Miracle-dz/UniFAR-FTUs huggingface dataset:
Leader–Follower Interactive Potential for Target Enclosing of Perception-Limited UAV Groups

Target enclosing is representative of the multirobot intelligence and coordination as it is pervasively involved in many practical scenarios. However, the limited perceptions of an individual robot bring in many challenges for the enclosing control such as the requirements to connectivity preservation and improvement of robustness. Motivated by this topic, a general control law based on an enclosing potential combined with a consensus-based target observer is presented for the target enclosing of unmanned aerial vehicles with limited perceptual abilities. Asymptotic stability can be guaranteed under some assumptions of target's property and network connectivity, whereas the enclosing control law may lose efficacy when capturing an agile target. Then, a novel group of leader–follower interactive potentials combined with a modified observer are derived to promote the robustness by strengthening the interactions between informed and uninformed nodes in a bicyclic spatial layout. Comparative results show the advantages of the proposed algorithms over state-of-the-art approaches on the strong stabilities of enclosing layouts with limited sensing.
Topology Identification Method of Station Area Based on Step by Step Analysis of Electric Quantity and Voltage

Low voltage station has been facing the problem of lack of topology for a long time. In recent years, the appearance of distributed generation and the operation of its regulation urgently need a clear substation topology. A step-by-step topology identification method based on cosine similarity of electric quantity and voltage is proposed in this paper. Firstly, according to the similarity of electric energy, the feeder subordinate nodes are determined, and the Apriori algorithm is used to generate the set of feeder subordinate nodes that meet the constraints. At the feeder level, the voltage curve is changed into voltage duration curve and binarized to reduce the impact of sampling time error on the model and the amount of calculation. The correlation analysis of voltage duration curve is done to generate node connection relationship. Furthermore, the influence of the amount of sampling data on the accuracy of the method is analyzed. Finally, the effectiveness of the proposed algorithm is verified by comparing the results of an actual station area in Yizheng.
Distinguishing Relational Pattern Languages With a Small Number of Short Strings

This paper studies the equivalence problem for relational pattern languages, where a relation im-poses dependencies between the two strings with which two variables in a pattern can be replaced simultaneously. Our focus is on the question whether the non-equivalence of two relational patterns is witnessed by short strings, namely those generated by replacing variables in the patterns by strings of length bounded by some (small) number z . After establishing a close connection between this problem and the study of the notions of teaching dimension and no-clash teaching dimension , we investigate speciﬁc classes of relational pattern languages. We show that the smallest number z that serves as a bound for testing equivalence is 2 when the relation between variable substitutions is that of equal string length, and the alphabet size it at least 3. This has interesting implications on the size and form of non-clashing teaching sets for the corresponding languages. By contrast, not even z = 3 is sufﬁcient when the constraints require two substituted strings to be the reversal of one another, for alphabets of size 2. We conclude with a negative result on erasing pattern languages.
A Emission-constrained Unit Commitment Model Considering Randomness of Wind Power and Electric Vehicle

Considering the randomness of wind power generation and electric vehicles, as well as the emission of thermal units, an emission-constrained unit commitment model considering the cooperative dispatch of wind power and electric vehicle is established. The model minimizes the total cost and carbon emission of the system. While meeting the operational constraints of electric vehicles, the operational constraints of wind power units, the operational constraints of conventional thermal units and the system operation constraints, the proposed model dispatch wind power generation and electric vehicles cooperatively. Due to the complexity and non-linearity of the stochastic unit commitment model, it is modeled as a mixed integer linear programming model and solved by using commercial solver CPLEX. The simulation results of the numerical example verify the necessity of the unit commitment model considering cooperative dispatch of wind power and electric vehicle.
A 10.1-ENOB, 6.2-fJ/conv.-step, 500-MS/s, Ringamp-Based Pipelined-SAR ADC With Background Calibration and Dynamic Reference Regulation in 16-nm CMOS

This work presents a single-channel, fully dynamic pipelined-SAR ADC with relaxed architectural tradeoffs thanks to the use of ring amplification and background calibration. It leverages a novel SAR quantizer and narrowband dither injection to achieve fast and comprehensive background calibration of DAC mismatch, interstage gain, and ring amplifier (ringamp) linearity and bias optimality. The ADC also includes an on-chip, wide-range, fully dynamic reference regulation system. Implemented in 16-nm CMOS, it consumes 3.3 mW at 500 MS/s (including regulation) and achieves 10.1 ENOB and 75.5-dB SFDR, resulting in Schreier and Walden figure-of-merit (FoM) values of 171.1 dB and 6.2 fJ/conv. -step, respectively.
Multipath Routing Strategy of Integrated Energy Communication System based on SDN Architecture

The integrated energy system is a new energy system that coordinates a variety of energy subsystems such as electricity, cooling and heating system. While amount of renewable energy devices energy storage devices are allowed to be connected to integrated energy system. Therefore, the integrated energy system has more types of data flow and larger scale of topo. To solve the problem of large-scale real-time data communication in this system, this paper proposed a multi-path routing method based on the classification of data flows’ tasks. Besides, geographic information is considered in heuristic operator to accelerate the computation of routing. Finally, we verify the effectiveness of the proposed algorithm and model on different kinds of numerical examples.
License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)

Although the majority of existing License Plate (LP) recognition techniques have significant improvements in accuracy, they are still limited to ideal situations in which training data is correctly annotated with restricted scenarios. Moreover, images or videos are frequently used in monitoring systems that have Low Resolution (LR) quality. In this work, the problem of LP detection in digital images is addressed in the images of a naturalistic environment. Single-stage character segmentation and recognition are combined with adversarial Super-Resolution (SR) approaches to improve the quality of the LP by processing the LR images into High-Resolution (HR) images. This work proposes effective changes to the SRGAN network regarding the number of layers, an activation function, and the appropriate loss regularization using Total Variation (TV) loss. The main paper contribution can be summarized into presenting an end-to-end deep learning framework based on generative adversarial networks (GAN), which is able to generate realistic super-resolution images. Also, proposed adding a TV regularization to the loss function to help the model enhance the resolution of images. The proposed SRGAN can handle tiny $72\times 72$ images of LPs. The paper explores how SRGAN performed over different datasets from many aspects, such as visual analysis, PSNR, SSIM, and Optical Character Recognition (OCR). The experiments demonstrate that the suggested SRGAN can generate high-resolution images that improve the accuracy of the license plate recognition stage compared to other systems.
Highly Pathogenic Avian Influenza H5Nx in Poland in 2020/2021: a Descriptive Epidemiological Study of a Large-scale Epidemic

Abstract Introduction Highly pathogenic avian influenza (HPAI) outbreaks caused by the Gs/Gd lineage of H5Nx viruses occur in Poland with increased frequency. The article provides an update on the HPAI situation in the 2020/2021 season and studies the possible factors that caused the exceptionally fast spread of the virus. Material and Methods Samples from poultry and wild birds delivered for HPAI diagnosis were tested by real-time RT-PCR and a representative number of detected viruses were submitted for partial or full-genome characterisation. Information yielded by veterinary inspection was used for descriptive analysis of the epidemiological situation. Results The scale of the epidemic in the 2020/2021 season was unprecedented in terms of duration (November 2020–August 2021), number of outbreaks in poultry (n = 357), wild bird events (n = 92) and total number of affected domestic birds (approximately ~14 million). The major drivers of the virus spread were the harsh winter conditions in February 2020 followed by the introduction of the virus to high-density poultry areas in March 2021. All tested viruses belonged to H5 clade 2.3. 4.4b with significant intra-clade diversity and in some cases clearly distinguished clusters. Conclusion The HPAI epidemic in 2020/2021 in Poland struck with unprecedented force. The conventional control measures may have limited effectiveness to break the transmission chain in areas with high concentrations of poultry.
Fake Data Injection Attack Detection in AMI System Using a Hybrid Method

The Advanced Metering Infrastructure of Smart Metering technology is playing an increasingly important role in the smart grid by introducing intelligent information processing capabilities. At the same time, various nodes in AMI for information collection and communication have become new entry points for attacks. Among them, False Data Injection Attack, which disrupts the normal operation of the system by destroying the integrity of the grid data, has gradually become a major attack threat to the security operation of the power system in recent years. Therefore, this paper constructs a FDIA intrusion detection model combining convolutional neural network and weighted random forest. The feature selection of the meter data management system in AMI is performed by convolutional neural network. Meanwhile, we adjust the weights of different decision trees by introducing the posterior probabilities of the decision trees in the random forest, which can solve the lack of classification accuracy due to the same weights of each decision tree in the random forest during the classification process. Then a weighted random forest is used in the output layer of the convolutional neural network to construct a classifier. The simulated experiment is conducted suffering FDIA of AMI to validate the model in the paper. Experimental results show that model in the paper is able to detect the spurious data more accurately with low detection false alarm rate, comparing with other detection models.
Non-intrusive Load Monitoring Method Based on Transfer Learning and Sequence-to-point Model

Non-intrusive load monitoring (NILM) or load disaggregation refers to the task of estimating the appliance power consumption given the aggregate power consumption readings. The sequence-to-sequence model with neural networks is widely used in the NILM, but the data processing of this method is complicated and the accuracy of load disaggregation is low. The sequence-to-point model improves the accuracy of load disaggregation by changing the data processing method and the neural network structure. The UK-DALE dataset is used to compare the load disaggregation effects of seq2seq and seq2point models. In addition, the problem of insufficient training data is solved by a model training method based on transfer learning. The effectiveness of this training method is verified by collecting a small amount of data from private dataset.
Multi-Objective Optimal Dispatch of Integrated Electricity and Heat System

With the increasing penetration of renewable energy, the dispatch of integrated electricity and heat system (IEHS) needs to fully consider the uncertain factors and a variety of optimization indicators. This paper proposes a bus-based multi-objective optimal dispatch model to realize the global optimal dispatch of IEHS considering the uncertainty of renewable energy. The proposed model is established according to the multi-objective optimization theory. Economic index and environmental index are the primary concerns in the multi-objective. The impact of uncertainty in the power supply of wind turbines and photovoltaics are analyzed by introducing chance constrained programming (CCP). We pay attention to comparing three optimization algorithms in terms of the optimal results and calculation speed. Three algorithms are analyzed in detail: (a) simplex method combined with branch-and-bound method (SCBB); (b) combination of KKT condition, the interior point method, and the BFGS method (KIPB); (c) particle swarm optimization (PSO). The results show that the proposed model is reasonable and the first algorithm obtains a more accurate result within the shortest time when it is applied to the proposed model.
A Novel LVRT Strategy for Grid-Connected Photovoltaic Inverters under Very Weak Grid

Under grid faults, the stability of the grid-connected inverter (GCI) system can be seriously threatened. Especially, under weak grid conditions, the high grid impedance will challenge the grid synchronization and the fault detection a lot. In this paper, by adopting the inverter-side current feedforward, the ideal voltage synchronization signal can be obtained and more accurate fault detection can be achieved because the voltage component across the grid impedance can be basically eliminated. Then, to tackle the challenge of grid voltage sag under very weak grid, a novel low-voltage-ride-through (LVRT) control strategy based on current feedforward is proposed. The simulation results have confirmed that the proposed LVRT strategy is effective to keep the stable operation of GCI system even when the short-circuit-ratio (SCR) is 1.3.
Access Frameworks and Application Scenarios for Hybrid VLC and RF Systems: State of the Art, Challenges, and Trends

The further demand for data traffic growth makes the conventional radio frequency (RF) wireless network face ever increasing challenges such as spectrum shortage and access congestion. Visible light communication (VLC) is envisioned as a promising candidate for wireless communication in the future, but it also has limitations like limited coverage and inconvenient uplinks. Naturally, it is a rational strategy to combine VLC and RF systems to form hybrid systems, which can take full advantage of high data rate in VLC and wide coverage in RF. In this article, the current research on hybrid VLC and RF systems is reviewed. First, the structures of hybrid access frameworks are introduced as complementary single-hop networks and heterogeneous multihop networks, while common frameworks in the current literature are summarized. Moreover, the technical characteristics and specific advantages of hybrid VLC and RF systems are discussed in different application scenarios for high data rate, high energy efficiency, and high reliability. Finally, the future trends of hybrid VLC and RF systems are looked forward to in some typical enabling technologies.
A Hybrid Model for Building-level Day Ahead Power Load Forecasting Based on Gated Recurrent Unit Networks

Accurate forecasting of day-ahead electrical load is a key to optimize the operation of the integrated energy system (IES) and a fundamental function of the cyber-physical system (CPS) of IES. Due to the characteristics of different features, data collected by CPS often have different sampling frequencies, resulting in mining data trends inadequately when making a load forecast. This paper proposes an GRU-based model to combine historical data with different sampling frequencies in forecasting day-ahead building-level load. First, day-level features are clustered by K-means. Hour-levels features are then partitioned according to clustering results and inputted separately into a gated recurrent unit (GRU) based neural network to capture relationships between external features and load fluctuations. The temporal dependencies of minute-level data are captured by the GRU network directly without any external features. Finally, the least square method is used to combine forecasting results at each hour of two models. The simulation results present that the proposed model performs better than a single model when applying to real-world data.
Construction of Low Voltage DC Smart Buildings with Generation-Storage-Distribution-Utilization

In order to achieve the goal of “carbon peak and carbon neutralization”, this project has established a set of building low-voltage DC power distribution system, including roof DC parallel grid distributed photovoltaic system, DC power loads, etc.The mode of “DC source-network-load” is adopted to realize the organic integration of DC power grid and new energy, which not only creates a safe, intelligent, reliable and efficient power consumption environment for users, but also realizes the improvement of energy efficiency.The paper can play a demonstration role in the research of low-voltage DC distribution related topics, and accumulate a lot of construction experience and actual operation data for DC smart building.
Boring Survey Based Fracture Detection (BSFD) for Fragility Fracture of the Pelvis in CT Images

Becoming a super-aging society, fragility fracture of the pelvis (FFP) with osteoporosis is a common disease. However, it is hard to find FFP in CT images because of the numerous images and tiny appearance of FFP. This paper proposes a novel method, which is called boring survey based fracture detection (BSFD), to automatically detect FFP in 3D CT images. FFP appears as a tiny crack of the bone that is hard to find by surface observation. Thus, BSFD surveys the FFP by boring from the pelvic surface. It first segments the pelvic bone region from CT images. At every surface point, it bores a small quadrangular prism whose center is the point of interest. The prism is composed of voxels with CT values and is feed to a 3D convolutional neural network which predicts a probability of fracture. The proposed method was evaluated by using 110 elderly subjects with pelvic fractures. The AUC was 0.84 for training subjects and 0.77 for evaluation subjects. In addition, 3D visualization of fracture probability superimposed on the pelvic bone surface is provided for qualitative evaluation and supporting FFP diagnosis by physicians. The boring survey approach will be effective to detect FFP because the bored volume extracts the unique characteristics of tiny fractures in CT images.
Unified-Phase-Shift Control Method of Dual Active Bridge DC-DC Converter for Data Center Application

In order to ensure the reliability of data center, the uninterrupted power supply is very important. Usually, supply power is derived from the ac system, and the isolated dc-dc converter is required for converting the suitable low voltage. In this paper, the dual-active-bridge (DAB) dc-dc converter is adopted. To improve performance of DAB converter, numerous studies have focused on some kinds of phase-shift control methods when both-side voltages are not matched. In order to integrated analysis of those phase-shift control methods, this paper proposes a unified-phase-shift (UPS) control method. On this basis, this paper gives the detail relationship and comparative analysis of those phase-shift control about some characteristic parameters. And the experimental results showed agreement with theoretical analysis.
Improved Model Predictive Control for Three-Level T-type Inverter under imbalanced Neutral Point Voltage Condition

The three-level T-type inverter 3LT2 I is widely adopted in photovoltaic (PV) systems. To improve separate maximum power point tracking (MPPT), the voltages of the top and bottom side capacitor are controlled to be unequal in PV modules. However, in terms of the deformation of space vector diagram, the current is distorted when applying the conventional model predictive control (CMPC) method. To overcome the limitation and mitigate current ripples under imbalanced neutral point (NP) voltage condition, an improved model predictive control (IMPC) based on asymmetric voltage vector diagram is proposed in this paper. Firstly, the imbalanced mechanism is analyzed in detail. Secondly, the imbalanced capacitor voltages are introduced to 3LT2 I model to generate the predictive output voltage. Finally, the current ripple is mitigated by penalizing the error produced in the cost function with IMPC. Moreover, the fast response has been obtained by the proposed method. To verify the practicability of the proposed method, the conventional and proposed method have been compared in simulation.
Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems

Many constrained, nonconvex and nonsmooth optimization problems can be tackled using the Majorization-Minimization (MM) method which alternates between constructing a surrogate function which upper bounds the objective function, and then minimizing this surrogate. For problems which minimize a ﬁnite sum of functions, a stochastic version of the MM method selects a batch of functions at random at each iteration and optimizes the accumulated surrogate. However, in many cases of interest such as variational inference for latent variable models, the surrogate functions are expressed as an expectation. In this contribution, we propose a doubly stochastic MM method based on Monte Carlo approximation of these stochastic surrogates. We establish asymptotic and non-asymptotic convergence of our scheme in a constrained, nonconvex, nonsmooth optimization setting. We apply our new framework for inference of logistic regression model with missing data and for variational inference of Bayesian variants of LeNet-5 and Resnet-18 on benchmark datasets.
Energy and Reserve Scheduling Based on Security Constrained Convex Optimal Power Flow Considering Wind Power Generation in Europe

Based on security constrained convex optimal power flow (SC-COPF), this paper proposes an energy and operational reserve scheduling approach for the power system. The proposed approach is capable of scheduling energy and operational reserve dynamically according to the power load forecast and the power generation from renewable energy. Reserve requirements to keep the power system in the acceptable security level is equivalently formulated as the linear constraints in the SC-COPF model. The reserve requirements are considered in a statistical way which is a common practice in the power system operations in Europe. For example, the practice in Switzerland of reserve scheduling is to ensure that the maximum probability of power insufficiency should be less than 0.2%. The objective is to minimize the economic cost of the energy production and the reserve provision. The convex optimal power flow (COPF) is used to model the AC power flows of the power network. The capacity constraints of transmission lines are considered in our SC-COPF model by equivalently constraining the power loss variables. The proposed approach is validated by using the modified European transmission network 1354pegase test case. The power load forecast and power generation from renewable energy data from Réseau de Transport d’EÉlectricite (RTE) in France are used for the validation. Numerical results show efficiency of our approach to dynamically schedule the energy and reserve.