Miracle-dz/UniFAR-FTUs huggingface dataset:
A Method for Guaranteeing Wireless Communication Based on a Combination of Deep and Shallow Learning

Wireless communication has changed and improved people’s lives and society, especially with the arrival of the Internet of Things (IoT) era. Despite the maturity of wireless communication, the security issue of communication remains the most stubborn and troublesome problem due to the increasingly complex and large amounts of data. An intrusion detection system is the guarantee of secure communication. However, variable protocols and drastic growth in data volume make intrusion detection a difficult task. In this paper, we proposed a framework of anomaly-based network intrusion detection system to finish the detection job. First, UNSW-NB15 is selected as the research object. Based on this new dataset, we built a detection model combining a deep learning method and a shallow learning approach. The former one is a deep auto-encoder used for feature learning, which can discover important representations of data and accelerate detection. The latter one is a powerful support vector machine (SVM), where the artificial bee colony (ABC) algorithm is used to find optimal parameters for SVM with five-fold cross validation (5FCV). Various experiments are conducted and the simulation results prove that the proposed method performs quite better than some of state-of-the-art intrusion detection approaches, including the method based on the principal component analysis (PCA) and some other machine learning strategies.
Transmission Line Ice Coating Prediction Model Based on EEMD Feature Extraction

Transmission line icing is a common natural phenomenon, but it is the most dangerous factor that severely threatens the safety and stability of the power grid operation. Transmission line icing involves many factors, including temperature, humidity, wind speed, light intensity, wire tension, pressure, and wind deflection angle. Because of the high dimensionality, nonlinearity, multi-modality, and heterogeneity of the data generated by these factors, it is difficult to establish an accurate prediction model based on these data adopting traditional data mining methods. How to establish an accurate and effective new model of transmission line icing prediction has become a key problem to be addressed urgently. To address these problems, the paper collects the data monitored by the China Southern Power Grid Online Monitoring System from 2011 to 2016 to study the prediction model of the icing level of the transmission lines. Since the values affecting the icing level are dynamically changing with time, this paper first uses the time series analysis method to process the icing data and proposes an ensemble empirical mode decomposition (EEMD) method to adaptively decompose the meteorological and mechanical data, which reduces the impact of noise and outliers in high-dimensional data, and maximizes the use of the inherent law of time-frequency to effectively analyze icing data. The feasibility of this method is verified with real data. The experimental results show that the prediction model based on EEMD time-frequency is more accurate than the prediction model based on the original data. Compared with the five prediction models as random forest, support vector machine, BP neural network, Elman neural network, and Bayesian network, the accuracy has increased by 0.47%, 2.93%, 1.85%, 0.92%, and 1.86%, respectively. In addition, this new method is more sensitive to the serious situation of icing on the transmission lines. Compared with the prediction model based on the original data, this method improves the accuracy of prediction for icing level 4 and 5 by 17.5%, 16.67%, 50%, 3.13%, and 10.26%, respectively.
Event-Triggered Adaptive Neural Network Controller in a Cyber–Physical Framework

The importance of remotely placed controller in a cyber space with sensor–controller–actuator network has increased significantly in industrial, defense, and surveillance sector. Such network has large amount of sensor and controller data. A time-triggered control technique may generate redundant control signals and put unnecessary data on network. Therefore, an event-triggered adaptive controller that generates control action at required instants using state- and error-based conditions has been developed in this paper. A data transmission framework has also been designed in this paper that addresses network delay and packet losses. The proposed controller-communication methodology has been validated through two case studies, first, temperature tracking for heating ventilation and air conditioning system and, second, real-time path tracking by automated guided vehicle. The proposed methodology has also been duly compared with its time-triggered counterpart. The control updates are reduced to approximately <inline-formula><tex-math notation="LaTeX">${\text{41}}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${\text{64}}\%$</tex-math></inline-formula> in the two case studies, respectively. The experimental results also prove the designed controller to be efficient when compared with event-triggered incremental PID controller using the same data transmission framework.
Novel Space Vector Pulsewidth Modulation Strategies for Single-Phase Three-Level NPC Impedance-Source Inverters

This paper presents new space vector pulsewidth modulation (PWM) strategies for a single-phase three-level buck-boost neutral point clamped inverter coupled with impedance-source (IS) network. These strategies can be implemented for systems with any IS network with neutral point. The case study system is based on the quasi-Z-source inverter with continuous input current. To demonstrate an improved performance, the strategies are compared with a traditional PWM strategy. The advantages lie in reduced switching number, without output voltage quality distortion. The simulation and experimental results confirm the theoretical predictions.
Comparison of the Heat Transfer Capabilities of Conventional Single- and Two-Phase Cooling Systems for an Electric Vehicle IGBT Power Module

This paper presents a comparison of conventional single-phase water/glycol liquid and innovative two-phase cooling technology for thermal management of high-power electronics automotive insulated-gate bipolar transistor modules during a full drive cycle. The proposed two-phase cooling system is built using conventional automotive air conditioning components (a condenser, an expansion valve, a compressor, and vapor and liquid lines) and a conventional cold plate as used for single-phase cooling; thus, the design does not require the development of new technology for its implementation. Three-dimensional numerical simulation in COMSOL and experimental results of two-phase cooling have been obtained on a prototype and compared to conventional water/glycol cooling high-power electronics modules, with a considerable improvement on working temperature, power transfer capacity, and equalization of die temperatures during a full driving cycle. These results suggest that two-phase cooling using the same cold plates as in single-phase cooling can be used to substantially improve the performance and reliability of electric vehicle power converters without major changes.
Synchronous Current Switching Technique for Adjusting the CMOS Ring Oscillator Duty Cycle

The present work proposes a low cost, synchronous current switching technique for adjusting a 60MHz, medium frequency, oscillator duty cycle. Current switching is necessary due to the use of both charge and discharge delays of the oscillator topology. Added circuitry consists on trimmable current mirrors and synchronizing circuitry. Main goal is not to affect the functionality of the system, nor initial frequency or duty cycle variation. The circuit is designed using sub micrometer technologies, for a supply voltage range between 1.6V and 2V.
Data Compression in Wireless Sensor Nodes with LoRa

In Wireless Sensor Networks (WSN), LoRa technology permits the data communication on large areas, allowing to eliminate multi-hop data transmission that requires a very precise synchronization. Due to these advantages it is being used more and more in practical solution through LoRaWAnprotocol. However, although it is a low power protocol, comparing with other data transmission protocols used in WSN, the timeframe for transmitting data is significantly longer and the energy consumption is increased. Thereby, reducing the data volume through data compression has a major impact on node current consumption. This paper presents results of data compression in wireless sensor nodes that uses LoRa technology to transmit data. An energy consume comparison is made with other data communication protocols used in WSN, like ZigBee or Enhanced ShockBurst.
ESD-Reliability Enhancement of Circular UHV 300-V Power nLDMOS by the Drain-side Superjunction Structure

For circular ultra-high voltage (UHV) 300-V power nLDMOS devices, a novel device architecture of high voltage p-well (HVPW) zones is embedded into a high voltage n-well (HVNW) drift region to form superjunction (SJ) structures (radial-type). And, by the HVPW/HVNW SJ of the radial-type, eight types of area ratios are fabricated. However, the human-body model (HBM) capacity of this UHV nLDMOS reference group is only 2500 V. For equal partition radial-type SJ, due to more even HVPW distribution in the equal 32-partitions, the HBM level (6750 V) was the highest. Meanwhile, it can maintain a fairly high breakdown voltage as with the reference sample. Finally, for the radial-type 30 equal partitions with different HVPW/HVNW area ratios, because the HVPW area was large in the HVPW/HVNW ratio(≈2:1), more conduction current flowed into the BNW layer under these new inserted HVPWs and dispersed the conduction current; thus, the HBM (7000 V) was the best. Thus, the more even partition and higher proportion of HVPW/HVNW area ratio in drain-side SJ architecture, the stronger its electrostatic discharge reliability performance.
Efficient Optical Trapping and Detection of Nanoparticle Via Plasmonic Bowtie Notch

For manipulating nanoparticles, we propose a nanotweezer named plasmonic bowtie notch (PBN) by adding a thin metal film to the bottom of plasmonic bowtie aperture. The PBN can exert a large trapping force on nanoparticle because the enhanced field of resonance mode can be much accessed by nanoparticle. The optical properties and factors influencing trapping force of PBN are fully investigated and discussed. The optimized PBN shows an excellent trapping capability with ultralow threshold excitation intensity of 0.64 mW/μm2 for stable trapping of a 100 nm polystyrene particle. In addition, it provides a label-free detection of the trapped particle by observing the extinction spectrum. The sensitivity to trapped target size of 71 pm redshift in peak wavelength per 1 nm increase of particle size is relatively high compared with those obtained from most near-field tweezers. Furthermore, the footprint of PBN is only 200 nm × 220 nm. The capabilities of the proposed design show a great potential in the application of nanoparticle trapping and sensing.
Deep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit

Facial expression is the most powerful and natural non-verbal emotional communication method. Facial Expression Recognition(FER) has significance in machine learning tasks. Deep Learning models perform well in FER tasks, but it doesn't provide any justification for its decisions. Based on the hypothesis that facial expression is a combination of facial muscle movements, we find that Facial Action Coding Units(AUs) and Emotion label have a relationship in CK+ Dataset. In this paper, we propose a model which utilises AUs to explain Convolutional Neural Network(CNN) model's classification results. The CNN model is trained with CK+ Dataset and classifies emotion based on extracted features. Explanation model classifies the multiple AUs with the extracted features and emotion classes from the CNN model. Our experiment shows that with only features and emotion classes obtained from the CNN model, Explanation model generates AUs very well.
Implementing an Impedance Compression Network to Compensate for Misalignments in a Wireless Power Transfer System

This paper presents an impedance compression network (ICN) design in a wireless power transfer (WPT) system to compensate for distance or alignment variations between coils. In midrange WPT applications, magnetic resonant coupling coils are mainly implemented to achieve high efficiency for charging batteries. However, a distance or horizontal alignment variation between coils changes their coupling coefficient, and it decreases the overall performance of WPT systems because the zero voltage switching in a resonant inverter is lost. In order to reduce coil impedance variation, we propose an ICN that compresses variations in coil impedance. The ICN consists of a resistance compression network and a phase compression network to suppress magnitude and phase variations, respectively, in the coil impedance. Only passive components, such as inductors and capacitors, are used to implement an ICN.
Optimized Luminaire Allocation and Configuration with Luminaire Failure Compensation

In this research, we aim to solve the luminaire allocation and configuration problem. Additionally, considering the difficult of movement or adjustment of fixed luminaire, the luminaire failure issue is also taken consideration in the proposed model. To verify the proposed lighting configuration based on the GA, the athletic fields is taken as an example. According to two experiments results, compare to the lighting simulation software DIALux, in scenes with average illuminance of 750 lux and 350 lux, the error rate was less than 5%. In scenes with average illuminance of 150, the error rate was relatively high. The disparity value of illuminance between each point was between 0.9 lux and 37.4 lux, and the average disparity value of illuminance was 16.6 lux. In the two experiments, it is notable that the lighting configuration proposed based on the GA in this research has extraordinary effects. The traditional method based on experience can be replaced. The result can be verified by the DIALux software recognized in the industry, proving the feasibility of the model.
A Method for Substrate Permittivity and Dielectric Loss Characterization Up to Subterahertz Frequencies

In this paper, a method for substrate permittivity and loss tangent characterization applicable for frequencies in the subterahertz range is presented. Planar transmission lines fabricated on three different printed circuit board (PCB) materials and four different on-chip/on-wafer materials are being used for measurement along with a wafer probing station equipped with on-wafer probes and frequency extenders. A comprehensive procedure for calculation of substrate permittivity, total loss, characteristic impedance, and dielectric loss tangent is presented. Measurement data up to 67 GHz is given in case of PCB and up to 500 GHz in case of on-chip/on-wafer materials and compared against reference data and competitive materials from recent publications.
Plane Based Visual Odometry for Structural and Low-Texture Environments Using RGB-D Sensors

This paper exploits planes instead of feature points, and proposes a visual odometry approach to estimate sensors' poses for structural and low-texture environments. We construct constraints for factor graph by utilizing the associated planes based on line features, and we derive the analytic jacobian for planes' minimal representation to speed up the optimize process. Experiments demonstrate the effectiveness and robustness of our proposed method and show its advantages over alternative methods.
Synchronized Monitoring of Droplet Transition and Keyhole Bottom in High Power Laser-MAG Hybrid Welding Process

This present paper addresses the process monitoring of laser-MAG hybrid welding by using a double high-speed camera sensing system. Different welding parameter experiments are conducted under the normal penetration condition. The real-time surface physical phenomena of top and bottom in welding process are captured by the sensing system. The image processing method is proposed to quantify the process status, such as droplet transition, spatter, keyhole bottom, and metal vapor. The statistical method combined with detail analysis method is used to analyze the effects of droplet transition on keyhole status under different welding parameters. Also, the time distribution of opened keyhole bottom is analyzed. The synchronization analysis is conducted to demonstrate the status of the droplet transition and laser-induced keyhole, and the status of the opened keyhole bottom is separated as two types: opened keyhole bottom occurs during droplet transition or during droplet growing. The experimental analysis results demonstrate that the droplet transition mainly affects the status of keyhole under normal penetration condition, and the opened time of keyhole bottom is quite different which means the effects are unstable. Furthermore, the experimental results confirmed that the process monitoring method in this paper is an effective way for laser-MAG hybrid welding evaluation.
Detecting Forged Images a Robust Java Implementation Algorithm

Image manipulation represents a true “art”, which takes into account the images with a lot of their properties. There are many ways a digital photo can be manipulated, so the forger can use one or more combinations of these in order to make their detection more difficult. On the other hand, only one detection technique is specific to a single image manipulation. For instance, a duplication detection method is based on an existing correlation between the segment of the original image and the copied image. This paper presents a robust Java implementation of an algorithm detecting such a Copy-Move forged pictures.
Magic Cube Bloom Filter: Answering Membership Queries for Multiple Sets

Given $d$ sets without intersection, the multi-set membership query problem is to decide which set an incoming item $e$ belongs to. Multi-set membership query is a fundamental problem in a variety of fields of computer science, especially computer networking. Although several approaches have been proposed in the literature to address this problem, they cannot achieve a high processing speed, a high accuracy, and a small memory usage at the same time. In this paper, we propose a novel data structure, namely Magic Cube Bloom Filter (MCBF), which outperforms the state-of-the-art in terms of accuracy and query speed with a limited memory usage. The key idea of the Magic Cube Bloom filter is that items from the same set are stored in different Bloom filters, improving the accuracy by the redistribution of items. The MC-BF also improves the query speed by utilizing spatial locality. Experimental results show that the MC-BF outperforms the state-of-the-art BUFFALO by up to 148.5 times in terms of accuracy, and up to 3.16 times in terms of query speed. We have made the source code of our MC-BF available on Github [1].
Study the Effect of Different Irrigation and Fertigation Levels on Growth and Yield Parameters of Cucumber crop under Naturally Ventilated Polyhouse

In nutritional welfare, economic viability, vegetables play a vital role and fit well into the main intensive crop system in various parts of our world. In our country vegetable production is consciously predisposed by the conditions of seasonality and temperature. It is primarily India's extensively grown warm season crop and can be closely incorporated into vegetable and non-vegetable crop rotation. It has huge potential for generating yield, revenue, and service per area and time unit.
Digital Manufacturing of Pathologically-Complex 3D Printed Antennas

In the last decade, the proliferation of new 3D printing technologies has enabled the fabrication of complex geometries in manifold materials for novel applications. One discipline that has been explored extensively in the context of additive manufacturing is electromagnetic devices such as antennas. Difficult-to-fabricate geometries are now possible and can deliver new antenna functionality and extend performance (e.g., lower frequency resonance in small volumes, wider bandwidth, narrow-beam directionality, and so on). Coupled with accurate 3D electromagnetic simulations, a new paradigm is emerging for antenna design and manufacture. Starting from a seed geometry, the state space can now be explored to identify new combinations and permutations of electromagnetically-beneficial shapes through multiple simulation iterations. Subsequently, the identified structures can be further validated and improved through rapid manufacturing using 3D printing for hardware evaluation in an anechoic chamber. However, to fully benefit from this emerging paradigm, an up-to-date survey of the most recent metal processes is required. This survey would determine which processes are well suited for building the next generation of antennas. For this purpose, a variety of metal 3D printing was employed to fabricate benchmark antennas with pathological geometries, including thin walls, overhanging features, and large aspect ratios. This survey can inform designers about potential structures to serve in novel antennas. A total of five processes have been preliminarily explored including selective laser melting, binder jetting, and plated vat photopolymerization, all of which delivered different advantages and disadvantages in terms of mechanical and electromagnetic performance.
Active SOC Balancing Control Strategy for Modular Multilevel Super Capacitor Energy Storage System

This paper presents an active state-of-charge (SOC) balancing control strategy for modular super capacitor energy storage system (ESS). The strategy has a master–slave structure, including a common dc bus voltage loop and individual slave current loop for each submodule. The common voltage loop ensures the ability of the system to stabilize dc bus voltage cannot be affected by SOC balancing control, and provides a same original current reference for all submodules. SOC information is introduced into current loops to achieve SOC balancing control. The highest SOC is taken as a reference for low SOC modules, thus, the low SOC modules can regulate the submodule average operation current according to the SOC reference and their own SOC. In order to minimize system current deviation caused by the introduction of SOC, a current correction method is proposed to improve system current regulate performance. The proposed strategy can achieve SOC balancing control during ESS charging and discharging process dynamically, in addition, the modularity of the system is improved and the proposed strategy has favorable stability. A modular ESS prototype consisting of three half-bridge submodules is tested in the laboratory, and the experimental results verify the effectiveness of the proposed control strategy.