Miracle-dz/UniFAR-FTUs huggingface dataset:
Seamless Transfer of Renewable-Based Microgrid Between Utility Grid and Diesel Generator

This paper deals with the renewable energy-based microgrid (MG) to feed the community loads. It has the capability to synchronize with the utility as well as with the diesel generator (DG) set. During healthy grid condition at high renewable power generation and when load demand is less, it feeds power to the grid while at low renewable generation power is taken from the grid. During outage and low renewable power generation, it synchronizes with the DG set to meet the load demand and forms a hybrid microgrid. In islanding, the voltage source converter (VSC) operates in a voltage control mode for realizing a renewable MG and operates in the current control mode for implementing a hybrid MG and switching to the grid-connected mode. An incremental conductance and perturb & observe approaches with a boost converter are implemented for the maximum power extraction of the renewables. A battery bank is connected at the dc link of VSC. The multifunctional MG synchronization and de-synchronization with ac mains/DG set are discussed in conjunction with variety of test results. In this topology, a single bidirectional converter is implemented and controlled using various control algorithms to manage MG power among generation, storage, and load units. A MATLAB/Simulink model is developed for a wind–solar–diesel based MG. Simulated results are validated with test results on a developed laboratory prototype.
Hybrid and Reconfigurable IPT Systems With High-Misalignment Tolerance for Constant-Current and Constant-Voltage Battery Charging

Inductive power transfer (IPT) for battery charging applications has significant advantages over the traditional plug-in system. Since misalignment between the primary and secondary windings is inevitable, it is of significance to improve the misalignment tolerance of IPT systems with constant-current (CC) and constant-voltage (CV) outputs for battery charging. In this paper, the load-independent output characteristic of the hybrid topology and the function switching between CC and CV of the reconfigurable topology are analyzed. Besides, a hybrid and reconfigurable IPT system with 3-D misalignment tolerance for CC and CV outputs is proposed, simplifying or even canceling control schemes. Moreover, a novel parametric design method is given for the IPT system, which can suppress the fluctuation of the output voltage/current within a certain range of misalignment. In order to validate the performance of the proposed topology, a 1-kW prototype is built, and the corresponding experiments are carried out. In the CC/CV mode, the system can operate with the longitudinal misalignment to 50% when the load varies from 36 to 480 Ω, and the fluctuation of the output current/voltage is within 5%. Similarly, the misalignment in Y- and Z-axis is 12.5% and 33.3%, respectively.
MORC Domain Definition and Evolutionary Analysis of the MORC Gene Family in Green Plants

Abstract Microrchidia (MORC) proteins have been described as epigenetic regulators and plant immune mediators in Arabidopsis. Typically, plant and animal MORC proteins contain a hallmark GHKL-type (Gyrase, Hsp90, Histidine kinase, MutL) ATPase domain in their N-terminus. Here, 356 and 83 MORC orthologues were identified in 60 plant and 27 animal genomes. Large-scale MORC sequence analyses revealed the presence of a highly conserved motif composition that defined as the MORC domain. The MORC domain was present in both plants and animals, indicating that it originated in the common ancestor before the divergence of plants and animals. Phylogenetic analyses showed that MORC genes in both plant and animal lineages were clearly classified into two major groups, named Plants-Group I, Plants-Group II and Animals-Group I, Animals-Group II, respectively. Further analyses of MORC genes in green plants uncovered that Group I can be subdivided into Group I-1 and Group I-2. Group I-1 only contains seed plant genes, suggesting that Group I-1 and I-2 divergence occurred at least before the emergence of spermatophytes. Group I-2 and Group II have undergone several gene duplications, resulting in the expansion of MORC gene family in angiosperms. Additionally, MORC gene expression analyses in Arabidopsis, soybean, and rice revealed a higher expression level in reproductive tissues compared with other organs, and showed divergent expression patterns for several paralogous gene pairs. Our studies offered new insights into the origins, phylogenetic relationships, and expressional patterns of MORC family members in green plants, which would help to further reveal their functions as plant epigenetic regulators.
Speech Activity Detection in Naturalistic Audio Environments: Fearless Steps Apollo Corpus

Speech activity detection (SAD) is a fundamental building block for most spoken language technology systems. Developing efficient SAD systems in highly naturalist data scenarios is a challenge. In this study, we investigate the SAD problem on NASAs Apollo space mission data <xref ref-type="bibr" rid="ref1">[1]</xref>. Apollo data consists of long-term naturalistic audio recordings (i.e., 6–12 day missions). The Apollo data poses various challenges like: 1) noise distortion with variable SNR, 2) channel distortion, 3) very high density of speech, 4) foreground versus background speech, and 5) extended periods of nonspeech activity. In this study, we use threshold optimized combo-SAD <xref ref-type="bibr" rid="ref21">[21]</xref> as our baseline unsupervised system. This technique was developed to address variable speech/nonspeech density issues in long-term audio data. To mitigate issues related to Apollo audio loops, multispeaker scenarios including foreground versus background conversations within loops, and highly noisy background, a new curriculum learning (CL) based convolutional neural network (CNN) model is developed. This efficient method leverages the long-term learning capability of CNN and CL strategies where data are trained in a manner that improves the efficiency during the learning process. Here, we use signal-to-noise ratio as the learning parameter. Our experiments on free flowing Apollo audio data show that the proposed approach provides a significant improvement in SAD performance (<inline-formula><tex-math notation="LaTeX">$> 10\%$</tex-math></inline-formula>).
Optimizing Multistage Discriminative Dictionaries for Blind Image Quality Assessment

State-of-the-art algorithms for blind image quality assessment (BIQA) typically have two categories. The first category approaches extract natural scene statistics (NSS) as features based on the statistical regularity of natural images. The second category approaches extract features by feature encoding with respect to a learned codebook. However, several problems need to be addressed in existing codebook-based BIQA methods. First, the high-dimensional codebook-based features are memory-consuming and have the risk of over-fitting. Second, there is a semantic gap between the constructed codebook by unsupervised learning and image quality. To address these problems, we propose a novel codebook-based BIQA method by optimizing multistage discriminative dictionaries (MSDDs). To be specific, MSDDs are learned by performing the label consistent K-SVD (LC-KSVD) algorithm in a stage-by-stage manner. For each stage, a new quality consistency constraint called “quality-discriminative regularization” term is introduced and incorporated into the reconstruction error term to form a unified objective function, which can be effectively solved by LC-KSVD for discriminative dictionary learning. Then, the latter stage takes the reconstruction residual data in the former stage as input based on which LC-KSVD is repeatedly performed until the final stage is reached. Once the MSDDs are learned, multistage feature encoding is performed to extract feature codes. Finally, the feature codes are concatenated across all stages and aggregated over the entire image for quality prediction via regression. The proposed method has been evaluated on five databases and experimental results well confirm its superiority over existing relevant BIQA methods.
Robust Face Recognition Using Radial Basis Probabilistic Neural Network

In this paper, we present a human face recognition method based on radial basis probabilistic neural network (RBPNN). Before classification, discrete cosine transform (DCT) is used to extract textural features from frontal face images. Dimension reduction is achieved considering high frequency coefficients as redundant features, thereby omitting them. Subtractive clustering-based RBPNN is used for classification. Two databases - ORL database and LU database are used to evaluate the model. Experimental results demonstrate better classification efficiency of the model with respect to other popular artificial neural networks (ANN) classifiers. Recognition rate of 98% is achieved using this model. 3% and 6% change in recognition rate are observed in ORL and LU database respectively for applying different noise level. Training time remains below 2 sec for both databases.
Implementation of Postfault Decoupling Vector Control and Mitigation of Current Ripple for Five-Phase Fault-Tolerant PM Machine Under Single-Phase Open-Circuit Fault

The multiphase machine is a competitive candidate for applications where uninterrupted operation is demanded under postfault conditions. In this paper, the decoupling vector control for a surface mounted five-phase permanent-magnet synchronous machine (PMSM) under single-phase open-circuit fault is investigated. The decoupled mathematical model of the five-phase PMSM under single-phase open-circuit fault is derived. Then, two postfault control strategies are obtained, which are based on the principles of minimum copper loss and equal amplitudes of currents, respectively. The postfault control strategy based on the feed-forward compensation is proposed in order to eliminate the influences of certain factors and decrease the current ripple. The validity of the postfault control strategies is proved by experiments.
Suppression of an Electric Field and a Local Heat Generation With the Equivalent Circuit of Stator Coil End of an Inverter-Fed High-Voltage Electrical Machine

We have been developing the insulation design techniques based on an equivalent circuit to predict the voltage distribution of the stator coil end of high-voltage inverter-fed electrical machines. A new equivalent circuit was proposed by an additional modeling of cover insulation, the corona protection shield, and the measurement circuits to the conventional equivalent circuit for mains-fed electrical machines. The applicability of our proposed equivalent circuit was verified experimentally and it was confirmed that simulation results of voltage distribution agreed with experiment results with uncertainty of less than 10%. Furthermore, in order to suppress the insulation degradation caused by inverter surge, sensitive parameters to reduce electric field concentration and local heat generation were explored. The capacitance of insulation layer affects both electric field and local heat generation, and those effects are reduced by 17% and 9% by halving the capacitance.
Research on the Prediction Method of Power Battery SOC Based on Deep Learning

Precise prediction of power battery SOC plays a key role in the accurate navigation of electric vehicles that are running on the way. Since traditional models adopt methods of manual intervention to extract characteristic factors that affect the battery, they fail to find the correlations between these factors, which leads to the unsatisfactory prediction precision of SOC. This article proposes a power battery SOC prediction model based on DBN-SVM through analyzing the nonlinear characteristics of power battery in an innovative way. By using deep belief networks, this model automatically learns high nonlinear combination characteristics of the power battery from basic data including the physical parameters of the power battery and the environmental factors. Afterwards, this model improves the precision via classifier. Test results show that the prediction model based on deep learning achieves a higher precision, which approves to be a reliable prediction method of battery power SOC.
Characteristic Modeling Approach for Complex Network Systems

This paper proposes a sampled data-based modeling approach for complex network systems. A compression method, known as characteristic modeling, is used to construct microscopic models from macroscopic observations. Based on this model, a novel control method is also developed to achieve network synchronization. The proposed approach can reduce both the complexity of microscopic dynamics and overall networks. Its application to pinning control design validates the effectiveness of this approach.
Prediction of Twist Angle for Assistive Exoskeleton Based on EMG Signals

In this study, we propose a method based on electromyography (EMG) for real time prediction of human joint angle, which can be used as upper layer control in assistive exoskeleton. Light assistive exoskeleton can provide help in specific ways for old people hold or get articles, however, due to4the restriction of upper control method and the intrinsic shortage of structure, many exoskeletons can merely be driven in a certain mode, sometimes may not be appropriate to particular situation. Purpose of this work is to figure out the corresponding relationship between EMG and kinematics of human limb, at the same time, to increase diversity of usage of EMG. Prediction of joint angle can save time for control and mechanical delay in service. Without the model of exoskeleton, evaluation of our model is based on the graphics of the error between practical signal curves and predicted signal curves.
Requirements for Testing and Validating the Industrial Internet of Things

The latest advances in industry have been accomplished within the 4th Industrial Revolution, mostly noted as Industrie 4.0. This industrial revolution is boosted by the application of Internet of Things (IoT) technologies into the industrial contexts, also known as Industrial Internet of Things (IIoT), which is being supported by the implementation of Cyber-Physical Production Systems (CPPS). In this context, most of the existing work concentrates on developing IIoT models and CPPS architectures, laking the identification of validation requirements of these platforms. By rushing into releasing state-of-the-art IIoT applications, developers usually forget to implement methodologies to validate these applications. In this paper, we propose a list of requirements for IIoT platform validation, based on its architecture, as well as in requirements established by the industrial reality. A CPPS case study is presented, in order to illustrate some of these requisites and how validation of these type of system could be achieved.
Advanced Diagnosis of Stator Turn-to-Turn Faults and Static Eccentricity in Induction Motors Based on Internal Flux Measurement

Large induction motors used in variable speed industrial applications (digging, traction, etc.) undergo dynamic stresses at high power levels. Their lifetime is reduced as compared with constant speed motors and downtime costs are very high. To monitor the condition of such motors, advanced sensor instrumentation is considered, including installation of Hall effect flux sensors inside the motor airgap. Such an advanced instrumentation enables continuous measurement of the airgap flux in both time and space domains simultaneously. This allows for advanced fault detection algorithms. This paper presents a method for early diagnosis of stator turn-to-turn faults and static eccentricity, and differentiation between these two conditions. The paper demonstrates that the proposed method detects, localizes, and quantifies both faults with high accuracy and sensitivity. Furthermore, each fault can be detected at an incipient stage and its development can be predicted. The results of the presented study have been supported by both simulation and experimental results obtained on a laboratory scale induction motor. Based on the results presented in the paper, a real-time condition monitoring system has been developed.
Evaluating Test Data Generation for Untyped Data Structures Using Genetic Algorithms

Untyped data such as the byte streams used in communications between spacecraft and ground stations present a specifically challenging field for automatic test data generation. We investigate variations of genetic algorithms to improve test data generation, and present measurements and preliminary results obtained using our prototype. The future goal is to extend our white-box random testing tool DCRTT with these methods and thus apply the approach to industry-grade software.
Influence of uridine diphosphate-glucuronosyltransferases (1A9) polymorphisms on mycophenolic acid pharmacokinetics in patients with renal transplant

Abstract Background: There are differences in pharmacokinetic of mycophenolic acid among individuals. The UGT1A9 enzyme is of special interest since it is the main enzyme involved in the glucuronidation of MPA. Single nucleotide polymorphisms in the UGT1A9 gene may be responsible for individual differences in the pharmacokinetics of MPA. The aim of this study was to explain MPA pharmacokinetics in UGT1A9 1399 C > T polymorphisms in Turkish renal transplant patients. Patients and methods: One hundred and twenty-five living-donor transplant recipients and 100 healthy control subjects underwent UGT1A9 1399 C > T genotyping using polymerase chain reaction–restriction fragment length polymorphism. Concentrations of MPA were determined with Cloned Enzyme Donor Immunoassay (CEDIA). Besides that, all the patients were monitored for acute rejection and graft function during the study period. Results: The UGT1A9 1399 C > T CC, CT, and TT genotype frequencies among patients were, respectively, 68.0%, 23.2%, and 8.8%. The CC, CT, and TT genotype frequencies among controls were, respectively, 63.0%, 23.0%, and 14.0%. There was no significant difference between patients and controls (p = .480, p = .999, p = .286, respectively). At first month, respectively, through blood concentrations of MPA were significantly higher in UGT1A9 1399 C > T TT carriers than in CT and CC carriers (p = .046). The doses for these patients were lower at first month (p = .021). Acute rejection episodes were not associated with the CC vs CT or TT genotypes (p = .064). Conclusions: Our results demonstrated a correlation between the UGT1A9 1399 C > T polymorphism and MPA pharmacokinetics among renal transplant patients. Determination of UGT1A9 polymorphism may help to achieve target of MPA blood concentrations.
Research of Urban Land Use and Regional Functions Based on Mobile Data Traffic

Urbanization and enlargement of cities contributes to different functional regions in urban area. In this paper, we investigate several main patterns of human online behavior to discover different functional areas in cities. To that end, we conclude a method for automatic identification of urban land use from the information obtained by mobile data traffic. We consider data traffic as reflection of human activities due to its timeliness and pervasiveness. During the process we also propose two possible indicators from traffic flows to build bridge between data traffic and human activities. We apply our technique to real-world mobile data traffic collected by major mobile operators in China. Our results prove the existence of several typical patterns in large-scale data traffic of city and link them to specific urban land uses.
Indemnity for Frequency Disruption in a Smart Grid during Cyber–Attack

Indemnity for frequency disruption in smart grid system due to cyber-attack is essential to avoid unstable power supply which can cause severe blackout. In this paper, a protective model is proposed for providing robust control of frequency in any situation, especially during cyber-attack on automatic generation control. A controlled switching unit is included in the protective model for ensuring protection against disruptive frequency during cyber-attack. The proposed control scheme restores frequency two times faster than the existing method.
A Novel Approach for Detecting Browser-Based Silent Miner

Transactions in the cryptocurrency market has been extremely hot in recent years, with the price of cryptocurrency climbing all the way. Hackers have turned their attentions to cryptocurrencies, and have used various means to acquire cryptocurrencies illegally, which caused huge losses to the victims. Some browsers block malicious mining activities from the network protocol level, but they do not have the ability to detect mining samples themselves, and it is difficult to make effective detection of homogenous mining samples of the network layer. To solve these problems, based on the attack pattern of browser mining, the browser-based silent mining features are analyzed, and a method to detect browser silent mining behavior is proposed. This method drives known malicious mining samples, extracts heap snapshots and stack code features of a dynamically running browser, and performs automated detection based on recurrent neural network. By modifying the kernel code of Chrome, a browser-based silent miner detection prototype system BMDetector was designed and implemented. With 1159 samples detected and analyzed, experimental results show that the recognition rate of the original mining sample is 98%, and 92% for the encrypted and confused, which is an effective and feasible method.
A Bidding Strategy for Virtual Power Plants With the Intraday Demand Response Exchange Market Using the Stochastic Programming

This paper presents a mathematical model for the energy bidding problem of a virtual power plant (VPP) that participates in the regular electricity market and the intraday demand response exchange (DRX) market. Different system uncertainties due to the intermittent renewable energy sources, retail customers’ demand, and electricity prices are considered in the model. The DRX market enables a VPP to purchase demand response services, which can be treated as “virtual energy resources,” from several demand response providers to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. This could increase the expected profit and the renewable energy utilization of the VPP. The overall energy bidding problem is modeled as a three-stage stochastic program, which can be solved efficiently by the scenario-based optimization approach. Extensive numerical results show that the DRX market participation can improve the VPP's energy management.
A Brief Overview of Existing Tools for Testing the Internet-of-Things

Systems are error-prone. Big systems have lots of errors. The Internet-of-Things poses us one of the biggest and widespread systems, where errors directly impact people's lives. Testing and validating is how one deals with errors; but testing and validating a planetary-scale, heterogeneous, and evergrowing ecosystem has its own challenges and idiosyncrasies. As of today, the solutions available for testing these systems are insufficient and fragmentary. In this paper we provide an overview on test approaches, tools and methodologies for the Internet-of-Things, its software and its devices. Our conclusion is that we are still lagging behind on the best practices and lessons learned from the Software Engineering community in the past decades.
Regulate to reduce chemical mixture risk

Regulatory systems must better provide for risks from exposure to multiple chemicals Humans and wildlife are continuously exposed to multiple chemicals from different sources and via different routes, both simultaneously and in sequence. Scientific evidence for heightened toxicity from such mixtures is mounting, yet regulation is lagging behind. Ensuring appropriate regulation of chemical mixture risks will require stronger legal stimuli as well as close integration of different parts of the regulatory systems in order to meet the data and testing requirements for mixture risk assessment.