Soochow University

Centec Networks (su Zhou) Co., Ltd.

Suzhou Neologics Bioscience Co., Ltd.

Positec Power Tools Co., Ltd.

Dong Wang

Jun Wang

Weiguo Huang

Zhongkui Zhu

Changqing Shen

Xingxing Jiang

Aiwen Zhang

Wei He

Jiang Du

Juanjuan Shi

Xu Wang

Yongjun Shen

Yu Xia

Liang Chen

Yu Zhang

Dongmiao Song

Bincai Lan

Guifu Du

Qian Gao

Liegang Shao

Xiaofeng Shang

Haojie Wang

Jun Ma

Baiyang Wang

Shuangjie Liu

Zaigang Chen

Jing Xie

Xin Dong

Jinyu Dong

Binbin Wang

Hongbo Que

Minjie Chen

Piao Lei

Chuancang Ding

Lin Kong

Chunjian Yuan

Disclosed herein are anti-TIM-3 antibodies and antigen-binding fragments, polynucleotides encoding the antibodies and antigen-binding fragments, and pharmaceutical compositions comprising the antibodies and antigen-binding fragments. Uses of the anti-TIM-3 antibodies and antigen-binding fragments described herein in cancer treatment are also disclosed.

TIM-3-targeting antibodies and uses thereof

The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the deep convolutional neural network model.

Dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions

The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.

Intra-class adaptation fault diagnosis method for bearing under variable working conditions

The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.

Adaptive manifold probability distribution-based bearing fault diagnosis method

The present invention provides a method and system for accurate IPG compensation of USXGMII multi-channel. The method comprises acquiring the length of a correspondingly deleted IPG unit between the inserted two sets of AM corresponding to each logical channel according to the working rate of a physical link, the number of distributed logical channels, the effective transmission rate corresponding to each logical channel, and the length of a set of AM inserted into each logical channel; and acquiring the data deletion interval corresponding to each logical channel, and the length of the IPG data to be deleted between the data deletion intervals according to the length of the correspondingly deleted IPG unit of each logical channel and the number of the inserted AM. The data transmission rate is enabled to be precisely matched with the bearing rate of the logical channel, thereby meeting the performance requirements.

Method and system for accurate IPG compensation of USXGMII multi-channel

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Suzhou, China

Dong Wang