Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of Slow Crack Growth Parameters of Advanced Ceramics by Constant Stress-Rate Flexural Testing at Elevated Temperatures

1.1 This test method covers the determination of slow crack growth (SCG) parameters of advanced ceramics by using constant stress-rate flexural testing in which flexural strength is determined as a function of applied stress rate in a given environment at elevated temperatures. The strength degradation exhibited with decreasing applied stress rate in a specified environment is the basis of this test method which enables the evaluation of slow crack growth parameters of a material.
Text Mining at Feature Level: A Review

Text Mining is the technique that helps users to find out useful information from a large amount of text documents on the web or database. Most popular text mining and classification methods have adopted term-based approaches. The term based approaches and the pattern-based method describing user preferences. This review paper analyse how the text mining work on the three level i.e sentence level, document level and feature level. In this paper we review the related work which is previously done. This paper also demonstrated that what are the problems arise while doing text mining done at the feature level. This paper presents the technique to text mining for the compound sentences.
A TURBULENT COMBUSTION MODEL FOR IGNITION OF RAPIDLY EXPANDING HYDROGEN JETS: VALIDATION FOR 1-D LAMINAR FLOWS

A turbulent combustion model based on the Linear Eddy Model for Large Eddy Simulation (LEMLES) is currently proposed to study self-ignition events of rapidly expanding hydrogen jets. The model is a one-dimensional treatment of a diffusion-reaction system within each multi-dimensional LES cell. This reduces the expense of solving a complete multi-dimensional problem while preserving micro-scale hotspots and their effects on ignition. The current approach features a Lagrangian description of fluid particles on the sub-grid for increased accuracy. Also, Adaptive Mesh Refinement (AMR) is implemented for increased computational efficiency. In this paper, the model is validated for various inviscid laminar 1-D mixing and ignition problems, shock tube problems, flames, and detonations.
Hepatic iron load differs strikingly between peritoneal dialysis and hemodialysis patients

Introduction Iron overload is one of the most controversial topics in the management of anemic dialysis patients. Parenteral iron supplementation is commonly prescribed to hemodialysis (HD) patients but less frequently to peritoneal dialysis (PD) patients. Moreover, ferritin targets are far lower and more physiological in PD than in HD. Methods We compared the liver iron concentration (LIC) measured by means of Signal-Intensity ratio (SIR) magnetic resonance imaging (MRI) according to Rennes University method in a cohort of 32 PD patients living in the Paris region published in 2017, with two cohorts of French HD patients studied in the same way (119 patients reported in 2012 and 80 further patients reported in 2014). Results Normal hepatic iron load (LIC ≤ 50 µmol/g of dry weight) was observed in 81.3% of the 32 PD patients (CI: 64.3-91.5%), as compared to only 16% (CI: 10.4-23.7%) in the first HD cohort and 35% (CI: 25.4-45.9%) in the second HD cohort (p 200 µmol/g) in only one PD patient (who had received IV iron) (3.1%; CI: 0-17.1%). Conversely, severe iron overload was found in 30.3% of patients in the first HD cohort (CI: 22.7-39%) and 11.3% of those in the second HD cohort (CI: 5.8-20.2%) (p= 0.0033 versus the first HD cohort, X2 test). Conclusion Contrary to hemodialysis patients, iron overload is rare and mostly mild in peritoneal dialysis patients.
Power Divider and Balun Filters With a Single Cross-Coupled Quadruplet

A filtering power divider and a filtering balun of fourth-order are synthesized with a single cross coupled quadruplet filter. The entire circuit uses only five resonators and both three-port circuits share four of the five resonators, leading to a compact circuit size and low insertion loss. In synthesis, given bandwidth, in-band return loss and transmission zeros, all coupling coefficients can be determined. For demonstration, a filtering divider and a filtering balun are designed at 2.4 GHz with a bandwidth of 5%. Measured responses of the experimental circuits have good agreement with the simulated data, and this validates the synthesis.
Multimodal Bilinear Fusion Network With Second-Order Attention-Based Channel Selection for Land Cover Classification

As two different tools for earth observation, the optical and synthetic aperture radar (SAR) images can provide complementary information of the same land types for better land cover classification. However, because of the different imaging mechanisms of optical and SAR images, how to efficiently exploit the complementary information becomes an interesting and challenging problem. In this article, we propose a novel multimodal bilinear fusion network (MBFNet), which is used to fuse the optical and SAR features for land cover classification. The MBFNet consists of three components: the feature extractor, the second-order attention-based channel selection module (SACSM), and the bilinear fusion module. First, in order to avoid the network parameters tempting to ingratiate dominant modality, the pseudo-siamese convolutional neural network (CNN) is taken as the feature extractor to extract deep semantic feature maps of optical and SAR images, respectively. Then, the SACSM is embedded into each stream, and the fine channel-attention maps with second-order statistics are obtained by bilinear integrating the global average-pooling and global max-pooling information. The SACSM can not only automatically highlight the important channels of feature maps to improve the representation power of networks, but also uses the channel selection mechanism to reconfigure compact feature maps with better discrimination. Finally, the bilinear pooling is used as the feature-level fusion method, which establishes the second-order association between two compact feature maps of the optical and SAR streams to obtain the low-dimension bilinear fusion features for land cover classification. Experimental results on three broad coregistered optical and SAR datasets demonstrate that our method achieves more effective land cover classification performance than the state-of-the-art methods.
Non-Visual to Visual Translation for Cross-Domain Face Recognition

Reducing the cross-modality gap between two different domains is a challenging problem for heterogeneous face recognition (HFR). The current visual domain face recognition system is not easy to solve the discrepancy of cross-modality when two comparing domains are heterogeneous. Moreover, the amount of HFR dataset is significantly insufficient, making it considerable difficulty in training. This paper proposes a novel two-step framework that consists of the image translation module and the feature learning module to obtain an enhanced cross-modality matching system for heterogeneous datasets. First, the image translation module consists of a Preprocessing Chain (PC) method, CycleGAN, and the Siamese network. It enables to meet the conditions for preserving contents along with changing the styles from the source domain to the target domain. Second, in the feature learning module, the training dataset and its translated images are used together for fine-tuning the pre-trained backbone model in the visual domain. This allows for discriminative and robust feature matching of the probe and gallery test datasets in the visual domain. The experimental results are evaluated with two scenarios, using the CUHK Face Sketch FERET (CUFSF) dataset and the CASIA NIR-VIS 2.0 dataset. The proposed method achieves a better recognition performance in comparison to the state-of-the-art methods.
Modeling Students’ Academic Performance Using Bayesian Networks

Dropping out from STEM higher education is a severe global issue that needs action and solution, in Hungary the dropout rate is particularly high. Exploring the causes of early school leaving is a well-studied research topic of great interest. In this paper, we study the relationship between pre-enrollment achievement measures and indicators of first-year university performance, using Bayesian networks, based on the academic data of students of the mechanical engineering bachelors program at the Budapest University of Technology and Economics. Bayesian networks are having their renaissance, thanks to the high number of openly available software tools. We demonstrate the applicability of Bayesian networks in the educational domain, moreover, we also include a high-level but detailed description of the algorithms, which other similar papers usually lack. Although the models perform generally quite well, we also point out inconsistencies in the results and some limitations of the models.
Multi-Feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Given the similarity of handwritten formula symbols and various handwriting styles, this paper proposes a squeeze-extracted multi-feature convolution neural network (SE-MCNN) to improve the recognition rate of handwritten formula symbols. The system proposed in this paper integrates the eight-directional feature of the original sequence in the convolutional layer, which significantly compensates for the lost dynamic trajectory information in the handwritten formula symbol. Meanwhile, the joint loss is constructed to improve the discriminability of features in the way of supervised learning, which enlarges the inter-class difference and decreases inner-class similarity. The standard mathematical formula symbol library provided by the Competition Organization on Recognition of Online Handwritten Mathematical Expression (CROHME) is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed SE-MCNN approach outperforms the state-of-the-art methods even at the condition of without using the data augmentation.
PROCESS FOR THE RECOVERY OF HEAVY OIL FROM AN UNDERGROUND

(26) Publication Language: English SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (30) Priority Data: MI2009A001809 20 October 2009 (20.10.2009) ΓΓ (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (71) Applicant (for all designated States except US): ENI GM, KE, LR, LS, MW, MZ, NA, SD, SL, SZ, TZ, UG, S.P.A. [IT/IT]; Piazzale E. Mattel, 1, 1-00144 Roma (IT). ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, MD, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, (72) Inventors; and EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, (75) Inventors/ Applicants (for US only): DEL GAUDIO, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, Lucilla [IT/IT]; Via Trieste, 16, 1-20097 San Donate M i SM, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, lanese (MI) (IT). PRATESI, Claudio, Riccardo [IT/IT]; GW, ML, MR, NE, SN, TD, TG). Via Trieste, 16, 1-20097 San Donato Milanese (MI) (IT). BELLONI, Alessandra [IT/IT]; Via F. Turati, 3/B, Published: 1-20070 Cerro Al Lambro (MI) (FT). MORONI, Iole [IT/ — with international search report (Art. 21(3)) IT]; Via Arno, 46, 1-29010 Monticelli d'Ongina (PC) (IT).
Short-Term Hand Gesture Recognition using Electromyography in the Transient State, Support Vector Machines, and Discrete Wavelet Transform

Hand gesture recognition models determine what gesture and when this gesture was performed by a person. We propose a recognition model of hand gestures which lasted a short time (i.e., short-term gestures) using surface electromyography in the transient state, support vector machines (SVM), and discrete wavelet transform (DWT). The proposed model recognizes five gestures (wave in, wave out, fist, fingers spread, and double tap) using 8 EMG surface sensors at a sampling rate of 200Hz. Moreover, this model uses the DWT using Daubechies wavelets of order 25 as the feature extraction method, SVM with Gaussian Kernel as the classifier, and a window size of 750ms. The recognition accuracy of the proposed model is 87.5% using the EMG data of 60 people. The data analysis times of the proposed model are 382ms and 258ms using a personal computer and a server, respectively. Also, the data collection time in both devices is 750ms.
Equivalent Thermal Model of Stator Slots using FEA

The rapid evolution of electric propulsion requires the design of high-performance electric motor. During the operations, the electric power train can operate in critical ambient condition, with high mechanical and thermal stresses. Therefore, several accurate analyses/simulations must be done during the design procedure, with an increase of simulation time. The paper proposes a method to obtain an equivalent thermal model of the slots of a PMS M using the finite element analysis. The equivalent model is calculated solving an optimization procedure by means of heuristic method. Some numerical simulations are reported in order to show the goodness of the proposed solution.
The vehicle classification based on neural networks in intelligent transport systems

This article is aimed to new methods of measurement, transfer and processing of data from complex system of FibreBragg Grating (FBG) sensors. State of the art technologies and algorithms for vehicle recognition and categorisation in university campus are used. Content of the Article is divided into two parts. The first part is devoted to fibre optic FBG sensors and their basic properties. The second part ofarticle is dedicated to visual data processing by neural networks for visual vehicle categorisation as visual confirmation of data from the fibre Bragg grating sensor arrays. Experimental results show that using AlexNet neural network can be achieved an accuracy for categorization of vehicles of 87.1%. Research is done under the grant project dealing with coexistence of photonic sensor systems and photonic networks within the Internet of Things.
High Gain Soft Switched DC-DC Converter for Renewable Applications

In this paper, high gain soft switched DC-DC converter is proposed. It consists of two switches, coupled inductor, three diodes and five capacitors. An active snubber circuit, where an auxiliary switch is included, helps to achieve zero voltage switching (ZVS) of switch. Soft switching is also achieved for auxiliary switch. The leakage energy of the coupled inductor is reused at output by using auxiliary switch which helps to increase voltage gain. All the diodes are turned-off with zero current switching (ZCS) condition which reduces reverse recovery losses in diodes. The active clamp also reduces the voltage stress across the devices, thus facilitating the use of low rated devices reducing the cost and improves the overall efficiency. Brief mathematical analysis and simulation results are presented PSIM for 500 W converter.
Phase-Shifting Transformer Application for Dynamic Stability Enhancement of Electric Power Stations Generators

This article represents the application of the thyristor controlled phase-shifting transformer (PST) capable to maintain the phase shift between the input and output voltage phasor within defined range during rapid transient. The positive influence was illustrated by PST installation in power distribution system of Boguchany Dam, which led to increasing of maximum generating capacity of Boguchany Dam, which was limited due to dynamic transient instability of generators.
A Simplified DC Capacitor Active Voltage-Balance Algorithm with Common-Mode Voltage Reduction using Active-Zero SVPWM for Three-Level Photovoltaic Grid-Tie Inverter Application

This paper presents: 1) a modulation method that combine active-zero PWM and conventional Space-Vector PWM (SVPWM) for three-level inverter, 2) a simplified DC capacitor active voltage-balance algorithm with common-mode voltage reduction using proposed modulation method. The presented modulation method and voltage-balance algorithm simplified calculation of both DC capacitor voltage balance and PWM generation, and take AC load current into consideration, which is suitable for wider range of load condition. In addition, both proposed methods can be programmed as add-on function of conventional SVPWM, which allow online enable/disable during operation of inverter for more flexible system control. The performance of proposed methods are demonstrated in both computer simulation and prototype test. A patent has been granted for the proposed methods and has been adopted in an industrial wide-bandgap semiconductor based PV prototype.
A Trace Agent with Code No-invasion Based on Byte Code Enhancement Technology

With the popularity of microservice architecture and the increasing complexity of services, how to effectively monitor application performance becomes more and more important. Under the guidance of the Dapper system of Google, many distributed tracking systems have emerged. The most essential part of the distributed tracking system is how to access the information of trace. In this paper, a trace agent based on byte code enhancement technology is presented. Then, the design of the agent and the construction of the trace logic are described in detail. The availability of agents is verified from the aspects of overhead and effectiveness under real environment. Finally, the result of experiment proves that it is convenient and effective to use the trace agent to get the information of trace from applications in Java language.
An Integrated CNN-RNN Framework to Assess Road Crack

Road crack detection and road damage assessment are necessary to support driving safety in a route network. Several unexpected incidents (e.g. road accidents) take place all over the world due to unhealthy road infrastructure. This paper proposes a deep learning approach for road crack detection and road damage assessment which will contribute to the transport sector of a country like Bangladesh where a plethora of roads undergo the crack problem. The proposed model consists of two phases. In the first phase, the model is trained using transfer learning (VGG16) to detect the existence of crack on the road surface. In the second phase, an integrated framework, combining CNN(VGG16) and RNN(LSTM), is trained to classify the crack in one of the two categories-severe and slight. After experiments, the validation accuracies obtained by the proposed models (VGG16 and VGG16-LSTM) are respectively 99.67% and 97.66%.
Composite L-Type TL for Spurious Band Suppression in Dual-Band Power Divider Studies

In this paper, a dual-band equal-split Wilkinson power divider (PD) featuring good spurious band suppression performance is proposed, which characteristic is attributed to a new composite L-type transmission line (TL) composed of an impedance matching line and a lumped-inductor loaded open-end stub. According to the analysis, the proposed TL features high design flexibility, bandstop filtering response, and compact size. The stopband bandwidth of the TL is influenced by the characteristic impedance of the utilized open-end stub, while the size is influenced by the value of the utilized lumped inductor that a larger value can lead to a shorter length of the open-end stub which means a smaller size of the utilized L-type TL. Closed-form design equations are derived, and the proposed theory is verified by a dual band PD centered at 1.0 GHz and 2.45 GHz. According to the simulated and measured results, a good spurious band suppression can be observed such that the measured minimum return loss S11 and the maximum insertion loss S21 between the two passbands are 1.4 dB and 14.6 dB, respectively.
Review of Energy Efficiency Technologies in the Food Industry: Trends, Barriers, and Opportunities

The industry sector has a significant responsibility for the depletion of fossil fuels and emission of carbon dioxide. Thus, several initiatives have been implemented by the industry sector to mitigate those issues. One initiative corresponds to the implementation of energy efficiency strategies. In particular, the food industry is heavily dependent on fossil fuels, and the food demand is expected to grow significantly in the coming years. Therefore, developing energy efficiency strategies for this particular industrial sector is crucial. This paper investigates the different opportunities for energy efficiency in the food industry. It first provides a brief overview of the various food industries and related energy consumption. Then, the different options for energy efficiency in the thermal and electric sector are discussed. New trends and opportunities, arising from industry 4.0 and demand response, are also presented.
Design and Development of a Benchmark for Dynamic Multi-objective Optimisation Problem in the Context of Deep Reinforcement Learning

Different benchmarks have played an important role in analysing algorithms for dynamic multi-objective optimisation problems. According to the literature, there are several benchmarks to deal with the dynamic multi-objective optimisation problems, especially in the evolutionary approaches. In this study, a comprehensive review has been done regarding the existing benchmarks in the single objective and multi-objective reinforcement learning (MORL) settings. To the best of our knowledge, there is no benchmark in the context of dynamic multiobjective reinforcement learning (DMORL). Therefore, this study has addressed this gap by applying the existing knowledge to propose a benchmark which may help to investigate the performance of different algorithms. It can also support to understand the dynamics while objectives are conflicting with each other and deal with the constraints and problem parameters that change over time. The proposed benchmark is the modified version of the deep-sea treasure hunt problem where several features such as changing parameters and objectives have been integrated to support the dynamics in a multi-objective environment. This paper highlights the methodology part of designing and developing a benchmark.
Diagnosis of Chronic Obstructive Pulmonary Disease Based on Transfer Learning

Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously endangers human health and has high incidence and mortality worldwide. Therefore, an effective predictive model is required for COPD diagnosis. Given the limited data samples available in current COPD studies, we propose a method for diagnosing COPD based on transfer learning called balanced probability distribution (BPD) algorithm; this algorithm integrates instance- and feature-based transfers to improve the prediction accuracy of the model. First, instance-based cascaded transfer learning was used to initialize the weight distribution of the training data and obtain instances closer to the target domain. Second, the cross-domain feature filtering algorithm was adopted to filter irrelevant features, eliminate redundant features, and obtain the co-occurrence features of the source and target domains. Moreover, the remaining features were assigned different weights and transformed into the same space to reduce the distribution difference between the domains. Third, the BPD algorithm was used to balance the examples and the co-occurrence features from multiple disease source domains and construct a more suitable classification model of the target domain. Finally, the elastic network was used to further improve the generalization performance of the model. The experimental results show that the prediction effect of the BPD model is better than that of state-of-the-art methods and has strong generalization ability and robustness. We proved that our proposed BPD method works well in the COPD prediction model when the sample size is small.