Proposed Design Model Research Articles

Axial Compression Performance of Concrete-Filled Steel Tubular Columns with Different D/t Ratios

The impacts of three diameter/thickness (D/t) ratios (21.22, 25.46, and 31.83) and concrete strengths (40 N/mm2, 50 N/mm2, and 60 N/mm2) on the strength capabilities of concrete-filled steel tubular (CFST) columns are investigated in this study. The central composite design (CCD) of the response surface methodology (RSM) was used to design the trials in order to complete the tests in a cost-effective manner. 13 (9 distinct tests) columns were evaluated according to the CCD experimental design, and the failure mode of the specimens, load–deformation behavior, and ultimate strength capacity were investigated. Concrete strength improves, resulting in a decrease in steel tube confinement on the core. Because the steel tube longitudinal compressive stress (fsl) increases as the D/t ratio lowers, the confinement is reduced by inhibiting the circumferential tensile stress (fsc). The Reynolds stress model’s, analysis of variance (ANOVA), Pareto chart, and contour plot demonstrated that the column D/t ratio, rather than the in-filled concrete strength, has a considerable impact on the CFST column’s strength capability. The proposed design models in different international codes and literature were evaluated for their effectiveness in predicting the strength capacities of CFST columns subjected to axial compression load. Using regression analysis, a simple design model was suggested to predict the axial strength capacities of CFST short columns, taking into account material strength and column shape. In comparison to other existing and suggested design models, the proposed design model of the present study delivers a more accurate and stable forecast.

"Food Village": An Innovative Alternative Food Network Based on Human Scale Development Economic Model.

Although the different alternative food networks (AFNs) have experienced increases worldwide for the last thirty years, they are still unable to provide an alternative capable of spreading on a large scale. They in fact remain niche experiments due to some limitations on their structure and governance. Thus, this study proposes and applies a design method to build a new sustainable food supply chain model capable of realizing a “jumping scale”. Based on the theoretical and value framework of the Civil Economy (CE), the Economy for the Common Good (ECG), and the Development on a Human Scale (H-SD), the proposed design model aims to satisfy the needs of all stakeholders in the supply chain. Max-Neef’s Needs Matrix and Design Thinking (DT) tools were used to develop the design model. Applying the design method to the food chain has allowed us to develop the concept of the “Food Village”, an innovative food supply network far from the current economic mechanisms and based on the community and eco-sustainability.

Simulation of interaction of production systems of industrial enterprises and business groups

The article considers the system of industrial enterprises constituting a business group, jointly producing some final product, supplying it with their private products. Since, in general, the capacities of enterprises are different, their products are not produced in proportional volumes, which simultaneously leads to both overstocking and the formation of a shortage of individual components of the final product. This leads to a decrease in the overall efficiency of the business group and to the instability of its individual enterprises. In the study, the task has been set to develop a mathematical model and a mechanism for a coordinated and fairly evaluated activity of enterprises using the basic concepts of the theory of active systems. The proposed model design is based on the requirements: private products must be supplied by enterprises in the required proportions, and penalties for failure to complete tasks must be characterized by a single penalty coefficient. In particular, in the development of well-known approaches to reconcile the conflicting interests of two different economic entities, it is proposed:– to clarify the area of agreed decisions by expanding it, taking into account not only the possibility of exceeding the optimal task for the enterprise, but also its underestimation;– to form a criterion for assigning penalty coefficients, as the equality of the lower value of the region of feasible solutions of a stronger enterprise and the upper value of the region of feasible solutions of a weaker enterprise.The solution of the problem is generalized to the case of an arbitrary number of enterprises. A general algorithm for solving the problem has been developed, including determining two critical enterprises, calculating the allowable (agreed) volume of product output for them, calculating the corresponding penalty coefficient and the values of output of individual products for each enterprise. An illustrative example has been given.

Axial compressive behaviour of CFST column externally reinforced by CFRP strips – A design of experiments approach

The purpose of this research is to investigate the influence of CFRP strips wrapping scheme (partial wrapping) in strengthening of CFST column subjected to axial compression. To complete the experimental investigation in an economical way, the central composite design (CCD) of the response surface methodology (RSM) was used to design the experiments. Based on the CCD experimental model, 9 unique tests were performed and the failure mode of the specimens, load–deformation behaviour, ultimate strength and ductility behaviour were studied. The test results showed that externally bonding CFRP strips is an effective approach to restraining axial deformation and enhancing the ultimate capacity of the column under compression. By bonding three layers of CFRP strips, the deformation of the columns was restricted to a maximum of 55.78% and 50.27% compared to the column bonded with one layer, when set at spacings of 20 mm and 30 mm, respectively. The ANOVA, Pareto chart and contour plot of RSM revealed that the layers of the CFRP wrapping scheme was more significant than the strips spacing in increasing the strength capacity of the CFST column. The suitability of design models proposed in various literatures for predicting the strength capacities of CFST columns strengthened with CFRP wrapping scheme was also examined. A simple design model, derived considering the column geometry and material strength of column and CFRP composites, was proposed to predict the axial strength capacities of CFST short columns confined by CFRP strips by using regression analysis. The proposed design model provides a more accurate and reasonably stable prediction compared with other existing design models.

Maritime search and rescue probability of containment model design and simulation

Abstract In maritime search and rescue operations, in order to improve the success rate of search and rescue, it is necessary to quickly determine the search area after target drift according to the existing accident and Marine environment information, and establish the probability of containment model, to provide information support for subsequent search and rescue planning. AP98 wind pressure drift model is used to model the drowning person at sea, and Monte Carlo simulation is used to simulate the floating situation of the drowning person. A large number of initial hypothetical points were arranged, the factors affecting the drift trajectory were quantitatively analyzed, and the probability density of the hypothetical points in the search area after a certain period of time was counted to form the inclusive probability model. Matlab software is used to test the model in different scenarios to prove the effectiveness of the proposed design model.

Predicting shear strength of FRP-reinforced concrete beams using novel synthetic data driven deep learning

Machine learning algorithms have emerged as a powerful technique to predict the engineering properties of composite materials and structures where traditional statistical methods have resulted in poor accuracy and high uncertainty. However, the lack of reliable and comprehensive experimental data hinders developing high-throughput computational models. To mitigate such a limitation, this study deploys the state-of-the-art tabular generative adversarial network (TGAN) to generate synthetic data for training generalized ML models. ‘Train on Synthesized - Test on Real’ approach was used to pioneer a novel framework for predicting the shear capacity of FRP-reinforced concrete beams. Accordingly, the models were trained using 8816 synthesized design data and tested using 304 real experimental data. The TGAN approach exhibited tremendous potential in generating credible data for training robust machine learning models, achievingR2of 0.96 when tested on the entire experimental dataset. Furthermore, a Bayesian optimization was performed on the extensive synthesized data to propose a new predictive shear strength equation. Results demonstrate that the proposed design model attained superior accuracy and vastly outperformed both existing design code provisions and empirical and theoretical models in the literature. The TGAN technique could transcend the lack of available experimental datasets in engineering problems via synthetizing numerous plausible data points to enhance the prediction accuracy and generalization ability of machine learning models.

Experimental and statistical modelling on copper recovery from low-grade mixed sulphide-oxide ore by direct leaching

ABSTRACT This study aims to investigate the recovery of copper from a low-grade source of an ore deposit in sulphuric acid solution. In this study, a full-factorial statistical design model and Artificial Neural Networks (ANN) are applied to develop an analytical approach to understand the dissolution parameters better. Temperature, time, particle size, and acid concentration were selected as the leaching parameters. The experimental design set by the full-factorial model was again trained with the ANN model to compare the predicted results. The correlation coefficients (R2) in both the models are 0.9962 and 0.99815, respectively, indicating both the models offered a well-aligned explanation for the variability of the experimental values to the predicted values. Therefore, the outcome of this study demonstrates that the proposed design model results are well-aligned with the experimental results, and 88% of copper metal can be recovered at a condition of 120 minutes, 90°C temperature, the particle size of -75 + 63 µm, and acid concentration of 1M.

A New PIFA Antenna For Future Mobile and Wireless Communication

In this work, a new antenna will be presented in the form of F-inverted working at 28Ghz frequency is proposed for 5G wireless com- munications. This antenna has been designed based on an FR-4. with shaped slots on the patch and interdigital slots in the ground for band- width enhancement. The PIFA antenna has an operating frequency of 28 GHz with S11,-39.0885 dB, while the peak gain is 7.85dBi. Therefore, it is suitable for applications of 5G wireless communications with good matching and return loss characteristics. The proposed design model and performance evaluation of the PIFA antenna has been executed by CST Microwave Studio and HFSS.

Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition.

In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available.

MICROSTRIP LINE-FED BASED QUAD-PORT WIDEBAND MIMO ANTENNA FOR K BAND APPLICATION

In this communication, a compact (30×30×1.6 mm3) microstrip line fed loaded quad-port and T-shaped wideband MIMO (Multiple-Input Multiple-Output) antenna for K band (18-27GHz) application is introduced. Here three simulated antennas (Antenna-1, Antenna-2 and Antenna-3) are analytically inspected in expressions of simulated gain, return loss (|S11|, |S22|, |S33| and |S44|), total active reflection coefficient (TARC), isolation (mutual coupling), diversity gain (DG), radiation efficiency, radiation pattern and envelope correlation coefficient (ECC). Antenna 3 disclaimers have been elevated for chosen antenna considerations and performance. The proposed antenna has |S11|, |S22|, |S33| and |S44| not more than -10 dB and the percentage impedance bandwidth is 19.54% (19.48-23.70GHz), 18.74% (19.58-23.63), 16.31% (19.54-23.01GHz) and 18.27% (19.39-23.29GHz) at ports 1, 2, 3 and 4 respectively. Simulated isolation is < -15 dB, ECC obtained between ports 1-2, 1-3 and 1-4 is not more than 0.06, DG originated among the ports 1-2, 1-3 and 1-4 is in the range of 9.969-9.999 (dB), TARC < 0 dB and radiation efficiency of 60% is obtained. The proposed design model of antenna has been analyzed with HFSS simulation tool by ANSOFT version 13.

Central Courtyard Feature Extraction in Remote Sensing Aerial Images Using Deep Learning: A Case-Study of Iran

Central courtyards are primary components of vernacular architecture in Iran. The directions, dimensions, ratios, and other characteristics of central courtyards are critical for studying historical passive cooling and heating solutions. Several studies on central courtyards have compared their features in different cities and climatic zones in Iran. In this study, deep learning methods for object detection and image segmentation are applied to aerial images, to extract the features of central courtyards. The case study explores aerial images of nine historical cities in Bsk, Bsh, Bwk, and Bwh Köppen climate zones. Furthermore, these features were gathered in an extensive dataset, with 26,437 samples and 76 geometric and climatic features. Additionally, the data analysis methods reveal significant correlations between various features, such as the length and width of courtyards. In all cities, the correlation coefficient between these two characteristics is approximately +0.88. Numerous mathematical equations are generated for each city and climate zone by fitting the linear regression model to these data in different cities and climate zones. These equations can be used as proposed design models to assist designers and researchers in predicting and locating the best courtyard houses in Iran’s historical regions.

MODELLING OF LOSSLESS AND CONTACTLESS POWER TRANSFORMER USING IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM

The contactless power transmission is applicable recently for various real time applications like electric vehicle charging, in space travelling, and other distribution systems. The transformer winding has magnetic coupling with each phases; it analyze the primary and secondary winding for core structure of transformer. The conventional Loosely coupled transformer with mixed winding and electromagnetic shielding for contactless power transmission method used various traditional Genetic algorithm and Particle Swarm Optimization algorithms for power loss reduction. This increases the transmission time and it reduces the efficiency. In proposed methodology, the optimal model of contactless power transformer approach uses improved particle swarm optimization algorithm for reducing the power loss in grid connected PV module. The maximum power of solar panel is tracked by using MPPT algorithm and it is fed with the switching controller for reducing the overlap. Here in the IPSO, the frequency dependent scale selection algorithm selects the fitness frequency for optimizing the swarm particle positions to reduce the losses. From this, the impedance matching approach is used for eliminating the frequency scale splitting because it may cause over-coupled signal and the fixed mutual inductance also helps to transfer maximum power. This proposed approach improves the result of power transfer efficiency. Various analytical calculations, numerical simulations and experimental results are taken to address the loss diminution in contactless power transfer approach with better efficiency of power transmission than other existing approaches. Overall the proposed design model is done by MATLAB 2018a/Simulink. Keywords: Contactless power transfer: Frequency dependent; improved particle swarm optimization; optimal frequency scale selection; swarm position updates; PV module; MPPT; Charging station

A New Design Model of an MR Shock Absorber for Aircraft Landing Gear Systems Considering Major and Minor Pressure Losses: Experimental Validation

This work presents a novel design model of a magnetorheological (MR) fluid-based shock absorber (MR shock absorber in short) that can be applied to an aircraft landing gear system. When an external force acts on an MR shock absorber, pressure loss occurs at the flow path while resisting the fluid flow. During the flow motion, two pressure losses occur: the major loss, which is proportional to the flow rate, and the minor loss, which is proportional to the square of the flow rate. In general, when an MR shock absorber is designed for low stroke velocity systems such as an automotive suspension system, the consideration of the major loss only for the design model is well satisfied by experimental results. However, when an MR shock absorber is applied to dynamic systems that require high stroke velocity, such as aircraft landing gear systems, the minor loss effect becomes significant to the pressure drop. In this work, a new design model for an MR shock absorber, considering both the major and minor pressure losses, is proposed. After formulating a mathematical design model, a prototype of an MR shock absorber is manufactured based on the design parameters of a lightweight aircraft landing gear system. After establishing a drop test for the MR shock absorber, the results of the pressure drop versus stroke/stroke velocity are investigated at different impact energies. It is shown from comparative evaluation that the proposed design model agrees with the experiment much better than the model that considers only the major pressure loss.

PERFORMANCE MEASURE OF SERIES-PARALLEL PIEZOELECTRIC TRANSDUCER CIRCUITY FOR IMPROVEMENT OF OUTPUT VOLTAGE

This project presents a comparative study between power generation using piezoelectric transducers available in the literature and the proposed model design. The parallel and series-connected piezoelectric transducers arrangement in this project, when subjected to mechanical stress (footsteps), generates an electric charge. The power generated from piezoelectric transducers will be given as input to bridge rectifier and chopper for conversion to dc quantity and its amplification respectively. This energy will be stored in a Li-ion battery. The paper mainly highlights aspects of improvement of power generation by optimizing circuit design and adapting output enhancement and amplification. Comparative analysis of power generated from predecessor circuit designs and output voltage obtained from current circuit design is validated and presented

Design of Wireless Power Transfer System for Charging of Electronic Gadgets

Wireless charging of gadgets is one of the new emerging technologies in the world at present moment. The most common method used is wireless power transfer is by using the method of inductive coupling. Wireless power transfer is one of the simplest and inexpensive ways of charging as it eliminates the use of conventional copper cables and current carrying wires. The inductive coupling technique is used since currently it is the easiest method of wireless power transfer because of high efficiency and large amount of the energy transferred. Its versatility and range of applications the power transferred will be used to charge a battery with the aid of additional circuitry. This research work focuses on the study of wireless power transfer for the purpose of transferring energy at maximum efficiency within a small range or in the near field region. The motivation to this proposed design model is contact less mobile device charging about 5cm-100cm distance range, which can be used to charge the mobile electronic devices like mobile phones, laptops, smart phones wirelessly. This proposed design is very helpful in the pandemic situation to charge electronic gadgets contactless as well wirelessly in the hospitals, public transports like metro stations, educational institutions, and in office communication systems.

Experimentally verified behavior and confinement model for concrete in circular stiffened FRP-concrete-steel double-skin tubular columns

A hybrid double-skin tubular column (DSTC) is a modern composite column with concrete sandwiched between outer fibre-reinforced polymer (FRP) and inner steel tube. Recently, a new variation form of DSTC, called stiffened DSTC, has evolved with longitudinal stiffeners provided on the outer surface of the inner steel tube. The stiffened DSTCs have better composite action between the DSTC components and mitigate the negative effects of inward local buckling of the inner steel tube. Scant research or design guidelines from standard specifications are presently available on the compressive behavior of this variation form of DSTC. This paper proposes the first ever design-oriented compressive strength model for confined concrete in circular stiffened DSTCs. The proposed axial stress model is based on the experimental results of the present study and a similar test dataset available from the literature. The experimental test results consisted of both normal FRP and large-rupture-strain FRP outer tubes used in circular DSTCs. In total, twelve circular DSTC specimens were tested in this study, and stiffener characteristics including the quantity, configuration, dimensions (i.e., width and thickness), and shape of the stiffeners, were studied. The test results showed that stiffened DSTCs had an enhanced compressive strength of confined concrete (up to about 29% increase in this study), resulting in higher axial load capacity and more ductile behavior compared to DSTCs without stiffeners. The proposed design model for compressive stress performed reasonably well and depicted close agreement with the experimental results for circular DSTCs, using both normal and large-rupture-strain FRP-based outer tubes.

Modelling of Multiband Patch Antenna Based on I Shaped Slots on DGS for Wireless Applications

This paper presents the modelling of multiband micro strip patch antenna resonates between 2.3719GHz to 8GHz frequencies for WLAN & C– a band that comes under the radar communications applications. The proposed model design by using parasitic elements surrounded on square patch, etched with I-slots on the top of the patches well as on ground plane supported by low cast FR4 substrate, it is fed 50Ω micro strip to excite antenna. The parameters of an I-slot are optimized to achieve a multiband frequency of 2.371GHz, 4.5176GHz, 4.6583GHz, 5.1509GHz, 6.5578GHz, 7.0503GHz, & 7.8241GHz respectively. The simulated results show that the antenna has a maximum bandwidth of 6.023% bandwidth of having VSWR ≤ 2 as compared to the conventional antenna. Also, the antenna has a stable radiation pattern at all desired frequencies. The proposed model was designed and optimized by assigning variables to each component of the model. The patch antennas were simulated using HFSS.

High-efficiency asymmetric transmission of linear polarized waves based on tri-layered metamaterial structure

In this work, a broadband polarization converter in transmission mode based on tri-layered metamaterial structure is proposed to realize asymmetric transmission and linear polarization conversion simultaneously. The proposed design model consists of a hexapolar split ring structure and two orthogonal sub-wavelength gratings embedded in two dielectric substrates. The appropriate parameters of the unit cell are optimized and selected. Sequentially, a prototype is fabricated and measured in order to confirm performance of the proposed design structure. The simulation and measurement results consistently demonstrate that the conversion polarization transmission coefficient is greater than 0.9 within the frequency region of 4.516.7 GHz, which is equivalent to 115% of the relative bandwidth. Based on the Fabry–Perot-like cavity theory, the working mechanisms of this structure have been discussed in detail. Thus, the proposed design structure is expected high potential applications in irreversible electromagnetic devices.

Novel Joint Object Detection Algorithm Using Cascading Parallel Detectors

Object detection is an essential computer vision task that aims to detect target objects from an image. The traditional models are insufficient to generate a high-quality anchor box. To solve the problem, we propose a novel joint model called guided anchoring Region proposal networks and Cascading Grid Region Convolutional Neural Networks (RCGrid R-CNN), enhancing the ability of object detection. Our proposed model design is a joint object detection algorithm containing an anchor-based and an anchor-free branch in parallel and symmetry. In the anchor-based, we use nine-point spatial information fusion to obtain better anchor box location and introduce the shape prediction method of Guided Anchoring Region Proposal Networks (GA-RPN) to enhance the accuracy of the predicted anchor box. In the anchor-free branch, we introduce the Feature Selective Anchor-Free module (FSAF) to reduce the overlapping anchor boxes to obtain a more accurate anchor box. Furthermore, inspired by cascading theory, we cascade the new-designed detectors to improve the ability of object detection by setting a gradually increasing Intersection over Union (IoU) threshold. Compared with typical baseline models, we comprehensively evaluated our model by conducting experiments on two open datasets: Pascal VOC2007 and COCO2017. The experimental results demonstrate the effectiveness of RCGrid R-CNN in producing a high-quality anchor box.

MODEL PROPOSAL ON THE DETERMINATION OF STUDENT ATTENDANCE IN DISTANCE EDUCATION WITH FACE RECOGNITION TECHNOLOGY

The aim of this study is to present a model proposal on determining the student participation rate in synchronous courses given in Learning Management Systems (LMS). Especially in situations where equal opportunities cannot be provided or opportunities are limited, distance education provides benefits for learning anytime and anywhere (ubiquitous learning) with the support of educational technologies. When the literature is examined, thanks to distance education; It is seen that it offers a very advantageous teaching environment in terms of location, time, convenience in accessing the resources needed and cost-benefit. However, when the literature is analyzed, it is found that there is a problem in determining the participation levels and rates of students in the Learning Management Systems used in distance education. Students access the activity or course in LMS using text-based user information and passwords. Unfortunately, it is not possible to determine with the current LMS whether the participant is the real responsible person or he/she is actively/synchronously following the course. In this context, a design model has been presented using face recognition algorithms to determine attendance in distance education, to ensure more active participation and to increase success indirectly. In the proposed model, tests were made using special filters for image processing, and in cases where the number of samples was increased, more than 80% accuracy was provided. The proposed design model was developed in Visual Studio.Net platform and coded on C# programming language. SQL server is used as database management system and EmguCV library is used for the image processing stages.