scholarly journals Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4701
Author(s):  
Yunxia Liu ◽  
Zeyu Zou ◽  
Yang Yang ◽  
Ngai-Fong Bonnie Law ◽  
Anil Anthony Bharath
Keyword(s):  
Performance Comparison ◽  
Training Data ◽  
Patch Selection ◽  
Fine Grained ◽  
Evaluation Protocol ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Prediction Module ◽  
Training Cost ◽  
The Impact

Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently. However, identification performance is susceptible to image content and is far from satisfactory for small image patches in real demanding applications. In this paper, an efficient patch-level source camera identification method is proposed based on a convolutional neural network. First, in order to obtain improved robustness with reduced training cost, representative patches are selected according to multiple criteria for enhanced diversity in training data. Second, a fine-grained multiscale deep residual prediction module is proposed to reduce the impact of scene content. Finally, a modified VGG network is proposed for source camera identification at brand, model, and instance levels. A more critical patch-level evaluation protocol is also proposed for fair performance comparison. Abundant experimental results show that the proposed method achieves better results as compared with the state-of-the-art algorithms.

Application of vacuum belt press filters for cane mud filtration and performance comparison with rotary filters

2014 ◽  
pp. 298-301 ◽  
Author(s):  
Arnaud Petit
Keyword(s):  
Electricity Consumption ◽  
Performance Comparison ◽  
Operating Conditions ◽  
Vacuum Filter ◽  
Sugar Mill ◽  
Belt Press ◽  
Vacuum Belt ◽  
And Performance ◽  
Mud Filtration ◽  
The Impact

Bois-Rouge factory, an 8000 t/d cane Reunionese sugarcane mill, has fully equipped its filtration station with vacuum belt press filters since 2010, the first one being installed in 2009. The present study deals with this 3-year experience and discusses operating conditions, electricity consumption, performance and optimisation. The comparison with the more classical rotary drum vacuum filter station of Le Gol sugar mill highlights advantages of vacuum belt press filters: high filtration efficiency, low filter cake mass and sucrose content, low total solids content in filtrate and low power consumption. However, this technology needs a mud conditioning step and requires a large amount of water to improve mud quality, mixing of flocculant and washing of filter belts. The impact on the energy balance of the sugar mill is significant. At Bois-Rouge mill, studies are underway to reduce the water consumption by recycling low d.s. filtrate and by dry cleaning the filter belts.

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals Potential Impacts of Future Climate Change Scenarios on Ground Subsidence

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 219 ◽  
Author(s):  
Antonio-Juan Collados-Lara ◽  
David Pulido-Velazquez ◽  
Rosa María Mateos ◽  
Pablo Ezquerro
Keyword(s):  
Climate Change ◽  
Land Subsidence ◽  
Ground Subsidence ◽  
Lower Percentage ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Fine Grained ◽  
Fine Grained Material ◽  
The Impact

In this work, we developed a new method to assess the impact of climate change (CC) scenarios on land subsidence related to groundwater level depletion in detrital aquifers. The main goal of this work was to propose a parsimonious approach that could be applied for any case study. We also evaluated the methodology in a case study, the Vega de Granada aquifer (southern Spain). Historical subsidence rates were estimated using remote sensing techniques (differential interferometric synthetic aperture radar, DInSAR). Local CC scenarios were generated by applying a bias correction approach. An equifeasible ensemble of the generated projections from different climatic models was also proposed. A simple water balance approach was applied to assess CC impacts on lumped global drawdowns due to future potential rainfall recharge and pumping. CC impacts were propagated to drawdowns within piezometers by applying the global delta change observed with the lumped assessment. Regression models were employed to estimate the impacts of these drawdowns in terms of land subsidence, as well as to analyze the influence of the fine-grained material in the aquifer. The results showed that a more linear behavior was observed for the cases with lower percentage of fine-grained material. The mean increase of the maximum subsidence rates in the considered wells for the future horizon (2016–2045) and the Representative Concentration Pathway (RCP) scenario 8.5 was 54%. The main advantage of the proposed method is its applicability in cases with limited information. It is also appropriate for the study of wide areas to identify potential hot spots where more exhaustive analyses should be performed. The method will allow sustainable adaptation strategies in vulnerable areas during drought-critical periods to be assessed.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

scholarly journals A Study of the Impact of Graphite on the Kinetics of SPS in Nano- and Submicron WC-10%Co Powder Compositions

Ceramics ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 331-363
Author(s):  
Eugeniy Lantcev ◽  
Aleksey Nokhrin ◽  
Nataliya Malekhonova ◽  
Maksim Boldin ◽  
Vladimir Chuvil'deev ◽  
...  
Keyword(s):  
Activation Energy ◽  
Solid Phase ◽  
Continuous Heating ◽  
Diffusion Creep ◽  
Hard Alloys ◽  
Fine Grained ◽  
Plasma Sintering ◽  
Spark Plasma ◽  
The Impact ◽  
Kinetics Of

This study investigates the impact of carbon on the kinetics of the spark plasma sintering (SPS) of nano- and submicron powders WC-10wt.%Co. Carbon, in the form of graphite, was introduced into powders by mixing. The activation energy of solid-phase sintering was determined for the conditions of isothermal and continuous heating. It has been demonstrated that increasing the carbon content leads to a decrease in the fraction of η-phase particles and a shift of the shrinkage curve towards lower heating temperatures. It has been established that increasing the graphite content in nano- and submicron powders has no significant effect on the SPS activation energy for “mid-range” heating temperatures, QS(I). The value of QS(I) is close to the activation energy of grain-boundary diffusion in cobalt. It has been demonstrated that increasing the content of graphite leads to a significant decrease in the SPS activation energy, QS(II), for “higher-range” heating temperatures due to lower concentration of tungsten atoms in cobalt-based γ-phase. It has been established that the sintering kinetics of fine-grained WC-Co hard alloys is limited by the intensity of diffusion creep of cobalt (Coble creep).

Thermal performance comparison of different sun tracking configurations

2019 ◽  
Vol 88 (2) ◽  
pp. 20902
Author(s):  
O. Achkari ◽  
A. El Fadar
Keyword(s):  
Thermal Performance ◽  
Electricity Generation ◽  
Arid Regions ◽  
Performance Comparison ◽  
Water Desalination ◽  
Parabolic Trough ◽  
Parabolic Trough Collector ◽  
The Impact ◽  
The Mathematical Model ◽  
Good Agreement

Parabolic trough collector (PTC) is one of the most widespread solar concentration technologies and represents the biggest share of the CSP market; it is currently used in various applications, such as electricity generation, heat production for industrial processes, water desalination in arid regions and industrial cooling. The current paper provides a synopsis of the commonly used sun trackers and investigates the impact of various sun tracking modes on thermal performance of a parabolic trough collector. Two sun-tracking configurations, full automatic and semi-automatic, and a stationary one have numerically been investigated. The simulation results have shown that, under the system conditions (design, operating and weather), the PTC's performance depends strongly on the kind of sun tracking technique and on how this technique is exploited. Furthermore, the current study has proven that there are some optimal semi-automatic configurations that are more efficient than one-axis sun tracking systems. The comparison of the mathematical model used in this paper with the thermal profile of some experimental data available in the literature has shown a good agreement with a remarkably low relative error (2.93%).

scholarly journals BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yikui Zhai ◽  
He Cao ◽  
Wenbo Deng ◽  
Junying Gan ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Learning Strategy ◽  
State Of The Art ◽  
Activation Function ◽  
Training Data ◽  
Fine Grained ◽  
Pattern Recognition Problem ◽  
Face Features ◽  
Facial Beauty

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

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