The analysis of available literature indicates that tests of products sound quality, which would not involve participation of groups of listeners supposed to evaluate the sounds emitted by these products, are neither carried out in Poland, nor in the world. That results in the fact that the products sound quality is determined on the basis of psychoacoustic information and comprises both objective and subjective factors of sound perception. With reference to those factors and to different life cycles of the machine, an original definition of the “sound quality of the machine” has been developed and presented in this article. The global index of the acoustic quality of the machine, accounting for the relations between the noise level at the workstation and the selected parameters characterising both the machine's sound activity and the working environment, was adopted as the measure of the sound quality of the machine. The experiments that followed confirmed the appropriateness of the assessment made with the use of the global index of acoustic quality.
Development of mineral deposits located at significant depth may be carried out by means of vertical shafts. Shaft sinking technology usually requires a number of works to be carried out, including the selection of appropriate excavating techniques adapted to geological and hydrological conditions, including natural hazards. The production technology and the machines used determine the level of sinking costs and execution period. The article discusses the excavating technologies currently used across the world. Then the assumptions, concept and construction of a new generation of shaft sinking system were presented. The proposed new solution of the system and the excavating technology allow for parallel execution of key processes related to winning, loading, transport and shaft wall-side lining, which significantly increases the progress of sinking. The shaft sinking system was created by scientists from AGH in cooperation with KOPEX – Przedsiębiorstwo Budowy Szybów S. A. and Instytut Techniki Górniczej KOMAG.
The article presents the results of research on the finishing of M63 Z4 brass by vibratory machining. Brass alloy was used for the research due to the common use of ammunition elements, cartridge case and good cold forming properties on the construction. Until now, the authors have not met with the results of research to determine the impact of abrasive pastes in container processing. It was found that the additive for container abrasive treatment of abrasive paste causes larger mass losses and faster surface smoothing effects. The treatment was carried out in two stages: in the first stage, the workpieces were deburred and then polished. Considerations were given to the impact of mass of workpieces, machining time and its type on mass loss and changes in the geometric structure of the surface. The surface roughness of machining samples was measured with the Talysurf CCI Lite optical profiler. The suggestions for future research may be to carry out tests using abrasive pastes with a larger granulation of abrasive grains, and to carry out tests for longer processing times and to determine the time after which the parameters of SGP change is unnoticeable.
The present paper aims at presenting a short study of the prefixed forms of the Polish verb pić (‘to drink’) (napić, wypić, popić, przepić, opić, zapić, etc.) and their French equivalents found in two parallel corpora: Glosbe and Reverso Context. In the first part, selected theoretical approaches concerning the verbal prefixation in Polish are discussed, with particular attention to the hypothesis of “perfective hypercategory” by Włodarczyk and Włodarczyk (2001b). The second part focuses on the results of the contrastive Polish-French analysis. The research is carried out in the general framework of the Aktionsarten theory and tries to discover by which linguistic means (grammatical and/or lexical) the French language expresses different semantic values conveyed by the Polish prefixes. The results of the analysis are appropriately formalized according to the principles of the object-oriented approach by Banyś (2002a, b), i.e. described by the syntactic-semantic schemes (which, after several changes of specifi cation, can be applied in the machine translation programs). The purpose of the investigation is, therefore, twofold: theoretical, since it is the matter of discovering certain relations between two languages expressing differently a given linguistic phenomenon, and practice, which consists in formulating interlinguistic correspondence rules for the purpose of the Polish-French translation.
Traffic classification is an important tool for network management. It reveals the source of observed network traffic and has many potential applications e.g. in Quality of Service, network security and traffic visualization. In the last decade, traffic classification evolved quickly due to the raise of peer-to-peer traffic. Nowadays, researchers still find new methods in order to withstand the rapid changes of the Internet. In this paper, we review 13 publications on traffic classification and related topics that were published during 2009-2012. We show diversity in recent algorithms and we highlight possible directions for the future research on traffic classification: relevance of multi-level classification, importance of experimental validation, and the need for common traffic datasets.
The study aimed to apply the protection from damage to engineering facilities located near a planned underwater aggregate extraction. The analysis was conducted in compliance with mining regulations and expert opinions. The study also aimed to assess the precision and correctness of the extraction, due to economic aspects. To reach the goals, in-situ research of the mining area was conducted, with the help of an advanced bathymetric device, based on the USV methodology. The instrument – named by the author as Smart-Sonar-Boat – was especially designed for underwater surveys in open-pit aggregate mines. The study analyzed the “Dwory” open-pit mine, located in southern Poland in the city of Oświęcim. The bathymetric results obtained contributed to improving the observation of changes in the bottom during the extraction. The applied USV method allowed for conducting the reliable evaluation of the mining work.
Quality evaluation is very important for haptic rendering. In this paper, an objective evaluation method for a haptic rendering system based on haptic perception features is proposed. In the method, the haptic rendering process is compared to the real world perception process in a simple standardized procedure based on feature extraction and data analysis. A complete evaluation process for a simple haptic rendering task of pressing a virtual spring is presented as an example to explain the method in detail. Compared with the traditional objective method based on error statistics, the method is more concerned about the consistency of human subjective feelings rather than physical parameters, which makes the evaluation process more consistent with the haptic perception mechanism. The results of comparative analysis show that the method presented in this paper is simple, gives reliable results reflecting the consistency with subjective feeling and has a better discrimination ability for different kinds of devices and algorithms compared with the traditional evaluation methods.
This paper presents a study of control strategies for 5-phase permanent magnet synchronous motors (PMSMs) supplied by a five-leg voltage source inverter. Based on the vectorial decomposition of the multi-phase machine, fictitious machines, magnetically decoupled, allow a more adequate control. In this paper, our study focuses on the vector control of a multi-phase machine using a linear proportional-integral-derivative (PID) current regulator in the cases of sinusoidal and trapezoidal back-electromotive force (EMF) waveforms. In order to determine currents’ references, two strategies are adopted. First one aims to minimize copper losses under constant torque, while the second one targets to increase torque for a given copper losses. These techniques are tested under a variable speed control strategy based on a proportional-integral (PI) regulator and experimentally validated.
The NOMAD project was a survey to examine the noise-related content of instructions supplied with machinery offered for purchase in Europe. The project collected more than 1 500 instructions from machines covering 40 broad machine-families and from 800 different manufacturing companies. These instructions were analyzed to determine compliance with the requirements of the Machinery Directive, and assess the quality of information. The general state of compliance of machinery instructions with the noise-related requirements of the Machinery Directive was found to be very poor: 80% of instructions did not meet legal requirements. Some required numerical values relating to noise emissions were often missing. Where values were given, they were often not traceable to machine operating conditions or measurement methods, and not credible either against stated conditions/methods or as warnings of likely risk in real use. As a consequence, it is considered highly likely that, in making a machinery procurement decision, employers are prevented from taking noise emissions into account, and understanding what is necessary to manage the risks from noise relating to equipment that is procured. Recommendations are made for actions aimed at bringing about a global improvement to the current situation. Targeted actions are now proposed by “ADCO Machinery Group” aimed at raising awareness of the legal requirements, responsibilities and actions required among the various groups who have parts to play in the system - machine manufacturers, machine users, occupational safety and health professionals, and standards-makers. Recommendations are also made aimed at providing, or improving, tools and resources for all these actors.
The aim of the study was to identify acoustic and structural modes in the spectrum obtained exper-imentally inside an operator's cab in a bulldozer. Measurements were taken inside the operator's cab in a caterpillar-track bulldozer Polremaco TD12NPH2E-2000, designed for work in underground mine enclosures. The acoustic pressure spectrum was obtained for varied rotational speeds of the engine during the free run of the machine. The reverberation time of the cab was determined basing on the pulse-type excited pressure response, followed by identification of the spectral components registered by measurements. Thus, identified frequencies were compared with natural acoustic frequencies registered inside the operator's cab and with frequencies associated with the valves and ignition frequencies due to rotational speed and natural frequencies of structural vibrations of the cab's walls. This study was conducted in an attempt to reduce the noise inside the operator's cab using passive methods
The study presented here offers an analysis of the heat flow through the wall of the Yankee cylinder when regarded as a thin-walled vessel. The effect of the selected design and process parameters (i.e. cylinder diameter and steam pressure) on density of the heating stream has been analyzed and discussed for both cast iron and steel cylinders. Based on the work presented here, the optimal ranges for steam pressure have been derived and proposed for cylinders mounted at various locations within the drying section.
The condition monitoring techniques like acoustic emission, vibration analysis, and infrared thermography, used for the failure diagnosis of bearings, require longer processing time, as they have to perform acoustical measurement followed by signal processing and further analysis using special software. However, for any bearing, its period of usage can be easily determined within an hour, by measuring the bearing sound, using sound level meter (SLM). In this paper the acoustical analysis of the spindle bearing of a radial drilling machine was performed using SLM, by measuring the sound pressure level of the bearing in decibels, for different frequencies, while muting all the other noises. Then using an experimental set up, two database readings were taken, one for new bearing and the other for completely damaged bearing, both are SKF6207, which itself is the spindle bearing. From these three sets of sound pressure level readings, the period of usage of the spindle bearing, was calculated using an interpolation equation, by substituting the life of the bearing from the manufacturer’s catalogue. Therefore, for any machine with a SKF6207 bearing, its usage time can be estimated using the database readings and one measurement on that machine, all with the same speed.
In this paper a scaling approach for the solution of 2D FE models of electric machines is proposed. This allows a geometrical and stator and rotor resistance scaling as well as a rewinding of a squirrel cage induction machine enabling an efficient numerical optimization. The 2D FEM solutions of a reference machine are calculated by a model based hybrid numeric induction machine simulation approach. In contrast to already known scaling procedures for synchronous machines the FEM solutions of the induction machine are scaled in the stator-current-rotor-frequency-plane and then transformed to the torque- speed-map. This gives the possibility to use a new time scaling factor that is necessary to keep a constant field distribution. The scaling procedure is validated by the finite element method and used in a numerical optimization process for the sizing of an electric vehicle traction drive considering the gear ratio. The results show that the scaling procedure is very accurate, computational very efficient and suitable for the use in machine design optimization.
Virtual machine described in the paper is a runtime program for controllers in small distributed systems. The machine executes intermediate universal code similar to an assembler, compiled in CPDev engineering environment from source programs written in control languages of IEC 61131-3 standard. The machine is implemented as a C program, so it can run on different target platforms. Data formats and commands of the machine code are presented, together with the machine’s Petri-net model, C implementation involving universal and platform-dependent modules, target hardware interface, input/output programming mechanisms, and practical applications.
This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, k-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring.
We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R 2 . These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for five percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifier. For the threshold of relevant change set as ten percent all approaches performed satisfactory.
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
In the article problems related to human labor and factors affecting the increasing use of industrial robots are discussed. Since human factors affect the production processes stability, robots are preferred to apply. The application of robots is characterized by higher performance and reliability comparing to human labor. The problem is how to determine the real difference in work efficiency between human operator and robot. The aim of the study is to develop a method that allows clearly definition of productivity growth associated with the replacement of human labor by industrial robots. Another aim of the paper is how to model robotized and manual operated workstation in a computer simulation software. Analysis of the productivity and reliability of the hydraulic press workstation operated by the human operator or an industrial robot, are presented. Simulation models have been developed taking into account the availability and reliability of the machine, operator and robot. We apply OEE (Overall Equipment Effectiveness) indicator to present how availability and reliability parameters influence over performance of the workstation, in the longer time. Simplified financial analysis is presented considering different labor costs in EU countries.
Touch-trigger probes for CNC milling machines usually use wireless communication in the radio or IR band. Additionally they enable triggering signal filtering in order to avoid false triggers of the probe. These solutions cause a delay in trigger signal transmission from the probe to the machine tool controller. This delay creates an additional pre-travel component, which is directly proportional to the measurement speed and which is the cause of a previously observed but not explained increase of the pre-travel with the increase of the measurement speed. In the paper, a method of testing the delay time of triggering signal is described, an example of delay time testing results is presented and the previous, unexplained results of other researchers are analysed in terms of signal transmission delay.
This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector) and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the realtime operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPU- and GPU-based solutions.
This paper presents the design process and the results of a novel fall detector designed and constructed at the Faculty of Electronics, Military University of Technology. High sensitivity and low false alarm rates were achieved by using four independent sensors of varying physical quantities and sophisticated methods of signal processing and data mining. The manuscript discusses the study background, hardware development, alternative algorithms used for the sensor data processing and fusion for identification of the most efficient solution and the final results from testing the Android application on smartphone. The test was performed in four 6-h sessions (two sessions with female participants at the age of 28 years, one session with male participants aged 28 years and one involving a man at the age of 49 years) and showed correct detection of all 40 simulated falls with only three false alarms. Our results confirmed the sensitivity of the proposed algorithm to be 100% with a nominal false alarm rate (one false alarm per 8 h).
This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable.
This paper presents a multivariate regression predictive model of drift on the Coordinate Measuring Machine (CMM) behaviour. Evaluation tests on a CMM with a multi-step gauge were carried out following an extended version of an ISO evaluation procedure with a periodicity of at least once a week and during more than five months. This test procedure consists in measuring the gauge for several range volumes, spatial locations, distances and repetitions. The procedure, environment conditions and even the gauge have been kept invariables, so a massive measurement dataset was collected over time under high repeatability conditions. A multivariate regression analysis has revealed the main parameters that could affect the CMM behaviour, and then detected a trend on the CMM performance drift. A performance model that considers both the size of the measured dimension and the elapsed time since the last CMM calibration has been developed. This model can predict the CMM performance and measurement reliability over time and also can estimate an optimized period between calibrations for a specific measurement length or accuracy level.