14 research outputs found
Classification of colorimetric sensor data using time series
Colorimetric sensors are widely used as pH indicators, medical diagnostic devices and detection devices. The colorimetric sensor captures the color changes of a chromic chemical (dye) or array of chromic chemicals when exposed to a target substance (analyte). Sensing is typically carried out using the difference in dye color before and after exposure. This approach neglects the kinetic response, that is, the temporal evolution of the dye, which potentially contains additional information. We investigate the importance of the kinetic response by collecting a sequence of images over time. We applied end-to-end learning using three different convolution neural networks (CNN) and a recurrent network. We compared the performance to logistic regression, k-nearest-neighbor and random forest, where these methods only use the difference color from start to end as feature vector. We found that the CNNs were able to extract features from the kinetic response profiles that significantly improves the accuracy of the sensor. Thus, we conclude that the kinetic responses indeed improves the accuracy, which paves the way for new and better chemical sensors based on colorimetric responses
Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal
Most deep-learning-based continuous sign language recognition (CSLR) models
share a similar backbone consisting of a visual module, a sequential module,
and an alignment module. However, due to limited training samples, a
connectionist temporal classification loss may not train such CSLR backbones
sufficiently. In this work, we propose three auxiliary tasks to enhance the
CSLR backbones. The first task enhances the visual module, which is sensitive
to the insufficient training problem, from the perspective of consistency.
Specifically, since the information of sign languages is mainly included in
signers' facial expressions and hand movements, a keypoint-guided spatial
attention module is developed to enforce the visual module to focus on
informative regions, i.e., spatial attention consistency. Second, noticing that
both the output features of the visual and sequential modules represent the
same sentence, to better exploit the backbone's power, a sentence embedding
consistency constraint is imposed between the visual and sequential modules to
enhance the representation power of both features. We name the CSLR model
trained with the above auxiliary tasks as consistency-enhanced CSLR, which
performs well on signer-dependent datasets in which all signers appear during
both training and testing. To make it more robust for the signer-independent
setting, a signer removal module based on feature disentanglement is further
proposed to remove signer information from the backbone. Extensive ablation
studies are conducted to validate the effectiveness of these auxiliary tasks.
More remarkably, with a transformer-based backbone, our model achieves
state-of-the-art or competitive performance on five benchmarks, PHOENIX-2014,
PHOENIX-2014-T, PHOENIX-2014-SI, CSL, and CSL-Daily
Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources
In order to replace fossil fuels with the use of renewable energy resources,
unbalanced resource production of intermittent wind and photovoltaic (PV) power
is a critical issue for peer-to-peer (P2P) power trading. To resolve this
problem, a reinforcement learning (RL) technique is introduced in this paper.
For RL, graph convolutional network (GCN) and bi-directional long short-term
memory (Bi-LSTM) network are jointly applied to P2P power trading between
nanogrid clusters based on cooperative game theory. The flexible and reliable
DC nanogrid is suitable to integrate renewable energy for distribution system.
Each local nanogrid cluster takes the position of prosumer, focusing on power
production and consumption simultaneously. For the power management of nanogrid
clusters, multi-objective optimization is applied to each local nanogrid
cluster with the Internet of Things (IoT) technology. Charging/discharging of
electric vehicle (EV) is performed considering the intermittent characteristics
of wind and PV power production. RL algorithms, such as deep Q-learning network
(DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy
optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for
simulations. Consequently, the cooperative P2P power trading system maximizes
the profit utilizing the time of use (ToU) tariff-based electricity cost and
system marginal price (SMP), and minimizes the amount of grid power
consumption. Power management of nanogrid clusters with P2P power trading is
simulated on the distribution test feeder in real-time and proposed GCN-PPO
technique reduces the electricity cost of nanogrid clusters by 36.7%.Comment: 22 pages, 8 figures, to be submitted to Applied Energy of Elsevie