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The purpose of this paper is to propose an efficient method for detection and modification of erroneous branch parameters in real time power system simulators. The aim of the proposed method is to minimize the sum of squared errors (SSE) due to mismatches between simulation results and corresponding field measurements. Assuming that the network configuration is known, a limited number of erroneous branch parameters will be detected and corrected in an optimization procedure.

Proposing a novel formulation that utilizes network voltages and last modified admittance matrix of the simulation model, suspected branch parameters are identified. These parameters are more likely to be responsible for large values of SSE. Utilizing a Gauss-Newton (GN) optimization method, detected parameters will be modified in order to minimize the value of SSE. Required sensitivities in optimization procedure will be calculated numerically by the real time simulator. In addition, by implementing an efficient orthogonalization method, the more effective parameter will be selected among a set of correlated parameters to avoid singularity problems.

Unlike state estimation-based methods, the proposed method does not need the mathematical functions of measurements to simulation model parameters. The method can enhance other parameter estimation methods that are based on state estimation. Simulation results demonstrate the high efficiency of the proposed optimization method.

Incorrect branch parameter detection and correction procedures are investigated in real time simulators.

The purpose of this paper is to study the application of advanced computer image processing techniques for solving the problem of solder position error correction for flexible printed circuit (FPC) solder.

To correct position error, image information is defined according to information theory, and the mutual information entropy (MIE) is applied in evaluating the correlation between the images. A reference image is acquired which is used to evaluate the correlation with the inspecting image. The MIE increases as the FPC solder positioning accuracy increases. When the referent and inspecting FPC solders are aligned with the same place, the MIE is at a maximum. According to the principle, a genetic algorithm integrated with MIE as a fitness function is applied to search for the best optimal correction parameters to improve positioning accuracy.

The method is verified by a simulation and applied to the inspection system. As a result of experiment using four FPC solder samples, it has been demonstrated that the method can correct position errors.

The method of defective detection is not involved, although that of searching for and locating FPC solder is presented.

The method of correcting position error based on MIE has high flexibility and can help improve positioning accuracy. In particular, it provides a new way to correct position error and can be implemented on any sort of target which is regular or irregular based on image technology.

This paper aims to investigate the attitude regulation for spacecraft in the presence of time-varying inertia uncertainty and exogenous disturbances.

The high gain approaches are typically used in existing researches for theoretical advantages, bringing better performance but sensitive to parameter selection, making the controller conservative. A reset-control policy is presented to achieve the spacecraft attitude control with easy control parameter tuning.

The reset-control policy guarantees satisfying control performance despite using performance tuning function and saturation function besides reducing the conservativeness of the controller, thus reducing the effort in tuning control parameters.

Notably, the adaptive function owns a reset mechanism, which is reset to a preset condition when the controlled variable crosses zero. The mathematical analysis also shows the system trajectory can converge to a set centered at the origin.

The properties of materials under impact load are introduced in terms of metal, nonmetallic materials and composite materials. And the application of impact load research in biological fields is also mentioned. The current hot research topics and achievements in this field are summarized. In addition, some problems in theoretical modeling and testing of the mechanical properties of materials are discussed.

The situation of materials under impact load is of great significance to show the mechanical performance. The performance of various materials under impact load is different, and there are many research methods. It is affected by some kinds of factors, such as the temperature, the gap and the speed of load.

The research on mechanical properties of materials under impact load has the characteristics as fellow. It is difficult to build the theoretical model, verify by experiment and analyze the data accumulation.

This review provides a reference for further study of material properties.

This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics.

A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately.

A machine learning framework for predicting the life of solder joints accurately has been developed in this study. To validate its accuracy and efficiency, it is applied to predict the long-term reliability of lead-free Sn96.5Ag3.0Cu0.5 (SAC305) for three commonly used surface finishes such OSP, ENIG and IAg. The obtained results show that the predicted failure based on the machine learning method is much more accurate than the Weibull method. In addition, solder ball/bump joint failure modes are identified based on various solder joint failures reported in the literature.

The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization.

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.

For many manufacturing companies reaching forward into Europe. Just‐in‐time (JIT) implementation is merely the start of the race. This may be difficult for those in the materials function who saw MRP as the be‐all‐and‐end‐all. Yet the integrated supply chain (ISC) cannot be tackled without the experience gained from MRP and JIT. Discusses, given that JIT is a prerequisite for ISC, how JIT can be used to advantage in an ISC culture.

The purpose of this paper is to present the problem of implementation of the differential global navigation satellite system (DGNSS) differential technique for aircraft accuracy positioning. The paper particularly focuses on identification and an analysis of the accuracy of aircraft positioning for the DGNSS measuring technique.

The investigation uses the DGNSS method of positioning, which is based on using the model of single code differences for global navigation satellite system (GNSS) observations. In the research experiment, the authors used single-frequency code observations in the global positioning system (GPS)/global navigation satellite system (GLONASS) system from the on-board receiver Topcon HiperPro and the reference station REF1 (reference station for the airport military EPDE in Deblin in south-eastern Poland). The geodetic Topcon HiperPro receiver was installed in Cessna 172 plane in the aviation test. The paper presents the new methodology in the DGNSS solution in air navigation. The aircraft position was estimated using a “weighted mean” scheme for differential global positioning system and differential global navigation satellite system solution, respectively. The final resultant position of aircraft was compared with precise real-time kinematic – on the fly solution.

In the investigations it was specified that the average accuracy of positioning the aircraft Cessna 172 in the geocentric coordinates XYZ equals approximately: +0.03 ÷ +0.33 m along the x-axis, −0.02 ÷ +0.14 m along the y-axis and approximately +0.02 ÷ −0.15 m along the z-axis. Moreover, the root mean square errors determining the measure of the accuracy of positioning of the Cessna 172 for the DGNSS differential technique in the geocentric coordinates XYZ, are below 1.2 m.

In research, the data from GNSS onboard receiver and also GNSS reference receiver are needed. In addition, the pseudo-range corrections from the base stations were applied in the observation model of the DGNSS solution.

The presented research method can be used in a ground based augmentation system (GBAS) augmentation system, whereas the GBAS system is still not applied in Polish aviation.

The paper is destined for people who work in the area of aviation and air transport.

The study presents the DGNSS differential technique as a precise method for recovery of aircraft position in civil aviation and this method can be also used in the positioning of aircraft based on GPS and GLONASS code observations.

The aim of this paper is to propose a new coning correction algorithm, based on the singular perturbation technique, for the attitude update computation with non‐ideal angular rate information.

Unlike conventional coning correction algorithms, the new method uses angular rate two‐time scale model to construct the coning correction term of attitude update. In order to achieve balanced real/pseudo coning correction performance, the selection guidelines of the new algorithm parameters are established.

Performance of the new algorithm is evaluated by comparison with the conventional algorithm in no ideal sensors undergoing stochastic coning environments. The accuracy of attitude update can be improved effectively with reduced computational workload by using this new coning algorithm as compared with conventional ones.

The proposed coning correction algorithm can be implemented with angular rate sensors in UAV (unmanned aerial vehicle) and other aircrafts attitude estimation for navigation and control applications.

Singular perturbation is an effective method for structuring coning correction algorithm with filtered angular rate outputs in stochastic coning environments. The improved coning correction algorithm based on singular perturbations reduces the real and pseudo coning effects effectively as compared with conventional ones. It is proved to be valid for improvement of accuracy with reduced computations of the attitude update.