International Business Machines Corporation

Hon Hai Precision Industry Co., Ltd.

Hongfujin Precision Electronics (zhengzhou) Co., Ltd.

Boehringer Ingelheim International Gmbh

Other

Ambit Microsystems (shanghai) Ltd.

Shanghai Fenqi Industrial Co., Ltd.

Jun Chi Yan

Jian Ding

Guo Qiang Hu

Jing Chang Huang

Wei Shan Dong

Peng Gao

Yu Wang

Min Gong

Dong Sheng Li

Stephen Mingyu Chu

Zhi Hu Wang

Wei-Da Yang

Eddy Liu

Xin Hao Cheng

Han Yang

Er-Wei Chen

Hsin-Ta Lin

Xiao-Ming Lv

Shu-Fa Jiang

Jun-Hui Zhang

Chih-Yuan Cheng

Qi Feng

Zhe-Qi Zhao

Ning Duan

Chao Zhang

Peng Ji

Chang Li

Qian Zhao

Jun Chen

Wei Peng Zhang

Wei Sun

Chun Ma

Min Li

Jianbin Tang

Weitong Dong

Ulrich Scholz

Feng Juan Wang

Da Deng

Chao E Zhang

Lidong Xing

Xin Zhang

Bin Wang

Ya Ming Zhang

Shi Lei Zhang

Jun Lu

Liang Wu

Jian Huang

Qin Wang

Yan Fu

Lijun Sun

Jian Dong Ding

Ren Jie Yao

Xiangle Jin

Jinsong Yang

Joerg Brandenburg

Yonghong Wang

Xiang Fang

Xin Wang

Jutta Kroeber

Qijun Zhang

Yuchuan Du

Bo Han

Weipeng Zhang

Jiangbin Wu

Shengchuan Jiang

Chenglong Liu

Zuodong Liang

Wengang Zhang

Juncheng Zheng

Guanghua Lv

Chengyue Jia

Ning Pan

Chang Chun Liu

Peiyuan Lin

Junning Xiang

Yan-Hua He

Wenzhuo Liu

A receiving structure providing real-time information as to the materials constituting its contents includes a box body, a first side plate, and a second side plate. The box body, the first side plate, and the second side plate forming a receiving space, and the receiving space receives materials. A discharge port is formed between the second side plate and the first side plate, the discharge port is in communication with the receiving space, and the discharge port is configured to take out the material. A material checking unit is provided on the first side plate, the material checking unit detects and enables real time identification of the materials in the receiving structure.

Automatic feeding system

Packing box

A method and system obtaining positioning data from an object traveling on a plurality of routes; mapping the data into a plurality of points on a digital map; identifying points that are unmatched to the stored route trajectory; obtaining candidate transition points from the unmatched points; aggregating the candidate transition points by applying a clustering algorithm; selecting a first cluster of points and a plurality of second clusters of points, determining a confidence level that the first cluster of points are transition points indicating a transition between the routes, classifying the first cluster of points as a first plurality of traveling points having a first direction in response to the confidence being below a threshold confidence and automatically adjusting the stored route trajectory to indicate that the first cluster of points are on the route trajectory.

Method and apparatus of data classification for routes in a digitized map

Embodiments of the present invention facilitate product defect detection. A computer-implemented method comprises: receiving, by a device operatively coupled to one or more processors, a template image of a normal product; generating, by the device, one or more geometric training parameters for transforming the template image; and transforming, by the device, the template image using the one or more geometric training parameters to generate a transformed image for training a data model, wherein the trained data model being used for aligning the template image and an image under inspection of a product.

Product defect detection

Receiving structure and automatic feeding system

A computer-implemented method applies labels to unlabeled public data for use by a global model. One or more processors train one or more local machine learning models with local private data to create one or more trained models. Processor(s) generate a label for each of the local private data using the one or more trained models, where each label describes the local private data, and then apply the label to unlabeled public data to create labeled public data. One or more processors then input the labeled public data into a global model that uses the public data.

Training a machine learning model in a distributed privacy-preserving environment

A concrete inside interface processing structure for secant pile construction, including secant concrete pile, formed plain concrete pile, rotary jet grouting piles, steel mesh sheet, ribbed steel bar, Y-shaped connecting piece, reinforced concrete pile, and U-shaped connecting piece; drilled holes are arranged at intervals along axis of the formed plain concrete pile; the ribbed steel bar is anchored in each drilled hole; the steel mesh sheet is formed by connecting longitudinal bars and transverse bars, and driven into a soil body between the formed plain concrete piles; the concrete layer is formed by spraying concrete on the steel mesh sheet; the reinforced concrete pile is cast at outside of the formed plain concrete pile, and tangent to the formed plain concrete pile, and interlocked with the rotary jet grouting piles; the rotary jet grouting piles are tangent to the formed plain concrete pile or secant concrete pile.

Concrete inside interface processing structure and method for secant pile construction

A method, system, and computer program storage product determine determining a trajectory information type of a receipt submitted by an employee. Trajectory information associated with the receipt submitted by the employee is retrieved based on the trajectory information type. Trajectory information corresponding to a device associated with the employee is also retrieved. The receipt is determined as a valid receipt in response to the trajectory information associated with the receipt submitted by the employee matching the trajectory information associated with the device associated with the employee.

Expense compliance checking based on trajectory detection

A method for leakage detection of underground pipeline corridor based on dynamic infrared thermal image processing. The leakage can be detected by the following steps, including: converting infrared thermal videos into infrared thermal images; obtaining the gray scale information and temperature information of internal environment of the underground pipeline corridor. The gray scale information can realize the conventional target of pipe line state identification inside the pipeline corridor, and the temperature information can be used to detect the pipe leakage.

Method for leakage detection of underground pipeline corridor based on dynamic infrared thermal image processing

Embodiments of the present disclosure provide method, system and computer program product for privacy preserving smart metering. According to one embodiment of the present disclosure, a plurality of customer-encrypted meter readings are received, each of the plurality of customer-encrypted meter readings being encrypted by a customer using a customer key, then a summation of customer keys is obtained, next the plurality of customer-encrypted meter readings are summed, and the summation of the plurality of customer-encrypted meter readings is decrypted based on the obtained summation of customer keys.

Privacy-preserving smart metering

A computer-implemented method is provided for image-based, self-guided object detection. The method includes receiving, by a processor device, a set of images. Each of the images has a respective grid thereon that is labeled regarding a respective object to be detected using grid level label data. The method further includes training, by the processor device, a grid-based object detector using the grid level label data. The method also includes determining, by the processor device, a respective bounding box for the respective object in each of the images, by applying local segmentation to each of the images. The method additionally includes training, by the processor device, a Region-based Convolutional Neural Network (RCNN) for joint object localization and object classification using the respective bounding box for the respective object in each of the images as an input to the RCNN.

Self-guided object detection in regular images

A method for object defect detection includes receiving digital data representing an image of an object with a repeated pattern. The method identifies a part of the image of the object as defined by a sample window of the digital data. The method generates one or more functions from at least the part of the image, wherein each of the one or more functions corresponds to one component of a pixel contained in the part of the image. Responsive to performing self-similarity analytics on the one or more functions, the method identifies a defect area of the object.

Self-similarity analysis for defect detection on patterned industrial objects

This disclosure provides a method for object detection. The method comprises receiving a user input that specifies one or more first regions and one or more second regions in a template image. The one or more second regions include one or more objects of interest. The method further comprises for each of the one or more first regions discovering a third region in an image under detection corresponding to the first region in the template image by matching the image under detection with the template image. The method further comprises computing a transformation function based on the matching from each of the one or more first regions to its corresponding third region. The method further comprises applying the computed transformation function to the one or more second regions to localize one or more fourth regions in the image under detection for the object detection.

Object detection

Growing community of inventors

Shanghai, China

Jun Chi Yan