Output Error Research Articles

Data-compatibility Analysis Based on Expectation-Maximization Algorithm with Unknown Noise Statistics

In this paper, data-compatibility analysis (DCA) problem is investigated with both the measurement noise and input noise considered. The expectation-maximization (EM) algorithm is exploited to deal with the unknown noise statistics. Motivated by the fact that although there exists an excellent EM based estimator for dynamic systems, it cannot be used in the context of DCA due to the presence of non-additive input noises, a generalized EM based estimator is developed to reconstruct the flight path, as well as to estimate the sensor model parameters. Making full use of the special dependencies among the state components and the fact that the associated dynamic system has an affine nonlinear structure, the generalized estimator retains many advantages of the existing EM based one, such as the simple optimization process for the unknowns. Experimental results show that it can give more accurate results than the classical output error method and the existing EM based one in the case of high input noise.

A Theoretical Framework for End-to-End Learning of Deep Neural Networks With Applications to Robotics

Deep Learning (DL) systems are difficult to analyze and proving convergence of DL algorithms like backpropagation is an extremely challenging task as it is a highly non-convex and high-dimensional problem. When using DL algorithms in robotic systems, theoretical analysis of stability, convergence, and robustness is a vital procedure as robots need to operate in a predictable manner to ensure safety. This paper presents the first unified End-to-End (E2E) learning framework that can be applied to both classification problems and real-time kinematic robot control tasks. In the proposed forward simultaneous learning method, the weights of all layers of the fully connected neural networks are updated concurrently. The proposed E2E learning framework uses an adjustable ReLU activation function so that convergence of the output error to a bound, which is dependent on the neural network approximation error, can be ensured. In particular, it is shown that the error between ideal output and estimated output of the network can converge to and stay in a certain bound, and this bound reduces to zero when the approximation error is zero. Therefore, the robustness of the learning system can be ensured even in the presence of the approximation error. Two case studies are done on classification tasks using MNIST and CIFAR10 datasets by using the proposed learning method, and the results show that the E2E learning method achieves comparable test accuracy as the gradient descent method and the main advantage is that convergence can be ensured during training. The framework is also implemented on a UR5e robot with unknown kinematic model, which is the first result on real-time E2E learning of deep neural networks for kinematic control of robots with guaranteed convergence.

Time-domain system identification of Li-ion batteries from non-zero initial conditions

Time-domain system identification of Li-ion batteries from non-zero initial conditions

Seasonal Photon Output Trend from Linear Accelerator in Sokoto, North-Western Nigeria

Background: Linear accelerators are so sensitive to variability of temperature, pressure, humidity and dust; thus, weather factors can cause common errors in photon output which in turn can compromise constancy and consistency in precise delivery of tumor dose. Dose checker is such a dose-measuring devices that has an inbuilt correction factor for bunker temperature and pressure changes which ensures the photon output is within the accepted values to the patient. The aim of the study is to find the effect of seasonal changes on Linac and its influence on the patient. Methods: This is a retrospective study and daily records of 6 and 15 MV photon output with their corresponding temperature and pressure from January 2019 and December 2021 were extracted and reviewed. Precision mercury-in-glass thermometer and Barometer (OPUS 10 THI) were used to keep daily records of bunker temperature and pressure while their seasonal behaviours were recorded. Mean values of each month of the year was calculated and arranged according to three seasons of the year; results were represented in graphs, tables and bar charts. Results: The overall mean photon outputs were 0.997 and 0.828 cGy/MU for 6 and 15 MV respectively. The deviation of photon outputs from their reference values were ±2% for 6MV and ±3% for the 15MV energy. The mean bunker temperature was 27.07oC. The overall mean bunker pressure was 977.23 hpa. During harmattan season the temperature and pressure ranged between 26.8-25.4oC and 978-976.1 hpa respectively. In hot season bunker temperature was between 25.7-29.5oC and pressure was between 976.1-975.0 hpa. For the wet season, the temperature was between 29.5-25.7oC, and the corresponding bunker pressure ranged between 980.0-976.3 hpa. Conclusion: The seasons of year influenced variation in bunker temperature and pressure with corresponding seasonal photon output variation despite the use of dose checker. Low photon outputs were recorded during harmattan and hot seasons while higher outputs were recorded during wet season. Keywords: Season; Trend; Photon output, Linear Accelerator

A Backpropagation Artificial Neural Network Approach for Loan Status Prediction

Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into credit risk. This risk occurred if debtors unable to complete their obligations that will led loss for creditors. It is necessity for company to create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) is introduced in solving this typical classification case. Furthermore, one of learning algorithm in ANN namely Backpropagation is able to minimizing error of output in order to receive accurate result. This research aims to form models that capable in classifying the loan status of applicants by utilizing historical data. The method developed in this research is Backpropagation with activation function is a sigmoid function. In addition, this research formed two data model for analyzed; with first data model is every variable given in dataset and for the second data model is the variables that influenced the loan acceptance. Backpropagation shows high performance with more or less data variables. The results of this research show that the both data model has highest accuracy of prediction is 94.37% while the lowest accuracy prediction is 80.28%.

Closed-loop identification of multi-axis active magnetic bearing systems based on decentralized and decoupling control

Closed-loop identification of multi-axis active magnetic bearing systems based on decentralized and decoupling control

A decoupled design of a robust sliding mode observer

A decoupled design of a robust sliding mode observer

Does a Formal Post-editing Training Affect the Performance of Novice Post-editors? An Experimental Study

Machine translation systems led to the creation of a new role for translators: the post-editor. With the birth of neural machine translation systems, the demand for post-editing has been increasing in the recent years, and it has now become a common service given by language service providers and professional translators. Such a change in the landscape of the translation industry might evolve the translation training programs worldwide. It is still heavily discussed whether post-editing and translation skills overlap, and post-editing courses are now included into the curriculum by several translation departments. We set out to investigate whether post-editing training influences the performance of student post-editors in order to explore the necessary background and skills in post-editing tasks. We measured productivity parameters and quality of the final outputs produced by two groups of participants, one of which was previously trained on post-editing. Our results show that, the experimental and control groups did not differ significantly from each other in terms of productivity. There was also little to no difference when we evaluated the post-edited outputs produced by both groups against a reference text using automatic machine translation evaluation metrics. However, we detected a statistical significance between the groups when we analyzed the number of errors in the final output. The post-editors in the experimental group were more aware of the typical errors of machine translation engines.

Double-layer Optimization Study of Virtual Power Plants with Source-load-storage

In order to effectively solve the supply-demand imbalance of the grid caused by intermittent new energy penetration and improve the scheduling flexibility of virtual power plants, a two-layer optimization model of virtual power plants with source-load-storage is proposed. The upper layer of the model is designed to reduce the forecast error of wind power output and improve the revenue of the virtual power plant. On the basis of the maximum wind power consumption, distributed energy storage and fuel cells are used to coordinate and compensate for the deviation between the planned and actual output of renewable energy. The active output of the upper layer is transferred to the lower layer. The lower layer of the model is solved by using the improved linear decreasing inertia weight particle swarm algorithm for the output of the gas turbine unit. The analysis results show that the model can absorb the excess new energy generation in the grid, achieve the balance of supply and demand regulation in the grid, and effectively reduce the economic operation cost of the system.

A Prediction of Fingertip Force and Joint Kinematics of Individual Fingers using Motoneuron Firing Activities

<h3>Research Objectives</h3> Advanced assistive robotic hands can help individuals with hand impairment to perform daily activities. The detection of intended motor output of individual fingers, however, remains a challenge. The objective of this study was to develop a decoding method to continuously and simultaneously predict fingertip forces and joint kinematics of individual fingers using motoneuron firing frequency. <h3>Design</h3> Blind source separation method was applied to EMG signals to obtain motor unit (MU) firing activities. MU refinement procedure was used to determine finger- and task-specific MU pools. Obtained MU pools were tested on different datasets involving both isometric and dynamic tasks (combined). Populational MU firing frequencies were calculated to estimate the fingertip forces and joint angles concurrently using regression models. Model performance was compared with conventional EMG amplitude-based approach. <h3>Setting</h3> The study was carried out at the NC State/UNC Joint Department of Biomedical Engineering. <h3>Participants</h3> Seven healthy volunteers (Age: 28±7) participated in this study. No participant had any prior neuro-muscular ailments. All participants provided an informed consent. <h3>Interventions</h3> Not applicable. <h3>Main Outcome Measure(s)</h3> Concurrent and continuous prediction of finger kinetics and kinematics via motoneuron activities. <h3>Results</h3> MU approach led to better performance (higher correlation between the predicted and measured output and lower prediction error) in predicting both joint angles and fingertip forces than the EMG amplitude approach. <h3>Conclusion(s)</h3> MU pools can be identified for specific finger-task combinations, which allows concurrent control of fingertip forces and joint angles with assistive robot hands. Separation matrix derived from single finger trials was used applied to combined trials to avoid the computationally intensive nature of HD-EMG decomposition and to determine its usability in real-time. Further development of this method could provide concurrent and continuous control of individual finger movements of highly dexterous robotic hands.

IMAGIN: Library of IMPLY and MAGIC NOR-Based Approximate Adders for In-Memory Computing

In-memory computing (IMC) has attracted significant interest in recent years as it aims to bridge the memory bottleneck in the Von Neumann architectures. IMC also improves the energy efficiency in these architectures. Another technique that has been explored to reduce the energy consumption is the use of approximate circuits, targeted toward error resilient applications. These applications have addition as one of their most frequently used operations. In literature, CMOS-based approximate adder libraries have been implemented to help designers choose from a variety of designs depending on the output quality requirements. However, the same is not true for memristor-based approximate adders targeted for IMC architectures. Hence, in this work, we developed a framework to generate approximate adder designs with varying output errors for the 8-, 12-, and 16-bit adders. We implemented a state-of-the-art scheduling algorithm to obtain the best mapping of these approximate adder designs for IMC. We performed an exhaustive design space exploration to obtain the pareto-optimal approximate adder designs for various design and error metrics. We then proposed IMAGIN, a library of approximate adders compatible with the memristor-based IMC architecture, which are based on the IMPLY and MAGIC design styles. We also performed mean filtering on the Kodak image dataset using the approximate adders from the IMAGIN library. IMAGIN can help designers select from a wide variety of approximate adders depending on the output quality requirements and serve as benchmarks for future research in this direction. All pareto-optimal designs will be made available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/agra-uni-bremen/JxCDC2022-imagin-add</uri> .

Control Strategy for Resonant Inverter in High Frequency AC Power Distribution System with Harmonic Suppression and Transient Performance Improvement

In high frequency AC (HFAC) distribution system, the resonant inverter is used to improve power quality and keep the stability of the output AC voltage. Aiming at the problems of poor output power quality and slow transient performance caused by unreasonable filter parameter design and load change during inverter operation, a single-phase H-bridge LCLC resonant inverter based on analog circuit controller implement is introduced in this paper for HFAC power distribution system (PDS). In this study, to design harmonic compensator and analyze the responsiveness of the inverter, it is necessary to analyze the output voltage total harmonic distortion (THD) of LCLC resonant inverter and the performance of the open loop system in detail. On the one hand, a proportional-integral-resonant (PIR) controller is designed to maintain the zero static error of the voltage output and suppress the output voltage THD of LCLC resonant inverter. On the other hand, an integral controller combines with phase-shift modulation (PSM) method is presented to effectively improve the transient performance of resonant inverter and provide the fixed frequency of the output voltage. On the basis of the above, the experimental prototype is implemented with the output AC voltage root mean square of 28 V, and the output voltage frequency for resonant inverter is equal to switching frequency. A rated output power of 130 W experimental platform is built to verify the effectiveness of the theoretical analysis, control strategy, and modulation method.

Error detection using EPID-based 3D in vivo dose verification for lung stereotactic body radiotherapy

To investigate the error detectability limitations of an EPID-based 3D in vivo dosimetry verification system for lung stereotactic body radiation therapy (SBRT). Thirty errors were intentionally introduced, consisting of dynamic and constant machine errors, to simulate the possible errors that may occur during delivery. The dynamic errors included errors in the output, gantry angle and MLC positions related to gantry inertial and gravitational effects, while the constant errors included errors in the collimator angle, jaw positions, central leaf positions, setup shift and thickness to simulate patient weight loss. These error plans were delivered to a CIRS phantom using the SBRT technique for lung cancer. Following irradiation of these error plans, the dose distribution was reconstructed using iViewDose™ and compared with the no error plan. All errors caused by the central leaf positions, dynamic MLC errors, Jaw inwards movements, setup shifts and patient anatomical changes were successfully detected. However, dynamic gantry angle and collimator angle errors were not detected in the lung case due to the rotation-symmetric target shape. The results showed that the γmean and γpassrate indicators can detect 13 (81.3%) and 14 (87.5%) of the 16 errors respectively without including the gantry angle error, collimator angle error and output error. In summary, iViewDose™ is an appropriate approach for detecting most types of clinical errors for lung SBRT. However, the phantom results also showed some detectability limitations of the system in terms of dynamic gantry angle and constant collimator angle errors.

Development of the piezoelectric dynamometer for brittle material drilling

ABSTRACT When processing deep and small holes in optical glass with a diamond core drill, the cutting force is minimal and very variable. It is challenging to measure the cutting force because of the high cutting frequency and small value of the cutting operation. In this study, a piezoelectric three-component dynamometer with excellent axial sensitivity is developed based on the measuring properties of quartz wafers and characteristics of cutting forces during processing. The dynamometer’s four sensors are positioned vertically, which could increase the dynamometer’s axial sensitivity. The dynamometer’s calibration studies were conducted in both static and dynamic modes. The output consists of the dynamometer was verified using a multi-point loading experiment. According to the experimental findings, the dynamometer’s linearity error and repeatability error are both less than 0.5%. Less than 1.6% and 2.9%, respectively, are found in the output error and crosstalk of multi-point loading experiment. The frequency of nature is higher than 1500 Hz. An experiment measuring cutting force was completed lastly. The findings of the experiment confirm the viability of the dynamometer by demonstrating that it performs well and can identify changes in cutting force caused by two distinct cutting tools when operating under various conditions.

Adaptive Internal Model Control Based on Parameter Adaptation

In actual industrial control, many objects have the characteristics of large lag and time-varying parameters, which makes it difficult for traditional PID control to obtain satisfactory control effects. Therefore, engineers need a control algorithm with a better control effect and a simple structure, and thus the internal model control is introduced into industrial process control. Internal model control has received a lot of attention in the control field because of its excellent control effect, and in ideal conditions, it can perfectly suppress external disturbances, and the output strictly tracks the input. On the basis of the internal model control, a two-degrees-of-freedom internal model control was developed. Compared with the conventional feedback control, the internal model control structure mainly embeds an internal model consistent with the control object in the control object, so the deviation between the internal model and the control object determines the quality of the control effect. In this paper, the internal model is changed to an adjustable parameter model, and the model parameters are adjusted in real time using the parameter adaptive algorithm, so that the model output error is as small as possible, or even zero. In order to solve the influence of interference on the system, compensation based on MRAC theory is used. In order to verify the feasibility of the algorithm, it was applied to the landing process control of fixed-wing Unmanned aerial vehicle) UAV and achieved satisfactory results.

Distributionally Robust Optimization of an Integrated Energy System Cluster Considering the Oxygen Supply Demand and Multi-Energy Sharing

Regional integrated energy systems (IESs) have emerged to satisfy the increasing diversified energy demand in Tibet. However, limited resource allocation of a given IES can occur because of the uncertainty in the output and prediction error of distributed renewable energy (DRE). A distributionally robust optimization (DRO) model was proposed for the joint operation of multiple regional IESs, and multi-energy sharing and multi-energy flow coupling of electricity, heat, and oxygen were considered. The probability distribution of the DRE output was described using 1− norm and ∞− norm constraints, and the minimum operating cost under adverse scenarios was determined through DRO. Furthermore, on the premise of ensuring cluster profit, a pricing mechanism of the energy supply within the cluster was proposed. Finally, a typical model involving eight cases was established and analyzed. The results revealed that multi-energy sharing and multi-energy flow coupling improved the economy of IES cluster operation and realized the coordination of robustness and economy. The energy supply price within the cluster enhanced enthusiasm on the demand side.

Trajectory tracking in an underactuated, non-minimum phase two-link multibody system through model predictive control with embedded reference dynamics

• A scheme for tip trajectory tracking in a two-link underactuated system is proposed. • The method handles non-minimum phase multibody systems. • A novel and extended formulation of Model Predictive Control is proposed. • The time-varying reference is embedded through its autonomous model. • The theory of Dynamic Mode Decomposition is exploited to model the reference. This paper proposes a novel formulation of Model Predictive Control (MPC), based on linearized models, for tip trajectory tracking of time-varying references in an underactuated, non-minimum phase, two-link multibody system. Indeed, tip trajectory tracking in such systems is challenging due to the unstable internal dynamics. Additionally, when traditional MPC is used, the reference is usually tracked with delay, since MPC is intrinsically devoted to regulation or to step tracking. To overcome these issues, the proposed approach embeds an autonomous state-space model of the time-varying reference into the constrained optimization process of MPC. The controller is therefore named MPC with Embedded Reference Dynamics, MPC-ERD. By embedding the reference in the controller design, tracking with negligible error and delay is achieved, and no feedforward control is required, thus overcoming the difficulties in model inversion. The MPC-ERD is experimentally validated and the results are compared with those provided by standard MPC with embedded integrator (also including output error constraints). Besides inheriting the benefits of standard MPC, MPC-ERD ensures precise tracking of the tip time-varying reference, with negligible delay and error.

Purity Control Based on a Type-II Fuzzy Controller for a Simulated Moving Bed

The control of a simulated moving bed (SMB) is always a challenging chemical control topic due to its complexity and nonlinearity. Its mathematical model must undergo an affine transformation and digitization before it can be controlled. Basically, there are three aspects that need to be considered in the nonlinear control of an SMB. First, the nonlinear characteristics are more complicated due to the switching time parameters of discrete events. Second, the control objective is not to minimize the control output error, but to make the separated concentrations between the components of the substance reach a certain ratio. Finally, the control variables are highly coupled. So far, the vast majority of the industry still uses relatively simple PLC controls; a few use specific controllers based on materials to be separated such as model predictive controls and PID controllers. Therefore, there is no unified intelligent processing mode. In this paper, a type-II fuzzy controller is presented and used as an SMB control. The interference of the related parameters was tested to observe the stability and robustness of the controller. The type-II fuzzy control was based on type-II fuzzy sets, which resulted in the type-II fuzzy controller having more flexible attribution function values. The results showed that the type-II fuzzy controller was not only more accurate in the control, but also better for robustness and adaptability than an ordinary fuzzy controller and PID controller.

Linear Quadratic Regulator Weighting Matrices for Output Covariance Assignment in Nonlinear Systems

A time-varying output covariance assignment problem in the presence of a stochastic disturbance is solved using finite-horizon optimal control formulation. It is shown that an assignment of time-varying output error covariance is possible in the presence of model error by utilizing a time-varying linear quadratic regulator controller with a class of output and control weighting sequences. This paper develops a systematic algorithm to calculate the sequence of such time-varying output weights that are further shown to be the Lagrange multipliers associated with the covariance constraints. A short horizon attitude control problem with stringent covariance constraints and a more nonlinear example concerning a low-thrust interplanetary maneuver are solved to demonstrate the utility of the proposed approach. Numerical results offer a degree of optimism about the broad applicability of the time-varying covariance assignment approach to solve guidance and control problems associated with nonlinear dynamic systems.

Minimal-norm state-feedback globally non-overshooting/ undershooting tracking control of multivariable systems

The non - overshooting/undershooting (NOUS) tracking control objective in the output response of the closed-loop system has several practical applications in “positioning” control problems. Recently, a state-feedback gain matrix based on Moore’s eigenstructure assignment technique to achieve the above control objective with a desired convergence rate in the output error from any initial condition is designed, referred to as globally monotonic tracking control, which is a convex optimization problem. From a practical viewpoint, a feedback gain matrix of a minimal norm is often required to install low-cost actuators in the overall closed-loop system. However, there is always a trade-off between the control objectives of achieving a NOUS tracking response with the desired convergence rate and a minimal norm of the feedback gain matrix. Besides, the latter control requirement renders the globally NOUS control optimization problem nonconvex. In this paper, a convex optimization-based iterative algorithm is developed to synthesize a minimal-norm state-feedback globally NOUS tracking controller. An upper bound on the achievable settling time in all output responses for the closed-loop eigenvalues lying in a prespecified region of the complex plane is also provided. The effectiveness of the proposed algorithm is demonstrated through a numerical example, followed by experimental validation on a multitank system.