Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dual-Band MIMO Antenna With Enhanced Isolation for 5G Smartphone Applications

In this paper, a compact dual-band MIMO antenna array for 5G smartphone applications is proposed. The MIMO antenna consists of eight folded monopole antennas operating at 3.45-GHz band (3300~3600MHz). And the antenna elements with a size of <inline-formula> <tex-math notation="LaTeX">$6.8\times 6.6\times4$ </tex-math></inline-formula> mm<sup>3</sup> (about <inline-formula> <tex-math notation="LaTeX">$0.078\lambda \times 0.075\lambda \times 0.046\lambda $ </tex-math></inline-formula> at 3.45 GHz), are disposed along each edge of the system circuit board. By introducing the decoupling structures, the isolation between inner antenna elements is improved from 10dB to 15.1dB in 3.45-GHz band (3300~3600MHz). Meanwhile the inner antenna units can generate an additional operating band covering 2400~2700MHz due to the coupling effect between the antenna elements and the proposed decoupling structures, which is promising for solving the problem of terminal space shortage. To verify the design principle of the proposed MIMO antenna array, the antenna is fabricated and measured. The measured results of efficiency and ECC are analyzed as well to demonstrate the performance of the proposed MIMO array. Channel capacity, user proximity and SAR analysis and are also given in this paper.
Energy efficiency at wind power systems operating at time varying wind speed

The present paper determines the optimum energy operation area of an electric wind power system, under time varying wind speed conditions. The determinations of the optimal area, from the energy point of view, is done based on experimental values from the Romanian black sea cost, Dobrogea area. The optimum operation area is defined by the optimal mechanical velocity, ωOPTIM. For this, the dependence between the wind speed, v, and ωOPTIM are computed based on the current wind speed and the current mechanical velocity ω. Due to the high inertia of the wind turbine (WT), the generators speed is not able to follow the changes of the wind speed. Knowing the optimum value ωOPTIM, the solution is to determine the load at the permanent magnet synchronous generator (PMSG) in order that the mechanical speed should timely reach his optimum.
Time-Varying Tan-Type Barrier Lyapunov Function-Based Adaptive Fuzzy Control for Switched Systems With Unknown Dead Zone

This paper investigates the output constraint problem for switched uncertain nonlinear systems with unknown dead-zone input. The system output is constrained by a time-varying bound, and the time-varying Barrier Lyapunov Function (BLF) is employed to guarantee that the constraint condition is not violated. The parameter of considered dead-zone is unknown but bounded. In order to remove the effectiveness caused by dead-zone, an adaptive scheme is used to adjust the bound of the unknown parameter. In addition, the unknown system dynamic is taken into account, which is modeled by using fuzzy logic systems (FLSs). Combing the backstepping technique, an adaptive controller is constructed such that all the signals in the closed-loop system are bounded under arbitrary switching signal, and the system out can track the reference signal as soon as possible. Moreover, the constraint condition for system output is ensured. Finally, an example is employed to explain the effectiveness of the proposed method.
Evolutionary Strategies of Supply Chain Finance From the Perspective of a Return Policy

Return policy and supply chain finance are significant measures for banks and enterprises to improve the overall competitiveness of their supply chain. Supervision and guidance by banks are prerequisites to the smooth implementation of return policy and supply chain finance. This study analyzes the evolutionary stable strategy of three parties, namely, one bank, one supplier and one retailer, by establishing an asymmetric evolutionary game model. The model assumes that banks apply a reward and punishment policy for suppliers and retailers. Results show that regardless of the strategy the bank chooses, one party will always choose non-implementation of the return policy or non-adoption of the supply chain finance. From a short-term perspective, regardless of the strategy the bank chooses, suppliers and retailers will select the strategy of non-implementation and non-adoption, respectively. From the long-term perspective, suppliers and retailers will actively choose implementation and adoption whether or not there is bank supervision.
Systematic Polar Codes Based on 3x3 Kernel Matrix

In this paper, the construction of polar codes based on multidimensional kernel matrix is analyzed. In order to analyze the performance of the codes efficiently, the performance of systematic polar codes (SPCs) and non-systematic polar codes (NSPCs) constructed by 3×3 kernel matrix is analyzed. Simulation results show that SPCs have advantage over NSPCs in bit error rate (BER) performance while they have the same frame error rate (FER). So we may conclude that SPCs based on 3×3 kernel matrix have better performance than NSPCs.
Ontology-Based Security Context Reasoning for Power IoT-Cloud Security Service

For a variety of cyber-attacks occurring in a power IoT-Cloud environment, conventional security intrusion incident detection and response technologies typically use pattern- and behavior-based statistical methods. However, they cannot provide fundamental solutions for a security intrusion or attacks, which are becoming more intelligent and diverse as time passes. Therefore, an effective response method that can respond to security intrusions intelligently while using an access control technique based on ontology reasoning is required. This can be achieved by adopting a variety of intelligent reasoning technologies for security intrusion incidents of power systems, such as various reasoning technologies based on the ontology and semantic-web technologies being actively studied in the field of intelligent systems, and malicious code detection technologies based on an intelligent access control model, text mining, and natural language processing technologies. Accordingly, a security context ontology was modeled by analyzing the security vulnerabilities of a power system in a power IoT-Cloud environment, and security context inference rules were defined. Furthermore, this paper presents an appropriate power IoT-Cloud security service framework that can be used in a power IoT-Cloud environment. In addition, a security mechanism that can be efficiently operated in such an environment is implemented. In experiments conducted for this application, attack context scenarios that commonly occur were created using a smart meter as an example, which is an essential power system device. Inference rules were then composed for each attack stage to check the paths of attacks those that exploit the vulnerability of a smart meter system. As a result, it was confirmed that a high level attack detection results can be obtained based on the inference rules.
Multi-level Modeling as a Language Architecture for Reference Models: On the Example of the Smart Grid Domain

In this paper, we propose the use of multi-level modeling as an underlying language architecture for the creation and use of reference models. We focus on the added flexibility and expressiveness offered by multi-level modeling, such as, among others, the ability to express domain information at the appropriate level of abstraction, and removing the need for additional consistency checking mechanisms. We illustrate the use of multi-level modeling on the basis of a multi-level reference model for smart grid cybersecurity created using the Flexible Meta Modeling and Execution Language (FMMLx), and briefly showcase an implementation in the integrated modeling and programming environment XModeler.
Topology Analysis of Basic Non-isolated DC/DC Converter and DC Distributed Power System

DC/DC converter and DC distributed power system (DC-DPS) have been widely used in many industrial fields. This paper systematically studies the rule and method of deriving the basic topologies of them. Starting with the overall structure of basic non-isolated DC/DC converters, it can be divided into three cascaded units. Each unit can further be divided into voltage-type (V-type) and current-type (C-type). Then, according to the basic circuit theory, four categories of basic non-isolated DC/DC converter generalized structures and twelve specific non-isolated DC/DC converter topologies are all derived out. Based on the same topology construction principle above, the basic DC V-type and Ctype distributed power system generalized structures are both established, which can be named DCV-DPS and DCC-DPS. On this basis, two practical DC-DPS topology structures are also given out as an application example. Finally, combined with high-power LED driving applications, a 90W prototype with three channel outputs based on the DCC-DPS topology structure is built and the experimental results are presented. It is proved that the proposed topology construction rule and analysis method are valid.
Automated Mapping Of Accessibility Signs With Deep Learning From Ground-level Imagery and Open Data

In some areas or regions, accessible parking spots are not geolocalized and therefore both difficult to find online and excluded from open data sources. In this paper, we aim at detecting accessible parking signs from street view panoramas and geolocalize them. Object detection is an open challenge in computer vision, and numerous methods exist whether based on handcrafted features or deep learning. Our method consists of processing Google Street View images of French cities in order to geolocalize the accessible parking signs on posts and on the ground where the parking spot is not available on GIS systems. To accomplish this, we rely on the deep learning object detection method called Faster R-CNN with Region Proposal Networks which has proven excellent performance in object detection benchmarks. This helps to map accurate locations of where the parking areas do exist, which can be used to build services or update online mapping services such as Open Street Map. We provide some preliminary results which show the feasibility and relevance of our approach.
A Flexible Tactile Sensor to Detect Stiffness Distribution Without Measuring Displacement

In this study, we developed a flexible tactile sensor that scans the surface to detect the stiffness distribution. Note that the sensor does not need to know the displacement or strain of the target surface.Endoscopic palpation is a promising technology to detect tumors that have larger stiffness than normal tissues. The stiffness can be deduced by acquiring the load applied and the displacement, whereas the displacement or strain of the tissue is difficult to obtain given that endoscope has an arbitrary shape when in use and the tissue surface is not flat. In this study, we propose a new stiffness sensor composed of four strain gauges with different Young’s modulus to detect the target without measuring the strain. The developed flexible sensor uses encapsulated liquid metal as the electrodes and can scan the surface.
Viewpoint Networking for Trusted Reality

With the advancement of immersive applications, edge computing and IoT technologies to deliver application service in an intuitive, immediate and reliable manner, we propose a viewpoint networking framework for Trusted Reality which is to bridge the real-cyber world based on trust by integrating individual preferences and surrounding objects to provide users with desired application services and perform a preliminary proof of concept to verify the feasibility of the proposed framework.
Protein Lithography beyond the Diffraction Limit Using Terahertz Metamaterials

Here we report nanolithography of silk proteins using ultrafast terahertz (THz) pulse with a center wavelength of ~300 μm. This is achieved by metamaterial enhanced local high-field THz field. The THz electric field (E-field) is localized inside the capacitive gaps of the gold split ring resonator (SRR) and enhanced by more than two orders of magnitude [1]. By adjusting the strength of the incident THz pulse, we can precisely control the secondary structure of the silk fibroin in the gap of SRRs, which provides a pertinent technique for protein-based lithography beyond the diffraction limit.
A hybrid alarm management strategy in signature-based intrusion detection systems

Signature-based Intrusion Detection Systems (IDS) are a key component in the cybersecurity defense strategy for any network being monitored. In order to improve the efficiency of the intrusion detection system and the corresponding mitigation action, it is important to address the problem of false alarms. In this paper, we present a comparative analysis of two approaches that consider the false alarm minimization and alarm correlation techniques. The output of this analysis provides us the elements to propose a parallelizable strategy designed to achieve better results in terms of precision, recall and alarm load reduction in the prioritization of alarms. We use Prelude SIEM as the event normalizer in order to process security events from heterogeneous sensors and to correlate them. The alarms are verified using the dynamic network context information collected from the vulnerability analysis, and they are prioritized using the HP Arsight priority formula. The results show an important reduction in the volume of alerts, together with a high precision in the identification of false alarms.
A Novel Non-Coupled Non-Isolated Double-Input Bidirectional High-Gain Converter for Hybrid Energy Storage System

In this paper, a novel non-coupled non-isolated double-input bidirectional high-gain converter is proposed for integrating hybrid energy storage systems (HESS) to DC micro-grids. The converter does not use the traditional techniques such as coupled-inductor or multi-winding transformer to achieve high-gain, hence problems such as voltage spikes or circulating currents are not present. It also has the ability of charging and discharging battery and supercapacitor independently. Moreover, it only utilizes six switches, which is less than its counterparts use. In addition, unlike other high gain converters it uses only three active switches in every mode, so conduction losses are decreased by a steep rate. The steady-state analysis of proposed converter is presented, to validate the analysis, detailed PSPICE simulations were carried out for calculating the precise gain of converter and power losses in several cases. Finally, a lab prototype of the converter is constructed, and experimental results are presented.
DNN-Based Cepstral Excitation Manipulation for Speech Enhancement

This contribution aims at speech model-based speech enhancement by exploiting the source-filter model of human speech production. The proposed method enhances the excitation signal in the cepstral domain by making use of a deep neural network (DNN). We investigate two types of target representations along with the significant effects of their normalization. The new approach exceeds the performance of a formerly introduced classical signal processing-based cepstral excitation manipulation (CEM) method in terms of noise attenuation by about 1.5 dB. We show that this gain also holds true when comparing serial combinations of envelope and excitation enhancement. In the important low-SNR conditions, no significant trade-off for speech component quality or speech intelligibility is induced, while allowing for substantially higher noise attenuation. In total, a traditional purely statistical state-of-the-art speech enhancement system is outperformed by more than 3 dB noise attenuation.
Application of experiential analogy in event exhibition

This paper reports on an application of experiential analogy in event exhibition. Experiential learning is not a new concept and, as the name suggests, the concept involves learning from experience, distinguishing it from rote or didactic learning. Due to increased ‘marketing clutter’ and the overuse of traditional media, marketers have had to find new ways to position their brands in consumers’ minds. One way to accomplish this is through the use of experiential analogy where consumers are able to view the target product in terms of the experience it offers, not just as a bundle of functional attributes and benefits. An example of a novel experiential analogy that draws individuals’ base experience and has high emotional inferences is provided in the paper.
Urban surface recovery through graph-cuts over SAR tomographic reconstruction

The understanding of tomograms can be a difficult task when the observed scene is complex. The Synthetic Aperture RADAR (SAR) ranging acquisition induces geometrical distortions depending on the local slope of the back-scattering surface. Over dense urban areas, this particular geometry produces tilted facades instead of straight buildings. Moreover the high dynamic in SAR images of urban areas may lead to 3-D representation with very bright voxels eclipsing other echoes. In this paper we present a graph-cut approach adapted to any tomographic reconstruction technique to segment the urban surface in a 3-D tomographic reconstruction. Results on real data from a stack of 40 TerraSAR-X images are presented.
I2V Multi-Hop Broadcast Communication by TC-BF Method

A novel Infrastructure to Vehicle (I2V) multi-hop broadcast communication technique is proposed. The proposed Time-Controlled Broadcast Forwarding (TC-BF) method enables more efficient information delivery on the road compared to the existing broadcast packet forwarding schemes such as flooding and Irresponsible Forwarding (IF) methods. Packet delivery rate (PDR) and forwarding efficiency of the proposed scheme are evaluated by computer simulation. It is concluded that the proposed TC-BF can cut unnecessary forwarding and keep high PDR with extended communication range.
Guidelines for the Design of Vehicular Cloud Infrastructures for Connected Autonomous Vehicles

Initiatives all around the world, funded by industries and governments, have devoted tremendous efforts to develop connected and autonomous vehicles (CAVs). Nowadays, different CAV prototypes are being tested on the roads. The natural next step in the evolution of this area will be the provisioning of infotainment applications for enjoyable commutes and trips. In this article, we discuss the motivation for vehicular clouds for content delivery aimed to support applications for CAVs. First, we review the main concepts of vehicular clouds. Consequently, we discuss the needs of vehicular clouds to support content distribution for applications in CAV scenarios. In addition, we highlight fundamental challenges regarding vehicular cloud that need to be tackled: self-organization, service discovery and management, and resource discovery and allocation. By doing so, we analyze the current works in the literature and portray their limitations. Moreover, we provide some guidelines to deal with the challenges of vehicular clouds for CAV applications. Finally, we present future research directions that might be considered for the design of large-scale vehicular cloud infrastructures for CAV applications.
Research on Performance Automation Testing Technology Based on JMeter

[Objective] With the development of Internet technology, web-based application systems have been widely used in various fields of social life. With the increasing number of users, the performance of the system is related to whether it can provide services normally, and affects the users experience directly. How to introduce the automation testing technology in the software development stage to expose the performance defects of the system effectively and improve the performance of the system continuously has attracted more and more attention. [Method] Based on JMeter testing tool, the author deeply discusses the technology of web performance automation testing, and shows the application of JMeter in performance testing by examples. [Conclusion] It is concluded that the flexible application of JMeter performance automation testing technology can simulate large-scale concurrent user requests, improve the testers' work efficiency and verify whether the performance of web application systems can meet the organization requirements effectively.
Change Your Cluster to Cold: Gradually Applicable and Serviceable Cold Storage Design

Because of its low cost per gigabyte, hard disk drive (HDD)-based storage are still extensively used despite consuming more power than flash-based storage. In particular, HDDs can be effectively used as cold storage for energy efficiency by making some drives in spin down. However, typical cold storage cannot be used owing to high access latency, unless it is used for archival or backup purposes. Furthermore, it is difficult to apply many power-proportional solutions because they require reconfiguration of the server power domain and adjustments to the data layout. In this paper, we propose a serviceable cold storage design that can be applied gradually to online services. The proposed design only modifies the data server of a typical distributed storage system to let it utilize the spin-up or spin-down features of disk drives and determine the data location. Because the modified data server appears identical to the existing data nodes, it can be implemented in the same manner as the addition or removal of a data server. Our prototype is implemented on Ceph, a well-known distributed storage system, and its effectiveness in managing the skewed I/O pattern of applications is demonstrated using a benchmark that can reproduce the real I/O patterns of the LINE mobile messenger application.
Fault Monitoring and Diagnosis of Actuators in Electromagnetic Valve-Train Based on Neural Networks Optimization Algorithm

In order to realize fully flexible variable valve lift and improve the intake efficiency of the engine, a new electromagnetic valve-train (EMVT) was designed to replace the traditional CAM in the engine valve-train. However, as the durability of electromagnetic linear actuator (ELA) in the EMVT was lower than that of CAM, and there might be some problems during a long running time. This paper presented an improved online fault diagnosis method combining BP Neural Networks with Grey Relation Analysis (GRA) to analyze current signals, which could realize the monitoring, diagnosis of electromagnetic linear actuator faults and early warning so as to prevent unnecessary situations such as accidental cylinder stop of the engine. The results showed that this method had a high fault diagnosis rate, high speed, reliability and practicability.
MPPT for Small Wind Turbines: Zero-Oscillation Sensorless Strategy

To increase the adoption of micro- and pico-grid systems, easy to use and commercially viable distributed generation solutions are required. The proliferation of distributed generation systems has accelerated the research and implementation of small wind turbines (SWTs) as viable renewable energy solutions. Sophisticated Maximum Power Point Tracking (MPPT) algorithms, used for large wind turbine installations, are not implemented with SWTs as they require turbine parameterization and costly sensors such as anemometers. Due to financial considerations, traditional SWTs implement either no MPPT or very simple algorithms such an Incremental Conductance (InCond) or Perturb and Observe (P&O). In this work, a novel and computationally efficient SWT MPPT algorithm, derived from the InCond method, is proposed. A control-oriented model of the SWT system is also presented which facilitates fast simulations using conventional power electronics software. The proposed MPPT algorithm offers 3 key advantages: 1) elimination of algorithm confusion due to change in wind velocity, 2) fast and accurate tracking of the maximum power point (MPP) and 3) improved steady state efficiency. The behavior of the proposed algorithm is presented and corroborated in simulations and experimental validation, using a custom-built Turbine Emulation Platform (TEP), is also included.