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Abstract

The process of designing and creating an integrated distributed information system for storing digitized works of scientists of research institutes of the Almaty academic city is analyzed. The requirements for the storage of digital objects are defined; a comparative analysis of the open source software used for these purposes is carried out. The system fully provides the necessary computing resources for ongoing research and educational processes, simplifying the prospect of its further development, and allows to build an advanced IT infrastructure for managing intellectual capital, an electronic library that is intended to store all books and scientific works of the Kazakhstan Engineering Technological University and research institutes of the Almaty academic city.

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Authors and Affiliations

Nurlan M. Temirbekov
Tahir M. Takabayev
Dossan R. Baigereyev
Waldemar Wójcik
Konrad Gromaszek
Almas N. Temirbekov
Bakytzhan B. Omirzhanova
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Abstract

A review of a new Polish translation of Aristophanes’ Clouds by Olga Śmiechowicz.
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Authors and Affiliations

Tomasz Mojsik
1

  1. Wydział Historii i Stosunków Międzynarodowych, Uniwersytet w Białymstoku
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Abstract

A response to the review of a new Polish translation of Aristophanes’ Clouds, which appeared in the previous issue of “Meander”.
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Authors and Affiliations

Olga Śmiechowicz
1
ORCID: ORCID

  1. Wydział Polonistyki, Uniwersytet Jagielloński
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Abstract

This paper deals with a methodology for the implementation of cloud manufacturing (CM) architecture. CM is a current paradigm in which dynamically scalable and virtualized resources are provided to users as services over the Internet. CM is based on the concept of coud computing, which is essential in the Industry 4.0 trend. A CM architecture is employed to map users and providers of manufacturing resources. It reduces costs and development time during a product lifecycle. Some providers use different descriptions of their services, so we propose taking advantage of semantic web technologies such as ontologies to tackle this issue. Indeed, robust tools are proposed for mapping providers’ descriptions and user requests to find the most appropriate service. The ontology defines the stages of the product lifecycle as services. It also takes into account the features of coud computing (storage, computing capacity, etc.). The CM ontology will contribute to intelligent and automated service discovery. The proposed methodology is inspired by the ASDI framework (analysis–specification–design–implementation), which has already been used in the supply chain, healthcare and manufacturing domains. The aim of the new methodology is to propose an easy method of designing a library of components for a CM architecture. An example of the application of this methodology with a simulation model, based on the CloudSim software, is presented. The result can be used to help the industrial decision-makers who want to design CM architectures.

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Authors and Affiliations

E. Talhi
J.-C. Huet
V. Fortineau
S. Lamouri
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Abstract

The rapid development of the global economy has led to an increasing demand for resources. The disparity between the supply and demand of resources continues to be prominent and shows a situation of short supply. Resource investment projects with large amounts and long construction periods face many risks due to various unpredictable factors. Cultural, legal, economic and other environments vary between different countries. Therefore, comprehensive risk identification, understanding, evaluation, and analysis are important prerequisites for the success of mineral investment. In this paper, the risk of mineral resources investment in host countries is identified. A risk evaluation index system is established to objectively evaluate the risk environment of the host country. The risk evaluation index system includes four first-level indexes: political and legal risk, social and cultural risk, economic and financial risk, and natural risk. The subjective weight was determined by sending questionnaires to experts and scholars in the industry and conducting data processing. The entropy method was used to determine the objective weight. Finally, the subjective weight and the objective weight were combined to obtain a group of scientific and accurate combined weights. The matter-element theory was introduced into the cloud model and a risk assessment model based on the cloud matter-element theory was constructed with comprehensive consideration of the fuzziness and randomness of risks. Eight countries with relatively rich mineral resources were taken as cases to verify the model application. The research results provide a theoretical basis and decision-making methods for mineral enterprise investment.
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Authors and Affiliations

Jie Hou
1
Guoqing Li
1
Jiahong Ling
1
Lianyun Chen
2
Wei Zhao
3
ORCID: ORCID
Baoli Sheng
3

  1. University of Science and Technology Beijing, China
  2. University of Science and Technology Beijing, China; Shandong Gold Group Co., Ltd., Jinan, China
  3. Sanshandao Gold Mine, Shandong Gold Group Mining (Laizhou) Co., Ltd., Yantai, China
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Abstract

The paper aims at the higher reactive power management complexity caused by the access of distributed power, and the problem such as large data exchange capacity, low accuracy of reactive power distribution, a slow convergence rate, and so on, may appear when the controlled objects are large. This paper proposes a reactive power and voltage control management strategy based on virtual reactance cloud control. The coupling between active power and reactive power in the system is effectively eliminated through the virtual reactance. At the same time, huge amounts of data are treated to parallel processing by using the cloud computing model parallel distributed processing, realize the uncertainty transformation between qualitative concept and quantitative value. The power distribution matrix is formed according to graph theory, and the accurate allocation of reactive power is realized by applying the cloud control model. Finally, the validity and rationality of this method are verified by testing a practical node system through simulation.

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Authors and Affiliations

Wei Min Zhang
Yan Xia Zhang
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Abstract

Recently, Google Earth Engine (GEE) provides a new way to effectively classify land cover utilizing available in-built classifiers. However, there have a few studies on the applications of the GEE so far. Therefore, the goal of this study is to explore the capacity of the GEE platform in terms of land cover classification in Dien Bien Province of Vietnam. Land cover classification in the year of 2003 and 2010 were performed using multiple-temporal Landsat images. Two algorithms – GMO Max Entropy and Classification and Regression Tree (CART) integrated into the Google Earth Engine (GEE) plat-form – were applied for this classification. The results indicated that the CART algorithm performed better in terms of mapping land use. The overall accuracy of this algorithm in the year of 2003 and 2010 were 80.0% and 81.6%, respective-ly. Significant changes between 2003 and 2010 were found as an increase in barren land and a reduction in forest land. This is likely due to the slash-and-burn agricultural practice of ethnic minorities in the province. Barren land seems to occur more at locations near water sources, reflecting the local people’s unsuitable farming practice. This study may provide use-ful information in land cover change in Dien Bien Province, as well as analysis mechanisms of this change, supporting en-vironmental and natural resource management for the local authorities.

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Authors and Affiliations

Luong B. Nguyen
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Abstract

With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data. Remote sensing data clear showing the characteristics of Big Data such as hyper spectral, high spatial resolution, and high time resolution, thus, resulting in a significant increase in the volume, variety, velocity and veracity of data.This paper proposes a feature supporting, salable, and efficient data cube for timeseries analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change. In this system, the feature data cube building and distributed executor engine are critical in supporting large spatiotemporal RS data analysis with spatial features. The feature translation ensures that the geographic object can be combined with satellite data to build a feature data cube for analysis. Constructing a distributed executed engine based on dask ensures the efficient analysis of large-scale RS data. This work could provide a convenient and efficient multidimensional data services for many remote sens-ing applications.
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Authors and Affiliations

Yassine Sabri
1
Fadoua Bahja
1
Henk Pet
2

  1. Laboratory of Innovation in Management and Engineering for Enterprise (LIMIE), ISGA Rabat, 27 Avenuel Oqba, Agdal, Rabat, Morocco
  2. Terra Motion Limited, 11 Ingenuity Centre, Innovation Park, Jubilee Campus, University of Nottingham, Nottingham NG7 2TU, UK
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Abstract

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks.
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Authors and Affiliations

Abbas M. Ali Al-muqarm
1 2
Naseer Ali Hussien
3

  1. University of Kufa, Iraq
  2. Computer Technical Engineering Department, The Islamic University, Iraq
  3. Alayen University, Iraq
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Abstract

Workflow Scheduling is the major problem in Cloud Computing consists of a set of interdependent tasks which is used to solve the various scientific and healthcare issues. In this research work, the cloud based workflow scheduling between different tasks in medical imaging datasets using Machine Learning (ML) and Deep Learning (DL) methods (hybrid classification approach) is proposed for healthcare applications. The main objective of this research work is to develop a system which is used for both workflow computing and scheduling in order to minimize the makespan, execution cost and to segment the cancer region in the classified abnormal images. The workflow computing is performed using different Machine Learning classifiers and the workflow scheduling is carried out using Deep Learning algorithm. The conventional AlexNet Convolutional Neural Networks (CNN) architecture is modified and used for workflow scheduling between different tasks in order to improve the accuracy level. The AlexNet architecture is analyzed and tested on different cloud services Amazon Elastic Compute Cloud- EC2 and Amazon Lightsail with respect to Makespan (MS) and Execution Cost (EC).
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Bibliography

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Authors and Affiliations

P. Tharani
1
A.M. Kalpana
1

  1. Department of Computer Science and Engineering, Government College of Engineering, Salem-636011, Tamil Nadu, India
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Abstract

The problem of performing software tests using Testing-as-a-Service cloud environment is considered and formulated as an~online cluster scheduling on parallel machines with total flowtime criterion. A mathematical model is proposed. Several properties of the problem, including solution feasibility and connection to the classic scheduling on parallel machines are discussed. A family of algorithms based on a new priority rule called the Smallest Remaining Load (SRL) is proposed. We prove that algorithms from that family are not competitive relative to each other. Computer experiment using real-life data indicated that the SRL algorithm using the longest job sub-strategy is the best in performance. This algorithm is then compared with the Simulated Annealing metaheuristic. Results indicate that the metaheuristic rarely outperforms the SRL algorithm, obtaining worse results most of the time, which is counter-intuitive for a metaheuristic. Finally, we test the accuracy of prediction of processing times of jobs. The results indicate high (91.4%) accuracy for predicting processing times of test cases and even higher (98.7%) for prediction of remaining load of test suites. Results also show that schedules obtained through prediction are stable (coefficient of variation is 0.2‒3.7%) and do not affect most of the algorithms (around 1% difference in flowtime), proving the considered problem is semi-clairvoyant. For the Largest Remaining Load rule, the predicted values tend to perform better than the actual values. The use of predicted values affects the SRL algorithm the most (up to 15% flowtime increase), but it still outperforms other algorithms.

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Authors and Affiliations

J. Rudy
C. Smutnicki
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Abstract

Cloud-based computational environments can offer elastic and flexible services to wide audiences. Małopolska Educational Cloud was originally developed to support the day-to-day collaboration of geographically scattered schools with universities which organized online classes, led by university teachers, as an amendment to face-to-face teaching. Due to the centralized management and ubiquitous access, both the set of services provided by MEC and their usage patterns can be adjusted rapidly. In this paper we show how – during the COVID-19 pandemic – the flexibility of Małopolska Educational Cloud was leveraged to speed up the transition from in-class to remote teaching, both in the classes and schools which were already involved in the MEC project, and newly added ones. We also discuss the actions that were required to support the smooth transition and draw conclusions for the future.
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Bibliography

  1.  K. Zieliński, Ł. Czekierda, F. Malawski, R. Straś, and S. Zieliński, “Recognizing value of educational collaboration between high schools and universities facilitated by modern ICT,” J. Comput. Assisted Learn., vol. 33, no. 6, pp. 633–648, 2017.
  2.  Ł. Czekierda, K. Zieliński, and S. Zieliński, “Automated orchestration of online educational collaboration in cloud-based environments,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 17, no. 1, pp. 1–26, 2021.
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Authors and Affiliations

Łukasz Czekierda
1
Filip Malawski
1
Robert Straś
1
Krzysztof Zieliński
1
ORCID: ORCID
Sławomir Zieliński
1

  1. AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland
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Abstract

The research was aimed at analysing the factors that affect the accuracy of merging point clouds when scanning over longer distances. Research takes into account the limited possibilities of target placement occurring while scanning opposite benches of quarries or open-pit mines, embankments from opposite banks of rivers etc. In all these cases, there is an obstacle/void between the scanner and measured object that prevents the optimal location of targets and enlarging scanning distances. The accuracy factors for cloud merging are: the placement of targets relative to the scanner and measured object, the target type and instrument range. Tests demonstrated that for scanning of objects with lower accuracy requirements, over long distances, it is optimal to choose flat targets for registration. For objects with higher accuracy requirements, scanned from shorter distances, it is worth selecting spherical targets. Targets and scanned object should be on the same side of the void.

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Authors and Affiliations

G. Lenda
P. Lewińska
J. Siwiec
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Abstract

This article reflects on key concepts of historical thinking proposed by doctoral students and young researchers. Established concepts such as the social role of history, professional historian and (imagined) space are still important to the new generation of historians. At the same time, some new concepts are emerging, such as political exhumations, mass graves, motion, embodied historical research, ahistorical memory politics, websites as historical sources, critical heritage studies and heritagisation, treason, preposterous history – an idea taken from Mieke Bal, and “Supreme Peace” – a notion drawn from the Chinese philosophy of history. To interpret these concepts, I build word clouds as a way of creating knowledge involving non‑human factors (algorithms) while enabling speculative interpretations of the relations between words. The idea of a secure past comes to the fore and I therefore examine whether historical security and being secure in history could be considered important elements of interdisciplinary security studies.
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Bibliography

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Bal, Mieke. Quoting Caravaggio: Contemporary Art, Preposterous History. Chicago: University of Chicago Press, 1999.
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White, Hayden. Przeszłość praktyczna. Kraków: Universitas, 2014.
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Authors and Affiliations

Ewa Domańska
1
ORCID: ORCID

  1. Uniwersytet im. Adama Mickiewicza w Poznaniu
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Abstract

The paper presents the idea of a prosumer energy cloud as a new service dedicated to electricity prosumers. The implementation of the cloud should generate a number of benefits in the following areas: settlements between prosumer and electricity supplier, the development of distributed energy sources in microprocessors and the development of e-mobility. From the prosumer point of view, the proposed idea of a prosumer cloud of energy is dedicated to the virtual storage of energy excess generated in the micro-installation. Physical energy storage in the cloud means recording the volume of electricity introduced into the electricity system from the prosumer’s microprocessors. It is assumed that the energy equivalent to the volume registered in the prosumer cloud can be used at any time at any point in the network infrastructure of the National Power System. Any point of network infrastructure shall be understood as any locally located point of connection of an electricity consumer provided with access authorization. From the point of view of the power grid operators, the idea of a prosumer energy cloud is a conceptual proposition of a service dedicated to the new model of the power system functioning, taking future conditions concerning the significant development of prosumer energy and e-mobility into account. In this concept, electricity would be treated as a commodity only to partial physical storage and above all to trade. In this model a key aspect would be virtual energy storage, that is, the commercial provision by the cloud operator (trading company) of any use of the electricity portfolio by its suppliers. It should be stressed, however, that in the prosumer’s energy cloud functioning, a significant factor would be the cost of guarantees of the use of energy by prosumers at any time and point of connection to the network. This results in the need of taking the presence of certain market risks, both volumetric and cost incurred by clouds operator, which can be minimized by passing a portion of the accumulated volume of generated energy to the cloud operator into account. It should be emphasized that this article presents the first phase of the development of the concept of prosumer energy cloud. However, it is planned to be expanded by the following stages, which include the possibility of controlling and supervising the operation of prosumer installations such as: sources, receivers and physical energy stores, e.g. home energy storage or batteries installed in electric vehicles. Ultimately, it is assumed that the proposed prosumer energy cloud will be outside of the storage of energy (virtual and partly physical) and that aggregation of prosumer resources will create new possibilities for their use to provide a variety of regulatory services, including system ones.

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Authors and Affiliations

Piotr Rzepka
Maciej Sołtysik
Mateusz Szablicki
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Abstract

Based on the theory of computer vision, a new method for extracting ore from underground mines is proposed. This is based on a combination of RGB images collected by a color industrial camera and a point cloud generated by a 3D ToF camera. Firstly, the mean-shift algorithm combined with the embedded confidence edge detection algorithm is used to segment the RGB ore image into different regions. Secondly, the effective ore regions are classified into large pieces of ore and ore piles consisting of a number of small pieces of ore. The method applied in the classification process is to embed the confidence into the edge detection algorithm which calculates edge distribution around ore regions. Finally, the RGB camera and the 3D ToF camera are calibrated and the camera matrix transformation of the two cameras is obtained. Point cloud fragments are then extracted according to the cross-calibration result. The geometric properties of the ore point cloud are then analysed in the subsequent procedure.
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Authors and Affiliations

Feng Jin
1
ORCID: ORCID
Kai Zhan
2
Shengjie Chen
2
Shuwei Huang
2
ORCID: ORCID
Yuansheng Zhang
2

  1. BGRIMM Technology Group University of Science and Technology Beijing, China
  2. BGRIMM Technology Group, China
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Abstract

Afeeder automation (FA) system is usually used by electricity utilities to improve power supply reliability. The FA system was realized by the coordinated control of feeder terminal units (FTUs) in the electrical power distribution network. Existing FA testing technologies can only test basic functions of FTUs, while the coordinated control function among several FTUs during the self-healing process cannot be tested and evaluated. In this paper, a novel cloud-based digital-physical testing method is proposed and discussed for coordinated control capacity test of the FTUs in the distribution network. The coordinated control principle of the FTUs in the local-reclosing FA system is introduced firstly and then, the scheme of the proposed cloud-based digital-physical FA testing method is proposed and discussed. The theoretical action sequences of the FTUs consisting of the FTU under test and the FTUs installed in the same feeder are analyzed and illustrated. The theoretical action sequences are compared with the test results obtained by the realized cloud-based simulation platform and the digital-physical hybrid communication interaction. The coordinated control capacity of the FTUs can be evaluated by the comparative result. Experimental verification shows that the FA function can be tested efficiently and accurately based on our proposed method in the power distribution system inspection.

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Authors and Affiliations

Guoyan Chen
Wenxiong Mo
Hongbin Wang
Jinrui Tang
Xinhao Bian
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Abstract

Transmission lines’ live working is one of an effective means to ensure the reliable operation of transmission lines. In order to solve the unsafe problems existing in the implementation of traditional live working, the paper uses ground-based lidar to collect point cloud data. A tile based on the pyramid data structure is proposed to complete the storage and calling of point cloud data. The improved bidirectional filtering algorithm is used to distinguish surface features quickly and obtain a 3D model of the site. Considering the characteristics of live working, the speed of data reading and querying, the nearest point search algorithm based on octree is used to acquire a real- time calculation of the safe distance of each point in the planned path, and the safety of the operation mode is obtained by comparing with the value specified in the regulation, and assist in making decisions of the operation plan. In the paper, the simulation of the actual working condition is carried out by taking the “the electric lifting device ascending” as an example. The experimental results show that the established three-dimensional model can meet the whole process control of the operation, and has achieved practical effect.
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Authors and Affiliations

Ying Wang
1
ORCID: ORCID
Haitao Zhang
1 2 3
Qiang Lv
3
Qiang Gao
3
Mingxing Yi
3

  1. School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Gansu, China
  2. Key Laboratory of Opto-Electronic Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University Gansu, China
  3. The UHV Company of State Grid Gansu Electric Power Company, Gansu, China
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Abstract

This paper proposes an advanced Internet of Things (IoT) system for measuring, monitoring, and recording some power quality (PQ) parameters. The proposed system is designed and developed for both hardware and software. For the hardware unit, three PZEM-004T modules with non-invasive current transformer (CT) sensors are used to measure the PQ parameters and an Arduino WeMos D1 R1 ESP8266 microcontroller is used to receive data from the sensors and send this data to the server via the internet. For the software unit, an algorithm using Matlab software is developed to send measurement data to the ThingSpeak cloud. The proposed system can monitor and analyse the PQ parameters including frequency, root mean square (RMS) voltage, RMS current, active power, and the power factor of a low-voltage load in real-time. These PQ parameters can be stored on the ThingSpeak cloud during the monitoring period; hence the standard deviation in statistics of the voltage and frequency is applied to analyse and evaluate PQ at the monitoring point. The experimental tests are carried out on low-voltage networks 380/220 V. The obtained results show that the proposed system can be usefully applied for monitoring and analysing chosen PQ parameters in micro-grid solutions.
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Bibliography

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Authors and Affiliations

Ngo Minh Khoa
1
ORCID: ORCID
Le Van Dai
2
Doan Duc Tung
1
ORCID: ORCID
Nguyen An Toan
1
ORCID: ORCID

  1. Faculty of Engineering and Technology, Quy Nhon University, Vietnam
  2. Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Vietnam
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Abstract

The number of scanner stations used to acquire point cloud data is limited, resulting in poor data registration. As a result, a cloud point block registration approach was proposed that took into account the distance between the point and the surface. When registering point cloud data, the invariant angle, length, and area of the two groups of point cloud data were affine transformed, and then the block registration parameters of point cloud data were determined. A finite hybrid model of point cloud data was created based on the coplane four-point nonuniqueness during the affine translation. On this basis, the point cloud data block registration algorithm was designed. Experimental results prove that the proposed method has great advantages in texture alignment, registration accuracy and registration time, so it is able to effectively improve the registration effect of point cloud data. The point cloud data block registration algorithm was built on this foundation. Experiments show that the suggested method has significant improvements in texture alignment, registration accuracy, and registration time, indicating that it can significantly improve point cloud data registration.
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Authors and Affiliations

Yinju Lu
1 2
Mingyi Duan
2
Shuguang Dai
1

  1. School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  2. School of Information Engineering, Zhengzhou Institute of Technology, Zhengzhou 450044, China
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Abstract

3D scanning measurements are gaining popularity every year. Quick inspections on already captured point clouds are easy to prepare with the use of modern software and machine learning. To achieve repeatability and accuracy, some surface and measurement issues should be considered and resolved before the inspection. Large numbers of manufacturing scans are not intended for manual correction. This article is a case study of a small surface inspection of a turbine guide vane based on 3D scans. Small surface errors cannot be neglected as their incorrect inspection can result in serious faults in the final product. Contour recognition and deletion seem to be a rational method for making a scan inspection with the same level of accuracy as we have now for CMM machines. The main reason why a scan inspection can be difficult is that the CAD source model can be slightly different from the inspected part. Not all details are always included, and small chamfers and blends can be added during the production process, based on manufacturing standards and best practices. This problem does not occur during a CMM (coordinate measuring machine) inspection, but it may occur in a general 3D scanning inspection.
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Authors and Affiliations

Marcin Jamontt
1
Paweł Pyrzanowski
2
ORCID: ORCID

  1. General Electric Company, al Krakowska 110-114, 02-265 Warsaw, Poland
  2. Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology, ul. Nowowiejska 24, 00-665 Warsaw, Poland
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Abstract

The building information modeling (BIM) method is one of the newest methods that has been widely used in many parts of the building business, including energy management. The aim of this research is to analyze the most holy theotokos in order to find the best set of modifications that result in an optimal energy cost. The analysis was conducted through the use of building information modeling (BIM) technology and the associated programmers such as Auto Desk Revit 2020 and Auto Desk Insight 360, in order to determine the optimal strategies by which the most applicable alternative construction materials and procedures are considered in order to obtain an environmentally and economically sustainable most holy theotokos. Applying this analyze to the most holy theotokos revealed that many alternatives are capable of making a tangible reduction in the cost of electrical energy consumption and the cost of fuel for generators. Such reductions are noticed when altering in the optimum manner. The alteration of construction materials for walls and roofs also reduces the cost of electrical energy consumption and fuel for generators. The results show that changing the plug load efficiency in the optimal manner reduces the cost of electrical energy consumption by approximately 933913 US dollar ($), and changing the heating, ventilating, and air conditioning systems (HVAC) reduces the cost of fuel energy consumption by approximately 13522 US Dollar ($). Green building studio (GBS) is a tool that helps in the early stages of a project to find the best ways to save energy.
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Authors and Affiliations

Oday Hammody Abdullah
1
ORCID: ORCID
Wadhah Amer Hatem
2
ORCID: ORCID

  1. University of Baghdad, Civil Engineering Department, Baghdad, Iraq
  2. Middle Technical University, Baquba Technical Institute, Baquba, Iraq
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Abstract

Using a lower-cost laser scanner for generating accuracy in 3D point-cloud has been a concern because of economic issues; therefore, this study aims to create a 3D point cloud of a target object using a low-cost 2D laser scanner, Hokuyo UTM 30LX. The experiment was carried out in November 2019 with 16 single scans from 8 different viewpoints to capture the surface information of a structure object with many intricate details. The device was attached to a rail, and it could move with stable velocity thanks to an adjustable speed motor. The corresponding 16 point-clouds were generated by using the R language. Then, they were combined one by one to make a completed 3D point cloud in the united coordinate system. The resulted point cloud consisted of 1.4 million points with high accuracy (RMSE = 1:5 cm) is suitable for visualizing and assessing the target object thanks to high dense point-cloud data. Both small details and characters on the object surface can be recognized directly from the point cloud. This result confirms the ability of generated the accuracy point cloud from the low-cost 2D laser scanner Hokuyo UTM 30LX for 3D visualizing or indirectly evaluating the current situation of the target object.
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Authors and Affiliations

Anh Thu Thi Phan
1
ORCID: ORCID
Ngoc Thi Huynh
2 3
ORCID: ORCID

  1. Department of Geomatics Engineering, Faculty of Civil Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
  2. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
  3. Department of Bridge and Highway Engineering, Faculty of Civil Engineering, HoChi Minh City University of Technology, 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
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Abstract

The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of topographic data acquisition has caused cartographic industry to move from classical surveying methods to passive and active detection methods. The introduction of remote sensing technology has not only improved the speed of data acquisition but has also provided elevation data for areas that are difficult to access and survey. The aim of the work is to analyse consistency of elevation data from the Georeference Database of Topographic Objects (Pol. Baza danych obiektów topograficznych – BDOT500) with data from airborne laser scanning (ALS) for selected 15 research areas located in the City of Kraków. The main findings reveal discrepancies between elevation data sources, potentially affecting the accuracy of various applications, such as flood risk assessment, urban planning, and environmental management. The research gap identified in the study might stem from the lack of comprehensive investigations into the consistency and accuracy of elevation data across different databases and technologies in urban areas. This gap highlights the need for a thorough examination of the reliability of various data sources and methods of urban planning, disaster management, and environmental analysis. The integration of diverse databases and technologies, like ALS and geodetic measurements, in various applications introduces potential discrepancies that can significantly impact decision-making and outcomes.
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Authors and Affiliations

Izabela Piech
1
ORCID: ORCID
Agnieszka Policht-Latawiec
1
ORCID: ORCID
Lenka Lackóová
ORCID: ORCID
Paulina Inglot
1 2

  1. University of Agriculture in Krakow, Faculty of Environmental Engineering and Land Surveying, al. Adama Mickiewicza 21, 31-120 Kraków, Poland
  2. Slovak University of Agriculture in Nitra, Faculty of Horticulture and Landscape Engineering, Department of Landscape Planning and Ground Consolidation, 949 76 Nitra, Slovak Republic

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