Abstract Industry 4.0 (I4) as a concept offers powerful opportunities for many businesses. The set of Industry 4.0 technologies is still discussed, and boundaries are not perfectly clear. However, implementation of Industry 4.0 concept becomes strategic principle, and necessary condition for succeeding on turbulent markets. Radio Frequency Identification (RFID) was used before I4 emerged. However, it should be treated as its important part and even enabler. The question arises how adoption of RFID was impacted by I4 paradigm. Therefore, to answer this question a set of technology management tools was selected and applied to forecast RFID potential development in forthcoming years. Moreover, case studies were conducted for technology management tools and their applications for RFID for qualitative discussion of its relevance. It aimed to prove that existing toolset should be applied for modern technologies related to I4. Tools were proven to be necessary and successful. However, some specific challenges were observed and discussed.

The presence of the spare parts stock is a necessity to ensure the continuity of services. The supply of spare parts is a special case of the global supply chain. The main objective of our research is to propose a global spare parts management approach which allows decision makers to determine the essential points in stock management. Thus, it is important for the stock manager to evaluate the system considered from time to time based on performance indicators. Some of these indicators are presented in the form of a dashboard. The presentation of this chapter chronologically traces the progress of our research work. In the first part, we present the work related to the forecast of spare parts needs through parametric and statistical methods as well as a Bayesian modelling of demand forecasting. To measure the appreciation of the supply of spare parts inventory, the second part focuses on work related to the evaluation of the performance of the spare parts system. Thus, we concretize the link between the management of spare parts and maintenance in the third part, more precisely, in the performance evaluation of the joint -management of spare parts and maintenance, in order to visualize the influence of parameters on the system. In the last section of this chapter, we will present the metaheuristic methods and their use in the management of spare parts and maintenance and make an analysis on work done in the literature.

So far, numerous studies have been published on the selection of appropriate maintenance tactics based on some factors affecting them such as time, cost, and risk. This paper aims to develop the TRIZ contradiction matrix by explaining the dimensions and components of each of the following Reactive maintenance tactics. The related findings of previous studies were analyzed by adopting “Rousseau and Sandoski” seven-step method to identify and extract the relationships between TRIZ principles and Reactive maintenance tactics. Thereafter, 5 Reactive maintenance tactics were replaced TRIZ’s 40 principles in the TRIZ contradiction matrix. Finally, the ANP method were used to extract and prioritize the appropriate Reactive maintenance tactics. The proposed matrix in this research was used in the desalination section of one of the oil companies to select on the appropriate Reactive maintenance tactics. The results of this research is useful for managers and maintenance specialists of units in making decisions to provide appropriate Reactive maintenance tactics for the desired equipment.

Industry 4.0 promises to make manufacturing processes more efficient using modern technologies like cyber-physical systems, internet of things, cloud computing and big data analytics. Lean Management (LM) is one of the most widely applied business strategies in recent decades. Thus, implementing Industry 4.0 mostly means integrating technologies in companies that already operate according to LM. However, due to the novelty of the topic, research on how LM and Industry 4.0 can be integrated is still under development. This paper explores the synergic relationship between these two domains by identifying six examples of real cases that address LM-Industry 4.0 integration in the extant literature. The goal is to make explicit the best practices that are being implemented by six distinct industrial sectors

The industrial revolution taking place since the 18th century has brought the global economies to the stage of mass production, mass industrialization and spreading ideas connected with its efficiency. The most famous of its kind is Fordism and its modern variations called Post- Fordism or Neo-Fordism. We can still see traditional way of producing things in some parts of the world, and the leading economies are using Ford’s ideas or the modifications of the Ford’s concepts. But there is a question about the place of these models in the modern economy, especially because mass-production causes mass-waste and modern societies has woken up to the reality of the global pollution, climate change or just the simple fact that the amount of the raw materials is limited. The social mood is slowly changing so there should be a change to the way we produce and consume things as well. There is a question: can we proceed within existing models or should we think outside the box so we can invent more suitable way of looking at efficiency and effectiveness. The objective of this paper is to contribute to the discussion about the future of how are we going to produce things. It is based on the literature review considering Fordism and its variations, Product Life Cycle facing issues like pollution, massive waste and changes in modern economy, as well as on the case study of implementing waste reduction activities in the product’ design phase in the industrial plant based in one of the EU countries – Poland.

As the corporate culture and re/setting of employer – employee relations is crucial due to changes in workplace due to impact of COVID-19, this article aims to identify types of organizational culture, and to find impact on the implementation of HR activities and employer branding, including classification of organizations by their defined strategies. A model of organizational culture, including its systematic relationships, is proposed and tested using a sample of 402 organizations across sectors operating in the Czech Republic as a characteristic economy in Central Eastern European region. This model includes different dimensions of internal brand management and manifestations of organizational culture. Data are analyzed using bivariate and multivariate statistics. Identification of a suitable type of organizational culture leads towards successful employer branding and work engagement; brand identification and communication directly raise positive perception of organizational culture. Three major areas of use of organizational culture and branding have been identified: re-setting of personnel processes depending on the change of organization’s size, on the decline in labor productivity and on organizational mergers, changes in scope of business and in market position. The results suggest that orientation on employee engagement is a better predictor of (positive) organizational culture than increase in productivity. Furthermore, the results explain supportive roles of organizational culture towards customers and employees. The results extend theory by empirical analysis of organizational culture and internal brand management from the employers’ perspective.

Commercialization processes are modeled and analyzed from the point of view of the implementation of activities under particular stages. These issues are the subject of many studies and analyzes, which is why the extensive literature is available on this subject. Technology valuation at various stages of the commercialization process is a separate issue. Such valuation is prepared in most cases by consulting companies for determining the price in the buying and selling processes. These valuations use known methods also used in other cases, e.g., real estate valuation. The work carried out presents the author’s concept of the commercialization process model, taking into account the costs and value of the technology at various stages of the product life cycle. The model uses a stochastic approach to determine future revenues and costs, which allows estimating the value of the technology by or in determining the probability of assessment validity. The proposed stochastic approach greatly increases the chances of using the presented solutions in practical activities related to technology valuation for the purposes of purchase and sale transactions.

The market of consumer goods requires nowadays quick response to customer needs. As a consequence, this is transferred to the time restrictions that the semi-finished product manufacturer must meet. Therefore the cost of manufacturing cannot determine how production processes are designed, and the main evaluation function of manufacturing processes is the response time to customers’ orders. One of the ideas for implementing this idea is the QRM (Quick Response Manufacturing) production organization system. The purpose of the research undertaken by the authors was to develop an innovative solution in the field of production structure, allowing for the implementation of the QRM concept in a Contract Manufacturer, which realizes its tasks according to engineering-to-order (ETO) system in conditions defined as High Mix, Low Volume, High Complexity. The object of the research was to select appropriate methods for grouping products assuming that certain operations will be carried out in traditional but well-organized technological and/or linear cells. The research was carried out in one of the largest producers of sheet metal components in Europe. Pre-completed groupings for data obtained from the company had indicated that – among the classical methods – the best results had been given by the following methods: King’s Algorithm (otherwise called: Binary Ordering, Rank Order Clustering), k-means, and Kohonen’s neural networks. The results of the tests and preliminary simulations based on the data from the company proved that the implementation of the QRM concept does not have to be associated with the absolute formation of multi-purpose cells. It turned out that the effect of reducing the response time to customer needs can be obtained by using hybrid structures that combine solutions characteristic of cellular systems with traditional systems such as a technological, linear, or mixed structure. However, this requires the application of technological solutions with the highest level of organization.

Artificial neural network (ANN), a Computational tool that is frequently applied in the modeling and simulation of manufacturing processes. The emerging forming technique of sheet metal which is typically called single point incremental forming (SPIF) comes into the map and the research interest towards its technological parameters. The surface quality of the end product is a major issue in SPIF, which is more critical with the hard metals. The part of the brass metal is demanded in many industrial uses because of its high load-carrying capacity and its wear resistance property. Considering the industrial interest and demand of the brass metal products, the present study is done with the SPIF experiment on calamine brass Cu67Zn33 followed by an ANN analysis for predicting the absolute surface roughness. The modeling result shows a close agreement with the measured data. The minimum and maximum errors are found in experiment 3 and experiment 7 respectively. The error of predicted roughness is found in the range of –30.87 to 20.23 and the overall coefficient of performance of ANN modeling is 0.947 which is quite acceptable.

A film stress measurement system applicable for hyperbaric environment was developed to characterize stress evolution in a physical simulation test of a gas-solid coupling geological disaster. It consists of flexible film pressure sensors, a signal conversion module, and a highly-integrated acquisition box which can perform synchronous and rapid acquisition of 1 kHz test data. Meanwhile, we adopted a feasible sealing technology and protection method to improve the survival rate of the sensors and the success rate of the test, which can ensure the accuracy of the test results. The stress measurement system performed well in a large-scale simulation test of coal and gas outburst that reproduced the outburst in the laboratory. The stress evolution of surrounding rock in front of the heading is completely recorded in a successful simulation of the outburst which is consistent with the previous empirical and theoretical analysis. The experiment verifies the feasibility of the stress measurement system as well as the sealing technology, laying a foundation for the physical simulation test of gas-solid coupled geological disasters.

Liquid-gas flows in pipelines appear in many industrial processes, e.g. in the nuclear, mining, and oil industry. The gamma-absorption technique is one of the methods that can be successfully applied to study such flows. This paper presents the use of the gamma-absorption method to determine the water-air flow parameters in a horizontal pipeline. Three flow types were studied in this work: plug, transitional plug-bubble, and bubble one. In the research, a radiometric set consisting of two Am-241 sources and two NaI(TI) scintillation detectors have been applied. Based on the analysis of the signals from both scintillation detectors, the gas phase velocity was calculated using the cross-correlation method (CCM). The signal from one detector was used to determine the void fraction and to recognise the flow regime. In the latter case, a Multi-Layer Perceptron-type artificial neural network (ANN) was applied. To reduce the number of signal features, the principal component analysis (PCA) was used. The expanded uncertainties of gas velocity and void fraction obtained for the flow types studied in this paper did not exceed 4.3% and 7.4% respectively. All three types of analyzed flows were recognised with 100% accuracy. Results of the experiments confirm the usefulness of the gamma-ray absorption method in combination with radiometric signal analysis by CCM and ANN with PCA for comprehensive analysis of liquid-gas flow in the pipeline.

In this paper a new method of frequency jumps detection in data from atomic clock comparisons is proposed. The presented approach is based on histogram analysis for different time intervals averaging phasetime data recorded over a certain period of time. Our method allows identification of multiple frequency jumps for long data series as well to almost real-time jump detection in combination with advanced filtering. Several methods of preliminary data processing have been tested (simple averaging, moving average and Vondrak filtration), to achieve flexibility in adjusting the algorithm parameters for current needs which is the key to its use in determining ensemble time scale or to control systems of physical time scales, such as UTC(PL). The best results have been achieved with the Vondrak filter.

Falls are one of the leading causes of disability and premature death among the elderly. Technical solutions designed to automatically detect a fall event may mitigate fall-related health consequences by immediate medical assistance. This paper presents a wearable device called TTXFD based on MPU6050 which can collect triaxial acceleration signals. We have also designed a two-step fall detection algorithm that fuses threshold-based method (TBM) and machine learning (ML). The TTXFD exploits the TBM stage with low computational complexity to pick out and transmit suspected fall data (triaxial acceleration data). The ML stage of the two-step algorithm is implemented on a server which encodes the data into an image and exploits a fall detection algorithm based on convolutional neural network to identify a fall on the basis of the image. The experimental results show that the proposed algorithm achieves high sensitivity (97.83%), specificity (96.64%) and accuracy (97.02%) on the open dataset. In conclusion, this paper proposes a reliable solution for fall detection, which combines the advantages of threshold-based method and machine learning technology to reduce power consumption and improve classification ability.

Whatever the type of surgery related to inner organs, traditional or robotic, the contact with them during surgery is a key moment for pursuing the intervention. Contacts by means of surgery instruments namely scalpels, staples, clamps, graspers, etc. are decisive moments. False, and erroneous touching and manoeuvring of organs operated on can cause irreversible damage as regard morphological aspects (outer impact) and physiological aspects (inner impact). The topic is a great challenge in the effort to measure and characterize damages. In general, electrical instruments for surgery employ the following technologies: ultrasound, radiofrequency (monopolar, and bipolar), and laser. They all result in thermal damages difficult to evaluate. The article proposes a method for a pre-screening of organ features during robotic surgery sessions by pointing out mechanical and thermal stresses. A dedicated modelling has been developed based on experimental activities during surgery session. The idea is to model tissue behaviour from real images to help surgeons to be aware of handling during surgery. This is the first step for generalization by considering the type of organ. The measurement acquisitions have been performed by means of an advanced external camera located over the surgery quadrant. The modelling and testing have been carried out on kidneys. The modelling, carried out through Comsol Multiphysics, is based on the bioheat approach. A further comparative technique has been implemented. It is based on computer vision for robotics. The findings of human tissue behavior exhibit reliable results.

High-speed serial standards are rapidly developing, and with a requirement for effective compliance and characterization measurement methods. Jitter decomposition consists in troubleshooting steps based on jitter components from decomposition results. In order to verify algorithms with different deterministic jitter (DJ) in actual circuits, jitter generation model by cross-point calibration and timing modulation for jitter decomposition is presented in this paper. The generated jitter is pre-processed by cross-point calibration which improves the accuracy of jitter generation. Precisely controllable DJ and random jitter (RJ) are generated by timing modulation such as data-dependent jitter (DDJ), duty cycle distortion (DCD), bounded uncorrelated jitter (BUJ), and period jitter (PJ). The benefit of the cross-point calibration was verified by comparing generation of controllable jitter with and without cross-point calibration. The accuracy and advantage of the proposed method were demonstrated by comparing with the method of jitter generation by analog modulation. Then, the validity of the proposed method was demonstrated by hardware experiments where the jitter frequency had an accuracy of 20 ppm, the jitter amplitude ranged from 10 ps to 8.33 ns, a step of 2 ps or 10 ps, and jitter amplitude was independent of jitter frequency and data rate.

In order to find the defects in ferromagnetic materials, a non-contact harmonic detection method is proposed. According to the principle of frequency modulated carrier wave, a tunnel magneto resistance harmonic focusing vector array detector was designed which radiates lower and higher frequency electromagnetic waves through the coil array to the detection targets. We use bistable stochastic resonance to enhance the energy of collected weak target signal and apply quantum computation and a Sobol low deviation sequence to improve genetic algorithm performance. Then we use the orthogonal phase-locked loop to eliminate the intrinsic background excitation field and tensor calculations to fuse the vector array signal. The finite element model of array detector and the magnetic dipole harmonic numerical model were also established. The simulation results show that the target signal can be identified effectively, its focusing performance is improved by 2 times, and the average signal-to-noise ratio is improved by 9.6 times after the algorithm processing. For the experiments, we take Q235 steel pipeline as the object to realize the recognition of three defects. Compared with the traditional methods, the proposed method is more effective for ferromagnetic materials defects detection.

This paper presents a method of optical fluorescence analysis for the evaluation of homogeneity of multicomponent grain mixtures. This method is based on the evaluation of the content of fluorescent marker. Maize with two degrees of fineness d1 = 1:25 mm and d2 = 2:00 mm was used as a tracer. Maize was covered with Rhodamine B, which emits red light under the influence of ultraviolet radiation. The tracer was introduced into the mixture before the mixing process began. Nine multicomponent grain mixtures were used. The proportion of fluorescent maize was evaluated on the basis of computer image analysis. Additionally, the fraction of the tracer was evaluated using a control method (validation of the accuracy of the proposed method). The results indicate that the degree of the tracer’s fineness influences the results obtained. The use of fluorescent maize with particle size d2 = 2:00 mm allowed to obtain results which differed less from the control method. The average size of the difference in results ranged from 0.20–0.38 for the 2.00 mm tracer and 0.38–1.34 for the 1.25 mm tracer.

Active thermography is an efficient tool for defect detection and characterization as it does not change the properties of tested materials. The detection and characterization process involves heating a sample and then analysing the thermal response. In this paper, a long heating pulse was used on samples with a low thermal diffusivity and artificially created holes of various depths. As a result of the experiments, heating and cooling curves were obtained. These curves, which describe local characteristics of the material, are recognized using a classification tree and divided into categories depending on the material thickness (hole depths). Two advantages of the proposed use of classification trees are: an in-built mechanism for feature selection and a strong reduction in the dimensions of the pattern. Based on the experimental study, it can be concluded that classification trees are a useful tool for the thinning detection of homogeneous material.

Angle calibrations are widely used in various fields of science and technology, while in the high-precision angle calibrations, a complete closure method which is complex and time-consuming is common. Therefore, in order to improve the measurement efficiency and maintain the accuracy of the complete closure method, an improved calibration method was proposed and verified by the calibration of a high-precision angle comparator with sub-arc-second level. Firstly, a basic principle and algorithm of angle calibration based on complete closure and symmetry connection theory was studied. Then, depending on the pre-established calibration system, the comparator was respectively calibrated by two calibration methods. Finally, by comparing En values of two calibration results, the effectiveness of the improved method was verified. The calibration results show that the angle comparator has a stable angle position error of 0:1700 and a measurement uncertainty of 0:0500 (k = 2). Through method comparisons, it was shown that the improved calibration method can greatly reduce calibration time and improve the calibration efficiency while ensuring the calibration accuracy, and with the decrease of measurement interval, the improvement of calibration efficiency was more obvious.

Low-cost Micro-Electromechanical System (MEMS) gyroscopes are known to have a smaller size, lower weight, and less power consumption than their more technologically advanced counterparts. However, current low-grade MEMS gyroscopes have poor performance and cannot compete with quality sensors in high accuracy navigational and guidance applications. The main focus of this paper is to investigate performance improvements by fusing multiple homogeneous MEMS gyroscopes. These gyros are transformed into a virtual gyro using a feedback weighted fusion algorithm with dynamic sensor bias correction. The gyroscope array combines eight homogeneous gyroscope units on each axis and divides them into two layers of differential configuration. The algorithm uses the gyroscope array estimation value to remove the gyroscope bias and then correct the gyroscope array measurement value. Then the gyroscope variance is recalculated in real time according to the revised measurement value and the weighted coefficients and state estimation of each gyroscope are deduced according to the least square principle. The simulations and experiments showed that the proposed algorithm could further reduce the drift and improve the overall accuracy beyond the performance limitations of individual gyroscopes. The maximum cumulative angle error was - 2:09 degrees after 2000 seconds in the static test, and the standard deviation (STD) of the output fusion value of the proposed algorithm was 0.006 degrees/s in the dynamic test, which was only 1.7% of the STD value of an individual gyroscope.

In recent years, assessing supply system impedance has become crucial due to the concerns on power quality and the proliferation of distributed generators. In this paper, a novel method is shown for passive measurement of system impedances using the gapless waveform data collected by a portable power quality monitoring device. This method improves the overall measurement accuracy through data regrouping. Compared with the traditional methods that use the consecutive measurement data directly, the proposed method regroups the data to find better candidates with less flotation on the system side. Simulation studies and extensive field tests have been conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method can serve as a useful tool for impedance measurement tasks performed by utility companies.

The present article relates to the subject of the relocation of buildings. It presents a historical background of operations of this type carried out in the past around the world, as well as in Poland. It goes on to discuss in more detail some pioneering structural and technological solutions used during the relocation of the Rogatka Grochowska (Grochowska tollgate) building, carried out in 1961 in Warsaw. The article’s main theme is the process of relocation of a historic building No. 15 within the old Norblin Factory in Warsaw, which took place during the final months of 2018. The article briefly presents the factory’s history. It also describes the assumptions of the related project, which covers the development of the old Norblin Factory. It discusses in detail the concept and the scope of the relocation of building No. 15, with the description of the structural and technical design related to this process. The progress of the relocation which took place in 2018 has been reported in detail.

This paper compares the measurement results of dynamic characteristics, including natural frequencies, damping ratio, and wind-induced responses of the Stuttgart TV Tower (TV Tower), obtained by Lenk in 1959 using classical instruments with those obtained by the authors a few decades later using Global Positioning System (GPS). The objective of this paper was to monitor the response of the TV Tower under wind loading, which is an important tool for the validation of its design, construction, and structural health. During the authors’ GPS measurements, weak and moderate wind speeds occurred most of the time. Only in 2007, the stronger wind observed (90 < V < 100 km/h) at the head of the TV Tower (H=157 m), which caused displacements in the decimetre range. Further measurements in 2011 were carried out, using additional GPS receivers with a higher data rate. The results achieved by the GPS prove that the cross-wind response was larger than the along-wind component for all ranges of wind speed, which occurred during the measurement periods, i.e. from 2002 to 2015. The authors of this paper extended Lenk’s results, by the static and along-wind components, confirmed the first natural frequency, and damping ratio, evaluated by the Random Decrement technique. Mounting a GPS receiver, on the steel antenna mast tip, enabled detection of the second natural frequency fs2 = 0.800 Hz, which is the frequency of the mode shape of the TV Tower steel antenna mast. Lenk did not measure this frequency.

The purpose of the following paper is to present the experimental field investigations in jointless railway track subjected to the author’s generated imperfections on its static work. The main concept for the executed investigations is to induce an intentional imperfection (both a concave and convex irregularity) in an actual railway track, propose a way of appropriate measurement (using the PONTOS system), and utilize author’s field investigations results to calibrate necessary parameters for theoretical calculations. An experimental formula describing the value of the force transferred from the rail to the railway sleeper on the grounds of the survey site caused by a locomotive is provided. Furthermore, the deflection of the chosen railway rail and sleeper due to the generated imperfection is subjected to analysis. Finally the objective of the present consideration is to resolve the calculations into the beam element such that the results can be used in computational railway practice. The scheme of the so-called a “hanging sleeper” is particularly unfavourable, a gap arises between the sleeper and the foundation, for which the significant changes appear, especially in the rail deflections and stresses. A work scheme of the railway track elements is described on the generated short concave and convex irregularity.