Miracle-dz/UniFAR-FTUs huggingface dataset:
Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

In traditional fault diagnosis methods in power systems, it is difficult to accurately classify and predict the types of faults. With the emergence of big data technology, the fault classification and prediction methods based on big data analysis and processing have been applied in power systems. To make the classification and prediction of the fault types more accurate, this paper proposes a hybrid data mining method for power system fault classification and prediction based on clustering, association rules and stochastic gradient descent. This method uses a three-layer data mining model: The first layer uses the $K$ -means clustering algorithm to preprocess the original fault data source, and it proposes to use self-encoding to simplify the data form. The second layer effectively eliminates the data that have little impact on the prediction results by using association rules, and the highly correlated data are mined to become the regression training data. The third layer first uses the cross-validation method to obtain the optimal parameters of each fault model, and then, it uses stochastic gradient descent for data regression training to obtain a classification and prediction model for each fault type. Finally, a verification example shows that compared with a single data mining algorithm model, the proposed method is more comparative in terms of the data mining, and the established power system fault classification and prediction model has global optimality and higher prediction accuracy, which has a certain feasibility for real-time online power system fault classification and prediction. This method reduces the disturbances from low-impact or irrelevant data by mining the fault data three times, and it uses cross-validation to optimize the multiple regression parameters of the regression model to solve the problems of low accuracy, large errors and easily falling into a local optimum, given the conduct of fault classification and prediction.
Coordinated Multiple Access Point Multiuser Beamforming Training Protocol for Millimeter Wave WLANs

Millimeter wave (mmWave) systems have attracted considerable attention as a benefit of their Gbps services. Therefore, wireless-gigabit (WiGig) transmission has been developed based on the IEEE 802.11ad and 802.11ay standards of wireless local area networks (WLANs) in the 60-GHz band. To overcome the high path loss of mmWave, beamforming and beam-sector based training protocols are specified in 802.11ad/ay. With the increase in service demand, more WiGig access points (APs) will be deployed, hence the transmission frame structure of beam control should be adjustable for supporting both multiple APs and multiple users. The conventional exhaustive beam search (EBS) scheme has a potentially excessive complexity for high number of beams. Furthermore, assigning fixed training slots for users will lead to high-latency beam training under EBS. Hence, we design an advanced protocol for multiple APs and multiple users. Based on this protocol, we propose coordinated multiple AP multiuser training (CMMBT) including variable length framing and adjustable beam training. Our simulations demonstrate that CMMBT provides flexible training control and achieves the lowest training latency and highest average throughput performance amongst the state-of-the-art solutions in the open literature.
Parallel State Machine Replication from Generalized Consensus

State machine replication (SMR) is a established approach to building fault-tolerant services. In search for high SMR throughput, approaches that exploit semantic information in the ordering and execution of commands have emerged. Generalized consensus and parallel state machine replication are two representative examples, respectively. Although both approaches have been proved effective in isolation, no study in the literature has considered their integration. In this paper, we investigate the integration of generalized consensus and parallel SMR. We derive algorithms to parallelize the execution of commands based on the ordering of commands provided by consensus. As a prototype, we extended Egalitarian Paxos and conducted many experiments varying conflict rates, command computational costs, and number of cores at replicas. Compared to Egalitarian Paxos, the integrated approach (a) results in important throughput gains, as command independency and computational cost increase, and (b) converges to the same performance with high conflict rates or reduced number of cores. Index Terms– State Machine Replication, Generalized Consensus, Distributed Algorithms
An Easy-to-Use Multi-Source Recording and Synchronization Software for Experimental Trials

A typical routine in many scientific studies consists of recording data from devices and identifying which segment of data corresponds with an experimental interval. However, many current applications have been designed to obtain and save data from one single device, and synchronizing data with the markers that delimit the test phases can be difficult. To address this issue, we have developed LSLRec, which is based on Lab-Streaming Layer, a C++ library that allows data synchronization. LSLRec is an easy-to-use, open-source, multi-platform, recording system developed on Java that can save data from several devices at the same time, while maintaining synchronization with the experimental phase markers. It supports three explicit sync methods: the first uses one-integer-channel input Lab-Streaming Layer streams. The others use TCP/UDP socket messages and allow any existing software that generates sync markers through TCP/UDP messages to be used under the same conditions. In LSLRec, the markers are saved together with input data, facilitating the process of linking data with test stages. A prerequisite for recording software is to guarantee suitable timing performance, with no data loss and an easy user interface. These features were assessed for LSLRec, with no data loss detected for 20 hours (25 sessions). We evaluated performance by measuring timestamp deviations for sync marker and input data. The sync methods exhibited an average of deviations of around $-34~\mu s$ (which is more than acceptable, e.g., in studies involving living beings), whereas the absolute value of deviation for one-channel data was lower than $40~\mu s$ . Finally, we assessed the system’s usability with the technology acceptance model 3 survey (6 volunteers). Subjects saw the software as useful, and intended to use it in the future. In conclusion, LSLRec is a useful tool for those who need to record data from multiple sources, and maintain synchronization with experimental phases with low delay.
Patient Monitoring System using Computer Vision for Emotional Recognition and Vital Signs Detection

Patient monitoring is a pivotal part of the healthcare system nowadays, either at hospitals or at home. Critical patients require to be monitored consistently and less human involvement, 24 hours a day to enable them to get medical assistance in the moment of need. However, these types of services are only available in private hospitals. Typically, there is a small number of patients in private hospitals, especially from the higher socio-economic backgrounds. Conversely, in public hospital, a huge number of patients require medical attention due to the imbalance between staff to patient ratio. The patient monitoring system is restricted when they are asleep or unconscious due to incapability to call for assistance during an emergency. This may delay the treatment as the medical staff are unaware of the patients’ condition, hence resulting fatality. This work proposes a smart integrated patient monitoring system that automatically detects patient’s emotional state and heartbeat levels through face recognition algorithms, heartbeat and temperature sensors. A Raspberry Pi and NodeMCU are used as client nodes to collect the patient data. These data are then transmitted to an IoT cloud for realtime visualization. Through this monitoring system, critical patients can get immediate attention without the requirement of the staff being present there 24 hours a day. This system offers a faster response from medical staff to provide treatment in critical times.
Model Predictive Control with Reduced Integration Step Size of a Switched Reluctance Drive

The problem of torque pulsation and its stabilization by means of control is typical for switched reluctance drives. This drives usually controlled using hysteresis current regulators, which produce audible noise and torque ripple. The method of model predictive control with reduced integration step size was introduced in order to implement precise torque stabilization using pulse width modulation. Active thermal control was developed as well to increase the continuous output power of the drive. The developed control strategy was simulated and shows accurate torque regulation even when only few integration steps inside single PWM cycle are used for duty cycle evaluation. Active thermal control reduced the temperature of the hottest power module by 7.4%, which allows to increase the continuous output power of the drive.
Towards Efficient, Credible and Privacy-Preserving Service QoS Prediction in Unreliable Mobile Edge Environments

With the widespread adoption of the fifth-generation (5G) cellular network and Mobile Edge Computing (MEC), numerous Internet of Things (IoT) applications are emerging in many critical areas. IoT applications are typically running on mobile devices to provide real-time interaction with users by connecting with smart IoT devices and remote cloud services. In order to ensure the performance of IoT applications, Quality of Service (QoS) is commonly used as a key metric for the selection and adaptation of high-quality services at runtime. Collaborative QoS prediction methods have been proposed in the literature to predict personalized QoS values, enabling QoS-based selection and adaptation. However, privacy issues in collaborative QoS prediction discourage users from collaborating by sharing data in practice. Furthermore, there are untrusted users in the unreliable MEC environment, which makes the prediction encounter serious reliability issues. As a result, privacy and reliability issues have become key challenges to make QoS prediction approaches feasible. In this paper, we proposed a credible and privacypreserving QoS prediction approach by leveraging federated learning techniques and developing reputation mechanisms to address this critical challenge. We evaluate the method on a large- scale real QoS dataset and the experimental results demonstrate the effectiveness and efficiency of the method.
Analysis of diffraction efficiency of phase gratings in dependence of grooves number

The analysis of dependence of diffraction efficiency on number of grooves of phase gratings with square and sine profiles was performed by means of diffraction modeling using angular spectrum method. Phase gratings with different number of grooves from 1 to 100 was modeled and diffraction efficiency was estimated from the obtained diffraction pattern. It was shown that the main dependence of diffraction efficiency on the number of grooves is regarding to the ratio of the reading beam diameter to the grating area. As this ratio increases the DE of non-zero orders decreases up to zero. Using the appropriate diameter of reading beam the maximum value of diffraction efficiency could reach the value of 40.5% and 33.9% for square and sine profile correspondingly. Obtained results make it possible to tune smoothly the diffraction efficiency changing the diameter of the reading beam.
Characterization of Subnets, Virtual Channel and Routing on Wireless Network-on-Chip Performance

Wireless network-on-chip (WiNoC) was introduced to enhance the efficiency and performance of network-on-chip (NoC). WiNoC architecture efficiency depends on a good choice of virtual channels (VC), subnets and routing algorithm. This work investigates the effect of VC, subnet size and routing on WiNoC performance efficiency and energy at varying packet injection rate (PIR) at different traffic distribution and the number of cores (64, 256 and 1024). This paper showed that subnets have a more significant impact on the performance efficiency of network architecture in WiNoC. However, VCs only improve energy consumption in NoC while it has little effect on WiNoC. The research evaluation was done using Noxim simulator, which is based on system C.
Self-optimising Application-agnostic Multithreading for Replicated State Machines

State-machine replication (SMR) is a well-known approach for fault-tolerant services demanding fast recovery. It is not easy, however, to parallelise SMR in order to exploit modern multicore architectures. Two main approaches have been extensively studied; one focusing on request-level concurrency using prior knowledge, the other utilising application-agnostic and lock-level deterministic scheduling. We show that significant performance improvements for the latter approach require deterministic scheduler configurations to be dynamically adapted to the current application load during runtime. First, we summarise current research on parallel SMR execution. Second, an analysis of obstacles in lock-level deterministic multithreading approaches shows how static scheduler configurations can lead to poor performance when load on the system varies over time. Third, we present a simple yet effective automatic adaptation solution, which provides significantly better overall system behaviour compared to static configurations. This is demonstrated by evaluations using a full system setup.
Quality metrics of variational autoencoders

Variational autoencoders (VAEs) are popular models for the generation of realistic images. Usually, the quality of generated images is estimated only by visual inspection, as the methods for the quantitative estimation are not fully developed. In this work, we present and test the methods that allow the possibility to evaluate the quality and the diversity of the images generated by a VAE objectively using an auxiliary classifier. The corresponding quality metrics are calculated for several implementations of VAE. The proposed metrics are especially valuable for the problems of preventing catastrophic forgetting during sequential learning. The experiments are performed on publicly available datasets.
Deep Learning Approach for Radar Applications

Automatic Target Recognition (ATR) is one of the strongest growing technology topics, especially in commercial applications like image recognition and automotive. ATR applications in radars established in the fields of defense and protection stayed on a certain level for years, so today there is a strong need to participate in the performance growth enabled by modern technologies. In this paper the ATR potential of the Deep Learning (DL) approach is proved by comparing the performance of a highly mature “classical” concept using feature extraction and several types of classifiers versus several state of the art DL networks and training concepts. The comparison is done on the same large database of Micro Doppler signatures from several classes, as typically used for moving object classification and drone threat assessments. The comparison shows the benefit and potential drawbacks of the different approaches.
Development of a beam-forming circuit for the antenna array operating in the W-band

The paper presents the development of a beamforming circuit aimed at controlling the state of an orbital angular moment of the output radio beam from a uniform circular antenna array operating in the W-band. The proposed device is based on the Rotman lens, the shape of which is calculated taking into account the configuration of the antenna array and the required states of its radiation pattern. The Rotman lens circuit has five input beam ports, eight output antenna ports, and eight dummy ports. The application of such a beam-forming scheme can significantly reduce the overall dimensions of the antenna system and can be implemented based on the microstrip technology together with the antenna array.
Hybrid Multiport Converter for High Step-Up Renewable Energy Applications

In this paper, a hybrid converter configuration is proposed suitable for high step-up renewable energy applications. The proposed topologies are derived by combining quadratic boost converter with Cuk and SEPIC converter. The proposed topologies are capable of producing high gain power conversion with single power control switch. The proposed topologies are suitable for applications requiring DC-DC multi-output converter.
Emotions Polarity of Tweets Based on Semantic Similarity and User Behavior Features

In social networks, people share their emotions and opinion towards specific subjects in the form of comments. These comments are positive, negative, or neutral based on their content. Therefore, people try to pick out the comment’s content; this content reflects the behaviour and style of expressing their emotions. In recent years, researchers interested in sentiment analysis (SA) achieved results using different methods, the lexical features and semantic similarity. But most of them neglect parts of the content of comments, considering it has no importance such as URLs, Numbers and Marks. They use the value of semantic similarity between comments direct to identify emotions polarity or to find synonyms of lexical features. This paper introduces an implementation that uses a combination of user behaviour, semantic and lexical features together for finding polarity emotions of Tweets. This proposed method examines some of the neglected content of tweets as features and adapts the semantic similarity value to emotional polarity as a feature in addition to lexical features. The main objective of this paper is to improve the classification accuracy. The performance of the proposed method evaluated using Naive Bayes and SVM as two popular machine learning classifiers. The best-obtained result is 94% using Naive Bayes and Sentiment140 dataset.
Efficient Airborne Network Clustering for 5G Backhauling and Fronthauling

Interests in the exploration and use of Unmanned Aerial Vehicles (UAVs) for service provision have risen in recent years. However, the use of UAVs to extend or solely provide Fifth Generation (5G) wireless network coverage in rural and low income areas is one application area that is yet to be extensively researched. To this end, this paper proposes a topological design model for building an airborne network to provide coverage in rural areas. The model uses a combination of Low Altitude Platform (LAP) as a base station and a cluster of UAVs as cellular access points to provide wireless access to ground users. A combination of performance metrics including Signal-to-Noise Ratio, communication range and residual energy of UAVs are used as guide for designing a robust airborne network with multiple sink nodes. Topology relaxation techniques are applied to design these meshed airborne networks respectively called Multi-Sink Airborne Network with Inter-Cluster Communication through the LAP (MSLBACK) and Multi-Sink Airborne Network with an Inter-Cluster Connection through UAV Gateways (MSGBACK). Compared to myopic approaches (that may lead to isolated UAVs), these airborne networks have more economic relevance as they ensure effective utilization of all UAVs in providing 5G connectivity to ground users. Simulation results reveal that MSLBACK and MSGBACK outperform the state-of-the-art algorithms.
Joints Steering Gear Detection Based on Semantic Segmentation

Aiming at the problem of inaccurate detection of joints steering gears in the industrialized inspection of automobiles, a method for joints steering gear detection based on image semantic segmentation is proposed. In this method, considering that this learning tasks are usually easy to overfit, appropriate regularization is needed to generalize well. In data augmentation, we randomly cover the input square area and randomly fill the corresponding square areas of other data into the area which is covered. Adopting encoder-decoder structure to obtain semantic segmentation graph, in downsampling we use dilated convolution to increase feature map size. And we also use label smoothing and adjust the loss function to improve the ability to predict the test set more accurately. Experiments show that this method is effective and has a good generalization for the detection problem of joints steering gear grooves.
Road Detection Based on Color and Geometry Characteristics

Many modern systems use road detection methods. The proposed automatic method for road detection consists of two steps. In the first step, the color segmentation in HSV color space is applied. The second step aims to improve the quality of color segmentation results by removing the false segmented areas. On the second step, the geometry features are used to classify segments into two classes – roads and background. We use four characteristics: shape index (SI), linear index (LI), elongation index (EI), eccentricity (Ecc). The geometry features are compared with the thresholds to classify segments. The presented results show the effectiveness of the proposed method.
Empowering Process and Control in Lean 4.0 with Artificial Intelligence

Lean Manufacturing is well known as an effective means to improve productivity and decrease costs of operations, using a series of management practices developed first in Japan and then adapted to worldwide circumstances. Rapid technological progress has opened new business potentials and opportunities forcing companies to constantly introduce more and more advanced solutions to remain competitive. However, the potential of such technologies to support the implementation of Lean Manufacturing is not completely perceived yet. After defining Lean 4.0 which aims to provide useful insight for the integration of lean and Industry 4.0 in the manufacturing companies, AI is then briefly introduced, as a key enabler. This paper focuses on the integration and support of AI to Lean 4.0, mainly investigating the process and control issues through better use of data and knowledge.
Focusing of pulsed linearly polarized Gaussian laser beams by refractive microaxicons

We perform and investigate focusing features of refractive microaxicons illuminated by pulsed linearly polarized Gaussian laser beams by means of with the finite difference in time domain (FDTD) method realized in the Lumerical software product. The changes in the distribution of transverse and longitudinal components of the electromagnetic field in dependence of size the refractive microaxicon and the pulse duration are shown.