Large Estimation Errors Research Articles

In silico data-based comparison of the accuracy and error source of various methods for noninvasively estimating central aortic blood pressure

Background and objectivesThe higher clinical significance of central aortic blood pressure (CABP) compared to peripheral blood pressures has been extensively demonstrated. Accordingly, many methods for noninvasively estimating CABP have been proposed. However, there still lacks a systematic comparison of existing methods, especially in terms of how they differ in the ability to tolerate individual differences or measurement errors. The present study was designed to address this gap. MethodsA large-scale ‘virtual subject’ dataset (n = 600) was created using a computational model of the cardiovascular system, and applied to examine several classical CABP estimation methods, including the direct method, generalized transfer function (GTF) method, n-point moving average (NPMA) method, second systolic pressure of periphery (SBP2) method, physical model-based wave analysis (MBWA) method, and suprasystolic cuff-based waveform reconstruction (SCWR) method. The errors of CABP estimation were analyzed and compared among methods with respect to the magnitude/distribution, correlations with physiological/hemodynamic factors, and sensitivities to noninvasive measurement errors. ResultsThe errors of CABP estimation exhibited evident inter-method differences in terms of the mean and standard deviation (SD). Relatively, the estimation errors of the methods adopting pre-trained algorithms (i.e., the GTF and SCWR methods) were overall smaller and less sensitive to variations in physiological/hemodynamic conditions and random errors in noninvasive measurement of brachial arterial blood pressure (used for calibrating peripheral pulse wave). The performances of all the methods worsened following the introduction of random errors to peripheral pulse wave (used for deriving CABP), as characterized by the enlarged SD and/or increased mean of the estimation errors. Notably, the GTF and SCWR methods did not exhibit a better capability of tolerating pulse wave errors in comparison with other methods. ConclusionsClassical noninvasive methods for estimating CABP were found to differ considerably in both the accuracy and error source, which provided theoretical evidence for understanding the specific advantages and disadvantages of each method. Knowledge about the method-specific error source and sensitivities of errors to different physiological/hemodynamic factors may contribute as theoretical references for interpreting clinical observations and exploring factors underlying large estimation errors, or provide guidance for optimizing existing methods or developing new methods.

Collision probability sliding mode guidance for spacecraft autonomous obstacle avoidance under state uncertainty

Spacecraft must possess the capability of autonomous obstacle avoidance to ensure flight safety. Currently, the available methods for obstacle avoidance guidance are primarily deterministic and will fail with large state estimation errors, which necessitates the robustness of guidance algorithms towards uncertainties. A collision probability sliding mode guidance method is presented for the autonomous obstacle avoidance of spacecraft. Based on the multiple sliding mode surfaces guidance law, a sliding mode structure is proposed to realize obstacle avoidance in the presence of state uncertainty, taking into account the collision probability constraint. First, A multiple power reaching law is designed to enhance the convergence speed of the first sliding mode surface, so that the spacecraft system reaches the target state within a finite time. Subsequently, the collision probability gradient information function is defined according to the relative relation between the spacecraft and obstacles. This function is then combined with the improved reaching law to develop the second sliding mode surface. The sliding mode surface can quantify the collision probability in real-time, and generate corresponding obstacle avoidance guidance commands to prompt the spacecraft to move away from the obstacles according to the magnitude of collision probability, which is robust to the state uncertainty. The capability of the proposed guidance algorithm is validated by a series of simulations, including scenarios involving asteroid landing and spacecraft rendezvous, and the effectiveness and robustness in the face of uncertainties are confirmed.

Using Satellite Data Assimilation Techniques to Combine Infrasound Observations and a Full Ray-Tracing Model to Constrain Stratospheric Variables

Abstract Infrasound waves generated at Earth’s surface can reach high altitudes before returning to the surface to be recorded by microbarometer array stations. These waves carry information about the propagation medium, in particular temperature and winds in the atmosphere. It is only recently that studies on the assimilation of such data into atmospheric models have been published. Intending to advance this line of research, we here use the modulated ensemble transform Kalman filter (METKF)—commonly used in satellite data assimilation—to assimilate infrasound-related observations in order to update a column of three vertically varying variables: temperature and horizontal wind speeds. This includes stratospheric and mesospheric heights, which are otherwise poorly observed. The numerical experiments on synthetic data but with realistic reanalysis product atmospheric specifications (following the observing system simulation experiment paradigm) reveal that a large ensemble is capable of reducing errors, especially for wind speeds in stratospheric heights close to 30–60 km. While using a small ensemble leads to incorrect analysis increments and large estimation errors, the METKF ameliorates this problem and even achieves error reduction from the prior to the posterior mean estimator. Significance Statement The stratosphere and mesosphere have significantly less observational coverage compared to the troposphere, especially for the winds. This lack of information can reduce the accuracy of medium-range weather forecasts. By mimicking a realistic setup, this study paves the way for including novel observations in the estimation of the atmospheric state in these heights using an ensemble data assimilation method. These observations come from a dataset of opportunity containing infrasound-related measurements that are routinely carried out at several stations around the world.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

Carbohydrate Estimation Accuracy of Two Commercially Available Smartphone Applications vs Estimation by Individuals With Type 1 Diabetes: A Comparative Study.

Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce. The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D). Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire. Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods. SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.

Multi-lane vehicle load measurement using bending and shear strains

Abstract Many load identification methods have been proposed, but most are affected by the basic axle parameters and lateral distribution of vehicles. To effectively measure traffic flow with lateral distribution information, this article presents an innovative method that employs a strain decoupling model (SDM) and a vehicle information identification model (VIDM) to measure multi-lane vehicle load depending on the bending strain and shear strain from long-gauge fiber bragg grating (FBG) sensors. The SDM decouples the measured coupling strain into the strain for a single lane load, thereby simplifying the complex structural response resulting from lateral distributed vehicles. By exploiting the distinct characteristics of different strain types that reflect various aspects of the structure, the VIDM establishes a sophisticated mapping relationship between bending, shear strain and axle parameters, which enables the accurate determination of axle parameters including axle speed and spacing. The real-time estimation of the multi-lane vehicle load is achieved by combining the obtained axle information with the decoupled bending strain. This method effectively solves the problem of large load estimation error caused by inaccurate identification of axle parameters, and enables accurate acquisition of vehicle load in lateral distribution using bending and shear strains near the bridge entrance. Both numerical studies and laboratory tests are carried out on a simply supported beam for conceptual verification. The results demonstrate that the proposed method successfully improves the measurement of multi-lane vehicle load.

A transfer learning-based ensemble learning model for electric vehicles lithium-ion battery capacity estimation using electrochemical impedance spectroscopy

Lithium-ion battery capacity estimation is crucial to ensure the operational reliability and safety of electric vehicles. Electrochemical Impedance Spectroscopy (EIS) can provide rich physical degradation information of the battery, which makes the EIS-based data-driven method a promising solution for accurate battery capacity estimation. However, batteries tend to present diverse degradation patterns due to the diverse operating conditions and manufacturing, resulting in large capacity estimation errors in practical applications. Therefore, this paper proposes a transfer learning (TL) based ensemble learning (EL) method based on multi-layer perception for the battery capacity estimation. First, the influential impedance features are identified from the EIS using Spearman rank coefficient analysis. Second, three TL models are developed based on distinct TL strategies to enable sufficient knowledge transfer considering the different degradation pattern. Further, an EL method with Gaussian kernel-based weighted assignment technique is proposed to integrate the developed TL models and improve the estimation robustness. The performance of the developed model has been successfully validated only using the first 1/3 cycles of the historical data from target battery obtained under different operating conditions and batteries. The results reveal that the proposed method can largely suppress estimation errors, presenting more accurate capacity estimation compared to other methods with an RMSE lower than 1%.

Robust Design Based on Cost-Quality Model in Micro-Manufacturing

This paper proposes a novel total cost model for the micro‐products' entire life cycle that takes into account the uncertainty of the model parameters. The total cost includes pre-sale manufacturing and post-sale warranty costs. Additionally, different marketing strategies are also given based on the weight of internal and external costs. Furthermore, limited data and unknown effects in experiments may cause large errors in parameter estimates. This could prevent the achievement of reliable designs. To address this, robust optimization and interval estimation are used. This approach reduces the impact of uncertainty on parameter estimates. It ensures optimality and robustness in micro-manufacturing parameters. Example analysis and numerical simulation results show that the proposed method assists companies in selecting the optimal manufacturing parameter level that aligns with their marketing strategies. Besides, considering uncertainty factors can ensure that the optimization results remain guaranteed, even under the worst-case scenarios.

Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method

Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (ks,i and keq,i) and nonlinear parameters (ni and qmax,i) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (kp,i and kkin,i) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter qmax. Therefore, the inverse method was introduced to further calibrate the parameter qmax, thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of qmax without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, qmax would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals.

Function observer based on model compensation control for a fixed-wing UAV

Fixed-wing unmanned aerial vehicle (UAV) is a nonlinear system characterized by strong coupling, high complexity, and sensitivity to external interference and model uncertainty. For the fixed-wing UAV having eight degrees of freedom, the uncertainty and wind disturbance parts of the model are clarified, and a model with lump disturbance is established through the mechanic analysis. To get the high performance of control, a highly accurate observer for the lump disturbance is necessary. A second-order compensation function observer (CFO) with feedforward is proposed with new structure. The CFO accuracy is much higher than the same order of the extended state observer (ESO) by convergence analysis. A hyperbolic tangent function is combined with the CFO for effectively reducing the large estimation error at the initial moment. A CFO-based model compensation control (MCC) algorithm is provided for controlling the longitudinal and lateral channels of the fixed-wing UAV. The MCC contains the known model, the compensation of unknown model via the CFO, feedback of error and the dynamic information of the given information. The MCC realizes the desired linear characteristics for the feedback system because it decouples the channels, restrains the lump disturbance. The stability of the closed-loop system is proved by Lyapunov theorem. The proposed CFO-based MCC is applied to the fixed-wing UAV, and compared with the traditional PID and ESO-based on sliding mode control (SMC) algorithm through simulation and the control test platform experiment based on Pixhawk, showing the superiority of the MCC and highly accuracy of the CFO.

An improved electrochemical equivalent circuit model and precise state-of-charge estimation comparative study for lithium-rich manganese-based battery

State-of-charge (SOC) serves as a crucial metric for lithium-ion batteries. A precise battery model is an essential factor influencing the accuracy of SOC estimation. Notably, lithium-rich manganese-based (LRMB) layered oxide batteries exhibit distinct voltage characteristics that differentiate them from other battery types. Experimental findings reveal that traditional equivalent circuit models (ECMs) are not well-suited for capturing the behavior of LRMB batteries. To address the aforementioned problem and its attendant phenomenon of large SOC estimation error, in this paper, we proposed an improved electrochemical equivalent circuit model (IEECM) for LRMB batteries that concurrently enhances accuracy and mitigates error. The proposed model comprehensively accounts for the long-term diffusion process of lithium ions within the solid phase, thereby offering a more precise representation of the battery's behavior. The results indicate that under different operating conditions, the RMSE of voltage error remains below 31.3 mV. Notably, under UDDS conditions, the MAE and RMSE are merely 15.1 mV and 20.6 mV, respectively. To further validate its effectiveness, we utilize three common SOC estimation algorithms under various operation conditions. Experimental results show that the RMSEs of SOC estimation based on the proposed model under different algorithms are lower than 2.21 %, which is better than ECMs. Consistently, our findings indicate that this model is particularly well-suited for LRMB batteries, leading to a notable enhancement in SOC estimation accuracy.

Comprehensive assessment of five near-real-time satellite precipitation products in the Lower Yangtze River Basin and the Lixiahe region, China: Dual perspectives from time series and extreme events

Near real-time satellite precipitation products (NSPPs) have significant potential in meteorological hydrological monitoring and forecasting due to their timeliness. Previous evaluation-oriented studies have either focused on the uncertainty of time series or on the monitoring capability of extreme events. There is limited research that comprehensively considers both aspects to provide a systematic knowledge. Moreover, there is still insufficient understanding of the accuracy of hourly precipitation estimates. This study conducts a comprehensive evaluation between five NSPPs (IMERG Early, IMERG Late, GSMaP NRT, GSMaP NRT Gauge and PERSIANN CCS) from two main perspectives at hourly scale: an assessment of time series spanning from 2018 to 2022 over the Lower Yangtze River Basin and the Lixiahe region and an examination of the four extreme events over the Tai Lake Basin and the Chao Lake Basin. HP-metrics are proposed to evaluate bias in the location of extreme rainfall, rainy area, and precipitation amount over time, comprehensively analyzing performance in terms of extreme events. The results show that, for general and seasonal assessment, IMERG Late generally performed best with good statistical metrics, with the highest CSI (0.343), CC (0.445), KGE ‘(0.295) in annual results, and the best agreement in capturing convective rain diurnal curves (summer CC = 0.820). GSMaP showed a better detection capability especially in spring and summer, and also performed better in capturing frequency curves, but always showed large estimation errors. IMERG and GSMaP detected worse CC region close to the water body. And the effect of high terrain on GSMaP NRT is more obvious in winter. For the evaluation in detecting the extreme events, IMERG and GSMaP overestimated the area and intensity and had a certain ability to discriminate the location of extreme precipitation, based on the temporal variation of HP-metrics. The rapid expansion or contraction of the extent of the extreme precipitation region could degrade the detection capability of HP-π. The comprehensive understanding of NSPPs provides valuable insights for upgrading satellite retrieval algorithms and making informed selection for precipitation products in meteorological hydrological applications.

Quantitative evaluation of the impact of variation of optical parameters on the estimation of blood hematocrit and oxygen saturation for dual-wavelength photoacoustics

Photoacoustic (PA) spectroscopy is considered to be one of the most effective ways to measure the levels of hematocrit (H) and oxygenation saturation (SO2) of blood, which are essential for diagnosing blood-related illnesses. This simulation study aims to investigate the impact of individual optical parameters, i.e., optical absorption coefficient (μ a ), scattering coefficient (μ s ), and anisotropy factor (g), on the accuracy of this technique in estimating the blood properties. We first performed the Monte Carlo simulations, using realistic optical parameters, to obtain the fluence maps for various samples. The wavelengths of the incident light were chosen to be 532, 700, 1000, and 1064 nm. Thereafter, the k-Wave simulations were executed, incorporating those fluence maps to generate the PA signals. The blood properties were obtained using the PA signals. We introduced variations in μ a , μ s , and g ranging from −10% to +10%, −10% to +10%, and −5% to +1%, respectively, at 700 and 1000 nm wavelengths. One parameter, at both wavelengths, was changed at a time, keeping others fixed. Subsequently, we examined how accurately the blood parameters could be determined at physiological hematocrit levels. A 10% variation in μ a induces a 10% change in H estimation but no change in SO2 determination. Almost no change has been seen for μ s variation. However, a 5% (−5% to 0%) variation in the g factor resulted in approximately 160% and 115% changes in the PA signal amplitudes at 700 and 1000 nm, respectively, leading to ≈125% error in hematocrit estimation and ≈14% deviation in SO2 assessment when nominal SO2=70%. It is clear from this study that the scattering anisotropy factor is a very sensitive parameter and a small change in its value can result in large errors in the PA estimation of blood properties. In the future, in vitro experiments with pathological blood (inducing variation in the g parameter) will be performed, and accordingly, the accuracy of the PA technique in quantifying blood H and SO2 will be evaluated.

Bayesian Learning in an Affine GARCH Model with Application to Portfolio Optimization

This paper develops a methodology to accommodate uncertainty in a GARCH model with the goal of improving portfolio decisions via Bayesian learning. Given the abundant evidence of uncertainty in estimating expected returns, we focus our analyses on the single parameter driving expected returns. After deriving an Uncertainty-Implied GARCH (UI-GARCH) model, we investigate how learning about uncertainty affects investments in a dynamic portfolio optimization problem. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize her expected utility from terminal wealth under an Affine GARCH(1,1) model. The corresponding stock evolution, and therefore, the wealth process, is treated as a Bayesian information model that learns about the expected return with each period. We explore the one- and two-period cases, demonstrating a significant impact of uncertainty on optimal allocation and wealth-equivalent losses, particularly in the case of a small sample size or large standard errors in the parameter estimation. These analyses are conducted under well-documented parametric choices. The methodology can be adapted to other GARCH models and applications beyond portfolio optimization.

An improved analysis of small punch deformation for evaluating tensile properties

Small punch (SP) method is a promising technique for evaluating tensile properties using small volumes of test material, yet its applicability is bound by large errors in estimation of tensile yield strength (YS) using the yield force (FY) determined by the existing two tangent, offset or bilinear function fitting methods. In this study, a yield force (FY) based on the occurrence of yielding across the specimen thickness is identified through finite element analysis and a methodology is formulated for determination of FY from experimental data, supported with acoustic emission (AE) technique. The YS-FY correlation for various structural steels, Inconel and Aluminium exhibit excellent linearity and standard errors within ±7 %. Based on the onset of plastic instability in SP deformation at an inflection point (Fs, us) of force-deflection curve, the specimen deflection us is shown to correlate with tensile uniform strain better than parameter um (deflection at peak load). The tensile properties of neutron irradiated SS 316 evaluated from SP tests using the methodologies and SP-tensile correlations formulated in this work, are found to be within 6–8 % of the uniaxial tensile test results.

An LDP Compatible Sketch for Securely Approximating Set Intersection Cardinalities

Given two sets of elements held by two different parties separately, computing the cardinality (i.e., the number of distinct elements) of their intersection set is a fundamental task in applications such as network monitoring and database systems. To handle large sets with limited space, computation, and communication costs, lightweight probabilistic methods (i.e., sketch methods) such as the Flajolet-Martin (FM) sketch and the HyperLogLog (HLL) sketch are extensively used. However, when a set's probabilistic data summary and the hash functions used to construct the sketch are disclosed to an untrusted third party, the set's privacy is compromised. Directly applyingLocal Differential Privacy (LDP) techniques to safeguard the sketch collection results in extremely large estimation errors of set intersection cardinalities. To address this issue, we propose a novel sketch method that makes it easier to incorporate noise into the constructed sketch to achieve differential privacy. More importantly, our sketch method is compatible with the LDP noise. In other words, the probabilistic model underlying our LDP-based data summary is quite basic, allowing us to eliminate the estimation error generated by the noise. We perform extensive experiments on various synthetic and real-world datasets and the experimental results demonstrate that our method is orders of magnitude more accurate and several times faster than state-of-the-art methods.

Ocean-colour anomalies quantified by the human eye.

Phytoplankton turn seawater green when their concentration increases. This allows us to monitor them using ocean colour. However, as the spectral properties of phytoplankton and their relationship with other coloured substances in seawater vary, subtle differences (anomalies) in ocean colour occur that can cause large errors in estimates of phytoplankton abundance. Identifying and understanding these anomalies is required to interpret ocean-colour data properly, but not all scientists have access to, or can afford, the in-situ instrumentation needed to do this. We show that practical, low-cost tools developed in the 19th century (a Secchi disk and Forel-Ule colour scale) can be used to quantify a colour anomaly in the Weddell Sea. Our findings imply that ocean-colour anomalies can be identified using affordable methods. Furthermore, records collected over the last century may contain clues on how ocean ecosystems have changed with climate.

Indoor environment PV applications: Estimation of the maximum harvestable power

The use of PV modules for powering sensors in an indoor environment requires that, during the design process, the harvestable power be evaluated and compared with the power requirements of the load device to validate their compatibility for a given type of light source and level of illumination. The models reported recently to relate power to illumination during power estimation are seen to result in large estimation errors in the case of an indoor environment. This paper seeks to propose an appropriate model that can be exploited during maximum power estimation in an indoor environment to relate power to illumination level. Proceeding from fitting measured data to the three-diode model of the PV module and based on the errors and execution time, the Grey Wolf Optimisation Algorithm, among others, proved to be accurate for estimating maximum power. The relationship between maximum power and illumination was seen to take the form of a second-degree polynomial. This was validated by comparing the values of power estimated by this model to the measured values as well as values estimated with the linear model, the exponential model, and the model proposed by Joseph Amajama. The proposed model proved to be more accurate than the models reported, recording errors of not more than 5 %, which is acceptable for system design. This will go a long way towards facilitating the design of indoor energy harvesting systems that can be used for powering smart sensors and other IoT devices.

Prediction of Fusion Rod Curvature Angles in Posterior Scoliosis Correction Using Artificial Intelligence.

This study aimed to estimate post-operative rod angles in both concave and convex sides of scoliosis curvature in patients who had undergone posterior surgery, using neural networks and support vector machine (SVM) algorithms. Radiographs of 72 scoliotic individuals were obtained to predict post-operative rod angles at all fusion levels (all spinal joints fused by rods). Pre-operative radiographical indices and pre-operatively resolved net joint moments of the apical vertebrae were employed as inputs for neural networks and SVM with biomechanical modeling using inverse dynamics analysis. Various group combinations were considered as inputs, based on the number of pre-operative angles and moments. Rod angles on both the concave and convex sides of the Cobb angle were considered as outputs. To assess the outcomes, root mean square errors (RMSEs) were evaluated between actual and predicted rod angles. Among eight groups with various combinations of radiographical and biomechanical parameters (such as Cobb, kyphosis, and lordosis, as well as joint moments), RMSEs of groups 4 (with seven radiographical angles in each case, which is greater in quantity) and 5 (with four radiographical angles and one biomechanical moment in each case, which is the least possible number of inputs with both radiographical and biomechanical parameters) were minimum, particularly in prediction of the concave rod kyphosis angle (errors were 5.5° and 6.3° for groups 4 and 5, respectively). Rod lordosis angles had larger estimation errors than rod kyphosis ones. Neural networks and SVM can be effective techniques for the post-operative estimation of rod angles at all fusion levels to assist surgeons with rod bending procedures before actual surgery. However, since rod lordosis fusion levels vary widely across scoliosis cases, it is simpler to predict rod kyphosis angles, which is more essential for surgeons.

TrackPose

Several studies have explored the estimation of finger pose/angle to enhance the expressiveness of touchscreens. However, the accuracy of previous algorithms is limited by large estimation errors, and the sequential output angles are unstable, making it difficult to meet the demands of practical applications. We believe the defect arises from improper rotation representation, the lack of time-series modeling, and the difficulty in accommodating individual differences among users. To address these issues, we conduct in-depth study of rotation representation for the 2D pose problem by minimizing the errors between representation space and original space. A deep learning model, TrackPose, using a self-attention mechanism is proposed for time-series modeling to improve accuracy and stability of finger pose. A registration application on a mobile phone is developed to collect touchscreen images of each new user without the use of optical tracking device. The combination of the three measures mentioned above has resulted in a 33% reduction in the angle estimation error, 47% for the yaw angle especially. Additionally, the instability of sequential estimations, measured by the proposed metric MAEΔ, is reduced by 62%. User study further confirms the effectiveness of our proposed algorithm.