Image Data Augmentation from Small Training Datasets Using Generative Adversarial Networks (GANs)

The scarcity of labelled data is a serious problem since deep models generally require a large amount of training data to achieve desired performance. Data augmentation is widely adopted to enhance the diversity of original datasets and further improve the performance of deep learning models. Learning-based methods, compared to traditional techniques, are specialized in feature extraction, which enhances the effectiveness of data augmentation. Generative adversarial networks (GANs), one of the learning-based generative models, have made remarkable advances in data synthesis. However, GANs still face many challenges in generating high-quality augmented images from small datasets because learning-based generative methods are difficult to create reliable outcomes without sufficient training data. This difficulty deteriorates the data augmentation applications using learning-based methods. In this thesis, to tackle the problem of labelled data scarcity and the training difficulty of augmenting image data from small datasets, three novel GAN models suitable for training with a small number of training samples have been proposed based on three different mapping relationships between the input and output images, including one-to-many mapping, one-to-one mapping, and many-to-many mapping. The proposed GANs employ limited training data, such as a small number of images and limited conditional features, and the synthetic images generated by the proposed GANs are expected to generate images of not only high generative quality but also desirable data diversity. To evaluate the effectiveness of the augmented images generated by the proposed models, inception distances and human perception methods are adopted. Additionally, different image classification tasks were carried out and accuracies from using the original datasets and the augmented datasets were compared. Experimental results illustrate the image classification performance based on convolutional neural networks, i.e., AlexNet, GoogLeNet, ResNet and VGGNet, is comprehensively enhanced, and the scale of improvement is significant when a small number of training samples are involved

Small facial image dataset augmentation using conditional GANs based on incomplete edge feature input

Image data collection and labelling is costly or difficult in many real applications. Generating diverse and controllable images using conditional generative adversarial networks (GANs) for data augmentation from a small dataset is promising but challenging as deep convolutional neural networks need a large training dataset to achieve reasonable performance in general. However, unlabeled and incomplete features (e.g., unintegral edges, simplified lines, hand-drawn sketches, discontinuous geometry shapes, etc.) can be conveniently obtained through pre-processing the training images and can be used for image data augmentation. This paper proposes a conditional GAN framework for facial image augmentation using a very small training dataset and incomplete or modified edge features as conditional input for diversity. The proposed method defines a new domain or space for refining interim images to prevent overfitting caused by using a very small training dataset and enhance the tolerance of distortions caused by incomplete edge features, which effectively improves the quality of facial image augmentation with diversity. Experimental results have shown that the proposed method can generate high-quality images of good diversity when the GANs are trained using very sparse edges and a small number of training samples. Compared to the state-of-the-art edge-to-image translation methods that directly convert sparse edges to images, when using a small training dataset, the proposed conditional GAN framework can generate facial images with desirable diversity and acceptable distortions for dataset augmentation and significantly outperform the existing methods in terms of the quality of synthesised images, evaluated by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) scores

Refrigerant- Lubricant Mixture Properties Influencing Bubble Dynamic Parameters and Heat Transfer Coefficient in Nucleate Pool Boiling

We have been successfully developed a model regarding lubricant effect on individual processes of bubble nucleation, growth and departure period for nucleate pool boiling heat transfer. In this study, three type POE refrigeration lubricants with different refrigerant miscibility (POEA/POEB/POEC), two viscosity grades (ISO68 & 170), three kind of refrigerants (R-134a/R-1234ze/R-134yf), and three different saturated temperatures (10℃/0℃/10℃) are taken into calculation under different heat flux ranging from 10 KW/m2 to 80 KW/m2. Based on this model, a knowledge of chemical structures and physical properties of lubricant and refrigerant is sufficient to get bubble dynamic parameters and predict the boiling performance near metal surface. According to calculating results, several key factors play an important role in pool boiling heat transfer and show drastic influence on bubble parameters and HTC, such as refrigerant type, saturated temperature, heat flux and lubricant concentration. Regarding lubricant chemical structure effect on heat transfer performance, it will be direct related to OCR and following influence on HTC in real evaporator environment. But if keeping same lubricant concentration, different results will appear. Various lubricant structures may provide different volume size, adsorption energy on metal surface and interaction force between refrigerant and lubricant, but these factors sometimes offset each other and lead to only a slight difference in bubble size, contact angle, surface coverage concentration, and HTC. The calculation indicates that the presence of lubricant imposes a negative effect on HTC during waiting period of bubble formation and departure period, but a positive effect on HTC may prevail in bubble growth period. Such two effects compete during the boiling process and could lead increase or impair heat transfer performance at a low lubricant concentration

The Uses of a Dual-Band Corrugated Circularly Polarized Horn Antenna for 5G Systems

This paper presents the development of a wide-beam width, dual-band, omnidirectional antenna for the mm-wave band used in 5G communication systems for indoor coverage. The 5G indoor environment includes features of wide space and short range. Additionally, it needs to function well under a variety of circumstances in order to carry out its diverse set of network applications. The waveguide antenna has been designed to be small enough to meet the requirements of mm-wave band and utilizes a corrugated horn to produce a wide beam width. Additionally, it is small enough to integrate with 5G communication products and is easy to manufacture. This design is simple enough to have multi-feature antenna performance and is more useful for the femtocell repeater. The corrugated circularly polarized horn antenna has been designed for two frequency bands; namely, 26.5–30 GHz for the low band and 36–40 GHz for high band. The results of this study show that return-loss is better than 18 dB for both low and high band. The peak gain is 6.1 dBi for the low band and 8.7 dBi for the high band. The beam width is 105 degrees and 77 degrees for the low band and the high band, respectively. The axial ratio is less than 5.2 dB for both low and high band. Generally, traditional circularly polarized antennas cannot meet the requirements for broadband. The designs for the antennas proposed here can meet the requirements of FR2 bandwidths. This feature limits axial ratio performance. The measurement error in the current experiment comes from the high precision control on the size of the ridge

The Effect of Refrigeration Lubricant Properties on Nucleate Pool Boiling Heat Transfer Performance

Refrigeration lubricant plays a key role in lubricating and sealing during vapor compression processes. However, it may migrate to the evaporator to influence the heat transfer characteristics, either enhancement or degradation. The aim of this study is to fundamentally understand the effect of lubricant properties and bubble parameters on heat transfer performance. To clarify parameters affecting the heat transfer coefficient, several experiments were conducted on a horizontal flat surface, and pool-boiling phenomenon was recording by high-speed camera. Comparisons of heat transfer measurements for different refrigerant/lubricant mixtures were made, including two different refrigerants (R-134a & R-1234ze) and eight POE lubricants with different miscibility, ISO68 to ISO170 viscosity range. This study shows that improvements over pure refrigerant heat transfer can be obtained for refrigerant /lubricant mixtures with small lubricant mass fraction, high lubricant viscosity, and a low critical solution temperature (CST). The presence of lubricant will decrease the departure bubble diameter and may deteriorate heat transfer performance when the lubricant mass fraction is higher than 3%. A mechanistic explanation was provided for the observed refrigerant/lubricant boiling phenomenon, and we were successfully in creating a new model to quantify the effect of lubricant properties on the heat transfer performance. This model was developed based on cavity boiling theory, interfacial energy calculation between metal-liquid surface, and liquid-bubble interface. According to the model, the presence of lubricant layer on metal surface and surrounding the bubble will significantly alter waiting time of boiling, bubble departure time, activity site density of boiling incipience and superheat on heating surface

Perceptions, Behavioral Expectations, and Implementation Timing for Response Actions in a Hurricane Emergency

This study examined the perceived attributes, behavioral expectations, and expected implementation timing of 11 organizational emergency response actions for hurricane emergencies. The perceived attributes of the hurricane response actions were characterized by two hazard-related attributes (effectiveness for person protection and property protection) and five resource-related attributes (financial costs, required knowledge/skill, required equipment, required time/effort, and required cooperation). A total of 155 introductory psychology students responded to a hypothetical scenario involving an approaching Category 4 hurricane. The data collected in this study explain previous findings of untimely protective action decision making. Specifically, these data reveal distinctly different patterns for the expected implementation of preparatory actions and evacuation recommendations. Participants used the hazard-related and resource-related attributes to differentiate among the response actions and the expected timing of implementation. Moreover, participants’ behavioral expectations and expected implementation timing for the response actions were most strongly correlated with those actions’ effectiveness for person protection. Finally, participants reported evacuation implementation times that were consistent with a phased evacuation strategy in which risk areas are evacuated in order of their proximity to the coast. However, the late initiation of evacuation in risk areas closest to the coast could lead to very late evacuation of risk areas farther inland

Clinical application of tumor volume in advanced nasopharyngeal carcinoma to predict outcome

<p>Abstract</p> <p>Background</p> <p>Current staging systems have limited ability to adjust optimal therapy in advanced nasopharyngeal carcinoma (NPC). This study aimed to delineate the correlation between tumor volume, treatment outcome and chemotherapy cycles in advanced NPC.</p> <p>Methods</p> <p>A retrospective review of 110 patients with stage III-IV NPC was performed. All patients were treated first with neoadjuvant chemotherapy, then concurrent chemoradiation, and followed by adjuvant chemotherapy as being the definitive therapy. Gross tumor volume of primary tumor plus retropharyngeal nodes (GTVprn) was calculated to be an index of treatment outcome.</p> <p>Results</p> <p>GTVprn had a close relationship with survival and recurrence in advanced NPC. Large GTVprn (≧13 ml) was associated with a significantly poorer local control, lower distant metastasis-free rate, and poorer survival. In patients with GTVprn ≧ 13 ml, overall survival was better after ≧4 cycles of chemotherapy than after less than 4 cycles.</p> <p>Conclusions</p> <p>The incorporation of GTVprn can provide more information to adjust treatment strategy.</p

Altered myocardial response in patients with diabetic retinopathy: an exercise echocardiography study

Additional file 1. Multivariable analysis for individual echocardiography parameters