scholarly journals Investigation of artificial neural network performance in the aerosol properties retrieval

2021 ◽  
Author(s):  
Nishi Srivastava ◽  
D. Vignesh ◽  
Nisheeth Saxena
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Performance ◽  
Computational Cost ◽  
Research Problem ◽  
Learning Rate ◽  
Computational Time ◽  
Research Fields ◽  
Artificial Neural ◽  
Number Of Iterations

Abstract Aerosols are an integral part of the earth's climate system and their effect on climate makes this field a relevant research problem. The artificial neural network (ANN) technique is an upcoming technique in different research fields. In the current work, we have evaluated the performance of an ANN with its parameters in simulating the aerosol's properties. ANN evaluation is performed over three sites (Kanpur, Jaipur, and Gandhi College) in the Indian region. We evaluated the performance of ANN for model's hyperparameter (number of hidden layers) and optimizer's hyperparameters (learning rate and number of iterations). The optical properties of aerosols from AERONET (AErosol RObotic NETwork) are used as input to ANN to estimate the aerosol optical depth (AOD) and Angstrom exponent. Results emphasized the need for optimal learning rate values and the number of iterations to get accurate results with low computational cost and to avoid overfitting. We observed a 23–25% increase in computational time with an increase in iteration. Thus, a meticulous selection of these parameters should be made for accurate estimations. The result indicates that the developed ANN can be utilized to derive AOD, which is not assessed at AERONET stations.

Analysis of artificial neural network performance based on influencing factors for temperature forecasting applications

2020 ◽  
Vol 26 (3) ◽  
pp. 209-223
Author(s):  
M. Madhiarasan ◽  
M. Tipaldi ◽  
P. Siano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Network Performance ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Research Fields ◽  
Temperature Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN)-based methods belong to one of the most growing research fields within the artificial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “influencing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artificial neural network-based applications. This paper provides a deep analysis about artificial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own specific applications with the aim of achieving better performance indexes.

An Artificial Neural Network Approach for Generating High-Resolution Designs From Low-Resolution Input in Topology Optimization

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Nicholas Napier ◽  
Sai-Aksharah Sriraman ◽  
Huy T. Tran ◽  
Kai A. James
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Topology Optimization ◽  
Structural Integrity ◽  
Computational Cost ◽  
Computational Time ◽  
Boundary Edge ◽  
Fine Mesh ◽  
Artificial Neural

Abstract We address a central issue that arises within element-based topology optimization. To achieve a sufficiently well-defined material interface, one requires a highly refined finite element mesh; however, this leads to an increased computational cost due to the solution of the finite element analysis problem. By generating an optimal structure on a coarse mesh and using an artificial neural network to map this coarse solution to a refined mesh, we can greatly reduce computational time. This approach resulted in time savings of up to 85% for test cases considered. This significant advantage in computational time also preserves the structural integrity when compared with a fine-mesh optimization with limited error. Along with the savings in computational time, the boundary edges become more refined during the process, allowing for a sharp transition from solid to void. This improved boundary edge can be leveraged to improve the manufacturability of the optimized designs.

scholarly journals Pengaruh Cahaya dan Kualitas Citra dalam Klasifikasi Kematangan Pisang Cavendish Berdasarkan Ciri Warna Menggunakan Artificial Neural Network

2021 ◽  
Vol 21 (1) ◽  
pp. 215-228
Author(s):  
Aditya Dwi Putro ◽  
Arief Hermawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Rate ◽  
Artificial Neural

Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu buah pisang. Tujuan dari penelitian ini adalah menganalisa pengaruh cahaya dan kualitas citra dalam mengklasifikasikan tingkat kematangan buah pisang berdasarkan ciri warna buah pisang di Kebun Pisang Cavendish kabupaten banyumas jawa tengah sesuai dengan SNI 7422:2009[1]. Pisang yang terdapat di Kebun Pisang Cavendish ini beraneka ragam kualitas, sebagai buah lokal yang memiliki nilai ekonomi tinggi dan memiliki potensi pasar yang masih terbuka luas, pisang menjadi salah satu komoditas buah-buahan yang dapat diandalkan. Permasalahan yang sering ditemukan selain resource dan ketelitian yakni kurang tepatnya dan kurang pengetahuannya karyawan dalam membedakan tingkat kematangan pisang terutama karyawan baru. Artificial Neural Network digunakan sebagai metode dalam proses pengklasifikasian. Dataset pada penelitian ini adalah 80 citra buah pisang yang diambil per tandan terdiri dari 40 tandan citra pisang Cavendish yang diambil di pagi hari dengan kualitas citra bagus 20 dan kualitas citra tidak bagus 20, 40 tandan citra pisang Cavendish yang diambil di sore hari dengan kualitas citra bagus 20 dan kualitas citra tidak bagus 20. Tingkat kematangan pisang pada penelitian ini yaitu mentah dan matang. pengujian menghasilkan Akurasi tertinggi dalam proses klasifikasi kategori buah pisang cavendish menggunakan epoch 5000, goal 0.0001 dan learning rate 0.1 dengan jumlah akurasi sebesar 100% dengan model trainlm dan waktu 1.6 detik.

Daily Surface Solar Radiation Prediction Mapping Using Artificial Neural Network: The Case Study of Reunion Island

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Peng Li ◽  
Miloud Bessafi ◽  
Beatrice Morel ◽  
Jean-Pierre Chabriat ◽  
Mathieu Delsaut ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Computational Time ◽  
Reunion Island ◽  
Surface Solar Radiation ◽  
Clear Sky ◽  
Réunion Island ◽  
Artificial Neural

Abstract This paper focuses on the prediction of daily surface solar radiation maps for Reunion Island by a hybrid approach that combines principal component analysis (PCA), wavelet transform analysis, and artificial neural network (ANN). The daily surface solar radiation over 18 years (1999–2016) from CM SAF (SARAH-E with 0.05 deg × 0.05 deg spatial resolution) is first detrended using the clear sky index. Dimensionality reduction of the detrended dataset is secondly performed through PCA, which results in saving computational time by a factor of eight in comparison to not using PCA. A wavelet transform is thirdly applied onto each of the first 28 principal components (PCs) explaining 95% of the variance. The decomposed nine-wavelet components for each PC are fourthly used as input to an ANN model to perform the prediction of day-ahead surface solar radiation. The predicted decomposed components are finally returned to PCs and clear sky indices, irradiation in the end for re-mapping the surface solar radiation's distribution. It is found that the prediction accuracy is quite satisfying: root mean square error (RMSE) is 30.98 W/m2 and the (1 − RMSE_prediction/RMSE_persistence) is 0.409.

scholarly journals Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hung Vo Thanh ◽  
Yuichi Sugai ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Recovery ◽  
Early Stage ◽  
Computational Time ◽  
Residual Oil ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
And Storage

Abstract Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

The use of genetic algorithm and self-updating artificial neural network for the inverse design of cabin environment

2016 ◽  
Vol 26 (3) ◽  
pp. 347-354 ◽  
Author(s):  
Tian-hu Zhang ◽  
Xue-yi You
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Computational Cost ◽  
The Self ◽  
Inverse Design ◽  
Artificial Neural ◽  
The Impact

The inverse process of computational fluid dynamics was used to explore the expected indoor environment with the preset objectives. An inverse design method integrating genetic algorithm and self-updating artificial neural network is presented. To reduce the computational cost and eliminate the impact of prediction error of artificial neural network, a self-updating artificial neural network is proposed to realize the self-adaption of computational fluid dynamics database, where all the design objectives of solutions are obtained by computational fluid dynamics instead of artificial neural network. The proposed method was applied to the inverse design of an MD-82 aircraft cabin. The result shows that the performance of artificial neural network is improved with the increase of computational fluid dynamics database. When the number of computational fluid dynamics cases is more than 80, the success rate of artificial neural network increases to more than 40%. Comparing to genetic algorithm and computational fluid dynamics, the proposed hybrid method reduces about 53% of the computational cost. The pseudo solutions are avoided when the self-updating artificial neural network is adopted. In addition, the number of computational fluid dynamics cases is determined automatically, and the requirement of human adjustment is avoided.

scholarly journals Klasifikasi Tanaman Obat Berdasarkan Ekstraksi Fitur Morfologi Daun Menggunakan Jaringan Syaraf Tiruan

2018 ◽  
Vol 5 (2) ◽  
pp. 169-174
Author(s):  
Kana Saputra S ◽  
Mochammad Iswan Perangin-Angin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Medicinal Plants ◽  
Medicinal Plant ◽  
Leaf Morphology ◽  
Recognition Rate ◽  
Learning Rate ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network

Abstrak Indonesia telah lama mengenal dan menggunakan tanaman yang berkhasiat sebagai obat. Dari banyaknya tanaman obat yang ada di dunia, 80% tanaman obat tumbuh di hutan tropika yang berada di Indonesia. Sekitar 28.000 spesies tanaman tumbuh dan 1.000 spesies diantaranya telah digunakan sebagai  tanaman obat. Dengan banyaknya spesies tanaman obat dan tingkat kemiripan yang tinggi dapat menyebabkan kesalahan dalam proses identifikasi jenis tanaman obat. Sehingga dibutuhkan bantuan komputer untuk mengenali jenis tanaman obat tersebut. Tujuan dari penelitian ini adalah untuk mengidentifikasi jenis tanaman obat menggunakan jaringan syaraf tiruan backpropagation berdasarkan ekstraksi fitur morfologi daun. Hasilnya menujukkan bahwa perubahan nilai learning rate mempengaruhi hasil identifikasi jenis tanaman obat berdasarkan fitur morfologi daun. Hasil perhitungan rata-rata nilai recognition rate sebesar 90% untuk data training dan 75,56% untuk data testing terjadi saat learning rate 0,01. Nilai learning rate terbaik untuk identifikasi jenis tanaman obat adalah 0,01 dengan jumlah rata-rata epoch sebesar 11,67 dan MSE sebesar 0,13. Ini menunjukkan bahwa metode ekstraksi fitur morfologi daun dan algoritma jaringan syaraf tiruan backpropagation sangat baik digunakan untuk mengidentifkasi jenis tanaman obat. Kata Kunci: Ekstraksi Fitur, Jaringan Syaraf Tiruan Backpropagation, Morfologi Daun, Tanaman Obat Abstract Indonesia has known and used a nutritious plant as a medicine. most of the medicinal plants in the world that is 80% of medicinal plants grown in tropical forests in Indonesia. the plant grows about 28,000 species and 1,000 species of which have been used as medicinal plants. Many species of medicinal plants with a high degree of similarity can cause errors in the process of identifying medicinal plants. Because the problem was needed computer assistance to recognize the types of medicinal plants. This research proposed to identify species of medicinal plants using backpropagation artificial neural network based on leaf morphological feature extraction. The results showed that changes in the value of learning rate influence the identification of medicinal plant species based on leaf morphology features. The calculation average of recognition rate is 90% for training data and 75.56% for data testing occurs at learning rate 0.01. The best learning rate for plant species identification is 0.01 with epoch average is 11.67 and MSE is 0.13. The results of this research concluded that the leaf morphology feature extraction method and backpropagation artificial neural network algorithm are very well used to identify the types of medicinal plants. Keywords: Backpropagation Artificial Neural Network, Feature Extraction, Leaf Morphology, Medicinal Plant

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