Multi-input Single-output Research Articles

GenCo: A Generative Learning Model for Heterogeneous Text Classification Based on Collaborative Partial Classifications

The use of generative learning models in natural language processing (NLP) has significantly contributed to the advancement of natural language applications, such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources, such as web-pages, blogs, emails, social media, and articles, one of the most common tasks in NLP is the classification of a text corpus. This is important in many institutions for planning, decision-making, and creating archives of their projects. Many algorithms exist to automate text classification tasks but the most intriguing of them is that which also learns these tasks automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables a reduction in the curse of dimensionality in text classification. Experiments with the model were carried out on the Twitter US Airline dataset, the Conference Paper dataset, and the SMS Spam dataset, outperforming baseline models with 98.40%, 89.90%, and 99.26% accuracy, respectively.

Multi‐input multi‐output converter for simultaneous buck and boost voltage conversion

SummaryInternet of Things (IoT) devices, portable electronics, and electric vehicle (EV) accessories have increased the need for DC power distribution with various outputs. Multiport converters (MPCs) handle numerous inputs and loads with fewer components, low power losses, and less cost. Most multi‐input converters are time‐shared and restrict the duty cycle's working range, limiting energy sources and output voltage. Single‐input multi‐output (SIMO) converters have inductor current and duty cycle limits. In this study, a dual‐input dual‐output (DIDO) converter is proposed to overcome the limitations mentioned above. It can operate multiple loads without restrictions on charging inductor currents and cross‐regulation problems. Any variations will not influence the proposed converter's output voltage VO1 (VO2) by the load current iO2 (iO1). To verify the feasibility of the proposed configuration, a 250 W laboratory prototype has been developed, and experimental results are validated.

Algebraic Formulation and Application of Multi-Input Single-Output Hierarchical Fuzzy Systems With Correction Factors

Hierarchical fuzzy systems (HFSs) are successfully applied to control systems and computer networks. Particularly, correction factor is introduced to regulate fuzzy rules and alleviate the problems of memory usage and real-time performance of systems. This paper considers the multi-input single-output HFSs with correction factors, and proposes an algebraic framework by using the semi-tensor product of matrices. Firstly, a kind of fuzzy correction factor matrix (FCFM) is constructed to realize the fuzzification of correction factor, based on which, an algebraic form is developed for two-input single-output fuzzy systems. Secondly, the structure diagram of HFSs is decomposed into several fuzzy logic units, which have the form of two-input singleoutput fuzzy systems with FCFM. Based on this operation, the algebraic formulation of serial, parallel and hybrid HFSs with correction factors is proposed. Finally, the effectiveness of HFSs with FCFM is verified by studying the on-ramp metering of freeway.

UKF-Based Channel Tracking Method for IRS-Aided mmWave MISO Systems

In this paper, we propose an unscented Kalman filtering (UKF)-based method to track the channel parameters of the intelligent reflecting surface (IRS) aided millimeter wave (mmWave) multi-input single-output (MISO) systems. To minimize the mean squared error (MSE) of the tracking parameters, the beamforming vector of the base station (BS) and the reflecting vector of the IRS are designed iteratively, considering the transmit power constraint of the BS and the unit module constraint of the IRS. The resulting sub-problems can be transformed into quadratic constraint quadratic problems (QCQPs), which are solved by the semidefinite relaxation (SDR) method and Gaussian randomization. Simulation results demonstrate that the proposed method outperforms existing benchmark methods.

Optimal FOPI-FOPD controller design for rotary inverted pendulum system using grey wolves’ optimization technique

The rotary inverted pendulum (RIP) has been used in various control application areas. This system can be represented as two degree of freedom (2-DOF), consisting of a rotating arm and rotating pendulum rod. RIP is an excellent example of designing a single-input multi-output (SIMO) system. Due to unstable RIP system dynamics, and its nonlinear model, multiple control techniques have been used to control this system. This paper uses integer and fractional order proportional integral-proportional derivative (PI-PD) controllers to stabilize the pendulum in the vertical direction. Constrained optimization approaches, such as the grey wolf optimization (GWO) methodology, are utilized to estimate the parametric values of the controllers. The simulation results showed that the fractional order PI-PD controller outperforms the integer order PI-PD controller with and without disturbance signal existence. A multiple results comparison has illustrated the superiority of fractional order controller over a previous work.

Fundamental Limits of Cache-Aided MIMO Wireless Networks

This paper studies the Multi-Input-Multi-Output (MIMO) interference networks with arbitrary number of transmitters and receivers, where both the transmitters and receivers are equipped with caches. The main goal is to design content placement and delivery schemes that minimize the worst case normalized delivery time (NDT). First, we propose a delivery scheme for the cache-aided Single-Input-Multiple-Output (SIMO) interference networks. Then, we obtain the achievable NDT of the cache-aided MIMO interference networks using the decomposition property of splitting each multi-antenna transmitter into multiple single antenna transmitters. Furthermore, we derive an information-theoretic bound on the optimal NDT of the cache-aided MIMO interference network. Analytical results show that the proposed scheme is within a multiplicative gap of 2 from the derived lower bound independent of all system parameters for any uncoded cache placement scheme. We also derive a novel delivery scheme for the cache-aided Multi-Input-Single-Output (MISO) interference network outperforming our proposed scheme for the cache-aided MIMO interference network. The numerical results show the superiority of our proposed scheme over the state-of-the-art schemes in the literature. Our results show that the coded caching gain has a more significant contribution in reducing the transmission latency than the spatial multiplexing gain. Our results indicate that the receive-antennas become more effective in reducing the NDT than the transmit-antennas in the presence of caches at the receiver-side. In addition, we show that increasing the number of transmit-antennas has a higher gain in reducing the NDT than adding more transmitters in the cache-aided MISO interference network.

Space time adaptive processing for airborne MIMO radar based on space time sampling matrix

Different from the traditional airborne single-input multi-output (SIMO) radar, each array element of airborne multiple-input multiple-output (MIMO) radar transmits mutually orthogonal waveforms, and multiple array elements receive at the same time. A set of matched filters are used to separate the transmitted waveforms in the echo signal. When the elements of the transmitting antenna are close to each other, the airborne MIMO radar can gain the benefit of waveform diversity and produce a large spatial virtual receiving array. It has certain advantages in signal-to-noise ratio, spatial degree of freedom (DoF) and spatial resolution. In this paper, the clutter DoF estimation formula of airborne MIMO radar is given and can accurately estimate the clutter DoF values under different parameters. On this basis, this paper proposes a space-time adaptive processing (STAP) method based on space-time sampling matrix. This method utilizes the low rank characteristic of clutter covariance matrix, constructs the clutter covariance matrix based on space-time sampling matrix, and estimates the clutter power through subaperture smoothing. In the non-homogeneous clutter background, the method only needs a single sample to effectively suppress clutter. In the homogeneous clutter background, as the space-time equivalent array is a dense non-uniform array, the robustness of the method is further improved by adding a small amount of training sample information.

Audio SIMO system based on visible light communication using cavity LEDs

The necessity for access to a high data rate has been the motivation behind expanding new technologies. The increasing of wireless devices has caused the radio wave spectrum to become congested. Additionally, radio waves cannot be used in EMI-vulnerable places. Hence, the interest in visible light communication (VLC) offers real alternatives to radio-based communications. This paper focuses on the use of visible light as a data communication medium and introduces a designed audio SIMO (Single-Input-Multi-Output) data transmission system from point to multipoint to demonstrate the functionality of the VLC system in audio transmission through modulated LEDs light. The system is made up of the transmitting unit and multi-receiving units. The transmitting unit consists of three stages: audio in, preamplifier, and white LEDs array that transmits the data using the ON-OFF keying (OOK) modulation technique to all receivers. On the other hand, the receiving unit consists of three receivers that have different photodetectors aspects with fixed distances separation between them. The line of sight (LOS) communication between transmitter and receivers for the purpose to transfer audio data has been employed. Based on the LOS link, simulation and experimental analysis have been done in multiple semi-angles for studying the output performance of receivers and the characteristics of the white LEDs such as luminous intensity and received power at 20,45 and 70 semi-angle at half-power through MATLAB® software.

Constrained temperature and relative humidity predictive control: Agricultural greenhouse case of study

The importance of Model Predictive Control (MPC) has significant applications in the agricultural industry, more specifically for greenhouse’s control tasks. However, the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the system. Subspace methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed data. In this paper, we introduce an application of Constrained Model Predictive Control (CMPC) for a greenhouse temperature and relative humidity. For this purpose, two Multi Input Single Output (MISO) systems, using Numerical Subspace State Space System Identification (N4SID) algorithm, are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation actions. In this sense, linear state space models were adopted in order to evaluate the robustness of the control strategy. Once the system is identified, the MPC technique is applied for the temperature and the humidity regulation. Simulation results show that the regulation of the temperature and the relative humidity under constraints was guaranteed, both parameters respect the ranges 15 °C ≤ Tint ≤ 30 °C and 50 % ≤ Hint ≤ 70 % respectively. On the other hand, the control signals uf and uh applied to the fan and the heater, respect the hard constraints notion, the control signals for the fan and the heater did not exceed 0 ≤ uf ≤ 4.3 Volts and 0 ≤ uh ≤ 5 Volts, respectively, which proves the effectiveness of the MPC and the tracking tasks. Moreover, we show that with the proposed technique, using a new optimization toolbox, the computational complexity has been significantly reduced. The greenhouse in question is devoted to Schefflera Arboricola cultivation.

Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling

In the face of increasing water scarcity and uncertainties of climate change, improving crop water use efficiency and productivity, while minimizing negative environmental impacts, is becoming crucial to meet the surging global food demand. Smart irrigation has a potential of improving water use efficiency in precision agriculture especially when efficient irrigation control strategies are adopted. Conventionally, irrigation systems rely on heuristic methods to schedule irrigation which either leads to over-irrigation or under-irrigation. This influences the crop physiological characteristics as well as the water use efficiency. To tackle this menace, model-based irrigation management has been overemphasized. A closed-loop irrigation control strategy relies on a mathematical model of the system for irrigation scheduling decisions. In this study, a data-driven approach was used to learn soil moisture dynamics from a drip irrigated tomato in an open field agricultural system. A total number of 9674 data samples were collected using an ATMOS41 weather station, TERROS 12 soil moisture sensor and a YFS-201 flow sensor for crop evapotranspiration and precipitation, soil moisture and irrigation volumes respectively. Data driven modelling was then performed using the system identification toolbox in a MATLAB environment. The model formulation was a multi-input single-output (MISO) system with reference evapotranspiration, irrigation and rainfall as inputs and soil moisture as the output. Different model structures including transfer functions, state space models, polynomial models and ARX models were evaluated. Model performance was evaluated using the mean square error (MSE), final prediction error (FPE) and estimated fit of the model approaches. Simulation results indicate that the soil moisture dynamics model provides a satisfactory approximation of the process dynamics with a state space model giving an estimated fit of 97.04 %, MSE and FPE of 1.74×10−7 and 1.75×10−7 respectively. This model will be used to design a model predictive controller for smart irrigation scheduling in open field environmental conditions.

A Method of Applying 81-Rule Fuzzy Control For Three-Linked Acrobot

Acrobot, which is also called gymnastic robot, is a single input-multi output (SIMO) system which imitates motion of a barbell athlete. However, two links of this model are just equivalent to body and hand of athlete. To imitate better motion of athlete, three-linked acrobot- a complicated multi input-multi output (MIMO) under-actuated system – is developed. Parts of barbell athlete including leg, body and hand, are modelled well by this new system. In this paper, we present a method to apply an 81-rule fuzzy algorithm, which is popularly used for invert pendulum systems, to balance this model at upward equilibrium point. Instead of using a complicated 729-rule fuzzy controller which is set by knowledge of experts, we use more simple fuzzy structure. Through genetic algorithm (GA), the pre-processing and post-processing coefficients of fuzzy controller are chosen. With these suitable coefficients, our fuzzy controller is proved to work well through Matlab/Simulink simulation.

Joint Data Transmission and Energy Harvesting for MISO Downlink Transmission Coordination in Wireless IoT Networks.

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greedy, and random algorithms, respectively, in comparison in the simulated environment.

Design of multi‐input single output DC–DC converter with preserved output voltage under source‐fault

AbstractMulti‐input converters play an important role in integrating the independent energy sources utilized in the grid‐connected system and electric vehicle (EV) applications. In this scenario, several types of multi‐input converters are presented in the literature. Most of the MICs are operated using a time‐sharing scheme. This leads to a restricted duty cycle which limits the energy sources utilization and output voltage. To overcome the aforementioned limitations, a multi‐input single‐output (MISO) boost converter is proposed. It can improve the utilization of energy sources and output voltage with a reduction in the part count. To verify the feasibility of the proposed scheme, a 200 W prototype circuit is developed; simulation and experimental results are validated.

Joint Secure Transmit Beamforming Designs for Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC), which allows individual radar and communication systems to share the same spectrum bands, is an emerging and promising technique for alleviating spectrum congestion problems. In this paper, we investigate how to exploit the inherent interference from strong radar signals to ensure the physical layer security (PLS) for the considered multi-user multi-input single-output (MU-MISO) communication and colocated multi-input multi-output (MIMO) radar coexistence system. In particular, with known eavesdroppers' channel state information (CSI), we propose to jointly design the transmit beamformers of communication and radar systems to minimize the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) on multiple legitimate users, while guaranteeing the communication quality-of-service (QoS) of legitimate transmissions, the requirement of radar detection performance, and the transmit power constraints of both radar and communication systems. When eavesdroppers' CSI is unavailable, we develop a joint artificial noise (AN)-aided transmit beamforming design scheme, which utilizes residual available power to generate AN for disrupting malicious receptions as well as satisfying the requirements of both legitimate transmissions and radar target detection. Extensive simulations verify the advantages of the proposed joint beamforming designs for ISAC systems on secure transmissions and the effectiveness of the developed algorithms.

Zone-wise surrogate modelling (ZSM) of univariate systems

Many complex systems display distinctly different behaviors across regions, zones, or sub-domains. A single surrogate may not suffice in modelling such systems. A better approach would be to identify the various zones and model them individually. In this work, we propose a zone-wise surrogate modelling (ZSM) algorithm to identify various zones in a system's input domain based on a user-specified acceptable goodness of fit and recommend the best surrogate for each identified zone from a library of potential surrogates. We have assessed ZSM on ten case studies involving complex 1-D functions and compared its modelling performance against some non-linear and piecewise models. We also show how ZSM can help in global optimization using five complex multimodal functions and found that a ZSM-based approach successfully identifies the true global optima of these functions. In future, we aim to extend ZSM for the modelling and optimization of complex multi-input single-output (MISO) systems.

Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data

Long short-term memory neural networks have been proposed as a means of creating accurate models from large time series data originating from various fields. These models can further be utilized for prediction, control, or anomaly-detection algorithms. However, finding the optimal hyperparameters to maximize different performance criteria remains a challenge for both novice and experienced users. Hyperparameter optimization algorithms can often be a resource-intensive and time-consuming task, particularly when the impact of the hyperparameters on the performance of the neural network is not comprehended or known. Teacher forcing denotes a procedure that involves feeding the ground truth output from the previous time-step as input to the current time-step during training, while during testing feeding back the predicted values. This paper presents a comprehensive examination of the impact of hyperparameters on long short-term neural networks, with and without teacher forcing, on prediction performance. The study includes testing long short-term memory neural networks, with two variations of teacher forcing, in two prediction modes, using two configurations (i.e., multi-input single-output and multi-input multi-output) on a well-known chemical process simulation dataset. Furthermore, this paper demonstrates the applicability of a long short-term memory neural network with a modified teacher forcing approach in a process state monitoring system. Over 100,000 experiments were conducted with varying hyperparameters and in multiple neural network operation modes, revealing the direct impact of each tested hyperparameter on the training and testing procedures.

MISO hierarchical inference engine satisfying the law of importation with aggregation functions

Fuzzy inference engine, as one of the most important components of fuzzy systems, can obtain some meaningful outputs from fuzzy sets on input space and fuzzy rule base using fuzzy logic inference methods. In multi-input–single-output (MISO) fuzzy systems, in order to enhance the computational efficiency of fuzzy inference engine, this paper aims mainly to investigate three MISO fuzzy hierarchical inference engines employed fuzzy implications satisfying the law of importation with aggregation functions (LIA). We firstly find some aggregation functions for well-known fuzzy implications such that they satisfy LIA. The fuzzy implication satisfying LIA with respect to a given aggregation function is then characterized. Finally, three fuzzy hierarchical inference engines in MISO fuzzy systems are constructed according to aforementioned theoretical developments. Three examples are also provided to illustrate our theoretical arguments.

Four Speed Auto Transmission DC-DC Converter Control for E-Vehicle and Regenerative Braking Based on Simulation and Model Investigation

A novel single input multioutput (SIMO) output DC-DC converter with circular capacitors is intended to function with a single independent source to create numerous transmissions to regulate the speed at the same switching intervals. A novel single input multi output (SIMO) output DC-DC converter employing circular capacitors is designed to work with a single independent source to create numerous transmissions. The proposed converter generates 7 outputs in total, with 4 outputs generated simultaneously for forward operation (speed transmission) using the preferred source (fuel cell/battery) and 3 other different outputs used for regenerative transmissions, with the battery being the preferred storage device. This converter can control the speed of an electric vehicle (EV) by using the SIMO is output voltage to regulate the vehicle is speed, allowing for higher accelerations and decelerations with minimal energy loss and degradation of the main battery pack. It can also be used to power electronic components such as LED lights, audio systems, and mobile charging etc. The energy recovered during regenerative braking is used to charge the battery via a buck/boost operation for varied speed transmission. In addition, simulation, modelling, and analysis are used to validate the proposed system. It is intended for a fixed voltage of 12 V and output voltages ranging from 12 to 48 V. The PSIM simulation tool is used to validate the system. The validation demonstrates the suggested converter is good evidence in both braking and regenerative braking operations.

Performance prediction of a clean coal power plant via machine learning and deep learning techniques

Computer simulation of energy resources has led to significant achievements in the interdisciplinary fields of energy and environment. Apart from renewable resources, fossil-fuel power generation plants can be made cleaner to satisfy the future climate targets while keeping secure and stable grid. Clean coal power plants are still among the dominant options for power generation, which are committed through energy-efficient operation, carbon capture and storage, or combination of both strategies. On the other hand, machine learning and deep learning techniques have a leading integrity in the field of simulation. This paper presents accurate models of a cleaner coal-fired supercritical (SC) unit using two types of artificial neural network, which are Elman neural network (ENN) and generalized regression neural network (GRNN). The models newly embed higher coverage range and more accurate results than previously published models. Each subsystem of the models has been structured as a multi-input single-output (MISO) component to predict the behavior of significant variables in the plant, mainly the supercritical pressure in MPa, the steam temperature in °C and the production in MW. Those variables have been intentionally selected as they are clear indicators for the energy-efficient and cleaner production. Simulation results of four sets of data have indicated satisfactory performance of both models with a bit higher superiority of the GRNN that has given negligible or zero Mean Squared Error (MSE) for all outputs, whereas the minimum MSE of the deep ENN is 3.131 × 10−3.

The data-filtering based bias compensation recursive least squares identification for multi-input single-output systems with colored noises

For the multi-input single-output (MISO) system corrupted by colored noise, we transform the original system model into a new MISO output error model with white noise through data filtering technology. Based on the newly obtained model and the bias compensation principle, a novel data filtering-based bias compensation recursive least squares (BCRLS) identification algorithm is developed for identifying the parameters of the MISO system with colored noise disturbance. Unlike the exiting BCRLS method for the MISO system (see, in Section 3), without computing the complicated noise correlation functions, still the proposed method can achieve the unbiased parameters estimation of the MISO system in the case of colored process noises. The proposed algorithm simplifies the implementation of and further expands the application scope of the existing BCRLS method. Three numerical examples clearly illustrate the validity of and the good performances of the proposed method, including its superiority over the BCRLS method and so on.