Applied sciences

Bulletin of the Polish Academy of Sciences Technical Sciences

Content

Bulletin of the Polish Academy of Sciences Technical Sciences | 2024 | 72 | 4

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Abstract

Traditional industrial robots come with prime movers, i.e. electric motors (EMs), which range from a few hundred to just a few kilo watts of power ratings. However, for autonomous robotic navigation systems, we require motors which are lightweight with the aspect of high torque and power density. This aspect is very critical when the EMs in robotic navigations are subjected to harsh high temperature survival conditions, where the sustainability of the performance metrics of the electromagnetic system of the EMs degrades with the prevailing high temperature conditions. Hence, this research work addresses and formulates the design methodology to develop a 630 W high temperature PMSM (HTPMSM) in the aspect of high torque and power density, which can be used for the autonomous robotic navigation systems under high temperature survival conditions of 200◦C. Two types of rotor configurations i.e. the surface permanent magnet type (SPM) and the interior permanent magnet type (IPM) of HTPMSM are examined for its optimal electromagnetic metrics under the temperature conditions of 200◦C. The 630 W HTPMSM is designed to deliver the rated torque of 2 Nm within the volumetric & diametric constraints of DxL, which comes at 80 × 70 mm at the rated speed of 3 000 rpm with the survival temperature of 200◦C and target efficiency greater than 90%. The FEM based results are validated through the hardware prototypes for both SPM and IPM types, and the results confirm the effectiveness of the proposed design methodology of HTPMSM for sustainable autonomous robotic navigation applications.
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Authors and Affiliations

M Anand
1
ORCID: ORCID
M Sundaram
1
P Sivakumar
1
ORCID: ORCID
A Angamuthu
1

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

In the present paper, the most important aspects of computer algebra systems applications in complicated calculations for classical queueing theory models and their novel modifications are discussed. We mainly present huge computational possibilities of Mathematica environment and effective methods of obtaining symbolic results connected with the most important performance characteristics of queueing systems. First of all, we investigate effective solutions to computational problems appearing in queueing theory such as: finding final probabilities for Markov chains with a huge number of states, calculating derivatives of complicated rational functions of one or many variables with the use of classical and generalized L’Hospital’s rules, obtaining exact formulae of Stieltjes convolutions, calculating chosen integral transforms used often in the above-mentioned theory and possible applications of generalized density function of random variables and vectors in these computations. Some exemplary calculations for practical models belonging both to classical models and their generalizations are attached as well.
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Authors and Affiliations

M. Ziółkowski
1
ORCID: ORCID

  1. Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland
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Abstract

In recent years, the use of the interior permanent magnet synchronous machine (IPMSM) in various applications has grown significantly due to numerous benefits. Sensors are used to achieve high efficiency and good dynamic response in IPMSM drives but due to their high cost and reduced overall size of the system, sensorless control techniques are preferred. Non-sinusoidal distribution of rotor flux and slot harmonics are present in the considered IPMSM. In this article, these problems are considered control system disturbances. With the above-mentioned problems, the classical observer structure based on (d-q) fails to estimate at low-speed ranges. This article proposes an observer structure based on a rotor flux vector in (��-��) stationary reference frame, which works using the adaptive control law to estimate speed and position, and a non-adaptive EEMF-based observer to estimate speed and position. Moreover, a comparative analysis between both observer structures at different speed ranges is also considered in this article. The effectiveness of the observer structure is validated by simulation tests and experimental tests using the sensorless control system with a field-oriented control scheme for a 3.5 kW IPMSM drive system.
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Authors and Affiliations

Deepak Vyas
1
Marcin Morawiec
1
ORCID: ORCID
Daniel Wachowiak
1
ORCID: ORCID

  1. Department of Electric Drives and Energy Conversion, Faculty of Electrical and Control Engineering and EkoTech Center, Gdansk University of ´Technology, ul. Narutowicza 11/12, 80-233 Gdansk, Poland
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Abstract

In this work, the level of influence of the posts published by famous people on social networks on the formation of the cryptocurrency exchange rate is investigated. Celebrities who are familiar with the financial industry, especially with the cryptocurrency market, or are somehow connected to a certain cryptocurrency, such as Elon Musk with Dogecoin, are chosen as experts whose influence through social media posts on cryptocurrency rates is examined. This research is conducted based on statistical analysis. Real cryptocurrency exchange rate forecasts for the selected time period and predicted ones for the same period, obtained using three algorithms, are utilized as a dataset. This paper uses methods such as statistical hypotheses regarding the significance of Spearman’s rank correlation coefficient and Pearson’s correlation. It is confirmed that the posts by famous people on social networks significantly affect the exchange rates of cryptocurrencies.
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Authors and Affiliations

Sergii Telenyk
1
ORCID: ORCID
Grzegorz Nowakowski
1
ORCID: ORCID
Olena Gavrilenko
2
ORCID: ORCID
Mykhailo Miahkyi
2
ORCID: ORCID
Olena Khalus
2
ORCID: ORCID

  1. Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland
  2. National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,” Prosp. Peremohy 37, Kyiv, Ukraine
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Abstract

Cloud computing has become ubiquitous in modern society, facilitating various applications ranging from essential services to online entertainment. To ensure that quality of service (QoS) standards are met, cloud frameworks must be capable of adapting to the changing demands of users, reflecting the societal trend of collaboration and dependence on automated processing systems. This research introduces an innovative approach for link prediction and user cloud recommendation, leveraging nano-grid applications and deep learning techniques within a cloud computing framework. Heuristic graph convolutional networks predict data transmission links in cloud networks. The trust-based hybrid decision matrix algorithm is then employed to schedule links based on user recommendations. The proposed model and several baselines are evaluated using real-world networks and synthetic data sets. The experimental analysis includes QoS, mean average precision, root mean square error, precision, normalized square error, and sensitivity metrics. The proposed technique achieves QoS of 73%, mean average precision of 59%, root mean square error of 73%, precision of 76%, normalized square error of 86%, and sensitivity of 93%. The findings suggest that integrating nano-grid and deep learning techniques can effectively enhance the QoS of cloud computing frameworks.
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Authors and Affiliations

Nagaraju Sonti
1
ORCID: ORCID
M S S Rukmini
1
Venkatappa Reddy Pamulapati
2

  1. Department of ECE, Vignan’s Foundation for Science, Technology & Research, Vadlamudi, Andhra Pradesh, India
  2. Azista Industries Pvt Ltd, Advanced Pixel Research Intelligence Lab, Hyderabad, Telangana, India
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Abstract

This research presents an advanced control approach for battery management in battery electric utility vehicles (BEUV) operating in indoor logistics environments. The proposed approach utilizes a combination of proportional-integral (PI), fuzzy PI, and interval type 2 fuzzy PI (IT2fuzzyPI) control structures to augment the state space model for battery management. The state space model incorporates the voltage and current of each battery cell as state variables and considers the current demand from the electric motor as an input. By integrating fuzzy logic with PI control and considering uncertainty, the IT2fuzzyPI structure offers improved control recital and system robustness in the occurrence of nonlinearities, uncertainties, and turbulences. The outcomes of the simulation validate the effectiveness of the proposed scheme in managing the battery pack system’s state of charge and controlling the rates of charging and discharging. The IT2fuzzyPI control significantly improves the overall proficiency and longevity of the battery system, making it suitable for battery electric utility vehicles in logistics environments. This research contributes to the field of battery management systems, providing a valuable tool for designing and evaluating high-performance electric vehicles with enhanced control capabilities.
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Authors and Affiliations

R. Arun Kumar
1
R. Sankar Ganesh
2

  1. Electrical and Electronics Engineering, V.S.B. Engineering College, Karur, Tamil Nadu, India
  2. Electrical and Electronics Engineering, K.S.R. College of Engineering, Tiruchengode, Tamil Nadu, India
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Abstract

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.
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Authors and Affiliations

Tao Ding
1
ORCID: ORCID
Wenhan Zhong
2
Yufeng Cai
1

  1. China Jiliang University, Hangzhou, China
  2. Zhejiang Light Industrial Products Inspection and Research Institute, Hangzhou, China
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Abstract

The power sector confronts a crucial challenge in identifying sustainable and environmentally friendly energy carriers, with hydrogen emerging as a promising solution. This paper focuses on the modeling, analysis, and techno-economic evaluation of an independent photovoltaic (PV) system. The system is specifically designed to power industrial loads while simultaneously producing green hydrogen through water electrolysis. The emphasis is on utilizing renewable sources to generate hydrogen, particularly for fueling hydrogen-based cars. The study, conducted in Skikda, Algeria, involves a case study with thirty-two cars, each equipped with a 5 kg hydrogen storage tank. Employing an integrated approach that incorporates modeling, simulation, and optimization, the techno-economic analysis indicates that the proposed system provides a competitive, cost-effective, and environmentally friendly solution, with a rate of 0.239 $/kWh. The examined standalone PV system yields 24.5 GWh/year of electrical energy and produces 7584 kg/year of hydrogen. The findings highlight the potential of the proposed system to address the challenges in the power sector, offering a sustainable and efficient solution for both electricity generation and hydrogen production.
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Authors and Affiliations

Toufik Sebbagh
1
ORCID: ORCID

  1. LGMM Laboratory, University of Skikda, PoBox 26, Road of ElHadaiek, Skikda, 21000, Algeria
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Abstract

Motion planning for autonomous vehicles relies heavily on perception and prediction results to find a safe, collision-free local trajectory that adheres to traffic rules. However, vehicle perception is frequently limited by occlusion, and the generation of safe local trajectories with restricted perception poses a significant challenge in the field of motion planning. This paper introduces a collision avoidance trajectory planning algorithm that considers potential collision risks, within a hierarchical framework of sampling and optimization. The primary objective of this work is to generate trajectories that are safer and align better with human driver behavior while considering potential collision risks in occluded regions. Specifically, in occlusion scenarios, the state space is discretized, and a dynamic programming algorithm is used for a sampling-based search to obtain initial trajectories. Additionally, the concept of a driving risk field is introduced to describe potential collision risk elements within the human-vehicle-road environment. By drawing inspiration from graph search algorithms, potential collision risk areas are accurately described, and a cost function is proposed for evaluating potential risks in occluded regions. Drivers typically exhibit conservative and cautious driving behavior when navigating through occluded regions. The proposed algorithm not only prioritizes driving safety but also considers driving efficiency, thereby reducing the vehicle’s conservativeness when passing through occlusions. The research results demonstrate that the ego vehicle can actively avoid blind spots and tends to move away from occluded regions, aligning more closely with human driver behavior.
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Authors and Affiliations

Yubin Qian
1
ORCID: ORCID
Chengzhi Deng
1
ORCID: ORCID
Jiejie Xu
1
Xianguo Qu
2
Zhenyu Song
1

  1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
  2. Defective Product Administrative Center, State Administration for Market Regulation, Beijing, China
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Abstract

The paper presents the analysis of modern Artificial Intelligence algorithms for the automated system supporting human beings during their conversation in Polish language. Their task is to perform Automatic Speech Recognition (ASR) and process it further, for instance fill the computer-based form or perform the Natural Language Processing (NLP) to assign the conversation to one of predefined categories. The State-of-the-Art review is required to select the optimal set of tools to process speech in the difficult conditions, which degrade accuracy of ASR. The paper presents the top-level architecture of the system applicable for the task. Characteristics of Polish language are discussed. Next, existing ASR solutions and architectures with the End-To-End (E2E) deep neural network (DNN) based ASR models are presented in detail. Differences between Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Transformers in the context of ASR technology are also discussed.
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Authors and Affiliations

Karolina Pondel-Sycz
Piotr Bilski
ORCID: ORCID

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Abstract

The knowledge of the impact and the load-bearing capacity of unstrengthened/strengthened structures is a crucial source of information about the safety of masonry buildings near deep excavations, especially in dense urban areas. Incorrect calculations made for such designs can seriously affect not only an analyzed object but also the adjacent buildings. The safety of masonry buildings can be determined by many factors that are closely related to the hazards presented during the performance of deep excavations. These factors are at first identified and then prioritized. The AHP process in the multi-criteria analysis was used to support the decision-making process related to the verification of factors affecting the safety assessment of masonry buildings in the area of deep excavations. The proper design of building structures, including the verification of the structure strengthening near deep excavations, was found to be the most significant factor determining the safety of such buildings. The methodology for proceeding with the verification of ultimate (ULS) and serviceability (SLS) limit states in accordance with the literature data, current regulations, such as Eurocode 6 and other design standards, and the know-how of the authors, described in this paper was the next stage of the discussed analysis.
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Authors and Affiliations

Radosław Jasiński
1
ORCID: ORCID
Izabela Skrzypczak
2
ORCID: ORCID
Agnieszka Leśniak
3
ORCID: ORCID
Eduardo Natividade
4
ORCID: ORCID

  1. Faculty of Civil Engineering, Silesian University of Technology, ul. Akademicka 5, 44-100 Gliwice, Poland
  2. 2 Faculty of Civil and Environmental Engineering and Architecture, Rzeszow University of Technology, Al. Powstanców Warszawy 12, ´35-959 Rzeszów, Poland
  3. Faculty of Civil Engineering, Krakow University of Technology, Al. Warszawska 24, 31-155 Kraków, Poland
  4. Instituto Politecnico de Coimbra, Instituto Superior de Engenharia de Coimbra, Rua Pedro Nunes, Quinta da Nora,3030-199 COIMBRA, Portuga
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Abstract

In order to study the influence of multiple karst cave factors on surface settlement during tunnel boring machine (referred to TBM hereinafter) tunnelling, a three-dimensional numerical model is built by taking a subway project as an example and combining it with MIDAS GTS NX finite element software. Secondly, the influence of the radius, height, angle, vertical net distance and horizontal distance of the karst cave on maximum surface settlement is studied and sorted under the two working conditions of treatment and lack of treatment using the gray correlation analysis method. Additionally, a multi-factor numerical model of the untreated karst cave is established. Finally, based on the preceding research, a multi-factor prediction model for maximum surface settlement is proposed and tested. The results reveal that when the karst cave is not treated, the radius and height of the karst cave have a significant effect on maximum surface settlement. Following cave treatment however, the influence of the cave parameters on maximum settlement of the surface is greatly reduced. The calculating model created in this study offers excellent prediction accuracy and good adaptability.
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Authors and Affiliations

Bichang Dong
1
ORCID: ORCID
Tao Yang
1
ORCID: ORCID
Binbin JU
1
Zhongying QU
2
Chao Yi
3

  1. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan Hubei 430063, China
  2. Wuhan Municipal Engineering Design and Research Institute Co., Ltd. Wuhan Hubei 430063, China
  3. China Railway Seven Bureau Group Fourth Engineering Co., Ltd. Wuhan 430200, China
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Abstract

Face sketch synthesis (FSS) is considered an image-to-image translation problem, where a face sketch is generated from an input face photo. FSS plays a vital role in video/image surveillance-based law enforcement. In this paper, motivated by the recent success of generative adversarial networks (GAN), we consider conditional GAN (cGAN) to approach the problem of face sketch synthesis. However, despite the powerful cGAN model ability to generate fine textures, low-quality inputs characterized by the facial sketches drawn by artists cannot offer realistic and faithful details and have unknown degradation due to the drawing process, while high-quality references are inaccessible or even nonexistent. In this context, we propose an approach based on generative reference prior (GRP) to improve the synthesized face sketch perception. Our proposed model, which we call cGAN-GRP, leverages diverse and rich priors encapsulated in a pre-trained face GAN for generating highquality facial sketch synthesis. Extensive experiments on publicly available face databases using facial sketch recognition rate and image quality assessment metrics as criteria demonstrate the effectiveness of our proposed model compared to several state-of-the-art methods.
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Authors and Affiliations

Sami Mahfoud
1 2
ORCID: ORCID
Messaoud Bengherabi
2
ORCID: ORCID
Abdelhamid Daamouche
3
ORCID: ORCID
Elhocine Boutellaa
3
ORCID: ORCID
Abdenour Hadid
4
ORCID: ORCID

  1. University of Algiers 3, Faculty of Economic, Commercial and Management Sciences, Laboratory of Governance and Modernizationof Public Management, 02 Ahmed Ouaked Street Dely Ibrahim 16302, Algiers, Algeria
  2. Center for Development of Advanced Technologies, Telecom Division, P.O. Box 17 Baba-Hassen 16303, Algiers, Algeria
  3. SUniversity M’Hamed Bougara of Boumerdes, Institute of Electrical and Electronic Engineering, Laboratory of Signals and Systems,Boumerdes, 35000, Algeria
  4. 4 Sorbonne University Abu Dhabi, Sorbonne Center for Artificial Intelligence, Abu Dhabi, UAE
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Abstract

This paper presents a concept of architecture and ontology layouts for the development of multiagent model-based predictive control systems. The presented architecture provides guidelines to simplify the development of agent-based systems and improve their maintainability. The proposed multiagent system (MAS) layout is split into multiple subsystems that include agents dedicated to performing assigned tasks. MAS implementation was prepared which can use provided algorithms and actuators and can react to changes in its environment to reach the best available control quality. An example of MAS based on the proposed architecture is shown in the application of dissolved oxygen (DO) concentration control in a laboratory-activated sludge setup with a biological reactor. For that application, MAS incorporates agent-based controllers from the boundary-based predictive controllers (BBPC) family. Presented experiments prove the flexibility, resilience, and online reconfiguration ability of the proposed multiagent system.
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Authors and Affiliations

Jakub Pośpiech
1
ORCID: ORCID
Witold Nocoń
1
ORCID: ORCID
Krzysztof Stebel
1
ORCID: ORCID

  1. Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
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Abstract

The selection of a reference model (RM) for a Model-Reference Adaptive Control is one of the most important aspects of the synthesis process of the adaptive control system. In this paper, the four different implementations of RM are developed and investigated in an adaptive PMSM drive with variable moment of inertia. Adaptation mechanisms are based on the Widrow-Hoff rule (W-H) and the Adaptation Procedure for Optimization Algorithms (APOA). Inadequate order or inaccurate approximation of RM for the W-H rule may provide poor behavior and oscillations. The results prove that APOA is robust against an improper selection of RM and provides high-performance PMSM drive operation.
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Authors and Affiliations

Rafał Szczepański
1
ORCID: ORCID
Tomasz Tarczewski
1
ORCID: ORCID
Lech Grzesiak
2
ORCID: ORCID

  1. Department of Automatics and Measurement Systems, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland
  2. Institute of Control and Industrial Electronics, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland

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