Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.
Introduction To Artificial Neural Network By Zurada Pdf To Jpg
Artificial neural networks (ANNs) are well suited to model the complex behavior of most geotechnical engineering materials which, by their very nature, exhibit extreme variability. ANNs have also demonstrated superior predictive ability when compared with traditional methods. Since the early 1990s, ANNs have been applied successfully to virtually every problem in geotechnical engineering. In this section, post-2001 applications of ANNs in geotechnical engineering are briefly examined, and interested readers are referred to Shahin et al. [1], where the pre-2001 papers are reviewed in some detail.
Despite the success of ANNs in geotechnical engineering and other disciplines, they suffer from some shortcomings that need further attention in the future, including model robustness, transparency and knowledge extraction, extrapolation, and uncertainty. In addition and according to Flood [115], ANNs in civil engineering, including geotechnical engineering, were used mostly as simple vector mapping devices for function modeling of applications that require rarely more than a few tens of neurons without higher-order structuring. Together, improvements in these issues will greatly enhance the usefulness of ANN models and will provide the next generation of applied artificial neural networks with the best way for advancing the field to the next level of sophistication and application. Until such an improvement is achieved, the authors agree with Flood and Kartam [105] that neural networks for the time being might be treated as a complement to conventional computing techniques rather than as an alternative or may be used as a quick check on solutions developed by more time-consuming and in-depth analyses.
Abstract:Intermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an experimental study to predict the amount of solar radiation using a classical artificial neural network (ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce University in Turkey. Moreover, we included meteorological data collected from the Meteorological Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of determination (R2), mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean biased error (MBE). LSTM outperformed ANN with R2, MSE, RMSE, MAE, and MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with two similar studies in the literature. The proposed study paves the way for utilizing renewable energy by leveraging the usage of PV panels.Keywords: renewable energy; solar energy; artificial neural network; deep learning; LSTM; radiation prediction
Abstract:Traditional rehabilitation systems are evolving into advanced systems that enhance and improve rehabilitation techniques and physical exercise. The reliable assessment and robotic support of the upper limb joints provided by the presented elbow exoskeleton are important clinical goals in early rehabilitation after stroke and other neurological disorders. This allows for not only the support of activities of daily living, but also prevention of the progression neuromuscular pathology through proactive physiotherapy toward functional recovery. The prices of plastics are rising very quickly, as is their consumption, so it makes sense to optimize three dimensional (3D) printing procedures through, for example, improved artificial intelligence-based (AI-based) design or injection simulation, which reduces the use of filament, saves material, reduces waste, and reduces environmental impact. The time and cost savings will not reduce the high quality of the products and can provide a competitive advantage, especially in the case of thinly designed mass products. AI-based optimization allows for one free print after every 6.67 prints (i.e., from materials that were previously wasted).Keywords: neural network; 3D printing; reduction of waste; elbow exoskeleton
The clinical manifestations of FS-DFSP and C-DFSP are similar but have large differences in immunohistochemistry. The classification accuracy and feasibility of the BP neural network model are high in FS-DFSP.
An artificial neural network (ANN) is an intelligent system that learns how the brain processes information by imitating the human nervous system. ANNs can make correct predictions of unknown data by learning and testing known data, and they do this by mathematically and physically abstracting and mimicking the structure and function of the human brain [18]. A back-propagation (BP) neural network is a kind of multilayer feedforward network that uses the error back-propagation algorithm. It has been reported that approximately 90% of neural networks are based on the BP algorithm, which has been widely used in disease recognition and diagnosis [19, 20].
Currently, few studies have reported the differences of conventional DFSP (C-DFSP, without fibrosarcomatous change) and FS-DFSP in the clinical features. In order to deeply understand the clinical characteristics of DFSP, we conducted a retrospective cohort study to evaluate the clinical characteristics of FS-DFSP and C-DFSP and build a recognition model with a BP neural network.
Wu et al. [17] proposed a probabilistic neural network for leaf recognition using 12 digital morphological features, derived from 5 basic features (diameter, physiological length, physiological width, leaf area, leaf perimeter). The authors collected a publicly available plant leaf database named Flavia.
Kadir et al. [24] prepared the Foliage dataset, consisting of 60 classes of leaves, each containing 120 images. The best reported result on this dataset reported by Kadir et al. [18] was achieved by a combination of shape, vein, texture and colour features processed by principal component analysis before classification by a probabilistic neural network.
Several methods have been proposed and evaluated on datasets which are not publicly available. Chi et al. [31] proposed a method using Gabor filter banks. Wan et al.[32] performed a comparative study of bark texture features: the grey level run-length method, co-occurrence matrices method, histogram method and auto-correlation method. The authors also show that the performance of all classifiers improved significantly when color information was added. Song et al. [33] presented a feature-based method for bark recognition using a combination of Grey-Level Co-occurrence Matrix (GLCM) and a binary texture feature called long connection length emphasis. Huang et al. [34] used GLCM together with fractal dimension features for bark description. The classification was performed by artificial neural networks.
Recently, Cimpoi et al. [58, 59] pushed the state-of-the-art in texture recognition using a new encoder denoted as FV-CNN-VD, obtained by Fisher Vector pooling of a very deep convolutional neural network (CNN) filter bank pre-trained on ImageNet by Simonyan and Zisserman [60]. The CNN filter bank operates conventionally on preprocessed RGB images. This approach achieves state-of-the-art accuracy, yet due to the size of the very deep VGG networks it may not be suitable for real-time applications when evaluated without a high-performance graphics processing unit (GPU) for massive parallelization.
Our model for PlantCLEF 2017 was based on the state-of-the-art convolutional neural network architecture, the Inception-ResNet-v2 model [70], which introduced residual Inception blocks - a new type of the Inception block making use of the residual connections from [67]. Both the paper [70] and our preliminary experiments show that this network architecture leads to results superior to other state-of-the-art CNN architectures. The publicly available [91] Tensorflow model pretrained on ImageNet was used to initiate the parameters of convolutional layers. The main hyperparameters were set as follows:
Leaf classification with deep convolutional neural networks is hard to apply to experiment with small leaf datasets. To get a comparison with our textural method, we performed our experiment on the Middle European Woods dataset, fine-tuning from an ImageNet-pretrained model. Note that due to high computational complexity and limited GPU resources, we only evaluated this method on one random data split (in both directions), while Ffirst was evaluated on 10 random splits. After 200,000 steps, the Inception-ResNet-v2 network with maxout outperforms previous results significantly, achieving 99.9 and 100.0% accuracy respectively. Moreover, the correct class always appears among the top 5 predictions. 2ff7e9595c
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