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This paper aims to deal with the formulation of the technological principle for precise positioning using global navigation satellite systems (GNSS) in railway engineering during construction and maintenance of a railway line and its spatial position. Solution of optimal route is based on finding the shortest Hamiltonian path in the graph method with additional conditions in nodes.

The core of the algorithm is a dynamic data structure which is based on events list. The optimization of field measurement solves the time demands and brings economic effectiveness.

The technology enables to determine the precise position with absolute difference limit from 10 to 15 mm within GNSS CZEPOS permanent network in the territory of Czech Republic.

Technology is the result of applied research.

This technology innovates the current procedure of geodetic control network determination used by Railway Infrastructure Administration (state organization) in Czech Republic.

The event means measurement at a given track point and time for a specified duration of observation. The algorithm was realized in Borland Delphi. The optimization of field measurement solves its time demands and increases economic effectiveness. The technology enables precise position determination with absolute difference limit from 10 to 15 mm within GNSS CZEPOS permanent network in the territory of Czech Republic. It has been verified in field selected electrified and non-electrified railway lines.

The purpose of this paper is to optimize and improve a bipolar charge transport (BCT) model used to simulate charge dynamics in insulating polymer materials, specifically low-density polyethylene (LDPE).

An optimization algorithm is applied to optimize the BCT model by comparing the model outputs with experimental data obtained using two kinds of measurements: space charge distribution using the pulsed electroacoustic (PEA) method and current measurements in nonstationary conditions.

The study provides an optimal set of parameters that offers a good correlation between model outputs and several experiments conducted under varying applied fields. The study evaluates the quantity of charges remaining inside the dielectric even after 24 h of short circuit. Moreover, the effects of increasing the electric field on charge trapping and detrapping rates are addressed.

This study only examined experiments with different applied electric fields, and thus the obtained parameters may not suit the experimental outputs if the experimental temperature varies. Further improvement may be achieved by introducing additional experiments or another source of measurements.

This work provides a unique set of optimal parameters that best match both current and charge density measurements for a BCT model in LDPE and demonstrates the use of trust region reflective algorithm for parameter optimization. The study also attempts to evaluate the equations used to describe charge trapping and detrapping phenomena, providing a deeper understanding of the physics behind the model.

The purpose of this paper is to propose a method for selecting the position and attitude trajectory of error measurement to improve the kinematic calibration efficiency of a one translational and two rotational (1T2R) parallel power head and to improve the error compensation effect by improving the properties of the error identification matrix.

First, a general mapping model between the endpoint synthesis error is established and each geometric error source. Second, a model for optimizing the position and attitude trajectory of error measurement based on sensitivity analysis results is proposed, providing a basis for optimizing the error measurement trajectory of the mechanism in the working space. Finally, distance error measurement information and principal component analysis (PCA) ideas are used to construct an error identification matrix. The robustness and compensation effect of the identification algorithm were verified by simulation and through experiments.

Through sensitivity analysis, it is found that the distribution of the sensitivity coefficient of each error source in the plane of the workspace can approximately represent its distribution in the workspace, and when the end of the mechanism moves in a circle with a large nutation angle, the comprehensive influence coefficient of each sensitivity is the largest. Residual analysis shows that the robustness of the identification algorithm with the idea of PCA is improved. Through experiments, it is found that the compensation effect is improved.

A model for optimizing the position and attitude trajectory of error measurement is proposed, which can effectively improve the error measurement efficiency of the 1T2R parallel mechanism. In addition, the PCA idea is introduced. A least-squares PCA error identification algorithm that improves the robustness of the identification algorithm by improving the property of the identification matrix is proposed, and the compensation effect is improved. This method has been verified by experiments on 1T2R parallel mechanism and can be extended to other similar parallel mechanisms.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

The need for an instrumented grinding system that addresses the requirements of ductile regime machining of brittle materials is implemented. The static and dynamic stiffness of the structural loop of the grinding system meets or exceeds those of previous researchers. An instrumented spindle is introduced which features capacitance gages embedded in the stator of an air bearing spindle.

The instrumented spindle is demonstrated to provide valuable force feedback for fine grinding and is capable of resolving intra‐revolution force components. Tests are performed to demonstrate the use of the instrumented spindle for ductile grinding of brittle materials with superabrasive wheels.

The results of the test show that the instrumented spindle is capable of determining intra‐revolution force components for square alumina‐titanium carbide wafers.

Outlines some important work developing and building instrumented spindles to aid the precision grinding industry.

Labor supply data seldom include detailed information on hours and wages in secondary job or overtime work. Based on survey information on hours and wages in overtime work and second job which is merged to administrative register information on income taxes and deductions we estimate a “Hausman labor supply model,” which allows for a detailed treatment of nonconvexities. Including explicit information on overtime pay and second job wages increase the estimated elasticities compared to a standard labor supply model without this information. However, allowing a more flexible treatment of nonconvexities the estimated elasticities are reduced; even below the estimates of the baseline results. In simulations we show that these findings have significant consequences when evaluating the degree of self-financing of various tax reforms.

In light of the increasingly aging workforce, it is interesting from both a theoretical and practical perspective to investigate empirically the commonly held stereotype that older workers are more resistant to change (RTC). Thus, the main purpose of this paper is to investigate the age/RTC relationship, considering tenure and occupational status (blue/white collar employees) as additional boundary conditions. Furthermore, the paper investigates the relationship between RTC and individual performance, thereby introducing RTC as a mediator in the age/job performance relationship.

Study hypotheses are tested among a sample of 2,981 employees from diverse companies. Structural equation modeling with bootstrapping procedures is applied to investigate the moderated-indirect model.

Contrary to common stereotypes, employee age is negatively related to RTC. Tenure and occupational status are further identified as boundary conditions for this relationship. Moreover, RTC also shows an association with individual job performance, which allows for the establishment of an indirect-mediation mechanism from age to job performance via the intermediation of RTC. These results can be explained using current life span concepts, particularly the selective optimization with compensation (SOC) model.

Hypotheses were tested in a cross-sectional data set, which does not allow for conclusions of causality.

This study contributes to the age stereotyping literature that has thus far neglected the age/RTC relationship. Furthermore, the age/job performance literature is extended by introducing RTC as an important mediating factor. In sum, this study should help provide a more positive and more differentiated picture of older employees in the workplace.

A growing body of literature has begun in the direction of supply chain performance measurement. However, selecting the appropriate set of key performance indicators (KPIs) for measuring supply chain performance have always remained a challenge. The purpose of this paper is to identify the KPIs and categorize them specifically for measuring retail supply chain performance.

A qualitative approach, based on literature has been adopted. Published literature from refereed journals on supply chain performance measurement has been considered and various approaches for developing KPIs have been studied to develop a theoretical framework for performance measurement in retail supply chain.

The paper identifies key indicators for performance measurement and classifies them into four major categories: transport optimization, information technology optimization, inventory optimization and resource optimization. These key indicators are arranged precisely for retail industry. A theoretical framework is proposed to link the performance of these constructs on financial performance of the firm.

Future research can be carried out to validate the relevance and applicability of identified indicators. The study can be further conducted to measure the interrelationships between the KPIs and their impact on financial performance of the firm.

This study proposes a list of indicators for retail industry, which are presented in appropriate categories so that it can be used by the focussed teams for further improvement.

To the best of authors’ knowledge, no other study has categorized the KPIs into groups, specifically for measuring retail supply chain performance. The researcher also intends to carry out further empirical study to test the proposed theoretical framework.

The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor.

The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation.

This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy.

In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system.

This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries.

The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

This paper presents a method to improve inverse problem resolution. This method focuses on the measurement set and particularly on sensor position. Based on experiment, it aims at finding sensor position criteria to insure the least bad inverse problem solving.

The studied device is a magnetized steel sheet measured by four sensors. Three optimization techniques are compared: condition number, solid angle and signature optimization.

An efficient criterion to compare the inverse problem resolution quality is presented. The comparison of optimization techniques shows that only signature optimization gives accurate results.

A relative simple case is studied in this paper: only four sensors are used to measure a steel sheet. Moreover magnetostatic low‐field case is supposed. Nevertheless techniques presented could be applied to more complex studies. Condition number and solid angle optimizations techniques should be tested with more sensors to confirm or infirm their inefficiency.

This paper presents the first step of a larger study concerning ships for naval application. The aim is to predict magnetic anomaly created by ship to compensate it. This anomaly could be computed through the resolution of an inverse problem based on internal measurements. The signature optimization technique could be used to find the optimal sensor location onboard.

Traditional regularization techniques are focusing on adding mathematical or physical information to the system in order to improve it. This paper provides another approach to improve inverse problem resolution through measurement set. It shows that sensor position optimization should be efficient.