Adaptive Backstepping-based H∞ Robust controller for Photovoltaic Grid-connected Inverter

To improve the robustness and stability of the photovoltaic grid-connected inverter system, a nonlinear backstepping-based H∞ 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 H∞ 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 H∞ 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 étude est d améliorer l extension du logiciel de programmation hors-ligne RobotStudio existante et de développer une nouvelle stratégie pour générer la trajectoire du robot par rapport aux paramètres essentiels de projection thermique. Notamment, l historique de la température par rapport à la trajectoire générée est prise en compte dans cette étude.L extension logicielle Thermal Spray Toolkit (TST) intégrée dans le cadre de RobotStudio est spécialement développée pour générer la trajectoire du robot en projection thermique. L amélioration de l extension TST dans la nouvelle version de RobotStudio est mise au point sur deux modules principaux :PathKit : génération de la trajectoire sur des pièces complexes.ProfileKit : modélisation du cordon singulier du dépôt et prédiction de son épaisseur en fonction des paramètres opératoires.Les déficiences existantes de l extension TST impliquent de mettre en œuvre une méthode plus avancée qui permettra de générer la trajectoire du robot en utilisant le maillage pour le calcul d élément finis. Ainsi, l opération de projection thermique pourra être menée. Dans cette étude, la méthodologie de maillage est introduite afin de fournir une stratégie de choix de points de trajectoire et l obtention d orientations de ces points de trajectoire sur la surface à revêtir. Un module dit MeshKit est donc ajouté dans l extension TST afin de lui apporter ces fonctionnalités nécessaires.Un couplage entre la trajectoire du robot et la répartition de chaleur du substrat a été développé, ce qui permet d étudier l évolution de température 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

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 μA/cm2. The BPV induced photocurrent can be approximate described as Jx ≈ 2.23cos(2θ), 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 (D$^2$Co), 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

The effect of continuous venovenous hemofiltration on neutrophil gelatinase-associated lipocalin plasma levels in patients with septic acute kidney injury

International audienceCe texte d’introduction au dossier de Flux 2017/2 (N° 108) questionne l’émergence de la thématique de la circularité des matières dans les politiques publiques urbaines contemporaines. Les articles ont en commun de porter une attention minutieuse à la matérialité des flux qui traversent et constituent la ville et aux objets sociaux qui la composent. Ils analysent les modalités et les conséquences de leur mise en circulation, ainsi que les régulations et les conflits qui l’accompagnent. Que l’ensemble des articles traite de pratiques et de politiques ancrées dans l’espace de la région de Lyon résulte moins d’une volonté monographique que d’une rencontre en partie fortuite. Mais cela souligne en tout cas l’importance d’une approche toujours attentive aux faits géographiques et aux effets de lieu dans la diversité de leurs échelles. Trois thématiques transversales sont présentes : d’abord, en identifiant de nouvelles ressources, les articles permettent de réfléchir à l’invention et à la construction de nouveaux circuits pour les matières. Ensuite, la régulation de ces circuits implique l’identification de nouveaux acteurs et la mise en place de nouvelles formes de relations avec les producteurs et gestionnaires des matières, formant donc l’espace d’une gouvernance renouvelée. Enfin, si ces circuits se structurent dans un espace qui est celui de la proximité géographique, ils s’inscrivent néanmoins dans une logique relationnelle qui ne cesse de questionner les normes et les échelles. Ce numéro permet ainsi de nuancer et de re-matérialiser les injonctions à faire advenir l’économie circulaire dans les villes