Appl. Syst. Innov., Volume 7, Issue 3 (June 2024) – 21 articles

The research aims to develop a neural network-based lost information restoration method when the complex nonlinear technical object (using the example of helicopter turboshaft engines) sensors fail during operation. The basis of the research is an auto-associative neural network (autoencoder), which makes it possible to restore lost information due to the sensor failure with an accuracy of more than 99%. An auto-associative neural network (autoencoder)-modified training method is proposed. It uses regularization coefficients that consist of the loss function to create a more stable and common model. It works well on the training sample of data and can produce good results on new data. Also, it reduces its overtraining risk when it adapts too much to the training data sample and loses its ability to generalize new data. This is especially important for small amounts of data or complex models. It has been determined based on the computational experiment results (the example of the TV3-117 turboshaft engine) that lost information restoration based on an auto-associative neural network provides a data restoring error of no more than 0.45% in the case of single failures and no more than 0.6% in case of double failures of the engine parameter registration sensor event. Full article

Parking occupancy is difficult in most modern cities because of increases in the accessibility and use of motor vehicles, and users generally take several minutes or even hours to find a place to park. In this work, we propose a smart parking prediction model in order to help users locate in advance the availability of parking near the places they plan to visit. For this it is proposed a fog computing architecture that integrates a machine learning algorithm based on AdaBoost to predict parking places hours or days in advance. Additionally, a user interface was developed, which involves the collection of user inputs through a mobile application where the user is prompted to enter the destination location and the prediction time interval. Through extensive experimentation using real-world parking flow data, our proposed algorithm demonstrated an improved level of accuracy compared with alternative prediction methods. Moreover, a simulation was conducted to evaluate the system’s latency when using cloud computing versus our hybrid approach combining both fog and cloud computing. The results showed that employing the fog module in conjunction with cloud computing significantly reduced response delay in comparison with using cloud computing alone. Full article

In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components that constitute this innovative system, we explore its fundamental architecture and how each element contributes to seamless information flow. The benefits of adopting a medical information system are highlighted, emphasizing improved patient care, streamlined processes, and enhanced decision making for healthcare professionals. Full article

This article is about experiments investigating teaching and learning processes and their effects on students. Specifically, the laboratory experiment method aims to determine if using virtual reality in classes leads to better learning outcomes, knowledge retention, satisfaction, engagement, and attractiveness compared to traditional teaching methods. The study found that students who used VR (Experimental Group—EG) had significantly better learning outcomes (with an average of 5.9747) compared to the control group (Control Group—CG), who only had traditional classes (with an average of 4.6229). The study employed a Likert scale from 1 to 7. The difference between EG and CG was 29.2%. Furthermore, the study found that students in the EG had higher knowledge retention, satisfaction, engagement, and attractiveness compared to the CG. All measurements were above 6.4 on the same scale. This study is important because it explores innovative teaching methods and their potential to improve learning outcomes, satisfaction, and efficiency. It also opens up avenues for further research on teaching methodologies for undergraduate students. Full article

This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet, and gray-level co-occurrence matrix (GLCM) (with angles of 0°, 45°, 90°, and 135°), supported by contrast, correlation, energy, homogeneity, and entropy matrices. The test results showed that the k-NN algorithm excelled at identifying the texture of fresh (99%), frozen (99%), and rotten (96%) meat, with high accuracy. The GLCM provided good results, especially on texture images of fresh (183.21) and rotten meat (115.79). The Haar wavelet results were lower than those of the k-NN algorithm and GLCM, but this method was still useful for identifying texture images of fresh meat (89.96). This research development is expected to significantly increase accuracy and efficiency in identifying and classifying types of meat based on texture in the future, reducing human error and aiding in prompt evaluation. Full article

This study examines the global literature that looks at spatial–visual abilities (SVA) while considering the numerous differential studies, methods of evaluation designed over a century, and multiple external influences on its development. The dataset was retrieved from Google Scholar and publisher databases such as Elsevier, Taylor & Francis, Springer, etc. Only factual reports and bibliographic reviews were included in an analysis of a total of 87 documents. Each study of SVA is classified based on information, country, year, and age groupings. SVA has been extensively studied in the areas of “STEM (Science, Technology, Engineering and Mathematics) fields”, “demographic factors” and “other activities”. “Spatial visualisation” or “visual ability” is the term employed to refer to the cognitive ability that allows one to comprehend, mentally process, and manipulate three-dimensional visuospatial shapes. One of the most crucial distinct abilities involved is spatial aptitude, which aids in understanding numerous aspects of everyday and academic life. It is especially vital for comprehending scientific concepts, and it has been extensively studied. Nearly all multiple-aptitude assessments include spatial ability. It is determined that over the past two decades, the study of SVA has gained momentum, most likely because of information being digitised. Within the vast reservoir of spatial-cognition research, the majority of the studies examined here originate from the United States of America, with less than a quarter of the studies based in the Asia–Pacific region and the Middle East. This paper presents a comprehensive review of the literature on the assessment of SVA with respect to sector, year, country, age and socio-economic factors. It also offers a detailed examination of the use of spatial interventions in educational environments to integrate spatial abilities with training in architecture and interior design. Full article

Abstract: This research addresses the critical gap in enabling effective coopetition networks through technological innovation with the development of Cockpit4.0+, an Industrial Internet of Things (IIoT) artefact tailored for small- and medium-sized enterprises (SMEs). By employing the principles of Service-Dominant Logic (S-D Logic) and leveraging the Design Science Research (DSR) methodology, Cockpit4.0+ represents a pioneering approach to incorporating the IIoT within ecosystems for value co-creation. This facilitates competition and cooperation among firms, enhancing the operational dynamics within SME networks. Evaluated by experts in the ornamental stone sector, a significant sector of the Portuguese economy, the system demonstrated a positive functional acceptance rate of 78.9%. An experimental test was conducted following the positive preliminary functional evaluation of Cockpit4.0+, especially among more digitally advanced companies. The findings revealed that the on-time delivery performance under current best practices (CB.Ps) was 67.1%. In contrast, implementing coopetition network practices (CN.Ps) increased on-time delivery to 77.5%. These positive evaluations of Cockpit4.0+ underscore the practical applicability of S-D Logic and provide fresh insights into the dynamics of coopetition, particularly beneficial for SMEs. Despite its promising results, the real-world efficacy of IIoT systems like Cockpit4.0+ requires further empirical studies to verify these findings. Future research should focus on examining the scalability of Cockpit4.0+ and its adaptability across various sectors and enhancing its cybersecurity measures to ensure its long-term success and broader adoption. Full article

Total real and reactive power losses in electrical power systems are an inevitable phenomenon and occur due to several factors, such as conductor resistance, transformer impedance, line reactance, equipment losses, and phase unbalance. Minimizing them is crucial to the system’s efficiency. In this study, an artificial neural network, specifically a Multi-layer Perceptron, was employed to predict total real and reactive power losses in electrical systems. The network is composed of three layers: an input layer consisting of the variables loading factor, real and reactive power generated on the slack bus, a hidden layer, and an output layer representing the total real and reactive power losses. The training method used was backpropagation, adjusting the weights based on the desired output. The results obtained, using datasets from IEEE systems with 14, 30, and 57 buses, showed satisfactory performance, with a mean squared error of around 10⁻⁴ and a coefficient of determination (R²) of 0.998. In validation with 20% of the data that was not part of the training, the network demonstrated effectiveness, with a mean squared error around 10⁻³. This indicates that the network was able to accurately predict total power losses based on loads, generating estimates close to the desired values. Full article

Extended Reality (XR) is increasingly gaining momentum in industries such as retail, health, and education. To protect users’ personal data, establishing a secure authentication system for XR devices becomes essential. Recently, the focus on authentication methods for XR devices has been limited. To further our understanding of this topic, we surveyed authentication schemes, particularly systems and methods deployed in XR settings. In this survey, we focused on reviewing and evaluating papers published during the last decade (between 2014 and 2023). We compared knowledge-based authentication, physical biometrics, behavioral biometrics, and multi-model methods in terms of accuracy, security, and usability. We also highlighted the benefits and drawbacks of those methods. These highlights will direct future Human–computer Interaction (HCI) and security research to develop secure, reliable, and practical authentication systems. Full article

In this research, a proposed model aims to automatically identify patterns of spatial and temporal behavior of moving objects in video sequences. The moving objects are analyzed and characterized based on their shape and observable attributes in displacement. To quantify the moving objects over time and form a homogeneous database, a set of shape descriptors is introduced. Geometric measurements of shape, contrast, and connectedness are used to represent each moving object. The proposal uses Granger’s theory to find causal relationships from the history of each moving object stored in a database. The model is tested in two scenarios; the first is a public database, and the second scenario uses a proprietary database from a real scenario. The results show an average accuracy value of 78% in the detection of atypical behaviors in positive and negative dependence relationships. Full article

This paper outlines the numerical modeling procedure aimed at defining the guidelines for the development of a continuous microwave-assisted pilot plant for shelled almond disinfestation, as an alternative to the use of chemicals. To this end, a 3D Multiphysics numerical tool involving both electromagnetic and thermal models was developed to predict the temperature and electric field profiles inside the microwave treatment chamber. Three different microwave sources arrangements were simulated and the accuracy of the model was verified under different residence times of almonds in the treatment chamber using the developed prototype. The modeling results demonstrated that the arrangement having five microwave sources, each delivering a maximum power of 1.5 kW and frequency of 2.45 GHz, ensures good heating uniformity. The obtained results proved that the model enables the accurate prediction of the temperature trend (root-mean-square error/RMSE = 0.82). A strong linear regression was detected for the standard deviation between the simulated and experimental data (linear regression, R² = 0.91). The very low COV value for the experimental temperature data demonstrated the heating uniformity as the treatment time changed. The developed model and the simulation strategy used may provide useful design guidance for microwave-assisted continuous plants for disinfestation, with a significant impact on the almond industry. Full article

Social robots are being conceived with different characteristics and being used in different applications. The growth of social robotics benefits from advances in fabrication, sensing, and actuation technologies, as well as signal processing and artificial intelligence. This paper presents a design and implementation of the humanoid robotic platform Adam, consisting of a motorized human-like head with precise movements of the eyes, jaw, and neck, together with capabilities of face tracking and vocal conversation using ChatGPT. Adam relies on 3D-printed parts together with a microphone, a camera, and proper servomotors, and it has high structural integrity and flexibility. Adam’s control framework consists of an adequate signal exploitation and motor command strategy that allows efficient social interactions. Adam is an innovative platform that combines manufacturability, user-friendliness, low costs, acceptability, and sustainability, offering advantages compared with other platforms. Indeed, the platform’s hardware and software components are adjustable and allow it to increase its abilities and adapt them to different applications in a variety of roles. Future work will entail the development of a body for Adam and the addition of skin-like materials to enhance its human-like appearance. Full article

Aging, degradation, or damage to internal insulation materials often contribute to transformer failures. Furthermore, combustible gases can be produced when these insulation materials experience thermal or electrical stresses. This paper presents an artificial neural network for pattern recognition (PRN) to classify the operating conditions of power transformers (normal, thermal faults, and electrical faults) depending on the combustible gases present in them. Two network configurations were presented, one with five and the other with ten neurons in the hidden layer. The main advantage of applying this model through artificial neural networks is its ability to capture the nonlinear characteristics of the samples under study, thus avoiding the need for iterative procedures. The effectiveness and applicability of the proposed methodology were evaluated on 815 real data samples. Based on the results, the PRN performed well in both training and validation (for samples that were not part of the training), with a mean squared error (MSE) close to expected (0.001). The network was able to classify the samples with a 98% accuracy rate of the 815 samples presented and with 100% accuracy in validation, showing that the methodology developed is capable of acting as a tool for diagnosing the operability of power transformers. Full article

Due to the prevalent challenges of inadequate accuracy, unstandardized parameters, and suboptimal efficiency with regard to icing prediction, this study introduces an innovative online method for icing prediction based on Robust STL–BTSF and IBWO–LSSVR. Firstly, this study adopts the Robust Seasonal Decomposition of Time Series and Bilinear Temporal–Spectral Fusion (Robust STL–BTSF) approach, which is demonstrably effective for short-term and limited sample data preprocessing. Subsequently, injecting a multi-faceted enhancement approach to the Beluga Whale Optimization algorithm (BWO), which integrates a nonlinear balancing factor, a population optimization strategy, a whale fall mechanism, and an ascendant elite learning scheme. Then, using the Improved BWO (IBWO) above to optimize the key hyperparameters of Least Squares Support Vector Regression (LSSVR), a superior offline predictive part is constructed based on this approach. In addition, an Incremental Online Learning algorithm (IOL) is imported. Integrating the two parts, the advanced online icing prediction model for transmission lines is built. Finally, simulations based on actual icing data unequivocally demonstrate that the proposed method markedly enhances both the accuracy and speed of predictions, thereby presenting a sophisticated solution for the icing prediction on the transmission lines. Full article

Mechanical seals ensure the internal sealing of centrifugal pumps from the surrounding environment. They are one of the most critical components in a centrifugal pump. For this reason, the condition of mechanical seals should be monitored during operation. Mechanical seal friction power is an important component of mechanical losses in centrifugal pumps and is used as an indicator of wear and therefore seal condition. The soft sensor described in this paper is based on temperature measurements at the seal and can be used for determining the frictional power performance. A major factor in determining frictional power performance is the heat transfer between the mechanical seal and the medium inside the pump. For calculating the heat transfer, the stationary temperature fields in the rings of the mechanical seal are described by transmission efficiencies. The root mean squared error was determined for steady-state operating conditions to assess the quality of the soft sensor calculation. The frictional power performance can be determined by recording the temperature at the mechanical seal mating ring and the medium. The algorithm detects when the steady-state operating conditions change but does not map the dynamic changes between the stationary operating conditions. Full article

As technology evolves, more complex non-affine systems are created. These complex systems are hard to model, whereas most controllers require information on systems to be designed. This information is hard to obtain for systems with varying control directions. Therefore, this study introduces a novel data-driven estimator and controller tailored for single-input single-output non-affine discrete-time systems. This approach focuses on cases when the control direction varies over time and the mathematical model of the system is completely unknown. The estimator and controller are constructed using a Multiple-input Fuzzy Rules Emulated Network framework. The weight vectors are updated through the gradient descent optimization method, which employs a unique cost function that multiplies the error by a hyperbolic tangent. The stability analyses demonstrate that both the estimator and controller converge to uniformly ultimately bounded (UUB) functions of Lyapunov. To validate the results, we show experimental tests of force control that were executed on the z-axis of a drive-controlled 3D scanning robot. This system has a varying control direction, and we also provide comparison results with a state-of-the-art controller. The results show a mean absolute percentage tracking error smaller than one percent on the steady state and the expected variation in the system’s control direction. Full article

In this work, an organ pipe instrument with a mechatronic control system including the Passive Haptic Feedback technology is implemented. The test bed consists of a motorized positioning stage mounted to a brace that is attached to a bridge on a platform. A simple pneumatic mechanism is designed and realized to achieve the same dynamics pressure for each measurement attempt on the keyboard. This system contain pipes, an air compressor, valves, and a piston connected to applied force pressure on the keyboard of the organ pipe. The pneumatic components, like valves and pressure regulators, mounted on the profile plate are connected to the main air supply line via flexible tubing or hoses to the air compressor and mechanical trucker. The pneumatic system has many types of valves that regulate the air speed, air flow, and power. The combination of valves and air compressor control the air flow and the mechanism of piston and pressure on the keyboard. The mechanical actuator presses the key to be tested, and a load cell detects the applied key force. A laser triangulation measurement system based on a Laser Displacement Sensor measures the displacement of the key during the key depression. The velocity of the key motion is controlled by the pneumatic actuator. A miniature-sized strain gauge load cell, which is mounted on a musical keyboard key, measures the contact force between the probe and the key. In addition, the quality of the audio signal generated by the organ instrument is estimated using the Hilbert transform. Full article

Although commercial smart speakers are becoming increasingly popular, there is still much potential for investigation into their usability. In this study, we analyzed the usability of commercial smart speakers by focusing on the learnability of young users who are not yet familiar with voice user interface (VUI) operation. In the experiment, we conducted a task in which users repeatedly operated a smart speaker 10 times under four conditions, combining two experimental factors: the presence or absence of a screen on the smart speaker and the operation method (voice control only or in conjunction with remote-control operation). The usability of the smart speaker was analyzed in terms of task-completion time, task-completion rate, number of errors, subjective evaluation, and retrospective protocol analysis. In particular, we confirmed and compared the learning curves for each condition in terms of the performance metrics. The experimental results showed that there were no substantial differences in the learning curves between the presence and absence of a screen. In addition, the “lack of feedback” and “system response error” were identified as usability problems, and it was suggested that these problems led to “distrust of the system”. Full article

Within recent years, considerable progress has been made regarding high-performance solvers for partial differential equations (PDEs), yielding potential gains in efficiency compared to industry standard tools. However, the latter largely remains the status quo for scientists and engineers focusing on applying simulation tools to specific problems in practice. We attribute this growing technical gap to the increasing complexity and knowledge required to pick and assemble state-of-the-art methods. Thus, with this work, we initiate an effort to build a common taxonomy for the most popular grid-based approximation schemes to draw comparisons regarding accuracy and computational efficiency. We then build upon this foundation and introduce a method to systematically guide an application expert through classifying a given PDE problem setting and identifying a suitable numerical scheme. Great care is taken to ensure that making a choice this way is unambiguous, i.e., the goal is to obtain a clear and reproducible recommendation. Our method not only helps to identify and assemble suitable schemes but enables the unique combination of multiple methods on a per-field basis. We demonstrate this process and its effectiveness using different model problems, each comparing the resulting numerical scheme from our method with the next best choice. For both the Allen–Cahn and advection equations, we show that substantial computational gains can be attained for the recommended numerical methods regarding accuracy and efficiency. Lastly, we outline how one can systematically analyze and classify a coupled multiphysics problem of considerable complexity with six different unknown quantities, yielding an efficient, mixed discretization that in configuration compares well to high-performance implementations from the literature. Full article

In the early software development stages, the aim of estimation is to obtain a rough understanding of the timeline and resources required to implement a potential project. The current study is devoted to a method of preliminary estimation applicable at the beginning of the software development life cycle when the level of uncertainty is high. The authors’ concepts of the estimation life cycle, the estimable items breakdown structure, and a system of working-time balance equations in conjunction with an agile-fashioned sizing approach are used. To minimize the experts’ working time spent on preliminary estimation, the authors applied a decision support procedure based on integer programming and the analytic hierarchy process. The method’s outcomes are not definitive enough to make commitments; instead, they are supposed to be used for communication with project stakeholders or as inputs for the subsequent estimation stages. For practical usage of the preliminary estimation method, a semistructured business process is proposed. Full article

This article examines the performance of the proposed complex-order, conventional and fractional-order controllers for process automation and control in process plants. The controllers are compared regarding disturbance rejection and set-point tracking, considering variables such as response time, robustness to uncertainty, and steady-state error. The study shows that a complex PI-PD controller has better accuracy, faster response time, and better noise rejection. Still, implementation is challenging due to increased complexity and processing requirements. In contrast, a standard PI-PD controller is a known solution but may have problems with accuracy and robustness. Fractional-order controllers based on fractional computations have the potential to improve control accuracy and robustness of non-linear and time-varying systems. Experimental insights and real-world case studies are used to highlight the strengths and weaknesses of each controller. The findings provide valuable insights into the strengths and weaknesses of complex-order and fractional-order controllers and help to select the appropriate controller for specific process plant requirements. Future perspectives on controller design and performance optimization are detailed, identifying the potential benefits of using complex and fractional-order controllers in process plants. Full article