236 research outputs found
Adaptive Backstepping-based Hâ Robust controller for Photovoltaic Grid-connected Inverter
To improve the robustness and stability of the photovoltaic grid-connected inverter system, a nonlinear backstepping-based Hâ controller is proposed. A generic dynamical model of grid-connected inverters is built with the consideration of uncertain parameters and external disturbances that cannot be accurately measured. According to this, the backstepping Hâ controller is designed by combining techniques of adaptive backstepping control and L2-gain robust control. The Lyapunov function is used to design the backstepping controller, and the dissipative inequality is recursively designed. The storage functions of the DC capacitor voltage and grid current are constructed, respectively, and the nonlinear Hâ controller and the parameter update law are obtained. Experimental results show that the proposed controller has the advantage of strong robustness to parameter variations and external disturbances. The proposed controller can also accurately track the references to meet the requirements of high-performance control of grid-connected inverters
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
Click-through rate (CTR) prediction is one of the fundamental tasks for
online advertising and recommendation. While multi-layer perceptron (MLP)
serves as a core component in many deep CTR prediction models, it has been
widely recognized that applying a vanilla MLP network alone is inefficient in
learning multiplicative feature interactions. As such, many two-stream
interaction models (e.g., DeepFM and DCN) have been proposed by integrating an
MLP network with another dedicated network for enhanced CTR prediction. As the
MLP stream learns feature interactions implicitly, existing research focuses
mainly on enhancing explicit feature interactions in the complementary stream.
In contrast, our empirical study shows that a well-tuned two-stream MLP model
that simply combines two MLPs can even achieve surprisingly good performance,
which has never been reported before by existing work. Based on this
observation, we further propose feature gating and interaction aggregation
layers that can be easily plugged to make an enhanced two-stream MLP model,
FinalMLP. In this way, it not only enables differentiated feature inputs but
also effectively fuses stream-level interactions across two streams. Our
evaluation results on four open benchmark datasets as well as an online A/B
test in our industrial system show that FinalMLP achieves better performance
than many sophisticated two-stream CTR models. Our source code will be
available at MindSpore/models.Comment: Accepted by AAAI 2023. Code available at
https://xpai.github.io/FinalML
Programmation robotique en utilisant la méthode de maillage et la simulation thermique du procédé de la projection thermique
L objectif de cette Ă©tude est d amĂ©liorer l extension du logiciel de programmation hors-ligne RobotStudio existante et de dĂ©velopper une nouvelle stratĂ©gie pour gĂ©nĂ©rer la trajectoire du robot par rapport aux paramĂštres essentiels de projection thermique. Notamment, l historique de la tempĂ©rature par rapport Ă la trajectoire gĂ©nĂ©rĂ©e est prise en compte dans cette Ă©tude.L extension logicielle Thermal Spray Toolkit (TST) intĂ©grĂ©e dans le cadre de RobotStudio est spĂ©cialement dĂ©veloppĂ©e pour gĂ©nĂ©rer la trajectoire du robot en projection thermique. L amĂ©lioration de l extension TST dans la nouvelle version de RobotStudio est mise au point sur deux modules principaux :PathKit : gĂ©nĂ©ration de la trajectoire sur des piĂšces complexes.ProfileKit : modĂ©lisation du cordon singulier du dĂ©pĂŽt et prĂ©diction de son Ă©paisseur en fonction des paramĂštres opĂ©ratoires.Les dĂ©ficiences existantes de l extension TST impliquent de mettre en Ćuvre une mĂ©thode plus avancĂ©e qui permettra de gĂ©nĂ©rer la trajectoire du robot en utilisant le maillage pour le calcul d Ă©lĂ©ment finis. Ainsi, l opĂ©ration de projection thermique pourra ĂȘtre menĂ©e. Dans cette Ă©tude, la mĂ©thodologie de maillage est introduite afin de fournir une stratĂ©gie de choix de points de trajectoire et l obtention d orientations de ces points de trajectoire sur la surface Ă revĂȘtir. Un module dit MeshKit est donc ajoutĂ© dans l extension TST afin de lui apporter ces fonctionnalitĂ©s nĂ©cessaires.Un couplage entre la trajectoire du robot et la rĂ©partition de chaleur du substrat a Ă©tĂ© dĂ©veloppĂ©, ce qui permet d Ă©tudier l Ă©volution de tempĂ©rature pendent le processus de projection thermique.The objective of this study is to improve the add-in package of off-line programming software RobotStudio and to develop a new strategy for generating the robot trajectory according to the kinematic parameters of thermal spraying. The computed temperature evolution relative to the generated robot trajectory on the coating surface is also considered in this study.The add-in package Thermal Spray Toolkit (TST) integrated in RobotStudio is developed to generate the robot trajectory for thermal spraying. The improved TST for new version of RobotStudio is composed of two principle modules:PathKit: generation of robot trajectory on the free-form coating surface.ProfileKit: modeling the coating profile and prediction the coating thickness based on kinematic parameters.The existing deficiency of TST leads to the development of an advanced robot trajectory generation methodology. In this study, the new approach implements the robotic trajectory planning in an interactive manner between RobotStudio and the finite element analysis software (FES). It allows rearranging the imported node created on the surface of workpiece by FES and in turns generating the thermal spraying needed robot trajectories.A coupling between the robot trajectory and the heat distribution on the substrate has been developed, which allows analyzing the temperature evolution during the thermal spray process, it helps to minimize thermal variations on the substrate and to select the appropriate execution sequence of trajectory.BELFORT-UTBM-SEVENANS (900942101) / SudocSudocFranceF
DRKF: Distilled Rotated Kernel Fusion for Efficient Rotation Invariant Descriptors in Local Feature Matching
The performance of local feature descriptors degrades in the presence of
large rotation variations. To address this issue, we present an efficient
approach to learning rotation invariant descriptors. Specifically, we propose
Rotated Kernel Fusion (RKF) which imposes rotations on the convolution kernel
to improve the inherent nature of CNN. Since RKF can be processed by the
subsequent re-parameterization, no extra computational costs will be introduced
in the inference stage. Moreover, we present Multi-oriented Feature Aggregation
(MOFA) which aggregates features extracted from multiple rotated versions of
the input image and can provide auxiliary knowledge for the training of RKF by
leveraging the distillation strategy. We refer to the distilled RKF model as
DRKF. Besides the evaluation on a rotation-augmented version of the public
dataset HPatches, we also contribute a new dataset named DiverseBEV which is
collected during the drone's flight and consists of bird's eye view images with
large viewpoint changes and camera rotations. Extensive experiments show that
our method can outperform other state-of-the-art techniques when exposed to
large rotation variations.Comment: 8 pages, 7 figure
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
Sequential recommender systems aim to model users' evolving interests from
their historical behaviors, and hence make customized time-relevant
recommendations. Compared with traditional models, deep learning approaches
such as CNN and RNN have achieved remarkable advancements in recommendation
tasks. Recently, the BERT framework also emerges as a promising method,
benefited from its self-attention mechanism in processing sequential data.
However, one limitation of the original BERT framework is that it only
considers one input source of the natural language tokens. It is still an open
question to leverage various types of information under the BERT framework.
Nonetheless, it is intuitively appealing to utilize other side information,
such as item category or tag, for more comprehensive depictions and better
recommendations. In our pilot experiments, we found naive approaches, which
directly fuse types of side information into the item embeddings, usually bring
very little or even negative effects. Therefore, in this paper, we propose the
NOninVasive self-attention mechanism (NOVA) to leverage side information
effectively under the BERT framework. NOVA makes use of side information to
generate better attention distribution, rather than directly altering the item
embedding, which may cause information overwhelming. We validate the NOVA-BERT
model on both public and commercial datasets, and our method can stably
outperform the state-of-the-art models with negligible computational overheads.Comment: Accepted at AAAI 202
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
Industrial recommender systems face the challenge of operating in
non-stationary environments, where data distribution shifts arise from evolving
user behaviors over time. To tackle this challenge, a common approach is to
periodically re-train or incrementally update deployed deep models with newly
observed data, resulting in a continual training process. However, the
conventional learning paradigm of neural networks relies on iterative
gradient-based updates with a small learning rate, making it slow for large
recommendation models to adapt. In this paper, we introduce ReLoop2, a
self-correcting learning loop that facilitates fast model adaptation in online
recommender systems through responsive error compensation. Inspired by the
slow-fast complementary learning system observed in human brains, we propose an
error memory module that directly stores error samples from incoming data
streams. These stored samples are subsequently leveraged to compensate for
model prediction errors during testing, particularly under distribution shifts.
The error memory module is designed with fast access capabilities and undergoes
continual refreshing with newly observed data samples during the model serving
phase to support fast model adaptation. We evaluate the effectiveness of
ReLoop2 on three open benchmark datasets as well as a real-world production
dataset. The results demonstrate the potential of ReLoop2 in enhancing the
responsiveness and adaptiveness of recommender systems operating in
non-stationary environments.Comment: Accepted by KDD 2023. See the project page at
https://xpai.github.io/ReLoo
Ferroelectric Photovoltaic Effect
Tetragonal BiFeO3 films with the thickness of 30 nm were grown epitaxially on (001) oriented LaAlO3 substrate by using pulsed laser deposition (PLD). The transverse photovoltaic effects were studied as a function of the sample directions in-plane as well as the angle between the linearly polarized light and the plane of the sample along X and Y directions. The absorption onset and the direct band gap are ~2.25 and ~2.52 eV, respectively. The photocurrent depends not only on the sample directions in-plane but also on the angle between the linearly polarized light and the plane of the sample along X and Y directions. The results indicate that the bulk photovoltaic effect together with the depolarization field was ascribed to this phenomenon. Detailed analysis presents that the polarization direction is along [110] direction and this depolarization field induced photocurrent is equal to ~3.53 ΌA/cm2. The BPV induced photocurrent can be approximate described as Jx â 2.23cos(2Ξ), such an angular dependence of photocurrent is produced as a consequence of asymmetric microscopic processes of carriers such as excitation and recombination
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
In the video recommendation, watch time is commonly adopted as an indicator
of user interest. However, watch time is not only influenced by the matching of
users' interests but also by other factors, such as duration bias and noisy
watching. Duration bias refers to the tendency for users to spend more time on
videos with longer durations, regardless of their actual interest level. Noisy
watching, on the other hand, describes users taking time to determine whether
they like a video or not, which can result in users spending time watching
videos they do not like. Consequently, the existence of duration bias and noisy
watching make watch time an inadequate label for indicating user interest.
Furthermore, current methods primarily address duration bias and ignore the
impact of noisy watching, which may limit their effectiveness in uncovering
user interest from watch time. In this study, we first analyze the generation
mechanism of users' watch time from a unified causal viewpoint. Specifically,
we considered the watch time as a mixture of the user's actual interest level,
the duration-biased watch time, and the noisy watch time. To mitigate both the
duration bias and noisy watching, we propose Debiased and Denoised watch time
Correction (DCo), which can be divided into two steps: First, we employ a
duration-wise Gaussian Mixture Model plus frequency-weighted moving average for
estimating the bias and noise terms; then we utilize a sensitivity-controlled
correction function to separate the user interest from the watch time, which is
robust to the estimation error of bias and noise terms. The experiments on two
public video recommendation datasets and online A/B testing indicate the
effectiveness of the proposed method.Comment: Accepted by Recsys'2
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