National Yunlin University of Science and Technology

Chuan-Yu Chang

Matthew Huei-Ming Ma

Yueh-Min Huang

Ching-Ju Chen

Fu-Jen Tsai

Hsiang-Chi Liu

Chia-Yan Cheng

Jun-Ying Li

Man-Ju Cheng

Meng-Syue Li

A buoy position monitoring method includes a buoy positioning step, an unmanned aerial vehicle receiving step and an unmanned aerial vehicle flying step. In the buoy positioning step, a plurality of buoys are put on a water surface. Each of the buoys is capable of sending a detecting signal. Each of the detecting signals is sent periodically and includes a position dataset of each of the buoys. In the unmanned aerial vehicle receiving step, an unmanned aerial vehicle is disposed on an initial position, and the unmanned aerial vehicle receives the detecting signals. In the unmanned aerial vehicle flying step, when at least one of the buoys is lost, the unmanned aerial vehicle flies to a predetermined position to get contact with the at least one buoy that is lost.

Buoy position monitoring method and buoy position monitoring system

A method for correcting infant crying identification includes the following steps: a detecting step provides an audio unit to detect a sound around an infant to generate a plurality of audio samples. A converting step provides a processing unit to convert the audio samples to generate a plurality of audio spectrograms. An extracting step provides a common model to extract the audio spectrograms to generate a plurality of infant crying features. An incremental training step provides an incremental model to train the infant crying features to generate an identification result. A judging step provides the processing unit to judge whether the identification result is correct according to a real result of the infant. When the identification result is different from the real result, an incorrect result is generated. A correcting step provides the processing unit to correct the incremental model according to the incorrect result.

Method and system for correcting infant crying identification

A mouth and nose occluded detecting method includes a detecting step and a warning step. The detecting step includes a facial detecting step, an image extracting step and an occluded determining step. In the facial detecting step, an image is captured by an image capturing device, wherein a facial portion image is obtained from the image. In the image extracting step, a mouth portion is extracted from the facial portion image so as to obtain a mouth portion image. In the occluded determining step, the mouth portion image is entered into an occluding convolutional neural network so as to produce a determining result, wherein the determining result is an occluding state or a normal state. In the warning step, a warning is provided according to the determining result.

Mouth and nose occluded detecting method and system thereof

A facial stroking detection method includes a detecting step and a determining step. The detecting step includes a pre-processing step, a feature extracting step and a feature selecting step. In the pre-processing step, an image is captured by an image capturing device, and the image is pre-processed so as to obtain a post-processing image. In the feature extracting step, a plurality of image features are extracted from the post-processing image so as to form an image feature set. In the feature selecting step, a determining feature set is formed by selecting a part of the image features from the image feature set and entered into a classifier. In the determining step, wherein the classifier provides a determining result according to the determining feature set.

Facial stroking detection method and system thereof

A heart rate detection method includes a facial image data acquiring step, a feature points recognizing step, an effective displacement signal generating step and a heart rate determining step. The feature points recognizing step is for recognizing a plurality of feature points, wherein a number range of the feature points is from three to twenty, and the feature points include a center point between two medial canthi, a point of a pronasale and a point of a subnasale of the face. The effective displacement signal generating step is for calculating an original displacement signal, wherein the original displacement signal is converted to an effective displacement signal. The heart rate determining step is for transforming the effective displacement signals of each of the feature points to an effective spectrum, wherein a heart rate is determined from one of the effective spectrums corresponding to the feature points, respectively.

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Yunlin County, Taiwan

Chuan-Yu Chang