Miracle-dz/UniFAR-FTUs huggingface dataset:
Sliding-Window-Based Real-Time Model Order Reduction for Stability Prediction in Smart Grid

In this paper, a new real-time model order reduction technique for stability prediction in the smart grid is proposed. The proposed method uses an online proper orthogonal decomposition algorithm. A snapshot matrix on a sliding sampling window is used for extracting the main components of the system states by performing a randomized singular value decomposition. After reducing the order of the system, a local linear model is estimated for this snapshot matrix. Then, the state of the system is predicted in a sliding prediction window. Finally, a suitable stability index is calculated and the stability of the system is forecasted in this prediction window. The proposed method is capable of predicting the transient stability, unstable/critical machines and the stability limit. In addition, it can be used for the first swing and multiswing instability detection. The simulations on three test systems show that the proposed technique can predict system stability with the high precision in real time. The computational burden and the length of prediction horizon is suitable for practical applications and the proposed algorithm has significant advantages in case of large-scale power systems.
An Improved H5 Topology With Low Common-Mode Current for Transformerless PV Grid-Connected Inverter

Transformerless photovoltaic (PV) grid-connected inverters have become more and more popular in distributed PV power generation systems due to the advantages on high efficiency, low cost, and small size. However, common-mode (CM) current in the transformerless PV inverters can result in serious electromagnetic interference and insecurity, which will further reduce the reliability of the PV inverter systems. In this paper, an improved H5 topology, namely H5-D topology, is proposed, in which a clamping diode is added on the basis of H5 topology to eliminate the CM voltage fluctuation in H5 topology. Further, the PSIM simulation results of the H5-D topology and H5 topology are given and compared, especially, the performance of H5-D topology for CM current suppression is presented and analyzed concretely. Finally, the experimental prototypes of the H5-D topology and H5 topology are built and tested, and the experimental results validate the advantages of the H5-D topology. The proposed H5-D topology provides a new practical topology for distributed PV grid-connected power generation systems.
Distributed Energy Storage System Planning in the Distribution Network Considering the Reactive Power Capability and Network Reconfiguration

This paper presented a mixed-integer second order conic programming (SOCP) model for the distributed energy storage system (DESS) planning in the distribution network. Aimed at minimizing the present value of total investment of DESSs and operation costs of distribution network with high DG penetration, active and reactive power regulation and reconfiguration of the distribution network in different periods were investigated simultaneously. The proposed model used second order conic relaxation to enlarge the feasible region and make the optimal problem convex. Effectiveness of the model was tested on the Wulong distribution network, which is a practical network with high hydroelectric penetration. Result of DESS planning in the distribution network was compared with the simulation of distribution network operation without DESSs. Besides, the effectiveness of SOC relaxation was verified.
Recognition of Chinese Text in Historical Documents with Page-Level Annotations

In historical documents, Chinese characters can be categorized into more than 8000 categories and are hard to recognize by directly applying classic methods. Furthermore, the lack of well-labelled data makes it difficult to recognize them using deep learning based methods. In this paper, we introduce a historical Chinese text recognizer that is trained by data labelled in page-level without the alignment of each text line. We propose Adaptive Gradient Gate AGG) to reduce the influence of misalignments between text line images and labels. With the help of the AGG, the error rate of the proposed text recognizer can be reduced by over 35%. Furthermore, we find that the implicit language model induced by a text recognition network with Convolutional Neural Networks(CNNs) and Connectionist Temporal Classification (CTC) as one important factor to achieve high recognition performance. Lastly, we compare the proposed recognizer with the three leading optical character recognition systems on the market. Our comparison reveals that the proposed recognizer outperforms other character recognition systems significantly.
Combining Fingerprints and their Radon Transform as Input to Deep Learning for a Fingerprint Classification Task

Fingerprint classification is a successful technique to reduce the search space of a large fingerprint database in identification systems. It is based on determining the class of a query fingerprint. In this work, we investigate the use of Conic Radon Transform (CRT) as a feature extractor and a deep learning technique in order to solve fingerprint classification tasks. The proposed approach is based on the CRT. The latter represents an extension of classical Radon Transform (RT) to integrate an image function f(x, y) over conic sections. The Radon technique enables the extraction of fingerprint's global characteristics which are invariant to geometrical transformations such as translation and rotation. We perform CRT on a source image, then we combine the Radon result with an original image and use them as an input for convolutional neural networks (CNN). In our experiments, our approach has yielded better accuracy than state-of-the-art methods based on explicit feature extraction, even others CNN methods based on original images.
A New Construction Method for Load Signature Database of Load Identification

Non-intrusive load monitoring plays an important role in realizing energy efficiency tracking and intelligent power consumption. Aiming at the current deficiency that it is necessary for load identification to establish the load signature database in advance, this paper proposed a method that the database can be established in actual operation based on muti-Iabel classification matching. Household electric appliances are classified multiple times by analyzing their inherent running characteristics. In this paper, the mixed signals obtained from real users are decomposed and highly stable signatures are used to detect the type numbers of household appliances. The characteristics extracted from the waveform of the individual running load are matched with the signatures to determine the types of appliances in the mixed signal. The validity of the method is verified by the actual collected data. Enabling database established in actual operation is a dynamic signature library special for each individual family, which provides a guarantee for subsequent load identification accuracy.
Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering

The identification of shrimp organs in biology using histological images is a complex task. Shrimp histological images pose a big challenge due to their texture and similarity between classes of organs. Feature engineering and convolutional neural networks (CNN), as used for image classification, are suitable methods to assist biologists when performing organ detection. This work evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bag-of-Words (PBOW) models for image classification using big data techniques and transfer learning for the same classification task by using a pre-trained CNN. A comparative analysis of these two different techniques is performed, highlighting the characteristics of both approaches on the problem of identification of shrimp organs.
Stochastic Transmission Expansion Planning Considering Uncertain Dynamic Thermal Rating of Overhead Lines

Dynamic thermal rating (DTR) is an important smart grid technology that can bring considerable economic benefits. One of the most important benefits of DTR is to postpone new investment. This paper proposes a novel stochastic transmission expansion planning (STEP) model considering the DTR of overhead lines. The objective function of the STEP model includes operational costs and the investment costs of new line construction and DTR systems installation. The model can determine where to build new lines and install DTR systems. The model cannot only realize the benefits that occur when the DTR is higher than the static thermal rating (STR) but also avoid overload risk, i.e., the power flow on a line being larger than the line's real capacity, caused by the DTR being lower than the STR. The model can consider both the voltage magnitude and phase angle of each bus. The model is linearized and therefore can be effectively solved by a Benders decomposition method. Furthermore, a new way of scenario reduction is proposed to obtain a better set of reduced scenarios. The effectiveness of the model is verified on a modified IEEE reliability test system and a modified IEEE 300-bus system.
An Improved Droop Controller for Three-phase Multi-junction Inverters in the Micro-grid

Three-phase multi-function inverters (TPMFIs) are able to generate active power and improve the power qualities of micro-grid simultaneously. However, to cope with the frequently switching of loads in micro-grid, the self-adaptive control of the output power of the TPMFI is also required. Therefore, an improved droop controller is proposed in this paper. The proposed controller concludes a modified allocation strategy of output power for TPMFIs in micro-grid, and a switching controller for power quality detection function. By adopting the proposed droop controller, the TPMFIs in the micro-grid could compensate the power quality problems in real time and self-control the power output at the same time. Verified by Matlab/Simulink simulations, the proposed droop controller for TPMFIs could guarantee the stability of microgrid and enhance the power quality of micro-grid at the same time.
Matching Table Structures of Historical Register Books using Association Graphs

In this paper we present a template-based table structure matching using association graphs for handwritten/printed historical documents. The recognition of the table structure consisting of column and header information is the prerequisite for the subsequent row detection and handwritten text recognition used for information extraction. The table matching is done by detecting the maximum clique in an association graph, which represents the matching of the line information of the template and a document of interest. This allows for variations of widths and heights of rows and columns. The presented methodology is evaluated on historical register books (death records) of the Archive of the Diocese of Passau. The method shows a reliable detection of the structure of handwritten/printed tables with a mean cell match of 88.28%.
An Intelligent Human Activity Recognition Method with Incremental Learning Capability for Bedridden Patients

Human activity recognition (HAR) is now valuable for bedridden patients to prevent falling, bedsore or other dangerous situation. This work proposes an intelligent broad learning system (BLS) recognition method based on the random vector functional-link neural network (RVFLNN) to identify the actions of bedridden patients. And the actions cover six types, including turning over to left, turning over to right, sitting up, lying down, stretching out for something and exiting from the bed. With the data collected from four pressure sensors that installed at four corners of an intelligent nursing bed, first, some pivotal preprocessing such as median filtering and down sampling are adopted to make a good performance. Then sparse auto encoder (SAE) is adopted for feature extraction. Finally, the RVFLNN is used for classification. Besides, for both new samples and new categories, the proposed method offers an incremental learning ability that can easily update the model with no need of model retaining. Compared with the convolutional neural network (CNN), the proposed method has superiority in training time while the accuracy is guaranteed.
3D Coronary Blood Flow Imaging: A Comparison of Automatic Adaptive Clutter Filters

Blood flow in the coronary arteries may be detected using transthoracic echo Doppler. However, measurements are challenging due to strong tissue signal overlapping with the blood signal. In this work, high frame rate imaging is proposed in conjunction with eigenvector based adaptive clutter filtering in order to improve coronary flow detection. Plane wave transmissions were used for 3D recordings of the left circumflex and posterior descending artery of healthy volunteers over one cardiac cycle. Two automatic eigen-based filters, similarity matrix diagonal projection (SMDP) and whitening filter were compared to a standard high pass Butterworth filter. 2D power Doppler projections at different time points showed that SMDP is the preferred candidate since it removes flashing artifacts without resulting in dropouts. For future work, SMDP will be further investigated in order to be able to select the optimal eigenvectors even when blood and tissue region are not clearly separated due to spatial overlap between blood and clutter.
Clustering Multivariate Time Series Data via Multi-Nonnegative Matrix Factorization in Multi-Relational Networks

In multivariate time series clustering, the inter-similarity across distinct variates and the intra-similarity within each variate pose analytical challenges. Here, we propose a novel multivariate time series clustering method using multi-nonnegative matrix factorization (MNMF) in multi-relational networks. Specifically, a set of multivariate time series is transformed from the time–space domain into a multi-relational network in the topological domain. Then, the multi-relational network is factorized to identify time series clusters. The transformation from the time–space domain to the topological domain benefits from the ability of networks to characterize both the local and global relationships between the nodes, and MNMF incorporates inter-similarity across distinct variates into clustering. Furthermore, to trace the evolutionary trends of clusters, time series is transformed into a dynamic multi-relational network, thereby extending MNMF to dynamic MNMF. Extensive experiments illustrate the superiority of our approach compared with the current state-of-the-art algorithms.
Comparison of Hexagonal and Square Coils for Use in Wireless Charging of Electric Vehicle Battery

The interest in electric vehicles is increasing day by day due to the reduction of fossil fuel resources and the environmental pollution caused by the use of these resources. Increased attention to electrical vehicles has also led the way to search for more practical and reliable methods to use them. Wireless power transfer (WPT) is also receiving attention for several applications and it is viewed as the choice of battery charging systems for near future. In this study, the coupling coefficients of different coil structures with equal winding area are compared. Taking into consideration the obtained results, a 2.2 kW WPT system to charge has been designed with square and hexagonal coil and its Finite Element Analysis (FEA) simulation has been carried out by using Maxwell Ansys. Simulation results show, hexagonal coil shape is less sensitive to misalignment conditions and is a good candidate for EV applications.
Development of Application for Forest Insect Classification using CNN

Insect identification has the disadvantage that it is difficult for non-experts to carry out due to the specificity of insects. Therefore, it is necessary for the general user to use auxiliary tools such as books to identify insects for education such as ecological learning. In recent years, researches using Deep Learning in fields such as object detection, behavior recognition, voice recognition as well as cancer detection in medical field have been actively conducted and show excellent results. In this paper, we developed a classification application that can be used in mobile phones with high automation and portability to solve the above insect classification problems. Experiments were conducted on 30 insect species selected for observable insects irrespective of environmental factors such as habitat and season, and the transform learning were applied to ResNet, which showed excellent performance in ILSVRC to classify forest insect. Our system achieved an average insect classification accuracy of 94%, an insect classification speed of 0.03 sec, and an insect photo transmission of 0.5 sec to output this information. This has proven that non-experts provide the appropriate performance to use.
Comparative Performance Analysis of IIR and FIR Filters for 5G Networks

This paper comprehensively quantifies and compares Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) bandpass filters that can optimize bandwidth allocation by eliminating noise, side lobes and Intersymbol Interference (ISI) in Orthogonal Frequency Division Multiplexing (OFDM) systems. Our goal is to choose a feasible filter that can accommodate more carriers in a bandwidth, so that the spectral efficiency can be increased. For IIR filtering, we employ, namely, Butterworth, Chebyshev and Elliptic, while for FIR Equiripple, Bohman and Hamming are studied. We evaluate these filters in terms of magnitude response and group delay, and also identify the minimum filter order that characterized nearly to an ideal filter response. The results show that the IIR filter has steep transition region when compared to the FIR filter with the same order, but the trade-off is high group delay. Due to the fastest roll-off, small transition region and low cost, we found that Elliptic filter is a good candidate to be implemented in the filtered-OFDM (f-OFDM) 5G system.
SDN-based Multimedia Content Delivery in 5G MmWave Hybrid Satellite-Terrestrial Networks

The outcomes in terms of very high data rates, low latencies, almost full-time availability and wide coverages promised by the fifth generation (5G) networking technology should cope with the increasing customers demand for high quality multimedia content and services. The attempt to fulfill 5G pledges has seen the move to explore other groundbreaking technologies. These will include, among the others, the exploitation of millimeterwave (mmWave) bandwidths in both satellite and terrestrial communications, the novel paradigm of Software Defined Networking (SDN), the use of smart satellite gateways, etc. This paper aims at proposing an SDN-based seamless integration of ultra-broadband short-range wireless network segments, namely information showers with fixed fiber-based terrestrial network infrastructures for internetworking and mmWave geostationary satellite links in order to provide multi-gigabit multimedia content delivery to nomadic users. The impact of different parameters (including tropospheric conditions, latency and technical faults) on network performance have been fully appreciated by means of realistic emulations in Mininet environment. Preliminary results evidenced the clear advantages in terms of increased throughput and reduced latencies of the proposed SDN-based approach.
Enhance Neighbor Reversibility in Subspace Learning for Image Retrieval

Two images that describe similar content usually have the neighbor-reversibility (NR) correlation, i.e., each image is among the neighbors of the other one. This phenomenon can be frequently observed in image retrieval. Some previous works have successfully utilized the NR correlation to improve search accuracy. In these methods, the retrieved images that have the NR correlation with the query image will rank ahead in the returned list. In this manner, the chance of finding the positive result in the top position will be increased. However, these methods will suffer from high computational cost when they deal with massive images. During the training phase, these methods need to calculate a weighting factor for each database image. When a database grows to be huge, this calculation will be disastrous. In order to overcome this shortcoming, we want to design a new method that can enhance the NR correlation without utilizing the whole database images for training. Our idea is to enhance the NR correlation within subspace learning. In the proposed method, we utilize a small number of training images to learn a low-dimensional subspace, where the NR correlation is best preserved. Then, images are transformed from their global features into the low-dimensional representation with the learned mapping function. In this manner, the efficiency of training will be improved. Experiment on four public retrieval datasets demonstrates that our method compares favorably with the subspace learning baseline methods for image retrieval.
RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles

We present here a framework to integrate into a motion planning method the interaction zones of a moving robot with its future surroundings, the reachable interaction sets. It can handle highly dynamic scenarios when combined with path planning methods optimized for quasi-static environments. As a demonstrator, it is integrated here with an artificial potential field reactive method and with a Bézler curve path planning. Experimental evaluations show that this approach significantly improves dynamic path planning methods, especially when the speeds of the obstacles are higher than the one of the robot. The presented approach is used together with a global planning approach in order to handle complex static environments in presence of fast-moving obstacles. When the ego vehicle is not holonomic the presented approach is able to take dynamic constraints into account, which improve the prediction accuracy.
QoE-driven and Network-assisted Naked-eye 3D Adaptive Video Streaming over Wireless Network

Fluctuations in wireless channel conditions have a negative impact on naked-eye three-dimensional (3D) video streaming. While offering free stereo perception, it poses a challenge to wireless networks for the large volumes of video traffic. To obtain better viewing experiences, a trade-off between stereo perception improvement and freeze avoidance is necessary. Though adaptive streaming seems to work well, traditional bandwidth estimation methods are at risk of overestimation which may lead to stall or underestimation which may cause resource waste. To improve the quality of experience (QoE), a novel scheme named QoE-driven and Network-assisted Naked-eye 3D Adaptive Video Streaming over Wireless Network (QNANA) is proposed to transmit naked-eye 3D video streaming over wireless networks adaptively with network assistance. In view of the constraint of bandwidth capacity, the scheme can select the video of the highest QoE under the current bandwidth conditions, so as to achieve the highest QoE overall. Besides, the clients can follow the fluctuating channel conditions timely and accurately. Results show that the scheme can realize the lowest prediction error and the best resource utility with up to 54% reduction in total stall durations compared to traditional bandwidth estimation methods.