Elektronika ir Elektrotechnika

Editorial Board

Elektronika ir Elektrotechnika — Wed, 06 Mar 2024 00:00:00 +0200

Optimal Design and Techno-Economic Analysis of a Hybrid System to Supply a Remote Fishpond with Electricity and Heat

Tue, 20 Feb 2024 00:00:00 +0200

This paper deals with the design of a hybrid system for the generation of electricity and heat that will supply a remote fishpond in eastern Serbia. The proposed hybrid system consists of a micro-hydro power plant (MHPP), a photovoltaic (PV) generator, a combined heat and power (CHP) unit with one diesel generator, batteries, a converter, a thermal load controller (TLC), and a boiler. A comprehensive techno-economic analysis is performed in the HOMER Pro software, which evaluated and compared 12 possible configurations with different combinations of system components. The results show that the optimal system has the lowest total net present cost (NPC) and the lowest levelized cost of energy (COE) amounting to 284421.0 $ and 0.178 $/kWh, respectively. Compared to a diesel/batteries/converter/boiler hybrid system, the proposed system produces 65.4 % less greenhouse gas (GHG) emissions, while the shares of electricity, heat, and renewable energy generation are increased by 31.1 %, 5.0 %, and 51.2 %, respectively. It is shown that covering the demand for heat by regenerating the waste heat from the diesel generator and excess electricity from renewables contributes to reducing the total cost of the system and the GHG emissions. This finding finally emphasised the necessity of applying TLCs in off-grid hybrid systems.

Sparse Point Cloud Registration Network with Semantic Supervision in Wilderness Scenes

Zhichao Zhang, Feng Lu, Youchun Xu, Jinsheng Chen, Yulin Ma — Wed, 06 Mar 2024 00:00:00 +0200

The registration of laser point clouds in complex conditions in wilderness scenes is an important aspect in the research field of autonomous vehicle navigation. It serves as the foundation for solving problems such as environment reconstruction, map construction, navigation and positioning, and pose estimation during the motion process of autonomous vehicles using laser radar sensors. Due to the sparse structured features, uneven point cloud density, and high noise levels in wilderness scenes, achieving reliable and accurate point cloud registration is challenging. In this paper, we propose a semantic-supervised sparse point cloud registration network (S3PCRNet) aiming to achieve effective registration of laser point clouds in wilderness large-scale scenes. Firstly, a local feature aggregation module is designed to extract the local structural features of the point cloud. Then, based on rotation position encoding, a randomly grouped self-attention mechanism is proposed to obtain the global features of the point cloud through learning. A semantic information weight matrix is calculated to filter out negligible points. Subsequently, a semantic fusion feature module is utilised to find reliable correspondences between point clouds. Finally, the proposed method is trained and evaluated on both the RELLIS-3D dataset and a self-made Off-road-3D dataset.

Performance Analysis of PSO-Based SHEPWM Control of Clone Output Nine-Switch Inverter for Nonlinear Loads

Nirmala Muthusamy, Beena Stanislaus Arputharaj, Vijayanandh Raja — Tue, 20 Feb 2024 00:00:00 +0200

A comprehensive mathematical model of the inverter using nine switches is derived and its carrier high-frequency signal-based Pulse Width Modulation (PWM) is developed for the control of dual nonlinear loads. The Carrier-Based Pulse Width Modulation (CBPWM) provides excellent quality output to linear loads, and it provides high value of Total Harmonic Distortion (THD) for the nonlinear load, where the 5^th, 7^th, 11^th, 13^th, and 17^th harmonics are highly manifest. Particle Swarm Optimisation (PSO) constructed Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) scheme is proposed to eliminate a higher number of harmonic components and enhance the harmonic profile with reduced number of active semiconductor switches in Nine-Switch Inverter (NSI) control of nonlinear load. The PSO algorithm is proposed to adjust the triggering angles of the SHEPWM scheme and eliminate the targeted harmonics. The main concern associated with the proposed technique is the degree of freedom to lower the harmonics when operated over a comprehensive scale of Modulation Index (MI). To prove the usefulness of the proposed carrier-based PWM, PSO-based SHEPWM technique for NSI, MATLAB-SIMULINK is used to perform the simulations. The experimental prototype of the NSI topology is developed using an ATmega162 microcontroller. The experimental results and its Fast Fourier Transform (FFT) spectrum are over a broad scale of MI, revealing the expertise and efficacy of the proposed control scheme.

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Elektronika ir Elektrotechnika — Wed, 06 Mar 2024 00:00:00 +0200

Development of a Position Control System for Wheeled Humanoid Robot Movement Using the Swerve Drive Method Based on Fuzzy Logic Type-2

Bhakti Yudho Suprapto, Suci Dwijayanti, Djulil Amri — Tue, 20 Feb 2024 00:00:00 +0200

A humanoid robot is capable of mimicking human movements, which poses a challenge for researchers. This has led some to utilise wheels to facilitate its motion. However, achieving smooth and accurate movements at desired positions remains a challenge, necessitating the development of an optimal control system and movement method. In this study, solutions to address these challenges include the use of type-2 fuzzy logic controller (FLC) and the swerve drive method. During the steering rotation movement testing, type-1 FLC exhibits the fastest response time of 0.8 seconds, but oscillations occur, reaching up to 117 degrees to achieve the set point of 90 degrees. Additionally, type-1 FLC cannot reach the set point of -90 degrees. On the contrary, type-2 FLC aligns successfully with both set points of 90 and -90 degrees. In coordinate movement testing, type-1 FLC still shows an error between 1 cm and 2 cm compared to type-2 FLC, particularly with 3 and 5 members, which are equal to the given set point. The results of the tests indicate that type-2 FLC is reliable, showing a small steady-state error, stability, and no overshoot, despite its longer response time and processing duration compared to type-1 FLC.

Spider Monkey Metaheuristic Tuning of Model Predictive Control with Perched Landing Stabilities for Novel Auxetic Landing Foot in Drones

Magesh M, P.K. Jawahar, Saranya S.N., Raj Jawahar R — Tue, 20 Feb 2024 00:00:00 +0200

The study focuses on improving drone landing gear dynamics through an innovative auxetic foot design, leveraging Spider Monkey Optimization for Model Predictive Control adjustment, facilitated by an Arduino-MATLAB interface. The auxetic foot design incorporates materials with a negative Poisson ratio, which allows the foot to expand and enhance energy absorption during landings. This design improves stability and safety during the perched landing process. The SMO-MPC approach is used to optimise the control of the perched landing gear. SMO, inspired by spider monkey search behaviour, optimises auxetic foot control input sequences with the limits of rotational displacement (theta = 30 deg to -30 deg) on the prediction horizon to improve landing gear performance. The real-time implementation of SMO-MPC is achieved through an Arduino-MATLAB interface on quadcopter drone. A comparative analysis is conducted to evaluate the benefits of SMO-MPC compared to conventional MPC methods. The results show that the SMO-MPC approach with auxetic foot design surpasses conventional MPC methods in terms of landing performance with 14.6 % improvement in damping force control and control of aerodynamic stability with pitch of 34.16 %, yaw of 16.87 %, and roll of 31.74 %.

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Nan Zhong, Munish Kumar Gupta, Orest Kochan, Xiangping Cheng — Tue, 20 Feb 2024 00:00:00 +0200

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

New Face Recognition System Based on DCT Pyramid and Backpropagation Neural Network

Badreddine Alane, Younes Terchi, Saad Bouguezel — Tue, 20 Feb 2024 00:00:00 +0200

Face recognition has emerged as a prominent biometric identification technique with applications ranging from security to human-computer interaction. This paper proposes a new face recognition system by appropriately combining techniques for improved accuracy. Specifically, it incorporates a discrete cosine transform (DCT) pyramid for feature extraction, statistical measures for dimensionality reduction of the features, and a two-layer backpropagation neural network for classification. The DCT pyramid is used to effectively capture both low- and high-frequency information from face images to improve the ability of the system to recognise faces accurately. Meanwhile, the introduction of statistical measures for dimensionality reduction helps in decreasing the computational complexity and provides better discrimination, leading to more efficient processing. Moreover, the two-layer neural network introduced, which plays a vital role in efficiently handling complex patterns, further enhances the recognition capabilities of the system. As a result of these advancements, the system achieves an outstanding 99 % recognition rate on the Olivetti Research Laboratory (ORL) data set, 98.88 % on YALE, and 99.16 % on AR. This performance demonstrates the robustness and potential of the proposed system for real-world applications in face recognition.

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Sunwen Du, Ruiting Song, Qing Qu, Zhiying Zhao, Hailing Sun, Yanwei Chen — Tue, 20 Feb 2024 00:00:00 +0200

At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.