Taiwan Association of Engineering and Technology Innovation: E-Journals
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A Review on Advances in Automated Plant Disease Detection
Get PDFPlant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images
Mixed Noise Removal by Processing of Patches
Get PDFSonar images are degraded by mixed noise which has an adverse impact on detection and classification of underwater objects. Existing denoising methods of sonar images remove either additive noise or multiplicative noise. In this study, the mixed noise in sonar images, the additive Gaussian noise and the multiplicative speckle effect are handled by the data adaptive methods. A patch based denoising is applied in two phases to remove the additive Gaussian and multiplicative speckle noises. In the first phase, the adaptive processing of local patches is used to remove the additive Gaussian noise by exploiting the sonar image local sparsity. The PCA and SVD methods are used for denoising the noisy image patches and blocks of patches. In the second phase, the weighted maximum likelihood denoising of the nonlocal patches reduces the speckle effect by exploiting the non-local similarity in a probability distribution. Experiments on side scan sonar images are conducted and the results show the importance of removing both the additive and multiplicative components from the sonar images
The Impact of Coating Ingredients on the Aging Resistance of Topcoat Paints by Model Trees
Get PDFTopcoat paint is mainly composed of resin and pigment and hence its quality highly depends on the type and proportion of these two ingredients. This study aims at testing the formula of the topcoat paint for finding one that can achieve better quality for anti-aging. Various formulas of paint are applied on boards that will be put into ultraviolet accelerated test machines to simulate weathering tests. The gloss and color, before and after the tests, are collected and numerical prediction method M5P is used to grow model trees for discovering the key factors affecting aging. Based on the structure and the linear regression models in the trees, a better topcoat paint should be composed of a high proportion of resin and generally a low proportion of pigment. Good types of resin and pigment are also identified for keeping color and gloss
An Experimental Study of Plastic Waste as Fine Aggregate Substitute for Environmentally Friendly Concrete
Get PDFDecomposing plastics, including plastic bottles, is a very difficult process because it takes 50-100 years. Every year, the use of plastic bottles is increasing, but only few people are willing to treat plastic bottle waste. In this study, plastic bottle waste is used as a substitute of fine aggregate and shaped in such a way to have a sand-like gradation. The variations of graded plastic bottle waste are 0%, 5%, 10%, and 12%. The test objects for each variation consist of three specimens. Data are analyzed by using regression and classical assumption test with SPSS program. The results of the data analysis show that there is a simultaneous effect on the compressive strength with variations in plastic waste substitution. The compressive strength decreases with the increase in the percentage of plastic added. Maximum compressive strength is at the variations of 0% and 5% with19.192 MPa and 16.414 MPa, respectively
Design Philosophy for Buildings’ Comfort-Level Performance
Get PDFThe data reported by Japan Meteorological Agency (JMA) show that the fatal casualties and severe injuries are due to heavy shaking during massive earthquakes. Current earthquake-resistant building standards do not include comfort-level performance. Hence, a new performance design philosophy is proposed in this research to evaluate the quantitative effect of earthquake-induced shaking in a building. The earthquake-induced response accelerations in a building are analysed, and the response accelerations related with the characteristic property of the building are used to evaluate the number of Seismic Intensity Level (SIL). To show the indispensability of the newly proposed comfort-level design philosophy, numerical simulations are conducted to evaluate the comfort level on different floors in a building. The results show that the evaluation of residents’ comfort levels should be considered in the current earthquake-resistant building design codes
A Hybrid Simulation of Converter-Interfaced Generation as the Part of a Large-Scale Power System Model
Get PDFThis study aims to propose an alternative hybrid approach to model renewable energy sources (RESs), which provide the most reliable results in comparison with the existing simulating tools. Within the framework of this approach, a specialized hybrid processor for modeling converter-interfaced generation (CIG) is developed. This study describes its structure and validation in the test system by comparing the results with commercial modeling tools, and also presents experimental studies of its operation as parts of the practical power system. The results obtained confirm the adequacy of the developed tools
Determination of the Compressive Strength of Concrete Using Artificial Neural Network
Get PDFThe objective of the work is to estimate the compressive strength of concrete by means of the application of Artificial Neural Networks (ANNs). A database is created with design variables of mixtures of 175, 210, and 280 kgf/cm², which are collected from certified laboratories of soil mechanics and concrete of the city of Jaen. In addition, Weka software is used for the selection of the variables and Matlab software is used for the learning, training, and validation stages of ANNs. Five ANNs are proposed to estimate the compressive strength of concrete at 7th, 14th, and 28th day. The results show that the networks obtain the average error of 4.69% and are composed of an input layer with eleven neurons, two hidden layers with nine neurons each, and the compressive strength of concrete as the output. This method is effective and valid for estimating the compressive strength of concrete as a non-destructive alternative for quality control in the construction industry
Free Vibration of Thick FGM Plates under TSDT and Thermal Environment
Get PDFThree parameters of thermal environment, varied calculated shear correction, and third-order shear deformation theory (TSDT) of displacement are important in the frequency study. These three effects have been studied on the non-dimensional and dimensional frequencies of thick FGM plates. An additional c1 displacement term in nonlinear coefficient of TSDT is used to present the frequency of vibration into the simply homogeneous equation of thick FGM plates. The determinant of the coefficient matrix containing the c1 displacement term in dynamic differential equilibrium equations can be derived into the five degree polynomial free vibration equation. The non-dimensional and dimensional of natural frequency can be obtained. The effects of plate thickness, temperature of environment and power law index of FGM on the non-dimensional and dimensional frequency of FGM plates are investigated.
 
Supervised Learning Based Classification of Cardiovascular Diseases
Get PDFDetecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists
Impact of Flax Fiber Reinforcement on Mechanical Properties of Solid and Foamed Geopolymer Concrete
Get PDFThe main objective of this study is to develop the advanced composites for civil engineering applications as material for the building industry, especially for an insulation purpose. The research processes include several steps. Firstly, the prototype elements, such as bricks and elevation elements were performed from eco-friendly composite -flax fiber reinforced geopolymer. The elements were designed to take into consideration for environment. Geopolymers are environmentally friendly, sustainable, and resource efficient, including energy demand. Next, the wall was built from these elements and exposed during the three months in a relevant environment. The main conclusion of the research is that the kind of fibers is important for the mechanical properties of the composite, including the fact that for those different fibers could be more beneficial for different raw materials, giving higher strength properties. The significant influence on the mechanical properties of the composites has the adhesion between fiber and material used as a matrix. The adhesion depends among others on the previous treatment of the fibers