Single-output Systems Research Articles

Use of Fuzzy Logic in Determining the Academic Achievement of Secondary School Students in Mathematics Class

Many studies have been found in the field of education using the fuzzy logic approach. However, no study has been found examining the academic achievement of secondary school students in mathematics. The research data consists of the exam notes of the 1st term 1st and 2nd Mathematics course of the 2023-2024 academic year in Kütahya. The study consists of the exam scores of 876 students studying in 4 secondary schools in the center of Kütahya. To study a fuzzy logic-based two-input and single-output system was designed using Fuzzy Logic Toolbox in the Matlab program. Totally 9 fuzzy rules were created. The academic success was evaluated first with classical method and then with the designed fuzzy-based model. According to the results obtained, it has been observed that there is a high positive relationship between fuzzy logic method and classical logic method. It was academic success score obtained fuzzy logic method was less than classical logic method for grades 5 and 7. But it was obtained fuzzy logic method was more than classical logic method for grades 6 and 8. In conclusion was reached in favor of fuzzy logic due to fuzzy logic method gives more flexible results.

Big Data Analysis of the Speed Performance of a 176k DWT Bulk Carrier in Real Operating Conditions

Assessment of ship performance under in-service conditions is challenging due to the complex effects of many environmental disturbances. ISO 15016 and ISO 19030 standards are commonly used to evaluate ship operating performance. However, ISO 15016 requires numerous variables, a complex calculation formula, and considerable time and cost, and ISO 19030 only evaluates the reduction of ship speed caused by wind and neglects the effect of waves. To improve both standards and achieve a more accurate ship performance assessment, this study proposes a new performance prediction model, the multi-input single-output (MISO) system, which assumes that each ship has specific frequency characteristics according to type and size. Based on this new model, in-service navigation data collected from a 176k DWT bulk carrier, which amount to 5.7 million data points, are analyzed to assess the speed performance of the vessel subject to environmental disturbances. The proposed model was validated by comparing its results with ISO 19030 and specifically assessing the speed–power curves and speed reduction measured in operational data with the influence of environmental disturbances removed.

Hybrid Beamforming Structure Using Grouping with Reduced Number of Phase Shifters in Multi-User MISO

This paper proposes a novel hybrid beamforming (HBF) structure for gain-aware grouping transmit antennas and users in multiuser multiple-input single-output (MU-MISO) systems. In the conventional HBF structure, all transmit antennas form a beam to each user. In this case, the gain of each antenna varies depending on the location of the base station and each user, and the transmit power after the digital beamformer is allocated to the antenna with the smallest gain. Signals transmitted from antennas with small gains are susceptible to noise and interference. Therefore, this paper proposes an HBF structure in which only the antenna with the highest gain forms the beam for each user. In the proposed scheme, the transmitting antennas are grouped and the beam is formed only by the group of antennas with the highest gain for each user. Simulation results show that the proposed scheme can reduce the number of phase shifters used on the transmit side compared to the conventional HBF scheme while maintaining sum-rate performance when the number of transmit antennas and users are the same. It was also shown that there is a trade-off between the reduction in the number of phase shifters used to form the beam and the improvement in performance as the number of transmit antennas increases. Furthermore, it is shown that when antenna selection is used, although there is a trade-off between the number of phase shifters and performance improvement, the number of phase shifters can be reduced while maintaining performance even when the number of transmit antennas increases.

Intelligent-Reflecting-Surface-Assisted Single-Input Single-Output Secure Transmission: A Joint Multiplicative Perturbation and Constructive Reflection Perspective.

Due to the inherent broadcasting nature and openness of wireless transmission channels, wireless communication systems are vulnerable to the eavesdropping of malicious attackers and usually encounter undesirable situations of information leakage. The problem may be more serious when a passive eavesdropping device is directly connected to the transmitter of a single-input single-output (SISO) system. To deal with this urgent situation, a novel IRS-assisted physical-layer secure transmission scheme based on joint transmitter perturbation and IRS reflection (JPR) is proposed, such that the secrecy of wireless SISO systems can be comprehensively guaranteed regardless of whether the reflection-based jamming from the IRS to the eavesdropper is blocked or not. Moreover, to develop a trade-off between the achievable performance and implementation complexity, we propose both element-wise and group-wise reflected perturbation alignment (ERPA/GRPA)-based IRS reflection strategies, respectively. In order to evaluate the achievable performance, we analyze the ergodic secrecy rate (ESR) and secrecy outage probability (SOP) of the SISO secure systems with the ERPA/GRPA-based JPRs, respectively. Finally, by characterizing the simulated and numerical ESR and SOP performance results, our proposed scheme is compared with the benchmark scheme of random phase-based reflection, which strongly demonstrates the effectiveness of our proposed scheme.

Design of a MIMO implantable antenna with ultra-miniaturized volume and reduced SAR

Abstract This paper presents an ultra-miniaturized skin implantable Multiple Input Multiple Output (MIMO) antenna for biotelemetry in the Industrial, Scientific, and Medical (ISM) band (2.4–2.48 GHz). The two-element configuration exhibits a compact planar volume of 4.5 × 6.2 × 0.25 mm³, with an edge-to-edge distance of 0.8 mm and a 75.85 % size reduction. Fabricated on a 0.25 mm thick Roger 10.2LM high permittivity substrate, the antenna achieves a Fractional Bandwidth (FBW) of 9.27 % and a realized gain of −28.2 dBi, with over 24.3 dB isolation across the entire band. Experimental results in multilayer animal tissue aligned well with simulations. Specific Absorption Rate (SAR) analysis at 1 W input power showed values of 848 W/kg (1-g) and 88.46 W/kg (10-g), yielding allowable input powers of 3.77 mW and 45.2 mW, respectively, adhering to international safety guidelines. The 2 × 2 element antenna demonstrated efficient communication over 7 m with a data rate of 78 Mbps/s. Multiple Input Multiple Output channel analysis revealed a Channel Capacity of 10.8 bps/Hz at a 20 dB SNR, nearly double that of an ideal Single Input Single Output system, making it suitable for high data rate skin-implantable applications.

A comprehensive control paradigm for prescribed performance attainment in complex nonlinear systems

This paper presents a comprehensive control framework tailored for achieving Prescribed Performance Control (PPC) in the context of complex nonlinear systems, addressing multifaceted challenges prevalent in control design. The focus is on a control scenario characterized by the simultaneous presence of nonlinearity, external disturbances, time delay, and unknown control direction—issues that pose considerable obstacles for existing solutions. To surmount these challenges, our proposed approach integrates well-established techniques, including neural networks, Nussbaum-type gains, and adaptive control strategies within a unified control design framework. The specific technical challenge addressed in this work involves the effective management of these intricate complexities in Single-Input Single-Output (SISO) systems. Our contributions extend the theoretical foundations, presenting an ideal PPC control design and introducing two adaptive neural network-based control methods capable of accommodating both known and unknown control directions. Utilizing Lyapunov–Krasovskii functionals, we showcase a unique integration that surpasses a mere combination of individual techniques. This work advances the theoretical underpinnings of control engineering tailored for real-world scenarios. The proposed controller’s efficacy is validated through rigorous simulations and compared with recent results and benchmark PPC controllers, establishing its superiority in addressing the intricacies of complex control scenarios.

Prediction of tool wear and surface finish using ANFIS modelling during turning of Carbon Fiber Reinforced Plastic (CFRP) composites

Carbon fiber-reinforced plastics (CFRP) are widely used in various industries due to their high strength to weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties. The machining of CFRP composites has always been a challenge for the manufacturers. In this study, CNC turning operation with coated carbide tool is used to machine a specific CFRP and the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated. An adaptive-network-based fuzzy inference system (ANFIS) model with two multi-input–single-output (MISO) system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameters. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. This model is capable of predicting the cutting tool wear and surface finish during turning of CFRP composite. The predicted tool wear and surface finish data are compared to the experimental results. The predicted data agreed well with the actual experimental data with 98.96 % accuracy for tool wear and 99.61 % accuracy for surface finish.

A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels

A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels

Quantifying rain-driven NO3-N dynamics in headwater: value of applying SISO system identification to multiple variables monitored at the same high frequency

The nitrate–nitrogen (NO3-N) concentration is a key variable affecting the ecosystem services supported by headwater streams. The availability of such data monitored continuously at a high frequency (in parallel to hydrometric and other water quality data) potentially permits a greater insight into the dynamics of this key variable. This study demonstrates how single-input single-output (SISO) system identification tools can make better use of these high-frequency data to identify a reduced number of numerical characteristics that support new explanatory hypotheses of rain-driven NO3-N dynamics. A second-order watershed managed for commercial forestry in upland Wales (United Kingdom) provided the illustrative data. Fifteen-minute rainfall time series were used to simulate NO3-N concentration dynamics and the potentially associated dynamics in dissolved organic carbon (DOC) and runoff, monitored at the same high resolution for two 30-day periods with a differing temperature regime. The approach identified robust, high-efficiency models needing few parameters. Comparison of only three derived dynamic response characteristics (DRCs) of δ, TC, and SSG for the three variables for the two different periods led to new hypotheses of rain-driven NO3-N dynamics for further exploratory field investigation.

Output fluctuation and overshoot restraining model-free adaptive control for a class of discrete-time nonlinear single-input single-output systems

Output fluctuation and overshoot restraining model-free adaptive control for a class of discrete-time nonlinear single-input single-output systems

A novel class of non-Gaussian system performance assessment and controller parameter tuning methods

Traditional variance-based control performance assessment (CPA) and controller parameter tuning (CPT) methods tend to ignore non-Gaussian external disturbances. To address this limitation, this study proposes a novel class of CPA and CPT methods for non-Gaussian single-input single-output systems, denoted as data Gaussianization (inverse) transformation methods. The idea of quantile transformation is used to transform the non-Gaussian data with the goal of maximizing mutual information into virtual Gaussian data. In addition, optimal system data for the virtual loop are mapped back to the actual non-Gaussian system using quantile inverse transformation. Furthermore, a CARMA model-based recursive extended least square algorithm and a CARMA model-based least absolute deviation iterative algorithm are used to identify virtual Gaussian and non-Gaussian system process models, respectively, while implementing the CPT. Finally, a unified framework is proposed for the CPA and CPT of a non-Gaussian control system. The simulation results demonstrate that the proposed strategy can provide a consistent benchmark judgment criterion (threshold) for different non-Gaussian noises, and the tuned controller parameters have good performance.

INVSID 1.0: An Inverse System Identification Toolbox for MATLAB

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process from theoretical derivation to toolbox creation of developing the toolbox is demonstrated. Afterwards, numerical examples are illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.

Stability Analysis for Large-Scale Multi-Agent Molecular Communication Systems.

Molecular communication (MC) is recently featured as a novel communication tool to connect individual biological nanorobots. It is expected that a large number of nanorobots can form large multi-agent MC systems through MC to accomplish complex and large-scale tasks that cannot be achieved by a single nanorobot. However, most previous models for MC systems assume a unidirectional diffusion communication channel and cannot capture the feedback between each nanorobot, which is important for multi-agent MC systems. In this paper, we introduce a system theoretic model for large-scale multi-agent MC systems using transfer functions, and then propose a method to analyze the stability for multi-agent MC systems. The proposed method decomposes the multi-agent MC system into multiple single-input and single-output (SISO) systems, which facilitates the application of simple analysis technique for SISO systems to the large-scale multi-agent MC system. Finally, we demonstrate the proposed method by analyzing the stability of a specific large-scale multi-agent MC system and clarify a parameter region to synchronize the states of nanorobots, which is important to make cooperative behaviors at a population level.

Enhancing aero-engine thrust response: Accounting for combustion delays

In scenarios like aircraft landing, Rapid Thrust Modulation (RTM) is required to achieve high-precision attitude and trajectory manipulations. However, the traditional manipulated variable in aero-engines, specifically the fuel flow, experiences sluggishness in adjusting thrust (Fn) due to combustion delays. In contrast, geometric variable areas, like the nozzle throat area (A8), offer rapid responsiveness but have limited thrust-adjusting capacity. Hence, the proposal of an innovative parallel Valve Position Control (VPC) scheme, combining redundant control inputs, emerges as a groundbreaking solution to enable RTM. In the pursuit of achieving RTM, the non-minimum phase thrust responses are first addressed, which has not been recognized previously to the best of the author's knowledge. This includes the implementation of a new valve position control (VPC) structure, incorporating a dual input Single Output (DISO) system, which is vital for optimizing response time and minimizing the delayed effects in combustion processes. After proper selection of operating conditions, the synthesis process of VPC is outlined by three key requirements, including: 1) realizing the 10 rad/s thrust bandwidth despite the combustion delay; 2) ensuring continuous modulation capability by mid-ranging the A8; 3) decoupling the A8 and thrust during flights. The VPC controller is implemented and compared with the classical Edmond algorithm in a turbofan engine. Results emphasize VPC's robustness in handling combustion delays, meeting specified requirements, and showcasing its potential as an effective RTM solution. Practically, the VPC system's architecture allows for critical rapid thrust changes within a 10 rad/s bandwidth, essential for operations like VSTOL aircraft maneuvers.

Demonstration of D-band 1×2 SIMO millimeter-wave wireless delivery over 1.2 km employing MRC technology

We experimentally demonstrate the transmission of 20-Gbaud QPSK millimeter-wave signals within a 1.2-km wireless range at 128-GHz using constant-modulus-algorithm (CMA) and the maximum ratio combining (MRC) technology with over 2.2-dB gain in a photonics-assisted millimeter wave (MMW) 1 × 2 Single Input Multi Output (SIMO) communication system. MRC is applied for SIMO signal merging which leads to Signal to Noise Ratio (SNR) enhancement and significant Bit Error Rate (BER) reduction. The comparison which is performed for Single Input Single Output (SISO) and SIMO system, demonstrates the BER improvement. To the best of our knowledge, this is the first experimental demonstration of high gain in a D-band MMW 1 × 2 SIMO system by using these technologies.

Comparative Analysis of SMC and INC MPPT Algorithm for Dual Input Single Output System

Many MPPT algorithms present to date have maximum output power from solar PV. This paper compares the SMC and INC algorithm results for the double input single output (DISO) system simulated on MATLAB/Simulink and verified with hardware. As the system is DISO, power is fed to the load, one from solar PV and another from the battery, with the help of a boost converter and bidirectional chopper. A control technique is designed to have constant voltage across the load and extract maximum power from solar PV under variable irradiance. In this project, the PI controller is used on the battery side, which helps to decide whether the battery works in undercharging or discharging mode. Different modes of operation are also shown in the paper, and the MPPT algorithm is used to extract maximum power from solar PV.

A Novel 4 × 1 MISO-VLC System with FBMC-OQAM Downlink Signals

A novel visible-light communication (VLC) system with 4 × 1 multi-input–single-output (MISO) channels is designed. In the system, the filter bank multicarrier (FBMC) and offset quadrature amplitude modulation (OQAM) techniques are used to generate downlink signals. The principles and implementation methods are proposed and analyzed, and the light intensity and received light power distribution of four LED emitters are discussed. The results demonstrate that it not only satisfies the requirements of indoor information access but also provides daily lighting. The used FBMC-OQAM signals exhibit better reception performance than orthogonal frequency division multiplexing (OFDM) signals. The system used has a lower bit error rate (BER) and larger access bandwidth compared to a 1 × 1 single-input–single-output (SISO) system. It has the potential for application advantages in future indoor VLC system applications.

A Wireless Power Transfer Mattress Based System for Perpetually Operating Physiological Monitoring Wearables.

This article presents a novel wireless power mattress-based system architecture tailored to guarantee continuous energy for in-home environment healthcare wearables intended to be used in the context of patients who would benefit from long-term monitoring of specific physiological biomarkers. The design demonstrates that it is possible to transfer over 20 mW at a primary-secondary distance of 20.7 cm, whilst still keeping within all FCC/ICNIRP safety regulations, using the proposed simplified beamforming-controlled power transfer multi-input single-output system. Compared with other beamforming-controlled based works, the proposed design used non-coupling coil arrays, significantly reducing the algorithmic complexity. An on-chip wireless power charger system was also designed to provide high-efficiency power storage (89.3% power conversion efficiency and 83.9% power charge efficiency), guaranteeing wearables can continuously maintain their functionality. In contrast with conventional NiMh chargers, this work proposes a trimming function that makes it compatible with batteries of varying capacities. It also employs a four-stage charge loop to ensure safety and sustainability during the charging process. Overall, this work shows that by relying on wireless power transfer, it is, in principle, possible to create a safe wearable that could enable continuous monitoring of certain healthcare biomarkers with little or zero maintenance burden for the patients or carers.

Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum

This study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives, in a data-dependent way, a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage, which constitutes the novel part of the presented study, builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning, ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as ℓ1,ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\ell}}_{1,{\\varvec{\\varepsilon}}}$$\\end{document}, namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error.

Robust Beamforming for Downlink Multi-Cell Systems: A Bilevel Optimization Perspective

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming technique that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties.