Minimum Error Algorithm Research Articles

Study of the filtration efficiency of radon progeny in facial masks

Radon exposure is the second-leading environmental cause of lung cancer, therefore finding ways to reduce the radon inhalation dose is a priority objective. Inhalation dose is calculated considering two contributions, the radon gas on the one hand and its progeny on the other. This paper studies the effectiveness of face masks (surgical masks, FFP2 and FFP3) to reduce the contribution of radon descendants that pass into the respiratory tract and thus reduce associated inhalation doses. To analyze the radon filtering potential of these masks, a continuous radon monitor has been used to estimate the radon concentration and the Potential Alpha Energy Concentration with and without facial masks. The data registered by this monitor, together with an error minimization algorithm developed in Matlab®, allow the Equilibrium Equivalent Concentration to be calculated. These results make it possible to compare the inhalation doses received by a person not wearing or wearing different types of masks, obtaining a dose reduction of 64% for surgical masks compared to the study without mask, a reduction of 71% for FFP2 and 72% for FFP3 masks.

Imageless robotic total knee arthroplasty determines similar coronal plane alignment of the knee (CPAK) parameters to long leg radiographs

BackgroundThe coronal plane alignment of the knee (CPAK) classification was first developed using long leg radiographs (LLR) and has since been reported using image-based and imageless robotic total knee arthroplasty (TKA) systems. However, the correspondence between imageless robotics and LLR-derived CPAK parameters has yet to be investigated. This study therefore examined the differences in CPAK parameters determined with LLR and imageless robotic navigation using either generic or optimized cartilage wear assumptions.MethodsMedial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA) were determined from the intraoperative registration data of 61 imageless robotic TKAs using either a generic 2 mm literature-based wear assumption (Navlit) or an optimized wear assumption (Navopt) found using an error minimization algorithm. MPTA and LDFA were also measured from preoperative LLR by two observers and intraclass correlation coefficients (ICCs) were calculated. MPTA, LDFA, joint line obliquity (JLO), and arithmetic hip-knee-ankle angle (aHKA) were compared between the robotic and the average LLR measurements over the two observers.ResultsICCs between observers for LLR were over 0.95 for MPTA, LDFA, JLO, and aHKA, indicating excellent agreement. Mean CPAK differences were not significant between LLR and Navlit (all differences within 0.6°, P > 0.1) or Navopt (all within 0.1°, P > 0.83). Mean absolute errors (MAE) between LLR and Navlit were: LDFA = 1.4°, MPTA = 2.0°, JLO = 2.1°, and aHKA = 2.7°. Compared to LLR, the generic wear classified 88% and the optimized wear classified 94% of knees within one CPAK group. Bland–Altman comparisons reported good agreement for LLR vs. Navlit and Navopt, with > 95% and > 91.8% of measurements within the limits of agreement across all CPAK parameters, respectively.ConclusionsImageless robotic navigation data can be used to calculate CPAK parameters for arthritic knees undergoing TKA with good agreement to LLR. Generic wear assumptions determined MPTA and LDFA with MAE within 2° and optimizing wear assumptions showed negligible improvement.

Complex error minimization algorithm with adaptive change rate

In this paper, we develop a new algorithm for estimating control parameter values of ordinary differential equation (ODE) models. We call it the complex error minimization algorithm with an adaptive rate of change. It efficiently determines the parameters of ODE models even over extremely short time series. The algorithm is based on the gradient method and successfully solves its major drawback, namely getting stuck in local minima. To detect the getting stuck, we first introduce the calculation of four different errors at once. Our studies show that they achieved the global minimum only when we observe simultaneous minimization of all these errors. When getting stuck in local minima takes place, only one (or several) errors are minimized, while the others remain unchanged or grow. Thus, the algorithm accurately detects getting stuck in local minima. After this, it applies special methods based on simulated annealing to escape from local minima. The algorithm selects the most optimal path for convergence of parameter values to the global minimum by applying random restarts and multi-start methods to avoid local minima. We have tested our method on models with regular and chaotic dynamics and have found that it can work with high accuracy in these cases. Our method is suitable for actual systems with complex dynamics and an extensive set of control parameters but short time series. Thus, the proposed algorithm is an effective method for solving the inverse problem of ODE models.

Predictive learning by a burst-dependent learning rule

Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the presence of sensory noise, in order to be useful for informing behavior. In contrast, typical machine learning approaches require many thousands of samples, and generalize poorly to unexperienced examples, or fail completely to predict at long timescales. Here, we propose a novel neural network module which incorporates hierarchy and recurrent feedback terms, constituting a simplified model of neocortical microcircuits. This microcircuit predicts spatiotemporal trajectories at the input layer using a temporal error minimization algorithm. We show that this module is able to predict with higher accuracy into the future compared to traditional models. Investigating this model we find that successive predictive models learn representations which are increasingly removed from the raw sensory space, namely as successive temporal derivatives of the positional information. Next, we introduce a spiking neural network model which implements the rate-model through the use of a recently proposed biological learning rule utilizing dual-compartment neurons. We show that this network performs well on the same tasks as the mean-field models, by developing intrinsic dynamics that follow the dynamics of the external stimulus, while coordinating transmission of higher-order dynamics. Taken as a whole, these findings suggest that hierarchical temporal abstraction of sequences, rather than feed-forward reconstruction, may be responsible for the ability of neural systems to quickly adapt to novel situations.

Multiple-Input-Single-Output prediction models of crowd dynamics for Model Predictive Control (MPC) of crowd evacuations

Predicting crowd dynamics in real-time may allow the design of adaptive pedestrian flow control mechanisms that prioritize attendees’ safety and overall experience. Single-Input-Single-Output (SISO) AutoRegresive eXogenous (ARX) prediction models of crowd dynamics have been effectively used in Linear Model Predictive Controllers (MPC) that adaptively regulate the movement of people to avoid overcrowding. However, an open research question is whether Multiple-Input, State-space, and Nonlinear modeling approaches may improve MPC control performance through better prediction capabilities. This paper considers a simulated controlled evacuation scenario, where evacuees in a long corridor dynamically receive speed instructions to modulate congestion at the exits. We aim to investigate Multiple-Input-Single-Output (MISO) prediction models such that the inputs are the control action (speed recommendation) and pedestrian flow measurement, and the output is the local density of the pedestrian outflow. State-space and Input–output MISO models, linear and neural, are identified using a data-driven approach in which input–output datasets are generated from strategically designed microscopic evacuation simulations. Different estimation algorithms, including the subspace method, prediction error minimization, and regularized AutoRegressive eXogenous (ARX) model reduction, are evaluated and compared. Finally, to investigate the importance of measuring and modeling the pedestrian inflow, the case in which the models’ structure is defined as a Single-Input-Single-Output (SISO) system has been explored, where the pedestrian inflow is considered an unmeasured input disturbance. This study has important implications for the design of more effective MPC controllers for regulating pedestrian flows. We found that the prediction error minimization algorithm performs best and that nonlinear state-space modeling does not improve prediction performance. The study suggests that modeling the inner state of the evacuation process through a state-space model positively influences predicting system dynamics. Also, modeling pedestrian inflow improves prediction performance from a predefined prediction horizon value. Overall, linear state-space models have been deemed the most suitable option in corridor-type scenarios.

Velocity structure above seismological bedrock estimated from earthquake recordings: an application of diffuse wave-field concept to strong motions in Iran

We present velocity structure inversion for three stations of Iranian Strong Motion Network (ISMN), and one KiK-net station that is used as a benchmark, for the application of diffuse wave-field concept in tectonic and geological setting of the west of Iran. This study compares the results of two existing computer codes for the velocity structure inversion at these sites. The computer codes use different search space parameterization, and error-minimization algorithms. Firstly, the available information on subsurface structure and surface geology from the strong motion stations is introduced. Then, ground motions of MW larger than 4, with PGA of all components less than 50 cm/s2 are used to calculate horizontal-to-vertical spectral ratios of earthquakes (eHVSR) at each station. The observed eHVSR curves are inverted for the velocity structure of the stations, and the results are compared with each other and the information of previous studies. Finally, the eHVSR curves of the mainshock records of the recent MW 7.3 earthquake at two ISMN sites are investigated. The velocity inversion is repeated by considering the shear modulus degradation of several shallow layers. Empirical nonlinear site amplification functions are calculated based on the modified VS structure and the VS structure of the linear ground response for two ISMN stations.Graphical

Cooperative hybrid beamforming design in Multi‐UAV MmWave MIMO systems

AbstractDue to the limitation of mobility and cost, it is inappropriate to utilize ground base station (GBS) to provide service in emergency scenarios or harsh environments. Unmanned aerial vehicle (UAV) is considered as a feasible way to solve the problem. Millimetre wave communication and UAV can be used together to offer wireless communications flexibly and rapidly for target area. However, fuselage jittering and energy supply would affect the quality and reliability of UAV communication seriously. To overcome the weakness of UAV, the authors prefer to use multiple UAVs to provide wireless communication service simultaneously. This paper investigates the design of hybrid beamforming in the scenarios including multiple UAVs and one end user. Cooperation strategy and alternating optimization method are applied in this design. The distributed UAV base stations are regarded as an entirety and the reference beamforming scheme obtained. Then, the beamforming problem for each UAV is formulated. Moreover, residual error minimization (REM) algorithm is proposed to find the solution of hybrid beamformers at UAV side. Finally, the beamformer of the receiver is optimized with the assist of effective channel, which includes the gain of both channel and beamformers of the transmitters. Simulation results show that the proposed algorithm can achieve near‐optimal spectral efficiency in different scenarios.

Learning from non-irreducible Markov chains

Most of the existing literature on supervised machine learning problems focuses on the case when the training data set is drawn from an i.i.d. sample. However, many practical problems are characterized by temporal dependence and strong correlation between the marginals of the data-generating process, suggesting that the i.i.d. assumption is not always justified. This problem has been already considered in the context of Markov chains satisfying the Doeblin condition. This condition, among other things, implies that the chain is not singular in its behavior, i.e. it is irreducible. In this article, we focus on the case when the training data set is drawn from a not necessarily irreducible Markov chain. Under the assumption that the chain is uniformly ergodic with respect to the L1-Wasserstein distance, and certain regularity assumptions on the hypothesis class and the state space of the chain, we first obtain a uniform convergence result for the corresponding sample error, and then we conclude learnability of the approximate sample error minimization algorithm and find its generalization bounds. At the end, a relative uniform convergence result for the sample error is also discussed.

Hydrodynamic Parameters Estimation Using Varying Forces and Numerical Integration Fitting Method

This paper presents a new experimental method for the estimation of hydrodynamic parameters of underwater vehicles, namely the added mass and drag coefficients. The principle is to accurately record the movement of the vehicle in response to a known time-varying actuating force, and find an optimal solution for the parameters that allows simulating a velocity trajectory that fits the recorded trajectory. The optimal solution is obtained using a Numerical Integration Fitting method, which, employing an error minimization algorithm, successively generates velocity trajectories through numerical integration until the parameters converge. This method is applied to determine the hydrodynamic parameters of a torpedo-shaped Autonomous Underwater Vehicle (AUV). The hydrodynamic parameters obtained are compared and found to be close to their theoretical values. In addition, the use of a time-varying force is shown to provide better and more reliable results than the use of a constant or zero external force.

Inverse algorithm for extraction of multiple parameters using analytical model of eddy current response

Eddy current (EC) technology is commonly used for detecting flaws, measuring geometric parameters, or determining properties of conducting materials. However, the measurement of a particular parameter can become more challenging if multiple influential parameters vary simultaneously. In particular, eddy current-based measurement of separation (gap) between a pressure tube (PT) and a calandria tube (CT) in the fuel channels of CANDU® reactors is made more difficult by variations in PT wall thickness, resistivity, and probe lift-off. An analytical model of the EC response to changes in PT–CT gap has been developed by approximating the geometry of the PT within the larger diameter CT as a pair of concentric tubes, where gap is varied by changing the CT radius. In this article, this model is used in combination with an error minimization algorithm to construct an inverse algorithm for the extraction of PT–CT gap, PT resistivity (ρ), and PT wall thickness (WT) from measured multi-frequency eddy current signals. Application of a linear regression tool in MATLAB, with fourth-order polynomial fitting of modeled data with varying ρ and WT as a function of PT–CT gap, is used to obtain coefficients that depend on ρ and WT. Output of multidimensional fitting of these coefficients is scaled and rotated to calibration data. Finally, implementation of an error minimization algorithm in MATLAB is used to produce estimates of multiple target parameters from experimental data. Simultaneous extraction of either one, two, or three parameters is examined, using experimental data obtained at frequencies used for in-reactor inspection of 4.2, 8, and 16 kHz, or just two frequencies of 4.2 and 8 kHz. Under full gap variation conditions, the inverse algorithm predicts gap to within 0.1 mm at gaps between 0 and 9 mm and to within 0.4 mm at gaps between 9 and 18 mm. PT resistivity is predicted to within 1 μΩ cm (2% relative error) and PT wall thickness within 0.03 mm (1% relative error) when each is the only extracted parameter. An excellent agreement between actual and predicted values of gap demonstrates the potential of the inverse algorithm for application to in-reactor gap measurement and simultaneous extraction of either PT wall thickness or resistivity when the other parameter is known. The extraction of PT resistivity may be particularly useful, as this parameter cannot otherwise currently be measured in-reactor.

Error analysis and reliability of zero-order Lamb mode inversion for waveguide characterization

In recent years, several fitting techniques have been presented to reconstruct the parameters of a plate from its Lamb wave dispersion curves. Published studies show that these techniques can yield high accuracy results and have the potential of reconstructing several parameters at once. The precision with which parameters can be reconstructed by inverting Lamb wave dispersion curves, however, remains an open question of fundamental importance to many applications. In this work, we introduce a method of analyzing dispersion curves that yields quantitative information on the precision with which the parameters can be extracted. In our method, rather than employing error minimization algorithms, we compare a target dispersion curve to a database of theoretical ones that covers a given parameter space. By calculating a measure of dissimilarity (error) for every point in the parameter space, we reconstruct the distribution of the error in that space, beside the location of its minimum. We then introduce dimensionless quantities that describe the distribution of this error, thus yielding information about the spread of similar curves in the parameter space. We demonstrate our approach by considering both idealized and realistic scenarios, analyzing the dispersion curves obtained numerically for a plate and experimentally for a pipe. Our results show that the precision with which each parameter is reconstructed depends on the mode used, as well as the frequency range in which it is considered.

YOLO 를 이용한 CNN 기반의 물체 인식과 파지 중심점 위치오차 최소화 알고리즘 연구 및 파지 성능 평가

In the last few years, the use of robot manipulators has attracted increasing attention in various industries. Accordingly. Researchers have proposed unique ideas for co-robot control using vision sensors. In this study, you only look once (YOLO) based on convolutional neural network (CNN), and grasping center point position error minimization algorithms were proposed to reduce object misrecognition and increase the performance for grasping an object. In addition, a gripping algorithm was designed for six degree of freedom (DOF) robot manipulators. In addition, machine vision algorithms, including a Grayscale, Gaussian filter, Canny edge, and Contouring, were implemented to detect objects features, such as centroids and orientation. Furthermore, the coordinate system of the vision sensor was converted into a coordinate system of the robot manipulator using a transformation matrix to accurately move the end effector of the robot arm to the center point of the object. The logic implemented in this study not only detected the trained object on the workstation, but also minimized the positional error of the transformation matrix. Additionally, experiments were performed on the 6-DOF robot manipulators. The results revealed that the end effector of the 6-DOF manipulators successfully moved to the center of the detected object, and each of the eight objects was normally gripped.

Group Secret-Key Generation Using Algebraic Rings in Wireless Networks

It is well known that physical-layer Group Secret-Key (GSK) generation techniques allow multiple nodes of a wireless network to synthesize a common secret-key, which can be subsequently used to keep their group messages confidential. As one of its salient features, the wireless nodes involved in physical-layer GSK generation extract randomness from a subset of their wireless channels, referred as the common source of randomness (CSR). Unlike two-user key generation, in GSK generation, some nodes must act as facilitators by broadcasting quantized versions of the linear combinations of the channel realizations, so as to assist all the nodes to observe a CSR. However, we note that broadcasting linear combination of channel realizations incurs non-zero leakage of the CSR to an eavesdropper, and moreover, quantizing the linear combination also reduces the overall key-rate. Identifying these issues, we propose a practical GSK generation protocol, referred to as Algebraic Symmetrically Quantized GSK (A-SQGSK) protocol, in a network of three nodes, wherein due to quantization of symbols at the facilitator, the other two nodes also quantize their channel realizations, and use them appropriately over algebraic rings to generate the keys. First, we prove that the A-SQGSK protocol incurs zero leakage to an eavesdropper. Subsequently, on the CSR provided by the A-SQGSK protocol, we propose a consensus algorithm among the three nodes, called the Entropy-Maximization Error-Minimization (EM-EM) algorithm, which maximizes the entropy of the secret-key subject to an upper-bound on the mismatch-rate. We use extensive analysis and simulation results to lay out guidelines to jointly choose the parameters of the A-SQGSK protocol and the EM-EM algorithm.

IDENTIFICATION OF MATHEMATICAL MODEL OF TURBINE GENERATOR UNIT IN PRESENCE OF UNCERTAINTY

An identification procedure of mathematical model of turbine generator unit in the presence of uncertainty is studied for using in the interconnected robust control automated system. The procedure is based on “worst-case” identification approach. The controlled object is modelled by the matrix transfer function with additive uncertainty. The identification consists of two stages: first is to identify transfer function with nominal parameters with the use of prediction error minimization algorithm, second – to determine weight function in additive uncertainty model using finding the worst-case log-magnitude curve of uncertainties. Identification is performed in active way, determining datasets for each control channel from individual experiments. A linear frequency-modulated signal is selected as the input test disturbance. A simulation model of the controlled object is constructed and the numerical experiment is conducted using the identification procedure. References 11, figures 7.

Cоздание высокоточных цифровых моделей высот с использованием ГНСС-БПЛА

Unmanned Aerial Vehicles (UAVs) are increasingly involved in surveying work, becoming a reliable basis for information on three-dimensional terrain features. Until now, ground reference points have been used to provide reliable planimetric evidence for measurements from drones. Their placement and coordinates measurement takes quite a long time, which increases proportionally to the area under study. In addition, the use of these marks produces model distortions (especially over large areas of 1 sq. km or more), which then lead to the appearance of areas of local depressions and uplifts where they are not present in the terrain. These distortions arise due to camera position optimization error minimization algorithms and cannot be corrected. Refusing to use ground control points leads to other geometric distortions associated with characteristics of survey system lenses mounted on UAVs (so-called “saddle-shaped” models). This paper presents the results of high-precision digital elevation models creation using built-in UAV Global Navigation Satellite System (GNSS) receivers. The methodology has been tested at ten sites in the Zakamye region of the Republic of Tatarstan (Russia) with an area ranging from 4 to 58 hectares. Correction of GNSS rover position was performed in post-processing from a virtual base station network located at a 6 to 70 km range from the surveyed sites. For all objects, DEM errors did not exceed 0.05 m on axes X, Y, and Z. At the same time, the dependence of the error value on the study area size was not revealed. The received results of the analysis of errors can indicate the prospect of the use of low-cost GNSS-UAVs without the necessity of organizing a network of ground reference points, being limited only to the necessity of installing control points will repeatedly reduce the time of the field works, in particular on hardly accessible objects.

Toward Practical Access Point Deployment for Angle-of-Arrival Based Localization

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2.

We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. The significance of these results for evaluating the impact of easing the lockdown measures is discussed.

Linearization of RF Power Amplifiers in Wideband Communication Systems by Adaptive Indirect Learning Using RPEM Algorithm

This paper proposes a new approach of digital predistortion (DPD) technique based on the adaptive indirect learning architecture (ILA) by using a recursive prediction error minimization (RPEM) algorithm for linearizing radio frequency (RF) power amplifiers (PAs) in emerging wideband communication systems. In the proposed RPEM-based linearization approach, the forgetting factor varies with time and is less sensitive to noise. Therefore, the predistorter (PD) parameter estimates become more consistent and accurate in steady state so that the mean square errors can be reduced. Both the error vector magnitude (EVM) and the adjacent channel power ratio (ACPR) are used to evaluate the DPD technique in RF PAs employing the proposed linearization. The efficiency validation of the proposed method is based on a simulated PA Wiener model. The simulation results have clarified the improvement of the proposed adaptive ILA-based DPD with RPEM algorithm in terms of both EVM and ACPR.

A Novel Iterative System Identification and Modeling Scheme With Simultaneous Time-Delay and Rational Parameter Estimation

In this work, a new method is proposed to identify systems in the form of first and second-order continuous time-delayed (CTD) models where time-delay and rational parameters are estimated simultaneously. The time delay is explicitly brought into system parameters by discretizing the CTD models. The sampled input-output data from the system is used in an iterative prediction error minimization (PEM) algorithm to identify the discrete-time delayed (DTD) model from which the equivalent CTD model is extracted. The DTD model includes the fractional and integer part of time-delay, where the phase contribution due to the fractional part is used to correct the estimation of delay as well as the termination of the algorithm until the phase contribution lies within one sample time. The efficacy of the proposed method is demonstrated by the simulation study with noisy measurements for four different systems along with the experimental validation.

Intelligent Safe Driving Methods Based on Hybrid Automata and Ensemble CART Algorithms for Multihigh-Speed Trains.

Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32%, 3.80%, and 4.30%, respectively.