Applied sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

Content

Bulletin of the Polish Academy of Sciences: Technical Sciences | 2022 | 70 | No. 3 |

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Abstract

Computer aided detection systems are used for the provision of second opinion during lung cancer diagnosis. For early-stage detection and treatment false positive reduction stage also plays a vital role. The main motive of this research is to propose a method for lung cancer segmentation. In recent years, lung cancer detection and segmentation of tumors is considered one of the most important steps in the surgical planning and medication preparations. It is very difficult for the researchers to detect the tumor area from the CT (computed tomography) images. The proposed system segments lungs and classify the images into normal and abnormal and consists of two phases, The first phase will be made up of various stages like pre-processing, feature extraction, feature selection, classification and finally, segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care of and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of crow search optimization algorithm, later artificial neural network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the fuzzy K-means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. The proposed system delivers accuracy of 96%, 100% specificity and sensitivity of 99% and it reduces false positives. Experimental results shows that the system outperforms many other systems in the literature in terms of sensitivity, specificity, and accuracy. There is a great tradeoff between effectiveness and efficiency and the proposed system also saves computation time. The work shows that the proposed system which is formed by the integration of fuzzy K-means clustering and deep learning technique is simple yet powerful and was effective in reducing false positives and segments tumors and perform classification and delivers better performance when compared to other strategies in the literature, and this system is giving accurate decision when compared to human doctor’s decision.
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Authors and Affiliations

J. Maruthi Nagendra Prasad
1
S. Chakravarty
1
M. Vamsi Krishna
2

  1. Centurion University of Technology and Management, Orissa, India
  2. Chaitanya Engineering College, Kakinada, India
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Abstract

Learning resources are massive, heterogeneous, and constantly changing. How to find the required resources quickly and accurately has become a very challenging work in the management and sharing of learning resources. According to the characteristics of learning resources, this paper proposes a progressive learning resource description model, which can describe dynamic heterogeneous resource information on a fine-grained level by using information extraction technology, then a semantic annotation algorithm is defined to calculate the semantic of learning resource and add these semantic to the description model. Moreover, a semantic search method is proposed to find the required resources, which calculate the content with the highest similarity to the user query, and then return the results in descending order of similarity. The simulation results show that the method is feasible and effective.
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Authors and Affiliations

Xiaocong Lai
1
Ying Pan
1
Xueling Jiang
1

  1. Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Nanning Normal University, Nanning 530001, People’s Republic of China
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Abstract

Object tracking based on Siamese networks has achieved great success in recent years, but increasingly advanced trackers are also becoming cumbersome, which will severely limit deployment on resource-constrained devices. To solve the above problems, we designed a network with the same or higher tracking performance as other lightweight models based on the SiamFC lightweight tracking model. At the same time, for the problems that the SiamFC tracking network is poor in processing similar semantic information, deformation, illumination change, and scale change, we propose a global attention module and different scale training and testing strategies to solve them. To verify the effectiveness of the proposed algorithm, this paper has done comparative experiments on the ILSVRC, OTB100, VOT2018 datasets. The experimental results show that the method proposed in this paper can significantly improve the performance of the benchmark algorithm.
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Authors and Affiliations

Zhentao Wang
1
Xiaowei He
1
Rao Cheng
1

  1. College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, Zhejiang, 321000, China
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Abstract

Convolutional neural networks have achieved tremendous success in the areas of image processing and computer vision. However, they experience problems with low-frequency information such as semantic and category content and background color, and high-frequency information such as edge and structure. We propose an efficient and accurate deep learning framework called the multi-frequency feature extraction and fusion network (MFFNet) to perform image processing tasks such as deblurring. MFFNet is aided by edge and attention modules to restore high-frequency information and overcomes the multiscale parameter problem and the low-efficiency issue of recurrent architectures. It handles information from multiple paths and extracts features such as edges, colors, positions, and differences. Then, edge detectors and attention modules are aggregated into units to refine and learn knowledge, and efficient multi-learning features are fused into a final perception result. Experimental results indicate that the proposed framework achieves state-of-the-art deblurring performance on benchmark datasets.
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Authors and Affiliations

Jinsheng Deng
1
Zhichao Zhang
2
Xiaoqing Yin
1

  1. College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410000, China
  2. College of Computer, National University of Defense Technology, Changsha 410000, China
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Abstract

Nowadays, Twitter is one of the most popular microblogging sites that is generating a massive amount of textual data. Such textual data is intended to incorporate human feelings and opinions with related events like tweets, posts, and status updates. It then becomes difficult to identify and classify the emotions from the tweets due to their restricted word length and data diversity. In contrast, emotion analysis identifies and classifies different emotions based on the text data generated from social media platforms. The underlying work anticipates an efficient category and prediction technique for analyzing different emotions from textual data collected from Twitter. The proposed research work deliberates an enhanced deep neural network (EDNN) based hierarchical Bi-LSTM model for emotion analysis from textual data; that classifies the six emotions mainly sadness, love, joy, surprise, fear, and anger. Furthermore, the emotion analysis result obtained by the proposed hierarchical Bi-LSTM model is being compared and validated with the traditional hybrid CNN-LSTM approach regarding the accuracy, recall, precision, and F1-Score. It can be observed from the results that the proposed hierarchical Bi-LSTM achieves an average accuracy of 89% for emotion analysis, whereas the existing CNN-LSTM model achieved an overall accuracy of 75%. This result shows that the proposed hierarchical Bi-LSTM approach achieves desired performance compared to the CNN-LSTM model.
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Authors and Affiliations

Dashrath Mahto
1
ORCID: ORCID
Subhash Chandra Yadav
2
ORCID: ORCID

  1. Department of Computer Science and Technology, Central University of Jharkhand, Ranchi, India
  2. Department of Computer Science and Technology,Central University of Jharkhand, Ranchi, India
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Abstract

The paper considers the problem of increasing the generalization ability of classification systems by creating an ensemble of classifiers based on the CNN architecture. Different structures of the ensemble will be considered and compared. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolution layer of CNN flattened to the form of a vector, are subjected to a few different selection mechanisms. Each of them chooses the independent set of features, selected according to the applied assessment techniques. Their results are combined with three classifiers: softmax, support vector machine, and random forest of the decision tree. All of them do simultaneously the same classification task. Their results are integrated into the final verdict of the ensemble. Different forms of arrangement of the ensemble are considered and tested on the recognition of facial images. Two different databases are used in experiments. One was composed of 68 classes of greyscale images and the second of 276 classes of color images. The results of experiments have shown high improvement of class recognition resulting from the application of the properly designed ensemble.
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Authors and Affiliations

Robert Szmurło
1
ORCID: ORCID
Stanislaw Osowski
2
ORCID: ORCID

  1. Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warszawa, Poland
  2. Faculty of Electronic Engineering, Military University of Technology, gen. S. Kaliskiego 2, 00-908 Warszawa, Poland
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Abstract

In this work, two robust zeroing neural network (RZNN) models are presented for online fast solving of the dynamic Sylvester equation (DSE), by introducing two novel power-versatile activation functions (PVAF), respectively. Differing from most of the zeroing neural network (ZNN) models activated by recently reported activation functions (AF), both of the presented PVAF-based RZNN models can achieve predefined time convergence in noise and disturbance polluted environment. Compared with the exponential and finite-time convergent ZNN models, the most important improvement of the proposed RZNN models is their fixed-time convergence. Their effectiveness and stability are analyzed in theory and demonstrated through numerical and experimental examples.
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Authors and Affiliations

Peng Zhou
1
Mingtao Tan
2
ORCID: ORCID

  1. College of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China
  2. School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
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Abstract

The iterative learning fault-tolerant control strategies with non-strict repetitive initial state disturbances are studied for the linear discrete networked control systems (NCSs) and the nonlinear discrete NCSs. In order to reduce the influence of the initial state disturbance in iteration, for the linear NCSs, considering the external disturbance and actuator failure, the iterative learning fault-tolerant control strategy with impulse function is proposed. For the nonlinear NCSs, the external disturbance, packet loss and actuator failure are considered, the iterative learning fault-tolerant control strategy with random Bernoulli sequence is provided. Finally, the proposed control strategies are used for simulation research for the linear NCSs and the nonlinear NCSs. The results show that both strategies can reduce the influence of the initial state disturbance on the tracking effect, which verifies the effectiveness of the given method.
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Authors and Affiliations

Fu Xingjian
1
Zhao Qianjun
1

  1. School of Automation, Beijing Information Science and Technology University, Beijing 100192, China
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Abstract

The inventory systems are highly variable and uncertain due to market demand instability, increased environmental impact, and perishability processes. The reduction of waste and minimization of holding and shortage costs are the main topics studied within the inventory management area. The main difficulty is the variability of perishability and other processes that occurred in inventory systems and the solution for a trade-off between sufficient inventory level and waste of products. In this paper, the approach for resolving this trade-off is proposed. The presented approach assumes the application of a state-feedback neural network controller to generate the optimal quantity of orders considering an uncertain deterioration process and the FIFO issuing policy. The development of the control system is based on state-space close loop control along with neural networks. For modelling the perishability process Weibull distribution and FIFO policy are applied. For the optimization of the designed control system, the evolutionary NSGA-II algorithm is used. The robustness of the proposed approach is provided using the minimax decision rule. The worst-case scenario of an uncertain perishability process is considered. For assessing the proposed approach, simulation research is conducted for different variants of controller structure and model parameters. We perform extensive numerical simulations in which the assessment process of obtained solutions is conducted using hypervolume indicator and average absolute deviation between results obtained for the learning and testing set. The results indicate that the proposed approach can significantly improve the performance of the perishable inventory system and provides robustness for the uncertain changes in the perishability process.
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Authors and Affiliations

Ewelina Chołodowicz
1
ORCID: ORCID
Przemysław Orłowski
1
ORCID: ORCID

  1. Faculty of Electrical Engineering, Department of Automation and Robotics, West Pomeranian University of Technology in Szczecin,al. Piastów 17, 70-310 Szczecin, Poland
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Abstract

Solar energy has become one of the most potential alternative energies in the world. To convert solar energy into electricity, a photovoltaic (PV) system can be utilized. However, the fluctuation of sunlight intensity throughout the day greatly affects the generated energy in the PV system. A battery may be beneficial to store the generated energy for later use. A DC–DC converter is commonly exploited to produce a constant output voltage during the battery charging process. A Zeta converter is a DC–DC converter which can be used to produce output values above or below the input voltage without changing the polarity. To deal with the inherent non-linearity and time-varying properties of the converter, in this paper the sliding mode control (SMC) is first analyzed and exploited before being integrated with a proportional-integral (PI) control to regulate the output voltage of the PV system. Disturbances are given in the form of changes in input voltage, reference voltage, and load. Voltage deviation and recovery time to reach a steady-state condition of the output voltage after disturbances are investigated and compared to the results using a proportional-integral-differential (PID) controller. The results show that the proposed control design performs faster than the compared PID control method.
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Authors and Affiliations

Rini Nur Hasanah
1
ORCID: ORCID
Lunde Ardhenta
1
ORCID: ORCID
Tri Nurwati
1
ORCID: ORCID
Onny Setyawati
1
Dian Retno Sawitri
2
Hadi Suyono
1
ORCID: ORCID
Taufik Taufik
3

  1. Electrical Engineering Department, Universitas Brawijaya, Indonesia
  2. Electrical Engineering Department, Universitas Dian Nuswantoro, Indonesia
  3. Electrical Engineering Department, Cal Poly State University, USA
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Abstract

The asymptotic stability of positive descriptor continuous-time and discrete-time linear systems is considered. New sufficient conditions for stability of positive descriptor linear systems are established. The efficiency of the new stability conditions are demonstrated on numerical examples of continuous-time and discrete-time linear systems.
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Authors and Affiliations

Tadeusz Kaczorek
1
ORCID: ORCID

  1. Bialystok University of Technology, Faculty of Electrical Engineering, Wiejska 45D, 15-351 Bialystok, Poland
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Abstract

This paper concerns the problem of empirical investigation and mathematical modelling of a novel controllable damper using vacuum packed particles. Vacuum packed particles tend to be placed among the group of so-called ‘smart structures’. The macroscopic mechanical features of such structures can be controlled by the partial vacuum parameter. The authors consider an application of Bouc-Wen model in order to represent the dynamic behaviour of the investigated device. The verification of the model response with experimental data is discussed. The Bouc-Wen model parameters identification is described.
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Authors and Affiliations

Anna Mackojc
1
ORCID: ORCID
Bogumil Chilinski
1
ORCID: ORCID
Robert Zalewski
1
ORCID: ORCID

  1. Institute of Machine Design Fundamentals, Warsaw University of Technology, Poland
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Abstract

Serious damage to the inner rim of the rear twin wheel in one dump truck was noted during the operation of the fleet performing transport tasks. It was a drive wheel, and its damage occurred while driving with a load exceeding the permissible value. The examination of selected fragments of the damaged rim surface was conducted visually as well as using a digital microscope with a portable head. The measurements of the Vickers hardness and microscopic observations of the material structure of the sample cut along the thickness of the rim disk were carried out. The drive torque loading of the twin wheels of the tipper-truck rear axle, under their mating with different kinds of road roughness and under various vertical loads of the wheels was calculated. An analysis of stress distributions in the rim modelled using the Finite Element Method was also conducted for several possible scenarios of wheel loading. The damage to the rim was caused by simultaneous action of several factors, such as overloading the car, poor condition of the tires, loading the drive wheel by a part of the vehicle weight and the driving torque, and hitting a wheel on a cavity in a dirt road, causing a temporary relief of one of the tires on a twin wheel.
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Authors and Affiliations

Przemyslaw Kubiak
1 2
Marek Wozniak
3
Sergiusz Zakrzewski
3
Krzysztof Siczek
3
Adam Rylski
3
Adam Mrowicki
1
Jan Matej
1
Jakub Deda
1
Lech Knap
1

  1. Faculty of Automotive and Construction Machinery Engineering, Warsaw University of Technology, Narbutta Str. 84, 02-524 Warsaw, Poland
  2. Ecotechnology Team, Lodz University of Technology, Piotrkowska 266, 90-924 Lodz, Poland
  3. Faculty of Mechanical Engineering, Lodz University of Technology, Stefanowskiego 1/15, 90-537 Lodz, Poland
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Abstract

The main focus of the article is an advanced actuator, designed and optimized for small dynamic legged robots. The presented actuator prototype is unique, as the market lacks similar solutions when dimensions and weight of the module are considered. The actuator has a modular structure, which makes it easy to replace in case of malfunction and simplifies the overall structure of the robot. High torque bandwidth, achieved by the module, is crucial to agile locomotion, obstacle avoidance and push recovery of the quadrupedal robot. The Authors have conducted a solution review aimed at similar small-size modules. It was found that there are no advanced actuators suitable for sub 5 kg quadruped robots. The unique design presented in this paper is described in all three aspects: mechanical, electrical and software. The mechanical section depicts the solutions implemented in the module, especially the low gear ratio gearbox. The custom brushless motor driver is presented in the electrical section, together with detailed diagrams and hardware descriptions. The last section depicts solutions implemented in the software, the main motor control algorithm and auxiliary modules such as automatic motor parameter identification and encoder misalignment correction. Tests performed in the last part of this paper validated the design goals established for the actuator. The results confirmed the high torque capability and exhibited the motor saturation region. Continuous and peak torque were measured based on the thermal characteristics of the module. Moreover, the automatic motor parameter identification process carried out by the controller itself was validated by manual measurements.
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Authors and Affiliations

Piotr Wasilewski
1
Rafał Gradzki
2
ORCID: ORCID

  1. Bialystok University of Technology, Faculty of Electrical Engineering, Wiejska 45D, 15-351 Bialystok, Poland
  2. Bialystok University of Technology, Faculty of Mechanical Engineering, Department of Robotics and Mechatronics, Wiejska 45C, 15-351, Bialystok, Poland
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Abstract

Maintenance of assets and equipment in power plants is essential for their safety and is required to help the plant stay active. In this paper, the specimens manufactured from a pipe of X10CrMoVNb9-1 (P91) power engineering steel in the as-received state and after operating for 80000 h at internal pressure of 8.4 MPa and temperature of 540ºC were subjected to tests using electronic speckle pattern interferometry (ESPI) under static loading of up to 2.5 kN. Such a procedure enables assessment of strain and stress distribution maps to compare material integrity in the as-received state and after exploitation in its elastic range. The measurements conducted showed no effect of long time operation on the mechanical response of P91 steel under the power installations conditions since the field strain distributions for each type of specimen were found to be similar.
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Authors and Affiliations

Mateusz Kopec
1 2
ORCID: ORCID

  1. Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland
  2. Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, UK
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Abstract

The generalized magnetizing curve series for the nonlinear magnetic circuit is proposed. Subsequently, three definitions of selfinductance for the nonlinear magnetic circuit are compared. The passivity of the magnetic circuit is reconsidered. Three theorems that describe features of Fourier harmonics of distorted waveforms have been proved.
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Authors and Affiliations

Dariusz Spałek
1
ORCID: ORCID

  1. Silesian University of Technology, Electrical Engineering Faculty, Akademicka 10, 44-100 Gliwice Poland
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Abstract

Discrete two-dimensional orthogonal wavelet transforms find applications in many areas of analysis and processing of digital images. In a typical scenario the separability of two-dimensional wavelet transforms is assumed and all calculations follow the row-column approach using one-dimensional transforms. For the calculation of one-dimensional transforms the lattice structures, which can be characterized by high computational efficiency and non-redundant parametrization, are often used. In this paper we show that the row-column approach can be excessive in the number of multiplications and rotations. Moreover, we propose the novel approach based on natively two-dimensional base operators which allows for significant reduction in the number of elementary operations, i.e., more than twofold reduction in the number of multiplications and fourfold reduction of rotations. The additional computational costs that arise instead include an increase in the number of additions, and introduction of bit-shift operations. It should be noted, that such operations are significantly less demanding in hardware realizations than multiplications and rotations. The performed experimental analysis proves the practical effectiveness of the proposed approach.
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Authors and Affiliations

Dariusz Puchala
1
ORCID: ORCID

  1. Institute of Information Technology, Technical University of Lodz, Poland
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Abstract

Since electrical drives have become an integral element of any industrial sector, power quality difficulties have been well expected, and delivering genuine quality of the same has proven to be a difficult challenge. Since power quality relies on load side non-linearity and high semiconductor technology consumption, it is a serious concern. The efficiency of the drive segment employed in the sector is increasingly becoming a topic of discussion in today’s market. Numerous reviews of available literature have found problems with the load side as well as with electrical drive proficiency, as a result of the issues listed above. A high level of power quality vulnerability is simply too much. Even the most advanced technology has its limits when it comes to drive operation. Research on the grid-side quality issues of electrical drives is the focus of this article. After field testing of grid power quality, each parametric analysis is performed to identify crucial parameters that can cause industrial drives to fail. Based on this discovery, a machine learning strategy was developed and an artificial intelligence technique was proposed to administer the fault deterrent prediction algorithm. An accurate forecast of anomalous behavior on the grid side ensures safe and dependable grid operation such that shutdown or failure probability is minimized to a greater extent by the results. Additional information gleaned from historical data will prove useful to equipment manufacturers in the future, providing a solution to this problem.
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Authors and Affiliations

Vishnu Murthy K.
1
ORCID: ORCID
Ashok Kumar L.
1
ORCID: ORCID

  1. Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamilnadu, India
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Abstract

The paper has investigated the effect of wind speed on selected thermal characteristics of the contemporary ACCR line. As wind speed functions, heating curves, stationary temperature profiles, steady-state current ratings and thermal time constants, have been determined. The composite core (Al–Al2O3) and the Al–Zr alloy braid were modeled as porous solids. As a result, the physical model is composed of a solid cylinder and a hollow cylinder with different material parameters of the above-mentioned elements. The mathematical model was formulated as the boundary-initial problem of the parabolic heat equation. The problem was solved by the state-superposition of and variable-separation method. On this basis, a computer program was developed in the Mathematica 10.4 environment and the velocity characteristics sought for were plotted. The results obtained analytically were positively verified by the finite-element method in the NISA v.16 environment. The physical interpretation of the determined characteristics has been given.
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Authors and Affiliations

Marek Zaręba
1
Jerzy Gołębiowski
1

  1. Faculty of Electrical Engineering, Bialystok University of Technology, ul. Wiejska 45D, 15-351 Białystok, Poland
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Abstract

This paper develops an automatic method to calculate the macrotexture depth of pavement roads, using the tire/road noise data collected by the two directional microphones mounted underneath a moving test vehicle. The directional microphones collect valid tire/road noise signal at the travel speed of 10–110 km/h, and the sampling frequency is 50 kHz. The tire/road noise signal carries significant amount of road surface information, such as macrotexture depth. Using bandpass filter, principal component analysis, speed effect elimination, Gaussian mixture model, and reversible jump Markov Chain Monte Carlo, the macrotexture depth of pavement roads can be calculated from the tire/road noise data, automatically and efficiently. Compared to the macrotexture depth results by the sand-patch method and laser profiler, the acoustic method has been successfully demonstrated in engineering applications for the accurate results of macrotexture depth with excellent repeatability, at the test vehicle’s travel speed of 10-110 km/h.
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Authors and Affiliations

Hao Liu
1
Yiying Zhang
2
Zhengwei Xu
2
Xiaojiang Liu
2

  1. China Merchants Chongqing Communications Technology Research & Design Institute Co., Ltd, 33 Xuefu Road, Nan’an District, Chongqing, PR China, 400067
  2. China Merchants Roadway Information Technology (Chongqing) Co., Ltd, 33 Xuefu Road, Nan’an District, Chongqing, PR China, 400067
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Abstract

Doping is one of the possible ways to significantly increase the thermoelectric properties of many different materials. It has been confirmed that by introducing bismuth atoms into Mg sites in the Mg2Si compound, it is possible to increase career concentration and intensify the effect of phonon scattering, which results in remarkable enhancement in the figure of merit (ZT) value. Magnesium silicide has gained scientists’ attention due to its nontoxicity, low density, and inexpensiveness. This paper reports on our latest attempt to employ ultrafast selfpropagating high-temperature synthesis (SHS) followed by the spark plasma sintering (SPS) as a synthesis process of doped Mg2Si. Materials with varied bismuth doping were fabricated and then thoroughly analyzed with the laser flash method (LFA), X-ray diffraction (XRD), scanning electron microscopy (SEM) with an integrated energy-dispersive spectrometer (EDS). For density measurement, the Archimedes method was used. The electrical conductivity was measured using a standard four-probe method. The Seebeck coefficient was calculated from measured Seebeck voltage in the sample subjected to a temperature gradient. The structural analyses showed the Mg2Si phase as dominant and Bi2Mg3 located at grain boundaries. Bismuth doping enhanced ZT for every dopant concentration. ZT = 0:44 and ZT=0.38 were obtained for 3wt% and 2wt% at 770 K, respectively.
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Authors and Affiliations

Bartosz Bucholc
1
ORCID: ORCID
Kamil Kaszyca
1
ORCID: ORCID
Piotr Śpiewak
2
ORCID: ORCID
Krzysztof Mars
3
ORCID: ORCID
Mirosław J. Kruszewski
2
ORCID: ORCID
Łukasz Ciupiński
2
ORCID: ORCID
Krystian Kowiorski
1
ORCID: ORCID
Rafał Zybała
1 2
ORCID: ORCID

  1. Łukasiewicz Research Network - Institute of Microelectronics and Photonics, Aleja Lotników 32/46, 02-668 Warsaw, Poland
  2. Faculty of Materials Science and Engineering, Warsaw University of Technology, Wołoska 141, 02-507 Warsaw, Poland
  3. Faculty of Materials Science and Ceramic, AGH University of Science and Technology, Kraków, Al. Mickiewicza 30, 30-059, Poland

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