Least-squares Error Research Articles

Deep Neural Network-Based Channel Estimation Scheme in Massive MIMO Systems

Channel estimation is one of the most essential technologies of wireless communication, as well as necessary indicators to measure the system’s quality. In recent years, the combination of channel estimation and deep learning techniques has raised wide attention and become one of the most popular research directions of machine learning applications in the wireless communication area. However, in wireless communication systems such as massive MIMO, it’s too complicated to take traditional channel estimation methods with shortcomings like insufficient precision or increased cost. With the combination of deep neural networks (DDN) and the least squares (LS) method, this paper focuses on the deep learning-based channel estimation for massive MIMO systems. Compared with traditional algorithms such as LS or minimum mean-square error method, the loss function of the results is significantly reduced while simulating for the same analog channel. The problem of lack of precision has been improved efficiently and the performance has been improved substantially, which can prove the adequacy of the design of the channel estimator.

Drought risk assessment for citrus and its mitigation resistance under climate change and crop specialization: A case study of southern Jiangxi, China

Crop specialization has become an important way to promote agricultural efficiency and increase incomes for farmers in China. Under extreme weather conditions, this is likely to increase resistance to drought risk mitigation and lead to greater drought damage. In order to make more sophisticated mitigation policy decisions, it requires efforts to assess crop drought risk variability and its risk mitigation resistance in detail. This study focuses on citrus as a cash crop, using a case of southern Jiangxi. We first constructed a drought risk assessment system for citrus, and then geographic information technology was used to characterize the risk dynamics. The factor contribution model was applied to calculate the risk mitigation resistance. And the least square error (LSE) model was finally employed to classify risk mitigation resistance types. The results showed that citrus drought risk had fluctuated moderately over macro-regions in the past 30 years, but had varied dramatically at local scales. Citrus drought risk significantly increases during the bud bust to flowering, fruit expansion, and fruit maturation stages. Exposure contributed 33.23 % to drought risk fluctuations, specifically, mainly attributed to the amplification of drought exposure in the farmer and agricultural production sectors, while 14.95 % was derived from climate change. In large-scale citrus expansion areas, the improvement in drought mitigation capacity generally lags behind the increase rate of the exposure system. The LSE model showed that risk mitigation resistance type varied across regions at system level and indicator level. In addition, water scarcity has emerged as a widespread issue that limits drought risk mitigation. Generally, this study provides new insights into the measurement of drought risk mitigation resistance and its type identification, thus contributing to the safe supply of agricultural products and human well-being.

Temperature-Robust Short-Term Memory Stored at Electrode/Ionic Liquid Interface and Its Applications for Energy-Efficient Information Processing

Electrochemical reactions (ERs) accompanied by electrodeposition at electrode/ionic liquid (IL) interface have been adopted to various application such as batteries [1] and metal plating for interconnects [2]. Herein, we demonstrate that the electrode/IL interface can be used for the physical device implementation of the advanced machine learning (ML) model called reservoir computing (RC), which is an edge AI computing-friendly ML model. In this application, the electrode/IL interface extracts the feature from the input data by converting the signal waveform via ER at the interface [3-5]. Our device having the functional electrode/IL interface is called “ionic liquid-based physical reservoir device (IL-PRD)”.Fig. 1(a) is an optical microscope image of the IL-PRD having Pt electrodes, where ERs occur. The external area of the white dotted line in Fig. 1(a) is covered by SiO2. Fig. 1(b) explains the data processing flow. We performed a benchmark task for ML called a short-term-memory (STM) task. The input signal u(TS) at the time step TS is a random time-series of the binary data (0 or 1), where the signals “0” and “1” are input to the IL-PRD as the triangular voltage pulses (TVPs) with the pulse height/width of -3.0 V/500 ms and +3.0 V/500 ms, respectively. By using the output current signal from the IL-PRD, the efficient ML in a simple neural network (NN) can be conducted. In the STM task, the target data of the ML is u(TS-TS delay), where TS delay was a delay time. The ML performance is evaluated by a memory capacity (MC), which was calculated by adding the square of the correlation coefficient between the target data and model output. A linear regression (LR) model was used after increasing the input variable (x i) for LR by a virtual node method [6]. The weight value w i (fitting parameters of LR) was optimized to minimize the least-square error. In the present study, we investigated the temperature dependence of MC using two different ILs, which were 1-Butyl-3-methylimidazolium Bis(trifluoromethanesulfonyl)imide ([C4mim][Tf2N]) containing Cu cations with the concentration C Cu of 0.1 mol/L and 0.4 mol/L. The ML at the moderately elevated temperature is indispensable when considering the high-temperature application such as the life-time prediction of batteries operated at high temperature [1] as well as the signal processing in the highly integrated circuits [7], which is quite thermogenetic.Fig. 2 is an example of the current-voltage (I-V) curves plotted using the randomly input TVPs and corresponding current obtained at 26 ºC using the IL-PRD with C Cu = 0.1 mol/L. The color-coding of the I-V curves in Fig. 2 was conducted according to the input signal history over three time steps. For instance, the figure legend of “001” shown in Fig. 2 indicates that the corresponding current data was obtained when “1” was input after “0” was input two times. As shown in Fig. 2, the shapes of the I-V curve differ depending on the input signal history, indicating that the output signal from the IL-PRD successfully extracted the feature of the input signal history, in other words, the memory about the time series input signal. The values of MC for IL-PRD with C Cu = 0.1 mol/L and 0.4 mol/L are plotted in Fig. 3 as a function of temperature. Compared with the case of C Cu = 0.4 mol/L, the IL-PRD with C Cu = 0.1 mol/L exhibited the better robustness against the temperature change. In our previous study [4], Cu and some Cu compounds are formed by the electrodeposition during the device operation, and Cu is especially soluble in the present IL even if no external voltage is applied. Such Cu dissolution is reasonably considered to be accelerated by the temperature rise. In the IL-PRD with C Cu = 0.1 mol/L, less amount of Cu is intrinsically involved in ERs at the Pt/IL interface compared to that with C Cu = 0.4 mol/L. Consequently, the electrical properties of the IL-PRD with C Cu = 0.1 mol/L and the consequent ML performance became more stable against the temperature change, which is preferred for the RC under temperature change.

Wind Speed Modeling under Quasi‐Linear Autoregressive Neural Network Model for Prediction of Production Power

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Machine tool thermal error measurement and prediction via wireless microscope

A novel method is proposed to measure the thermal errors of a three-axis machine tool by taking images of unique custom-designed fiducials attached to a worktable using a wireless microscope mounted to the spindle. Multiple fiducials are applied for the measurement of different thermal errors at various worktable locations. In addition, a least-squares thermal error model within the work volume is proposed in which the model parameters are determined from various fiducial measurements, and the use of various fiducials for the thermal error model is analyzed. The results show the method can measure thermal errors within four times the positioning resolution of the machine tool, with most of the errors being smaller in magnitude than twice the positioning resolution. Further, the results show the thermal error model depends on the location of the fiducials used to construct the model. The unique numbering system for the fiducial patterns also allows for the measurement of large planar thermal errors, which is advantageous for machine tools that undergo significant thermal drift.

Evaluating the Application of the SIR Model to a Localized Population

Objective Using data collected from the Johns Hopkins COVID-19 Repository, I investigated the reliability of the SIR (Susceptible-Infected-Removed) model. Summary of Background Data Modern pandemic responses have evolved into effective defenses when implemented correctly but present issues in epidemiological modeling efforts. Intervention strategies such as quarantining and masking limit population size (N), which affects the accuracy of modeling population-based rate systems especially in highly transmissible diseases. Methods I begin by reviewing the SIR model retroactively to the initial SARS-CoV-2 Wuhan strain. I compared the parameters available in the published literature (N = 2,717,000, β = 2, γ = 1/14) to the best-fitting SIR-yielded values by minimizing the root mean squared error function. Subsequently, I evaluated its predictive capabilities on the Delta variant using early surge data, which was later compared against a retroactive analysis. Results Using a least-squares error best-fit analysis allowed me to retroactively define remarkably accurate model parameters for the Wuhan waves. Parameters including N = 730, β = 0.46, γ = 0.043 in the first wave and N = 11,200, β = 0.198, γ = 0.07 in the second reflected effective intervention strategies. I show it is an effective predictive tool regarding the Delta variant, yielding parameters N = 50,900, β = 0.87, γ = 1/3.7 that proved accurate when compared with parameters from a full retroactive analysis (N = 60,000, β = 0.94, γ = 1/3.6). Conclusion The similarity of the yielded parameters in my results supports the SIR model’s utility in epidemiological monitoring of high-transmissibility, low-mortality outbreaks vis-à-vis various containment measures.

Estimating the power law exponent of shear waves in soft tissue with a time-domain parametric model

In soft tissue, the attenuation of shear waves satisfies a power law. However, the values of power law exponents for shear waves in various soft tissues remain unknown due to windowing artifacts that limit the effectiveness of present methods that are based on two-dimensional fast Fourier transforms. To solve this problem, a time-domain signal processing approach is proposed. Robust shear wave parameter estimates are obtained by minimizing the least-squares error between measured time-domain shear wave data and stable probability density functions that describe the solution to a closely related fractional-order diffusion equation. Assessments are performed with measured shear wave data collected from ex vivo pig liver using a Verasonics system with an L7-4 linear array transducer. Power law exponents are obtained from measured shear wave data at the focal depth between 1.2 and 13.9 mm from the center of the push beam. The mean estimated power law exponent for these locations is 0.74, and the standard deviation is 0.05. The results suggest that fractional calculus models that support power law exponents less than one are needed for shear waves in pig liver.

A new shape reconstruction method for monitoring the large deformations of offshore wind turbine towers

Offshore wind turbine (OWT) upsizing has become an inevitable trend. Compared with small OWTs, large OWT rotors exert greater horizontal loads and bending moments on the tower. These factors make a large OWT tower more prone to geometric nonlinear deformations, which can affect the normal operation of a wind turbine and even lead to damage to the wind turbine tower. Therefore, monitoring the deformation of large OWT towers is necessary. Shape sensing is a technique that uses surface strains to reconstruct deformed shapes. Currently, there is a lack of effective methods for geometric nonlinearity deformation shape sensing. In addition, the tower of OWT is a variable cross-section structure. The influence of varying cross-sections further increases the difficulty of developing a large deformation reconstruction algorithm. To solve this problem, this paper proposes a new method called rotation angle approximation (RAA). This method establishes a least-squares error functional using analytic curvature and section curvature. The rotation angles of the tower axis are obtained via this function. The deformed shape is then predicted via the boundary conditions and the rotation angles along the tower axis. Numerical simulations demonstrated this method can accurately predict the large deformations of a tower under different load conditions.

Identifying the dominant operating variables to evaluate the cascading failure potential in the power system by theory of mutual information

In this study, the potential of cascading failure as a result of transmission line outages through large power systems is evaluated. Transmission lines are more dispose of the outage resulting from different fault occurrences and extend the uncontrolled failures compared to other power system devices. In this case, power system dynamic security must evaluate the possibility of cascading failures using a power system control center (PSCC) in order to limit the spread of outages caused by line failures. For this issue, using the PSCC online information consisting of power system operating variables, it is possible to present a variables-based scheme to estimate power system cascading failures concerning fault events. In order to cover the issue, based on developing a set of dominant operating variables (DOVs), this paper presents an online scheme of identifying cascading failures caused by line outages in the presence of line operational diversities and communication restrictions. In this scenario, during each time interval, by evaluating the proposed DOVs, the line information consisting of dynamic securities and sensitivities to cascading failure are investigated which the lines with the potential of cascading failures are identified online. Proper DOVs are determined for this issue by studying a variety of mathematical formulations according to theory of mutual information (MI) and measuring the entropy among the variables. In a real-time environment, by using the proposed DOVs and substituting them through online boundary equations based on the Least Square Error (LSE) method, the potentials of power system cascading failures without acting any line outage are provided. The effectiveness of the suggested technique is evaluated using a typical IEEE-39 bus and IEEE-118 bus, and proper results with high accuracy are achieved by evaluating the system potential concerning large cascading failures.

Evaluation and comparison of infiltration models for estimating infiltration capacity of different textures of irrigated soils

Accurate estimation of infiltration rates is crucial for effective irrigation system design and evaluation by optimizing irrigation scheduling, preventing soil erosion, and enhancing water use efficiency. This study evaluates and compares selected infiltration models for estimating water infiltration rates in the Shillanat-iv irrigation scheme in northern Ethiopia. Soil samples were collected to determine textural classes using hydrometer soil texture analysis and the United States Department of Agriculture (USDA) textural triangle. The soil textural map of the study was created using the inverse distance weight interpolation technique in ArcGIS version 10.4. Infiltration rates were measured using the double-ring infiltrometer for five soil textures: clay loam, loam, sandy clay loam, clay, and sandy loam. Six infiltration models (Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel) were employed and evaluated using statistical parameters. Model calibration and validation were conducted using data from 38 points within the study area. The parameter values of the infiltration models were optimized using SPSS statistical software using least-squares errors. The results showed that, Revised Modified Kostiakov, Modified Kostiakov, and Novel infiltration models demonstrated superior capability in estimating infiltration rates for clay loam, loam, and sandy loam soil textures, respectively. Horton's model outperformed other models in estimating infiltration rates for both sandy clay loam and clay soil textures. The appropriately fitted infiltration models can be utilized in designing the irrigation system to estimate the infiltration rate of soil textures within the selected irrigation scheme and at similar sites with comparable soil textures.

Data-driven forced oscillation localization using inferred impulse responses

Poorly damped oscillations pose threats to the stability and reliability of interconnected power systems. In this work, we propose a comprehensive data-driven framework for inferring the sources of forced oscillation (FO) using solely synchrophasor measurements. During normal grid operations, fast-rate ambient data are collected to recover the impulse responses in the small-signal regime, without requiring the system model. When FO events occur, the source is estimated based on the frequency domain analysis by fitting the least-squares (LS) error for the FO data using the impulse responses recovered previously. Although the proposed framework is purely data-driven, the result has been established theoretically via model-based analysis of linearized dynamics under a few realistic assumptions. Numerical validations demonstrate its applicability to realistic power systems including nonlinear, higher-order dynamics with control effects using the IEEE 68-bus system, and the 240-bus system from the IEEE-NASPI FO source location contest. The generalizability of the proposed methodology has been validated using different types of measurements and partial sensor coverage conditions.

A new least squares error based method for accurate phasor estimation of fault currents in series-compensated transmission lines

Phasor estimation of the fundamental component of the fault current is of great importance for the fault location unit in distance relays. In series-compensated transmission lines, the transient component of the fault current may appear in the complicated wave shape of an exponential-sinusoidal function. Thus, phasor estimation is a major challenge owing to the fact that more unknown parameters should be found, especially during off-nominal frequency conditions. Therefore, in this paper, a new method is put forward, on the strength of the least squares error (LSE), in order to accurately estimate the fundamental component phasor of the fault current in a series-compensated transmission line for off-nominal frequency conditions. The performance of the established method is also investigated through extensive simulation studies, taking care of different fundamental components, harmonic contents, and transient components. Moreover, the performance of the proposed method is evaluated for off-nominal frequencies. The results obtained in this study demonstrate that the mean absolute error of the estimated amplitude and phase for the fundamental component is less than 1% and 1°, respectively. Furthermore, the accuracy of the proposed method is not affected by the noise up to a signal-to-noise ratio of 30 dB. Thus, the established method is capable to effectively be employed for the phasor estimation in fault location applications.

A Critical Comparison of Shape Sensing Algorithms: The Calibration Matrix Method versus iFEM.

Two shape-sensing algorithms, the calibration matrix (CM) method and the inverse Finite Element Method (iFEM), were compared on their ability to accurately reconstruct displacements, strains, and loads and on their computational efficiency. CM reconstructs deformation through a linear combination of known load cases using the sensor data measured for each of these known load cases and the sensor data measured for the actual load case. iFEM reconstructs deformation by minimizing a least-squares error functional based on the difference between the measured and numerical values for displacement and/or strain. In this study, CM is covered in detail to determine the applicability and practicality of the method. The CM results for several benchmark problems from the literature were compared to the iFEM results. In addition, a representative aerospace structure consisting of a twisted and tapered blade with a NACA 6412 cross-sectional profile was evaluated using quadratic hexahedral solid elements with reduced integration. Both methods assumed linear elastic material conditions and used discrete displacement sensors, strain sensors, or a combination of both to reconstruct the full displacement and strain fields. In our study, surface-mounted and distributed sensors throughout the volume of the structure were considered. This comparative study was performed to support the growing demand for load monitoring, specifically for applications where the sensor data is obtained from discrete and irregularly distributed points on the structure. In this study, the CM method was shown to achieve greater accuracy than iFEM. Averaged over all the load cases examined, the CM algorithm achieved average displacement and strain errors of less than 0.01%, whereas the iFEM algorithm had an average displacement error of 21% and an average strain error of 99%. In addition, CM also achieved equal or better computational efficiency than iFEM after initial set-up, with similar first solution times and faster repeat solution times by a factor of approximately 100, for hundreds to thousands of sensors.

Effective optimization strategies for abrasive water jet machining of glass-carbon fiber reinforced composites: A comparative study of evolutionary optimization techniques

This study applied evolutionary optimization techniques and neural networks to predict optimum machining parameters of Abrasive Water Jet Machining (AWJM) for machining Glass-Carbon Fiber Reinforced Composite (GCFRC) materials. Several researchers have employed different optimization techniques; however, evolving computational capabilities further open avenues to optimize such parameters. Five evolutionary techniques, namely Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Nonlinear Least Square Error (LSE), were applied to minimize surface roughness (Ra) and maximize kerf width (Kw) and material removal rate (MRR). The output performance parameters (Ra, Kw, and MRR) were formulated as a linear mathematical function of machining parameters: tool feed rate (TFR), cutting speed rate (CSR), and stand-off distance (SOD). Though this study solves linear optimization problems, the proposed technique will be a strong tool for solving complex associations of machining and performance parameters in the future. A dataset of machining parameters and subsequent performance parameters was adopted from the available literature. The results indicated that the LSE method outperformed other techniques, yielding the lowest Root Mean Square Error (RMSE) in predicting Ra, Kw, and MRR, thus ensuring high machining accuracy. LSE technique reported relatively least RMSE values of 0.37 µm, 0.149Mm, and 237.23 mm3/min for Ra, Kw, and MRR, respectively. SA and PSO displayed identical and competitive RMSE values, slightly higher than LSE (up to 20 % higher). ANN and GA techniques were not effective relative to other considered techniques. LSE, SA, and PSO provide superior performance in optimizing AWJM parameters. The significant contribution of this research is the proposed optimization technique, offering a clear direction for solving complex associations between the performances and machining parameters of AWJM. This work also provides a foundation for future research to optimize such associations for other machining setups.

Incremental Fuzzy Clustering-Based Neural Networks Driven With the Aid of Dynamic Input Space Partition and Quasi-Fuzzy Local Models.

Fuzzy clustering-based neural networks (FCNNs) based on information granulation techniques have been shown to be effective Takagi-Sugeno (TS)-type fuzzy models. However, the existing FCNNs could not cope well with sequential learning tasks. In this study, we introduce incremental FCNNs (IFCNNs), which could dynamically update themselves whenever new learning data (e.g., single datum or block data) are incorporated into the dataset. Specifically, we employ dynamic (incremental) fuzzy C-means (FCMs) clustering algorithms to reveal a structure in data and divide the entire input space into several subregions. In the aforementioned partition, the dynamic FCM adaptively adjusts the position of its prototypes by using sequential data. Due to the time-sharing arrival of training data, compared with batch learning models, incremental learning methods may lose classification (prediction) accuracy. In order to tackle this challenge, we utilize quasi-fuzzy local models (QFLMs) based on modified Schmidt neural networks to replace the popular linear functions in TS-type fuzzy models to refine and enhance the ability to represent the behavior of fuzzy subspaces. Meanwhile, the recursive least square error (LSE) estimation is utilized to update the weights of QFLMs from one-by-one or block-by-block (fixed or varying block size) learning data. In addition, the L2 regularization is considered to ameliorate the deterioration of generalization abilities caused by potential overfitting when carrying out weight estimation. The proposed method leads to the construction of FCNNs in a new way, which can effectively deal with incremental data as well as deliver sound generalization capability. Extensive machine-learning datasets and a real-world application are employed to show the validity and performance of the presented methods. From the experimental results, we show that the proposal can maintain sound classification accuracy when effectively processing sequential data.

Hybrid Optimized LMMSE-Based Channel Estimation with Low Power Trellis Coded Modulation

Data transfer in a wireless channel environment is plagued by numerous security issues and power transmission loss. Channel state information is crucial to attaining the performance benefit of reconfigurable intelligent surfaces (RISs) in wireless systems. Because of the Doppler effect, mobility makes accurate channel estimate (CE) more difficult. The parameter estimation approach is employed as an alternative to direct estimation in the channel matrix to address the aforementioned problems. Also, to lower the Mean Square Error (MSE) between the estimated and original channel, training-based CE, such as Linear Minimum Mean Square Error (LMMSE), and Least Square Error (LSE), are widely used in some wireless standards. To optimize the channel, certain intelligent optimized strategies are presented. For the Optimal CE, a Trellis Coded Modulation (TCM) with hybrid optimization in LMMSE has been provided. Moth amalgamated Elephant Herding Optimization (MAEHO) algorithm is the proposed hybrid optimization. Finally, the performance of the accepted model is determined in comparison to other existing techniques using a variety of metrics.

A high-order sharp-interface immersed boundary solver for high-speed flows

A sharp-interface immersed boundary (IB) solver for the three-dimensional compressible Navier-Stokes equations with shock discontinuities is presented. The IB method is based on high-order schemes and a ghost-cell method which uses Taylor polynomial interpolation and weighted least-squares error minimization to impose the boundary conditions on the immersed boundary. The results show that the method enables robust and accurate calculation of high-speed flow problems involving complex geometries, moving boundaries, and solid and membrane geometries. Third-order accuracy or higher is maintained even for supersonic conditions. The numerical framework is validated through several two-dimensional and three-dimensional benchmark problems at subsonic and supersonic speeds. Additionally, the method is demonstrated for airfoil dynamic stall and wing-store interaction investigations.

The role of tourism development in boosting export product diversification in China: A provincial perspective

Tourism has emerged as a major sector, and it is considered a significant contributor to the development of the host countries. Tourism development (TD) is believed, inter alia, to be a key promoter of export product diversification (EPD). This study explores the liaison between tourism development and export product diversification in China and across its regions (i.e., Eastern, Central, & Western) using the data for the period of 2011-2019. The Driscoll-Kraay standard errors (DKSEs) is adopted for empirical analysis along with feasible generalized least-squares (FGLS) and panel-corrected standard errors (PCSEs). The findings revealed an inverted U-shaped liaison between the TD and EPD. The estimates are robust and insensitive to econometric models, measures of tourism development, and the inclusion of other key covariates. The relationship between industrial structure, natural resource endowments, human capital, and economic growth is positive and statistically significant. The study revealed similar findings that were significant across regions. The policy insinuation is that the government should devise such policies to boost TD in order to promote EPD and sustainable development.

A suboptimal control scheme based on integrated real-time nonlinear estimation-identification approach for developed flexible joint manipulators structure

As the safety of human-robot interaction is of great interest in various applications, the flexibility of machine joints becomes increasingly important. In this paper, a general model is developed for electrically driven flexible-joint robots (ED-FJR), in which the coupled phenomena of stiffness-damping and friction in the joints are modeled as nonlinear and time-varying parameters. Because the output torque of the actuator mounted on the link cannot be measured, its state variables are estimated by the suboptimal estimator. Furthermore, the unknown parameters of joint flexibility are identified by the identification algorithm based on time-varying nonlinear least-squares error (TV-NLS), which are then used in the proposed control algorithm to compensate for negative effects and errors. The state-dependent Riccati equation (SDRE) method is developed in this paper using the combined estimator-identifier approach. The novel proposed method for a 3-degree-of-freedom (3-DOF) mechanical arm with electrically driven flexible joints is implemented and validated. The obtained results show that the system's performance has improved by 83% in terms of modeling accuracy. Furthermore, when compared to other methods, the proposed algorithm reduces tracking error by 59%, making it easier to reach the target point.

Optimizing the Heat Loss from an Insulation Material and Boundary Layer Thickness of Airflow through a Hot Plate Using Nonlinear Least-Squares Error and Linear Programming Algorithms.

Heat loss is a major challenge in heat transfer problems. Several researchers have minimized heat loss for different heat transfer cases, focusing on one optimization technique; however, not all optimization techniques are suitable for a given problem. A limited number of studies have compared different techniques for a given problem under boundary conditions and constraints. This review revisits basic heat transfer problems and identifies a promising technique for each problem to minimize heat loss. The paper considers three techniques: nonlinear least-squares error (LSE), interior point linear programming (IPLP), and genetic algorithm. Two cases are studied: 1. heat loss optimization from cylindrical insulating surfaces and 2. laminar airflow on a heated plate. The results are compared for each technique, and a suitable technique is recommended for each considered case. Nonlinear LSE is found to be most suitable for case 1. IPLP and GA are recommended for the Case 2 problem. The average thermal conductivity is found to be 0.081 W/mK. The average insulation thickness is found to be 213.25 mm. This research will act as a basis for future research to justify and implement suitable techniques for different heat transfer problems.