Class-Incremental Grouping Network for Continual Audio-Visual Learning

Continual learning is a challenging problem in which models need to be trained on non-stationary data across sequential tasks for class-incremental learning. While previous methods have focused on using either regularization or rehearsal-based frameworks to alleviate catastrophic forgetting in image classification, they are limited to a single modality and cannot learn compact class-aware cross-modal representations for continual audio-visual learning. To address this gap, we propose a novel class-incremental grouping network (CIGN) that can learn category-wise semantic features to achieve continual audio-visual learning. Our CIGN leverages learnable audio-visual class tokens and audio-visual grouping to continually aggregate class-aware features. Additionally, it utilizes class tokens distillation and continual grouping to prevent forgetting parameters learned from previous tasks, thereby improving the model's ability to capture discriminative audio-visual categories. We conduct extensive experiments on VGGSound-Instruments, VGGSound-100, and VGG-Sound Sources benchmarks. Our experimental results demonstrate that the CIGN achieves state-of-the-art audio-visual class-incremental learning performance. Code is available at https://github.com/stoneMo/CIGN.Comment: ICCV 2023. arXiv admin note: text overlap with arXiv:2303.1705

The effect of mandatory regulation on corporate social responsibility reporting quality: evidence from China

Corporate Social Responsibility (CSR) disclosure has attracted attention from regulatory bodies and academics over the past few decades. Due to the unreliability resulted from CSR voluntary disclosure, an increasing number of researchers are calling for more government regulation on CSR disclosure. Based on 1830 standalone CSR reports disclosed by the Chinese-listed firms during 2009-2012, we examine the effect of mandatory regulation on CSR reporting quality. We further hypothesize and test for the moderating effect of firm size and other characteristics on the link between government regulation on CSR reporting quality. Our results suggest that government mandatory regulation leads to an overall improvement in CSR reporting quality. We also find that this positive effect is greater when firms are larger and have better financial performance, but less when firms are controlled by government. Our study provides a direct answer to the recent calling for mandatory disclosure on CSR reports, and helps to understand why recent studies of social disclosure regulation suggest that government interventions do not seem to resolve the problems that are generally attributed to voluntary disclosures. Our findings should be of interest to the academics, regulators, and investors

Audio-Visual Class-Incremental Learning

In this paper, we introduce audio-visual class-incremental learning, a class-incremental learning scenario for audio-visual video recognition. We demonstrate that joint audio-visual modeling can improve class-incremental learning, but current methods fail to preserve semantic similarity between audio and visual features as incremental step grows. Furthermore, we observe that audio-visual correlations learned in previous tasks can be forgotten as incremental steps progress, leading to poor performance. To overcome these challenges, we propose AV-CIL, which incorporates Dual-Audio-Visual Similarity Constraint (D-AVSC) to maintain both instance-aware and class-aware semantic similarity between audio-visual modalities and Visual Attention Distillation (VAD) to retain previously learned audio-guided visual attentive ability. We create three audio-visual class-incremental datasets, AVE-Class-Incremental (AVE-CI), Kinetics-Sounds-Class-Incremental (K-S-CI), and VGGSound100-Class-Incremental (VS100-CI) based on the AVE, Kinetics-Sounds, and VGGSound datasets, respectively. Our experiments on AVE-CI, K-S-CI, and VS100-CI demonstrate that AV-CIL significantly outperforms existing class-incremental learning methods in audio-visual class-incremental learning. Code and data are available at: https://github.com/weiguoPian/AV-CIL_ICCV2023.Comment: Accepted at ICCV 202

Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network

AbstractMassive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology

Thermal simulation modeling of a hydrostatic machine feed platform

Hydrostatic guideways are widely applied into precision and ultra-precision machine tools. Meanwhile, the oil film heat transfer causes thermal disturbance to the machine accuracy. Therefore, it is necessary to study the mechanism of the oil film heat transfer and the heat-transfer-reducing method to improve the machine accuracy. This paper describes a comprehensive thermal finite element (FE) simulation modeling method for the hydrostatic machine feed platform to study methods of reducing machine thermal errors. First of all, the generating heat power of viscous hydraulic oil flowing between parallel planes is calculated based on the Bernoulli equation. This calculation is then employed for the simulation load calculations for the closed hydrostatic guideways, which is adopted by the hydrostatic machine feed platform. Especially, in these load calculations, the changing of oil film thickness (resulted from external loads) and the changing of oil dynamic viscosity (influenced by its temperature) are taken into account. Based on these loads, thermal FE simulation modeling of the hydrostatic machine feed platform is completed to predict and analyze its thermal characteristics. The reliability of this simulation modeling method is verified by experiments. The studies demonstrate that the hydrostatic machine thermal error degree is determined by the oil film heat transfer scale, and this scale is mainly influenced by the relative oil supply temperature to ambient temperature (quantitative comparison of oil supply temperature and ambient temperature). Furthermore, the reduction of the absolute value of this relative oil supply temperature can reduce the oil film heat transfer scale and improve the machine accuracy

Dynamic modeling and control of a novel XY positioning stage for semiconductor packaging

This paper presents the dynamic modeling and controller design of an XY positioning stage for semiconductor packaging. The XY stage is directly driven by two linear voice coil motors, and motion decoupling between the X and Y axes is realized through a novel flexible decoupling mechanism based on flexure hinges and preloaded spring. Through bond graph method, the dynamic models of X- and Y-axes servomechanisms are established, respectively, and the state space equations are derived. A control methodology is proposed based on force compensations and the performance of the XY stage is investigated by simulations and experimental tests. The results show that the XY stage has good performance. When the reference displacements are defined as 2 mm, the settling time of the X-axis movement is 64 ms, and the overshoot is 0.7%. Y-axis settling time is 62 ms, and the overshoot is 0.8%. X-axis positioning accuracy is 1.85 μm and the repeatability is 0.95 μm. Y-axis positioning accuracy and repeatability are 1.75 μm and 0.9 μm, respectively. In addition, the stage can track linear, circular and complex trajectories very well

A differentiated multi-loops bath recirculation system for precision machine tools

Traditional bath recirculation cooler for precision machine tools always has the uniform and open-loop cooling strategy onto different heat generating parts. This causes redundant generated heat being transferred into the machine structure, and results in unsatisfactory thermal errors of precision machine tools. For the solution of this problem, this paper presents the differentiated multi-loops bath recirculation system. The developed system can accomplish differentiated and close-loop cooling strategies onto machine heat generating parts during its operation. Specially, in order to illustrate the advantages of this system, constant supply cooling powers strategy is presented with its applications onto a certain type of built-in motorized spindle. Consequently, advantages of the proposed strategy based on the differentiated multi-loops bath recirculation system are verified experimentally in the environment within consistent temperature (TR = 20 ± 0.3°C). Compared with room temperature tracing strategy based on the traditional bath recirculation cooler, the constant supply cooling powers strategy is verified to be advantageous in spindle temperature stabilization and thermal errors decrease

Active and intelligent control onto thermal behaviors of a motorized spindle unit

Motorized spindle unit is the core component of a precision CNC machine tool. Its thermal errors perform generally serious disturbance onto the accuracy and accuracy stability of precision machining. Traditionally, the effectiveness of the compensation method for spindle thermal errors is restricted by machine freedom degrees. For this problem, this paper presents an active, differentiated, and intelligent control method onto spindle thermal behaviors, to realize comprehensive and accurate suppressions onto spindle thermal errors. Firstly, the mechanism of spindle heat generation/dissipation-structural temperature-thermal deformation error is analyzed. This modeling conveys that the constantly least spindle thermal errors can be realized by differentiated and active controls onto its structural thermal behaviors. Based on this principle, besides, the active control method is developed by a combination of extreme learning machine (ELM) and genetic algorithm (GA). The aim is to realize the general applicability of this active and intelligent control algorithm, for the spindle time-varying thermal behaviors. Consequently, the contrasting experiments clarify that the proposed active and intelligent control method can suppress accurately and synchronously all kinds of spindle thermal errors. It is significantly beneficial for the improvements of the accuracy and accuracy stability of motorized spindle units