Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Portable and Stationary Energy Storage Systems with Battery Charging and Swapping Coordination

As a key technology for renewable energy integration, battery storage is expected to facilitate the low-carbon transition of energy systems. The wider applications of battery storage systems call for smarter and more flexible deployment models. Here we propose a hybrid energy storage system (HESS) model that flexibly coordinates both portable energy storage systems (PESSs) and stationary energy storage systems (SESSs) in power grids. PESSs are batteries and power conversion systems loaded on vehicles that travel between grid nodes with locational marginal price (LMP) difference to alleviate grid congestion. Meanwhile, they integrate renewable energy by charging and discharging at these nodes or swapping (part of) batteries with SESSs. We introduce an on-demand spatiotemporal decision-making model for HESS and analyze the optimal operation strategy and economic benefits of HESS in the case study. The results show that, compared with the total revenue and battery degradation of SESS and PESS alone, the spatiotemporal arbitrage revenue of HESS could increase by up to 6.4% with the amount of battery degradation decreased by 19.6%, in some representative operating periods.
Highly robust and reliable power amplifiers in 22FDX and 45RFSOI technologies

This paper reports the VSWR (Voltage Standing Wave Ratio) ruggedness and aging measurements of two power amplifiers designed in 22FDX and 45RFSOI technologies. The PA is one of the most critical function in a front-end module as it is operating at high power and directly impacted by mismatch load. De-rating of 20% is applied on the supply voltage to ensure the operation and durability of the power amplifiers for a 10 years lifetime. The measured and modelled degradation versus time are presented and show excellent results on both technologies.
Neutrino-mass differences studied in a model with one basic neutrino

In this paper, we propose and study a model, in which there exists only one basic neutrino. Basically, the Lagrangian of the model is obtained from the Lagrangian in the Glashow-Weinberg-Salam electroweak theory by reducing the three flavor neutrinos there to one basic neutrino. In this model, neutrino states with a fixed flavor are interpreted as certain superpositions of states of the basic neutrino and states of the related charged lepton. Neutrino mass states are associated with low-lying eigenstates of the total Hamiltonian. We derive an approximate expression for a ratio of neutrino-mass differences, which gives a value $17$ for the ratio, about half of the experimental result of $33$.
Global Temporal Attention Optimization for Human Trajectory Prediction

Predicting human trajectory is one of the key knowledge required for autonomous driving and social robots in real scenarios. Recent studies based on Transformer networks have shown a great ability to model social behaviors. As far as we know, global trajectory information has an essential influence on prediction at a certain step. However, these methods only rely on the previous trajectory states/attention but ignore the important following states/attention of the trajectory for each pedestrian, which will generally collapse on some irregular movements (e.g. acceleration, deceleration, and motionless). To solve this issue, we propose a Global Temporal Attention optimization model (GTAO), which activates the utilization of the following states/attention of the trajectory, and jointly and iteratively optimizes the preliminary trajectory prediction through a global temporal attention (GTA) module. To effectively address the decline in the generalizability and abnormal processing of the model, we further introduce global temporal guidance (GTG) module to instruct the GTA to learn the features closer to realistic trajectories. Experimental results on commonly used real-world human trajectory prediction datasets (ETH and UCY) indicate that our GTAO can achieve better performance in terms of prediction accuracy.
Vision-based Toothbrush Quality Evaluation

The toothbrush is of key importance for oral health of human beings. Therefore it is necessary to make an accu-rate evaluation of toothbrush quality, which should include its cleaning ability and durability. In this paper, a vision-based toothbrush quality evaluation system is proposed, which consists of a tartar simulant measurement module and a bristle estimation module. In the tartar simulant measurement module, the cleaning ability of a toothbrush can be evaluated by comparing the variation of tartar simulant after tooth brushing. The tooth is firstly detected with deep learning method. Secondly, the tartar simulant is extracted by the combination of both thresholding and intensity-based denoising. Thirdly, the tartar extraction result is refined by memory-based denoising. Finally, the region of tartar is precisely measured and compared during the brushing process. In the bristle estimation module, the durability of a toothbrush may be estimated by its bristle deformation after the specified amount of tooth-brushing. The tooth bristle is quickly extracted by thresholding in a low-resolution tooth-brush bristle image. The boundary of the toothbrush bristle is accurately detected in a high-resolution sub-image with the approach of maximal connected components and a hole-filling method. The toothbrush bristle deformation is estimated based on the accurate segmentation of toothbrush bristle. Experiments applied on collected practical cases demonstrate that our system achieves desired performance for toothbrush quality evaluation in practical industrial scenarios.
Fault Diagnosis and Tolerant Method of Open-Circuit Fault for Triple Active-Bridge DC-DC Converter

Triple active-bridge (TAB) DC-DC converter is widely used in bidirectional DC distributed system. The open-circuit fault (OCF) of TAB may threaten the system safety operation and should be eliminated in time. This article proposed an OCF diagnosis and tolerant method for TAB three-port dc-dc converter consisting of one DC bus and two energy storage systems. Firstly, normal mode and OCF mode of TAB is analyzed. Base on the analysis, the average value of the midpoint voltage of each bridge arm can be used as an OCF diagnosis indicator. Then the overcurrent and the DC bias of transformer winding current are eliminated by blocking the gate-driving signal of the faulty bridge arm. Finally, fault-tolerant operation is achieved by regulating the phase shift angle. If the OCF occurs on the energy storage side, the energy storage cut off. If the OCF occurs on the DC bus side, the DC bus side can still absorb power. The OCF can be diagnosed quickly and accurately according to the experimental results. The impact of OCF can be reduced, which promotes the reliability of TAB converters.
Chinese Dish Detection Based on Dish-YOLOv5

In this paper, we construct two dish datasets, DISH-A and DISH-B, and use Copy-Paste method for data augmentation so that they can be better applied in practice. Then, we propose a specialized dish detection algorithm based on classical YOLOv5, i.e., DISH-YOLOv5 model. By optimizing the model frame, this improved detection network can be used to deal with the low accuracy issue emerging in existing single-stage detection methods. Furthermore, to better optimize the feature extraction capability, Coordinate Attention module is introduced to the residual network. The experimental results show that the entire scheme has good performance in detection accuracy while with no significant decrease in detection speed.
Renewable Energy Hosting Capacity Assessment and Optimization in Power Distribution System Considering Uncertainty

Wind power and photovoltaic (PV) are two crucial forms of clean energy utilization, and the renewable energy accessing capacity in power distribution system (PDS) is increasing rapidly. Nevertheless, the exploitation and utilization of wind and solar energy resources lack scientific and reasonable guidance, resulting in a great waste of resources, and even threaten the security and stability of PDS to a great extent. To this end, a renewable energy hosting capacity (REHC) assessment and optimization approach is proposed to effectively analysis and optimize the REHC of PDS. Based on mixed integer linear programming (MILP), a REHC assessment and optimization model is constructed, where all nodes of PDS allow wind power and PV access. Besides, on account of the obstacles brought by the bilinear terms in proposed model, a Big-M based relaxation technique is developed to reduce the complexity of model and improve the solving efficiency. Additionally, the uncertainty of wind power and PV is represented by several typical scenarios that obtained by the proposed scenario generation methodology, which can further reduce the computational burden and improve the accuracy of the proposed approach. Eventually, comprehensive case study on a modified 15-bus PDS validates the efficiency and practicability of the proposed model and approach.
Shell-model calculations of two-neutrino double-{beta} decay rates of {sup 48}Ca with the GXPF1A interaction

The two-neutrino double-{beta} decay matrix elements and half-lives of {sup 48}Ca were calculated within a shell-model approach for transitions to the ground state and to the 2{sup +} first excited state of {sup 48}Ti. We use the full pf model space and the GXPF1A interaction, which was recently proposed to describe the spectroscopic properties of the nuclei in the nuclear mass region A=47-66. Our results are T{sub 1/2}(0{sup +}{yields}0{sup +})=3.3x10{sup 19} yr and T{sub 1/2}(0{sup +}{yields}2{sup +})=8.5x10{sup 23} yr. The result for the decay to the {sup 48}Ti 0{sup +} ground state is in good agreement with experiment. The half-life for the decay to the 2{sup +} state is two orders of magnitude larger than that obtained previously.
Small-Signal Stability Assessment Method for MMC-MVDC System With High Penetration PV Based on Hybrid Impedance Stability Criterion

Medium-voltage direct current (MVDC) systems are increasingly used for renewable energy sources (RESs) integration and transmission with rapid improvements in power electronic technologies. However, some instability issues may arise due to the dynamic interactions among RESs, converters, and ac grid. This paper proposes a framework of small-signal modeling and stability assessment methods for MMC-MVDC systems with high penetration photovoltaics (PVs). First, a systematic hybrid impedance modeling method for MMC-MVDC system is developed. Then, based on the established model, a hybrid impedance criterion is proposed to evaluate the stability of MMC-MVDC system. And the stability properties of MMC-MVDC are investigated via generalized Nyquist plots. Finally, the time-domain simulation results verify the effectiveness of the proposed small-signal stability assessment method based on the hybrid impedance stability criterion.
A Comparison Study of Egocentric and Allocentric Visual Feedback for Motor-Imagery Brain-Computer Interfaces

Motor imagery (MI) based brain-computer interfaces (BCIs) have been studied as applications for the improving rehabilitation and recovery, as well as augmenting existing function. MI BCI systems typically provide feedback in an egocentric rather than an allocentric reference frame. This study aims to see if presenting stimuli in an allocentric reference frame is comparable to presenting egocentric stimuli. We used dynamic visual stimuli in egocentric and allocentric reference frames to induce motor imagery in a virtual reality (VR) environment. Eight participants imagined grasping actions with their left and right hands while observing egocentric or allocentric stimuli. The allocentric and egocentric reference frame tasks had comparable inter-rater agreement and precision, indicating that allocentric visual feedback is as effective as egocentric one for MI BCI.
LVRT Control Strategy of MMC-HVDC Converter Connected to DFIG Wind Farm

Wind power is often sent out through high voltage direct current transmission system due to its volatility and discontinuity. When wind power is connected to the HVDC, the control strategy of DFIG has certain challenges in dealing with the requirements of low voltage ride through. In this paper, the MMC auxiliary control strategy is used to assist DFIG to achieve low voltage ride through, and the control strategy of wind field side MMC is designed to improve the low voltage ride through ability of the system. The simulation model of DFIG connected to MMC is built in simulation platform to verify the feasibility and effectiveness of the control strategy under different voltage drops.
Weighting Sliding Tiles For Writer Identification in Handwritten Musical Scores

In this paper, we propose an approach along with an extended ablation study to address the writer identification task in handwritten scores. To benefit from machine learning methods and train them when working with these types of images, traditional approaches tend to apply some standard transformations, such as reshaping the image or randomly choosing a small image patch. This process can seriously degrade the information received by the model and therefore, it risks learning non-discriminatory information. To address these problems, we propose a sliding tile-based approach for the task of writer identification in music scores in two stages. The first stage benefits from symbol detection for the sole purpose of identifying optimal regions containing musical information. And a second stage uses this music content information to compute the final classification at the full-page. We present an ablation study together with a new database containing musical scores extracted from the music department of the National Library of France - BnF named REMDM Autographs. We present the results of our explorations for two databases, the new corpus, and the well-known public CVC-MUSCIMA database. When comparing the performance of the tile approach versus the full-page approach, we see an undeniable performance improvement of more than 45% in both databases.
Effect of activation temperature on the performance of Chitosan-based activated carbon for super-capacitor application

In the current study, the performance characteristics of symmetric super-capacitor (SC) utilizing activated carbon electrodes have been analyzed in an aqueous 1M potassium hydroxide (KOH) solution implemented as the electrolyte. Activated carbon (AC) is derived from Chitosan (CS), which possesses remarkable biodegradable characteristics via the chemical activation method at N2 atmosphere conditions using ZnCl2 as activating agent. To evaluate how CS performs at various activation temperatures of 500, 600, and 700°C, the experiment was conducted. The effect on activation temperature here on porous properties of prepared AC was investigated systematically, using surface area and pore size analysis. Additional morphological properties of synthesized AC were analyzed using the techniques of scanning electron microscopy (SEM) and Energy Dispersive X-Ray Analysis (EDX). Symmetric super-capacitor electrodes were fabricated, and their performance was analyzed in a two-electrode system applying standard electrochemical characterization methods using the potentiostat. Moreover, the electrochemical analysis of the CS-based materials has been studied utilizing cyclic voltammetry (CV) and cyclic charge-discharge (CCD) approaches. It was found that the super-capacitor electrode employing chitosan exhibits higher performance enhancement in the super capacitive characteristic of CS seems linked to the porous structure and pseudocapacitive mechanism of charge-storage, as compared to the super-capacitor cell using various biodegradable.
Null-space-based shared control of a mobile robot using motor imagery based brain-computer interface

The paper presents a shared control architecture for non-holonomic mobile robots commanded through a motor imagery based Brain-Computer Interface (BCI). The overall system is aimed at assisting people to teleoperate a mobile robot in a simulated house-like scenario by resorting to two motor imagery commands. The developed architecture is structured in such a way that the user can drive the mobile robot while safety tasks, e.g. obstacle avoidance, are autonomously achieved, leaving complete autonomy to the mobile robot to let the latter adjust its configuration, e.g. aligning itself with a narrow passage. The overall architecture has been realized by developing control modules with the ROS environment, while the OpenVibe framework has been adopted to process the EEG signals. The effectiveness of the proposed architecture has been validated through experiments where a healthy user, wearing a Unicorn g.tec BCI, performs an assisted teleoperation task through motor imagery sessions with a Turtlebot robot.
Constructing a hybird model for the evaluation of the service quality of O2O platforms

The spread of COVID-19 has led many people to turn to O2O platforms to buy daily supplies leading to a boom in the O2Oe-commerce industry. How to improve the service quality of O2O platforms to attract more customers has become an important concern for service providers. This study differs from previous statistical analysis studies in that it applies the data mining methodology to extract the key factors that affect the service quality of O2O e-commerce platforms. A hybrid multi-criteria decision-making method is then utilized to obtain the influence relationships and weights of the dimensions and criteria. The results suggest that privacy security, and reliability have a positive impact on social interaction, recommendation quality, efficiency and empathy. Empathy, social interaction and recommendation quality are the three most important factors for evaluating the service quality of O2Oe-commerce platforms. Finally, the theoretical implications and management implications based on the findings are discussed.
Coordination and Optimization Scheduling Strategy for Distribution Network Based on Composite Demand Side Response

How to improve the level of renewable energy consumption is the bottleneck of the vigorously developing of renewable energy. Distribution network has a certain amount of flexible load resources, how to effectively use these resources to fully absorb renewable energy and reduce the operation cost of power network is one of the research hotspots. The traditional time-of-use pricing policy is a rough demand side management mode for large regional distribution network, which has poor adaptability to regional load and power characteristics. In this paper, a control strategy of composite demand side response is proposed to further improve the regional photovoltaic consumption level. Firstly, the incentive demand response (IDR) is used to re-divide time-of-use price peak and valley periods based on user load curve and photovoltaic (PV) output curve. Then, based on the dissatisfaction function, the demand-side response model of reducible load and time-shifting load is modeled, and the adjustment range calculation method of user load is derived. Finally, based on the above results, centralized scheduling control is carried out by the dispatching center, and an optimal scheduling model is established to minimize the PV discarded and system operating cost. At last, the validity of the method is illustrated by examples.
An Extended Q-Routing-based Event-driven Dynamic Reconfiguration of Networked Microgrids

Large-scale integration of distributed energy resources (DER) in the power distribution grid leads to the possible operation of multiple microgrids (MG). DER management system (DERMS) is responsible for the optimal network configuration, dispatch of DERs, and providing appropriate set-points for the DERs. The network of multiple MGs needs to be reconfigured dynamically when extreme events occur in the distribution grid. Reinforcement learning methods are used in various optimal energy management, and dispatch of DERs. Q-routing is a reinforcement learning-based method to discover the optimal path between source and destination nodes. Since in networked MGs, each MG can have multiple boundaries, the Q-routing algorithm is enhanced with a reward mechanism depending on the events in the distribution grid. In this paper, extended Q-routing-based dynamic reconfiguration of a networked MG is proposed including the event-driven TCP/IP communication. Real-time results validate the effectiveness of the proposed framework for various reconfiguration test cases.
Prediction of lymph node metastasis in CT based on multi-scale attention fully convolutional network

With the application of artificial intelligence technology in the medical field, the use of deep learning methods to predict lymph node (LN) metastasis from Computed Tomography (CT) images has become one of the important studies in adjuvant cancer treatment. Although many studies have made some progress, the large difference in LNs size has always limited the performance of traditional convolutional neural network methods. In this paper, we propose a multi-scale attention fully convolutional network for LN metastasis prediction in CT images. We aim to make the network compatible with LNs of different sizes to improve the overall performance of the network. First, the network extracts image features from multiscale input data. Then, we use an attention-based fusion module to study the relationship between features at different scales and adaptively fuse image features. Furthermore, a feature consistency loss is proposed by us to enhance the similarity between image features at different scales. The experimental results show that our proposed network achieves the best performance with the accuracy 92.37%, the sensitivity 90.21% and the specificity 95.71%, which outperforms several state-of the-art methods.
Multi-objective Optimal Power Scheduling of A Residential Microgrid Considering V2G and Demand Response Techniques

Electrical load reshaping plays an important role in optimal power scheduling and emission reduction. Vehicle-to-grid (V2G) strategies offer an appropriate control option for flatting load duration curves despite the high burden of electric vehicles on the utility grid. Charging EV s from renewable sources is the only solution for reducing electric grid's emissions, so establishing new microgrids which depend mainly on renewable energy is highly encouraged these days. The novelty of this paper demonstrated in studying the emission reduction and multi-objective optimal power scheduling of the residential microgrids considering EVs' charging/discharging management and demand response programs. Two case studies are discussed in this research; the first one is the system without EV integration, and the second case study represents the integration of controlled EV s. The obtained results show a significant reduction in the system cost by integrating EV s to the system.
Improving Time Series Generation of GANs through Soft Dynamic Time Warping Loss

With the rising popularity of Generative Adversarial Networks (GANs) in generating synthetic data, time series are no exception to this trend. In this work, we propose two novel loss functions sDTW-p and sDTW-m based on SoftDynamic Time Warping that can be used to improve the generated time series without modifications to the existing architecture. We also present the first evaluation of the generated samples across different sequence length. Lastly, we show empirically that the result of leveraging our loss function can lead to a 9% improvement according to our metric.