Engineering Computer Research Articles

THE SIGNIFICANCE OF THE CREDIT-MODULE SYSTEM IN STUDYING THE SCIENCE OF ENGINEERING COMPUTER GRAPHICS

This article highlights the special importance of organizing the engineering computer graphics course through the credit-module system, which plays a significant role in developing students' creative abilities and ensuring quality in the lesson process through modular teaching.

A Survey on Ring Theory for its Mathematical Foundation and Applications

The origin of the ring theory can be dated to the early 19th century. With the rapid development of technology, more and more complex engineering problems and computer problems need to obtain support and results from more basic mathematics theory. Hence, based on this demand, the researcher discovered that ring theory is a very noteworthy math theory for solving the complicated problems above. This paper investigated the ring theory and related articles based on number theory and corresponding applications, which produces and results in a literature review in this particular direction.

Experimental Design and Modeling for Forward-Inverse Maps

In customer-driven design of systems or products, one has performance targets in mind and would like to determine values for product or system parameters that meet such targets. Engineering computer simulation models predict performance given design parameter values; meeting a target is done iteratively through an optimization search procedure, typically by optimizing a regression, neural network or other type of approximation metamodel of the computer model. We pose the thesis that, since forward metamodel construction is a key part of this strategy, constructing an inverse metamodel directly is advantageous. The inverse metamodel obviates the need for optimization in many settings. One can design experiments that allow simultaneous fitting of forward and inverse metamodels. We discuss the potential for this strategy, the connection with the calibration problem, and some of the issues that must be resolved to make the approach practical.

Cybersecurity Implications of Edge Computing in Data Engineering

Edge computing, as an extension of cloud computing, brings computation and data storage closer to the data source. This shift offers significant advantages in terms of latency reduction, bandwidth optimization, and real-time processing capabilities. By minimizing the distance that data needs to travel, edge computing enhances the performance of applications that require rapid data processing and immediate response times. This is particularly beneficial for Internet of Things (IoT) devices, autonomous vehicles, smart grids, and other applications that demand low-latency interactions. However, the decentralization inherent in edge computing introduces a unique set of cybersecurity challenges that are distinct from those faced in traditional centralized cloud environments. The distributed architecture of edge computing creates numerous points of vulnerability, each of which can be exploited by cyber attackers. Additionally, edge devices often operate with limited computational resources and power, which complicates the implementation of robust security measures. This paper explores the cybersecurity implications of edge computing in the context of data engineering. It begins with a comprehensive review of existing literature to establish the current understanding of edge computing security issues. The review identifies the primary vulnerabilities associated with edge computing, including those related to distributed architecture, data transmission, and resource constraints. Following the literature review, the paper delves into specific security threats that edge computing environments face. These include man-in-the-middle (MitM) attacks, distributed denial-of-service (DDoS) attacks, malware and ransomware, and insider threats. Each threat is analyzed to understand its potential impact on edge computing systems and the data they process.

Exploring Biomedical Engineering (BME): Advances within Accelerated Computing and Regenerative Medicine for a Computational and Medical Science Perspective Exploration Analysis

The field of molecular engineering in medicine has made significant strides in recent years, transforming healthcare, diagnostics, and therapy development. However, the COVID-19 pandemic highlighted the ongoing need for further innovative progress and detailed investigation. This research delves into the interdisciplinary domain of biomedical engineering with molecular engineering, examining its impact on the domains of regenerative medicine, biomaterials, tissue engineering, and the acceleration of health science through advanced biotechnologies. The primary objective of this research is to conduct an in-depth exploration of biomaterial applications and their roles in regenerative medicine, along with advancements in tissue engineering, organon-a-chip device mechanics, and the transformative potential of bioprinting in creating functional tissues and organs. This research also includes a case study analysis of drug discovery, immune engineering, precision medicine, and gene editing, with insights into the processing, design, and screening pipelines for biologics, as well as the future role of therapeutics and drugs in healthcare. Throughout this exploration, meaningful discussions with conclusions are drawn regarding the advanced technologies investigated, employing systematic technical computing methods at each step of the research process. The rapid advancements in terms of technological computing have brought about significant growth and transformation in various types of domains of biomedical engineering, particularly in the field of medical science and human health. With the progress in artificial intelligence (AI), computer vision, deep learning, image processing, machine learning there has been a revolutionary change in healthcare, addressing a wide range of medical conditions and human anatomy perspectives. The integration of these immersive technologies has not only improved in the realm of medication and disease control but has also provided solutions for complex tasks and issues related to human anatomy threats within the health sector. This research also focuses on the impact of accelerated computing in biomedical engineering, providing insights into the modern utility of toolsets in Bioinformatics and mechanics with artificial intelligence within medical science and also diving into understanding the human anatomy. Additionally, it explores the concept of functional genomics and its potential to provide insights into future disease and health issues, paving the way for advancements in healthcare for the foreseeable future and beyond.

Design of Variable Valve Timing and Electronic Control for Honda R18A

Abstract: This paper explores the Variable Valve Timing and Lift Electronic Control (VTEC) system developed by Honda, focusing on its mechanisms, design methodology, and impact on engine performance. VTEC optimizes engine efficiency and power by switching between cam profiles suited for low-RPM stability and high-RPM power output, controlled by the engine's computer based on operational conditions. We examine the intricate workings of the VTEC system, detailing its hydraulic actuation and the resulting enhancements in torque and horsepower. Our study centers on the Honda R18A engine, targeting increased maximum RPM and power output. Using theoretical analysis validated through Ricardo Wave software and MATLAB simulations, we determine optimal valve lift and timing to achieve the desired performance. Additionally, the research addresses the geometric parameters influencing airflow through the valves, providing a comprehensive understanding of the VTEC system's contribution to engine efficiency and driving experience. This investigation not only underscores the technological advancements in variable valve timing but also presents practical insights for automotive engineering applications.

An Overview of Digital Engineering Methods for Platform Integration of Power and Energy Systems

US Navy ships, and combatant ships in particular, have requirements for integrated systems that are designed and configured for operational efficiency, redundancy, and survivability. Mission systems today and in the future will not always come with their own energy and many may, at times, require extreme pulse power loads. In addition, the migration away from fossil fuels to hybrid systems with energy storage, or the requirements for autonomous platforms, is challenging our ability to design platforms for these systems. Understanding the interdependencies between components, the systems they support, and the energy domains they are member of is a critical ontological design requirement. This paper will address how we applied digital engineering principles and computer science to the design of ontologies that allow for the modeling of complex operational systems riding on the same shipboard energy network. This system network must support varying levels of detail needed during design while at the same time understanding the impacts of that design on ship system operations and their energy loads over time.

Simulasi Pemanfaatan Aplikasi Sistem Deteksi Pengguna Masker Pada Ruangan Laboratorium

During the post-pandemic or endemic period, the government continues to recommend the use of masks and maintenance of hygiene when engaging in activities outside the home. This extends to activities on the campus of the University of Mataram, particularly in the electrical engineering computer laboratory, where students are consistently encouraged to wear masks. To streamline activities in the laboratory, a Mask Detection System using the Haar Cascade Classifier (HCC) Method integrated with a Telegram Bot has been tested. The trial implementation aims to instill discipline among students in mask usage. This system was developed using Visual Studio Code and Python 3.10 tools, with image data input from a webcam. The HCC method is employed to detect students wearing masks or not in the laboratory, utilizing webcam-captured image data. Subsequently, the Telegram bot sends images if individuals are identified wearing masks. The study results demonstrate the system's capability to detect masks within a range of 30 cm to 120 cm. The warning feature, consisting of recorded images of students wearing masks sent via the Telegram bot, operates effectively.

IEEE Computers in Science and Engineering

IEEE Computers in Science and Engineering

3D printed titanium carbide MXene-coated polycaprolactone scaffolds for guided neuronal growth and photothermal stimulation

The exploration of neural circuitry is paramount for comprehending the computational mechanisms and physiology of the brain. Despite significant advances in materials and fabrication techniques, controlling neuronal connectivity and response in 3D remains a formidable challenge. Here, we introduce a method for engineering the growth of 3D neural circuits with the capability for optical stimulation. We fabricate bioactive interfaces by melt electrospinning writing (MEW) 3D polycaprolactone (PCL) scaffolds followed by coating with titanium carbide (Ti3C2Tx MXene). Beyond enhancing hydrophilicity, cell adhesion, and electrical conductivity, the Ti3C2Tx MXene coating enables optocapacitance-based neuronal stimulation, induced by localized temperature increases upon illumination. This approach offers a pathway for additive manufacturing of neural tissues endowed with optical control, facilitating functional tissue engineering and neural circuit computation.

Field investigation and numerical analysis of horizontal cracking in ledge girders of post-tension concrete bridges

This research addresses the pressing issue of horizontal crack initiation in ledge girders of post-tensioned concrete (PTC) bridges, which is a significant concern for the safety and longevity of long-span bridges. With field observations and advanced engineering computations, the study identifies the primary causes of these cracks and explores potential mitigation strategies. The research pinpoints the primary causes of horizontal crack initiation in PTC girders by analyzing 3D finite element modeling and comparing the results with the analytical solution suggested by a design code. The study also probes the potential of using external prestressing with a post-tensioning bar to mitigate horizontal cracking. The findings underscore the significance of adhering to the design code requirements for hanger reinforcement, which notably mitigates crack width in PTC girders. The research also highlights that higher prestress losses correspond to increased crack width, emphasizing the need to consider prestress losses for maintaining structural resilience. Furthermore, the study reveals that in all strengthening cases, the maximum crack width is notably reduced at the point of failure, albeit less effectively than when using rebar as per the design code. These findings underscore the potential of post-tensioning as a strengthening technique and the importance of adhering to design standards.

Solution of nonlinear models in engineering using a new sixteenth order scheme and their basin of attraction

The utilization of computers in various engineering and mathematical fields has become increasingly important in recent times. In computational and applied mathematics, computer programs can be used to efficiently process complex data in mere seconds. By utilizing different computer tools and programming languages, computers can manipulate various iterative algorithms to solve nonlinear problems. The convergence rate and computational costs per-iteration are essential factors that determine the effectiveness of an iterative algorithm. This article introduces a new and highly efficient iterative approach of sixteenth order. The authors propose a four-step iterative optimal derivative-free method for solving non-linear equations that arise in engineering application problems. The proposed method achieves an accuracy of order sixteen with only five functional evaluations, and the efficiency index is 1.741. To demonstrate the performance of the method, the authors present several numerical examples, application problems, and a study of dynamics, comparing the method with other available methods of the same order in the literature. Numerical results and dynamics are obtained using computer tools.

Editorial: Pushing Engineering Computation to address the UNSDGs

Editorial: Pushing Engineering Computation to address the UNSDGs

Quantum Tomography: From Markovianity to Non-Markovianity

The engineering of quantum computers requires the reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum likelihood estimation (MLE)-based methods. Furthermore, the performance of standard methods, including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation, neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce the details of basic linear inverse methods. Then, the framework of MLE methods with constraints is summarized. Finally, we briefly introduce recent further research in developing the performance of tomography, utilizing some symmetry properties of the target. This review provides a primary getting-start in developing quantum tomography, which promotes quantum computer development.

An Investigation on Utilizing Large Language Model for Industrial Computer-Aided Design Automation

Text-to-image or 3D content generation has attracted attention in academia and industry because of its impressive performance in high-fidelity content generation with natural language commands. However, its widespread application in industrial computation-aided design (CAD) is not yet found. The main issue lies in the end-to-end generation through implicit probabilistic learning from datasets. This presents difficulties in precise, controllable, and transparent creations. Also, it is time-consuming and labor-intensive for designers to verify and customize the generated result to fit their demands. Furthermore, popular generative models offer limited support for early-stage engineering computations, leaving tedious and massive calculations to human engineers. To overcome these challenges, this paper proposes a method to apply large language model (LLM) to assist standard CAD design workflow on both engineering computation and precise 3D geometry generation. During the designing process, LLM can support the computation process with human guidance and generate accurate 3D shapes with the capability of sequential code representation and transformation. To verify the feasibility of the proposed method, a use case for a one-stage reduction gear system consisting of two gears will be given as a proof-of-concept demonstration. The result shows that the LLM can carry out the computation and get the result as expected with only a few lines of human guidance and feedback. Then, it can create CAD models with accurate parameters aligned to the computational results. It can cope with various requirements given by users and allow users to further modify the generation using the same CAD environment without much effort. Authors believe that this piece of work can shed light on utilizing the capability of LLMs for a certain part of task automation based on human beings’ intentions. With the rise of LLMs and relevant technologies, conventional CAD may shift to a more user-friendly AI-aided design (AIAD) diagram.

Fracture Behavior Analysis of Defective Pipelines Based on Computer-Aided Engineering

Due to the existence of structural and material property discontinuities in the ring-welded joints of large-diameter, high-steel-grade pipelines, which are prone to the risk of fracture. Therefore, in this paper, computer-aided engineering techniques, based on finite element analysis and numerical calculations, have been used to establish a model of pipeline containing internal surface crack defects for simulation, and J-integral algorithms have been applied to evaluate the effect of crack parameters on fracture behavior. This research highlights the critical role of computers in pipeline engineering and provides an important theoretical and methodological basis for further optimizing pipeline design and maintenance.

Quantum Computing in Communication Engineering: Potential and Practical Implementation

Quantum Computing in Communication Engineering: Potential and Practical Implementation

Review of quantum computing and potential applications in nuclear engineering

Review of quantum computing and potential applications in nuclear engineering

Construction of Green Buildings by using AI in the Civil Engineering Field

Abstract: A branch of computer wisdom called Artificial intelligence (AI) deals with the study, creation, and use of intelligent machines. This includes the description of recently developed ideas and styles for the development and perpetuation of AI in civil engineering and also gives an overview of the field’s advancement. With the tremendous development and advancement in big data, deep literacy, and machine literacy technologies, it has been used effectively and successfully in colorful sectors of civil engineering. The important areas of artificial intelligence exploration in civil engineering include structural operation and conservation, as well as design optimization. Data collection, sustainability assessment, and productivity are just many advantages and prospects that the use of AI in civil engineering offers to civil masterminds. With the use of digital technology, the construction trend has now been converted into bone that emphasizes sustainability. The use of computers in civil engineering is primarily concentrated on numerical, algorithmic computations, which is unhappy for working with the empirical and inadequately structured problems that arise in factual practice and are handled by expert systems and artificial intelligence.

Implementation of IDS Using Snort with Barnyard2 Visualization for Network Monitoring in The Informatics Engineering Computer Lab at Muhammadiyah University Surakarta

The recent surge in cyberattacks should not be taken lightly, especially by large enterprises with sensitive data. Intrusion Detection Systems (IDS) are becoming a critical component for detecting network anomalies. One such network anomaly detection tool is SNORT, with a BASE (Basic Analysis and Security Engine) frontend for efficient data processing. Acting as a bridge between SNORT and BASE, the author uses barnyard2 as a backend to store logs obtained from SNORT into the database. The implementation methodology used in this research is an experimental approach, where the authors conduct experiments through trial and error to achieve the desired results. This IDS system was tested using two types of attacks, namely DDoS and SQL-Injection. The DDoS attack trial uses tools found in Kali Linux, namely Hping3 with 6 scenarios namely FIN, ACK, RST, UDP, SYN, and ICMP with the results detected in the snort database. SQL-Injection attack test using the DVWA vulnerable website with the result detected in the snort database when the attack is carried out. This proves that the accuracy level of the system reaches close to 100% with the rules given and the penetration testing given.