Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences

Volume Info.

  • Title

    Proceedings of the 2nd International Conference on Mechatronics and Smart Systems

    Conference Date






    978-1-83558-413-2 (Print)

    978-1-83558-414-9 (Online)

    Published Date



    Omar Marwan, Illinois Institute of Technology


  • Open Access | Article 2024-04-30 Doi: 10.54254/2755-2721/62/20240535

    Revolutionizing architecture: The synergy of computational design and digital fabrication

    This article delves into the profound impact of computational design and digital fabrication on the architectural landscape, presenting a comprehensive overview of their theoretical foundations, technological advancements, and environmental implications. It explores the transition from traditional design methodologies to algorithmic and generative approaches, highlighting how these technologies facilitate the creation of innovative, efficient, and sustainable architectural solutions. Through the lens of pioneering case studies, the analysis demonstrates significant efficiency gains and the potential for reducing construction waste and energy consumption. The integration of computational design with digital fabrication heralds a new era of architecture that not only challenges conventional construction practices but also aligns with the urgent need for sustainability in the built environment. The article further investigates the role of material innovation, robotic automation, and software development in pushing the boundaries of what can be achieved, ultimately underscoring the environmental benefits of these integrated technologies.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240371

    Research on wind resistance principles and design of super high-rise buildings

    Due to the high and flexible characteristics of super high-rise buildings, the structure is very sensitive to wind loads, and wind vibration effects and comfort are issues that cannot be ignored in the structural design of super high-rise buildings. The paper mainly introduces the problems in wind resistance design of super high-rise buildings. Including wind load values, wind vibration effects, and vibration control methods and applications. The result show that the wind induced vibration effect and comfort of the structure are often determined through wind tunnel tests. For super high-rise buildings with complex structural forms. There are two methods for controlling wind-induced vibration in high-rise buildings: Optimizing structural form through architectural methods; Take structural control measures. At the same time, with the cost of high-performance materials and engineering technologies, it is a challenge to find building materials and technologies that can meet wind resistance requirements while keeping costs in check. However, as computational technology continues to evolve, the wind-resistant design of tall buildings will benefit from more accurate wind-tunnel simulations and computational modelling, which will help to better predict and address wind-resistance challenges. Scientists and engineers will continue to research novel materials and structural designs to improve the wind resistance of tall buildings and reduce costs.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240372

    Virtual instrumentation-based data collection and analysis for CNC machining process

    Using Computer Numerical Control (CNC) machine tools in manufacturing is crucial for exact processing materials. The quality of the machined product heavily relies on the machine's condition. Therefore, monitoring and assessing the machine's status is essential to ensure product quality and enhance its lifespan. However, due to cost, space, and technical limitations, only a few physical variables, such as acceleration, force, displacement, acoustic radiation, temperature, and flow conditions, can be measured. Efficient transfer and extraction of these data are critical for monitoring machine status using statistical methods or valuable signatures. Traditional measurement instruments are complex and numerous, whereas measurement and analysis systems based on virtual instrumentation technology collect necessary data from sensors and data acquisition cards, meeting the needs of test analysis. Virtual instrumentation utilizes computer hardware resources, modularization hardware, and software systems for data analysis, communication, and operation interface, providing greater versatility, flexibility, compatibility, and repeatability. Previous research has shown successful attempts at developing LabVIEW-based systems for monitoring CNC milling machines and analyzing cutting parameters and forces. This project aims to collect and process data from the CNC machining process. The main objectives include writing a LabVIEW software program, recording data from CNC machine programs using the provided hardware (myRIO) and LabVIEW program, processing the data using MATLAB software, and analyzing and discussing the obtained results. The report consists of four parts, starting with an introduction to the project's background and literature review, followed by a description of the project steps, methods, experiments, and data processing. The processed data graphs will be presented and discussed, and recommendations for future research will be provided before concluding with a study summary.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240374

    Application of energy recovery mechanisms in wearable devices

    Wearable devices have almost become a necessity in modern-day society due to their multifaceted functions, providing fitness tracking and health monitoring. Apart from this foundational role, daily wearable devices play a significant role in helping with health recovery, such as Mechanical Exoskeletons, in our high-demand society. However, one of the primary limitations of these devices is their dependency on finite battery life. This review paper aims to address this problem with the concept of energy recovery (ER) technology, which could be a potential solution for prolonging the operational time of wearables. These methodologies are primarily based on the theory of energy conservation and efficiency models, branching out into different aspects of thermodynamics, piezoelectricity, and the basic principles of human motion. Through the analysis of academic journals and primary studies, this review aims to provide a detailed explanation of how ER technology would work from various perspectives, identify constraints within the use of this technology, and suggest directions for future investigation. The objective is to promote the integration of energy recovery technologies into wearable devices, ultimately enhancing their efficiency for users.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240376

    A comprehensive analysis of different types of proximity sensors in wearable electronic devices

    As the technological world has developed exponentially in recent decades, wearable electronics has been a growing industry in both size and significance. Sensors play an important role in these electronics, but research was rarely done on how different sensors play this role and serve different purposes. Thus, this paper focuses on the characteristics, pros and cons, and potential application on wearable electronics of different commonly seen proximity sensors. Namely, infrared sensors, ultrasonic sensors, and binocular vision. The research is done by analyzing different past papers and studies, piercing this information to gather to gain a comprehensive analysis and conclusions. The study reveals the vastly different characteristics of different proximity sensors and their different advantages and disadvantages displayed due to their diverse characteristics. The study also revealed potential applications of different sensors on wearable electronics. It is reasonably induced, from this study, that wearable electronics should adapt to use the most suitable proximity sensor and even use more than one type of sensor to tact the disadvantage of each to maximize its function.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240382

    Foldable display technology and wearable device integration design

    This paper explores the latest advancements in foldable display technology and its integration into wearable devices. By synthesizing relevant literature, the paper introduces the development history, principles, and application domains of foldable display technology. It subsequently discusses the advantages and challenges of incorporating foldable display technology into wearable devices, presenting various integration design approaches and future directions. The paper concludes by envisioning the potential applications of foldable display technology and wearable device integration in fields such as smart healthcare, fitness tracking, and fashion technology.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240384

    Research on the application of automatic control theory in automatic driving technology

    Autonomous driving technology has been widely used and studied in depth in recent years, and has become a hot field in the automotive industry. As one of the core methods to realize automatic driving, automatic control theory provides an important theoretical basis and practical guidance for automatic driving systems. This paper aims to explore the application of automatic control theory to autonomous driving and analyze its potential impact on improving driving safety, comfort and efficiency. This paper first introduces the basic principles and components of the autonomous driving system. Among them, automatic control theory plays an important role in decision-making and execution. Secondly, this paper discusses the specific application of automatic control theory in automatic driving. These include PID controller-based vehicle stability control, model predictive control (MPC) for path planning and trajectory tracking. These applications enable autonomous driving systems to respond in real time to environmental changes and maintain vehicle stability and safety. Finally, the paper discusses the challenges and future directions of automatic control theory in autonomous driving. Future research should focus on further improving the robustness and adaptability of automatic control algorithms to cope with complex driving scenarios and uncertainties. To sum up, automatic control theory plays an important role in automatic driving and has broad application prospects. Through continuous improvement and innovation, automatic control theory will make an important contribution to the realization of safer, more efficient, and more intelligent autonomous driving technology.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240385

    The impact of radio on the construction of smart cities

    In the process of smart cities, radio technology has played an increasingly important role. With the continuous acceleration of the global urbanization process, cities are facing many challenges. Driven by the government’s vigorous support and business promotion, wireless communication technology has developed rapidly, and new wireless communication technology has continued to emerge, providing more choices and possibilities for smart city construction. This article reviews and analyzes the actual case analysis, studies the impact of radio technology on the construction of smart cities, while explaining the importance of radio security management. The results showed that the application of radio technology enabled smart cities to achieve functions such as intelligent traffic management, intelligent communication, and intelligent energy management, but there were also problems such as leakage privacy, signal interference, and imbalance in regional development. In the process of future smart city construction, it is necessary to further optimize the safety and popularity of radio, promote the use of radio, narrow the gap between the use of different regions and cities, and enhance the radio management, so that it can better serve the construction of smart cities.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240386

    Research on man-machine cooperation and safety in intelligent manufacturing

    Securing our organization’s safety in this dynamic environment demands an all-encompassing approach, encompassing the physical, virtual, and human dimensions. It is imperative to understand that true security transcends the digital realm, encompassing the very physical spaces where innovation unfolds and the people who drive it. In order to effectively recognize and mitigate urgent threats in real-time scenarios, a commitment to adhering to established standards and leveraging state-of-the-art sensors becomes indispensable. These sensors serve as the vigilant sentinels of collaborative spaces, tirelessly monitoring interactions, and swiftly identifying anomalies. By adhering to rigorous standards, organizations ensure that their security protocols remain robust and responsive. Collaboration safety, meanwhile, hinges on the proficiency of trained and informed personnel. Properly equipping the human element within this collaborative landscape is paramount. Operators must be well-versed in the technologies they interact with, trained to navigate collaborative workflows, and imbued with a heightened sense of safety awareness. The success of collaborative ventures relies heavily on the competence and preparedness of the human workforce. From our extensive analysis, a resounding call to action emerges: the imperative to integrate diverse elements cohesively for optimal performance. This integration isn’t merely a technological endeavor; it’s a holistic approach that considers people, processes, and technology.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240387

    How pre-trained large model can help, when SAM meets image restoration

    In the process of image capture, degradation is inevitable due to noise, motion, down-sampling and so on. Therefore, image restoration is essential to improve the quality of images to enhance their visual effects and benefit downstream tasks. Adding prior knowledge can help the model better understand the image content and restoration requirements, thus improving the quality and efficiency. Semantic-level prior information can be generated by pre-trained large-scale models, such as segment anything models (SAM), and applied to a large number of downstream tasks. SAM has demonstrated powerful robustness and stability in restoration tasks, such as denoising, super-resolution, low-light enhancement, etc. Meanwhile, as an interactive component, SAM brings more control for users during the repair process. In this paper, we focus on the importance of SAM as prior information and systematically summarize a series of recent works combining SAM prior and low-level image restoration from three perspectives. In addition, we have summarized some potential problems and future directions of SAM.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240388

    Design and construction of small-scaled variable modular installation (SVMI) in the context of Chinese street renewal (CSR) : A case study of the "Jingzhang Box" (JZB) service installation in Beijing

    China's urban areas are entering an era of stock renewal, where street renewal is receiving policy attention and practical project implementation. However, the challenges posed by spatial constraints and insufficient functional activities on the streets have led to street renewal failing to meet public demands. Small Variable Modular Installations (SVMI) possess a high degree of adaptability to constrained spaces and a flexible capacity to shape diverse functions, making them a targeted strategy in theory to address the challenges of street renewal. Nevertheless, there is a research gap in the context of Chinese street renewal (CSR) concerning the design, and construction of SVMI: an insufficient exploration of combined variable and modular technology solutions and practical construction techniques and methods. In response to this research gap, this study focuses on the "Jing-Zhang Box" (JZB) service installation in Beijing, providing specific analysis of its design concept, design technical points, application functions, spatial structure and connection methods, construction methods, and construction outcomes. This study aims to provide specific supplementation to the research on SVMI design and construction in the context of CSR and to offer theoretical and engineering foundations for the future design and practical construction of SVMI in various scenarios.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240389

    Adaptable robotics for disaster response and search & rescue: Integration of deformable smart car design and pi control

    In the context of disaster response and search and rescue operations, the need for adaptable and efficient robotic systems has become increasingly evident. This research paper addresses the evolving challenges in this domain by introducing a novel DIY deformable robot equipped with advanced PI (Proportional-Integral) control. The background of this study emerges from the growing urgency to enhance the capabilities of robotics in post-disaster scenarios, where navigation through complex terrains and the swift delivery of supplies are paramount.The primary objective of this paper is to develop and assess a versatile robotic platform using a multidisciplinary approach encompassing mechanical engineering, electronics, and control systems. The core of the investigation is a DIY deformable car consisting of two sensor-equipped containers and a linking module, making it well-suited for search and rescue missions. This paper contains the implementation of a PI control system to govern the robot's mobility and adaptability. This includes a detailed examination and demonstration of the PI control mechanism, encompassing its proportional and integral components. The actual results also indicate that the smart car controlled by the PI controller has better performance in both stability and speed.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240392

    Analysis of gesture recognition applications based on deep learning

    With the advent of artificial intelligence, deep learning has been continuously evolving. It has progressed from its initial stages, exemplified by AlphaGo, to the development of related technologies exclusively centred around deep learning for the game of Go. Subsequently, deep learning has found applications in an increasing number of domains, including healthcare, sports, and more. This paper delves into the profound influence and development of deep learning as the foundation for analysing its impact on gesture recognition. Deep learning has brought about significant advancements in this field, enabling more precise and versatile gesture recognition systems. Furthermore, we explore specific use cases and contexts where deep learning has been harnessed, such as in medical diagnostics and sports performance analysis. While scrutinizing these applications, a promising future for deep learning becomes evident, with the potential to revolutionize various industries and enhance technology interaction through the development of more intuitive and sophisticated gesture recognition systems. The continuous growth and evolution of deep learning offer a bright prospect for the future of human-machine interfaces and artificial intelligence-driven solutions.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240396

    Accelerating handwritten number classifier with pipelined Multiply-Accumulate units in neural networks

    This research project explores the development and assessment of a Handwritten Number Classifier based on Neural Networks, employing the widely used MNIST dataset. The primary objective of this study is to enhance computational efficiency, and to achieve this, it concentrates on integrating and contrasting non-pipelined and pipelined structures within the Multiply-Accumulate (MAC) unit. The initial phase involved conducting MATLAB simulations, which yielded promising results regarding the accuracy of weight calculations. Subsequently, Hardware Description Language (HDL) testing was carried out to further validate the classifier’s performance. In the HDL testing phase, the classifier incorporating the pipelined MAC unit demonstrated a substantial 42.9% enhancement in processing speed when compared to its non-pipelined counterpart. These results highlight the potential advantages of employing pipeline processing in neural network architectures, emphasizing its effectiveness in achieving faster and more efficient image classification, particularly when dealing with extensive datasets. In conclusion, this research project not only presents valuable insights into improving the efficiency of neural network-based image classifiers but also lays the groundwork for potential future endeavors. These future directions may include adapting the classifier to handle more complex datasets and addressing emerging challenges.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240398

    Typical artificial intelligence algorithms and real-world applications related to handwritten number classifier

    Artificial Intelligence is a science that aiming to creating systems to imitate human wisdom, and perform tasks such as learning, reasoning, problem-solving, perception, natural language understanding, and some forms of creativity. This paper is about handwritten number classifier, a specific field within artificial intelligence, and its use across various industries. The basic concepts about artificial intelligence will be given at the first and the definition and structure of three representative artificial intelligence algorithms, convolutional neural networks, recurrent neural networks, and long short-term memory neural networks will be exemplified to better illustrate the concepts. Furthermore, the applications of handwritten number classifier will be analysed, especially in the areas of large-scale data statistics or survey, finance and taxation, and mail sorting. Eventually, a conclusion encompassing the challenges that the artificial intelligence systems are faced and reasoning regarding the significant role that artificial intelligence plays in the urban areas of the world is given for further discussion.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240399

    Optimization research of handwritten digit recognition algorithm based on hardware neural networks

    In the modern era of digitization, the widespread application of handwritten numerical data has led to an exponential increase in data volume, necessitating efficient real-time recognition and processing systems. This project is grounded in the foundational structure of neural networks, with a specific focus on integrating these networks into hardware through Very-Large-Scale Integration (VLSI). The primary objective is to achieve faster processing speeds, lower power consumption, and real-time operations. The central emphasis of this study is the design and implementation of a hardware neural network system tailored specifically for handwritten digit recognition. Drawing from neural network theory, we explore the construction of a hardware-based handwritten digit classifier. The fundamental model employed is the single-layer perceptron neuron model. Upon receiving 28x28 pixel grayscale images, the image classifier utilizes pre-trained weights, activation functions, and a maximum selector to compare and output recognition results for numerical digits. The concrete implementation is realized using the Verilog hardware description language, coupled with algorithm optimization strategies to enhance performance and efficiency. This research endeavor aims to provide an effective hardware solution for real-time handwritten digit recognition in the digital age.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240400

    Optimization of substation enclosure performance and carbon emission analysis based on OED

    To explore the thermal performance parameters of substation enclosure parameters and interaction and influence degree, using the DeST software simulation and orthogonal test method to building load minimum as the target parameters, select external wall heat transfer coefficient, roof heat transfer coefficient, outer window heat transfer coefficient and orthogonal test factors, carry out the four factors three level L9 (34) Orthogonal test analysis, studied the influence of each factor and its interaction on the building load, and finally obtained the optimal combination of factors under the comprehensive boundary conditions. At the same time, the correlation between load and outdoor temperature in the optimal scheme of unmanned substation and the carbon emission results before and after optimization are also explored. The results show that through univariate analysis, with the heat transfer coefficient of exterior wall, roof and window, the cumulative cooling load is not linear. Through the orthogonal experimental analysis, the significance of substation building is> roof exterior wall> south Angle> window, and the optimal is A1-B1-C2-D2The annual cumulative building cooling load is 20165.59 kW · h, and the peak cooling load appears on July 24. The operating condition of 0 ~ 9kW accounts for 90.6% of the annual operating time. There is a strong linear correlation between the building cooling load and the outdoor temperature (R2=0.65), the energy saving rate of the final optimized scheme is 17.71%, and the carbon saving rate is 26.72%. The research content of this paper provides reference for the renovation of existing buildings and the design of new buildings in substation buildings.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240406

    A hardware-implemented neural network for the analysis of handwritten numerals

    This study presents a specialized hardware-accelerated neural network tailored for the recognition of handwritten digits in 28x28 pixel grayscale images. Employing the perceptron model, our single-layer neural network is composed of 10 neurons, each handling inputs from all pixels to generate an output. The digit recognition is determined by the neuron with the highest output value. Implemented in synthesizable Verilog, the design complies with a constraint of 350 multipliers. To achieve this, this paper employs a combination of parallel processing and pipelining, breaking down the 785 multiplications needed for each digit into 8 stages, simultaneously processing 98 data points per clock cycle. In testbench evaluations, the final design exhibits impressive performance, successfully recognizing the majority of the provided images, and attaining a remarkable 99% accuracy rate, all with a minimal delay of just 115 clocks. This accomplishment is achieved using only 99 multipliers and 107 adders, showcasing the efficiency and effectiveness of our hardware-accelerated neural network for handwritten digit recognition.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240408

    Information management and optimization methods in architectural construction drawings: A case study of the "Coconut forest settlement" in Hainan

    This paper investigates information management and optimization methods within architectural construction drawings, using the “Coconut Forest Settlement” project in Hainan as a case study. Five key categories of methods are explored: clarification, structuring, standardization, precision, and lightweight. These methods address issues such as the lack of case-based analysis, the need for better information management, and the reduction of information redundancy. By classifying and discussing these strategies, the study highlights the enduring relevance of construction drawings in the digital age. Furthermore, it envisions the transferability of these methods to different contexts due to their emphasis on logical commonalities and the ever-present need for efficient information management. Practical examples from other fields, such as Revit, Rhino, and Grasshopper, are cited to demonstrate the potential applicability of these methods beyond traditional construction drawings. This paper contributes to the enhancement of information management in architectural design, fostering innovation and improved efficiency across various applications.

  • Open Access | Article 2024-05-20 Doi: 10.54254/2755-2721/62/20240411

    Research and application of power equipment monitoring and fault diagnosis

    Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

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