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<!DOCTYPE html>
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<h2>Our <span class="color">Researches</span></h2>
<h3>The research direction and results of our laboratory in recent years.</h3>
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<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/Dataset_web.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>CCUMVL-Vehicle-ReID DATASET</h2>
<p>
This dataset is intended for vehicle re-identification purposes.<br>
The vehicle route starts from the Taibao Branch of the Chiayi County Railway Police Bureau(612, Chiayi
County, Taibao City, 23), along the Gaoxie West Road extension of Jiapu Highway(No. 168, Gaotie W Rd, Taibao
City, Chiayi County, 612) to the Puzi Gas Station(Fumaopuzi Station), and a total of 8 cameras are used to
capture the footage along the way.<br><br>
More details record in <a href="download.html">Download page</a>.<br><br>
Please read the License first and submit the application form.<br>
link:<br>
<a href="https://drive.google.com/file/d/1vRMSBM-0VIfDtoUcx2tqD0I31Nvdx914/view">License Link</a><br>
<a href="https://forms.gle/6LRTicntpC2pCJbt7">Apply From Link</a>
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid"
src="images/research/Very_Long_Time_Series_Data_Augmentation_via_Deep_Learning_for_Silicon_Wafer_Quality_Prediction.png"
alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Very Long Time Series Data Augmentation via Deep Learning for Silicon Wafer Quality Prediction</h2>
<p>
In the wafer grinding process, sensors are usually installed in the grinding machine to monitor the
wearing process and predict the product quality. However, labeling the time series data is very
time-consuming. Therefore, this research aims for very long time series data augmentation based on
the deep learning model. The augmented data is verified by wafer quality prediction model.
In this report, the proposed data augmentation deep learning method is divided into three stages:
augmented data generation, feature representation learning, and prediction quality model.
The first stage is the augmented data generation. Temporal Pattern Attention Long short-term memory
(TPA-LSTM) model is utilized to realize data augmentation. The second stage is feature extraction,
which will extract data features based on the Long short-term memory (LSTM) model or the Auto-Encoder model.
It is expected to convert the high-dimensional space of the original signal data into a low-dimensional
feature representation to reduce the burden of model learning. Data slicing method is considered to
further reduce the model size and computational complexity during model training. The third stage is
quality prediction. This is to evaluate the stability under different augmentation settings, different
feature extraction methods and different models.
Experimental results demonstrate the superior performance improvement when the proposed data
augmentation method is exploited for very long time series data generation. The improvement ratio
goes up to 98.42% on the real-world wafer grinding dataset.
</p>
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</div>
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<div class="col-md-6">
<img class="img-fluid"
src="images/research/Generating-Adversarial-Examples-Based-on-Perceptual-Visual-Properties.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Generating Adversarial Examples Based on Perceptual Visual Properties</h2>
<p>
Recent studies show that deep neural networks (DNN) will be affected by
adversarial examples, and images after adding perturbation are called adversarial
examples. Among them, adversarial samples with a large amount of perturbation
can make the deep neural network greatly affected, but it is also easier to detect
the difference between the adversarial samples and the original image in visual
perception. To improve the image quality of adversarial samples, this paper proposes a general improvement
method and an adversarial attack method based on
the perceptual visual characteristics, where perceptual visual characteristics include
spectral sensitivity and just noticeable difference (JND). Our proposed method will
be combined with existing adversarial attack methods, showing the generality of
our method. In addition, it can also become an adversarial attack method independently. Experiments show
that our method can improve the attack performance
of existing methods and improve the image quality of adversarial samples in some
cases.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/calligraphy.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Writing calligraphy on robot</h2>
<p>
The research includes three areas of artistic creation, robotic automation, and
artificial intelligence(AI). The AI technology is used to construct writing style of
famous calligrapher, and writing with the arm.
<br>
<strong>Calligraphy style transfer:</strong>
<br>
 In the process of calligraphy style transfer, the method is based on CycleGAN. With a
improvement of adding embedding layers to overcome that a single model can only convert
a different style limit. By collecting the wrist movements during writing, the robot can
simulate the calligrapher's writing. After the calligraphy are written.
<br>
<strong>Generating stroke orders and robot trajectory:</strong><br>
 Thining the transferred calligraphy lets the robotic arm simulate the calligrapher's
writing action to write the calligraphy characters, we need to convert the coordinates
of the thinned images to get the six-axis data. The six-axis sequence data of the
calligraphy is provided to the robot arm for writing the calligraphy characters.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/makeup-attack-628x279.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Incorporating attack information into makeup to attack deep learning models</h2>
<p>
Machine learning has evolved very rapidly, with good results in both computer vision and
natural language processing. There are many deep learning techniques that are used in
everyday life of humans such as autonomous vehicles and face recognition systems.
Nowadays, the gradual dependence of human daily life on deep neural networks can lead to
serious consequences, so the security of neural networks becomes very important.
Therefore, the deep neural network has obvious weaknesses. We propose a method based on
generating a confrontation network to generate a facial makeup picture that can deceive
the face recognition system. We hide the perturbation of the attack in the results of
the abnormal makeup photos that humans can’t detect. The experimental results show that
we can not only generate high-quality facial makeup images, but also our attack results
have a high attack success rate in the face recognition system.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/music-game-628x396.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Using the Generative Adversarial Network(GAN) to generate music rhythm games</h2>
<p>
The music rhythm game is currently a very popular game, and we propose to generate a
music rhythm game spectrum based on the method of Generative Adversarial Network. The
music is separated into two parts: the vocal and the soundtrack, which makes the
generated spectrum closer to the real spectrum. The model consists of two concepts of
Generative Adversarial Network: Conditional Generative Adversarial Nets (CGANs) for
music information and Improved Wasserstein GAN (WGAN-GP) for better convergence of the
model.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/scribble-lines-to-painting-520x237.jpg" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Be an Artist! Scribble Lines to Painting.</h2>
<p>
We propose a fully automated system that converts random graffiti into a painting.
However, this is a serious challenge because the input graffiti can be very messy and
hide multiple objects, so finding the correlation between these repeated lines and
multiple objects is not a simple matter. In the system, we use selective search, sparse
coding and Convolutional Neural Network (CNN), in which we use selective search to find
the part of the object that may be the object of the graffiti; then use sparse coding to
find the corresponding element; CNN sets the style to be converted. The final
experimental results show that the methods we use have superior performance and produce
artistic works.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/full-model-2-844x484.jpg" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Clothing style analysis and popular element capture</h2>
<p>
With more and more styles of clothing and accessories, regardless of the physical or online store, consumers
will spend a lot of time looking for their favorite styles in many styles, so if consumers
can give some photos of their favorite costumes, systematic analysis Find out the
relevant information in the photo (such as the store address, matching related
accessories, etc.). For the store, if you can collect the relevant clothing styles of
the customers, you can adjust the purchase styles and the furnishings in the store
according to this information, further recommend the related accessories to consumers
according to the preferences of consumers and save consumers to find matching
accessories. time. For garment manufacturers, they can analyze the data collected by
various stores to know which styles are popular and those styles are unpopular, and thus
become the next batch of new style design references.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/60359706-2364327760514781-6025843136377389056-n-641x285.png"
alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Deep Learning for Sensor-Based Rehabilitation</h2>
<p>
In this work, we aimed to
evaluate four kinds of rehabilitation exercises at three levels: good, average, and bad.
We propose a novel evaluation method by learning the best feature of each class.
The idea was to design an evaluation matrix where each entry corresponded to one level
of one exercise. By setting the largest number in one entry, the evaluation matrix could
be used along with the output layer of the deep learning model to infer the best feature of that exercise at
a
particular level.
The evaluation score is obtained by examining the distance measure of the current feature and the best
feature of that class.
We also collect a new rehabilitation
exercise dataset for the rehabilitation exercise evaluation. It contains four different
rehabilitation
actions at three levels, defined by rehabilitation physicians.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/outdoor-low-resolution-face-recognition-554x261.png" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Outdoor low resolution face recognition</h2>
<p>
The goal of this project is to compare low-resolution face images to verify that they
are the same person. In today’s unrestricted environment, the effectiveness of face
recognition often decreases due to posture factors, so we establish a normalization
method to restore any face angle, thereby returning the face angle of any state to
increase The effectiveness of face recognition. The project uses two Caffe model
architectures: Matching-Convolutional NeuralNetwork (M-CNN) and Siamese Neural Network
(SNN). Finally, the accuracy of the SNN model is more than 90%, which is higher than
that of M-CNN.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/image-multi-label-classification-628x461.jpg" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Multiple attributes image classification</h2>
<p>
When sorting face images, there are inevitably some accessories in the images to be
identified, such as sunglasses, scarves, earrings, etc., or external environmental
factors such as light, angle, etc. These accessories or environmental factors are in
people. The face image is called multiple attributes. We uses the existing Local
Discriminant Embedding (LDE) algorithm as an extension to achieve multiple attribute
classification purposes.
</p>
</div>
</div>
<div class="row research-block">
<div class="col-md-6">
<img class="img-fluid" src="images/research/sparse-coding-628x463.jpg" alt="">
</div>
<div class="col-md-6 mt-4 mt-md-0">
<h2>Sparse Coding</h2>
<p>
In recent years, sparse coding has been very popular in the field of computer vision and
image processing. Sparse coding consists of a linear combination of input data,
dictionary and input data. Sparse coding can be used for image denoising, restoration,
and classification. The laboratory focuses on two research directions based on sparse
coding: multiple attribute image classification and sparse coding of huge amounts of
data.
</p>
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