Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Electric Vehicles Navigation Strategy Considering the Uncertainty of the Charging Load

With the increased penetration level of electric vehicles (EV), there is a virtual issue to address the uncertainty of EVs. Under the premise of uncertain information, how to maximize the revenue of charging stations (CSs) by guiding the EVs' charging behavior is the concern of this paper. In this paper, long short-term memory neural network has been used to forecast the charging load of the CSs, and the model predictive control (MPC) method is combined to formulate an effective two-stage energy scheduling strategy for CSs. n order to better motivate electric vehicles to achieve the corresponding CS, this paper constructs an incentive subsidy model, which effectively guides the EVs' charging behavior by changing the service price of the station. In the day-ahead phase, CSs trade with the grid based on forecast information. In the day-ahead market, energy is purchased at a lower price through the trading pool; in the real-time stage, by changing the price of charging services, price incentives are given to EVs, guiding electric vehicles to transfer between stations, and changing EVs at each station. load, thereby further compensating for the deviation of the charging load to reduce the cost of CSs. The effectiveness and economy of the two-stage EV navigation strategy based on incentive price is verified by experimental results.
Experiential Learning in Data Science Through a Novel Client-Facing Consulting Course

This Innovative-Practice Full Paper presents the curriculum development and our experiences in offering a client-facing consulting course in data science. Data science education has seen rapid growth over the past decade. To provide students with hands-on opportunities to work with real data, many data science programs have advocated for and implemented experiential learning opportunities throughout the curriculum, which has been shown in a wide variety of literature to have many benefits. Most experiential learning opportunities in STEM programs are provided through capstone and engineering design courses; this is becoming increasingly the case in data science programs as well where several universities have developed data science capstone programs in which students work with clients on the client’s real-world data sets. While client-sponsored capstone projects are an exemplar of experiential learning, they may pose major challenges to implement and can be particularly resource-intensive for institutions; this is especially the case in data science where the legalities of data sharing may come with additional hurdles. Because of this, we were motivated to develop a novel client-facing data science consulting course that provides a unique experiential learning scenario to both undergraduate and graduate students while requiring much fewer resources and legalities. In our novel data science consulting course, groups of students work directly with real clients in a consulting clinic setting to provide data science guidance and short-term help with data science challenges. Through this process, students learn about the diversity of real-world problems in data science, how to lead consultations with clients effectively as a team, how to frame data science challenges and research possible solutions, and how to communicate solutions to clients in reports and presentations. We leveraged best practices in consulting courses developed in business school settings to design our course. Additionally, the consulting course serves as a community service initiative whereby researchers, clinicians, non-profit and government workers, and industry professionals benefit from the advice and short-term help provided through consultation. In this paper, we report how our consulting course is set up, how clients from both within and outside the university can seek help at the consulting clinic, and how the structure of the course enables students to have firsthand experience working on many real-world data science problems with clients. Finally, we discuss how student performance is assessed in this course, the lessons learned from offering this course, and recommendations for other data science programs in universities that wish to design similar courses.
Fault Prediction Model for Motor and Generative Adversarial Networks for Acceleration Signal Generation

The manufacturing process must continuously be improved. One of the most efficient strategies is maintenance scheduling by predictive maintenance for early fault detection and assisting with real-time decisions. The major concern of developing a predictive maintenance system is the lack of abnormal data and the cost of a high-specification sensor device for collecting data. This paper introduces the unsupervised learning model called Generative Adversarial Networks (GANs) for generating abnormal data in the form of acceleration signals to provide a dataset for developing an early fault prediction model and assisting a real-time decision on a low-frequency sensor device. The prediction model dataset is labeled on IS010816 to classify the label of data by Velocity Vibration (mm/s). The machine learning classifier model implements a hyperparameters optimization framework called OPTUNA to provide the best model performance. The proposed system aims to assist in real-time decision and maintenance schedules for the injection molding machine and offer the prediction model based on low-frequency sensor data from a drive motor.
Bottom-up quantification of energy flexibility in cluster of residential heat pump systems

Electrification of residential heating demand of buildings has a game-changing impact on decreasing energy-related carbon emissions. However, the increased electrical demand also causes stress on the distribution grids in many ways including increased incidence of under voltage as well as grid congestion and introduction of new demand peaks. A single building’s heat pump hot water system, consisting of a heat pump and a storage vessel, often has a sporadic profile of energy demand and is therefore unable to provide sufficient flexibility to address these issues. However, these issues can often be addressed using clusters of heat pump hot water systems that exist behind the same feeder. This paper proposes a bottom-up approach, using the individual hot water energy demand of each building in the cluster, which can be used to quantify available energy flexibility (both as upwards and downwards regulation) as a function of cluster size and type of controller. Both of these factors have significant, but different, impact on the amount and direction of flexibility that can be offered. Moreover, they also greatly influence the flexibility longevity (i.e duration for which energy flexibility could be sustained to be used later), which also depends indirectly on user comfort. Finally, the paper also presents a novel method to decide the flexibility trajectory (a term that collectively refers to flexibility potential and flexibility longevity) of a cluster of buildings and achieve a desired flexibility potential from them.
Performance Investigation of Screen-Printed Carbon Electrodes Activated by MES-Acid

Carbon is a well-known material for its properties, versatility, availability, and wide range of applications. Screen-printed carbon electrodes (SPCEs) have several attractive properties like mass production, price effectiveness, and technical properties. However, SPCEs performance usually needs to be enhanced with several approaches, such as modifying with nanocomposite materials, electrochemical activation, or doping. In this work, an investigation focused on the effect of 2-(N-morpholino)ethanesulfonic-acid (MES) – electrochemical activation of SPCEs was conducted. Electrochemical methods, i.e., cyclic voltammetry and impedance spectroscopy, were utilized as characterization techniques. This study utilizes the concept of charge transfer resistance Ret and its drop as a measure to assess the enhancement in conductivity. The results show a significant enhancement in charge transfer resistance (Rct). For graphite electrodes, Ret was reduced by 52% after pretreatment. Meanwhile, the Rct dropped by 54% for the carbon nanotube-modified graphite electrodes.
Influence of Self-Perceived Burden on Quality of Life in Patients with Urostomy Based on Structural Equation Model: The Mediating Effects of Resilience and Social Support

Objective To investigate the current situation of self-perceived burden in patients with urostomy, analyze the correlation between self-perceived burden and quality of life, and explore the intermediary role of resilience and social support. Methods The convenience sampling method was used to select 303 patients with urostomy of outpatient departments of the three tertiary hospitals in Yinchuan, Ningxia region, China, from April 1, 2020, to October 1, 2020, who then completed a survey questionnaire. The survey questionnaire contained a general data questionnaire and self-perceived burden scale, city of hope-quality of life-ostomy questionnaire, Connor-Davidson resilience scale, and social support rating scale. Results Self-perceived burden was present among 89.8% patients with urostomy; the quality of life of patients with urostomy is low. The results showed that the self-perceived burden and quality of life, resilience, and social support are related in pairs; self-perceived burden was significantly negatively correlated with quality of life，resilience, and social support; there was a significant positive correlation between quality of life, resilience, and social support; resilience and social support were parallel mediators. Conclusions Patients with urostomy had a heavy self-perceived burden and low quality of life. Reducing the self-perceived burden of patients with urostomy by improving the level of resilience and social support, could raise the level of quality of life. This study could provide empirical basis for nurses to take continuous nursing intervention measures in order to reduce the self-perceived burden of patients with urostomy and ultimately to improve the quality of life.
Fall Detection Based on HOG Features for Millimeter Wave Radar

The secondary injury caused by falls poses a serious threat to the physical and mental health of the elderly. Millimeter-wave radar has the characteristics of all-weather, unaffected by light, privacy-friendly, and strong penetration. It has the ability to compete with wearable sensors and video monitoring systems. With its excellent performance, it has become a fall detection method that has attracted much attention. In this paper, we propose a novel human fall detection method for millimeter wave radar based on histogram of oriented gradient (HOG) features and machine learning. The manual extracted HOG features of time-range maps, time-velocity maps and time-angle maps are used as the input of support vector machine (SVM) classifier. We separately verify the accuracy of the SVM classifier with single HOG feature and multiple HOG features. Our experiments proved that the accuracy of fall detection can reach 95% by using the HOG features of three maps as the input of the SVM classifier, which is at least 12.5% higher than that of single map.
Robust Constrained Gain-Scheduled Static Output Feedback Controller for NLPV descriptor systems

This paper presents a new approach to design Gain-Scheduled Static Output Feedback (SOF) controller for constrained Non Linear Parameter Varying (NLPV) descriptor system, subject to input saturation and external disturbances. Moreover, we take into account both state and input constraints in the control design using set invariance arguments. For the control context, we provide an effective solutions with a new class of Parameter Dependent (PD) nonqudratic Lyapunov function to estimate the largest Domain of Attraction (DoA), wich can be nonconvex, it can be formulated and solved as an optimization problem under strit PD Linear Matrix Inequality (LMI) constraints, the $\mathfrak{L}_{2}$ gain performance is used to attenuate the effect of the external disturbances. Controller design and stability analysis are provided with techniques in order to obtaining less conservative results. The theoretical developments are provided to demonstrate the effectiveness of the proposed approach in reducing the design conservatism through an illustrative example.
Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints

Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents. However, the generative models used for multiple-trajectory forecasting suffer from a lack of diversity in their proposals. To avoid this form of collapse, we propose a novel method for structured prediction of diverse trajectories. To this end, we complement an underlying pretrained generative model with a diversity component, based on a determinantal point process (DPP). We balance and structure this diversity with the inclusion of knowledge-based quality constraints, independent from the underlying generative model. We combine these two novel components with a gating operation, ensuring that the predictions are both diverse and within the drivable area. We demonstrate on the nuScenes driving dataset the relevance of our compound approach, which yields significant improvements in the diversity and the quality of the generated trajectories.
A Novel lncRNA SAAL Suppresses IAV Replication by Promoting Innate Responses

Influenza A virus (IAV) infection has traditionally been a serious problem in animal husbandry and human public health security. Recently, many studies identified that long noncoding RNAs play an important role in the antiviral immune response after the infection of the influenza virus. However, there are still lots of IAV-related lncRNAs that have not been well-characterized. Using RNA sequencing analysis, we identified a lncRNA, named Serpina3i Activation Associated lncRNA (SAAL), which can be significantly upregulated in mice after IAV infection. In this study, we found that overexpression of SAAL inhibited the replication of A/WSN/33(WSN). SAAL upregulated Serpina3i with or without WSN infection. Overexpression of Serpina3i reduced influenza virus infection. Meanwhile, knockdown of Serpina3i enhanced the replication of WSN. Furthermore, knockdown of Serpina3i abolished the SAAL-mediated decrease in WSN infection. Overexpression of SAAL or Serpina3i positively regulated the transcription of interferon β (IFN-β) and several critical ISGs after WSN infection. In conclusion, we found that the novel lncRNA SAAL is a critical anti-influenza regulator by upregulating the mRNA level of Serpina3i.
Infrared Interferometers At 10 µm

The two infrared (10µm) wavefronts of an interferometer (Michelson or Mach-Zehnder) interfere separately with a tilted reference beam on a dynamic recording medium consisting of a thin oil film on a glass substrate. Illumination of this twin hologram with a visible (HeNe) beam reconstructs simultaneously, in the same direction, two wavefronts characteristic of each arm of the interferometer. Practical realization and experimental constraints, such as air turbulence and mechanical vibrations, are easily satisfied with these long-wave-length interferometers. Experimental results include air wedge determination, homogeneity test of a NaCl plate, step deposit evaluation and afocal assembly analysis. The real-time aspect of our recording medium also permits the visual observation of transient phenomena.
New Based GPR Model For FGCPW Coplanar Waveguide Analysis

In this work, we propose a new model using the machine learning method GPR to modelling the electromagnetic behaviour of a transmission line coplanar with finite ground FGCPW. This model is based on the relative permittivity the substrate and the geometrical dimensions of the line. The database was created by the use of a high-frequency simulator and for a multi-dimensional transmission line with several lossless materials whose relative permittivity varied between 2,1 and 9,2. The method of the S-parameters is exploited to find the effective permittivity; in addition, this parameter is defined as an output in our model. The results obtained show that our model is efficient to translate this electromagnetic behaviour by comparing with the CMT technique, and which show the criterion of the chosen method.
A Multi-Objective Optimization Design Method for Control Parameters of Single-Phase PWM Rectifier

The double closed-loop control of single-phase PWM rectifier can achieve high power factor and stable output DC voltage, which has been widely used in industry. In the stage of designing controller parameters, the traditional approach based on small signal model cannot quantitatively evaluate and optimize the effect of control, resulting in deviating of design parameters from the actual used. Accordingly, this paper proposes a multi-objective design method that depends on discrete time domain model to realize quantitative design of controller parameters. Firstly, the design space is derived from the traditional small signal model, and it is positively mapped to the time domain dynamic and steady-state performance space of the controller by PLECS to evaluate the controller parameters quantitatively. On top of that, with the optimization algorithm, it is possible to find the design space that meets the requirements of multi-objective optimization. Finally, experiments are presented to verify the correctness and effectiveness of the proposed method.
Investigations on the Degradation of Different Gate Oxide Regions of SiC MOSFET Under Different Repetitive DC Bus Voltage

Due to the exceptional electrothermal properties, SiC MOSFET is considered as one of the most potential candidates in power devices. However, the short circuit reliability of SiC MOSFET is the weak point. In this paper, the degradation of different gate oxide regions of 1.2kV SiC MOSFET under different repetitive short-circuit DC bus voltage is investigated. A short-circuit test system is built, which applied repetitive short-circuit stress on the SiC MOSFET. Capacitance-Voltage curves of the devices after the short-circuit tests were extracted by power device analyzer and were used for gate degradation extraction. The degradation of SiC MOSFET channel region and JFET region under the condition of DC bus voltage of 200V, 400V and 600V was studied. Under same SC energy, the degradation of the JFET region of the device becomes serious under high voltage conditions, while the degradation of the channel region is basically not affected by the DC bus voltage. Finally, the electrothermal stress of different gate oxide regions during the short circuit process is analyzed by Sentaurus TCAD simulation, which is helpful to analyze its degradation mechanism.
Robust Adaptive Finite-time Trajectory Tracking Control System for Underactuated Surface Vessels with Event-Triggered Input

This paper designs a finite-time trajectory tracking control system with event-triggered input for underactuated surface vessels(USVs) which are influenced by dynamic uncertainties, unknown time-varying disturbances and limited communication resources. In this system, the virtual reference heading is obtained by performing norm inverse calculation on the virtual control of sway and surge, and a new kinematic error equation is constructed based on this. Then, the internal dynamic uncertainty and the external time-varying disturbance in the system are compounded linearized, and the upper bound of the compound uncertainty term are approximated by adaptive technology. Combined with the finite-time control theory, the kinematic virtual control is optimized, and the finite-time control law is obtained. Through the event-triggered technology, the control frequency of the system is limited to ensure the effective use of communication resources. Finally, the design process of the whole system is strictly analyzed, and the performance of the system is effectively verified by two sets of simulations.
Sensorless Model Predictive Control Using Artificial Neural Network for Multilevel Flying Capacitor Boost Converters

In the article, one new sensorless model predictive control method using artificial neural network (ANN-SMPC) is presented for multilevel flying capacitor boost converter (FCBC) to address the issue of over-reliance on flying capacitor (FC) voltage sensors. Firstly, the sampling data obtained from simulation environment is used to train the ANN offline, then the trained ANN is applied to the MPC controller instead of the FC voltage sensors for multilevel FCBC. The ANN structure, data selection and training method of ANN-SMPC are introduced in detail. Its feasibility is proved by simulation and test results. The FPGA-based ANN-SMPC controller can provide control performance comparable to traditional MPC while significantly reduce the FC voltage sensors.
Predicting Drug-Drug Interactions with Graph Attention Network

Predicting Drug-Drug Interactions (DDIs) is usually a time-consuming and labour-intensive task. The undetected adverse interactions between drugs are a common cause of medical injuries. Recently, with the assistance of deep learning algorithms, the accuracy of prediction in DDIs significantly improved. However, previous methods do not take well into account both adapting to datasets and extracting neighbourhood information on the graph-structured data. In this study, we propose a new framework to fill this gap, named Interaction Prediction Graph Attention Network(IPGAT). This framework consists of two modules, i.e., the embedding module and the prediction module. Inspired by the Graph Embedding and Graph Attention Networks, the embedding module extracts features from graph-structured data with high-order neighbourhoods. Then, it directly transfers to the prediction module without an intermediate process. Our proposed IPGAT presents advantages compared to existing DDI prediction methods. Experimental results on the public DrugBank dataset reveal that IPGAT significantly outperforms the state-of-the-art methods such as AMF&AMFP, Conv-LSTM, Graph Auto-Encoder, etc. The corresponding results increase 6.9% in AUROC and at least 8.5% in AUPR for the retrospective experiment. Further studies verify the efficacy of multi-layer and multi-head in the model. The codes are available at https://github.com/yytfy/IPGAT.
A New Deep Learning-Based Strategy for Launching Timely DoS Attacks in PMU-Based Cyber-Physical Power Systems

Malicious attacks in the cyber-physical power systems (CPPS) can eventually result in cascading failure and even widespread blackout, if not rectified in a timely manner. The probability of success of most of these attacks mainly depends on their timeliness, as the degree of system vulnerabilities varies from time to time by changing its operating state. In this paper, we propose a new denial of service (DoS) attack strategy where the attackers leverage learning capabilities of convolutional neural networks in the encrypted domain to forecast the optimal time of launching a DoS attack. In our simulations Internet Protocol Security (IPsec) is used to secure communication channels between phasor measurement units, phasor data concentrators, and the regional/national control center. It is illustrated that, despite providing confidential communication channels by IPsec-based security gateways, an attacker still can estimate the future operating state of the power system in advance. This gives an opportunity to the attackers for initiating an effective DoS attack. The proposed method is validated by the simulation results, which show a significant increase in the success rate of DoS attacks.