energy efficiency and spectrum sensing performance are two crucial parameters in cognitive radio networks. Enhancing energy efficiency should not come at the cost of sacrificing spectrum sensing performance. This paper introduces a novel sensing node grouping algorithm to address the challenge of balancing energy efficiency and sensing performance. The proposed algorithm first prioritizes spectrum sensing training for each node and estimates their reliability. Nodes with reliability values exceeding a predefined threshold are selected for cooperative spectrum sensing. The selected nodes are divided into two groups using a staggered grouping approach, ensuring equivalent sensing performance between the groups. The groups alternate in performing spectrum sensing operations, with one group engaging in sensing while the other participates in data transmission or remains idle. The optimal number of cooperative nodes is determined through a greedy algorithm, formulated as a constrained optimization problem. The objective is to maximize energy efficiency subject to a constraint on the error probability, ensuring the highest possible energy efficiency while satisfying the required sensing performance criteria. Experimental results demonstrate that the proposed algorithm effectively maximizes energy efficiency while fulfilling the sensing performance requirements. Compared to traditional algorithms, the dynamic grouping algorithm significantly enhances spectrum sensing performance and improves energy efficiency. The algorithm is adaptable to various network configurations and environmental conditions, addressing the challenges faced by existing solutions. This research contributes to the advancement of cognitive radio networks and their applications in various domains, offering a comprehensive and adaptive solution to optimize energy efficiency and spectrum sensing performance.

Power transformers are indispensable components for energy transmission and voltage regulation. Since the leakage impedance affects the short-circuit current, magnetic leakage distribution, and manufacturing cost, the accurate calculation of transformer impedance is vital for transformer design. Generally, leakage impedance is mainly decided by the design parameters of the windings, both analytically and numerically. In most given literature studies, the leakage impedance was optimized and analyzed by adjusting the design parameters of the windings without considering the consequent influence on transformer loss and manufacturing costs. A multiobjective optimization model considering the leakage impedance, manufacturing cost, and operating loss of the windings is presented in this paper. The eXtreme Gradient Boosting (XGBoost) model is built using 2048 finite-element analysis (FEA) samples and utilized as a leakage impedance predictor. XGBoost shows better accuracy and consumes much less time compared to analytical and FEA methods. Subsequently, the presented multiobjective model is optimized using multiobjective algorithms and the NSGA-III shows the best performance among the NSGA-II, MOPSO, and MODE. The results show that the leakage impedance is closer to the required value. Besides, the winding manufacturing cost and loss are meanly decreased by 4.7% and 4.1%, respectively, in the engineering case.

The use of decentralized renewable energy systems will continue to play a significant role in electricity generation especially in developing countries where grid expansion to most remote areas is uneconomical. The income levels of these off-grid communities are often low, such that there is a need for the delivery of cost-effective energy solutions through optimum control and sizing of energy system components. This paper aims at minimizing the net present cost (NPC) and the levelised cost of energy (LCOE). The study presents a hybrid power system involving a hydroelectric, solar photovoltaic (PV), and battery system for a rural community in Cameroon. The optimization of the system was done using HOMER Pro and validated using a meta-heuristic algorithm known as genetic algorithm (GA). The GA approach was programmed using the MATLAB software. After the HOMER simulation, the optimal power capacity of 3 kW solar PV, 334.89 Ah battery, and 32.2 kW microhydropower was used to meet the load. The village load profile had a daily energy usage of 431.32 kWh/day and a peak power demand of 38.49 kW. The optimized results showed an NPC and LCOE of $90,469.16 and 0.0453 $/kWh, respectively. The system configuration was tested against an increase in hydropower capacity, and it was observed that increasing the hydropower capacity has the ability to significantly reduce the LCOE as well as the battery and solar PV size. A comparative analysis of the two approaches showed that the optimization using GA was more cost-effective than HOMER Pro with the least LCOE of 0.0344 $/kWh and NPC of $86,990.94 as well as a loss of power supply probability (LPSP) of 0.99%. In addition, the GA method gave more hydropower generation than HOMER Pro. This supports the fact that stochastic methods are more realistic and economically viable. They also accurately predict system operation than deterministic methods.