In this paper, a combined RBF neural network sliding mode control and PD adaptive tracking controller is proposed for controlling the directional heading course of a ship. Due to the high nonlinearity and uncertainty of the ship dynamics as well as the effect of wave disturbances a performance evaluation and ship controller design is stay difficult task. The Neural network used for adaptively learn the uncertain dynamics bounds of the ship and their output used as part of the control law moreover the PD term is used to reduce the effect of the approximation error inherited in the RBF networks. The stability of the system with the combined control law guaranteed through Lyapunov analysis. Numeric simulation results confirm the proposed controller provide good system stability and convergence.
This paper addressed the design of online uninterruptible power supply (UPS) system with a low frequency transformer for isolation, based on given specifications which include bypass switch and battery and taken into account the concentrated on open loop operation. Depending on the application, the online UPS system is composed by two stage conversions of AC/DC and DC/AC, the enclosure of these freeloading effects of all components and devices is very important to design the UPS system for acceptable performance. The initial stage of the design is based on the theoretical calculations and few assumptions have been made throughout the design. Simulation work has been carried out by MATLAB/Simulink program to validate the operation of the online UPS system with low frequency transformer isolation. The analysis of the results are presented and the justifications with regards to performance evaluation parameters which some are not satisfied the design specifications are discussed in details.
In this paper, we evaluate the performance of WiMAX downlink system in vicinity of UWB system. The study is achieved via simulating a scenario of an office building which utilizes from both WiMAX and UWB appliances. From the simulation results, we found that WiMAX system is largely affected by the UWB interference. However, in order to overcome the interference problem and achieve reasonable BER (Bit Error Rate) of 10 -4 , we found that it is very necessary to raise the WiMAX transmitted power in relative to that of UWB interferer. So, the minimum requirements for WiMAX system to overcome UWB interference are stated here in this work.
Epilepsy, a neurological disorder characterized by recurring seizures, necessitates early and precise detection for effective management. Deep learning techniques have emerged as powerful tools for analyzing complex medical data, specifically electroencephalogram (EEG) signals, advancing epileptic detection. This review comprehensively presents cutting-edge methodologies in deep learning-based epileptic detection systems. Beginning with an overview of epilepsy’s fundamental concepts and their implications for individuals and healthcare are present. This review then delves into deep learning principles and their application in processing EEG signals. Diverse research papers to know the architectures—convolutional neural networks, recurrent neural networks, and hybrid models—are investigated, emphasizing their strengths and limitations in detecting epilepsy. Preprocessing techniques for improving EEG data quality and reliability, such as noise reduction, artifact removal, and feature extraction, are discussed. Present performance evaluation metrics in epileptic detection, such as accuracy, sensitivity, specificity, and area under the curve, are provided. This review anticipates future directions by highlighting challenges such as dataset size and diversity, model interpretability, and integration with clinical decision support systems. Finally, this review demonstrates how deep learning can improve the precision, efficiency, and accessibility of early epileptic diagnosis. This advancement allows for more timely interventions and personalized treatment plans, potentially revolutionizing epilepsy management.
Optical OFDM based on discrete Hartley transform (DHT-O-OFDM) has been proposed for large-size data mapping intensity modulation direct detection (IM/DD) scheme as an alter- native to the conventional optical OFDM. This paper presents a performance analysis and evaluation of IM/DD optical DC-biased DHT-O-OFDM over diffused multipath optical wireless channels. Zero-padding guard interval along with minimum mean-square error (MMSE) equalizer are used in electrical domain after the direct detection to remove the intersymbol interference (ISI) and eliminate the deleterious effects of the multipath channels. Simulation results show that the ZP-MMSE can effectively reduce the effects of multipath channels. The results also show that the effects of optical wireless multipath channel become more serious as the data signaling order increases.
Clustering is a fundamental data analysis task that presents challenges. Choosing proper initialization centroid techniques is critical to the success of clustering algorithms, such as k-means. The current work investigates six established methods (random, Forgy, k-means++, PCA, hierarchical clustering, and naive sharding) and three innovative swarm intelligence-based approaches—Spider Monkey Optimization (SMO), Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)—for k-means clustering (SMOKM, WOAKM, and GWOKM). The results on ten well-known datasets strongly favor swarm intelligence-based techniques, with SMOKM consistently outperforming WOAKM and GWOKM. This finding provides critical insights into selecting and evaluating centroid techniques in k-means clustering. The current work is valuable because it provides guidance for those seeking optimal solutions for clustering diverse datasets. Swarm intelligence, especially SMOKM, effectively generates distinct and well-separated clusters, which is valuable in resource-constrained settings. The research also sheds light on the performance of traditional methods such as hierarchical clustering, PCA, and k-means++, which, while promising for specific datasets, consistently underperform swarm intelligence-based alternatives. In conclusion, the current work contributes essential insights into selecting and evaluating initialization centroid techniques for k-means clustering. It highlights the superiority of swarm intelligence, particularly SMOKM, and provides actionable guidance for addressing various clustering challenges.
Non-ideal channel conditions degrade the performance of wireless networks due to the occurrence of frame errors. Most of the well-known works compute the saturation throughput and packet delay for the IEEE 802.11 Distributed Coordination Function (DCF) protocol with the assumption that transmission is carried out via an ideal channel (i.e., no channel bit errors or hidden stations), and/or the errors exist only in data packets. Besides, there are no considerations for transmission errors in the control frames (i.e., Request to Send (RTS), Clear to Send (CTS), and Acknowledgement (ACK)). Considering the transmission errors in the control frames adds complexity to the existing analysis for the wireless networks. In this paper, an analytical model to evaluate the Medium Access Control (MAC) layer saturation throughput and packet delay of the IEEE 802.11g and IEEE 802.11n protocols in the presence of both collisions and transmission errors in a non-ideal wireless channel is provided. The derived analytical expressions reveal that the saturation throughput and packet delay are affected by the network size (n), packet size, minimum backoff window size (W min ), maximum backoff stage (m), and bit error rate (BER). These results are important for protocol optimization and network planning in wireless networks .
The primary goal of this study is to investigate and evaluate the performance of wireless Ad-Hoc routing protocols using the OPNET simulation tool, as well as to recommend the most effective routing strategies for the wireless mesh environment. Investigations have been testified to analyze the performance of the reactive and proactive Ad-Hoc routing protocols in different scenarios. Application and wireless metrics were configured that were used to test and evaluate the performance of routing protocols. The application metric includes web browsing metrics such as HTTP page response time, voice and video metrics such as end-to-end delay, and delay variation. The wireless network metrics include wireless media access delay, data dropped, wireless load, wireless retransmission attempts, and Packet Delivery Ratio. The simulations results show that the AODV overcome DSR and OLSR in terms of PDR (76%), wireless load (22.692 Mbps), voice delay variation (102.685 ms), HTTP page response time (15.317 sec), voice and video packet end-to-end delay (206.527 and 25.294 ms), wireless media access delay (90.150 ms), data dropped (10.003 Mbps), wireless load (22.692 Mbps), and wireless retransmission attempts (0.392 packets).
Recently, numerous researches have emphasized the importance of professional inspection and repair in case of suspected faults in Photovoltaic (PV) systems. By leveraging electrical and environmental features, many machine learning models can provide valuable insights into the operational status of PV systems. In this study, different machine learning models for PV fault detection using a simulated 0.25MW PV power system were developed and evaluated. The training and testing datasets encompassed normal operation and various fault scenarios, including string-to-string, on-string, and string-to-ground faults. Multiple electrical and environmental variables were measured and exploited as features, such as current, voltage, power, temperature, and irradiance. Four algorithms (Tree, LDA, SVM, and ANN) were tested using 5-fold cross-validation to identify errors in the PV system. The performance evaluation of the models revealed promising results, with all algorithms demonstrating high accuracy. The Tree and LDA algorithms exhibited the best performance, achieving accuracies of 99.544% on the training data and 98.058% on the testing data. LDA achieved perfect accuracy (100%) on the testing data, while SVM and ANN achieved 95.145% and 89.320% accuracy, respectively. These findings underscore the potential of machine learning algorithms in accurately detecting and classifying various types of PV faults. .
This paper presents an insufficient cyclic prefix (CP) Orthogonal Frequency Division Multiplexing (OFDM) system with equalizer whose coefficients are calculated using Least Mean Square (LMS) algorithm. The OFDM signal is passed through a channel with four multipath signals which cause the OFDM signal to be under high inter-symbol interference (ISI) and inter-carrier interference (ICI).8-QAM and 16-QAM digital modulation techniques are used to evaluate the performance of the proposed system. The simulation results have accentuated the high performance of the LMS equalizer via comparing its Bit Error Rate (BER) and constellation diagram with those of the Minimum Mean Square Error and Zero Forcing equalizers. Moreover, the results also reveal that the LMS equalizer provides BER performance close to that of the OFDM system with a hypothetical sufficient CP.