Mean Square Prediction Research Articles

Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate.

Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals. This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen. The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study's output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%. The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.

The Analysis and Rapid Non-Destructive Evaluation of Yongchuan Xiuya Quality Based on NIRS Combined with Machine Learning Methods

This paper attempts to analyze and assess Yongchuan Xiuya tea quality quickly, accurately, and digitally. The sensory evaluation method was first used to assess Yongchuan Xiuya tea quality, and then near infrared spectroscopy (NIRS) was obtained, and standard methods were applied to the testing of the chemical components. Next, principal component analysis (PCA) and the correlation coefficient method were used to comprehensively screen out the representative components. Finally, NIRS combined with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN) methods were applied to build quality evaluation models for Yongchuan Xiuya tea, respectively, and external samples were employed to examine the practical application results of the best model. The cumulative variance contribution rate of the first three principal components of the ingredients in tea was 97.73%. Seven components closely related to tea quality were screened out, namely, amino acids, total catechin, epigallocatechin gallate (EGCG), tea polyphenols, water extracts, epicatechin gallate (ECG), and epigallocatechin (EGC) (p < 0.01). Between the two models established to predict the tea quality, the model built by the PLS method had the better results, whose coefficient of determination of prediction (Rp2) and root mean square error of prediction (RMSEP) were 0.7955 and 1.2263, respectively, and the best results were obtained by the nonlinear BP-ANN model, whose Rp2 and RMSEP were 0.9315 and 0.6787, respectively. The 10 external Yongchuan Xiuya samples were employed to test the best BP-ANN model, and the results of R2 and RMSEP were 0.9579 and 0.6086, respectively, meaning that the model has good robustness. Therefore, the model established by NIRS combined with the BP-ANN method can be used to assess Yongchuan Xiuya tea quality rapidly, accurately, and digitally, and it can also provide new ideas and methods for evaluating the quality of other teas.

A GRNN informed ECMS-ALPF operational management strategy for reducing fuel consumption of diesel/battery/supercapacitor hybrid vehicles

The fuel consumption and pollution emission of ships have become the main focuses in ocean environment management. This paper proposes a new marine vehicular hybrid propulsion system (VHPS), which is composed of a diesel generator and a hybrid energy storage system consisted of batteries and supercapacitors. However, the distinct power and energy characteristics of the three power sources result in complex operational management and a negative impact on energy conservation and emission reduction. Therefore, a general regression neural network informed equivalent consumption minimization strategy and adaptive low pass filter (GRNN informed ECMS-ALPF) operational management strategy (OMS) is proposed to optimize the fuel consumption and energy loss. Finally, a hardware in the loop experimental platform is utilized to verify the performance of proposed OMS. From experimental results, the GRNN informed ECMS-ALPF strategy has accurate load predictive capability, the predictive average root mean square error value is only 2.1633 kW, whose fuel consumption is 14.9% lower compared with a conventional rule-based strategy. And the energy loss of the given hybrid energy storage system can reduce 0.0178 kWh (about 14.55%) compared with a conventional low pass filter strategy. The proposed VHPS and OMS offer insights for the study of ocean and coastal management.

Rapid and non-destructive detection of hard to cook chickpeas using NIR hyperspectral imaging and machine learning

Identification of Hard to Cook (HTC) chickpeas in a rapid, non-destructive manner is crucial for the pulse processing industry. This study investigated the potential of near infrared (NIR) hyperspectral imaging (HSI) system to classify chickpeas into HTC and Easy to Cook (ETC) (control) categories. Two types of HTC chickpeas were created using eight different varieties of chickpeas: the first type was created by storing under suboptimal conditions, while the second type was created with chemical treatment. A total of eight hundred sixty-four chickpea seeds ({control- 36; physically hardened-36 seeds; chemically hardened-36 seeds} × 8 varieties) were used in this study. The chickpeas were imaged using a NIR-HSI system in the spectral range of 900–2500 nm. The cooking time of individual chickpea seed was measured using an automated Mattson cooker and the spectral data was correlated with the measured reference cooking time of chickpeas to develop the calibration model. Partial Least Square Discriminant Analysis (PLSDA), Support Vector Classifier (SVC) and Convolutional Neural Network-Attention (CNN-ATT) models was used for model development based on full spectrum and significant wavelengths. The optimal models were obtained using the SVC and CNN-ATT which demonstrated 100% accuracy in classifying the chickpeas into HTC and ETC. Besides, the cooking time of control (ETC) and HTC chickpeas were predicted using One Dimensional Convolutional Neural Network (1D-CNN) with Correlation Coefficient of Prediction (R2p) and Root Mean Square Error of Prediction (RMSEP) values of 0.880 and 0.662 respectively indicating the potential of this approach in developing robust model for cooking time prediction in other pulses.

Spatial association-considered real-time risk rate assessment of high arch dams using observed displacement and combination prediction model

Structural health monitoring is an important approach to diagnose dam safety. The main purpose of this paper is to improve the accuracy of displacement prediction and real-time risk rate assessment of high arch dams by utilizing the deformation spatial association of multiple displacement monitoring points. Two strategies are integrated for the former. First, in addition to the traditional objective of minimizing the fitting error, deformation spatial association is also determined as an optimization objective for machine learning models. Second, five frequently used models are adopted as sub-models, and the overfitting degree is then used as an evaluation index to select optimal sub-models that included in the combination prediction model. On this basis, probability density analysis is conducted for displacement residuals, and a quantification function is defined to assess the real-time risk rate of a single monitoring point. The real-time risk rate of a high arch dam is then calculated according to the risk rates of multiple monitoring points, during which the spatial association are taken into account by the Copula function. Research results of the Jinping-I arch dam indicate that the deformation spatial association is very beneficial for improving the accuracy of dam safety diagnosis, where the prediction mean square error of four machine learning models decreases with an average rate of 66.92%, 45.37%, 35.45% and 73.12%, respectively, and the combination prediction method can further improve the displacement prediction accuracy by 23.79%. The observation data-based high-risk period of arch dams mainly occurs in operation scenarios that the rising or dropping rate of reservoir water level changes sharply. The joint risk rate can effectively avoid the accidental influence of a single monitoring point, and it exhibits an exponential decrease with the increasing number of used monitoring points. For the Jinping-I arch dam, a minimum number of 10 monitoring points is required.

Determination of total phosphorus concentration in water by using visible-near-infrared spectroscopy with machine learning algorithm.

Total phosphorus (TP) content is a crucial evaluation parameter for surface water quality assessment, which is one of the primary causes of eutrophication. High-accuracy, fast-speed approach for the determination of low-concentration TP in water is important. We proposed a rapid, highly sensitive, and pollution-free approach that combines spectroscopy with a machine learning algorithm we improved called synergy interval Extra-Trees regression (siETR) to determine TP concentration in water. Results show that the prediction model based on siETR can get a high coefficient of determination of prediction ([Formula: see text] = 0.9444) and low root mean square error of prediction (RMSEP = 0.0731), which performs well on the prediction of TP concentration. Furthermore, the statistical analysis results further prove that the model based on siETR is superior to other models we studied both in prediction accuracy and robustness. What is more, the prediction model we established with only 140 characteristic wavelengths has the potential for the development of miniature spectral detection instruments, which is expected to achieve in situ determination of TP concentration. These results indicate that Vis-NIR spectroscopy combined with siETR is a promising approach for the determination of TP concentration in water.

Prediction of KLCI Index Through Economic LASSO Regression Model and Model Averaging

The Financial Times Stock Exchange (FTSE) Bursa Malaysia KLCI Index is a key component in the development of Malaysia's economic growth and the complexity in terms of identifying the factors that have a substantial impact on the Malaysian stock market has always been a contentious issue. In this study, the macroeconomic factors of exchange rate, interest rate, gold price, consumer price index, money supply M1, M2, and M3, industrial production, and oil price were discussed by using economic LASSO regression and Bayesian Model Averaging (BMA) with monthly average and monthly end time-series data spanning from January 2015 to June 2021, with a total of 78 observations by using the R Studio. The findings demonstrate that month-end data is better suited for stock market prediction than month-average data and that the BMA model is more suitable than the LASSO model, as seen by lower Mean Square Error of Prediction, MSE(P) and Residual Mean Square Error of Prediction, RMSE(P) values. The exchange rate, gold price, and money supply have a negative association with the dependent variables, while the consumer price index has a positive relationship associated with the dependent variables. The consumer price index is the most significant contributing factor, whereas gold price is the least significant. The result depicted that the KLCI index has no significant relationship with the variables interest rate, money supply M2, M1, industrial production index, and oil price. In conclusion, investors could specifically focus on the positive contributor and put lesser attention on improving their portfolio return.

SAMA: Self-adjusting multi-cycle approximate adder

Approximate computing is a promising approach that leads to a better compromise between power and delay by trading an acceptable level of accuracy in inherently error resilient applications. In this paper, we present a runtime accuracy configurable approximate adder that can operate in both approximate and exact modes. It provides a better compromise between power, delay, and accuracy compared with state-of-the-art approximate adders at high accuracies. Our evaluations show that the proposed adder can make up to 45% and 79% improvements in power-delay product (PDP) and energy-delay product (EDP), respectively, compared with state-of-the-art approximate adders, at high accuracies. In the accurate mode, the improvements in the PDP and EDP are up to 58% and 82%, respectively. The use of the proposed adder in an image processing application and in a mean-squares prediction algorithm confirms its superiority over existing circuits in terms of PDP and EDP.

A robust hybrid methodology between applied linear regression model (alrm) and multilayer perceptron (mlp)

Background: The goal of this study is to illustrate an optimum variable selection method using established Multiple Linear Regression (MLR) models and to validate the variable using Multilayer Perceptron Neural Network (MLP) models. Initially, all selected variables will be passed through the bootstrap methodology, and they were screened for significant relationships. Objective: The goal of this work is to analyze and construct a model for the factor linked with total crime cases by combining an Applied Linear Regression Model (ALRM) and a Multilayer Perceptron (MLP). Material and Methods: Around 200 data was simulated to build the methodology. Advanced computational statistical modeling methodologies were used to evaluate data descriptions of several variables in this retrospective study, including the total victim, gender, age, marital status, social class, adult in the household, children in household, burglary’s victim, sexual’s victim, victim’s report, and household location. The case study was developed and implemented using the R-Studio program and syntax. Results: The statistical method demonstrated that regression modeling surpasses R-squared and mean square error test in most situations. Researchers observed that when data is divided into two datasets for training and testing, the hybrid model approach performs significantly better at predicting the experiment’s outcome. When it came time to determine variable validity, the well-established bootstrap-integrated MLR approach was applied. Ten characteristics are taken into consideration in this case: Gender (: -0.4369700; p< 0.25), age (: -0.0086757; p< 0.25), marital status (: 0.2646097; p< 0.25), social class ( : 0.0602540; p< 0.25), adult in household (: -0.0211293; p> 0.25), children in household (: -0.0025346; p> 0.25), burglary’s victim (: 1.3473593; p< 0.25), sexual’s victim (: 1.0382444; p< 0.25), victim’s report (: -0.3176104; p< 0.25), and location of household (: -0.1355046; p< 0.25).There is a 0.07745823 MSE for the linear model in this scenario. Conclusion: The neural network’s Predicted Mean Square Error (PMSE) was used to assess MLP’s performance (MSE-forecasts the Network). PMSE is used to determine how far our projections are from the actual data, and the lowest MSE from the MLP indicates the best achievement. The R syntax for MLR and MLP is also included in this research article.As a result, the study’s conclusion establishes the superiority of the hybrid model technique. Bangladesh Journal of Medical Science Vol. 22 No. 01 January’23 Page : 38-46

Cardiopulmonary bypass parameters improve the prediction of 30-day mortality following cardiac surgery.

Currently 30-day mortality is commonly used as a quality indicator for cardiac surgery; however, prediction models have not included the role of cardiopulmonary bypass (CPB). We hypothesized that reproducing currently utilised prediction model methods of 30-day mortality using the Australian and New Zealand Collaborative Perfusion Registry (ANZCPR) would identify relevant CPB predictors. Nine centers in Australia and New Zealand collected data using the ANZCPR between 2011-2020. CPB parameter selection was determined by evaluating association with 30-day mortality. Data were divided into model creation (n = 15,073) and validation sets (n = 15,072). Bootstrap sampling and automated variable selection methods were used to develop candidate models. The final model was selected using prediction mean square error and Bayesian Information Criteria. The average receiver operating characteristic (ROC), p-value for Hosmer-Lemeshow chi-squared test and MSE were obtained from multifold validation. In total, 30,145 patients were included, of which 735 (2.4%) died within 30 day of surgery. The area under the ROC curve for the model including CPB parameters was significantly greater than preoperative risk factors only (0.829 vs 0.783, p < 0.001). CPB parameters included in the predictive model were CPB time, red blood cell transfusion, mean arterial pressure <50mmHg, minimum oxygen delivery, cardiac index <1.6L/min/m2. CPB parameters improve the prediction of 30-day mortality. Randomised trials designed to evaluate modifiable CPB parameters will determine their impact on mortality.

Robust sequential adaptive Kalman filter algorithm for ultrashort baseline underwater acoustic positioning

Affected by dynamic changes in the complex marine environment, ultrashort baseline (USBL) systems may exhibit continuous gross errors in underwater target positioning, resulting in the distortion of the target coordinates. To effectively detect and eliminate continuous gross errors in USBL underwater acoustic positioning, a robust sequential adaptive Kalman filter (RSAKF) algorithm is proposed in this paper. The RSAKF algorithm employs sequential filtering to decompose all measurement updates into multiple submeasurement updates and uses the fading memory weighted average method to estimate the one-step prediction mean square error of the metrics for each submeasurement update. Then, the RSAKF algorithm adopts an adaptive correction method of submeasurement noise variance, which eliminates the influence of continuous gross errors through a more targeted adaptive correction of each submeasurement noise variance. The effectiveness of the algorithm was quantitatively analyzed using a USBL positioning simulation experiment, and the results showed that the continuous gross errors rejection rate of the RSAKF algorithm reached 84.12%. The point error of the RSAKF algorithm is improved by 62.65%, 46.76%, 36.09%, and 26.48% compared with the Kalman filter (KF), KF based on Huber, KF based on Institute of Geodesy and Geophysics, and the maximum correntropy KF, respectively. The USBL positioning remotely operated vehicle experiment was conducted in the South China Sea, and the results showed that the RSAKF has the best filtering accuracy. Simulation and actual measurement experiments verified that the RSAKF algorithm can effectively eliminate the influence of continuous gross errors.

Application of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed

Evaluating the protein content of a single chickpea seed in a rapid, non-destructive, and precise manner is crucial for facilitating the breeding of high-protein chickpeas. This study explored the potential of near-infrared (NIR) hyperspectral imaging (HSI) to predict the protein content in a single chickpea seed. Eight varieties of chickpeas with different protein contents were subjected to NIR reflectance hyperspectral imaging in the spectral range of 900–2500 nm at two different positions of chickpea seed (micropyle down and micropyle up). The spectral data was correlated with the measured reference protein content of chickpea seed for building the partial least square regression (PLSR) and support vector machine regression (SVMR) models based on different spectral preprocessing techniques, with full spectrum and effective wavelengths selected using competitive adaptive reweighted sampling (CARS) and iteratively retaining informative variables (IRIV) algorithms. When using the full spectrum, the optimal protein prediction model was obtained using PLSR, which yielded correlation coefficient of prediction (R2p) and root mean square error of prediction (RMSEP) values of 0.935 and 0.987, respectively, with external parameter orthogonalization (EPO)+standard normal variate (SNV) preprocessing for micropyle down position of chickpea seed. The IRIV selected wavelength with PLSR yielded the best model with R2p and RMSEP of 0.947 and 0.861, respectively, at the micropyle down position of chickpea seed. Hence, the optimal prediction models were obtained using PLSR with EPO+SNV at the micropyle down position of chickpea seed.

Study on egg sorting model based on visible-near infrared spectroscopy

To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate () and the rejection rate () are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.

Multi-head self-attention bidirectional gated recurrent unit for end-to-end remaining useful life prediction of mechanical equipment

In order to reduce error accumulation caused by multistep modeling and achieve a generally accurate model, this paper proposes an end-to-end remaining useful life (RUL) prediction model based on a multi-head self-attention bidirectional gated recurrent unit (BiGRU). Taking multivariable samples with long time series as the model input and multistep RUL values as the model output, the BiGRU model is constructed for continuous prediction of RUL. In addition, single-head self-attention models are applied for time series and variables of samples before or after the BiGRU, which can be fused into a multi-head attention BiGRU. Aeroengines and rolling bearings are selected to testify the effectiveness of the proposed method from the system level and component level respectively. The results show that the proposed method can achieve end-to-end RUL prediction efficiently and accurately. Compared with single-head models and individual deep learning models, the prediction mean square error of the proposed method is reduced by 20%–70%.

Stealthy attack detection method based on Multi-feature long short-term memory prediction model

Industrial control systems (ICS) face severe threats due to the inherent vulnerability of shared networks. Among the attacks against ICS, stealthy attack is an attack behavior in which an attacker injects false sensor measurements or drives signals into the control loop, evading detection by the intrusion detection system (IDS). They are highly destructive and difficult to detect because of concealment. In related studies, data-driven methods have the shortcomings of a large computation burden. Physics-based methods are usually difficult and expensive to change the system structure. Moreover, most of the current methods against stealthy attacks take up a considerable amount of system resources due to training or the pursuit of robustness. In this paper, using the importance and correlation of neglected feature data, an stealthy attack detection method based on the Multi-feature LSTM (MFLSTM) model is innovatively developed to predict and recover data attacked. Random Forest (RF) scores and heatmap are used to judge the importance and correlation of the feature data. Then the forget gates of the LSTM model are improved to construct an MFLSTM model that can learn predictive information from other feature data and decouples the attack conditions that stealthy attack relies on. The testing results indicated that MFLSTM has significant advantages in prediction accuracy, stability, and resource-saving. MFLSTM model saved 52.3% of the resources required for the same type of prediction. The single-point prediction mean square error (MSE) for the STEP 7 (S7) protocol attack signature prediction and secure water treatment (SWaT) dataset were 0.0471 and 0.0035, respectively, which also demonstrates the feasibility of our proposed method.

Performance Evaluation of Precision Poverty Alleviation with the Assistance of Wireless Communication Technology for Rural Revitalization

In the context of rural revitalization, it is imperative to evaluate the performance of poverty alleviation scientifically, which can not only promote the removal of poverty areas and their sustainable development but also comprehensively evaluate the achievements of targeted poverty alleviation work. In this work, the rough set and Support Vector Machine- (SVM-) related concepts are first introduced to establish the required index system. Long-Range Radio (LoRa) wireless communication technology is adopted to collect relevant data, rough set is utilized to preprocess the data, and the importance and relative weight of each indicator are calculated. After elimination of redundant indexes, a new decision table is established, and a prediction model of SVM is established. In parameter optimization, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and grid search are employed. Experimental data show that the minimum error of GA is 1.82%, the test error is 5.11%, and the training error is 3.18%. The minimum error of PSO is 1.86%, the test error is 5.62%, and the training error is 3.15%. The minimum error of grid search method is 2.11%, the test error is 10.73%, and the training error is 2.34%. These three algorithms can optimize SVM parameters and effectively improve the performance evaluation results of targeted poverty alleviation by the model. By comparing the effect of the SVM model without rough set, it is found that the prediction accuracy of model can be improved by using rough set. Given the cross-validation error rate of the model and the predicted mean square error, GA is better than others. The established model makes up for the situation of strong subjectivity and low universal applicability in typical performance evaluation, enriches performance evaluation methods of targeted poverty alleviation, and is of certain practical reference value in performance evaluation of targeted poverty alleviation.

Study on Model Evolution Method Based on the Hybrid Modeling Technology With Support Vector Machine for an SOFC-GT System

Abstract The mechanism models of solid oxide fuel cell–gas turbine (SOFC-GT) systems are very useful to analyze the thermodynamic performance details, including the internal complex transfers of mass, heat, and electrochemical processes. However, several physical-property parameters in the mechanism model are unmeasurable and difficult to accurately quantify from the operation data when the inevitable degradation occurs. As a result, it is difficult for the mechanism model to accurately capture the SOFC electrochemical characteristic during the full operating cycle. In this paper, a model evolution method based on hybrid modeling technology is proposed to address this problem. A hybrid modeling framework of a SOFC-GT system is designed by combining a least squares-support vector machine algorithm (LS-SVM) electrochemical model with our previous mechanism model. The electrochemical characteristic of SOFC is easily identified and evolved by re-training the LS-SVM model from operating data, no longer needing a mechanism electrochemical model. The validated full-mechanism model from our previous work is taken to simulate a physical SOFC-GT system to generate the operating data. Various LS-SVM models are trained by different data sets. The comparison results demonstrate that the LS-SVM model trained by large-size data set 3 performs the highest accuracy in predicting the local current density. The maximum absolute error of prediction is only about 1.379 A/m2, and the prediction mean square error of the normalized test data reaches 4.36 × 10−9. Then, the LS-SVM hybrid model is applied to evaluate the thermodynamic performance of a SOFC-GT system. The comparison results between the hybrid model and our previous full-mechanism model show that the hybrid model can accurately predict the SOFC-GT system performance. The maximum error is 1.97% at the design condition and 0.60% at off-design conditions. Therefore, the LS-SVM hybrid model is significant for accurately identifying the real electrochemical characteristic from operation data for a physical SOFC-GT system during the full operation cycle.

Improved Small Domain Estimation via Compromise Regression Weights

Shrinkage estimates of small domain parameters typically use a combination of a noisy “direct” estimate that only uses data from a specific small domain and a more stable regression estimate. When the regression model is misspecified, estimation performance for the noisier domains can suffer due to substantial shrinkage toward a poorly estimated regression surface. In this article, we introduce a new class of robust, empirically-driven regression weights that target estimation of the small domain means under potential misspecification of the global regression model. Our regression weights are a convex combination of the model-based weights associated with the best linear unbiased predictor (BLUP) and those associated with the observed best predictor (OBP). The mixing parameter in this convex combination is found by minimizing a novel, unbiased estimate of the mean-squared prediction error for the small domain means, and we label the associated small domain estimates the “compromise best predictor,” or CBP. Using a data-adaptive mixture for the regression weights enables the CBP to preserve the robustness of the OBP while retaining the main advantages of the EBLUP whenever the regression model is correct. We demonstrate the use of the CBP in an application estimating gait speed in older adults. Supplementary materials for this article are available online.

An MAP Method for Closed-Loop Channel Training in Massive MIMO Systems

Accurate channel state information is of great importance in high-speed wireless communication. In this work, the problem of channel training for massive multiple-input multiple-output (MIMO) systems is investigated and a closed-loop channel training scheme is devised upon the maximum <i>a</i> <i>posteriori</i> (MAP) criterion. More specifically, at each channel use, the channel estimation is performed at the receiver according to the MAP criterion, and the expectation-maximization (EM) algorithm is employed to cope with the MAP estimation problem. Based on the parameter estimates, the predicted mean square error (PMSE) of the channel estimate is minimized to determine a preferred sounding beam for the next channel use, of which the index is fed back to the transmitter via a limited-rate channel. The performance superiority of the proposed approach is evidenced by comparing with various channel training schemes.

Biomechanical Analysis of the Influence of SACH Foot and Dynamic-Response Foot in Individual With Unilateral Transtibial Amputee During Running

Objective: Amputation of the lower limb due to loss of part of the musculoskeletal structure reduces performance and increases injury during locomotion. The effect of various types of prosthetic feet has been analyzed in several studies during running. The purpose of this study was a biomechanical analysis of the influence of SACH and Dynamic-Response foot on several kinetic variables in the stance phase of running in individuals with unilateral transtibial amputation. Materials &amp; Methods: In this semi-experimental study, 8 left foot transtibial amputees were included in this study using an available or easy sampling method. The target population was unilateral transtibial amputees who were able to run and the available population included left transtibial amputees who were referred to Kosar Rehabilitation Center in Tehran from 2008 to 2012. To adapt to the foot, each foot was used by the subjects for at least one week before the experiment. All subjects participated in 3 running evaluation sessions; 1 session involving the use of their own foot (familiarization session), 1 session involving the use of SACH foot, and 1 session involving the use of Dynamic foot. Only data from the 2 last sessions were used to compare both feet. Each subject runs in 12-meter walkway 3 times at a speed of 2.5 meters per second. The same running speed was chosen for the comparability of kinetic variables. Sport shoes were used to bring the test conditions closer to the actual running conditions. In each session, 3 successful trials were performed so that the foot was in full and perfect contact with the force plate. Kistler force plate and three-dimensional motion analysis Vicon system were used to collect kinetic and kinematic data, respectively. The motion and the force plate data were sampled simultaneously at 200 and 1000 Hz, respectively. The trajectories of the markers and analog data were filtered using the predicted mean square error adaptive filter in version 1.7 of the Vicon software package. The Kinetic variables were generated using the dynamic model of the Vicon Plug-in-Gait. The vertical ground reaction force was normalized for body weight. In the present study, 5 variables were selected for biomechanical analysis of feet. The maximum vertical ground reaction force, power, spring efficiency, ankle moments at the amputated leg, and the symmetry ratio (percentage) of the maximum vertical ground reaction force between the amputated leg and the intact leg were calculated. All values ​​in each trial were averaged for each subject with each foot. A paired t-test and a Wilcoxon test were used to analyze the data based on normality (P ≤0.05). Results: In examining the normality of the data distribution, the results showed that the data of maximum power absorption of the ankle with the SACH foot and the maximum power absorption of the hip with the Dynamic-Response foot did not have a normal distribution and other variables had a normal distribution. The results of paired t-test and Wilcoxon showed that Spring Efficiency and Maximum Plantar Flexion were significantly different between the SACH and Dynamic-Response feet (P ≤0.05). The Spring Efficiency was greater with Dynamic-Response foot than the SACH foot (P =0.05), although the Maximum Plantar Flexion with the SACH foot was greater than Dynamic-Response foot (P =0.05). While there is no statistical difference between the maximum vertical ground reaction force, maximum power absorption and generation in the ankle, maximum power absorption and generation in the knee, maximum power absorption and generation in the hip, maximum dorsiflexion moment, and the symmetry ratio (percentage) of the maximum vertical ground reaction force between the amputated leg and the intact leg. Conclusion: The results of the study showed that the spring efficiency with Dynamic-Response foot was greater than SACH foot and closing to the spring efficiency of a normal foot. With this perspective, the Dynamic-Response foot has more natural performance than the SACH foot.