Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Scheduling in Edge Computing: A Survey

With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and functions located in the cloud to the proximity of users, edge computing can provide powerful communication, storage, networking, and communication capacity. The resource scheduling in edge computing, which is the key to the success of edge computing systems, has attracted increasing research interests. In this paper, we survey the state-of-the-art research findings to know the research progress in this field. Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed. Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning. Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared. Also, we summarize the main performance indicators based on the surveyed literature. To shed light on the significance of resource scheduling in real-world scenarios, we discuss several typical application scenarios involved in the research of resource scheduling in edge computing. Finally, we highlight some open research challenges yet to be addressed and outline several open issues as the future research direction.
Robust Multi-agent Q-learning in Cooperative Games with Adversaries

We present RoM-Q 1 , a new Q-learning-like algorithm for ﬁnding policies robust to attacks in multi-agent systems (MAS). We consider a novel type of attack, where a team of adversaries, aware of the optimal multi-agent Q-value function, performs a worst-case selection of both the agents to attack and the actions to perform. Our motivation lies in real-world MAS where vulnerabilities of particular agents emerge due to their characteristics and robust policies need to be learned without requiring the simulation of attacks during training. In our simulations, where we train policies using RoM-Q, Q-learning and minimax-Q and derive corresponding adversarial attacks, we observe that policies learned us-ing RoM-Q are more robust, as they accrue the highest rewards against all considered adversarial attacks.
Quality spectra fluctuation modeling for manufacturing process based on deep transfer learning

It is difficult to characterize and monitor the quality fluctuation caused by multi-correlation parameters in manufacturing process. Motivated by the powerful ability of digital images to characterize process states, this paper presents a quality spectra fluctuation modeling method based on deep transfer learning. Firstly, through the multi-parameter correlation of spectra pixels, the quality spectra is constructed to characterize quality fluctuation. Then, a deep residual network transfer learning model is used to identify the types of quality fluctuation. Finally, the effectiveness analysis of proposed model is demonstrated by the Tennessee Eastman process.
Design of Ka-band broadband low-noise amplifier using 100nm gate-length GaN on silicon technology

Due to the high breakdown voltage and better linearity of gallium nitride (GaN) high electron mobility transistor (HEMT), GaN based RF front-end is widely researched and studied. In this paper, a Ka-band cascode low-noise amplifier (LNA) designed with 100nm gate-length GaN-on-silicon technology is presented. With novel methods such as the introduction of high-pass filters and standing wave filtering capacitors, the LNA achieves a stable gain of 21-22.8dB and a noise figure (NF) of 0.9-1.3dB from 22GHz to 38GHz. The input and output return loss are better than-10dB in band of concern. This LNA occupies an area of 2.3mm × 0.9mm and consumes 265-mW DC power. Compared with silicon-on-insulator (SOI), indium phosphide (InP) and gallium arsenide (GaAs) LNAs, the proposed GaN LNA exhibits better performance of linearity, as well as competitive gain and NF.
Characterization of plastic scintillator bars using fast neutrons from D-D and D-T reactions

We report results of fast neutron response in plastic scintillator (PS) bars from deuterium-deuterium (D-D) and deuterium-tritium (D-T) reactions using Purnima Neutron Generator Facility, BARC, Mumbai. These measurements are useful in context of Indian Scintillator Matrix for Reactor Anti-Neutrino (ISMRAN) detection, an array of 10×10 PS bars, used to measure reactor anti-neutrinos through inverse beta decay (IBD) signal. ISMRAN detector, an above-ground experiment close to the reactor core (∼13m), deals with an active fast neutron background inside the reactor hall. A good understanding of fast neutron response in PS bars is an essential pre-requisite for suppression and discrimination of fast neutron background from IBD events. A monoenergetic neutron beam from the fusion reaction of D-D at 2.45 MeV and D-T at 14.1 MeV are used to characterize the energy response in these bars. The neutron energy response function has been simulated using the GEANT4 package and are compared with the measured data. A reasonable agreement of deposited energies by fast neutrons in PS bars between data and simulation are obtained for these reactions. The ratio of energy deposition in adjacent bars is used to discriminate between prompt IBD, fast neutron and neutron capture cascade gamma events.
Deep transfer attention network for intelligent fault diagnosis of rolling bearings

Deep learning based on fault diagnosis methods of rolling bearings has attracted much attention with the rapid development of artificial intelligence. However, these methods trained by bearing datasets from one equipment cannot be well applied to correctly identify the fault information of the bearing datasets from another equipment. The main reason for this problem is that the datasets from the different equipment have different probability distributions. In order to solve the above problem, a deep transfer attention network is proposed for intelligent fault diagnosis of the rolling bearings. The steps of this method are listed as follows. Firstly, the feature extraction network is used to extract the features from different bearing datasets. Secondly, the different transfer attention is given to different areas of the sample data by the attention mechanism and distribution differences among the features is reduced by domain adaptation. Finally, the fault recognition network is trained to identify fault status for different bearings. The proposed method is verified by using fault datasets from different bearings equipment. The results show that the proposed method can achieve the fault identification of different bearings equipment.
Visualization analysis method based on multidimensional scale transformation for indicators on simulation data of weapon system

Visual analytic methods are a growing area of research that targets the effective description of weapon system, which capitalizing on displaying of various indicators information of weapon system simulation. We provide a method which can display all kinds of indicators and architectural features of simulation results in the same screen. First of all, the simulation data of multidimensional information index system and the corresponding dimension is mapped into the two-dimensional matrix. Secondly, the index system of two-dimensional graphs and planar map simulation data are fused by computation. Finally, distinguish the architectural features by drawing the influence range of the indicators to the systems. The method can analyze the correlation between indicators, similarity comparisons system features, and describe some kind of index and the system characteristics of the correlation degree. Because of the above characteristics of the method, we can adjust the index system, using visualization method to display the comprehensive characteristic of the weapon equipment system, and select the weapon equipment system in accordance with the specific conditions of the system.
Financial performance management and economic cycle variations. Evidence for textile industry

The study analyses the dynamics of the financial performance of the companies in the textile industry in Romania and aims to identify its main determinants, corresponding to the different stages of the economic cycles. The selected sample was mixed; therefore, the econometric analysis (based on panel data) was performed on accounting data collected from secondary sources (for 2000-2019). Because some of the companies in the sample showed negative financial results, the financial performance was assessed based on sales’ volume. The regression model used included seven determinants related to the company (internal determinants) and three external variables (GDP growth rate, inflation rate and unemployment rate). The results of the analyses indicated that (for the whole period) 51% of the sales dynamics can be explained due to the variation of the determinants included in the model. The analysis at the level of internal determinants indicated that all seven variables have a significant statistically influence on financial performance. From the perspective of macroeconomic determinants, the study showed that performance is negatively and significantly correlated with the inflation rate and unemployment rate. Applying the regression model to distinct periods (pre-crisis, crisis and post-crisis) provided very useful information from a practical point of view.
Research on anti-pilot contamination in massive MIMO

In order for the pre-coding technology to suppress the influence of pilot contamination, a hybrid pre-coding scheme is proposed in this paper by combining a full-digital pre-coding technology and an analog beamforming in the context of current massive MIMO. Simulation results show that the proposed pre-coding scheme effectively suppresses the pilot contamination while reducing the system structure complexity and ensuring the system’s basic performance, which is of important significance in practical communication applications.
Study of Modular Multilevel Converter in AC/DC Hybrid Distribution Network

The AC/DC hybrid power system plays an important role in the intelligent distribution network with the advantages of large power supply radius and capacity, low line cost and convenient distributed access. In this paper, a modular multilevel converter based on AC-DC side of AC/DC hybrid distribution network is exploited. An improved MPC sub-module capacitor voltage balance control method is proposed to reduce the calculation burden of MPC. The cost function is defined for phase currents, circulating currents, and sub-modules capacitor voltages. The weight factors of the cost function are eliminated, and the sub-module capacitor voltage control algorithm is improved to reduce the computational complexity of MPC. Not all cost functions for possible switching states combinations of MMC are calculated using improved method. The effectiveness of proposed improved method is evaluated based on 11 levels MMC system using Matlab/Simulink.
The model of a permanent magnet DC motor in time domain and frequency domain based on Bond Graph modelling and design of position control using PID controller

During the research work, the modelling of a permanent magnetic DC (PMDC) motor was realised in time domain and frequency domain via 4 different types of model construction. To achieve this, the Bond Graph model of the motor was determined, and based on them the system equations were defined in time domain firstly and then in frequency domain by using the Laplace transform. According to the system equations in time domain, the state-space model of the motor was also realised and implemented. Based on the system equations in frequency domain, the transfer function of the position control loop was realised. As the further part of the research work, PID control-based position control was designed in both time domain and frequency domain. The controller was tuned according to the Ziegler-Nichols method. Finally, the models were compared from the point of view of behaviour during the tuning process. The models were implemented in LabVIEW environment produced by the National Instruments.
Performance and application of an evaluation method for flow conditioner based on CFD

As evaluation of the performance of flow conditioner mostly depends on the specific pipeline system, which cannot be timely feedback to improve the structure design, an evaluation method for flow conditioner based on computational fluid dynamics (CFD) is promoted. First, the evaluation coordinates for flow conditioner is established and the coordinate values of each sampling point are solved. Then, the judgment basis of whether the velocity distribution of pipe cross-section is fully developed is derived, and then come to the evaluation index of flow conditioner rectifier performance. Finally, with CFD simulation technology, the verified experiments of grid format flow conditioner are performed. The results showed that, the proposed evaluation method is qualified to evaluate the grid format flow conditioner’s performance quickly and effectively. Besides, it is helpful to analyze the function of flow conditioner’s performance on Reynolds numbers. What’s more, when 5.84 × 106≤Re≤5.84×108, the required minimum length of straight pipe for irregular downstream field of the grid format conditioner achieving fully developed is only 0.5-0.6 times by those without conditioner. It is of great significance to accelerate the flow conditioner designed and to promote fluid energy monitoring.
Research on measures suppressing high frequency transient overvoltage in offshore wind farms

A large number of different types of cables, wind turbines, transformers and vacuum circuit breakers (VCBs) are used in power collecting system of offshore wind farms (OWFs). Due to the phenomenon of traveling wave refraction and reflection in cable collector system, transient overvoltage with high amplitude and high steepness will be generated when reignition phenomenon occurs in frequent operation of vacuum circuit breakers on wind turbines, which will cause serious damage to transformers, the problem of overcurrent is also becoming more and more serious. Therefore, through the electromagnetic transient simulation software of ATP-EMTP, a single-phase model of VCB switching-off transformer in offshore wind farm is built to study the influence of the separation speed of contacts of vacuum circuit breaker, high-frequency arc quenching capability and arching time on overvoltage. Then, the medium voltage cable collector system of OWF is built. Installing ferrite ring near the VCB or on the high-voltage side of transformer can suppress high frequency transient overvoltage. The results show that the high frequency transient overvoltage generated by offshore wind farm can be well suppressed after the installation of overvoltage protection device.
Analysis and application of manufacturing data driven by digital twins

This article introduces the importance of data processing to the production process and the application of data in the digital twin workshop. Then the overall process of data processing is introduced. First, several typical data acquisition methods are listed, feed the collected data to the digital twin model, and then extract the model data for integration and convert it into the same XML text format. Perform data cleaning and data analysis on it, and then use the DBN in deep learning to extract and classify the data to complete the real-time matching and transmission of production information to employees. Finally, the future development direction of the data analysis will be briefly summarized.
Study on variation of thermodynamic parameters in vehicle-mounted LNG gas cylinder under static state

To study the variation regularities of the mass, evaporation, temperature, and pressure of LNG in the gas cylinder with time, LNG was used as the working medium, and three specifications of LNG gas cylinders (150L, 330L, and 450L) were selected for heat transfer calculation. For the liquefied medium in the LNG gas cylinders, a saturated homogeneous model was adopted. For the heat transfer calculation of the adiabatic layer of LNG gas cylinders, a multilayer insulation structure model of radiation and heat conduction was adopted. According to the thermodynamic relations in the saturated state, the relations between the temperature and the physical parameters of the liquefied medium were fitted. The calculation method and calculation model were verified by the experimental data of liquid nitrogen for three specifications of LNG cylinders (150L, 330L, and 450L). According to the calculation model, the variation regularities of LNG thermodynamic parameters with time were obtained at a normal temperature. In the initial period of evaporation, the increasing temperature rate of LNG, the evaporating rate of LNG, and the pressure-boost rate of LNG in the gas cylinder are slow, and then these rates increase. The final mass of boil-off gas (BOG) produced by the phase change finally reaches 0.68% of the total mass of the medium in the gas cylinder.
Improved Chinese Short Text Classification Method Based on ERNIE_BiGRU Model

This paper is aimed at traditional word embedding models and Bidirectional Encoder Representations from Transformers (BERT) that cannot learn text semantic knowledge, as well as convolutional neural network (CNN) and Bidirectional long short-term memory (BiLSTM) unable to distinguish the importance of words, proposing an improved Chinese short text classification method based on ERNIE_BiGRU model. Firstly, learning text knowledge and information through the Enhanced Representation through Knowledge Integration (ERNIE) enhances the model’s semantic representation capabilities. Secondly, considering that CNN can only extract local features of the text while ignoring the semantic relevance between contextual information, and the Bidirectional Gating Recurrent Unit (BiGRU) is simpler, has fewer network parameters and faster calculation speed than the BiLSTM, the combination of CNN and BiGRU enables the model to capture both local phrase-level features and contextual structure information. Finally, according to the importance of features, the attention mechanism is used to assign different weights to improve the classification effect of the model. The experimental results show that the ERNIE_CNN_BiGRU_Attention (ECBA) model used in this paper has achieved good results in the task of Chinese short text classification.
Active power improvement of three-phase grid-connected inverter under unbalanced grid faults

Unbalanced grid voltage causes fault-phase over-current, affects the life span of devices and even leads to control system instability. The conventional current limiting strategy reduces the output power of grid-connected inverter. In order to increase the active power injected by the grid-connected inverter into the grid, this paper proposes a new current limiting strategy. On the premise of ensuring that the DC side voltage fluctuation and grid-connected current harmonics meet the grid standards, the fault-phase current and the normal phase current are respectively limited. The simulation results verify the proposed strategy can effectively improves the active power output of the grid-connected inverter.
Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load

In Iran, no detailed information on the amount of erosion, sediment transport, and sedimentation of rivers, and in many cases, there are many differences between measurements. Since the flow regime and consequently the sediment regime in the drainage basins are not constant, estimation of sediment discharge can help estimate the sediment accumulated behind the water structures, especially the dams, and determining the dead volume of reservoirs in the coming months, and by adopting timely arrangements, the management of discharge will be facilitated to a certain extent during sedimentation. In this study, a hybrid method of the Whale optimization algorithm and the neuro-fuzzy inference system was used to estimate the suspended sediment load (SLL) of the Zarinehrood river. The performance of the proposed methods was evaluated by two statistics, including determination coefficient (R2) and normal root mean square error (NRMSE). SSL of the Zarinehrood river during 10 years with flow discharge was used as inputs. The results showed the high accuracy of the WOA-ANFIS with values R2=0.962 and NRMSE=0.051. In general, a comparison of the results obtained from the hybrid method used in this study showed the high ability and accuracy of the WOA-ANFIS method in estimating the SLL of the Zarinhrood river.
Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement

In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.