Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct Active and Reactive Power Control for Grid-Connected PEC9 Inverter Using Finite Control Set Model Predictive Method

This paper is dealing with designing an advanced Direct Power Control (DPC) technique for a grid-connected nine-level Packed E-Cell (PEC9) inverter through Finite Control Set Model Predictive Control (FCSMPC) and Single-phase Direct-Quadrature (s-d-q) frame. Generating a nine-level voltage waveform using minimum active and passive components count makes PEC9 a cost-effective compact multilevel converter in single-phase applications. FSMPC symmetrically stabilizes the nine-level voltage waveform and accurately synchronizes the inverter current with the grid voltage by applying direct control to the switching operation of PEC9 inverter. The phase and the amplitude of the reference current in the proposed DPC are adjusted regarding the desired level of active and reactive power through s-d-q frame. In order to address the FCSMPC weighting factors tuning difficulty, some new adaptive functions using the error value of the capacitors voltages and the inverter current are also proposed. The experiments and simulations approve that PEC9 accurately generates the desired amounts of active and reactive power with standard level of THD and dc voltage ripple.
Facial-Machine Interface-based Virtual Reality Wheelchair Control using EEG Artifacts of Emotiv Neuroheadset

This paper proposes a facial-machine interface system based on electroencephalogram (EEG) artifacts for mobility enhancement in people with paraplegia. We aim to employ the proposed system for cooperating with an EEG-based BCI system. Emotiv EPOC X neuroheadset used to acquire an EEG signal for verification of electrode positions and feature extraction algorithms. By using motion artifacts, two modalities of wheelchair control from only jaw movement and combing jaw and eye movements to online control a virtual reality wheelchair were proposed and evaluated the efficiency of the proposed control modalities. The results showed that combing eye and jaw movements can yield a high efficiency which can be used for a practical hybrid BCI system. We will further implement the proposed in a real power wheelchair.
Ship Classification Based on Sidelobe Elimination of SAR Images Supervised by Visual Model

SAR image of a ship at the ocean usually contains severe cross sidelobe due to the strong scatter points of the ship body, which increases the difficulty of detecting the ship type correctly. Aiming at this problem, this paper proposes a novel method to eliminate the sidelobe of the strong scattering points in the ship’s original SAR image, which utilizes the contour of the ship visual model to extract the ship hull information. We first estimate the ship’s aspect angle in the original SAR image and transmit this parameter into the top view dictionary of the ship visual model to find out the top view image with the same aspect angle. Then, we extract the ship visual model’s contour to merge with the original SAR image, preserving the ship body’s grayscale information in the visual model contour and rejecting the rest grayscale information outline. Finally, we construct a convolutional neural network with a simple structure to train the newly generated SAR image to finish the ship classification task. To validate our proposed method’s feasibility, we conduct the ship classification experiment on the latest benchmark SAR ship dataset. The proposed method achieves the final average classification accuracy of 98.21%, far exceeding the original method provided in this dataset.
Adaptive Signal Filtering Platform for a CPS/IoT Ecosystem

The rapid increase in number of devices in Internet-of-Things generates astronomic amounts of data. Dealing with noisy and low quality data uses more effort than the data analysis itself. Dealing with noisy data at the source would significantly reduce the effort of pre-processing during analysis, as well as the storage and bandwidth overhead. In this paper we introduce an Adaptive Signal Processing Platform (ASPF) for CPS/IoT Ecosystems. It provides ability to dynamically detect noise variation in a signal and successfully filter these components out of the signal leaving only clean and useful data. The paper shows two approaches with different requirements on effort and scalability.
Inventor profile’s development for acquiring technology exclusive rights in engineering students

In this paper, we present the results of a study carried out with Mechatronics engineering students at the Tecnologico de Mexico in Hermosillo, Mexico. The purpose of this research was to design a profile that will be the goal for our students inside of Internet of Things (IoT) laboratory.Students do not have training that allows them to acquire intellectual property. For this, it seeks to train them in an emerging technologies laboratory where students can develop projects from which they can acquire the exclusive right over the technology and generate in them the necessary skills to achieve it.
Dynamic Resource Allocation Scheme and Deep Deterministic Policy Gradient-Based Mobile Edge Computing Slices System

The development of multi-industry compatibility and the coexistence of multiple services and multiple functional communication networks will cause rapid growth in mobile communication system traffic. Users will have increasingly strict requirements for quality of service (QoS), e.g., a high rate, low latency, and low energy consumption. To address these problems, it is helpful to combine network slicing and mobile edge computing (MEC) to provide customized networks while reducing the service processing time. Due to the uncertainty of user requests and the environment, reasonable resource allocation is always particularly challenging. A novel dynamic resource allocation scheme for MEC slice systems, which formulates resource allocation and computation offloading issues as an optimization problem subject to the latency and rate, is proposed. Based on the dynamics of the slice requirements, quantity, and service time, the proposed problem is converted to a Markov decision process (MDP), and a state, action, and reward function are proposed. By exploiting the deep deterministic policy gradient (DDPG) algorithm, the wireless resources and computing resources are configured dynamically according to the requirements of different types of slices to maximize the revenue of the network operator. The simulation results demonstrate the influence of the slice arrival rate and total resources on the allocation policy. Compared with other schemes, the proposed scheme can provide a more effective performance when resources are scarce.
CNN for Radial Velocity and Range Components Estimation of Ground Moving Targets in SAR

Ground-moving objects in synthetic aperture radar (SAR) images appear defocused and azimuthally displaced using conventional SAR image formation algorithms. In this paper, a novel regression method based on convolutional neural networks (CNNs) for the estimation of radial velocity and slant range components of ground moving targets is proposed. Motion parameters estimation can be helpful for designing additional matched filters to focus and relocate moving targets. We have generated the training and the test data in such a way that each image is indeed a 2D data matrix of a moving target. In other words, each complex image contains the range-compressed signal of only one moving target with a specified pair of (range, radial velocity). To further decrease the estimation error, we employed transfer learning by fine-tuning the pretrained AlexNet architecture in a regression problem. To verify the effectiveness of the proposed method, simulations have been performed. The results demonstrate the effectiveness of the proposed method.
The Hydrophobicity Class of Porcelain Insulator Detection Based on Digital Image Processing : A Paper Review

The experiment of quality assurance of polymeric outdoor insulators service life is necessary to be checked the class of wettability by using the digital image processing tools. The image processing can improve its pictorial information for human interpretation, render it more suitable for autonomous machine perception. This paper provides a survey of the recent technology and theoretical concept to explain the development of the Hydrophobicity Class (HC) of porcelain insulator. The hydrophobic properties of the housing material were found degraded to a different extent between field-aged insulators due to differences in material structure and pollution conditions. This paper contributes to the latest development of reviews relating to image processing for the implementation of porcelain insulator hydrophobic systems testing. , and their related studies. We categorized the computer vision mainstream into 2 group such as Machine Learning Methods Applied to Digital Image Processing and Deep Learning Methods Applied to Digital Image Processing. Then compare pros/cons in each type of technique. We also provide brief explanation on the up-to-date information about the techniques and their performance.
Agent-Based Decentralised Process Planning and Evolutionary Change Propagation

The increasing dynamics of manufacturing systems promote the utilization of decentral system architectures, such as Multi-agent Systems (MAS). These are especially applicable for dynamic systems that exhibit structural and behavioural changes during runtime. This paper develops a MAS that is capable of decentralised process planning that does not require any a priori definition of compatibility or interfaces between machines. Furthermore, the paper outlines how the producibility graph that is derived by the agents during process planning is updated and shared between the agents to keep the knowledge bases of the agents updated at all times.
Heartbeat Measurement with Millimeter Wave Radar in the Driving Environment

Millimeter wave radar has proven to be an effective method to measure human vital signs in a non-contact manner. However, the driving context is complex and noisy, and this fact must be considered in the algorithm design as well as in testing and verification methodologies. We implemented an estimation algorithm based on arctangent demodulation and added refinements to optimize performance. Three different algorithm choices, peak spectrum, MUSIC, and particle filter were tested while driving in an on-road vehicle as well as in a high-fidelity driving simulator. Accuracy was explored over a parameter space by varying window size and error tolerance. We present results that help to select the method and parameter. Additionally, we conclude that a high-fidelity simulator is an appropriate environment to conduct radar tests for in-cabin monitoring.
Modeling and Predictability Analysis on Channel Spectrum Status Over Heavy Wireless LAN Traffic Environment

Using the real wireless spectrum occupancy status in 2.4 and 5 GHz bands collected at a railway station as representative of a heavy wireless LAN (WLAN) traffic environment, this paper studies the modeling of durations of busy/idle (B/I) status and its predictability based on predictability theory. We first measure and model the channel status in the heavy traffic environment over almost all of the WLAN channels at 2.4 GHz and 5 GHz bands in a busy (rush hour) period and non-busy period. Then, using two selected channels at 2.4 GHz and 5 GHz bands, we analyze the upper bound (UB) and lower bound (LB) of predictability of the busy/idle durations based on predictability theory. The analysis shows that the LB predictability of durations can be easily increased by changing their probability distribution. Based on this property, we introduce the data categorization (DC) method. By categorizing the busy/idle durations into different streams, the proposed data categorization can improve the prediction performance of some streams with large LB predictability, even if it employs a simple low-complexity auto-regressive (AR) predictor.
Control of a Single Phase Modular Multilevel Converter based on a New Modulation Technique

Modular Multilevel Converter (MMC) topologies have a bright future for high-power applications. In order to operate them, the first objective is to control the current at MMC terminals. Therefore, the arm currents must be controlled. The second objective is to control the capacitor voltages. Therefore, the internal circulating current must be controlled too. Additionally, the third objective is to reduce the switching losses. As of late, there has been an extensive effort towards addressing the technical constraints associated with the control and operation of MMC. The control of single phase MMC is however not widely developed. This paper proposes a complete scheme for controlling the MMC load current and capacitor voltages. The proposed control scheme presents a new modulation technique called the Integral Modulation technique. Finally, the effectiveness of the control scheme with its new modulation technique is tested on a single phase MMC using MATLAB/Simulink.
Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Sentiment analysis is the extraction and categorization of sentiments that have been expressed in text data using text analysis techniques. Manifested by earlier studies, sentiment analysis of drug reviews has a large potential for providing valuable insights to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Such insights help safeguard patients and increase their trust in medical companies. The existing systems either follow a lexicon-based approach or a learning-based approach for sentiment analysis in the medical domain. Learning-based techniques require annotated data while lexicon-based techniques tend to be domain-specific which restricts their wide use. This research embarks on a hybrid technique that utilizes both learning-based and lexicon-based approaches to achieve better results. General-purpose sentiment lexicons, such as AFFIN, TextBlob, and VADER, are used for annotating the reviews. Furthermore, several feature engineering techniques, such as term frequency (TF), term frequency-inverse document frequency (TF-IDF), and union of TF and TF-IDF (TF U TF-IDF) have been incorporated for the extraction of useful features. Finally, the learning models including logistic regression (LR), AdaBoost classifier (AB), random forest (RF), extra tree classifier (ETC), and multilayer perceptron (MLP) are used to classify sentiments of the reviews. The performance of the proposed hybrid approach is evaluated using accuracy, precision, recall, and F1-score. Experimental results indicate that the combination of learning-based and lexicon-based approaches provide improved results than their individual use. Moreover, TextBlob has shown promising results giving an accuracy of 96% with MLP when used with TF-IDF and with LR when used with TF U TF-IDF.
Review of Learning Design Choices of Primary School Programming Courses in Empirical Researches

Programming, most recently in the form of Computational Thinking (CT), is emerging as a new subject in primary schools worldwide. By data-based decision making, teachers, as learning designers, make decisions on instruction design based on a broad range of evidence, such as student assessment scores and classroom teaching observations. Given the current limitations on conclusive, field-proven teaching practices and an underlying “culture” to use as a basis, we turn to pertinent empirical studies that the growing scientific interest on introducing programming in primary school curricula has produced. The hypothesis is that we may overcome the lack of extensive experience in designing a programming course by reviewing the above evidence, in order to frame learning situations and methods.
Environmental pollution in relation to stone decay

Examines in detail each atmospheric pollutant which contribute either directly or indirectly to the deterioration of stone sulphur compounds (hydrogen sulphide diozide etc); nitrogen oxides; ozone; hydrogen chloride; carbon dioxide. The mechanism of stone deterioration and the formation of black scabs due to sulphur dioxide attack are slso described. Extensive
Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management

Currently, research work is primarily dependent on the collection of large sets of data from systems and making predictions based on the knowledge obtained from the data, which is generally termed as ‘data mining’. These data mining algorithms are of great importance in improving the performance of different applications. In this regard, Machine Learning (ML) algorithms have been demonstrated to be excellent tools to cope with difficult problems. In this paper, a classification learner based supervised ML algorithm is proposed for intentional islanding of DERs based on the live data collected from supervisory control and data acquisition (SCADA) system in post disaster situations. Literature presents various islanding detection techniques and also intentional islanding algorithms to address different problems in AC networks. These algorithms majorly work based on the control of current source or voltage source inverters. On the other hand, a low voltage DC distribution system allowing the removal of inverter is proposed, which is supposed to be more advantageous by reducing losses and is also economical when working with DERs. In this paper, ML based intentional islanding algorithm for DERs based low voltage DC distribution system is proposed by considering the effects of natural disasters. The learner models trained are fine tree, linear SVM, quadratic SVM and Gaussian SVM. The training of fine tree model is achieved with higher accuracy of 99.8%. The main objective of this work is to achieve a faster and accurate decision making. The performance of the ML based intentional islanding algorithm is compared with the earlier proposed artificial intelligence (AI) based intentional islanding algorithms. The AI algorithms proposed earlier are fuzzy inference systems (FIS), artificial neural networks (ANN) and adaptive network based fuzzy inference system (ANFIS). The comparison shows that, the decision making with ML based intentional islanding algorithm is faster and accurate than all other algorithms.
A phase based approach for machine inductance estimation via current slope detection of an inverter fed IPMSM

The following describes a new method for estimating the parameters of an interior permanent magnet synchronous machine (IPMSM). For the estimation of the parameters the current slopes caused by the switching of the inverter are used to determine the unknowns of the system equations of the electrical machine. The angle and current dependence of the machine parameters are linearized within a PWM cycle. By considering the different switching states of the inverter, several system equations can be derived and a solution can be found within one PWM cycle. The use of test signals and filter-based approaches is avoided. The derived algorithm is explained and validated with measurements on a test bench.
Nine-Phase-based Fractional-Slot Winding Layouts for Integrated EV On-board Battery Chargers

Permanent magnet (PM) machines configured with fractional slot concentrated winding (FSCW) have recently been employed in electric vehicle (EV) powertrains thanks to their myriad merits over conventional designs. The concept of integrated on-board battery chargers (OBCs), where the propulsion components are employed in the battery charging process, has also shown promise to provide a cost-effective alternative to conventional on-board and/or off-board chargers. This technology highly depends on the machine type as well as the employed winding design. Most of the up-to-date research covering this topic has mainly focused on multiphase machines with conventional distributed winding owing to their high-quality flux distribution. Despite the outstanding features of FSCW, their inevitable flux distortion constitutes the main drawback of this winding layout. This paper evaluates the performance of two nine-phase-based integrated OBCs using surface-mount permanent magnet (SPM) synchronous machines equipped with two different fractional-slot layouts, namely, the non-overlapped FSCW 18-slot/16-pole and overlapped fractional-slot winding 18-slot/10-pole with two-slot coil pitch. The design optimization of the two machines has been obtained based on analytical magnetic equivalent circuit (MEC) models. While, finite element simulation has been used to evaluate the two designs under both charging and propulsion operational modes.
Prototyping intrusion detection in an industrial cloud-native digital twin

Digital twins are poised to play a vital role in the industry 4.0 era. A cloud-based digital twin can augment the entity that it represents. To that effect, we envision that digital twins can have embedded control systems when paired with a cyber physical system, yielding significant performance and configurability advantages. However, relegating controllers to a cloud-based digital twin exposes them to a new set of attack surfaces. Given the intricacy of such systems and the plethora of mitigating actions they can take, intrusion detection is integral to maintaining the integrity of such system. In this paper, we propose and prototype a cloud-native digital twin proof of concept for evaluating the viability of the concept. The resulting platform is evaluated for its ability to host a cyber-physical system and its potential to incorporate an intrusion detection system.
Online Multi-target Tracking for Pedestrian by Fusion of Millimeter Wave Radar and Vision

Pedestrian trajectory tracking is crucial to ensure pedestrian safety in autonomous driving. Recently developed multi-sensor based multi-target tracking algorithms either overrely on the detection performance of some certain sensors or underutilize sensors’ inherent information. Aiming at improving reliability and robustness of tracking under complex autonomous driving scenes, a new multi-sensor based tracking algorithm for pedestrians is proposed in this paper, which realizes sensor-fusion tracking by employing a newly proposed back-projection mechanism and a novel multi-hypothesis association approach. For the performance evaluation, massive experiments are performed to produce the dataset with 20 sequences. Qualitative experiment results demonstrate the superiority of the proposed algorithm over the single sensor based conventional algorithm. Quantitative experiment results further verify that the proposed algorithm reduces false negatives and improves tracking accuracy as compared to conventional multi-sensor based tracking algorithms.