Decision-making model based multilayer perceptrons for estimation of optimum design properties for truss structure

Many branches of the structural engineering discipline have many problems, which require the generating an optimum model for beam-column junction area reinforcement, weight lightening for members such a beam, column, slab, footing formed as reinforced concrete, steel, composite, and so on, cost arrangement for any construction, etc. With this direction, in the current study, a structural model as a 5-bar truss is handled to provide an optimum design by determining the fittest areas of bar sections. It is aimed that the total bar length is minimized through population-based metaheuristic algorithm as teaching-learning-based optimization (TLBO). Following, the decision-making model is developed via multilayer perceptrons (MLPs) by performing an estimation application to enable directly foreseen of the optimal section areas and total length of bars, besides, the approximation and correlation success are evaluated via some metrics. Thus, determination of the real optimal results of unknown and not-tested designs can be realized with this model in a short and effective time.

Rockburst Interpretation by a Data-Driven Approach: A Comparative Study

Accurately evaluating rockburst intensity has attracted much attention in these recent years, as it can guide the design of engineering in deep underground conditions and avoid injury to people. In this study, a new ensemble classifier combining a random forest classifier (RF) and beetle antennae search algorithm (BAS) has been designed and applied to improve the accuracy of rockburst classification. A large dataset was collected from across the world to achieve a comprehensive representation, in which five key influencing factors were selected as the input variables, and the rockburst intensity was selected as the output. The proposed model BAS-RF was then validated by the dataset. The results show that BAS could tune the hyperparameters of RF efficiently, and the optimum model exhibited a high performance on an independent test set of rockburst data and new engineering projects. According to the ensemble RF-BAS model, the feature importance was calculated. Furthermore, the accuracy of the proposed model on rockburst prediction was higher than the conventional machine learning models and empirical models, which means that the proposed model is efficient and accurate.

Characteristics of raw water sources and analysis of the optimal model of the mixing process with parameter design in clean water pump installations

The quality characteristics of raw water sources in the regional integrated drinking water supply system (SPAM) of Banjarbakula were investigated and found to maintain the supply of drinking water quantity and quality in accordance with drinking water standards. The optimum model for the mixing process of raw water and poly aluminum chloride (PAC) and pump stroke for the input of water sources from rivers to obtain a composition setting that is in accordance with the raw water sources of each region in the region was selected and determined. So the optimum parameter setting model between alum water, raw water and pump stroke for each raw water source is known and is regionally integrated as a result of a comprehensive study. The integration of Taguchi parameter design and response surface can complement each other and become two methods that go hand in hand in the process of optimizing clean water products. Parameter design provides a very practical optimization step, the basis for this formation refers to the factorial fractional experimental design. However, the absence of statistical assumptions that follow the stages of analysis makes this method widely chosen by researchers and practitioners. With the experimental design of the raw water mixing process, turbidity such as 5 lt/sec, 10 lt/sec, 15 lt/sec, 20 lt/sec and 25 lt/sec and % PAC concentration 5 ppm, 10 ppm, 15 ppm, 20 ppm and 25 ppm with a pump installation stroke of 5 %, 10 %, 15 %, 20 % and 25 % were used. In the process of adding PAC, always pay attention and observe the behavior of the attractive force of the floating particles (flock). The particles were then subjected to SEM (scanning electron microscopy) to determine the dimensions of the flock grains deposited

An evaluation of Quasi-Moment-Method calibrated pathloss models for Benin City Nigeria

This paper introduces the Quasi-Moment-Method (QMM) as a novel radiowave propagation pathloss model calibration tool, and evaluates its performance, using field measurement data from different cellular mobile communication network sites in Benin City, Nigeria. The QMM recognizes the suitability of component parameters of existing basic models for the definition of ‘expansion’ and ‘testing functions’ in a Galerkin approach, and simulations were carried out with the use of a FORTRAN program developed by the authors, supported by matrix inversion in the MATLAB environment. Computational results reveal that in terms of both Root Mean Square (RMS) and Mean Prediction (MP) errors, QMM-calibrated models performed much better than an ‘optimum’ model reported for the NIFOR (Benin City), by a recent publication. As a matter of fact, the QMM-calibrated COST231 (rural area) model recorded reductions in RMS error of between 31.5% and 71% compared with corresponding metrics due to the aforementioned ‘optimum’ model. The simulation results also revealed that of the five basic models (COST231-rural area and suburban city, ECC33 (medium and large sized cities), and Ericsson models) utilized as candidates, the two ECC33 models, whose performances were consistently comparable, represented the best models for QMM-model calibration in the Benin City environments investigated.

Studies Based on CFD Behaviour of Aerofoil and Regression Analysis

The performance of an aerofoil depends upon the angle of attack, leading-edge radius, surface modifications, etc. The aerofoil which has a broader range of attack angle and surface area is responsible for the upliftment in the performance of the aerofoil. The present work deals with the evaluation of the aerofoil spread with dimples over the active surface. The positions and area of spread are modified accordingly and evaluated for the velocity and pressure lineation. The aerofoil with 30% dimples over the active surface is found to possess higher values for the required intents of velocity and pressure at an inlet velocity of 9 m/s. The optimum model with better lineation values is further evaluated for the co-efficient of lift and drag to propose the best design. The best result is obtained at an aerofoil of NACA 8412 series with 30% dimples extension at the rear end placed at 15° angle of attack and the regression analysis is done for the coefficient of lift values.

A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants

Agriculture is one of the primary occupations in many countries. Tomatoes are grown by many farmers in countries where the water resource is available in abundance. Improper methods of cultivation and failure to identify the diseases when it is in the nascent stage results in the reduction of crop yield thus affecting the outcome of cultivation. This paper proposes a novel method of early identification of diseases in tomato plants by making use of convolutional neural networks (CNN) and image processing. Dataset from an open repository was considered for training and testing and the algorithm was capable of identifying nine different varieties of diseases that affect the tomato plant at its early stages. The images of tomato leaves were fed for identification through processing and classification. An optimum model was developed by analyzing various architectures of CNN including the VGG, ResNet, Inception, Xception, MobileNet and DenseNet. The performance of each of these architectures was compared and various metrics like the accuracy, loss, precision, recall and area under the curve (AUC) were analyzed.

A Hierarchical Autonomous Driver for a Racing Car: Real-Time Planning and Tracking of the Trajectory

The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control(MPC) logic and solved as Linear Programming (LP) by Sequential Convex Programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car’s GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduction of the lap time of about 1 s compared to the offline optimizer and reproducibility of the performance obtained by the driver.

Estimating SARS-COV-2 Exposure Indoors in Delhi Given Outdoor Pollution Metrics Using Machine Learning

The Global Burden of Disease journal by the Lancet(Ritchie and Roser, 2013) and states that one million deaths have occurred from 1990 to 2017 due to air pollution. In 2018, the WHO estimated a death toll of 3.8 million due to indoor pollution(WHO,2018). In these times of the pandemic, it is quintessential for countries like India, with a huge population and high levels of pollution, to take severe measures for controlling pollution. The 2020 US Policy Report in the Lancet(2020) affirmed that there is a positive correlation between the PM2.5 or PM10 particles concentration and COVID-19 infection as the virus uses the particulate matter as a piggyback. The case study here, is based on the Indian urban locality and aims to analyze and estimate the correlations between PM2.5 particles, the AQI, weather conditions and COVID-19 particles using Machine Learning models. The optimum model is also to be used for predicting the outdoor AQI and Covid-19 infection rates in the suburban localities of northwestern Delhi and the data so obtained, would aid to calculating ,and extrapolating the mortality probability due to Covid-19 infection, indoors, in the metropolitan cities of India, like Delhi.

Modeling and Forecast of COVID-19 Vaccinations Population Size in China: A Non-Clinical Study

BACKGROUNDSince its outbreak in December 2019, severe acute respiratory syndrome coronavirus-2, the virus responsible for the COVID-19 pandemic, has considerably affected the worldwide population. Health authorities and the medical community identify vaccines as an effective tool for managing public health.METHODSIn this study, the autoregressive integrated moving average (ARIMA) model built-in Python was adopted to establish the COVID-19 vaccination forecast model. In this study, the sample data were selected from the Our World in Data website. COVID-19 vaccinations administered daily in China from December 16, 2020 to March 21, 2021 were analyzed to establish an autoregressive integrated moving average (ARIMA) model.RESULTSThe built-in ARIMA module function of Python was used, and the optimum model was ARIMA (3, 2, 3) according to the established time series analysis. The analysis showed that the predicted COVID-19 vaccination uptake supplemented well with the actual values with a small relative error.CONCLUSIONSThis indicated that the ARIMA(3, 2, 3) model could be used to forecast the number of COVID-19 vaccinations in China.