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In this paper, we describe how neural networks can be used for high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects...
We propose joint modeling strategies leveraging upon large-scale mixed-band training speech for recognition of both narrowband and wideband data based on deep neural networks (DNNs). We utilize conventional down-sampling and up-sampling schemes to go between narrowband and wideband data. We also explore DNN-based speech bandwidth expansion (BWE) to map some acoustic features from narrowband to wideband...
In this paper, a decentralized adaptive neural network sliding mode control scheme is proposed for trajectory tracking control problem of reconfigurable manipulators based on data-based modeling. This method can be implemented to reconfigurable manipulators with different configurations and degrees of freedom without modifying any control parameters. Different from the previous works, the proposed...
This paper deals with a new kind of fractional-order controllers with nonlinear part in order to eliminate nonlinear characteristic of the controlled system, actuator, wind-up effect, noise and so on, respectively. In this paper is presented the methods for controller implementation in digital form as well as neural network approach to design such kind of the controller. The main advantage of such...
In order to autonomously design the optimal descending trajectory for spacecraft soft landing on an asteroid, an onboard guidance based on the bidirectional extreme learning machine (B-ELM) is proposed. The optimization problem is formulated and transformed into a two-point boundary value problem (TPBVP). And then, based on the sample trajectories obtained off-line, a single-hidden layer feed-forward...
Saliency computational model with active environment perception can substantially facilitate a wide range of applications. Conventional saliency computational models primarily rely on hand-crafted low level image features, such as color or contrast. However, they may face great challenges in low lighting scenario, due to the lack of well-defined feature to represent saliency information in low contrast...
Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live/dead assay dataset from the Broad Bioimage Benchmark Collection. These architectures include a standard convolutional network which produces single pixel...
In the fine-grained categories, images have lager diversity in their intra categories. Meanwhile, they have more similarity in their inter categories. Therefore, images are difficultly distinguish during fine-grained visual classification(FGVC). This paper proposes a deep sparse coding framework to implement the fine-grained visual categorization. In our framework, deep layer structures with sparse...
Currently, the differential function has been widely used in transformer protection relay. However, the main issue of this technique is assigned to the relay misoperation during the presence of inrush currents and current transformer (CT) saturation. In the literature, these limitations have been overcome with the use of tools based on artificial intelligence and signal processing, such as the methods...
With the evolution of e-commerce and Online Social Networks, the web information has constantly increased, so the relevance to create methods for automatic knowledge extraction and data mining earned notoriety. Information as opinion evaluation is a point studied by Sentiment Analysis area, which is becoming important nowadays. Be aware of the best reviews is a factor that must be taken into account...
In this paper we propose a study to identify the best ANN configuration in terms of number of neurons, number of layers, training-set size, in order to perform the day-ahead energy production forecast for a Photo-Voltaic (PV) plant. This set up is applied to a novel hybrid method (PHANN Physic Hybrid Artificial Neural Network) in order to enhance the energy day-ahead forecast combining both the deterministic...
We propose a novel approach called, an orthogonal particle swarm optimization (OPSO) algorithm, for economic dispatch (ED) of thermal generating units (TGUs) in smart electric power gird (SEPG) environment. The characteristics of TGUs are nonlinear and the generation system becomes more and more complicated when these TGUs are subjected to ramp rate constraints and prohibited operating zones. In such...
Rule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. In this work the Discretized Multi Layer Perceptron (DIMLP) was trained...
Forest fires are a dangerous and devastating phenomenon. Being able to accurately predict the burned area of a forest fire could potentially limit human casualties as well as better prepare for the ensuing economical and ecological damage. A data set from the Montesinho Natural Park in Portugal provides a difficult regression task regarding the prediction of forest fire burn area due to the limited...
Malware classification has become an important task in protection of privacy and sensitive information from being stolen or modified. A number of malware categories and families emerged over last decade targeting Microsoft Windows since it is the most attractive platform for virus developers. Software for this OS is provided in a format of Portable Executable (PE) files. Majority of commercial anti-virus...
When photographs are being taken in an outdoor environment, the medium in air will cause light attenuation and further reduce image quality, and this impact is especially obvious in a hazy environment. Reduction of image quality results in the loss of information, which renders an image recognition system unable to identify objects in the image. In order to eliminate the hazy effect on images and...
Images, text, web documents, videos, real-world data are very often high-dimensional. Many researchers or users may need to construct accurate predictive models for a variety of applications, especially those that involve clustering. Handling high dimensional data is a reality in processing task involving areas such as high-throughput genotyping platforms and human genetic clustering in bioinformatics,...
The task of classifying data has been addressed in various works, and has been utilized in various areas of application, such as medicine, industry, marketing, financial market and many others. This work will present a data classifier proposal that combines the SOM (Self-Organizing Map) neural network with INN (Informative Nearest Neighbors). The combination of these two algorithms will be called...
This paper presents a nonlinear control of a quadrotor unmanned aerial vehicle(UAV) for trajectory tracking. The dynamic model is obtained by the Euler- Lagrange methodology. In this paper, the control strategy of the quadrotor is based on inner (attitude control) and outer (position control) loops. The outer loop generates the inputs for inverse dynamics and calculates instantaneous desired angles...
Network is a powerful paradigm for representing complex relationships and finding the community structure of networks can help people better understand the real world. Infomap, which employs the minimum description length as the optimization objective, is a competent algorithm for community structure analysis. In this paper, we propose a novel algorithm combining flow-based ensemble learning and Label...
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