Miracle-dz/UniFAR-FTUs huggingface dataset:
A Collective Entity Linking Method Based on Graph Embedding Algorithm

Aiming at the poor effect of traditional entity linking method combining context co-occurrence and objective knowledge, this paper proposes a collective entity linking framework based on graph embedding algorithm Node2vec. The framework first uses BiLSTM and CRF to do named entity recognition over a text, aiming to obtain entities related to mention in the text. Then, the Node2vec is used on the sub-graph of the knowledge graph for all entities corresponding to the candidate entity sets and the recognized entities to obtain the vector representation between different entities. The similarity between each entity and the text within a certain range is calculated, and the degree between each pair of entity elements is obtained. The most similar pair is selected as the target entity of the link. Experimental verification shows that $F1$ in this framework reaches 85.96% on NLPCC2014 data set, and the improved Node2vec algorithm performs better than some other graph embedding algorithms in this framework.
Research on Technology of Autonomous Inspection System for UAV Based on Improved Yolov4

With the continuous expansion of the grid scale and the development of artificial intelligence, UAV inspection is gradually replacing manual inspections and has become a mainstream of transmission line inspections by its high efficiency, low cost and high safety, which has greatly improved the inspection efficiency and inspection accuracy of transmission lines. However, the existing UAV inspections mainly rely on manual methods, requiring staff to operate and perform a lot of interventions in the field. The operating specifications are not uniform, which brings a lot of difficulties to the subsequent intelligent identification. Therefore, this paper proposes an autonomous transmission line UAV inspection system based on the improved yolov4. First, a priori box is set through K-means clustering to enhance the size adaptability, and the improved yolov4 could identify the key structure of the transmission tower. Second, the system could move the PTZ to place the relevant structure in the center of the image to complete the collection of image information. The test results show that the system can collect relevant information of transmission line towers in a standardized manner, improve the accuracy of image collection, and expand the scope of application of autonomous drone inspections, and provide a new direction for subsequent intelligent inspections.
Gait Phases Blended Control for Enhancing Transparency on Lower-Limb Exoskeletons

A major challenge in the design and control of exoskeletons is the preservation of the user.s natural behavior when interacting with these machines. From this point of view, one of the most important features is the transparency of the exoskeleton. An ideally transparent exoskeleton follows the user's movements without interaction forces. This is the goal of many control algorithms proposed in the literature. Traditional algorithms are based on finite state machines and are affected by assistive torque discontinuity problems in the transitions between phases. State-of-the-art methods approach this problem by imposing a smooth transition that does not account for variable walking speed. In this work, the authors propose an innovative control algorithm for a lower-limb exoskeleton. The proposed control aims at solving the torque discontinuity problem, without requiring a smooth transitions strategy. The proposed control algorithm continuously blends the output of two independent single stance dynamic models, by weighting the contribution of each stance model to the total assistance based on the gait phase. A linear regressor is used to produce the weights, and it requires a brief user-specific calibration. Results showed a significant reduction of interaction forces, and a longer stride length when compared to two finite-state-machine-based controls at two speeds on the treadmill and one self-selected-speed in an overground walk.
GAD: A Global Attraction Dataset and Attraction Classification Based on Residual Dense Convolutional Neural Networks

Over the past years, the development of big data have received great attention, and data growth continues to increase. Deep learning technologies have shown good performance in many practical applications. In this work, we proposed a new deep learning model with low complexity to deal with global attraction classification problem. Due to limited datasets in outdoor scenes, firstly, we constructed a new large-scale global attraction dataset (GAD), containing more than 380k images for the world’s 250 famous attractions. Then we propose a new attraction classification method, where a residual dense module is integrated into a low-complexity convolutional neural network model. Our proposed method is demonstrated to show better performance than several state-of-the-art deep convolutional neural network models on our constructed GAD dataset.
Fast and Safe Trajectory Planning: Solving the Cobot Performance/Safety Trade-Off in Human-Robot Shared Environments

The rise of collaborative robotics has offered new opportunities for integrating automation into the factories, allowing robots and humans to work side-by-side. However, this close physical coexistence inevitably brings new constraints for ensuring safe human-robot cooperation. The current paramount challenge is integrating human safety constraints without compromising the robotic performance goals, which require minimization of the task execution time alongside ensuring its accomplishment. This letter proposes a novel robot trajectory planning algorithm to produce minimum-time yet safe motion plans along specified paths in shared workspaces with humans. To this end, a safety module was used to evaluate the safety of a time-optimal trajectory iteratively. A safe replanning module was developed to optimally adapt the generated trajectory online whenever the optimal plan violates dynamically provided safety limits. In order to preserve performance, a recovery trajectory planning algorithm was included such that the robot is allowed to restore higher speed motions as soon as the safety concern has been resolved. The proposed solution's effectiveness was evaluated both in simulations and real experiments with two robotic manipulators.
An Average Current Control Method in Multiphase Interleaved Bidirectional DC/DC Converter Connected on DC Microgrids

The main objective of this paper is to present analytical and practical digital implementation of average current control methods with DSCs (Digital Signal Controllers) in multiphase interleaved bidirectional DC/DC converter topologies connected on DC Microgrids. The bidirectional DC-DC converter has a significant role in DC Microgrids as a charge and discharges controller interfacing the ESS(Energy Storage System) both of islanding and grid-tied mode. In this work, the average current control algorithms code, embedded and developed by low-cost 16-bit DSCs. A Bilinear Transformation is needed in this form to generate a discrete model. The average current control method with a closed loop proportional-integral algorithm gives results in a more stable control from noise, does not require slope compensation, and does not cause an error in the peak current value to the average current value. The use of multiphase interleaved topologies can minimize and reduce a current ripple. By controlling the current on the DC-DC Converter, will impact the power flow during the charge-discharge process and improve the efficiency of the DC Microgrids system. The analysis, modeling, simulation, hardware implementation, and laboratory verification show that this strategy can control the current and provide better performance.
A simulation calculation method for suppressing the magnetizing inrush current in the setting of the overcurrent protection of the connecting transformer in the hydropower station

In order to improve the reliability of power supply in adjacent hydropower stations, the auxiliary power systems of the two stations are connected through a contact transformer. The magnetizing inrush current generated by the connecting transformer of a hydropower station has the characteristics of high frequency, strong energy, and multi-coupling. The harm caused by the connecting transformer is huge. In order to prevent misoperation during the closing process of the connecting transformer, this article aims at the problem of setting the switching current of the connecting transformer of the two hydropower stations, and establishes the analysis model of the excitation inrush current with SimPowerSystem software, and carries out the quantitative simulation calculation of the excitation inrush current of the connecting transformer. A setting strategy for overcurrent protection of tie transformers to suppress the excitation inrush current is proposed. Under the conditions of changing switch closing time, generator load, auxiliary transformer load, tie transformer core remanence, the maximum amplitude of the excitation inrush current is comprehensively judged Value, and then achieve the suppression of the excitation inrush current, and accurately determine the protection setting of the switch.
Stochastic Event-Triggered Distributed Fusion Estimation Under Jamming Attacks

The paper concentrates on the distributed fusion estimation issue of a bandwidth-constrained multi-sensor nonlinear networked system suffering from jamming attacks. For each communication channel, a stochastic event-triggered transmission scheme is developed to reduce excessive communication between smart sensors and local estimators, and a Stackelberg game framework is established to analyze interactions between the smart jammer and smart sensors. Utilizing a sequential fast covariance intersection fusion rule, a distributed fusion estimation algorithm is designed by fusing local estimations from event-triggered unscented Kalman filter-based local estimators. Then convergence conditions are derived by analyzing behaviors of the fusion estimation error covariance, and the boundedness of communication rate for each communication channel is further discussed. Finally, a comparative simulation is given to testify the validity of the proposed fusion technique.
Research on small target detection algorithm based on improved yolov3

As a popular target detection algorithm, yolov3 has high detection speed and detection accuracy. However, compared with other target detection algorithms, yolov3 has poor performance in small target recognition, with higher missed detection rates and higher false detection rates. In response to the above problems, this paper draws on the idea of adaptive spatial feature fusion (ASFF), and makes full use of the semantic information of high-level features and the fine-grained, edge, and texture features of low-level features to fuse high-level feature maps and shallow feature maps. Secondly, the K-means clustering algorithm is used to generate a more suitable anchor frame, which is conducive to further improving the recognition accuracy of the model, and finally the network structure is further improved. Through training and recognition in the NWPU vhr-10 dataset, the mAP of the improved yolov3 algorithm is 79.89%, and the mAP of the unimproved yolov3 algorithm is 78.26%. After the improvement, the mAP of 1.63% is increased, and the improvement can solve the problem of low target recognition rate of yolov3 to a certain extent.
Extended User Preference Based Weighted Page Ranking Algorithm

Due to the massive amount of data and information available in web pages, users use search engines to find the desired information from the World Wide Web. Search engines encounter a very challenging task to retrieve relevant information efficiently. When a user inputs the search parameters and keywords into the search engine, there are many web pages that match the user’s query. To overcome this challenge, the search engine utilizes ranking algorithm to rank the results. One of the most popular ranking algorithms is the Page Rank algorithm which we consider in this study. One of the challenges related to Page Rank algorithm is that many web pages resulted from the algorithm may not meet the intended search query. So, studies are required to develop and enhance such sort of algorithms including the Page Rank algorithm. This study presents the traditional Page Rank algorithm which focuses on the number of links, Hyperlink-Induced Topic Search algorithms which emphasizes hubs and authorities, and Weighted Page Rank algorithm which weighs in-links and out-links to rank pages. Additionally, it reviews the latest studies that improve page rank algorithms based on user behavior and interest. This study proposes an algorithm called Extended User Preference Based Weighted Page Ranking Algorithm (EUPWPR), which enhances the latest studies of page rank algorithms. Moreover, this study considers more parameters to compute the page rank and the results showed that considering more parameters makes the page rank values more precise.
Accurate 2D Facial Depth Models Derived from a 3D Synthetic Dataset

As Consumer Technologies (CT) seeks to engage and interact more closely with the end-user it becomes important to observe and analyze a user’s interaction with CT devices and associated services. One of the most useful modes for monitoring a user is to analyze a real-time video stream of their face. Facial expressions, movements and biometrics all provide important information, but obtaining a calibrated input with 3D accuracy from a single camera requires accurate knowledge of the facial depth and distance of different features from the camera. In this paper, a method is proposed to generate synthetic high-accuracy human facial depth from synthetic 3D face models. The generated synthetic human facial dataset is then used in Convolutional Neural Networks (CNN’s) for monocular depth facial estimation and the results of the experiments are presented.
AI Management Platform with Embedded Edge Cluster

The area of AI application continues to grow from resource-rich environment such as cloud to resource-constraint environment such as embedded devices. However, management of AI on the embedded device is still a challenging task due to different environment other that resource-rich devices. This paper proposes an AI management platform for a cluster which consists of edge devices and cloud resources. The proposed system provides efficient methods to identify different hardware acceleration resources and resource management for a specific AI application. The implementation of the proposed platform on a cluster which consists of cloud VMs and embedded devices are described presented.
Analytical Hierarchy Process-Based Decision Making for Evaluating the Use of Pyrolytic Carbon Black as an Alternative Source of Heating in Refugee Camps in Jordan

Nearly 4 billion tons of waste tires are generated throughout the world. The accumulated tires are a global concern being undegradable therefore, hazardous to the environment. A recent breakthrough in recycling technology based on pyrolysis process decomposes tire’s rubber into high grade energy source which is pyrolytic oil and carbon black as a byproduct that commonly utilized as a filler in polymer industry and rarely as a solid fuel source. This work introduces the potential of utilizing carbon black from pyrolysis (CBP) as an alternative solution for heating and targeting refugee camps in Jordan.Analysis of different alternatives of heating sources currently used in refugees’ camps in Jordan is performed in comparison to energy alternative sources. A multi criteria decision making methodology has been used to choose the best alternative between four heating energy sources: LPG, Solar, CBP, and diesel. Four main criteria and two sub-criteria were studied by employing the Analytical Hierarchy process AHP methodology tool to prioritize each main and sub-criteria with respect to the alternatives to highlight the significant features for the decision makers when choosing heating sources in refugee camps in Jordan.The alternatives of this research were analyzed and prioritized, the results demonstrated that LPG is the most suitable source of heating to be used in refugee camps with a rank of 0.3921; followed by the new alternative CBP ranking 0.3049, solar and fossil fuel diesel ranked third and fourth with values 0.1946 and 0.1084, respectively. Results showed that the economic aspects were the most significant main criteria with respect to the target, while future availability and pollution were the most significant sub-criteria.
Hybrid Energy Storage for Underground Mining EV

One of the main concerns when it comes to contributions to the carbon footprint is the consumption of fuel by vehicles, more specifically that of mining vehicles. One solution to this problem is replacing the combustion engine vehicles with electric vehicles. This also poses the problem of low power density. This paper proposes a solution of using supercapacitors and hybrid capacitors with the aim of investigating how they can affect the performance of electric vehicles. For this purpose, a background of fuel consumption and alternative solutions to the problem are discussed, a prototype was designed and built. It was found that when supercapacitors and hybrid capacitors were used in combination with the battery the system had a notably longer endurance.
Stability Analysis of Two-Stage OTA with Frequency Compensation

In this paper, the design implementation of a two-stage operational transconductance amplifier (OTA) using a passive frequency compensation technique is presented. It is based on the Miller and passive frequency compensation techniques. The use of these techniques achieves the high gain-bandwidth product without an increase in the overall power consumption. It produced a -21.17dB of gain margin with a phase margin of 77° at a 1.8V power supply voltage. A resistor-capacitor pair in feed-forward compensation is connected in the first stage and a Miller compensation capacitor is introduced in the second stage of the circuit. The proposed op-amp design results in 1205 MHz of gain-bandwidth product. It is observed that the presented circuit performs better as compared to existing designed circuits.
An Ultra-Low Power MOS2 Tunnel Field Effect Transistor PLL Design for IoT Applications

This work presents the implementation of analytical transport model of MoS2tunnel field effect transistor (TFET) using Verilog-A in Cadence/Spectre. The parameters of the model are extracted, and most notable is its high ION/IOFF ratio, which makes it suitable for low power design, and IoT applications. Subsequently, we employ the TFET model in the design of ultralow power phase locked loop (PLL). Various components of PLL are examined in terms of their structures and functions. The results show that the voltage controlled oscillator (VCO) operates from 0.5 GHz to 2.9 GHz with a tuning range of 82.8%. It also consumes 1.91 μW power at 2 GHz carrier and has a phase noise of -117.3 dBc/Hz at 1 MHz offset frequency. The average power consumed by phase locked loop is 0.021 mW. In addition, the operation of the PLL is not sensitive to temperature variations.
A Mura Detection Method Based on an Improved Generative Adversarial Network

Mura is defined as visual unevenness on the display panel. It can cause unpleasant feelings, so it is necessary to perform Mura inspection during the display quality test. However, Mura is quite difficult to be detected because of its irregular shape and size as well as its low contrast. To solve this practical problem, we proposed a GAN-based model named UADD-GAN to detect Mura in this work. Consisting of a proposed UADD generator and a discriminator, the model is trained using only normal samples, after which the generator is able to simulate the distribution of normal samples. During training, the generator takes normal images as inputs and outputs their reconstructions, while the discriminator receives images and determines whether they are original or reconstructed ones, defiantly helping the generator to perform reconstructions better. The symmetric structure and the skip-adding operation make it easy for the UADD generator to reconstruct the normal samples well. In the detecting procedure, the generator performs worse in the reconstruction of samples with Mura so that we can distinguish them from the normal ones. To make full use of discriminator, we use multiple feature layers of the discriminator for supervision instead of using only the classification layer, helping the generator to reconstruct normal samples better. Meanwhile, a two-side detecting method was used to detect Mura since all the samples are not in a square shape and it greatly improved the detecting accuracy. We have conducted experiments of Mura data sets with different proportion and our research indicates that our proposed method surpasses other state of the art methods.
Modulation Strategies for Anisotropy-Based Position Estimation of PMSMs Using the Neutral Point Voltage

Information obtained about magnetic anisotropy via neutral point voltage measurements can be used to estimate the position of synchronous and induction machines at all speeds including standstill. Due to its usually high signal-to-noise ratio, this estimation method can be an attractive alternative to approaches that acquire anisotropy information via current measurements. The position estimation method looked at in this paper relies on anisotropy information in the form of position-dependent inductance ratios. These are obtained through measurement of the voltage between the machine’s neutral point and an artificial neutral point during the application of different voltage vectors via a two-level switching inverter. We analyze the conditions placed on and the consequences of implementing necessary modifications to a standard space vector modulation. Five different modified modulation strategies are compared and investigated, including two newly proposed strategies which use only a minimal set of active and zero voltage vectors for measurement of the inductance ratios and therefore allow high utilization of available voltage and a high update rate of the estimated position. Experimental results for three low-power three-phase permanent magnet synchronous machines are presented which suggest that modulation strategies that use active measurement vectors in all three instead of only two axes of the machine are less susceptible to systematic deviations in the position estimation that presumably result from nonlinear machine properties. As part of the machine model, a normalizing inductance variation ratio is introduced, which simplifies expressions and supports the comparison of motors.
Effect of modulation index on the performance of the three phase modular multi-level converters

The multi-level modular converters are the most advanced generation in the multi-level converter family. The value of these converters derives from their ability to overcome the drawbacks of conventional multi-level converters in terms of power and voltage levels. Since the modulation index is considered the main parameter for controlling the modular converters, this paper is devoted to discuss the effect of modulation index on the performance of three-phase modular multi-level converters in terms of output voltage, current amplitude and total harmonic distortion. Moreover, the sub-capacitors voltage ripple and the circulating current amplitude have been also investigated. This research work investigates three converter models. The first model has four sub-modules in each arm, the second model has seven sub-modules in each arm, and the third model has ten sub-modules in each arm. All the details about modular multi-level converter performance is stated and validated using MATLAB – SIMULINK