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In this paper, we study the global dissipativity for Cohen-Grossberg neural networks with both time-varying delays and infinite distributed delays. Based on Lyapunov functions, mean value theorem and inequality techniques, several algebraic criterions for the global dissipativity are obtained. Meanwhile, the estimations of the positive invariant set and globally attractive set are given out. Finally,...
Studies of paleoclimate variations in local regions are seriously restricted by the low resolution and uncertainties of the simulated data at present. In order to apply large-scale modeling data to paleoclimate research in local regions, an effective downscaling model based on three-layer back propagation neural network (BPNN) is developed. Observational and ECHO-G simulated data are employed to train...
For fire detection and alarm system with simple function, positioning difficulties, false positive and false negative in traditional intelligent building, the fire detection and alarm systems based on intelligent neural network have been designed. It can do integrated estimation with a variety of fire detection information detected by the microcontroller, neural network intelligent algorithm was joined...
This paper seeks to implement and test a financial forecasting agent which employs time series, derived time series data, and news that are retrieved and extracted from the Web. This research focuses on the time series data of some individual stocks from the Indonesian Stock Exchange as well as the index data. The financial forecasting agent implemented is based on a Multilayer Neural Network trained...
In the current power and energy scenario, distributed generation (DG) has generated a lot of interest across the globe due to the growing concerns about gradual depletion of fossil fuels, steep load growth, environmental pollution and global warming caused by greenhouse gas emissions. Renewable DGs such as wind generators and solar photovoltaic are well-recognized now-a-days as sources of clean energy...
In the mining industry, knowing the position of miners and/or equipments is an important safety measure that reduces risks and improves the security of that facility. Being an indoor environment, wireless transmitted signals in underground narrow-vein mines suffer multiple kinds of distortions due to extreme multipath and non-line of sight (NLOS) conditions. One of the proposed solutions to accurate...
In this paper, we discuss the extraction of relations between lecturer and students in lectures by using multi-layered neural networks. Here, the relations among a few features concerning on the behaviors of the lecturer and students can be represented by multi-layered neural networks with the time-delay. Furthermore, we introduce a structural learning algorithm with forgetting for neural networks...
Osteoarthritis is a degenerative joint disease, which causes the degradation of articular cartilage and subchondral bone. The disease may result in mechanical abnormalities of the joints, including weight bearing joints such as the knees and hips. In this work, we analyze gait biomechanical data using neural network models to predict the level of joint deterioration and the level of pain in participants...
Constant monitoring of a variety of physiological signals is vitally important in numerous clinical care settings. This signals are not perfect, however, and can be corrupted or lost. The loss of a signal can be devastating to the patient, as the physician may lose key information to understanding disease processes, or worse, be unaware of the patient's status in either surgery or the ICU. This study...
High-throughput microscopy allows fast imaging of large tissue samples, producing an unprecedented amount of sub-cellular information. The size and complexity of these data sets often out-scale current reconstruction algorithms. Overcoming this computational bottleneck requires extensive parallel processing and scalable algorithms. As high-throughput imaging techniques move into main stream research,...
Power utilization has become a major issue in portable designs, since its battery storage is less compared to its usage. One of the popular techniques to solve this problem is to use Dynamic Power Management (DPM) at the system level. Dynamic power management is a technique used to save power when the system is idle. Earlier it was assumed that the prediction can be done only in long range dependent...
The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements are quite small. On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced...
A novel neural network method to predict the spectral signature in the predicted meteorological image is presented here. Back propagation algorithm has been used in this work. Based on computation cost, three different dimensional feature vectors are provided from two consecutive images as input to neural net for training and testing. Various kinds of testing are made depending upon position of predicted...
A GPU-accelerated OpenCL implementation of a back-propagation artificial neural network for the creation of QSAR models for drug discovery and virtual high-throughput screening is presented. A QSAR model for HSD achieved an enrichment of 5.9 and area under the curve of 0.83 on an independent data set which signifies sufficient predictive ability for virtual high-throughput screening efforts. The speed-up...
Pipe separators are currently being assessed as substitutes for conventional separators in the oil and gas industry for the separation of gas, oil and water. In the process of separation, the interface levels between the different media are important measurands to be monitored to optimize the separation process. Electrical Capacitance Tomography (ECT) without too much focus on tomograms can be used...
This work focuses on developing cool store's thermal mapping system based on the neuro Wireless Sensor Network (nWSN). The network intelligence is taken care of by the sensor network embedded neural net. The target application of the architecture development is for cool stores with emphasis on meat storage. The meat quality is a significant characteristic within the cold chain management. Temperature...
This paper presents the development of paraplegic joint model using Artificial Neural Network (ANN). A series of experiments using Functional Electrical Stimulation (FES) with different stimulation frequencies, pulse width and pulse duration to investigate the impact on the leg swing angle is conducted. The data obtained is used to develop the paraplegic leg joint model. 74810 training data and 33602...
We introduce a mobile robotic system able to learn through reinforcement, which allows it to navigate within a dynamic environment (e.g. a room) avoiding any obstacle it might encounter. The robotic system must be able to locate and reach an established pattern, which has been previously learned. The learning system is implemented with two neural networks. Both neural networks use reinforcement learning...
Artificial intelligence (Al) functions are becoming important in smartphones, portable game consoles, and robots for such intelligent applications as object detection, recognition, and human-computer interfaces (HCI). Most of these functions are realized in software with neural networks (NN) and fuzzy systems (FS), but due to power and speed limitations, a hardware solution is needed. For example,...
With the advancement of wireless communication systems in recent years, small size multiband antennas are in great demand for both commercial and military applications. The fractal geometry concept can be used to reduce antenna size. So fractal shaped antennas are good choice to reduce antenna size and get multiband behavior. A dual band elliptical fractal patch antenna is presented in this paper...
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