scispace - formally typeset
Search or ask a question
Author

Yixuan Fu

Bio: Yixuan Fu is an academic researcher from South China University of Technology. The author has contributed to research in topics: Clathrate hydrate & Deep learning. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: In this paper , the potential of hydrate-based desalination (HBD) in treating brine with concentrations up to 16 wt% was demonstrated, where carbon material with high crystallinity is more effective in inducing hydrate nucleation in NaCl solution due to the properly tuned interface hydrogen-bonding network as confirmed by Raman spectra.

6 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: The segmentation results shown in the paper demonstrated that the combined image segmentation algorithms and image preprocessing methods can successfully segment the thyroid regions out of thyroid ultrasound images.
Abstract: Image segmentation for thyroid ultrasound images is a challenging task. As such, several image segmentation algorithms combined with different image preprocessing methods applied to thyroid ultrasound image segmentation are studied in this work. The image segmentation algorithms presented in this paper include edge detection, regional segmentation and active contour without edge algorithms. The image preprocessing methods presented in this paper include Butterworth low-pass filtering, Butterworth high-pass enhanced filtering, and adaptive weighted median filtering. In the experiments, the image segmentation algorithms and image preprocessing methods were combined to evaluate the segmentation results for thyroid ultrasound images. The segmentation results shown in the paper demonstrated that the combined image segmentation algorithms and image preprocessing methods can successfully segment the thyroid regions out of thyroid ultrasound images.

3 citations

Proceedings ArticleDOI
11 Sep 2021
TL;DR: In this paper, the relative position relationship between different tissues in ultrasound images is added to the loss function as a prior knowledge to improve the semantic expression of loss function in measuring the difference between the predicted results and the ground-truth labels.
Abstract: In deep learning, loss function plays a crucial role in training an effective neural network model. In the task of ultrasound image segmentation, the pixel-wise loss functions such as cross-entropy loss and dice loss are usually used to train a deep neural network model. These loss functions only count the distribution differences between the predicted results of the model and the ground-truth labels at pixel level, but do not pay attention to the consistence of the spatial relations between different tissues in the predicted results and the real images. In order to improve the semantic expression of the loss function in measuring the difference between the predicted results and the ground-truth labels, we use the concept of relative fuzzy connectedness to add the relative position relationship between tissues in ultrasound images to the loss function as a prior knowledge.
Journal ArticleDOI
TL;DR: In this paper , a Meyer rod-coating approach was proposed to fabricate large-size and flat MXene membranes at a scale up to 5 m. This roll-to-roll method will promote an industry-level fabrication and application of MXene-based membranes.
Abstract: Application-oriented assembly of two-dimensional nanosheets with uniform nanochannels is critical for fabricating sophisticated, high-performance membranes for water treatment. However, fabricating the desired membranes by a simple, fast, and effective method is a challenge as most of the previous methods are based on batch processes rather than a continuous roll-to-roll process. Here, a simple Meyer rod-coating approach to continuously fabricate large-size and flat MXene membranes at a scale up to 5 m is introduced. This study demonstrated that a high MXene concentration, above 10 mg mL−1, is critical in processability due to the desired viscosity, surface tension, and viscoelastic properties. The as-made MXene membranes show that shearing and solutal-Marangoni flow can considerably improve the ordering of the stacked MXene nanoflakes. Thus, the rod-coated MXene membranes demonstrate a smaller surface roughness and interlayer distance compared to the MXene membranes fabricated by the most commonly vacuum-assisted filtration. The rod-costed MXene membranes show superior performance in dye and mono/divalent cation separation. The proposed roll-to-roll Meyer rod-coating method can also be used to fabricate MXene-based composites, such as MXene/carbon nanotubes and MXene/polymer, using the inks containing high concentration MXene and other desired compositions. This roll-to-roll method will promote an industry-level fabrication and application of MXene-based membranes.

Cited by
More filters
Journal ArticleDOI
TL;DR: In this paper , the authors investigated the effect of Fe3O4 nanoparticles and magnetic field on the cyclopentane hydrate formation in seawater desalination process.

3 citations

Journal ArticleDOI
TL;DR: In this paper , an environmentally friendly hybrid capacitive deionization method was used to selectively remove Cu2+ from wastewater by using the redox-active CuS electrode for the first time.
Abstract: Resource shortage and industrial wastewater pollution are important problems concerning environmental safety. Copper is not only an important industrial metal but also a common heavy metal contamination in water. Selective extraction of copper from wastewater is a huge challenge. Here, an environmentally friendly hybrid capacitive deionization method was used to selectively remove Cu2+ from wastewater by using the redox-active CuS electrode for the first time. CuS as a cathode material can significantly reduce the concentration of Cu2+ in wastewater with the high adsorption capacity (350.04 mg·g–1) and excellent selective adsorption. The removal efficiency for Cu2+ is greater than 90%, and the distribution coefficient Kd is over 104 mL·g–1 in a variety of salt ions and heavy metal-ion mixtures. Additionally, the electrode shows high cyclic stability in the adsorption of Cu2+. Importantly, CuS exhibits an outstanding copper extraction performance in real water samples (e.g., industrial wastewater and natural lake water), which confirms the high applicability of CuS in real-world scenarios. Ex situ XRD and XPS tests were used to unveil the Cu2+ removal mechanism. This work provides a new direction for the removal of copper from wastewater and a possibility for the application of copper resource extraction from wastewater.

3 citations

Journal ArticleDOI
01 Dec 2022-Fuel
TL;DR: In this article , the authors investigated the reformation characteristics of gas hydrate in the aqueous solution with residual guest concentration, which was achieved by setting the dissociation (hold) pressure above the atmospheric pressure.

2 citations

Journal ArticleDOI
TL;DR: In this paper, a deep learning-based framework for diagnosing human malaria infection from microscopic images of thin blood smears is presented, which is based on a direct segmentation and classification approach which relies on the analysis of the parasite itself.
Abstract: Malaria is an infectious disease caused by Plasmodium parasites and is potentially human life-threatening. Children under 5 years old are the most vulnerable group with approximately one death every two minutes, accounting for more than 65% of all malaria deaths. The World Health Organization (WHO) encourages the research of appropriate methods to treat malaria through rapid and economical diagnostic. In this paper, we present a deep learning-based framework for diagnosing human malaria infection from microscopic images of thin blood smears. The framework is based on a direct segmentation and classification approach which relies on the analysis of the parasite itself. The framework permits to segment the Plasmodium parasite in the images and to predict its species among four dominant classes: P. Falciparum, P. Malaria, P. Ovale, and P. Vivax. A high potential of generalization with a competitive performance of our framework on inter-class data is demonstrated through an experimental study considering several datasets. Our source code is publicly available on https://github.com/Benhabiles-JUNIA/MalariaNet.

2 citations

04 Feb 2020
TL;DR: By implementing this system, the time for the diagnosis of malaria will be cut down, which will save lives and the medical resources that are used while waiting for the results of the tests from the old system.
Abstract: As technology has evolved it has become more and more efficient to diagnose, and treat multiple diseases. Malaria is one of the deadliest diseases on this pl anet. Each year it estimated that 1 million people die as a re sult of this disease. Furthermore 3.4 billion people are in dang er of contracting malaria. With advances in the field of medicine it is now entirely possible to not only tr eat but also prevent malaria. The way in which people are diagnosed for malaria today is through blood sample s. The techniques used currently are accurate however they are time consuming. This has necessitated doctors to st art the treatment for malaria before the blood work is finished, since in its later stage’s malaria can be very diff icult to cure. The system that is discussed aims to cut this time requirement by at least half and increase the accur acy of the tests. An automated system that gathers the ima ge data and analyses the images for malarial parasites is described. A system for collection of data and anal ysis is described. By implementing this system, the time ne eded for the diagnosis of malaria will be cut down. This will save lives and the medical resources that are used while waiting for the results of the tests from the old system. Keywords—Deep Learning, Malaria, Parasite

1 citations