Prototype Model Research Articles

Design and Development of IIOT Based Prototype Model by Using Machine Learning Techniques for Workflow Management in Industry 4.0

Manufacturing is undergoing a revolutionary shift known as Industry 4.0 (I4.0), which unifies production and business operations through data-driven End-to-End digital integration. I4.0 goes beyond the digital ecosystem of the Third Industrial Revolution to include autonomous tech-human convergence and real-time data interchange. The Industrial Internet of Things (IIoT) and machine learning are driving breakthroughs in cyber-physical systems, which form the basis of this. In order to advance I4.0, technology and human talents must be balanced. It is essential to upskill the workforce and engage in coordinated transdisciplinary activities. A holistic approach, integrating human factors and tech advancements, ensures smooth automation transition. The proposed study introduces a cost-effective liquid filling system based on a Raspberry Pi, addressing automation challenges for smaller firms.

OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-Based Weed Detection

Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology.

A Sustainable Residential Building Model in North Iraq by Considering Occupant Behaviour, Sociocultural Needs, and the Impact on Energy Use

Sustainable design, which aims to reduce energy consumption and mitigate climate change, is a primary concern of developing countries. Hence, it reduces CO2 emissions. Residential buildings in North Iraq account for approximately 69% of all electricity consumed. To mitigate this issue, this article investigates the design of a sustainable model by considering the local climate, building design occupant behaviour, and sociocultural needs in the region and their impact on energy use. This study used mixed research methods to develop a sustainable single-family house model in semi-arid climates, specifically Erbil (North Iraq), the process consisted of three phases. Phase One saw the collection of all data from analysed literature, observation, worship, case study simulations of the base, and an improved model. Phase Two defined the guidelines for creating sustainable model dwellings based on the main findings in Phase One. Phase Three created a prototype to evaluate the sustainable model, primarily focusing on meeting people’s design preferences while avoiding privacy concerns. In addition, DesignBuilder Software simulation was used to examine the impact of occupancy behaviour (based on local culture and traditions) on the building’s energy performance throughout two phases. In the first step, three occupancy profile types are compared with real-life study bills. These profiles were the base case, which came from an actual case; the statistical profile from surveys; and the international standard ASHRAE 90.1, which was used as the default. The second phase compared the base model with an improved model and developed a sustainable prototype that satisfies local climate and sociocultural needs. The result indicated that the standard occupant profile significantly differs from the actual bill by 40%, whereas the statistical profile and base case reduce the gap to 11% and 4%, respectively. The sustainable prototype model can enhance operative temperature by 4 °C and decrease total energy use by 50% compared to the base case model. Data also showed that occupants keep lights on even when rooms are unoccupied. Therefore, when designing sustainable dwellings, it is crucial to consider occupant behaviour and their sociocultural needs, as they have a significant impact on energy use as a result of their activity patterns and schedules. These factors should be considered in the local code.

Estimating Kolmogorov–Sinai entropy from time series of high-dimensional complex systems

A method is presented to estimate KS entropy from time series of a high-dimensional complex system. We focus on partitioned entropy, which measures the complexity of data points localized in a delay-coordinate space. By quantifying the time evolution of the partitioned entropy, we show that its growth rate gives a good estimate of the KS entropy. Our method is computationally efficient, since a time series is discretized into a coarse-grained symbolic sequence by a vector quantization technique. Using numerical data sets generated from prototypical models of chaotic systems, we showed that our approach works quite well for high-dimensional complex systems. Application to experimental data measured from physical models of the vocal membranes confirmed that our method is effective for characterizing the complexity of real-world systems.

Maximum Power Point Tracking with Artificial Neural Network

The urgent depletion of fossil fuel resources and the pressing need for environmental preservation have spurred a critical examination of alternative energy solutions. In response, this study focuses on the development of innovative approaches to mitigate the overreliance on conventional energy sources and transition towards sustainable alternatives. As part of a final year project, the research draws inspiration from this imperative and endeavors to design a prototype model that integrates advanced techniques for harnessing solar energy. Index Terms–Maximum Power Point, Buck-Boost Converter, Neural Network Architecture

Design of an Intelligent Energy Management System for Electric Vehicle Charging Station

In today’s world there are more outcomes in environmental change due to the overutilization of petroleum products in this manner prompting a genuine effect on the climate. So there is a need for a substitute answer for lessen the consumption of such non – sustainable assets. One such exertion made in the field of Freeways is the advancement of "Solar Freeways" which can be an elective arrangement. Sun oriented streets consolidate various arrangements in one – it can assist us with improving the creation of power utilizing sun based boards, to give a computerized stage to our future country's ventures like Smart Cities, and to work with the arising electric vehicles that supplant the petroleum driven vehicles and substantially more. Motivated by the fact that there are numerous amount of clean and sustainable energy we receive from roadways, the following study puts forward some of the event and application of an innovative charging method for the renewable energy driven electric cars, buses by using the roadway and also implementation of revolutionary nanotechnology along with the latest best in the house power electronics and power system analysis tools. A small scale prototype model was made by our team to attest the working of smart inductive charging process. Our project team was successful to improve the working of the model by improving the use of the preinstalled solar panels and also implement its use on the very concept we are trying to improve. On the vehicle, there will be the use of coils which are experimentally made for the flow of charges that are needed to provide charge to a moving electric vehicle (EV). The detailed strategy is presented in this report. Key Words: Environmental change, Overutilization of petroleum products, Solar Freeways, Renewable energy, Electric vehicles (EVs), Smart Cities, Nanotechnology

Few-shot classification of ultrasound breast cancer images using meta-learning algorithms

Medical datasets often have a skewed class distribution and a lack of high-quality annotated images. However, deep learning methods require a large amount of labeled data for classification. In this study, we present a few-shot learning approach for the classification of ultrasound breast cancer images using meta-learning methods. We used prototypical networks and model agnostic meta-learning (MAML) algorithms as meta-learning methods. The breast ultrasound images (BUSI) dataset, which has three classes and is difficult to use in meta-learning, was used for meta-testing in a cross-domain approach along with other datasets for meta-training. Our proposed approach yielded an accuracy range of 0.882–0.889, achieved by implementing the ResNet50 backbone with ProtoNet in a 10-shot setting. These results represent a significant improvement ranging from 6.27 to 7.10% over the baseline accuracy of 0.831. The results showed that ProtoNet outperformed the MAML method for all k-shot settings. In addition, the use of ResNet models as the backbone network for feature extraction was found to be more successful than the use of a four-layer convolutional model. Our proposed method is the first attempt to apply meta-learning for few-shot classification in the BUSI dataset while providing higher accuracy compared to deep learning methods for medical images with small-scale datasets and few classes. The methodology used in this study can be adapted to other datasets with similar problems.

A novel on high voltage gain boost converter with cuckoo search optimization based MPPTController for solar PV system

Traditionally, isolated and non-isolated boost converters are used for solar photovoltaic systems (SPV). These converters have limitations such as low voltage gain, less voltage ripples, temperature dependence, high voltage stress across the switches, and being bulky in size. Besides, the solar PV system also has non-linear characteristics between I–V and P–V, and the energy yield potential is affected by partial shading phenomena. Therefore, maximum power point tracking (MPPT) is being added to the SPV system to get the maximum output power under steady and dynamic climate conditions. Although the conventional MPPT has drawbacks such as less accuracy in predicting the MPP under partial shading conditions, low tracking speed, and more ripples, Hence, the research proposes a stackable single switch boost converter (SSBC) with a Cuckoo search MPPT controller for the SPV system. The efficiency of the proposed circuit topology has been compared with conventional boost converters with various MPPTs. Subsequently, the accuracy of tracking true MPPT by CSO is compared with that of PSO and FPNA. The results show, that the CMPPT with CBC has produced more ripples, whereas the BMPPT with SSBC produces ripple-free power under steady conditions. It is also observed that SSBC with BMPPT produces more power than SSBC with TMPPT. The efficiency of SSBC with BMPPT is better than other combinations. Finally, a prototype model has been developed and verified.

An effective aeration system for high performance pond aeration at low energy cost

With the effective use of aerators with low energy cost and high aeration performance, aquaculture areas can be developed and supported. The main objective of this study is to determine the optimal physical parameters of high aeration capacity high head conduits and to ensure low energy costs, thus to present a comprehensive review of its usability for pond aeration. In this study, a high-head gated conduit was used as the aeration unit. After obtaining the design that will maximize the aeration performance of the high-head gated conduit, a prototype model was developed to ensure its effective use in pond aeration. The parameters affecting the performance of this developed aeration system and the corresponding energy cost values were determined. According to the results, the most important parameters affecting the aeration performance are, respectively, flow rate, the opening of the gate, hydrostatic level, and jet plunge angle. In the optimal design, more than three times the unit volume of water circulating in the system is added to the system. The cost value of this operation in terms of energy was measured as 0.10 kWh/m3 air. The developed system was found to have significantly higher energy efficiency values compared to alternatives, based on specific energy consumption data.

Development of AI-based Arabic Learning Model to Improve non-native speaker Arabic Speaking Skills

This research delves into the development of an Artificial Intelligence (AI)-based Arabic language learning model with the aim of enhancing Arabic speaking skills for non-native speakers. The background of this research arises from the complexity of Arabic language learning, which requires not only an understanding of grammar and vocabulary but also proficiency in speaking with an accurate accent and intonation. The research methodology includes the selection of suitable AI algorithms, the development of a prototype model, and further testing on non-native speakers at the Sekolah Tinggi Agama Islam (STAI) al-Yasini, Pasuruan. The utilization of AI technology is expected to provide innovative solutions to address individual learning needs in the context of the Arabic language. Mid-term evaluation results indicate that the AI learning model can positively impact Arabic speaking skills, although some improvements are still necessary. User response analysis provides valuable insights that serve as the basis for further model refinements. Suggestions for future research include further development of the AI model, broader testing across various non-native speaker groups, and the integration of user feedback to enhance Arabic speaking skills holistically. The implications of these research findings are expected to make a significant contribution to the development of adaptive and innovative Arabic language learning.

Optimal Observer-Based Power Imbalance Allocation for Frequency Regulation in Shipboard Microgrids

This paper proposes a two-level control strategy based on a super-twisting sliding-mode algorithm (STA) to optimally allocate power imbalances in shipboard microgrids (SMGs) while achieving frequency regulation. The strategy employs an STA observer to estimate the unknown power load demand imbalances in finite time. This estimate is then passed to an online high-level optimal control framework to periodically determine the optimal sequence of power reference values for each energy storage device (ESS), minimising the operational cost of the SMG. The online optimised power reference values are interpolated and passed to the low-level STA control strategy to control the output power of each ESS. The efficacy of the proposed methods is demonstrated through numerical simulations conducted on a prototypical model of an SMG equipped with two ESSs, namely batteries and fuel cells with associated hydrogen storage.

Dynamic characteristics testing and modeling in electromagnetic-thermal-force coupling of electromagnetic devices using parallel finite element simulation

Electromagnetic systems of Electromagnetic devices involve multiphysics coupling of electromagnetic fields, electrical circuits, mechanical motion, and temperature fields. When the digital prototype model has a large number of meshes, the commercial finite element software also has the disadvantage of long computation time in the simulation of dynamic characteristics of electrical apparatus. This paper proposes a transmission line method (TLM) based digital modeling method for the electromagnetic-thermal-force coupling to test and evaluate the static and dynamic characteristics of electromagnetic devices more accurately and efficiently. The case study is developed on a direct current (DC) contactor, and the dynamic characteristics testing and modeling in the electromagnetic-thermal-force coupling of the contactor are investigated. The numerical results of the finite element method (FEM) model for the electromagnetic system of the contactor are compared with the commercial FEM software Altair Flux and the experimental results, which demonstrate the exceptional accuracy of the proposed digital modeling method for the multiphysics coupling of the DC contactor. Meanwhile, the dynamic characteristics of the DC contactor at different ambient temperatures are measured and evaluated, and the comparison with the experimental measurements shows the applicability of the proposed digital prototype model at different ambient temperatures. Compared with the conventional Newton-Raphson (NR) iteration, when dividing fine mesh and extremely fine mesh, the TLM parallel computing significantly improves the efficiency of solving the dynamic characteristics of multiphysics coupling by about 35 % and 44 % with the same computational accuracy, respectively. The dynamic characteristics evaluation method combined with digital modeling technology and experimental measurements proposed in this paper demonstrates high accuracy and solving efficiency, making a notable contribution to the field of design and optimization of electromagnetic devices.

Prototype Model of Forklift with Mecanum Wheel Controlled Using Mobile

Industrial forklifts are used to load and unload goods. Electrical forklifts consume less power to drive the vehicle. In crowded spaces with static and/or dynamic obstacles and small corridors, such as industrial floors, warehouses, offices, and hospitals, multidirectional cars could carry out crucial functions. Mecanum wheel-mounted forklifts have seen several designs developed recently to enhance their multidirectional portability and usefulness. In this project, the center of gravity of the forklift is 67mm from the front axle and mecanum wheel diameter is 60mm. The direction of the mecanum wheel is determined by the direction of the leading edge of the roller, if the roller is pointed left then it is a left wheel and if the roller is pointed right then it is a right wheel. The mecanum wheel is a device that can move a vehicle in all directions, including forward, backward, left, right, diagonal, and more. This design has empowered us to create mecanum wheel-based forklift prototype, showcasing the potential for engineering solution. Key Words: Forklift, Mecanum wheel, multidirectional,

Bridge Approach Model with Precast Concrete-cell Box Structure to Overcome Differential Settlement

The bridge approach road embankment has a very important role as a link between the road and the bridge. In the post-construction stage, there are many cases of bridge approach road embankments, especially those with high elevations, potentially experiencing differential settlement. To overcome this, an analysis and planning study was carried out in increasing stability and overcoming the differential decline that occurred in the bridge approach road structure, especially in the approach slab structure with the end of the bridge. So that a bridge approach road model was developed as a substitute for the mass of soil embankment by conducting experiments and making a prototype of the Precast Concrete-cell Box structure model, which was given the maximum repetition and gradual plan load. While the method used in this research is the prototype model method, by conducting loading and settlement experiments on the bridge approach, especially on the approach slab. Furthermore, analysis and simulation are carried out under the condition of reaction force and maximum loading. From the results of the analysis, in the loading test of 2,761.00 kg, 2,717.00 kg 2,974.00 kg on the prototype model, the value of the decrease in LVDT 1-2-3 was 0.00 mm, the loading test of 3,956.00 kg and 3,887.00 kg obtained the value of the decrease in LVDT 1 and LVDT 3 of 0.00 mm and LVDT 2 obtained a decrease value of -0.01 mm which indicates a bending / deflection occurs in the approach slab structure, but it is still safe and no structure is required..

MightyPix at the LHCb Mighty Tracker — verificationof an HV-CMOS pixel chip's digital readout

MightyPix is a high voltage complementary metal-oxide-semiconductor (HV-CMOS) active pixel sensor, currently being developed for the Mighty Tracker, an upgrade proposed for LHCb in anticipation of the High Luminosity LHC. To ensure that MightyPix will be able to handle the particle hit rates at the Mighty Tracker, which are expected to reach 17 MHz/cm2, simulations of the chip's digital readout mechanism were performed. Using simulated particle hits the chip's performance within the LHCb environment is characterised. For this, a behavioural model of the first prototype, MightyPix1, representing the analogue pixel matrix, together with the synthesised digital logic is used. Simulation results show the MightyPix1 readout mechanism having an efficiency over 99 % up to 20 MHz/cm2. The bottleneck was found to be the speed at which the hits are read out. This yielded new design ideas to improve the readout for MightyPix2, leading to an efficiency of over 99 % up to 30 MHz/cm2.

Consequence of anisotropy on flocking: the discretized Vicsek model

We numerically study a discretized Vicsek model (DVM) with particles orienting in q possible orientations in two dimensions. The study investigates the significance of anisotropic orientation and microscopic interaction on macroscopic behavior. The DVM is an off-lattice flocking model like the active clock model (ACM; Chatterjee et al 2022 Europhys. Lett. 138 41001) but the dynamical rules of particle alignment and movement are inspired by the prototypical Vicsek model (VM). The DVM shows qualitatively similar properties as the ACM for intermediate noise strength where a transition from macrophase to microphase separation of the coexistence region is observed as q is increased. But for small q and noise strength, the liquid phase appearing in the ACM at low temperatures is replaced in the DVM by a configuration of multiple clusters with different polarizations, which does not exhibit any long-range order. We find that the dynamical rules have a profound influence on the overarching features of the flocking phase. We further identify the metastability of the ordered liquid phase subjected to a perturbation.

Тождество теоретического и экспериментального подходов при прототипировании региональных проектов в сфере здравоохранения.

The formalization of processes for finding the parameters of a prototype model of a medical organization implementing a regional project is based on the unity of theoretical and experimental approaches. The purpose of the study was an attempt to create a unified and harmonious theoretical foundation for conducting an experiment in order to find a prototype of a medical organization implementing projects at the regional level. In order to reveal the potential for solving technological problems facing project stakeholders, formalization of approaches was carried out for experimentally finding the parameters of a prototype project for a medical organization implementing a regional project. Materials and methods. The research materials were the works of Russian and foreign scientists related to the issues of mathematical modeling and prototyping. In order to study the research question, more than 30 literature sources were analyzed, directly or indirectly addressing the problems of practical application of object prototyping methods. Methods of system analysis, comparison, systemic and integrated approaches, as well as mathematical modeling methods were used. Results. Complex, multi-level systems when implementing projects in the regions are poorly structured and, accordingly, not fully formalized, so the experimental approach is especially relevant when searching for prototyping tools. The degree of formalization of approaches to searching for a prototype of a project object depends on the completeness (saturation) of processes, their structure and the degree of correlation of process data in a medical organization to process performers, operations, and management objects. Prototyping is designed to optimize the procedures for planning, analyzing and processing the results of research data based on a mathematical foundation. The prototyping technique is primarily aimed at saving resources (labor, financial, time) as much as possible, therefore, justification of the need and mastery of mathematical foundations in formalizing approaches to prototyping is a fundamental factor and the purpose of this discussion. Findings. System analysis using the unity of theoretical and experimental approaches, in the author’s opinion, will allow for a deeper synthesis of knowledge about the potential possibilities for forming decisions through a prototype of a medical organization implementing projects in the healthcare industry.

IQuantum: A toolkit for modeling and simulation of quantum computing environments

SummaryQuantum computing resources are predominantly accessible through cloud services, with a potential future shift to edge networks. This paradigm and the increasing global interest in quantum computing have amplified the need for efficient, adaptable resource management strategies and service models for quantum systems. However, many limitations in the quantum resources' quantity, quality, availability, and cost pose significant challenges for conducting research in practical environments. To address these challenges, we proposed iQuantum, a holistic and lightweight discrete‐event simulation toolkit uniquely tailored to model hybrid quantum computing environments. We also present a detailed system model for prototyping and problem formulation in quantum resource management. Through rigorous empirical validation and evaluations using large‐scale quantum workload datasets, we demonstrate the flexibility and applicability of our toolkit in various use cases. iQuantum provides a versatile environment for designing and evaluating quantum resource management policies such as quantum task scheduling, backend selection, hybrid task offloading, and orchestration in the quantum cloud‐edge continuum. Our work endeavors to create substantial contributions to quantum computing modeling and simulation, empowering the creation of future resource management strategies and quantum computing's broader applications.

Web-Based Laundry Service Information System Using Rapid Application Development Method

In the current era of digitalization, human needs for time make all household and community activities want to be practical, especially such as washing clothes. Especially for people who have busy demands and do not have time to wash clothes, dry and iron so they prefer to entrust their laundry to the services of a laundry or laundry, for the reason of saving time and to concentrate more on completing their work. This study aims to design a service information system that can help in conveying information about laundry and knowing the valid and efficient website-based laundry service information system. This form of research uses the Rapid Application Development method to produce a product and uses a prototype model to design the system. This Information System is the best solution for solving problems in the management of laundry service systems. With the use of computer data technology that is managed becomes faster, reducing time inefficiencies and reducing the occurrence of errors in processing data.

Parameter Analysis of Vibration Damping and Energy Harvesting Performance of Bionic Suspension

The traditional damping structure usually transfers the vibration energy into heat, dissipated and without harvesting. But for harsh environments, such as military activities and lunar probe patrols, the vibration energy can be extracted, and realize the purpose of self-power condition monitoring. In this paper, a bionic suspension with energy-harvesting property is proposed based on bionic configurations. The bionic suspension adopts a symmetrical three-link arrangement to simulate the arrangement of bones, and a horizontal spring to simulate muscles. The micro-generator embedded in the knee joint provides the main damping force for the overall structure and also plays a role in harvesting energy. The dynamic equation and virtual prototype model are established, and the effects of parameter changes on the performance of vibration reduction and energy harvesting are analyzed, and the relationship between the two is revealed. The results show that the parameters to achieve their respective optimal performance are not consistent in the trend, though it is difficult to obtain the best performance of both, it can be designed according to the specific functions to be satisfied. This structure can be utilized as a general model for vibration reduction and energy harvesting scenarios, providing new ideas for self-powered sensing and condition monitoring configurations.