Software Approach Research Articles

A numerical research on the interaction between underwater explosion bubble and deformable structure using CEL technique

The dynamic process of an underwater explosion (UNDEX) bubble in the vicinity of deformable structures is a complex phenomenon that has been studied by many researchers. The dynamic process of a UNDEX bubble is a complex transient problem that results in a highly distorted bubble and large deformation of the structure. The previous work has introduced various solutions for studying the interaction between the UNDEX bubble and deformable structure. The interaction between the bubble and nearby structures has been widely solved by the combination of the boundary element method (BEM) and the finite element method (FEM). However, this couple requires tight time-step controlling, long-time analysis, and large computer resources. Furthermore, this combination is not widely used as the FEM code in commercially available software for solving UNDEX bubble problems. This paper presents a coupled Eulerian-Lagrangian (CEL) approach in commercial software to deal with the fluid-structure interaction (FSI). The numerical model of a UNDEX bubble is first developed and verified by comparing results with experimental, BEM, and empirical data. Then both bubble behavior and structural deformation are examined in various case studies. The numerical results show that the stiffness of the structure has strongly influenced the bubble behavior and the water jet development. The pressure pulse becomes significantly large as the bubble collapse. Besides, this numerical approach also can reproduce crucial phenomena of a UNDEX bubble, such as the whipping effect and water jet attacks. Although the numerical model is developed using simplified boundary conditions, the proposed approach shows the feasibility of simulating the important features of a UNDEX bubble process as well as the response of nearby structures.

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

AbstractFor decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single‐vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around and . This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2‐year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real‐world evaluation of the displayed concepts.

On COVID-19 Pandemic-Induced Attitudinal Changes in Software Engineering Teaching and Learning

This note outlines certain notable changes observed in the attitudes towards and approaches in teaching and learning software engineeringrelated courses following the end of the COVID-19 pandemic. It views these changes from the interrelated perspectives of the primary stakeholders of software engineering education (SEE), namely administrators, educators, and students. It seems that while some changes may be ephemeral and transient, the others may be essential and permanent. From a students' perspective, the transformation of SEE appears to be generally for the better.

Generatívny dizajn pri konštrukčnom návrhu tvarov telies čelných ozubených kolies

A paper was prepared related to current design engineering advancements. Topological optimization and generative design were some of these advancements. The process of topology optimization was explained in the following chapters. Each step is broken down into a paragraph, where it is explained and illustrated with a picture. In a SolidWorks program, topology optimization is detailed step-by-step. The last part, which lays out the subsequent approach and other possibilities, summarizes the benefits and drawbacks of this software's approach.

Automatic synchrotron tomographic alignment schemes based on genetic algorithms and human-in-the-loop software.

Tomography imaging methods at synchrotron light sources keep evolving, pushing multi-modal characterization capabilities at high spatial and temporal resolutions. To achieve this goal, small probe size and multi-dimensional scanning schemes are utilized more often in the beamlines, leading to rising complexities and challenges in the experimental setup process. To avoid spending a significant amount of human effort and beam time on aligning the X-ray probe, sample and detector for data acquisition, most attention has been drawn to realigning the systems at the data processing stages. However, post-processing cannot correct everything, and is not time efficient. Here we present automatic alignment schemes of the rotational axis and sample pre- and during the data acquisition process using a software approach which combines the advantages of genetic algorithms and human intelligence. Our approach shows excellent sub-pixel alignment efficiency for both tasks in a short time, and therefore holds great potential for application in the data acquisition systems of future scanning tomography experiments.

Input Spectrum Design for Identification of a Thermostat System

This paper considers the shaping of the amplitude spectra of perturbation signals for the identification of a thermostat system. The current approach in control engineering practice utilizes flat spectrum signals, which may not result in the highest possible accuracy. This research aims to investigate the effectiveness of optimal signals with amplitude spectra designed using two state-of-the-art software approaches, namely the model-based optimal signal excitation 2 (MOOSE2) design and the optimal excitation (optexcit) design, in improving estimation accuracy. Such a comparison on a real system is currently lacking. In particular, there exists a research gap on how the combined choice of signal and model structure affects performance measures. In this research, two model structures are used, which are the autoregressive with exogenous input (ARX) and the output error (OE) model structures. Four performance measures are compared, namely the determinant of the covariance matrix of the parameter estimates and the minimum error, mean error and maximum error in the frequency response. Results show that the optimal signals are effective in reducing the determinant of the covariance matrix and the maximum error in the frequency response for the thermostat system, when applied in combination with the ARX model structure. The flat spectrum signal remains very useful as a general broadband perturbation signal as it provides a good overall fit of the frequency response. The findings from this work highlight the benefits of applying optimal signals especially if the identification results are to be used for control, since these signals improve key performance measures which have direct implications on controller design.

Challenges and Opportunities Experienced by Iranian Researchers during the COVID-19 Pandemic: A Qualitative Study

The COVID-19 pandemic has caused new conditions, problems, and different research platforms for qualitative research. The aim of the present study was to analyse the challenges and opportunities facing qualitative researchers during the COVID-19 pandemic in Iran. This qualitative research was conducted with a conventional content analysis approach with twenty-four Iranian health sciences researchers. The participants were selected by snowball and purposive sampling. Data collection was carried out using semi-structured face-to-face and online interviews until reaching saturation. Data analysis was also carried out using the Graneheim & Lundman approach in MAXQDA-2018 software, and to improve the trustworthiness of the results, Guba and Lincoln’s criteria were used. Data analysis led to the identification of two main categories, 13 subcategories, and 69 primary codes: The challenges were in areas such as data collection methods, access to participants and how to conduct interviews. Opportunities also included formation of new topics for qualitative research, highlighting the importance of qualitative research, strengthening the technological knowledge of researchers, research cost-effectiveness, and presenting further information on sensitive topics. It is also possible to strengthen qualitative research by supporting qualitative researchers at universities and research centres, facilitating the administrative processes, providing communication infrastructure such as suitable Internet in universities, ensuring more diversity in data collection methods, developing an appropriate protocol during the COVID-19 pandemic, and dedicating some university rooms to qualitative researchers to conduct interviews.

Automatic detection of internal corrosion defect in a natural gas gathering pipeline using improved YOLOv5 model

The detection of internal corrosion of the very long gas pipeline is a fundamental task for the prevention of possible failures, also one of the major challenges facing gas companies. Pipeline endoscopy based on the corrosion inspection method provides direct visual observation of defects, but the post-video analysis is time-consuming and not practical. In this work, we propose an improved YOLOv5 model-based software approach to automatically detect inner corrosion defects in nature gas-gathering pipelines. Video streaming of pipe interiors is provided by an endoscope robot. Sample augmentation strategies such as affine transformation and defect segmentation with background fusion are used to generate defect images and expand the data set. The region-based recursive localization method can be effectively optimized to improve the localization of corrosion regions.

ПРОГРАММНЫЙ ПОДХОД К КОМПЛЕКСНОМУ СБОРУ И ПОДГОТОВКИ ДАННЫХ ОБ ИНТЕНСИВНОСТИ ДВИЖЕНИЯ ТРАНСПОРТНЫХ СРЕДСТВ, ПОГОДНЫХ УСЛОВИЙ И ЕСТЕСТВЕННОЙ ОСВЕЩЕННОСТИ В ЧАСОВЫХ ИНТЕРВАЛАХ

The steady growth in the level of motorization inevitably leads to congestion of the elements of the road network in the cities of our country. The intensity movements of road transport is an important indicator of accidents, as it determines the modes and conditions for the occurrence of accident-hazardous areas. At the same time, the state of controlled intersections is the most relevant. The consequence of an accident is a large number of factors of the Driver-Car-Road-Environment system. To predict this negative phenomenon, which is considered a failure in a harmoniously functioning transport infrastructure, it is necessary to study the accompanying conditions and their influence on each other in detail. Thus, in order to carry out the necessary reconstruction measures at emergency hazardous areas, including at controlled intersections, it is necessary to carry out high-quality calculations aimed at evaluating the effectiveness of the proposed measures, as well as designing control modes at intersections with the optimal configuration of elements. This article discusses a software approach to the integrated collection and preparation of data, the use of which makes it possible, on the basis of the obtained patterns, to solve the problems of harmonious, without significant failures, the functioning of any considered transport hub on the road network. These studies are a new round in the improvement of the road safety system, striving for zero deaths on roads. The complex solution, in the version proposed by the authors of the article, has a direct impact on the management of traffic flows in an accident-free mode, which has not been previously developed and applied, this is the scientific novelty of the work.

Intermittent Computing Emulation of Ultralow-Power Processors: Evaluation of Backup Strategies for RISC-V

With the progress in energy harvesting circuits and the decrease in power requirements of processing, sensing, and communication hardware, we have the potential of freeing the Internet of Things devices from their batteries. However, removing batteries introduces frequent power failures due to the irregular power availability from the environment. This situation leads devices to compute intermittently under transient environmental power. Intermittent computing requires significant microarchitectural modifications on existing processor designs to ensure automatic computation progress despite the power failures. For example, built-in nonvolatile memory components should be integrated in processor architectures. Consequently, different microarchitectural automatic backup strategies need to be implemented. In this work, we introduce different processor state backup strategies based on an interrupt-based software approach, which do not need modifications to the microarchitecture of existing processors. Therefore, we present a systematic approach to emulate different processor architectures with varying backup strategies under transient power. To justify our claims, we make Ibex RISC-V core, a popular ultralow-power processor architecture, suitable for intermittent computing. This is the first attempt to make a variety of existing and future ultralow-power processor architectures easily exploitable for transiently powered computing systems.

Energy Consumption Monitoring with Evaluation of Hidden Energy Losses

This article presents a computational method for monitoring the energy consumption of technological systems with the assessment of their hidden energy losses caused by erroneous actions of personnel or equipment failures. Herewith, energy losses are calculated as the difference between the actual energy consumed and the minimum energy required to conduct the process in all operating modes. The minimum required energy is determined by the machine learning method based on stationary consumption precedents. Two approaches to the implementation of energy consumption monitoring with the assessment of hidden energy losses are considered – hardware and software. The hardware approach is based on the preliminary definition of normative, or minimum specific energy consumption in each technological mode. The software approach is based on the modeling of stationary areas of energy consumption in the form of precedents and their further analysis in the space of influential technological parameters. The paper notes the advantages and disadvantages of the proposed monitoring method, it is emphasized that the method is able to work with both linear and non-linear functions of energy dependence on the parameters of the technological process. It is noted in the paper that the advantage of the proposed method is the automated construction of the minimum energy function.

Visualizing Data Security with AI/ML-Powered Data Classification

Data Protection is an integral part of cybersecurity. With enormous amounts of data being produced, transferred, and consumed every day, securing an organization’s sensitive data from leaking to the external internet is one of the challenges companies and cybersecurity professionals deal with every day. We found that the existing policy and criteria-based approach in cybersecurity software to identify anomalous activities limits the visibility, makes security administrators’ jobs time-consuming and ultimately leads to weakened data security. In this paper, we investigate how cybersecurity professionals can solve this problem with the power of AI/ML and we propose a more intuitive, novel dashboard and drill-down interface solution to make the sensitive data classification, organization, visibility, synthesis, and finally remediation efficient for security administrators

DoS Attack Detection Mechanism in Wireless Sensor Networks

Wireless sensor networks (WSNs) are not like traditional networks in terms of their characteristics. Unlike WSN, in classic networks, networking mechanisms have decision-making power with reference to the management of an incoming packet only based on its internet protocol (IP) destination address. Assailant nodes can activate a denial of service (DoS) attack after entering the network. This research work focuses on tracing these collusive nodes by applying a trust-based scheme. The trust-based scheme includes measuring the degree of trust among all nodes. Nodes with minimum trust are designated as malicious nodes. Trust is measured based on the overall packets transferred during the time slot allotted. The node forwarding maximal packets and exploiting minimal assists will be tagged as malicious. The nascent architecture was deployed in NS2, and the outcomes were analysed based on particular performance metrics. To calculate trust, the overall packets forwarded by nodes in the allotted slot were considered. The node that transmits the largest number of packets and uses negligible assets was declared a vindictive node. This task implementation presents the approach in the NS2 software and analyses the results based on certain metrics.

A Novel Hexagonal Psuedo framework for Edge Detection Operators on Hexagonal Framework

Edge detection using a gradient-based detector is a gold-standard method for identifying and analyzing different edge points in an image. A hexagonal grid structure is a powerful architecture dominant for intelligent human-computer vision. This structure provides the best angle resolution, good packing density, high sampling efficiency, equidistant pixels, and consistent connectivity. Edge detection application on hexagonal framework provides more accurate and efficient computations. All the real-time hardware devices available capture and display images in rectangular-shaped pixels. So, an alternative approach to mimic hexagonal pixels using software approaches is modeled in this paper. In this research work, an innovative method to create a pseudo hexagonal lattice has been simulated and the performance is compared with various edge detection operators on the hexagonal framework by comparing the quantitative and qualitative metrics of the grayscale image in both square and hexagonal lattice. The quantitative performance of the edge detection on the hexagonal framework is compared based on the experimental facts. The pseudo-hexagonal lattice structure assures to be aligned toward the human vision.

Development of a methodological approach to the research of special purpose communication systems

Hierarchical construction of special purpose systems, a large number of different types of communication devices that are the part of them necessitates the need to justify the order of research of special communication systems. Existing approaches to conduct research on special purpose communication systems are narrowly focused and aimed at researching certain types of communication. A large number of destabilizing factors affecting the functioning of special purpose communication systems and a priori uncertainty about the conditions of its application only complicate the mentioned issue. Also, the experience of the full-scale armed aggression of the armed forces of the Russian Federation on the territory of Ukraine showed the need to change approaches to the research of special purpose communication systems. That is why the issue of improving the effectiveness of the management of forces and devices of communication of groups of troops (forces) during operations is an important and urgent issue. The object of the research is the communication system of the group of troops (forces). The subject of the research is the effectiveness of the communication system of the group of troops (forces) in accordance with the purpose of the operation. The research developed a methodical approach to the research of special purpose communication systems. The novelty of the proposed methodical approach consists in taking into account the efficiency while choosing this or that method while investigating the state of a special-purpose communication system and the ability to calculate the reliability while choosing this or that method while investigating the state of a special-purpose communication system. Also, the element of novelty of this methodical approach is taking into account the efficiency of the decisions made regarding the assessment of the state of the special purpose communication system while choosing one or another research method. It is advisable to implement the mentioned approach in algorithmic and program software while studying the state of the special purpose communication system.

“When people see me, they know me; they trust what I say”: characterizing the role of trusted sources for smoke risk communication in the Okanogan River Airshed Emphasis Area

IntroductionAs wildfire smoke events increase in intensity and frequency in the Pacific Northwest, there is a growing need for effective communication on the health risks of smoke exposure. Delivery through a trusted source or intermediary has been shown to improve reception of risk communication messages. This is especially salient in rural and tribal communities who may be hesitant to trust information from state and federal agency sources. This study aims to identify and characterize trusted sources for smoke risk information in the Okanogan River Airshed Emphasis Area (ORAEA), a rural region of North Central Washington state that is heavily impacted by smoke from wildfires and prescribed fire.MethodsThe research team conducted a qualitative study using data collected through key informant interviews and focus groups to assess the role of various sources and intermediaries in disseminating smoke risk information. We used a consensual coding approach in NVivo Qualitative Analysis Software to sort data into preliminary categories, which were grouped into themes using a thematic analysis approach. We used member checking and iterative feedback processes with local project partners throughout the project to ensure credibility of results.ResultsThrough the analysis, we identified three themes characterizing trusted sources for smoke risk communication in the ORAEA. These themes were: (1) local and tribal sources of information are perceived as more trustworthy than state and federal government sources, (2) trustworthiness is determined by an evaluation of multiple factors, in particular, perceived credibility, quality of information, and relationship with the source, and (3) conservative political ideology and perceived parallels with COVID-19 communication influence perception of trust. Within each theme, we identified several sub-themes, which contributed additional nuance to our analysis.ConclusionThis study provides insights into which sources of information are trusted by rural and tribal community members in the ORAEA and why. Results from our study emphasize the importance of relationships and collaboration with local and tribal partners in smoke risk communication. In this paper, we discuss implications for state and federal agency practitioners and present recommendations for how to work with local and tribal partners on smoke risk communication.

THE FUTURE OF PARATRANSIT: A STUDY OF PROVIDER EXPERIENCES AND HOPES FOR CHANGE

Abstract The present study examined paratransit managers’ perceptions of a changing transportation market, and what resources or supports they might need to adapt to evolving market expectations. As transportation network companies (e.g., Uber, Lyft) have emerged, the landscape of demand-responsive transportation has changed. However, the experiences of those closest to the operations of paratransit programs have been largely neglected in research. In-depth, semi-structured interviews were conducted with 16 managers of paratransit services. The resulting transcripts were coded iteratively using NVivo software, using both inductive and deductive approaches. We found that participants were already innovating and often wanted to be even more creative with their services, but were hampered by inadequate funding, driver shortages, regulatory or policy challenges, and other barriers. This study’s findings advance the literature toward greater understanding of how policymakers can leverage existing paratransit infrastructure to better serve the transportation needs of older adults and other transportation disadvantaged groups.

Software approaches for resilience of high performance computing systems: a survey

Software approaches for resilience of high performance computing systems: a survey

GROSS DOMESTIC PRODUCT ANALYSIS IN INDONESIA FOR 2008-2021

Economic growth is an indicator that plays an important role in determining the prosperity of a country. This study aims to analyse the effect of growth of foreign debt, inflation, Exchange rate, growth of poverty, and regional minimum wage on gross domestic product (GDP) in Indonesia for 2008 - 2021. Data collected through library research from the Central Bureau of Statistics and Bank Indonesia Reports. Furthermore, the data was processed using descriptive statistical analysis and verification. Ordinary Least Square regression analysis using EViews software approach was adopted for analyse the effect of independent variables on dependent variable. The result shows that gross domestic product (GDP) is influenced by the growth of foreign debt, the growth of poverty, and the growth of the minimum wage. This result is supported by the value of t-statistics < t table and also simultaneously through the coefficient of determination shows that the three variables above have a significant effect on the gross domestic product variable.

Discovering the global landscape of vulnerability assessment method of transportation network studies: A bibliometric review

Vulnerability assessment is an effort to assess the risk of disasters on transportation networks. We conducted a bibliometric analysis of vulnerability assessment method of transportation networks research from 1978 to 2021 to see the trend of publications in this study area. To perform a comprehensive statistical analysis of worldwide vulnerability assessment method, research knowledge that exhibit research trends, growth, collaborations and hotspots, quantitative and qualitative data from bibliometrics were used. According to the findings of the study, the number of studies on vulnerability assessment methods have increased to a considerable degree since 2004. Researchers on the topic are predominantly from Europe, with the majority based in the United States, United Kingdom, Italy, and China. The contribution of developing countries was relatively minimal, with China being the one notable exception. Based on the analysis of the keywords and the review, simulations using computer software, statistical and mathematical approach are the most used methods in the vulnerability assessment of transportation networks. The results also indicated that research on traffic control and accident prevention on roadways is still the most prevalent. In addition, this study meticulously outlined the hotspots in this topic (floods and seismic hazards). However, the vulnerability assessment of transportation network studies concerning the aspects of climate change during disasters is less explored. Lastly, the findings are expected to provide a reference that is both helpful and relevant for academics and practitioners who are interested in the future paths of this study.