Miracle-dz/UniFAR-FTUs huggingface dataset:
Heat Transfer in BNNT-Polymer Nanocomposites from Non-equilibrium Molecular Dynamics

Doping high thermal conductivity fillers into polymers as a major way to improve the thermal conductivity of materials, but the failure mechanism of the coupling agents and entangled polymer chains on the thermal conductivity of nanofillers is missing. In order to explore those, the thermal conductivity of pure BN nanotube and BN nanotube with coupling agents was calculated by the nonequilibrium molecular dynamics method. It is shown that the thermal conductivity of BN nanotube with coupling agents decreased significantly at the full frequency range. The dynamical structural factor was calculated to exact the longitudinal acoustics mode of BNNT in the nanocomposites and found that the entangled polyethylene chains have little effect on the thermal conductivity of BNNT and the coupling agents are the main factor to destroy the thermal conductivity. Based on those results, a new branches method is proposed that the coupling agent should bond at the edge not on the surface for single-wall BNNT.
Comparison of Two Different Winding Sets for Dual Three-Phase Permanent Magnet Machines

This study presents investigations on different winding designs for dual three-phase permanent magnet synchronous machines. Dual three-phase machine design can enable a reduction of the maximum IGBT current by increasing the number of phase belts from three to six. Dual three-phase machines have important advantages compared to conventional three-phase machines such as decreased torque pulsation and increased fault tolerance. In this contribution a short pitch dual three-phase winding where the winding sets are placed in seperate halves of the stator with no-phase shift between the two subsystems is compared to a dual three-phase winding with thirty degree phase shift between the two winding subsystems. This paper presents 2D finite-element analysis of the different winding designs and provides acoustic measurements to verify the conclusions.
Research of Athletes’ Movements Biomechanics in Big Tennis Using Motion Capture Technology and Image Processing

In work the need for research of tennis players’ movements biomechanics for increase of trainings productivity and detection of the latent chronic diseases and micro-injuries is shown. Use of the Motion Capture technology for research of movements biomechanics allows to register and transfer information on the person movements to a digital format. Big arrays of information are useless without the revealed regularities in them. The approach allowing to transform the obtained digitized data to mathematical dependences, and then dependences to transfer to abstract vector variables is offered. It allows to automate processing of data files. The algorithm of secondary mathematical processing of the digitized data, an electronic template for the automated
Controlling Status Updates in a Wireless System with Heterogeneous Traffic and Age of Information Constraints

In this work, we derive optimal status update generation policies for a wireless source node that has a single queue which serves two applications, one that is Age of Information (AoI) sensitive and one that is not. Additionally, the AoI-sensitive application sets a strict upper bound on the AoI. The latter constraint is satisfied by utilizing an ultra-reliable low-latency communication link. We formulate a dynamic programming problem and utilize the framework of Markov Decision Processes to derive optimal policies for the generation of status update packets. We compare the derived optimal policies against baseline policies. Results indicate that existing status update policies fail to capture the effect of AoI insensitive traffic on queueing delay and AoI and thus perform suboptimally.
Dielectric Characterization of Printed Circuit Board by Microstrip Line Up to 110 GHz

This paper presents the characterization method and the measurement result of the dielectric property for printed circuit boards (PCBs) by microstrip transmission lines (TLs), with the multiline thru-reflect-line (MTRL) calibration and the probing techniques for the scattering parameter (S-parameter) measurements. The broadband TL propagation constant is evaluated accurately after the MTRL calibration, and the characteristic impedance is further determined by an additional resistor test item with the low substrate loss assumption. The extraction of the dielectric constant (DK) for the PCB substrate then becomes an inverse problem where the estimated DK can determine the TL propagation constant which is compared to the measured data for modifying the DK; this process is proceeded iteratively until the error between calculated value and the measured one satisfying the criterion. The proposed method is examined by the NF30 substrate, in the frequency range from 1 GHz to 110 GHz.
Handwritten Signature Verification using TensorFlow

Many different methods are being tested by researchers for the correct identification of original signatures from forged ones. The objective of the research reported in this paper is to set up a new system that can be used reliably for the recognition of signatures in legal documents and cheques used in banks. The proposed system was designed using TensorFlow, which is used widely for deep learning. The Convolutional Neural Network (CNN) used in the designed system is capable of accurately verifying the characters unique to the original signature. The effectiveness of the system is measured using two parameters which are False Rejection Rates (FRR) and False Acceptance Rate (FAR). The proposed system showed FAR and FRR values as 5 percent and 5 percent respectively while testing and the overall accuracy of the system is 90 percent.
Brain Fibre Tracking Improved by Diffusion Tensor Similarity using Non-Euclidean Distances

Fibre tracking is a non-invasive technique based on Diffusion Tensor Imaging (DTI) that provides useful information about biological anatomy and connectivity. In this paper, we propose a new fibre tracking algorithm, named TAS (Tracking by Angle and Similarity), which is able to overcome the shortfalls of existing algorithms by considering not only the main diffusion directions, but also the similarity of diffusion tensors using non-Euclidean distances. Quantitative comparison is carried out through a collection of simulation experiments using statistics of diffusion tensor anisotropy and volume, and tracking errors. Fibre tracking in Corpus Callosum from a healthy human brain dataset is presented.
Bioinformatics analysis of colorectal cancer related gene

Colorectal cancer (CRC) is ranked as one of the three most common cancers in the world. This research aims at exploring the molecular mechanisms of CRC. We used R language to analyze gene expression data from the Gene Expression Omnibus (GEO) database. By using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) database, the function and pathway enrichment of differentially expressed genes were analyzed. The Protein-protein interaction (PPI) network was constructed by STRING and Cytoscape. A total of 205 differentially expressed genes (DEGs) were screened out. These differentially expressed genes mainly enriched in the terms of regulation of leukocyte chemo taxis, chemokine receptor binding and chemokine-mediated signaling pathway. The PPI network of differentially expressed genes was constructed. Seven hub genes (CCL2, TPM1, DES, CXCL12, MYLK, MYL9 and TPM2) were identified. We conclude that these 7 genes may be involved in the occurrence of CRC.
Aided Diagnosis of Alzheimer's Disease Based on Structural Magnetic Resonance Imaging

With the vigorous development of computer and pattern recognition technologies, using magnetic resonance imaging (MRI) and machine learning methods to assist diagnosis of Alzheimer's disease (AD) has became a research hotspot. A new method based on Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Linear Discriminant Analysis (LDA) for aided diagnosis of AD is proposed in this study. Firstly, Structural MRI images including 34 patients with AD, 26 subjects with subjective memory complaints (SMC) and 50 normal controls (NC) from the ADNI database are preprocessed to obtain the gray matter volumes of 90 brain regions. Then the fusion idea on SVM-RFE and LDA is used to select the characteristics on the above gray matter volumes. Finally, the selected features are classified by support vector machine (SVM), and the average classification accuracies of AD/NC, AD/SMC and NC/SMC reach respectively 94%, 100% and 93.6%. Compared with SVM-RFE or LDA alone, the average classification results on the fusion idea have obvious advantages. The above experimental results show that the proposed method can effectively extract features and assist doctors for exact diagnosis of AD and SMC.
Algorithm for Estimating the Degree of Polymerization of Paper Insulation Impregnated with Inhibited Insulating Oil

Paper insulation in power transformers may deteriorate due to ageing processes during the operation. The widely used parameter for determining paper insulation condition is the degree of polymerization (DP). Nowadays, DP measurement techniques without damaging the components of the transformer insulation system or interfere with the transformer service time are needed. The most common method is using a furan analysis to estimate DP value. However, there are different factors and uncertainties such as the occurrence of local overheating at one spot, different periods of measurements after an oil processing, etc. thus, they have an impact on the accuracy of the furan measurement. Therefore, several estimation algorithms had been developed by considering the chemical and dielectric parameters of un-inhibited insulating oil.This contribution aims to develop an algorithm for estimating the DP value of paper insulation on transformers immersed with inhibited insulating oil. An algorithm was implemented to field operating transformer measurement data. The dielectric characteristic parameters (acidity, interfacial tension), as well as dissolved gases (carbon monoxide, carbon dioxide), were processed and evaluated by a specialized fuzzy inference system (FIS).
Utility and A*-Based Algorithm for Network Slice Placement and Chaining

Next Generation Mobile Network, 5G, will leverage heavily on Network Function Virtualisation for flexible and cost efficient deployments. 5G technology is to provide numerous innovative business opportunities through a key concept known as Network Slicing. This approach will require efficient placement and chaining of virtual network functions (VNFs) composing a network slice. The goal is to maximize network resources availability with minimal cost as network infrastructure is shared among different network slices. This paper proposes a time and resource-efficient VNF placement and chaining method, UA*, a Utility-based placement algorithm that extends the A* algorithm node classification set. UA* considers slice performance requirements and policies. Our simulations evaluate UA* and compares its performance with state of the art approaches.
Benefits of MEC in 5G Cellular Networks from Telecom Operator's View Points

In recent years, the demand for further expansion of the capacity of wireless communication is increasing due to the drastic growth in the number of connected devices and the emergence of various services and applications utilizing wireless networks. It is necessary to decrease the traffic on the backhaul networks and reduce the end- to-end (E2E) latency. In order to address the problems, Multi-Access Edge Computing (MEC) where services, computing resource, storage, etc., would be deployed at the edge of the network is proposed. However, it is difficult for the operators to determine if and how MEC should be deployed in cellular networks because it is still unclear whether a return on investment of MEC could be obtained. In order to deploy MEC on the cellular network, to the best of authors knowledge, no research has been conducted on whether a business model can be established when additional funds are paid for MEC. In this paper, we propose a more sophisticated business model with an ecosystem for MEC deployment. This paper investigates E2E latency and analyzes the optimal MEC resource deployment in the cellular network, and the numerical results verify our analysis. The analysis results reveal that the profit for telecom operatorâ€™s revenue tended to increase with the addition of MECs, and the optimal number of MEC maximizing the profit is a function of MEC resource cost and application requirements.
Application of SOS based PDPID Cascaded Controller with Double Derivative Filter for AGC of Interconnected Power System

This paper presents the automatic generation control of a two equal area thermal system employing Symbiotic Organism Search (SOS) based PDPID cascaded controller with double derivative filter. Initially SOS based PID controllers are optimally designed for both the areas and then the proposed controller is designed for the same power system. A disturbance of 0.1 p.u is employed in area-one and integral time absolute error is taken as objective function for the study. Results obtained are compared with the newly published article based on SOS and Quasi-Oppositional SOS (QOSOS) algorithm based PI controller. It is finally observed that SOS based PDPID cascaded controller with double derivative filter outperforms other controllers in every aspects.
Overview On 5G-Massive MIMO Cellular Communication

Massive MIMO is most fundamental and the basis of 5G. Whole evolution from older generation to 5G in context of data speed, huge amount of data traffic handling capacity, decrease in response time, providing very large range of bandwidth, increase in coverage are, high energy efficiency and many others has been done due to this new technology called Massive MIMO. It is the basic building block for 5G, whole 5G is built upon Massive MIMO. Objectives: This paper presents the basic working of 5G networks in terms of Massive MIMO and tries to find answer of question like how it increases the capacity of data handling by a huge amount as much as 1000 times more, how it gives such high speeds, what are the concept behind this significant improvements, will there be significant increase in size of BS since massive number of antennas are used. There is also a comprehensive and statistical analysis has been done on Spectral Efficiency and Energy Efficiency giving solid evidence for the improvement in 5G networks. Analysis on various digital signal processing done in Massive MIMO and comparing various processing techniques in terms of complexity and performance. The sole purpose of this paper is to provide the basic understanding on how is 5G networks are actually working and the reason behind there improvements. Brief information is added about the projects running worldwide on Massive-MIMO and its application in future communication systems of next generation. Findings: It has been observed that to provide the performance to such high standards, massive amount of antennas are used at BS to execute a directed beams focused towards users. The size of BS remains same as it was in 4G. There is no significant increase in the size of BS it just that number of transceiver chains increases by the same amount as number of antennas per BS.
Analysis and Criterion for Inherent Balance Capability in Modular Multilevel DC–AC–DC Converters

Modular multilevel dc–ac–dc converters (MMDAC) have emerged recently for high step-ratio connections in medium-voltage distribution systems. Extended phase-shift modulation has been proposed and was found to create the opportunity for inherent balance of submodule capacitor voltages. This letter presents fundamental analysis leading to clear criterion for the inherent balance capability in MMDAC. A sufficient and necessary condition, with associated assumptions, to guarantee this capability is established. Using the mathematics of circulant matrices, this condition is simplified to a coprime criterion which gives rise to practical guidance for the design of an MMDAC. Experiments on downscaled prototypes and simulations on full-scale examples both provide verification of the analysis and criterion for the inherent balance capability of MMDAC.
A Bipolar High Voltage Pulse Generator Used for Irreversible Electroporation Ablation

In order to meet the requirements of Irreversible Electroporation Ablation(IRE), a bipolar high-voltage microsecond pulse generator based on all solid-state Marx generator is put forward in this paper. The main topology circuit is a four-stage Marx generator cascades a full-bridge modulator. The fast rise time can be obtained by taking full advantage of fast turn-on speed of the MOSFET. The bipolar pulse is realized as few devices as possible with the high withstand voltage of IGBTs. The working principle of each mode on the condition of different loads and the switching strategies are illustrated clearly. Finally, the generator can output bipolar pulses with amplitude of 0~3.7 kV, pulse width 1.5~200 µs and pulse frequency 0.1~10 Hz. Moreover, it can also operate with burst mode in which the pulse number can be adjusted within 1~ 1 000. The generator has the advantages of strong load adaptability, small size and multi -parameter adjustable including voltage amplitude, pulse width, pulse number and repetition frequency. Hence, it is competent for further exploration on medical experiments under high-voltage pulsed electric field.
Improved fault detection using Dynamic Independent Component Analysis (DICA): An application to multi-variate system

In this paper, a statistical multi-variate technique based on Dynamic Independent Component Analysis (DICA) is proposed for monitoring abnormalities in a chemical process. Multi-variate fault detection (FD) technique based on Principal Component Analysis (PCA) is restricted in capturing gaussian features of industrial data and it also assumes that observations at present time instant are not dependent on previous time instant. These assumptions do not apply for industrial processes due to the random characteristics of the variables and the underlying dynamics of the process. Another multi-variate FD technique named Indendent Component Analysis (ICA) has the ability of representing data as a function of latent variables (IC‘s) which are independent and this assumption is crucial to capture non- gaussian features in the data. The dynamics in the process data could be incorporated through dynamic ICA modeling where ICA model is embedded with lagged variables for capturing plant dynamics. In the current work, dynamic ICA (DICA) is used as the modeling frame-work while I2d, I2e and SPE statistics are the fault detection indicators. In ICA model development, the conventional FastICA algorithm involves random initialization of matrix B which results in different solutions for each iteration. To avoid this concern, in the current work, the matrix B is be initialized to a identity matrix to provide constant solution in each iteration. The performance of developed DICA strategy is demonstrated on a multi-variate process and a simulated quadruple tank process. The simulation results clearly suggest that the DICA strategy is able to detect anomalies effectively.
Empirical Study on Influencing Factors of Knowledge Product Remixing in OIC

Remixing of knowledge products has become one of the mainstream innovation models for the online innovation community (OIC). It is of great significance to explore the influencing factors of knowledge product remixing in OIC for better stimulating the open innovation. We first propose an analytical model for influencing factors of knowledge product remixing, then come up with a method for identifying false product attributes based on deep learning, and finally sum up the influencing factors of knowledge product remixing after analyzing the knowledge product attributes. The study results show that attention degree and user interaction have a positive impact on remixing of knowledge products, and there exists an inversely U-shaped relationship between knowledge complexity and product remixing. Continuous innovation has no significant positive incentive effect on product remixing.
Linux CPU-Idle Menu Governor with Online Reinforcement Learning and Scheduler Load Balancing Statistics

Efficient power management is a key requirement for any battery powered devices. Majority of the mobile market is Android-Linux kernel driven. CPU-Idle is one of the Dynamic power management technique present in Linux. In CPU-Idle framework, the Menu governor decides the appropriate idle state. The deeper the idle state, more power saving it is, but deeper idle state comes at cost of its extra entry/exit power and latencies. It's necessary to choose the idle state optimally for better power and performance. In current work, a mathematical framework, for modelling the Idle state selection through Markov Decision Process, is proposed. SARSA (state-action-reward-state-action), mechanism has been employed to assess and improve the idle state selection policy. In addition, scheduler load balancing statistics have been considered for idle state selection as well. For quantifying the work, a CPU-Idle profiler has been developed, which will capture all idle statistics per CPU basis. Results obtained through profiler show definite improvement in idle state selection in many scenarios, but in some scenarios the results remain same. The results leave out opportunity for Linux open source community to use, improve and tune the logic further based on experiments performed on various hardware platforms.
One-stage object detection networks for inspecting the surface defects of magnetic tiles

One of the core components of the permanent magnet motor is magnetic tile and surface defect detection of it is of vital importance to ensure the performance and service life of the motor. This paper employs a deep learning method based on computer vision to detect the surface defects on magnetic tiles in order to replace manual inspection and increase productivity. Considering the real-time requirements of the industrial site, three designed one-stage object detection networks of different depth are compared on our Inner-R surface dataset of magnetic tiles. The whole image is input into the networks which regard the object detection as a regression problem and output the value of class probability and position coordinate of the object. This approach can detect more than one defects on the same image as well as the location of defects which provides advantages to find the number of defects per class and improve the manufacturing process. As the result shows, the YOLOv3 network is the most applicable one in this magnetic tile surface defect detection problem and the detection time is less than 23 ms, which is an eye-catching result.
Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale IP Backbone Networks

The end-to-end network traffic information is the basis of network management in large-scale IP backbone networks. To obtain exact network traffic data, a prevalent idea is to employ NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, it also affects the network load. Motivated by this issue, we propose an optimized traffic measurement method based on reinforcement learning in this paper, which can collect most of the network traffic data by activating NetFlow on a subset of interfaces of routers in a network. We use the Q- learning-based approach to deal with the problem of the interface-selection, and propose an approach to compute the reward. Furthermore, a modified Q- learning approach is proposed to handle the problem of interface-selection. The method is evaluated by the real data from the Abilene and GEANT backbone networks. Simulation results show that the proposed method can improve the efficiency of traffic measurement distinctly.