Abstract:
A dye-sensitized solar cell with hybrid nanostructures comprises a negative-polarity conductive substrate, a metal oxide layer, a positive-polarity conductive substrate and an electrolyte. The metal oxide layer has a plurality of nanoparticles and a plurality of nanotubes. The metal oxide layer and the electrolyte are arranged between the negative-polarity conductive substrate and the positive-polarity conductive substrate. The nanoparticles increase contact area with dye and thus enhance power generation efficiency. The nanotubes increase carrier mobility and thus effectively transfer electricity to electrodes. The solar cell integrates the advantages of nanoparticles and nanotubes and offsets the disadvantages thereof to effectively enhance the photovoltaic conversion efficiency of dye-sensitized solar cells.
Abstract:
A SnO2 ISFET device and manufacturing method thereof. The present invention prepares SnO2 as the detection membrane of an ISFET by sol-gel technology to obtain a SnO2 ISFET. The present invention also measures the current-voltage curve for different pH and temperatures by a current measuring system. The temperature parameter of the SnO2 ISFET is calculated according to the relationship between the current-voltage curve and temperature. In addition, the drift rate of the SnO2 ISFET for different pH and hysteresis width of the SnO2 ISFET for different pH loop are calculated by a constant voltage/current circuit and a voltage-time recorder to measure the gate voltage of the SnO2 ISFET.
Abstract:
A method and an apparatus for measuring the temperature parameters of an ISFET that uses hydrogenated amorphous silicon as a sensing film, which uses the measurements of the temperature parameters and the source/drain current and gate voltage in an unknown solution to sense the ion concentration and the pH value of the unknown solution.
Abstract:
A traceability management method for supply chains of agricultural, fishery and animal husbandry products provided by the invention is provided for a user for inputting an identification data, and searching for at least one first data, second data and/or third data matching the identification data from databases, and then performing correlated recursive mathematical operation on the first data, the second data, and/or the third data to search for and establish a traceability node having connection relationship with the first data, the second data and/or the third data. Thereby, relevant historical footprints of a product in a supply chain between manufactories can be traced.
Abstract:
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.
Abstract:
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.
Abstract:
A method for processing three-dimensional point cloud data includes a data creation step, a layering step, a gridding step, a data processing step and a two-dimensional image generation step, so that the three-dimensional point cloud data can be converted into a two-dimensional image, and the two-dimensional image can correspond to, identify and store the axial depth and information point features of the point cloud data in three axes.
Abstract:
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.
Abstract:
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.
Abstract:
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.