Miracle-dz/UniFAR-FTUs huggingface dataset:
Unknown Attack Detection Based on Zero-Shot Learning

In recent years, due to the frequent occurrence of network intrusions, more and more researchers have begun to focus on network intrusion detection. However, it is still a challenge to detect unknown attacks. Currently, there are two main methods of unknown attack detection: clustering and honeypot. But they still have unsolved problems such as difficulty in collecting unknown attack samples and failure to detect on time. Zero-Shot learning is proposed to deal with the problem in this article, which can recognize unknown attacks by learning the mapping relations between feature space and semantic space (such as attribute space). When the semantic descriptions of all attacks (including known and unknown attacks) are provided, the classifier built by Zero-Shot learning can extract common semantic information among all attacks and construct connections between known and unknown attacks. The classifier then utilizes the connections to classify unknown attacks although there are no samples for unknown attacks. In this article, we first propose to use Zero-Shot learning to overcome the challenge of unknown attack detection and illustrate the feasibility of this method. Secondly, we then propose a novel method of Zero-Shot learning based on sparse autoencoder for unknown attack detection. This method maps the feature of known attacks to the semantic space, and restores the semantic space to the feature space by constrains of reconstruction error, and establishes the feature to semantic mapping, which is used to detect unknown attacks. Verification tests have been carried out by using the public dataset NSL_KDD. From the experiments conducted in this work, the results show that the average accuracy reaches 88.3%, which performs better than other methods.
Boosting One-Shot Spectral Super-Resolution Using Transfer Learning

Though deep learning based spectral super-resolution (SSR) methods have state-of-the-art performances, most previous deep spectral super-resolution approaches require extensive paired RGB images and hyperspectral images (HSIs) for well-fitting learning. However, in real cases, the cost of generating such paired images is too prohibitive to collect sufficient training samples. To solve this problem, we investigated one-shot SSR in a target domain. To avoid over-fitting, we introduced knowledge from a source domain to guide the one-shot SSR in the target domain and use the idea of spectral unmixing to remove the interference of different spectral characteristics, with which we proposed a spectral-unmixing inspired deep SSR framework. Experimental results on three benchmark SSR datasets showed the effectiveness of the proposed method.
A method to estimate the worst-case torque ripple under manufacturing uncertainties for permanent magnet synchronous machines

The influences of manufacturing uncertainties on the cogging torque of permanent-magnet (PM) motor have been widely examined, and they are usually estimated by calculating and comparing several design models featuring small deviations to their ideal counterpart. To achieve high quality of the analysis results within a reasonable calculation time, a suitable selection of design variants featuring uncertainties for evaluation are crucial. However, lack of knowledge of the relationship between torque variations and uncertainties, a proper selection is difficult to accomplish. In the previous work related to the worst-uncertain-combination-analysis (WUCA) method, efforts were made to reveal the relationship between the additional cogging torque harmonics and combinations of uncertainties. In this paper, the WUCA method is extended to estimate the on-load torque ripple performance under manufacturing uncertainties in this paper, and its effectiveness in terms of identifying the worst-case combinations is verified through finite element analysis.
Measurement of CO2 and Ultrasonic Vibration Based on Tunable Fiber Ring Laser

In this paper, we propose a method to simultaneously measure carbon dioxide (CO2) concentrations and ultrasonic vibration using fiber sensors with a tunable fiber ring laser based on a loop design. The in-line interferometric vibration sensor consists of a single-mode-multimode-single-mode fiber structure and detects continuous dynamic vibrations through intensity demodulation. The evanescent wave absorption based CO2 sensor is composed of a no-core fiber coated with silica derived from tetraethylorthosilicate. The fiber ring laser output is tuned to the CO2 absorption wavelength of 1574.6 nm, which also best matches the quadrature bias point of the vibration sensor. The vibration frequency signals from 10 Hz to 50 kHz, and fast detection of CO2 concentrations from 10% to 100% are experimentally demonstrated. The proposed hybrid sensor system is a promising method for monitoring the gas pipelines operating state, gas turbines, nuclear power plants, and automotive engines, where both vibration and CO2 concentration measurements are essential.
Assessment on Image Quality Changes as a Result of Implementing Median Filtering, Wiener Filtering, Histogram Equalization, and Hybrid Methods on Noisy Images

The information content in an image can experience a decrease in quality due to noises. Accordingly, noise removal and histogram equalization (HE) are among the processes used to enhance image quality. The purpose of this research is to determine the effect of changes in image quality as a result of applying median filtering (MF), Wiener filtering (WF), HE, or hybrid methods on noisy images. Here, face images are used as input. The research stages began with preprocessing, i.e., cropping, size normalization, and color-to-gray conversion. Then, Gaussian and salt-and-pepper noises with intensities of 20%, 50%, and 80% are applied to the images. Consequently, the processes, i.e., HE, MF, WF, hybrid method 1 (HE and MF), and hybrid method 2 (HE and WF), are implemented, and then the image quality is evaluated. The results of the research show that the best methods to enhance Gaussian and salt-and-pepper noisy images are WF and MF, respectively. HE and the two hybrid methods generally do not improve image quality. The implementation of hybrid method 2 results in the maximum structural similarity index with 80% Gaussian noise. Meanwhile, MF provides the minimum mean-square error and maximum peak signal-to-noise ratio with 20% Gaussian noise.
Autonomous Current Sharing Control with Integrated Virtual Impedance and Self Synchronisation for Unbalanced Islanded Microgrids

This paper proposes a new autonomous current sharing control strategy for islanded microgrids that is simple and robust under unbalanced load conditions. The strategy estimates the distributed generator output voltage and current positive sequence components from the internal variables of a resonant current and resonant voltage regulator, and then uses them to create an integrated virtual impedance sharing mechanism. This avoids control complexity and the need for output current measurements, while maintaining a balanced voltage and accurate current sharing. The approach has been verified using a detailed switched simulation.
Optimisation and Design Performance of a Small-Scale DC Vernier Reluctance Machine for Direct-Drive Wind Generator Drives

The study begins with a proposal to design a small-scale DC Vernier reluctance machine (DC-VRM) for direct-drive wind generator drives. The motivation centres on the increasing need for renewable energy technologies, as well as the quest for non-PM and gearless wind generator drives upon which the DC-VRM is uniquely compatible. A global optimisation is first undertaken based on solutions from 2D finite element modelling (FEM) to produce optimal benchmark designs (OBDs). The OBDs are further evaluated for independent performance improvement of the torque ripple using the simple rotor tooth-pairing technique. The findings show that the torque ripple can be drastically reduced when absolved from the integrated optimisation process. On the average, at 0.8 tooth-paring ratio, the torque ripple reduces by at least by 50 %, with observation of special quid pro quo effects in torque ripple and average torque generation of this machine. Already, 3D FEM simulations have been issued to draw out the end-effect discrepancies, while laboratory validation is still pending.
Maximum Discriminant Difference Criterion for Dimensionality Reduction of Tensor Data

Discriminant analysis is an important tool in machine learning. One of the motivations of this paper is to judge whether a dataset is suitable for discriminant analysis. At present, tensor data are becoming more and more popular in machine learning. Another motivation is to propose a dimensionality reduction algorithm of tensor data which facilitates discriminant analysis of tensor data. The first contribution of this paper is to propose a criterion to measure the potential ability of discriminant analysis of a dataset, and the criterion is called Maximum Discriminant Difference (MDD). The second contribution is to propose a dimensionality reduction algorithm of tensor data based on the mode product of tensor and MDD, called MDD-TDR for short. The first innovation of this paper is that, although MDD is inspired by Linear discriminant analysis (LDA), MDD is a criterion, not an algorithm. MDD can be applied to many applications, not just dimensionality reduction. Furthermore, MDD can be applied to both supervised and unsupervised learning. The second innovation is that, unlike many other tenor dimensionality reduction algorithms that are linear and align tenor into a vector for processing, MDD-TDR is nonlinear and iterative, and in each iteration, each dimension of a tensor is dimensionally reduced separately. The experimental results on 5 widely-used landmark datasets show MDD-TDR outperforms 7 related algorithms published in top academic journals in recent years.
Optimizing Discharge Efficiency of Reconfigurable Battery With Deep Reinforcement Learning

Cell imbalance in a multicell battery occurs over time due to varying operating environments. This imbalance leads to overall inefficiency in battery discharging due to the relatively weak cells in the battery. Reconfiguring the cells in the battery is one option for addressing the problem, but relevant circuits may lead to severe safety issues. In this article, we aim to optimize the discharge efficiency of a multicell battery using safety-supplemented hardware. To this end, we first design a cell string-level reconfiguration scheme that is safe in hardware operations and also provides scalability due to the low switching complexity. Second, we propose a machine learning-based run-time switch control that considers various battery-related factors, such as the state of charge, state of health, temperature, and current distributions. Specifically, by exploiting the deep reinforcement learning (DRL) technique, we train the complex relationship among the battery factors and derive the best switch configuration in run-time. We implemented a hardware prototype, validated its functionalities, and evaluated the efficacy of the DRL-based control policy. The experimental results showed that the proposed scheme, along with the optimization method, improves the discharge efficiency of multicell batteries. In particular, the discharge efficiency gain is maximized when the cells constituting the battery are unevenly distributed in terms of cell health and exposed temperature.
Performance Comparison Between SiC Two-Level and Si Three-Level AFE Converters with Grid Filters

Advancement in semiconductors and power electronics industry along with new topology types for different application has provided new possibilities for more optimal designs. There is a lot of work in the literature focusing on comparing different topologies and switch types in one aspect of design such as power losses or EMC results. However, a generic consideration of all design criteria measures is missing. This paper compares two topology types and semiconductors for Active Front End (AFE) converters.
A Review of Thermal Designs for Improving Power Density in Electrical Machines

More electric aircraft is a promising solution to the problems of fuel consumption, emissions, and noise faced by the aviation industry. Electrical machines with high power density are essential to improve the overall performance of more electric aircraft. The huge difference between the maximum and the rated output of electrical machines shows that the performance does not reach the present limit of the electromagnetic design. Improving thermal designs is an important and effective way to increase power density at the present stage, as the temperature rise limit of materials is the main obstacle to restricting the performance of electrical machines. In this article, a variety of thermal designs are comprehensively reviewed and critically discussed according to different cooling positions. Next, an analysis of suitable thermal designs for different applications of electrical machines in more electric aircraft is presented. Subsequently, cooling capacity and additional mass of the thermal designs, which are the two key determinants of improving power density, are estimated and compared. Moreover, the reliability and cost of the thermal designs are discussed in depth. This work contributes to a thorough understanding of thermal designs in improving the power density of electrical machines and guides their future designs and applications in more electric aircraft.
Performance evaluations of UHF-RFID flexible antennas fully-integrated with epidermal sensor board

Bio-integrated wireless systems require to integrate electronic modules for bio-signal processing within a stretchable and soft skin-like device. The size and complexity of the PCB hosting circuitry cannot be separated from the design of the antenna used for the communication. Challenges arise when high frequencies are involved so that human body losses will deteriorate the radiation gain. This paper describes the design of a UHF-RFID epidermal antenna integrated with different types of EMG-sensor board arrangements in comparison with a benchmark configuration. The purpose is to evaluate the effect of the PCB metallization and identify the optimal antenna size. An overall 40 $\times 40\mathrm{m}\mathrm{m}^{2}$ device footprint is found to minimize the disturbing effects of the sensor circuit on the antenna performance and a read range up to 150 cm can be achieved.
Three-Phase Unified Power Quality Conditioner based on H-bridge and High-Frequency Link

In this paper, a three-phase unified power quality conditioner (UPQC) based on H-bridge and high-frequency link is proposed and investigated. The shunt converter compensates reactive power from the load and supplies the isolated H-bridges dc links of the series unit. The connection between the series and shunt dc links is performed applying a unidirectional isolated dc-dc converter. A multi-winding high-frequency transformer (HFT) is implemented allowing the dc-dc power conversion. The proposed UPQC is able to mitigate grid voltage sag using conventional active power compensation strategies. Hence, HFT along with unidirectional dc-dc converter might optimize the overall UPQC power density while reducing the control complexity of dc-dc stage. Operating principle, high-frequency link implementation, PWM strategy, control approach as well as comparison with similar structures are discussed in this work. Simulation and experimental results are presented in order to evaluate the proposed system.
Automated Smart Garbage Monitoring System with Optimal Route Generation for Collection

One of the major concerns of the environment that strongly impacts the health and well-being of society is the detection, monitoring, and management of solid wastes. Slowly the world is stepping towards smart systems enabling complete automation of societies. The concept of smart cities, which is heavily based on Internet of Things (IoT) systems makes human lives more comfortable and secure in every aspect. As a result, smart waste management systems form an essential part of the establishment of smart cities. The conventional method of manually monitoring the wastes in waste bins is a tedious process and uses a lot of human effort, time, and cost which can easily be avoided with the current innovations. The garbage collection process is also highly redundant, inefficient, and can be vastly improved using Machine Learning (ML) algorithms. The purpose of this paper is to use the powerful tools of IoT to completely automate the process of garbage monitoring using ultrasonic sensors and NodeMCU and provide an optimal route for garbage collection using the cluster first-route second ML algorithm.
IT Ticket Classification: The Simpler, the Better

Recently, automatic classification of IT tickets has gained notable attention due to the increasing complexity of IT services deployed in enterprises. There are multiple discussions and no general opinion in the research and practitioners’ community on the design of IT ticket classification tasks, specifically the choice of ticket text representation techniques and classification algorithms. Our study aims to investigate the core design elements of a typical IT ticket text classification pipeline. In particular, we compare the performance of TF-IDF and linguistic features-based text representations designed for ticket complexity prediction. We apply various classifiers, including kNN, its enhanced versions, decision trees, naïve Bayes, logistic regression, support vector machines, as well as semi-supervised techniques to predict the ticket class label of low, medium, or high complexity. Finally, we discuss the evaluation results and their practical implications. As our study shows, linguistic representation not only proves to be highly explainable but also demonstrates a substantial prediction quality increase over TF-IDF. Furthermore, our experiments evidence the importance of feature selection. We indicate that even simple algorithms can deliver high-quality prediction when using appropriate linguistic features.
Preserving Converter Lifetime by Active Thermal Boundary Control

The reliability of converters is primarily governed by thermal stress of semiconductor devices. By limiting devices cooling, the proposed control method effectively reduces thermal cycling of the device junction temperatures, ΔTj, improving reliability. To this end, natural switching surface control is utilized for active thermal control (ATC) on the dual active bridge. Electro-thermal semiconductor device modeling provides validation with quantified improvements over conventional ATC methods.
Chip Metallization Aging Monitoring with Induced Voltage veE between Kelvin and Power Emitter for High Power IGBT Modules

Online aging monitoring provides an effective way to get an insight into the current health state for the high power insulated gate bipolar transistors (IGBTs). With the development of wire-bonding and soldering technologies, chip metallization is also considered a bottleneck and the reconstruction failure mechanism is found to be the inducement to the wire lifting-off and solder degradation. In this paper, the peak voltage across the parasitic inductance between the Kelvin and power emitter in switching transient is proposed as an online chip metallization aging condition indicator. The monotonic dependence between the induced peak voltage and chip metallization aging is investigated and verified by the experimental results from the double-pulse test and accelerated aging test platform. Besides, the high-fitting-degree function of induced voltage peak with aging cycles and junction temperature is given. Hence, the induced peak voltage is validated as a promising chip metallization aging sensitive parameter.
Control and Dynamic Analysis of an Isolated Microgrid System: A Case Study

The operation of microgrids that contain microgeneration units such as wind, photovoltaic, and diesel power generations is always challenging towards the establishment of such microgrids. Such challenges arise due to the intermittent behavior of primary energy sources such as wind and solar. The largest island, Masirah, in the Sultanate of Oman, has significant potential of establishing microgrid using locally available sources of renewable energy. This paper presents the operation and control dynamics of a microgrid system that has significant potential to implement for Masirah Island. The case study microgrid system model has constructed using MATLAB/SIMULINK. The operational behavior of the case study microgrid is analyzed with and without a microgrid controller. It also presents the design and analysis of an efficient microgrid controller that is capable of maintaining an active power balance in the microgrid. The combination of a power absorber circuit and a power-frequency droop characteristic are incorporated in the proposed microgrid controller. The outcomes show that the designed controller is capable of maintaining microgrid frequency within the grid standard, which indicates the capability of operating such microgrid in isolated modes.
LLC Resonant Converter with Reconfigurable Voltage Rectifier for Wide Input Voltage Applications

In conventional LLC converters, when the input or output voltage is wide, the switching frequency must have a wide-swing range to achieve the desired output or input voltage that results in decreased efficiency. In this paper, a novel LLC converter with a reconfigurable rectifier is proposed to achieve high efficiency with a narrow switching frequency range. The proposed converter is suitable for applications that call wide input and wide output voltage ranges. This converter integrates three rectifier structures: a voltage-doubler rectifier (VDR), a voltage-tripler rectifier (VTR), and a voltage fifthfolder rectifier (VFR), all integrated into a novel rectifier structure that switches between them depending on the input voltage values. As a result, the switching frequency range becomes narrow with improved efficiency. This paper will show that Zero-voltage-switching (ZVS) is realized in all power MOSFETs, and zero-current-switching (ZCS) is achieved in all power diodes. A 250 W converter prototype was built and tested to verify the feasibility of the proposed converter.
Leveraging Prior Knowledge for Effective Design-Space Exploration in High-Level Synthesis

High-Level Synthesis (HLS) tools allow the generation of a large variety of hardware implementations from the same specification by setting different optimization directives. Each combination of HLS directives returns an implementation of the target application that is based on a particular microarchitecture. Designers are interested only in the subset of implementations that correspond to Pareto-optimal points in the performance versus cost design space. Finding this subset is hard because the relationship between the HLS directives and the Pareto-optimal implementations cannot be foreseen. Hence, designers must default to an exploration of the design space through many time-consuming HLS runs. We present a methodology that infers knowledge from past design explorations to identify high-quality directives for new target applications. To this end, we formulate a novel abstract representation of applications and their associated configuration spaces, introduce a similarity metric to compare quantitatively the configuration spaces of different applications, and a method to infer actionable information from a source space to a target space. The experimental results with the MachSuite benchmarks show that our approach retrieves close approximations of the Pareto frontier of best-performing implementations for the target application, in exchange for a small number of HLS runs.