Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Machine Olfaction is a bionic detection technique that adopts electronic device to simulate biological olfactory system. As an information acquisition device, MOS gas sensor array is an important component of machine olfactory system. In this paper, a novel gas concentration estimation method based on multivariate relevance vector machine (MVRVM) is proposed for mixed gas detection by using MOS gas...
Stochastic gradient algorithms are the main focus of large-scale optimization problems and led to important successes in the recent advancement of the deep learning algorithms. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose an adaptive learning rate algorithm, which utilizes stochastic...
In millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, channel estimation in the presence of sparse multipath fading boils down to two-dimensional (2D) direction-of-arrival (DOA) estimation followed by path gain estimation. To achieve super-resolution angle estimation at affordable complexity, this paper develops an efficient channel estimation approach by applying a truncated...
This paper extends the idea of Universum learning to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples, or Universum samples, belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons...
Hidden Markov Models are very efficient in speech recognition. Based on machine states, HMMs combine Bayesian probability and decision making to approximate each output to its appropriate class. In this paper, we propose to use HMMs for ECG QRS detection. We select a set of models to represent QRS complex and noise aiming to a better discrimination between them. For a total of 44510 beats of the MIT/BIH...
The process of mining comprises of supervised learning and unsupervised learning. It includes various approaches out of which data classification is one of the beneficial and constructive methods. This paper explores the effective functioning of the whole process. There are several cases in classification where the important data is missed during the process. It can hence be concluded that the process...
We herein propose an evolutionary multi-agent system (EMAS for short) to build an ensemble of surrogates for prediction. In our EMAS, we employ six kinds of basic surrogates, including Gaussian process, Kriging model, polynomial response surface, radial basis function, radial basis function neural network, and support vector regression machine. We define each surrogate as one agent and co-evolve parameters...
Indoor localization becomes a research focus in recent years since. Smartphone-based pedestrian dead reckoning (PDR) is one of the widely-adopted localization techniques with limiting problems such as the drift of inertial sensors. Bluetooth Low Energy (BLE) has better performance result which makes it an auxiliary tool for PDR to correct errors. But BLE fingerprint sampling and calibrating are time-consuming...
Food related web services, such as recipe websites and food journaling apps, are rapidly gaining popularity. Data from service providers and that generated by users often coexist in these services. Compared to the former, the latter, due to its randomness and lack of organization, is often difficult to incorporate into common service features like recommendation making and associative searching. This...
This paper aims to study the use of a multistep parallel-strategy (MSPS) for long-term estimation of the remaining useful life of the Li-ion batteries. Various extreme learning machines (ELMs) including standard ELM, kernel ELMs and online sequential ELM (OS-ELM) are used along with the parallel strategy for multi-step prognosis. These multistep predictors are trained by means of constant current...
In this paper, a deep-learning based architecture is proposed to estimate gender which includes both supervised and unsupervised facial feature extraction techniques and a deep network to fuse features and to classify them. As unsupervised techniques we have benefited deep-learning feature extraction method called convolutional neural network, and for supervised facial feature extraction we have used...
MRI parameter quantification has diverse applications, but likelihood-based methods typically require nonconvex optimization due to nonlinear signal models. To avoid expensive grid searches used in prior works, we propose to learn a nonlinear estimator from simulated training examples and (approximate) kernel ridge regression. As proof of concept, we apply kernel-based estimation to quantify six parameters...
Traditional data stream classification techniques assume that the stream of data is generated from a single non-stationary process. On the contrary, a recently introduced problem setting, referred to as Multistream Classification involves two independent non-stationary data generating processes. One of them is the source stream that continuously generates labeled data instances. The other one is the...
Crowd counting is useful and widely applied invideo surveillance. It remains a challenge task for thecharacteristics of crowd, such as severe occlusions, sceneperspective distortions and so on. The existing state-of-the-artmethods are spatial information ignored or spatial scalereduced. To solve these problems, we proposed a novel FullyConvolutional Networks (FCN) model, which is end-to-endlearned...
In this paper identification of electroencephalogram (EEG) based brain-computer interface (BCI) for motor imagery (MI) task is planned by an efficient adaptive neuro-fuzzy classifier (NFC). The linguistic hedge (LH) is used for proper elicitation and pruning of the fuzzy rules and network is trained using scaled conjugate gradient (SCG) and speeding up SCG (SSCG) techniques. The performance of the...
Convolution neural network (CNN) has been shown as one of state-of-the-art approaches for learning face representations. However, previous works only utilized identity information instead of leveraging human attributes (e.g., gender and age) which contain high-level semantic meaning. In this work, we aim to incorporate identity and human attributes in learning discriminative face representations through...
Automatic speech recognition is now playing an important role in volume control and adjustment of modern smart speakers. According to the recognition results by using the advanced deep neural network technology, this paper proposes an efficient processing system for automatic volume control (AVC) and limiter. The theoretical analyses, subjective and objective testing results show that the proposed...
Pitch is an important characteristic of speech and is useful for many applications. However, it is still challenging to estimate pitch in strong noise. In this paper, we propose a joint training approach to determinate pitch. First, a Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTMRNN) is trained to map the noisy to clean speech features. Second, the pitch estimation is also...
This paper describes an unsupervised method of adapting deep neural networks (DNNs) for sound source localization (SSL). DNNs-based SSL achieves high localization accuracy for sound data that are similar to training data. However, the accuracy deteriorates if a sound source is at an unknown position in unknown reverberant environments. We solve the problem by using unsupervised adaption of the DNNs'...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.