Utechzone Co., Ltd.

Great Computer Corporation

Cathay General Hospital

Tzu-chi University of Science and Technology

Chia-Liang Lu

Chih-Heng Fang

Arulmurugan Ambikapathi

Ming-Tang Hsu

Chien-Chung Lin

Liang Shih

Sheng-Chieh Lo

Hung-Ju Tsai

Ming-Chuan Kuo

Zu-Chun Lin

Chia-Hui Chu

Ping-I Li

Heng-Kuan Lin

Ming-Cheng Lai

A method for laser processing a packaging box is provided, which includes feeding a paper material printed with a pattern on the front side thereof to a cutting machine by back feeding using an automatic paper feeder; performing edge finding on the rear side of the paper material, and forming indentations on the rear side of the paper material corresponding to where the pattern is on the front side; conveying the paper material to a laser cutting machine to perform registration marking on the rear side of the paper material before laser cutting; and finally performing laser cutting based on the registration markings to obtain a packaging box with the pattern printed on the front side and the indentations formed on the rear side.

Method for laser processing packaging box

An optical inspection method for an optical inspection device comprising an optical lens is provided according to an embodiment of the disclosure. The optical inspection method includes: obtaining a first image of an object by the optical lens; performing an edge detection on the first image to obtain a second image comprising an edge pattern; and performing a defect inspection operation on the second image based on a neural network architecture to inspect a defect pattern in the second image. In addition, an optical inspection device and an optical inspection system are provided according to embodiments of the disclosure.

Defect inspection method, defect inspection device and defect inspection system

A defect inspection system, connected to an automatic visual inspection device, is provided, including the followings. A re-inspection server (VRS) receives a defect image and a defect location. A training terminal stores trained modules. A classification terminal receives the defect image and the defect location, reads a target trained module corresponding to the defect image, classifies the defect image according to the target trained module to obtain a labeled defect image, and sends the labeled defect image to the VRS. A re-inspection terminal receives the labeled defect image from the VRS, and sends a verified operation corresponding to the labeled defect image to the VRS. A labeling re-inspection terminal receives the verified operation and the labeled defect image, and a labeling result corresponding to the labeled defect image. The VRS sends the labeling result and the labeled defect image to the training terminal to train a corresponding training module.

Defect inspection system and method using artificial intelligence

The present invention is an automated optical inspection method using deep learning, comprising the steps of: providing a plurality of paired image combinations, wherein each said paired image combination includes at least one defect-free image and at least one defect-containing image corresponding to the defect-free image; providing a convolutional neural network to start a training mode of the convolutional neural network; inputting the plurality of paired image combinations into the convolutional neural network, and adjusting a weight of at least one fully connected layer of the convolutional neural network through backpropagation to complete the training mode of the convolutional neural network; and performing an optical inspection process using the trained convolutional neural network.

Automated optical inspection method using deep learning and apparatus, computer program for performing the method, computer-readable storage medium storing the computer program, and deep learning system thereof

The present invention provides a dynamic automatic focus tracking system, comprising an image capturing device for capturing an image of a workpiece in a target picture-taking region; a driving device for adjusting a spacing between the image capturing device and the workpiece; and a focal length adjustment module coupled to the image capturing device and the driving device to generate a control signal according to a figure feature and a predefined figure feature in the image of the workpiece and send the control signal to the driving device, thereby adjusting a position of the image capturing device with the driving device.

Dynamic automatic focus tracking system

An automatic sensing and warning device for placing an intravenous drip includes a transmitter unit, a blocking unit, a receiver unit, and a fixing device. The transmitter unit is attachable to an injection site on a patient's hand and is operable to transmit an identification signal. The receiver unit is arranged on the blocking unit and includes a drive circuit and a warning device. The fixing device supports the blocking unit and the receiver unit on the intravenous drip pole. The RF receiver is set at a predetermined vertical distance from a drip chamber and areas above and below the RF receiver are respectively defined as dangerous and safe areas. When the injection site is spaced from the drip chamber by a distance shorter than the predetermined distance, the RF receiver receives the identification signal from the RF transmitter and the drive circuit activates the warning device to issue warnings.

Automatic sensing and warning device for placing intravenous drip

Growing community of inventors

Taipei, Taiwan

Chia-Liang Lu