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This paper presents the DeepCD framework which learns a pair of complementary descriptors jointly for image patch representation by employing deep learning techniques. It can be achieved by taking any descriptor learning architecture for learning a leading descriptor and augmenting the architecture with an additional network stream for learning a complementary descriptor. To enforce the complementary...
A fault diagnosis model of gas pressure regulator based on deep belief networks (DBN) theory is proposed in this paper, which is applied to the fault diagnosis method of gas regulator. Through the DBN parameter setting, the distribution of the original data of the network architecture can be reassembled by the greedy unsupervised layering training algorithm in the depth network to reflect the more...
We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors. The idea is that in order to fully utilize the expressive power of the descriptor space, good local feature descriptors should be sufficiently “spread-out” over the space. In this work, we propose a regularization term to maximize...
Many prediction tasks contain uncertainty. In some cases, uncertainty is inherent in the task itself. In future prediction, for example, many distinct outcomes are equally valid. In other cases, uncertainty arises from the way data is labeled. For example, in object detection, many objects of interest often go unlabeled, and in human pose estimation, occluded joints are often labeled with ambiguous...
The low noise amplifier (LNA) is a significant device in RF front-end. In this paper, a straight and efficient modeling method for LNA based on the Volterra series with recursive least squares (RLS) algorithm is proposed. Instead of calculating the high nonlinearity order of Volterra kernels, the proposed method extracts the first three order Volterra kernels characterizing the memory effect to construct...
In the previous study, we have investigated that the Extended Kalman Filter (EKF) has the excellennt performance and very fast learning as the training of Feedforward Neural Network (FNN). In the expansion of Kalman filter algorithm for nonlinear estimation, the Unscented Kalman Filter (UKF) was proposed. Enlightened the UKF is superior to EKF, in this study, we investigate the UKF algorithm as the...
The benefits of well-informed water management systems are related to the forecasting skills of hydrological variables. These benefits can be reflected in reducing economic and social losses to come. Therefore, the optimal design of water management projects frequently involves finding the methods or techniques that generate long sequences of hydrological data. These sequences considered as time series...
In 2013, Tams et al. proposed a method to determine directed reference points in fingerprints based on a mathematical model of typical orientation fields of tented arch type fingerprints. Although this Tented Arch Reference Point (TARP) method has been used successfully for pre-alignment in biometric cryptosystems, its accuracy does not yet ensure satisfactory error rates for single finger systems...
Creativity is considered as a very important element of the society development. Having entered the big data era, people have been focusing on finding a pathway developing creativity for all applications. In the psychology and education domain, various approaches have been attempted, such as divergent thinking and brain storming. The psychology domain has researched on human's cognitive development...
Future high-performance computing (HPC) systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are among the major sources that corrupt HPC execution results. Unlike fail-stop errors, SDCs can be harmful and dangerous in that they cannot be detected by hardware....
Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify similar persons. Because of challenges regarding changes in lighting between views, occlusion or even privacy...
The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison...
In this paper it is proposed the methodology of analytic design of the optimum structure for stochastic control system used on multi-degree-of-freedom stand simulator of spacecraft motion in the presence of deterministic and stochastic disturbances acting on it.
Artificial Immune Recognition System (AIRS) is an algorithm inspired by animal immune system in the biological world. It is specialized in solving pattern recognition problems. Heating, Ventilation and Air Conditioning (HVAC) systems are widely installed in modern buildings to provide the occupants with a satisfactory indoor environment. HVAC systems are the some of the most power consuming equipment...
Estimating short-term power load is a fundamental issue in the power distribution system. Since short-term power load is related to many parameters such as weather conditions, and time. The aim of this study is to determine the relevant parameters in estimating short-term power load not only in order to decrease the computational cost, but also to achieve higher success rates. Furthermore, by using...
This study aims at developing an intelligent agent that can recognize user-specific emotions and can self-evolve. Previous studies have explored several methods to develop the model and improve the results while maintaining the feasibility of real-time implementation for later stages. We evolved the emotion recognition module by using Genetic Programming (GP) and explored several optimizations. We...
This paper presents a knee torque estimation in non-pathological gait cycle at stance phase. Comparative modelling by using dynamics model and neural network model is discussed. Dynamics modelling is constructed by using simple two degree of freedom dynamics with Newtonian calculation approach and more complex four degree of freedom dynamics with Lagrangian calculation approach. Neural network based...
The prosthetic knees have been improved and developed to support the amputee to be able to walk as normal people and help them on a daily basis. This research is concerned with a swing phase of a semi-active prosthetic knees utilizing magnetorheological (MR) damper. Although the referred work which use a neural network predictive control (NNPC) has a satisfying results with low error, it has a possibility...
The paper deals with the problem of stability during the solving of pattern recognition tasks from the point of view of transformation groups. It shows the possibility to avoid the necessity of regularization by using the geometric equaffine Lorentz transformation, exploiting as example the alpha-procedure.
Forecasting the returns of stock markets is gaining importance nowadays in finance. For this aim, in the last decade, Artificial Neural Networks (ANN) have been widely used to forecast stock market movements. In Baltic countries, artificial neural networks are not commonly used in predicting financial failures. This study aims using artificial neural networks to predict OMX Baltic Benchmark GI (OMXBBGI)...
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