Media interaktif berbantuan SWISHMAX-4: desain dan implementasinya dalam pembelajaran

Tujuan penelitian ini adalah untuk mendesain dan mengimplementasikan media pembelajaran interaktif berbantuan Swishmax-4 materi garis lurus. Jenis penelitian yang digunakan dalam penelitian ini adalah Research and Development, dengan menggunakan model penelitian ADDIE. Teknik pengumpulan adalah Wawancara, Angket dan Tes. Data penelitian berupa hasil pretest dan posttest dianalisis menggunakan rumus effect size. Hasil penelitian menunjukkan bahwa media pembelajaran yang dikembangkan sangat valid, ahli materi memperoleh skor rata-rata 3,73 dan ahli media skor rata-rata 3,27. Respon mahasiswa memperoleh hasil skor rata-rata 3,49 pada uji skala kecil dan 3,42 pada uji skala besar dengan criteria keduanya sangat menarik. Keefektifan media pembelajaran diuji mengunakan effect size yaitu dengan perolehan skor efektifitas  0,78 dengan kategori efektivitas sedang

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system

Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate

This study compares different methods to predict the simultaneous effects of conductive and radiative heat transfer in a Polymethylmethacrylate (PMMA) sample. PMMA is a kind of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical analysis. Computer implementation was obtained for the numerical solution of governing equation with the implicit finite difference method in the case of discretization. Kirchhoff transformation was used to get data from a non-linear equation of conductive heat transfer by considering monochromatic radiation intensity and temperature conditions applied to the PMMA sample boundaries. For Deep Neural Network (DNN) method, the novel Long Short Term Memory (LSTM) method was introduced to find accurate results in the least processing time than the numerical method. A recent study derived the combined heat transfers and their temperature profiles for the PMMA sample. Furthermore, the transient temperature profile is validated by another study. A comparison proves a perfect agreement. It shows the temperature gradient in the primary positions that makes a spectral amount of conductive heat transfer from a PMMA sample. It is more straightforward when they are compared with the novel DNN method. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems. By analyzing the results from the numerical solution it can be understood that the conductive and radiative heat flux is similar in the case of gradient behavior, but it is also twice in its amount approximately. Hence, total heat flux has a constant value in an approximated steady state condition. In addition to analyzing their composition, ROC curve and confusion matrix were implemented to evaluate the algorithm performance.Comment: 15 pages, 11 figure

Prediction of compression index of fine-grained soils using a gene expression programming model

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models

Thermodynamic assessment and multi-objective optimization of performance of irreversible Dual-Miller cycle

In this study, a new series of assessments and evaluations of the Dual-Miller cycle is performed. Furthermore, the specified output power and the thermal performance associated with the engine are determined. Besides, multi-objective optimization of thermal efficiency, ecological coefficient of performance (ECOP) and ecological function (Eun) by means of NSGA-II technique and thermodynamic analysis are presented. The Pareto optimal frontier obtaining the best optimum solution is identified by fuzzy Bellman-Zadeh, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved. For the results of the condition that (n k) the best point has been LINMAP and TOPSIS answer. The thermal efficiency for this point has been 0.5385. Also, ECOP and Eun have been 1.6875 and 279.7315, respectively. Furthermore, the errors are examined through comparison of the average and maximum errors of the two scenarios

Review of soft computing models in design and control of rotating electrical machines

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Learning and intelligent optimization for material design innovation

Learning and intelligent optimization (LION) techniques enable problem-specific solvers with vast potential applications in industry and business. This paper explores such potentials for material design innovation and presents a review of the state of the art and a proposal of a method to use LION in this context. The research on material design innovation is crucial for the long-lasting success of any technological sector and industry and it is a rapidly evolving field of challenges and opportunities aiming at development and application of multi-scale methods to simulate, predict and select innovative materials with high accuracy. The LION way is proposed as an adaptive solver toolbox for the virtual optimal design and simulation of innovative materials to model the fundamental properties and behavior of a wide range of multi-scale materials design problems