63,595 research outputs found
FloWaveNet : A Generative Flow for Raw Audio
Most modern text-to-speech architectures use a WaveNet vocoder for
synthesizing high-fidelity waveform audio, but there have been limitations,
such as high inference time, in its practical application due to its ancestral
sampling scheme. The recently suggested Parallel WaveNet and ClariNet have
achieved real-time audio synthesis capability by incorporating inverse
autoregressive flow for parallel sampling. However, these approaches require a
two-stage training pipeline with a well-trained teacher network and can only
produce natural sound by using probability distillation along with auxiliary
loss terms. We propose FloWaveNet, a flow-based generative model for raw audio
synthesis. FloWaveNet requires only a single-stage training procedure and a
single maximum likelihood loss, without any additional auxiliary terms, and it
is inherently parallel due to the characteristics of generative flow. The model
can efficiently sample raw audio in real-time, with clarity comparable to
previous two-stage parallel models. The code and samples for all models,
including our FloWaveNet, are publicly available.Comment: 9 pages, ICML'201
Deep Affordance-grounded Sensorimotor Object Recognition
It is well-established by cognitive neuroscience that human perception of
objects constitutes a complex process, where object appearance information is
combined with evidence about the so-called object "affordances", namely the
types of actions that humans typically perform when interacting with them. This
fact has recently motivated the "sensorimotor" approach to the challenging task
of automatic object recognition, where both information sources are fused to
improve robustness. In this work, the aforementioned paradigm is adopted,
surpassing current limitations of sensorimotor object recognition research.
Specifically, the deep learning paradigm is introduced to the problem for the
first time, developing a number of novel neuro-biologically and
neuro-physiologically inspired architectures that utilize state-of-the-art
neural networks for fusing the available information sources in multiple ways.
The proposed methods are evaluated using a large RGB-D corpus, which is
specifically collected for the task of sensorimotor object recognition and is
made publicly available. Experimental results demonstrate the utility of
affordance information to object recognition, achieving an up to 29% relative
error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201
Impact of marine power system architectures on IFEP vessel availability and survivability
In recent years integrated full electric propulsion (IFEP) has become a popular power system concept within the marine community, both for the naval and the commercial community. In this paper the authors discuss the need for a detailed investigation into the impact of different IFEP power system architectures on the availability of power and hence on the survivability of the vessel. The power system architectures considered here could relate to either a commercial or a naval vessel and include radial, ring and hybrid AC/DC arrangements. Comparative fault studies of the architectures were carried out in an attempt to make valuable observations on the survivability of a vessel. Simulation results demonstrate that the ring and hybrid AC/DC architectural contribute to a higher survivability than the radial architecture. However, there are still challenges that need to be addressed and therefore potential solutions such as fault current limiters will be considered
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Urban and extra-urban hybrid vehicles: a technological review
Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, âvehicle operating lifeâ is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid âelectric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use
(implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used
Modularization Assessment of Product Architecture
Modularization refers to the opportunity for mixing-and-matching of components in a modular product design in which the standard interfaces between components are specified to allow for a range of variation in components to be substituted in a product architecture. It is through mixing-and-matching of these components, and how these components interface with one another, that new systems are created. Consequently, the degree of modularization inherent in a system is highly dependent upon the components and the interface constraints shared among the components, modules, and sub-systems. In this paper, a mathematical model is derived for analyzing the degree of modularization in a given product architecture by taking into consideration the number of components, number of interfaces, the composition of new-to-the-firm (NTF) components, and substitutability of components. An analysis of Chrysler windshield wipers controller suggests that two product architectures may share similar interface constraints, but the opportunity for modularization of one module is significant higher than the other due to the higher substitutability of its components and lower composition of NTF components.Product architecture, modularization, substitutability, new product development
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory
Mental simulation is a critical cognitive function for goal-directed behavior
because it is essential for assessing actions and their consequences. When a
self-generated or externally specified goal is given, a sequence of actions
that is most likely to attain that goal is selected among other candidates via
mental simulation. Therefore, better mental simulation leads to better
goal-directed action planning. However, developing a mental simulation model is
challenging because it requires knowledge of self and the environment. The
current paper studies how adequate goal-directed action plans of robots can be
mentally generated by dynamically organizing top-down visual attention and
visual working memory. For this purpose, we propose a neural network model
based on variational Bayes predictive coding, where goal-directed action
planning is formulated by Bayesian inference of latent intentional space. Our
experimental results showed that cognitively meaningful competencies, such as
autonomous top-down attention to the robot end effector (its hand) as well as
dynamic organization of occlusion-free visual working memory, emerged.
Furthermore, our analysis of comparative experiments indicated that
introduction of visual working memory and the inference mechanism using
variational Bayes predictive coding significantly improve the performance in
planning adequate goal-directed actions
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