Research on forming quality of poly-wedge pulley spinning

As an important power transmission part, pulleys are widely used in automobile industry, agricultural machinery, pumps and machines. A near-net forming process for six-wedge belt pulleys manufacturing was put forward. For this purpose, the required tooth shape and size can be formed directly by spinning without machining. The whole manufacturing procedures include blanking, drawing and spinning. The spinning procedure includes five processes, performing, drumming, thickening, toothing and finishing. The forming defects occurred during each forming processes of poly-wedge pulley spinning, such as the drumming failure, flanged opening-end, folded side-wall, insufficient bottom size, flashed opening-end, cutting-off bottom, are introduced, and the factors influencing the defects are analyzed. The corresponding preventive measures are put forward

The Chinese Characteristics in the Planning and Design of Guangdong Greenway

As a typical example of greenway networks in China and Asia, Guangdong Greenway Network is of epoch-making and practical significance. The practice of Pearl River Delta Regional Greenway is instructive for other developing countries and densely-populated urban areas to build their greenway networks. The experience of Guangdong Greenway Network gives new Chinese characteristics to the concept and practice of greenway. China has a profound history of greenways. Even before the Qin Dynasty was established, there already existed a great number of prototypes of greenway. These greenways were born in ancient era where the original productivity remained at a low level and people had nothing to do but to revere nature. Today, when human being has accumulated a great deal of material and spiritual wealth, they come to realize the importance of balance between man and nature. Therefore, greenway becomes one of the active ways for balancing the development of man and nature. Pearl River Delta Region is one of the most developed areas in China and it is also where the first greenway programs in China are initiated. The Pearl River Delta Regional Greenway has set a typical sample for the greenway movement in China

Towards Realizing the Value of Labeled Target Samples: a Two-Stage Approach for Semi-Supervised Domain Adaptation

Semi-Supervised Domain Adaptation (SSDA) is a recently emerging research topic that extends from the widely-investigated Unsupervised Domain Adaptation (UDA) by further having a few target samples labeled, i.e., the model is trained with labeled source samples, unlabeled target samples as well as a few labeled target samples. Compared with UDA, the key to SSDA lies how to most effectively utilize the few labeled target samples. Existing SSDA approaches simply merge the few precious labeled target samples into vast labeled source samples or further align them, which dilutes the value of labeled target samples and thus still obtains a biased model. To remedy this, in this paper, we propose to decouple SSDA as an UDA problem and a semi-supervised learning problem where we first learn an UDA model using labeled source and unlabeled target samples and then adapt the learned UDA model in a semi-supervised way using labeled and unlabeled target samples. By utilizing the labeled source samples and target samples separately, the bias problem can be well mitigated. We further propose a consistency learning based mean teacher model to effectively adapt the learned UDA model using labeled and unlabeled target samples. Experiments show our approach outperforms existing methods

Consistency Regularization for Generalizable Source-free Domain Adaptation

Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset, making it applicable in a variety of real-world scenarios. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. This oversight leads to overfitting issues and constrains the model's generalization ability. In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets. Our method leverages soft pseudo-labels generated from weakly augmented images to supervise strongly augmented images, facilitating the model training process and enhancing the generalization ability of the adapted model. To leverage more potentially useful supervision, we present a sampling-based pseudo-label selection strategy, taking samples with severer domain shift into consideration. Moreover, global-oriented calibration methods are introduced to exploit global class distribution and feature cluster information, further improving the adaptation process. Extensive experiments demonstrate our method achieves state-of-the-art performance on several SFDA benchmarks, and exhibits robustness on unseen testing datasets.Comment: Accepted by ICCV 2023 worksho

A new alkaloid from Portulaca oleracea L. and its antiacetylcholinesterase activity

International audienc