Software Part Research Articles

Voice and Gesture Controlled D.C. Motor Fan’s Rotational Speed and Direction

Physically challenged people in Kenya and the rest of the developing countries in the world; encounter difficulties, when controlling most ofthe motor driven devices in industries and domestic areas of work. In this research paper, a system that utilizes voice and gesture commandsto control the rotational speed and direction of a d.c. (direct current) motor fan is developed. Arduino board is used for providing a link and developing the system hardware parts. The software part is accomplished in Arduino IDE (Integrated Development Environment) using embedded C language. Hardware is then carefully constructed and software porting is done successfully. Outcomes obtained after actual implementation and testing showed that through the use of voice and hand gesture commands speed and direction of a d.c. motor fan can easily be manipulated.

Developing a novel structured mesh generation method based on deep neural networks

In this paper, we develop a novel structured mesh generation method, MeshNet. The core of the proposed method is the introduction of deep neural networks to learn high-quality meshing rules and generate desired meshes. To accomplish this, MeshNet employs a well-designed physics-informed neural network to approximate the potential transformation (mapping) between computational and physical domains. The training process is governed by differential equations, boundary conditions, and a priori data derived from coarse mesh generation, which has been disregarded in previous studies. The automatic subdivision of a given domain into quadrilateral elements is achieved through efficient feed-forward neural prediction. A series of experiments are conducted to investigate the robustness of the proposed method. The results across different cases demonstrate that MeshNet is fast and robust. It outperforms state-of-the-art neural network-based generators and produces meshes of comparable or higher quality compared to expensive traditional meshing methods. Furthermore, the proposed method enables fast varisized mesh generation without re-training. The simplicity and computational efficiency of MeshNet make it a novel meshing tool in the discretization part of simulation software.

A multitask encoder–decoder model for quality prediction in injection moulding

It is desirable in injection moulding that every yielded product is of high quality and precision. Several studies are conducted on modelling the relationship between the process parameters e.g., holding pressure, mould temperature, etc. that are either time series or non-time series, and the output product quality characteristics e.g., weight, dimensions, and warpage. To build a more comprehensive/generalized quality prediction model with online learning, this study for the first time takes time series process parameters from 2 pairs of in-mould pressure and temperature sensors simultaneously with 18 non-time series parameters and introduces a single deep learning architecture to perform multitask learning i.e., to leverage information from the related sources to simultaneously predict multiple product quality characteristics on a real injection moulding machine. This along with the discussed modern containerization practices develops a fully automated software part of the quality control process. The non-time series process parameters are learned by fully connected neural network layers of the model while the time series process parameters are related through an attention-based encoder–decoder architecture. This model is ablated as well as compared with a benchmark model to validate the performance advantage in terms of the reduced error asymptote. The proposed model offers an order of magnitude lower average mean squared error than the benchmark model, and 1.2 to 3 times smaller mean squared errors than its ablated versions.

A Convolutional Neural Network for Electrical Fault Recognition in Active Magnetic Bearing Systems.

Active magnetic bearings are complex mechatronic systems that consist of mechanical, electrical, and software parts, unlike classical rolling bearings. Given the complexity of this type of system, fault detection is a critical process. This paper presents a new and easy way to detect faults based on the use of a fault dictionary and machine learning. The dictionary was built starting from fault signatures consisting of images obtained from the signals available in the system. Subsequently, a convolutional neural network was trained to recognize such fault signature images. The objective of this study was to develop a fault dictionary and a classifier to recognize the most frequent soft electrical faults that affect position sensors and actuators. The proposed method permits, in a computationally convenient way that can be implemented in real time, the determination of which component has failed and what kind of failure has occurred. Therefore, this fault identification system allows determining which countermeasure to adopt in order to enhance the reliability of the system. The performance of this method was assessed by means of a case study concerning a real turbomachine supported by two active magnetic bearings for the oil and gas field. Seventeen fault classes were considered, and the neural network fault classifier reached an accuracy of 93% on the test dataset.

Combination of Cross-Correlation-Based Analysis and Ultrasonic GW Tomography for Barely Visible Impact Damage Detection Preliminary Assessment

Statistical based reconstruction methods and signal processing tooling techniques are implemented and used to detect delaminations or debondings within composite complex items with very high precision. From the literature, it appears that although a single procedure for the estimation of structural health is a fast solution, multiple analyses based on different reconstruction methods or different damage parameters are the way to achieve maturation assessments of the methodology. This highlights the fact that the hardware and software parts of an SHM system need two different assessment and maturation ways. This work focuses on the software part by proposing a way to start assessing the outcomes. In this paper, the damage detection and localization strategy in CFRP plate-like structures with elastic guided waves excited and acquired with a circular array PWAS network is considered. Previous outcomes are compared by new analyses using a new post-processing approach based on a cross-correlation-based technique in terms of the BVID (Barely Visible Impact Damage) surface position and its center of mass. The advantage of this specific study is hopefully to enable confidence in the transition from R&D to field implementation. In addition, this work tries to evidence an improvement in terms of cost efficiency and reduced complexity while maintaining the same accuracy.

Easy to go and innovative validation process using the spot weld inspection system PHAsis and related software

In the industry 4.0 the need for a cost-saving and efficient production process is of high significance. This includes every area of the production processes and leads to appropriate testing procedures by means of the quality management. A combination of ultrasonic inspection devices and software tools for result post processing reduces the need of several cost-intensive validation circles with destructive testing to a very low level. This can be shown for example with the imaging phased array spot weld inspection system PHAsis in combination with a software app for post processing. This is including the software part for the data and result management as well as a device software for the ultrasonic inspection of the spot welds. In combination with the usage of an automation interface for robotic usage, even more cost reduction is possible. Several validation processes on typical resistance spot welding applications with plate pairings of two or three plates (aluminium as well as steel combinations) by the use of PHAsis and the connected PostProcessor will be exemplified. The general procedure of the validation process will be accentuated step by step. Therefore, the original data of phased array testing and a comparative method in the shape of a destructive testing (hammer & chisel test or horizontal respectively vertical cross section) will be presented. Furthermore, the steps of the post processing procedure to define the ultrasound system parameters for a best fit will be described. During these steps, the influence of the different sensitivity and system parameters on the spot weld size will be explained. Also, the measurement uncertainties given by the destructive testing that have an influence to the comparation with ultrasonic testing in general will be addressed. The efficiency of the post processing software tool due to reaching a high match between PHAsis and the comparative destructive method will be illustrated.

Pelatihan Pembuatan Pemodelan Dan Simulasi Banjir Di Kawasan Pesisir Kota Semarang Menggunakan ArcScene 10.4

<p><em>In the field of regional and urban planning, several media can be used to convey information about the problems of an area. The most commonly used medium is using maps. The analysis carried out to present information in the form of maps varies according to needs. To represent and present flood and tidal information in the coastal area of Semarang City, 3D modeling training was conducted using ArcScene 10.4 software, which is part of ArcGIS software. This modeling is carried out by utilizing DEM (Digital Elevation Model) data and data on the addition of water discharge in coastal areas which will then be processed and simulated in the 3-dimensional form that can be watched in video form. It is hoped that this community service in the form of training can provide further knowledge and understanding regarding mapping activities so that it can be applied in student learning and research activities. So second-semester students of the Urban and Regional Planning Department, Faculty of Engineering, Universitas Semarang have a basic understanding of planning and a more sensitive to urban issues and how to do the right analysis according to the goals and needs of urban planning.</em></p>

A deep learning-based method for detecting granular fertilizer deposition distribution patterns in centrifugal variable-rate spreader fertilization

Beginning with the problems of high cost, low efficiency and high working intensity of traditional granular fertilizer deposition distribution detection methods, this study proposes an automatic detection system of granular fertilizer deposition distribution pattern based on deep learning method. The system analyzes the distribution pattern of granular fertilizer deposition on the ground and allows for parameter calibration, performance evaluation and structural optimization of fertilizer spreading devices. In this study, hardware and software parts of the proposed system were designed. The hardware part primarily includes a customized fertilizer calibration mat, an image acquisition device, a system control terminal, and a processing core to realize image acquisition, storage, data processing, and system control functions. For the software part, a granular fertilizer identification algorithm was designed; in addition, the granular fertilizer instance segmentation network model F-TPP (Fertilizer-Swin Transformer PANet partition) was proposed that integrated the Swin Transformer network and path aggregation network (PANet) to address the problems associated with extremely small and relatively dense targets in the detection of granular fertilizers. Moreover, the F-TPP network model optimized the Anchor generation scale for appropriate segmentation detection of granular fertilizers. The experimental results show that the F-TPP network model outperformed comparison conventional network models, such as Mask R-CNN, SOLO, and YOLOv5. Indeed, the detection efficiency of the proposed model was higher than that of conventional models for small and dense granular fertilizers. The proposed model yielded a detection precision, recall rate, and F1 score of 93.8%, 96.3%, and 0.95, respectively. The F-TPP network model improves the segmentation precision of granular fertilizer; thus, it improves the accuracy of automatically-deposited fertilizers. According to the experimental results, the proposed detection method yielded maximum absolute and relative errors of 0.59 g and 5.25%, respectively.

Research on the Model Predictive Trajectory Tracking Control of Unmanned Ground Tracked Vehicles

This article summarizes the research significance and the development status of the unmanned ground tracked vehicles (UGTVs). According to the speed and steering principle of the UGTVs in plane motion, the kinematic state space equation of the vehicle is established. Based on the model predictive control (MPC), the UGTVs trajectory tracking controller is also established. After introducing the overall control system solution, based on the vehicle model, the track speed on both sides is used as the control input to solve the predicted output of the system. After constraining the control amount, the model prediction controller is established by using S function. Several simulations with different preset speeds are performed under linear conditions, and the numerical simulation with different prediction time domains and control time domains are performed under continuous curves. The test validates the effectiveness of the controller, and the effects of speed and time domain parameters on the deviation are analyzed based on the results. Based on satellite positioning technology, trajectory tracking tests of the UGTVs are carried out. After analyzing the positioning principle and common errors, the real-time kinematic technology is used to improve the positioning accuracy of the vehicle prototype. The hardware of the test platform includes a data acquisition system, a control execution system and a walking execution system, and the software part includes data processing and optimization control. The trajectory tracking experiments under different driving conditions are conducted, and the results show that the navigation tracking system can achieve good tracking performance.

USING OBD-2 TECHNOLOGY FOR VEHICLE DIAGNOSTIC AND USING IT IN THE INFORMATION SYSTEM

This article considers the research of OBD-2 technology for interaction with on-board vehicle systems, the creation of a unified system that can work with different makes and models of cars. The history of OBD-2 technology, its development, existing standards and their implementation in modern cars is described. Basic diagnostic functions are described, with which you can get information about the car, as well as perform its settings and send various commands to it. In addition, the hardware part of the scanner, its technical features, nuances of interaction with it and the purpose of each of its pins are considered. Also reviewed couple models of scanners that can be used to develop this system. Conclusions are drawn on the practicability of their use, taking into account their capabilities. Existing software analogues, their main functions, advantages and disadvantages are considered. The general concept of interaction with the car via OBD-2, the interaction algorithm and step-by-step analysis of the interaction between the scanner and the car are also considered. The software part of the interaction between the car and the scanner, special codes used to send requests and responses, their further analysis and interpretation in a human-friendly form are considered. The possibilities for the unification of these codes for different car manufacturers and the common code base to create a universal system that will be suitable for different cars from different car manufacturers are highlighted. A model of the software system is proposed, which can embody a large set of useful functions for any motorist and will be compatible with a large number of modern cars equipped with universal diagnostic tools, while using an affordable scanner model without the use of expensive professional equipment.

Analysis of Motivations, Process, and Implications of Elon Musk’s Acquisition of Twitter

After Elon Musk announced his plan to acquire Twitter, it attracted extensive attention from various sectors of society. In recent years, Musk, who is a maverick, not only has unquestionable solvency but also enjoys a great reputation in society. Twitter is a leader in social media platforms, frequently appearing in people's sight, and has become an indispensable part of social software. In order to explore the truth of the matter and bring enlightenment to investors as well as corporations, this study will analyze Musk's acquisition motivation, acquisition method, and financing method based on some news reports, Twitter's financial reports, as well as Musk's words and deeds. According to the analysis of this acquisition case, it is not difficult to notice that this event has a profound impact not only on Musk and Twitter but also on society and American politicians' elections. Overall, these results shed light on guiding further exploration of acquisition for KOL and social media industry.

CryptoArcade: A Cloud Gaming System With Blockchain-Based Token Economy

Cloud gaming is a novel service provisioning technology that offloads parts of game software from terminals to powerful cloud infrastructures. However, the commercial charging model for cloud gaming is still in its infancy. In this paper, we reveal the deficiencies of existing cloud gaming pricing models and propose CryptoArcade, a token-based cloud gaming system that adopts cryptocurrency as a payment method. Using cryptocurrency, CryptoArcade provides a transparent and resource-aware pricing method, enabling a time irrelevant silent payment on the floating price to protect players' interests, which avoids the Quality of Experience (QoE) degradation caused by traditional dynamic models. While CryptoArcade can solve the problem of pricing strategies, players still face decision headaches caused by having commission overhead and pre-deposit amounts on blockchains. To better understand players' trading behaviors in this decision-making, we consider a marketplace where players trade tokens through smart contracts before gaming sessions. Considering the uncertainty of future token consumption, we use Prospect Theory (PT) in modeling and obtain the optimal solution in closed form. When comparing with the benchmark expect utility theory (EUT), we show that with the same external factors, EUT players are more likely to buy tokens than PT ones.

DetAIL: A Tool to Automatically Detect and Analyze Drift in Language

Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based systems are as trusted as traditional software. Traditional software is made dependable by following rigorous practice like static analysis, testing, debugging, verifying, and repairing throughout the development and maintenance life-cycle. Similarly for machine learning systems, we need to keep these models up to date so that their performance is not compromised. For this, current systems rely on scheduled re-training of these models as new data kicks in. In this work, we propose DetAIL, a tool to measure the data drift that takes place when new data kicks in so that one can adaptively re-train the models whenever re-training is actually required irrespective of schedules. In addition to that, we generate various explanations at sentence level and dataset level to capture why a given payload text has drifted.

PROTOTYPE OF FUTURE FLYING TRANSPORT PLATFORM

This paper is devoted to the analysis of the existing eVTOL flying vehicles and consideration of the capabilities of the flight controller based on the STM32F722 microcontroller, as well as the evaluation of the characteristics of various sensors and devices and the study of their capabilities using the BetaFlight firmware. The main result of this work is the development of a prototype (prototype) of the flying transportation platform of the future, for which both hardware and software parts of the flying vehicle system have been developed and tested. Within the framework of the research there was created an interface between the flight controller based on STM32F722 and BetaFlight, worked out all technologies and systems for creating eVTOL-based flight vehicles. Also, comprehensive testing of the prototype system was performed and data transfer technologies were worked out. Functional and circuit diagrams of the prototype were also developed, as well as the simulation of mechanical-physical properties of the aircraft's frame reactions. An important result of this work is experimental research, the results of which show that the force structure is able to withstand the design loads. The results make it possible to improve the design of eVTOL aircraft and create more efficient data transfer systems. The results of the experimental studies show that the force structure is capable of withstanding the design loads.

Iranian Arabic teachers’ perception of applying educational software

Educational technology with its wide variety of usage has assisted teachers and learners to learn from the material which has been produced and reviewed by experts. However, the question may come to mind concerning the number of teachers applying such productions, how they perceive such productions, and if they use them, for what purpose they use them. This study was designed to investigate the Iranian Arabic teachers’ perception of applying educational software. To find the answer to these questions, the researchers have developed a survey and interview with 29 Arabic teachers in Ahar. The results from the interview and survey indicated that more than half of the teachers apply such productions for teaching grammar and pronunciation more than other skills and sections of the book. However, they also believe that not all parts of the available software are adequate for usage. Thus, the findings of the present study might be useful for Arabic language supervisor teachers and educational software designers, to improve the quality of the software and also to motivate the teachers to use them in their classrooms. 
 Keywords: Arabic language; educational software; foreign language.

A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this paper, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Grow-Shrink</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS</i> ), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Incremental Association MB</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IAMB</i> ), and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interleaved IAMB</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inter-IAMB</i> ). Our implementations are available as part of an open-source software called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ramBLe</i> , and are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality. Our implementations were selected for the reproducibility challenge component of the 2021 student cluster competition (SCC'21), which tasked undergraduate teams from around the world with reproducing the results that we obtained using the implementations. We discuss details of the challenge and the results of the experiments conducted by the top teams in the competition. The results of these experiments indicate that our key results are reproducible, despite the use of completely different data sets and experiment infrastructure, and validate the scalability of our implementations.

Development of an automated system for disinfection of premises

The article describes the development of an automated system for disinfection of premises. This task has become especially urgent during the Coronavirus pandemic. This is a type of SARS, which includes hundreds of different viruses that cause, at least, inflammation of the mucous membranes of the respiratory tract. The main way of protection is prevention. The relevance of the article is determined by the need for air purification in working rooms. During the pandemic, closed-type recirculators were used to solve the problem of disinfection of premises, which work only with the air passing through them, but do not affect microorganisms and viruses on surfaces and furniture in the room in any way. The article deals with the development of an automated cleaning system for premises using ultraviolet lamps operating in various modes in working rooms. The authors present a description of the structure of an automated system designed to disinfect air and surfaces in rooms of all categories in order to prevent bacterial contamination in the absence of people. The system consists of a software part with a user interface that allows you to configure devices in accordance with the working hours of employees, and a hardware part based on a microcontroller that determines the program of operation of the device. An original algorithm for the functioning of the system and the program interface has been developed. The development results have been tested. The advantages and novelty of the developed system are as follows: • The developed algorithm allows disinfection of the room (air and surfaces) in automatic mode with the ability to set operating modes in accordance with the nature of the tasks performed by employees. • Sound notification of the beginning and end of processing. • The proposed arrangement of disinfection lamps (integrated into the lighting system) allows you to clean both the indoor air space and the surfaces. The developed software and device can be used in all types of premises for their high-quality disinfection.

АНАЛІЗ ПРОФЕСІЙНОЇ ДІЯЛЬНОСТІ ФАХІВЦЯ З РОЗРОБКИ КОРИСТУВАЛЬНИЦЬКИХ ІНТЕРФЕЙСІВ

The article is dedicated to the challenging problem of improving the quality of professional education of future bachelors in the field of software engineering, in particular, defining qualification requirements, updating the goals and content of educational programs for their education by analyzing the professional activities of a user interface development specialist. As technology advances and integrates into our daily lives, the demand for such specialists has grown significantly. At the same time, the field of information technology is constantly evolving, creating a need for more specialized skills that can only be acquired through a professional education. During the analysis of the professional activity of a specialist in the development of user interfaces, the subjects, means, process, technologies, conditions and products at each stage of creating user interfaces of various software are considered. The user interface is defined as the means by which users interact with digital devices or software. The article considers the job responsibilities of a UI designer, UX designer, front-end developer, graphic designer and user experience researcher – specialists who develop user interfaces. The process of developing user interfaces is analyzed at the following stages: determining the needs, desires and behavioral patterns of users; defining the problem to be solved; generating ideas for solving the problem; prototyping; testing (in accordance with the concept of «Design Thinking»); development of the software part of the user interface (writing program code, front-end and back-end development, integration of front-end and back-end components into a single system, testing and quality control, cross-platform support, ensuring accessibility for people with disabilities). The qualification requirements for a specialist at each stage of user interface development are determined to improve the quality of professional training of future bachelors in the field of software engineering, to update the goals and content of educational programs for their training. Key words: professional activity, user interface development, UI design, UX design, design-thinking, qualification requirements.

Cyber-Physical System for Diagnostic Along the Controlled Section of the Oil Pipeline

The purpose of the work is to develop an experimental model of the control system for compliance with turns along the control length of the pipeline. The open issue of detecting oil product leaks along the controlled section of the pipeline. Preliminary analysis of leak detection methods and principles of operation of hardware and software security diagnostics of the state of pipe transport networks has been considered. A method of studying experimental data and presenting results has been developed. Different literature sources have been analyzed, these literature sources provide information about real cases of pipeline system diagnostics and leak or defect detection. The software and hardware part of the control systems for conducting checks along the control part of the pipeline have been developed, and checks and evaluations of the results of the system checks have been carried out.

Enhancing Traceability Link Recovery with Fine-Grained Query Expansion Analysis

Requirement traceability links are an essential part of requirement management software and are a basic prerequisite for software artifact changes. The manual establishment of requirement traceability links is time-consuming. When faced with large projects, requirement managers spend a lot of time in establishing relationships from numerous requirements and codes. However, existing techniques for automatic requirement traceability link recovery are limited by the semantic disparity between natural language and programming language, resulting in many methods being less accurate. In this paper, we propose a fine-grained requirement-code traceability link recovery approach based on query expansion, which analyzes the semantic similarity between requirements and codes from a fine-grained perspective, and uses a query expansion technique to establish valid links that deviate from the query, so as to further improve the accuracy of traceability link recovery. Experiments showed that the approach proposed in this paper outperforms state-of-the-art unsupervised traceability link recovery methods, not only specifying the obvious advantages of fine-grained structure analysis for word embedding-based traceability link recovery, but also improving the accuracy of establishing requirement traceability links. The experimental results demonstrate the superiority of our approach.