Miracle-dz/UniFAR-FTUs huggingface dataset:
Control and Fault Monitoring of Modular Dual Active Bridge Converters

Modular DC-DC converters are a promising solution for many applications due to their flexible adaption to various voltage and power levels. However, this modularization involves an increased control effort to achieve symmetrical voltage and current distribution among the converter modules. In this work, a three-phase modular Dual Active Bridge (DAB3) converter is considered, wherein the individual converters are connected in input-series-output-parallel (ISOP) configuration. In order to address the problems of unequal power distribution among the modules, a new control strategy with uniform input-voltage distribution is proposed and verified using real-time simulation. Furthermore, the fault ride-through (FRT) capability of two ISOP connected DAB3 converters is simulated for a short circuit DC fault at the output. Finally, a novel pulse pattern generator (PPG), executed in VHDL, is used to verify the modulation strategy employed for FRT.
Network Anomaly Detection With Temporal Convolutional Network and U-Net Model

Anomaly detection in network traffic is one of the key techniques to ensure security in future networks. Today, the importance of this topic is even higher, since the network traffic is growing and there is a need to have smart algorithms, which can automatically adapt to new network conditions, detect threats and recognize the type of the possible network attack. Nowadays, there are a lot of different approaches, some of them have reached relatively sufficient accuracy. However, the majority of works are being tested on old datasets, which do not reflect current network conditions and it leads to overfitted results. This is caused by high redundancy of the data and because they fail to reflect the performance of the latest methods in the real-world anomaly detection applications. In this work, we applied a couple of new methods based on convolutional neural networks: U-Net based and Temporal convolutional network based for network attack classification. We trained and evaluated methods on the old dataset KDD99 and the modern large-scale one CSE-CIC-IDS2018. According to results, Temporal convolutional network with LSTM has achieved accuracy 92% and 97% on the KDD99 and the CSE-CIC-IDS2018 respectively, the U-Net model has accuracy 93% and 94% on the KDD99 and the CSE-CIC-IDS2018 respectively. Additionally, we utilized the focal loss function in the Temporal convolutional network with Long Short-Term Memory model, which has positive effect on class imbalance in time-series data. We showed, that the Temporal convolutional network in combination with Long Short-Term Memory network and U-Net model can give higher accuracy compared to other network architectures for network traffic classification. In this work we also proved, that methods trained on the old dataset can easily overfit during training and achieve relatively good results on the testing set, but at the same time, these methods are not so successful on more complex and actual data.
Screening of COVID-19 Suspected Subjects using Multi-Crossover Genetic Algorithm based Dense Convolutional Neural Network

Fast and accurate screening of novel coronavirus (COVID-19) suspected subjects plays a vital role in timely quarantine and medical care. Deep transfer learning-based screening models on chest X-ray (CXR) are effective for countering the COVID-19 outbreak. However, an efficient screening of COVID-19 is still a huge task due to the spatial complexity of CXRs. In this paper, a dense convolutional neural network (DCov-Net) based transfer learning model is proposed for the screening of COVID-19 suspected subjects using CXR images. A modified multi-crossover genetic algorithm (MMCGA) is then proposed to tune the hyper-parameters of DCov-Net. Majority of the existing COVID-19 diagnosis models are not interpretable as they do not provide any transparency to the users. Therefore, the concept of heat-maps is used to achieve explainability and interpretability. MMCGA based DCov-Net is implemented on a multiclass dataset that contains four different classes. Experimental results reveal that MMCGA based DCov-Net achieves better performance than the existing models. The proposed MMCGA based DCov-Net can be utilized for initial screening of COVID-19 suspected subjects with an accuracy of 99.34 ± 0.51 %.
Simulation and experimental analysis of a cost-effective miniaturized transceiver for X-band application

In this paper, the design, development and realization of an X-band transceiver in a system-level solution with high performance and a compact size is investigated and verified by the experimental results. Multi-layer low loss substrate based system-in-package (SiP) technology, which integrates digital/analog integrated circuits, monolithic microwave integrated circuits (MMICs) and passive devices, is one of the best candidates for RF system-level integration due to its advantages of low loss, high integration capability and low cost. The electrical model of MMICs, such as: power amplifier (PA), low noise amplifier (LNA), have been respectively created and combined, in order to achieve an overall test-bench for the performance investigation of the X-band transceiver. The output amplitude and phase of the transmitter are simulated and characterized, respectively, in order to validate the design and to prove the accuracy of the MMIC models. In addition, the essential passive components and chip-chip interconnections such as wire-bonding, transmission lines are individually modeled and integrated in the SiP. The interconnection loss between MMICs should be carefully analyzed in the co-integration, and the choice of the packaging method and the in-package transmission line structure is crucial to ensure a good RF performance. A 3-dimensional (3D) system-level package is implemented on a low dk/df substrate which ensures a good radio-frequency (RF) performance with reasonable fabrication cost. Though wire-bonding technique is still feasible for the microwave and millimeter-wave application, the performance of flip-chip technology is more suitable for the high frequency application. Therefore, a combination of wire-bonding and flip-chip has been applied in the final prototype. Finally, both simulation and measurement results of the output amplitude and phase are analyzed and compared, in order to validate the package design flow and electrical modeling.
Temperature Considerations for Online Health Monitoring of DC-Link Capacitors in Modular Multilevel Converters

In this paper, the effect of temperature on online heath monitoring of dc capacitors in modular multilevel converters (MMCs) is studied. The submodule capacitance value is considered as the main indicator of health. Both self heating and ambient temperature are considered as sources of temperature variation. The study is conducted on a single phase MMC with full-bridge submodules. However, a similar analysis can be used for half-bridge MMCs and multiphase MMCs as well. The thermal behaviour of the capacitors is explained analytically and simulated using a simplified Cauer model. The effect of temperature on the estimated capacitance values is shown, and a method for reducing this effect is proposed. The results are verified using simulations in MATLAB/Simulink.
A Buck-Boost Embedded Multilevel Inverter with Double Voltage Gain

This work focuses on a Multilevel Inverter topology with an embedded voltage boosting feature. The inverter under investigation, integrates a DC/DC Buck-Boost converter to double the input voltage and the cascaded extension of the topology is presented. The power processing characteristics of the topology are evaluated to prove that the integrated DC/DC converter processes only a fraction of the total supplied active power. Also, the passive component selection of the topology is thoroughly analyzed under the DCM operation of the embedded DC/DC converter. A 1kVA prototype is implemented to study via experimental probing the performance and the efficiency of the topology.
A Magnetic Design Method for Powder Core Inductor with Concentrated Airgap Considering DC Superposition Characteristics Used for High Current Applications

Powder cores are attractive magnetic cores for high current applications such as bidirectional dc/dc converters used in power trains of eco-friendly automotive because of their high saturation flux density, high Curie temperature, and soft saturation characteristics. However, powder cores have nonlinear relative permeability characteristics dependent on the magnetic field intensity which results in a drop in the inductance value at high current values. There are two key solutions to keep constant inductance value at maximum power rating of circuit specifications: (1) inserting a concentrated airgap (2) reducing the number of turns to tackle the drop of relative permeability due to dc-bias. Therefore, this paper proposes a novel magnetic design method for powder core inductors with concentrated airgap which to keep the desired inductance constant under dc-bias conditions. The feature of the proposed design methodology is based on considering non-linear relative permeability directly based on the magnetic characteristics modeling. The validity of the proposed method is confirmed by experimental tests using EE core.
Model Predictive Control and the Selection of Cost Functions

In model predictive control strategy implementations, the cost function selection is very important in ensuring good performance. In this paper a study of the most common cost functions used for the control of two level voltage source inverters is presented. The paper compares several cost function alternatives that could be considered for different power electronic converter applications. The results show that Model Predictive Control is a suitable alternative for the implementation of different control objectives in power converters and show how to find the best balance in the cost function.
Multicast Scheduling in SDN WISE to Support Mobile Nodes in Industrial Wireless Sensor Networks.

The growing use of mobile nodes in manufacturing environments is increasing the Quality of Service (QoS) requirements of the communications infrastructures that support them, as is the case with Industrial Wireless Sensor Networks (IWSN). Managing networks using Software Defined Networks (SDN) addresses the challenge of handling multiple sources in a satisfactory manner. The global vision of the topology that the SDN controller has allows a logical segmentation of the network, through the allocation of dedicated resources for each information flow, thus ensuring independent service levels and complete isolation. Mobile nodes produce constant changes in topology, which lead to instability for routing protocols. Traditional routing solutions with protocols such as Routing Protocol for Low-Power and Lossy Networks (RPL) require additional time to make the parent node change. This paper proposes Mobile Multicast Forwarding with Software Defined Network (MMF-SDN), an approach using Software Defined Networking solution for WIreless SEnsor Network (SDN WISE) protocol that exploits the advantages of SDN and Time Slotted Channel Hopping (TSCH) synchronism. The mobile nodes are managed as multicast sources, through a cumulative allocation of resources from the controller. This allows reception states to be synchronized at the parent nodes to provide network stability, avoiding subsequent recalculations in the routing protocol. Parent node changes are transparent and immediate. This proposal improves on other SDN based solutions, reducing energy consumption in reception by up to 50%, 70% end-to-end delay and improving scalability with a 30% reduction in slotframe occupancy.
Unsupervised Domain-shared Convolutional Neural Network for Bearing Fault Transfer Diagnosis

Most previous models of intelligent fault diagnosis are built on the premise that adequate and labeled samples are taken from the same feature distribution. However, in industrial scenario, this assumption is mostly invalid. For this reason, a new method based on unsupervised domain-shared convolutional neural network is proposed for bearing fault transfer diagnosis. In this method, we propose a new method based on unsupervised domain-shared convolutional neural network (DSCNN) to extract transferable features of the original bearing vibration signals installed in two different machines. Then, the multi-kernel maximum mean discrepancy (MK-MMD) evaluation index is used for domain adaptation, trying to reduce the distribution discrepancy for source and target domains. Lastly, using the fault diagnosis knowledge of the source domain labeled samples to help classify the unlabeled samples in the target domain. We verify the effectiveness of proposed method with six transfer tasks of three bearing datasets. The results of case studies confirm that the proposed method can effectively learn the transferable features and reduce the distribution discrepancy between two domains, which also shows better transfer performance and classification accuracy compared to other methods.
Agricultural Fields Segmentation on Satellite Images Using Convolutional Neural Networks

Agricultural fields segmentation algorithms in satellite images are presented. Three convolutional neural networks were developed: U-Net with ResNet-34 and SE-ResNeXt-50 backbones and Deeplabv3+ with Xception backbone. All backbones were pretrained in Imagenet database. Training and testing of algorithms was carried out on NVIDIA DGX-I supercomputer using a dataset of high-resolution images from the Sentine1-2 satellite. The value of F1 and Dice were 0.706 and 0.942 for U-Net with SE-ResNeXt-50 backbone. Test results confirm high accuracy in determining the boundaries of agricultural fields by proposed algorithm.
A Novel Dynamic Appliance Clustering Scheme in a Community Home Energy Management System for Improved Stability and Resiliency of Microgrids

Power scheduling of domestic appliances is a vital preference for bridging the gap between demand and generation of electricity in a microgrid. For a stable microgrid, an acceptable mechanism must reduce the peak to average ratio (PAR) of power demand with supplementary benefits for consumers as reduced electricity charges. Recent studies have focused on PAR and cost reduction for a small consumer population. Furthermore, researchers have mainly considered homogeneous consumer loads. This study focuses on residential power scheduling for electricity cost reduction for consumers and load profile PAR curtailment for a relatively large consumer population with non-homogeneous loads. A sample population of 1000 consumers from various classes of society is considered. The proposed dynamic clustered community home energy management system (DCCHEMS) allows the clustering of appliances based on time overlap criteria. Comparatively flatter power demand is attained by utilizing the clustered appliances in conjunction with particle swarm optimization under the influence of user-defined constraints. Modified inclined block rates with real-time electricity pricing strategies are deployed to minimize the electricity costs. DCCHEMS achieved higher efficiency rates in contrast to the traditional non-clustering and static clustering optimization schemes. An improvement of 21% in peak to average ratio, 4% in cost reduction, and 19% in variance to mean ratio is obtained.
A Facile Bonding Material to Enable Interconnection among complex Surfaces through AgNWs Aerogel

Metal aerogels are a new material class with unique properties, including lightweight, high surface area, high reactivity, and high electrical conductivity, making them highly attractive in various applications, such as sensing, thermal insulation, energy generation/storage, and catalytic conversion. A novel flexible preformed film has been produced from the AgNW aerogel to realize the flexible integration among complex structures in this work. As the AgNW forming bulk 3D networks in the aerogel has excellent flexibility, stability and high reactivity and conductivity, the proposed preformed film can enable the interconnection between metals like Cu or Ag through self-sintering without additional flux or solders. According to the experimental data, reliable and flux-free bonds can be achieved between components with If-shaped, V-shaped, round-shaped or step-shaped structures through the preformed AgNW aerogel film, with a high shear strength over 30 MPa, a low electrical resistivity less than 4.5 J.1g. cm and a large thermal conductivity attaining 110 W/m*K, if proper temperature and stress were applied. Meanwhile, these flexible preformed films can also provide ultra-high electromagnetic shielding effect over 75 dB in the frequency range of 8–12 GHz, and be further combined with PDMS to serve as a flexible circuit with a high electrical conductivity over 1.4*106 S/m. In summary, this work provides novel approaches to realize the adaptive interconnection between complex structures and provide new material classes for electromagnetic shielding and flexible circuit, which may enable the extended usage of metal aerogel materials in flexible electronics and packaging.
Design Space Optimization of a SiC Drive Inverter with an Integrated All-Pole Sine Filter

The aim of this paper is to minimize an all-pole sine filter by increasing the switching frequency so that it can be integrated into a SiC drive inverter. For this purpose, theoretical considerations of the influence of the switching frequencies on the size and losses of the inductor, the capacitor, the semiconductor, and the heat sink size for the semiconductors were carried out. A parameter study of a filter concept as a function of the switching frequency has been discussed. Detailed analysis of three inductors is made, which consist of various simulation models.Two prototypes of all-pole sine filters are built for a switching frequency of 128 kHz. These two prototypes were operated in a drive system consisting of a SiC drive inverter and motor to measure and evaluate the filter effect and thermals of the filters. The filter behavior in the event of overload is also discussed.
Cross-Domain Few-Shot Micro-Expression Recognition Incorporating Action Units

Micro-expression, different from ordinary facial expressions, is an involuntary, spontaneous, and subtle facial movement that reveals true emotions which people intend to conceal. As it usually occurs within a fraction of a second (less than 1/2 second) with a low action intensity, capturing micro-expressions among facial movements in a video is difficult. Moreover, when a micro-expression recognition system works in cold-start conditions, it has to recognize novel classes of micro-expressions in a new scenario, suffering from the lack of sufficient labeled samples. Inconsistency in micro-expression labeling criteria makes it difficult to use existing labeled datasets in other scenarios. To tackle the challenges, we present a micro-expression recognizer, which on one hand leverages the knowledge of facial action units (AU) to enhance facial representations, and on the other hand performs cross-domain few-shot learning to transfer knowledge acquired from other domains with different data labeling protocols and feature distribution to overcome the scarcity of labeled samples in the cold-starting scenario. In particular, we draw inspirations from the correlation between micro-expression and facial action units (AUs), and design an action unit module, aiming to extract subtle AU-related features from videos. We then fuse AU-related features and general features extracted by optical-flow facial images. Through fine-tuning, we transfer knowledge from datasets in different domains to the target domain. The experimental results on two datasets show that: (1) the proposed recognizer can effectively learn to recognize new categories of micro-expressions in different domains with a very few labeled samples with the UF1 score of 0.544 on CASME dataset, outperforming the state-of-the-art methods by 0.089; (2) the performance of the recognizer is more competitive when it distinguishes micro-expression videos of more categories; and (3) the action unit module enables to improve the recognition performance by 0.072 and 0.047 on CASME and SMIC, respectively.
Microwave Sensor Dedicated to the Determination of the Dielectric Properties of 3D Biological Models from 500MHz to 20GHz

Biologists introduced some years ago an intermediate biological model between 2D cell culture and 3D in vivo tissues, called microtissues or spheroids. To study such 3D models, techniques are currently applied, including electromagnetic waves-based methods at low frequencies. This frequency range does not allow the penetration of the waves inside the cells, conversely to the microwaves. There is consequently a strong interest to develop an appropriate microwave-based sensor dedicated to microtissues analysis. This constitutes the topic of this paper with the introduction of a new microwave sensor, suitable for the dielectric characterization of microtissues. An analytic method is proposed to extract their electrical parameters. The evaluation of the biosensor is performed, while measuring large polystyrene beads with known dielectric properties, followed then by the dielectric evaluation of fixed spheroids from 500MHz to 20GHz.
A Two-stage Privacy Preservation and Secure Peer-to-Peer Energy Trading Model using Blockchain and Cloud based Aggregator

Local energy trading has attracted the attention of many researchers as a result of its promising benefits. These benefits include minimizing gas emission, reducing power shortage, and establishing a competitive energy market. However, the energy trading between several prosumers causes trust, security, and privacy challenges in energy systems. On the other hand, a single point of failure and an increase in overall system cost occur when the energy system is managed using a centralized model. Therefore, to tackle the mentioned issues, this work proposes a two-layered secure Peer-to-Peer (P2P) energy trading model based on blockchain. The proposed model has two layers: authentication, and secure energy trading. In the authentication layer, in order to protect the proposed model from impersonation attacks, a mutual authentication process is implemented. In the energy trading layer, a new consensus mechanism is proposed to minimize the number of malicious validators. Afterwards, an incentive-punishment algorithm is introduced to motivate energy prosumers to contribute more energy in the model. Next, a dynamic contract theory based on supply-demand ratio pricing scheme is proposed. The purpose of the proposed pricing scheme is to solve the issues associated with the existing pricing schemes. It also preserves the privacy of the actual energy consumption behavior of the trading participants. Furthermore, a consensus mechanism validators’ selection model is proposed. The aim of the proposed work is to have an efficient and secure P2P energy trading platform. Simulations are executed to show the performance of the proposed model in terms of communication and computational costs, reputation, energy contributed, reward, and prices. The results for the authentication process show 7.45 ms computational cost and 1152 bits communication cost, which are better than the existing works. In the consensus process, 66.67% of the validators are selected to conduct the consensus for every transaction. This selection efficiently improves the consensus process and minimizes the number of malicious validators. In the proposed model, the increase in reward is observed for increased energy contribution, decreased non-malicious transactions and adjustment of energy consumption. The proposed model shows a satisfactory performance in terms of trust, security, and privacy.