In this research work, three maximum power point tracking (MPPT) control algorithms based on power feedback are applied to two types of converters, namely Zeta and buck-boost, in the photovoltaic (PV) system. The PV system is intended to guarantee the power supply to a remote smart greenhouse. The control algorithms investigated are perturb and observe (P&O), incremental conductance (IncCond), and fuzzy logic (FLC) methods. Their performance are investigated and compared for each converter. It is found that the maximum power is always achieved, even during abrupt changes in irradiation or/and in temperature. The three methods have shown to have good performance; fast response time and very low steady-state error, with minor preference of the P&O method where the output voltage followed the input with high efficiency. The comparative study revealed that the power response time of the PV generator under stable conditions (constant irradiance and constant temperature) for P&O and IncCond was longer in the buck-boost converter than in the Zeta. On the other hand, the ripple level was better for the buck-boost. For the FLC, the maximum power was reached in a shorter time (short response time) with the smallest ripple. As for operation under variable environmental conditions, the Zeta outperformed the buck-boost for each control technique.

To solve the problems of large prediction error, slow convergence speed, and poor generalisation ability of traditional models in predicting surface deformation of open-pit mine slopes, this paper proposes a new intelligent prediction model based on the Mayfly algorithm-optimised support vector machine (MA-SVM). In this method, the MA is used to optimise the SVM parameters to reduce the uncertainty of the model and avoid time-consuming parameter adjustment. To evaluate the proposed prediction model, real-world deformation data of the north slope of the Anjialing open-pit mine in Pingshuo city, China, are collected using the microdeformation monitoring radar and used to investigate the deformation prediction performance of the proposed method. The results of the analysis demonstrate that the proposed method is able to accurately predict the deformation of the surface of the mine slope and outperforms three existing popular methods, including SVM, genetic algorithm (GA)-SVM, and particle swarm optimisation (PSO)-SVM). The mean absolute error (MAE) of the proposed MA⁃SVM is 2.52 % while 6.56 %, 4.95 %, and 5.16 % for the SVM, GA-SVM, and PSO-SVM, respectively; the root mean square error (RMSE) of the proposed MA⁃SVM is 10.21 % while 30.79 %, 17.38 %, and 22.54 % for the other three methods. Because the proposed MA⁃SVM model is able to predict slope deformation using actual monitoring data, it is of practical importance in real-world applications for early warning on landslides of mine slopes.

This paper introduces a reinforcement learning-based controller, utilising the deep deterministic policy gradient (DDPG) method, to mitigate low-frequency disturbances in electrical grids with renewable energy sources. It features a novel reward function inversely related to the control error and employs a state vector comprising absolute and integral errors to enhance error reduction. The controller, tested on a dual-region system with solar power, utilises phasor measurement unit (PMU) data for global inputs. Its performance is validated through time-domain simulations, pole-zero mapping, modal analysis, frequency response, and participation factor mapping, using a custom MATLAB and Simulink toolkit. The design accounts for communication delays and adapts to variable conditions, which proves to be effective in reducing oscillations and improving system stability.

As population increases, one of the factors affecting life is traffic. Efficient traffic management has a direct positive impact on issues such as time, carbon dioxide emissions, and fuel consumption. Today, an important parameter under the heading of traffic is the signalling systems for intersections, which are operated with fixed-time, semi-actuated, fully actuated, and fully adaptive control methods. In this study, an adaptive traffic management model is developed for signalised intersections. The adaptive traffic management model developed includes phase extension with minimum and maximum time intervals dependent on density and phase skip features. Additionally, the most distinctive feature of the model is its flexible phase structure rather than a sequential phase. The Heybe intersection, located within the boundaries of Antalya province, is modelled one-to-one in the simulation of urban mobility (SUMO) simulation programme with real intersection data. The developed adaptive traffic management model is applied to the Heybe intersection, and the effects of the model are revealed. Improvements obtained from the SUMO simulation programme were verified through visual inspection, and high-accuracy results were determined. As a result of the studies, it was found that the application of the adaptive traffic management model developed at Heybe intersection, which has approximately 50,000 vehicles passing daily, resulted in a 27.2 % improvement in the average delay per vehicle parameter, a 32.4 % improvement in the average waiting time per vehicle parameter, and a 16.7 % improvement in the average speed per vehicle parameter.

Automatic assembly equipment is the key to improving the efficiency and quality of workpiece assembly. The precision of assembly directly influences the overall quality of the assembled product. To optimise the position control accuracy in the automatic assembly equipment, a variable universe fuzzy proportional integral (VUFPI) controller optimised by the sparrow search algorithm (SSA) is developed in this paper. The developed controller adopts the SSA to adjust in real time the universe of the fuzzy controller according to the deviation of the servo system. The servo system model is established to evaluate the performance of the proposed SSA-VUFPI controller; furthermore, the SSA-VUFPI controller is implemented in the automatic assembly equipment for experimental evaluation. The analysis results demonstrate that the proposed SSA-VUFPI controller is capable of improving the anti-interference ability and position accuracy of the servo system compared to traditional PI, VUFPI, and currently used back propagation neural network proportional-integral-derivative (BP-PID), fractional-order PID (FOPID), and SSA-PID controllers. Moreover, it effectively improves the position accuracy of the workpiece and ultimately improves the quality of the assembly.

In this research, the impact of various weather conditions on digital television signals is investigated. Machine learning and nonlinear regression models were used to estimate the strength of the received signal. The received signal strength might vary significantly depending on the weather condition, especially in higher frequency ranges or millimetre wavelengths. Predictive analysis was performed for the radio-relay link Aval Tower-Vršac Hill, which is used for the distribution of television and radio programmes by the public company Broadcasting Technology and Connections in Serbia. The prediction was made using temperature, temperature index, relative humidity, and received signal strength data for the months of June, July, and August in 2022. The best results were obtained using the RandomForest model. Extreme variations in the strength of the received signal can be predicted by using the model mentioned above. More effective management of the broadcasting infrastructure can be done with the ability to predict sudden falls and fluctuations in received signal strength.

This research focusses on the potential application of artificial intelligence (AI) techniques in the analysis of behavioural addictions, specifically addressing problematic Internet use among adolescents. Using tabular data from a representative sample from Serbian high schools, the authors investigated the feasibility of employing eXplainable AI (XAI) techniques, placing special emphasis on feature selection and feature importance methods. The results indicate a successful application to tabular data, with global interpretations that effectively describe predictive models. These findings align with previous research, which confirms both relevance and accuracy. Interpretations of individual predictions reveal the impact of features, especially in cases of misclassified instances, underscoring the significance of XAI techniques in error analysis and resolution. Although AI’s influence on the medical domain is substantial, the current state of XAI techniques, although useful, is not yet advanced enough for the reliable interpretation of predictions. Nevertheless, XAI techniques play a crucial role in problem identification and the validation of AI models.

As an important enabling technology in the era of Industry 4.0, the intelligent connected vehicle (ICV) facilitates robust data interaction with the outside through sensors and communication technologies, ultimately making scientific decisions based on environmental perception information. However, due to constraints such as limited communication bandwidth and computing resources, the influx of data simultaneously impedes the sustainable optimisation of the vehicle decision making process at the same time. As a novel technology that effectively connects physical and virtual space, the special ability of the digital twin (DT) is to identify characteristics within a certain lifecycle, thereby garnering widespread attention across various industries. The purpose of this paper is to review the contribution of digital twins in the application field of intelligent vehicles and explore its potential for development. First, the key technologies of ICV provide a basis for the embedding of digital twins. Then, by analysing the development process and technical composition of digital twins, readers can better understand the concept of digital twins. Finally, the application of DTs in ICV is reviewed from the perspective of vehicles, traffic facilities, and occupants. Future challenges and opportunities in this direction are described at the same time.

The power industry has been dedicated to applying virtual reality (VR) technology to build training systems in virtual environments, enabling personnel to complete skill training in real simulated environments while ensuring their safety. Conventional action scoring systems struggle to provide accurate scores for fine movements. Accurate scoring of fine movements can help workers identify their shortcomings during power operations, thus improving learning efficiency. This is of great significance for training on virtual environment-based power operation. This paper proposes a power operation-orientated VR action evaluation method based on the Gaussian distance-improved dynamic time warping (DTW) algorithm and the temporal convolutional network (TCN) model. First, the adaptive adapter is used to extract one-dimensional features from the three-dimensional data of the data gloves. Then, based on the TCN model, action data with significant discrepancies are filtered out. Finally, the obtained data are input into the Gaussian distance-improved DTW algorithm, where the path size is calculated. Corresponding scoring criteria are established on the basis of the path size to evaluate the actions. The results demonstrate that the VR action evaluation method based on the Gaussian distance-improved DTW algorithm and the TCN model significantly improves the accuracy of evaluating fine movements compared to traditional evaluation algorithms.

This paper discusses the problem of optimal placement and sizing of passive harmonic filters to mitigate harmonics in unbalanced distribution systems. The problem is formulated as a nonlinear multiobjective optimisation problem and solved using the multiobjective genetic algorithm. The performance of the proposed algorithm is tested on unbalanced IEEE 13- and 37-bus three-phase systems. The optimal solutions are obtained based on the following objective functions: 1) minimisation of total harmonic distortion in voltage, 2) minimisation of costs of filters, 3) minimisation of voltage unbalances, and 4) a simultaneous minimisation of total harmonic distortion in voltage, costs of filters, and voltage unbalances. Finally, an analysis of the influence of uncertainties of load powers and changes in system frequency and filter parameters on filter efficiency was performed.