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In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

Objects fabricated using additive manufacturing (AM) technologies often suffer from dimensional accuracy issues and other part-specific problems. This study aims to present a framework for estimating the printability of a computer-aided design (CAD) model that expresses the probability that the model is fabricated correctly via an AM technology for a specific application.

This study predicts the dimensional deviations of the manufactured object per vertex and per part using a machine learning approach. The input to the error prediction artificial neural network (ANN) is per vertex information extracted from the mesh of the model to be manufactured. The output of the ANN is the estimated average per vertex error for the fabricated object. This error is then used along with other global and per part information in a framework for estimating the printability of the model, that is, the probability of being fabricated correctly on a certain AM technology, for a specific application domain.

A thorough experimental evaluation was conducted on binder jetting technology for both the error prediction approach and the printability estimation framework.

This study presents a method for predicting dimensional errors with high accuracy and a completely novel approach for estimating the probability of a CAD model to be fabricated without significant failures or errors that make it inappropriate for a specific application.

The purpose of this paper is to develop a risk assessment method for production processes of large-size steel ship hulls.

This study uses a quantitative-probabilistic approach with involvement of clustering technique in order to analyse the database of accidents and predict the process risk. The case-based reasoning is used in here. A set of technological hazard classes as a basis for analysing the similarities between the production processes is proposed. The method has been explained using a case study on large-size shipyard.

Statistical and clustering approach ensures effective risk managing in shipbuilding process designing. Results show that by selection of adequate number of clusters in the database, the quality of predictions can be controlled.

The suggested k-means method using the Euclidean distance measure is initial approach. Testing the other distance measures and consideration of fuzzy clustering method is desirable in the future. The analysis in the case study is simplified. The use of the method according to prediction of risk related to loss of health or life among people exposed to the hazards is presented.

The risk index allows to compare the processes in terms of security, as well as provide significant information at the technology design stage of production task.

There are no studies on quantitative methods developed specifically for managing risks in shipbuilding processes. Proposed list of technological hazard classes allows to utilize database of past processes accidents in risk prediction. The clustering method of analysing the database is agile thanks to the number of clusters parameter. The case study basing on actual data from the real shipyard constitutes additional value of the paper.

This paper aims to present a hybrid model to mid-term forecast the load of transmission substations based on the knowledge of expert site and multi-objective posterior framework. The main important challenges in load forecasting are the different behavior of load in specific days. Regular days, holidays and special holidays, days after a holidays and days of load shifting are characterized by abnormal load profiles. The knowledge of these days is verified by expert operators in regional dispatching centers.

In this paper, a hybrid model for power prediction of transmission substations based on the combination of similar day selection and multi-objective posterior technique has been proposed. In the first step, the important data for prediction is provided. Posterior method is used in the second step for prediction that it is based on kernel functions. A multi-objective optimization has been formulated with three type of output accuracy measurement function that it is solved by non-dominated sorting genetic technique II (NSGT-II) method. TOPSIS way is used to find the best point of Pareto.

The presented method has been tested in four scenarios for three different transmission stations, and the test results have been compared. The presented results indicate that the presentation method has better results and is robust to different load characteristics, which can be used for better forecasting of different stations for better planning of repairs and network operation.

The main contributions of this paper can be categorized as follows: A hybrid model based on similar days selection and multi-objective framework posterior is presented. Similar day selection is done by expert site that the day type and days with scheduled repair are considered. Hyperparameters of posterior process are found by NSGT-II based on TOPSIS method.

Aircraft structures are mainly connected by riveting joints, whose quality and mechanical performance are directly determined by vertical accuracy of riveting holes. This paper proposed a combined vertical accuracy compensation method for drilling and riveting of aircraft panels with great variable curvatures.

The vertical accuracy compensation method combines online and offline compensation categories in a robot riveting and drilling system. The former category based on laser ranging is aimed to correct the vertical error between actual and theoretical riveting positions, and the latter based on model curvature is used to correct the vertical error caused by the approximate plane fitting in variable-curvature panels.

The vertical accuracy compensation method is applied in an automatic robot drilling and riveting system. The result reveals that the vertical accuracy error of drilling and riveting is within 0.4°, which meets the requirements of the vertical accuracy in aircraft assembly.

The proposed method is suitable for improving the vertical accuracy of drilling and riveting on panels or skins of aerospace products with great variable curvatures without introducing extra measuring sensors.

The purpose of this paper is to present an improved model and its applied adaptive controller for a waste heat recovery generation system using a power turbine generator (PTG) with an accurate model on shipboard that is employed by an identification method on the basis of an overall system model.

The PTG system has been developed as a waste heat recovery type generation system making use of exhaust gas from the main shipboard diesel engines. Conventionally, control of a plant is exercised using the proportional‐integral‐derivative (PID)‐based controller. The PID controller, however, is difficult to keep in place because of fouling conditions and variations across time. Thus, the load bank controller is proposed using a PID‐based controller. The controller should take into account both the fouling conditions and variations across time because the exhaust gas contains considerable amounts of ash and soot. Hence, an accurate model needs to improve the dynamic characteristics of the PTG system. The identification method clarifies the PTG system. The unknown parameters of the PTG speed model can be estimated using the prediction error method after the mathematical model is transferred to the state‐space model.

Simulation results are verified with measured data of a prototype. In the transient response of the PTG speed, all the errors are within 0.23 percent. The proposed model using the identification method shows the error between the accurate model and the standard to be less than 10 percent. The proposed controller is evaluated by comparing it with the conventional controller. As a result of using the proposed controller, limit speed overshooting is improved by more than 25 percent. Hence, the proposed model is confirmed to have excellent property.

The PTG is an extremely effective system for fuel cost reduction in the face of rising fuel prices, and systems capable of providing several thousand kilowatts are being considered.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The purpose of this paper extends the authors’ previous contributions on aircraft system identification, such as open loop identification or closed loop identification, to cascade system identification. Because the cascade system is one special network system, existing in lots of practical engineers, more unknown systems are needed to identify simultaneously within the statistical environment with the probabilistic noises. Consider this problem of cascade system identification, prediction error method is proposed to identify three unknown systems, which are parameterized by three unknown parameter vectors. Then the cascade system identification is transferred as one parameter identification problem, being solved by the online subgradient descent algorithm. Furthermore, the nonparametric estimation is proposed to consider the general case without any parameterized process. To make up the identification mission, model validation process is given to show the asymptotic interval of the identified parameter. Finally, simulation example confirms the proposed theoretical results.

Firstly, aircraft system identification is reviewed through the understanding about system identification and advances in control theory, then cascade system identification is introduced to be one special network system. Secondly, for the problem of cascade system identification, prediction error method and online subgradient decent algorithm are combined together to identify the cascade system with the parameterized systems. Thirdly from the point of more general completeness, another way is proposed to identify the nonparametric estimation, then model validation process is added to complete the whole identification mission.

This cascade system corresponds to one network system, existing in lots of practice, such as aircraft, ship and robot, so it is necessary to identify this cascade system, paving a way for latter network system identification. Parametric and nonparametric estimations are all studied within the statistical environment. Then research on bounded noise is an ongoing work.

To the best of the authors’ knowledge, research on aircraft system identification only concern on open loop and closed loop system identification, no any identification results about network system identification. This paper considers cascade system identification, being one special case on network system identification, so this paper paves a basic way for latter more advanced system identification and control theory.

This paper is concerned with an online parameter estimation algorithm for nonlinear uncertain time‐varying systems for which no stochastic information is available.

The estimation procedure, called nonlinear learning rate adaptation (NLRA), computes an individual adaptive learning rate for each parameter instead of using a single adaptive learning rate for all the parameters as done in stochastic approximation, each individual learning rate being controlled by a meta‐learning rate rule for the sake of minimizing the measurement prediction error. The method does not require stochastic information about the system model and the measurement noise covariance matrices contrarily to the Kalman filtering. Numerical results about aircraft navigation trajectory tracking show that the method is able to estimate reliably time‐varying parameters even in presence of measurement noise.

The proposed algorithm is practically insensitive to changes in the meta‐learning rate. Therefore, the performance of the method is stable with respect to the tuning parameter of the algorithm.

The proposed NLRA method may be adopted for recursive parameter estimation of uncertain systems when no stochastic information is available. It may also be used for process regulation and dynamic system stabilization in feedback control applications.

Provides a method for fast and practical computation of parameter estimates without requiring to know the model and measurement noise covariance matrices contrarily to existing stochastic estimation methods.

The purpose of this paper is to identify potential helicopter pilots’ errors during their interaction with the flight deck in the process of starting a helicopter in night-time conditions.

Systematic Human Error Reduction and Prediction Approach is used for the analysis of the pilot–flight deck interaction. This methodology was used for the identification of errors for 30 pilots during a period of 10 years. In total, 55 errors were identified, and most common errors noted are: error of omission, caused by pilots’ lack of attention or longer periods of no flying, and error of wrong execution, caused by misunderstanding a situation.

Hierarchical task analysis and classification of pilot’s tasks were used for the analysis of consequences, probability of occurrence, criticality and remedial strategies for the identified pilot error.

This paper does not give an ergonomic analysis of the flight deck, as that is not its subject. However, results of the research presented in this paper, together with results presented in references, clearly show that there are disadvantages in the ergonomic design of flight decks.

Based on the identified pilot errors and with respect of existing ergonomic solution, it is possible to begin with the reconstruction of flight decks.

Higher quality of pilot–flight deck interaction must be ensured for both pilots’ and passengers’ safety, as even a slightest error can lead to catastrophic consequences.

The value of this paper lies in the fact that it points to the need for synergy of ergonomic design and human reliability methods.