Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss Breakdown of a Dual Conical Rotor Permanent Magnet Motor Using Grain Oriented Electrical Steel and Soft Magnetic Composites

This paper provides a loss breakdown of the NovaMAX® 215 frame, 1800 rpm motor at 5 kW. Mechanical losses, copper loss, and various types of electromagnetic losses are identified through experiment and simulation. The motor is able to achieve a very high efficiency, up to 96% at some operating points. A comparison with another highly efficient permanent magnet motor illustrates the benefits of the dual rotor axial flux topology, the use of grain oriented electrical steel, and the conical air gap and rotor flux focusing topology together with ferrite magnets. Additionally, a motor with a variable air gap was tested at different air gaps, and its losses are shown. At low speed and high torque, the losses are minimized with relatively small air gaps. However, at higher speeds and lower torques, the optimal air gaps are larger.
A Simple Distributed Adaptive Consensus Tracking Control of High Order Nonlinear Multi-Agent Systems

Recently, consensus tracking control of a higher order multi agent nonlinear system has become an interesting problem. In order to solve the explosion of design complexity, in this paper the Dynamic Surface Control (DSC) approach is used to present a new consensus method for an unknown multi agent high order nonlinear systems with uncertain disturbances. Compared to previous methods, the computational burden of the distributed adaptive control scheme is significantly reduced and a simpler distributed adaptive control scheme is designed. Moreover, unknown multi agent nonlinear system is estimated without using fuzzy logic systems or neural networks, thus the number of tuned parameters is significantly reduced. Finally, the global uniform boundedness of all the closed-loop signals and the convergence of the distributed consensus tracking errors to a small boundedness of the origin are proved. Simulation results verified that the proposed controller is effective and appropriate.
T-LSTM: A Long Short-Term Memory Neural Network Enhanced by Temporal Information for Traffic Flow Prediction

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transportation systems. It plays an important role in traffic information service and traffic guidance. However, complex traffic systems are highly nonlinear and stochastic, making short-term traffic flow prediction a challenging issue. Although long short-term memory (LSTM) has a good performance in traffic flow prediction, the impact of temporal features on prediction has not been exploited by existing studies. In this paper, a temporal information enhancing LSTM (T-LSTM) is proposed to predict traffic flow of a single road section. In view of the similar characteristics of traffic flow at the same time each day, the model can improve prediction accuracy by capturing the intrinsic correlation between traffic flow and temporal information. The experimental results demonstrate that our method can effectively improve the prediction performance and obtain higher accuracy compared with other state-of-the-art methods. Furthermore, we propose a novel missing data processing technique based on T-LSTM. According to the experimental results, this technique can well restore the characteristics of original data and improve the accuracy of traffic flow prediction.
Radar Cross Section Reduction of Conformal Patch Antenna Using Mantle Cloak

In this paper out-of-band radar cross section (RCS) reduction of conformal patch antenna without affecting the antenna characteristics is investigated by mantle cloaking. First, a conformal microstrip antenna with inset feed is considered on dielectric substrate with ε r = 10 grounded by a perfect electric conductor (PEC) cylinder. The working frequency and bandwidth of the patch antenna are 5 GHz and 80 MHz, respectively. Then, a patch frequency selective surface (FSS) is placed on top of the substrate layer except the regions covered by the patch and feed line of the antenna. The FSS, substrate and ground make a mantle cloak decreasing the monostatic and bistatic RCS of the antenna structure from 8.5 GHz to 10.5 GHz. The propagation characteristics of the new antenna are compared with the first one and it is shown that mantle cloaking has negligible effect on impedance bandwidth, gain and pattern of the antenna.
Transformer On-line Monitoring and Fault Diagnosis System Based on DRNN and PAS

This paper is a systematic study of artificial neural network applications for the diagnosis of power transformer incipient fault. Diagonal recurrent neural network (DRNN) is used to realize the intelligent fault diagnosis for oil-filled power transformer based on dissolved gas-in-oil analysis(DGA). In order to obtain better detection results and improve the accuracy of detection, the photoacoustic spectroscopy (PAS) technique is applied to the transformer on-line monitoring system. To overcome disadvantages of BP algorithm, a new recursive prediction error (RPE) algorithm is proposed and implemented in this paper. In addition, to demonstrate the effectiveness and veracity of the proposed on-line monitoring system, a large number of experimental studies have been carried out, and the experimental results are satisfactory.
Multi-modal Fusion Based Automatic Pain Assessment

Does he/she look painƒ It is a difficult question for computer to reply. Accurate pain assessment is important for proper treatment. It’s such a demanding work for human, so an automatic pain assessment system is needed. In this paper, a multi-modal based pain detection method was proposed. A series of features were learned from videos and biomedical signals. Dynamic facial expression analysis and dynamic biomedical signal analysis were conducted for multi-dimensional representation, such as appearance, geometric and head pose, which were presented in temporal manner. Both decision-level and feature-level fusion schemes were utilized to fuse multi-modal and multi-dimensional dynamic features for automatic pain assessment. The proposed scheme achieved the accuracy of 85% and 86% for feature-level fusion and decision-level fusion respectively, which outperformed the results of single-modality. Moreover, the biomedical signal played a key role in the fusion modal.
Deep Potential Geo-Social Relationship Mining for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has been an important research topic in data mining and the popularity of location-based social networks (LBSNs) has significantly contributed to POI recommendation. The existing POI recommendation models mostly adopt various explicit social relationships under geographical space. The implicit relationships among users under a certain geographical region are rarely taken into account, though they have major influence on user behaviors. Due to the above limitations, we were motivated to introduce an innovative Deep Potential Geo-Social Relationship mining model for POI Recommendation (DPGSR-PR). The proposed DPGSR-PR performs the personalization of geographical features, towards the determination of user check-in behaviors and choices in specific domains, which is achieved by estimating and considering kernel density. Moreover, user preferences and personalized geo-social influence are incorporated into a geo-social recommendation framework under a holistic view. Specifically, the deep potential geo-social relationships include the explicit-implicit user geo-social relationships between users (EIU-GSR) and the implicit common check-in POI-based geo-social relationships (ICP-GSR). The estimation of Kernel density and the two-hop random walk approach are employed in an effort to mine the EIU-GSR. The ICP-GSR is discovered by specifically determining and considering the Jaccard similarity coefficient and kernel density estimation. Due to the fact that the role of both EIU-GSR and ICP-GSR as regularization terms is quite significant, we used their combined impact to obtain a unique recommendation model that employs matrix factorization. The proposed DPGSR-PR was tested on two datasets, which has proven that DPGSR-PR outperforms other well-established models.
Force and Torque Model of Ironless Passive Magnetic Bearing Structures

This article presents the development and validation of a three-dimensional force and torque semi-analytical model for ironless permanent magnet structures. This method relies on the definition of the magnetic flux density generated by permanent magnets in terms of elliptic integrals, allowing a fast computation when compared with finite element methods (FEM). The approach is validated and shown for two ironless passive magnetic bearing configurations by studying the applied force, torque and stiffness when small displacements and tilting are present.
Improved Sensorless Control for Permanent-Magnet Synchronous Motor with Self-Inductance Asymmetry

An improved sensorless control for Permanent-Magnet Synchronous Motors (PMSM) with inductance asymmetry is proposed in this paper. Inductance asymmetry can be caused by manufacture tolerance, winding fault, or single winding operation of dual three-phase PMSM, etc. When the inductance is asymmetric, it not only makes the current unsmooth, but also yields inaccurate position and speed estimation of sensorless control. In this paper, an improved phase-locked loop (PLL) in the sliding mode observer (SMO) is proposed to improve the sensorless control in the case of asymmetric inductance. The improved PLL is based on a PIR controller for smooth current control. The improved method uses an additional resonant controller to compensate the second harmonic error. Furthermore, the second harmonic error is used to identify the value of the asymmetric self-inductance and correct the inductance parameters in the SMO. This method can accurately estimate the speed and position and ensure the performance of sensorless control. The simulation and experimental results obtained from a 400W SPMSM verify the effectiveness of the proposed method.
Localization-Aware Meta Tracker Guided With Adversarial Features

Deep learning has recently shown great potentials in learning powerful features for visual tracking. However, most deep learning-based trackers neglect localization accuracy in the evaluation process of candidates. What’s more, they usually over-rely on the discriminative features in a single frame in the training process. Consequently, they may fail when the discriminative features are occluded or changed in the tracking phase. In this paper, we propose a novel localization-aware meta tracker (LMT) guided with adversarial features to address the above issues. First of all, we design a novel intersection over union guided method to effectively balance the problem of classification and localization accuracy. To further improve the robustness of our classifier, we creatively use adversarial features during offline training phase. Those adversarial features can effectively guide the classifier in learning how to better deal with the situation where the discriminative features are occluded or changed. Finally, benefiting from meta learning, our algorithm only needs to perform one iterative update on the first frame and it can perform well on the tracking sceneries. The extensive experiments demonstrate the outstanding performance of our LMT compared with the state-of-the-art trackers on three benchmarks: OTB-2015, VOT-2016, and VOT-2018.
Incorporating Topic Information in a Global Feature Selection Schema for Authorship Attribution

Authorship attribution (AA) is a stylometric analysis task of finding the author of an anonymous/disputed text document. In AA, the performance improvement of class-based feature selection schemas, such as Chi-square, and Gini index over frequency-based feature selection schemas, such as document frequency, common n-grams, and inverted document frequency has been shown to be limited. In AA, the feature selection process is significantly affected by topic distributions. In this paper, we assess the performance of a global feature selection approach into which the document’s topic category is incorporated to scale the existing feature weights. In this approach, the common features of an author among different topics indicate higher relevance for the author and thus have higher weights. On the other hand, features with biased topic distributions are assumed to have high topic relevance and lower weights. In this approach, the global topic measure and the author specific topic measure are combined in order to scale the existing selection weights of the features. The ten-fold cross-validation experiment result on a multi-topic dataset with a random topic distribution indicates that our approach improves the performance of Chi-square, modified Gini index, and common n-grams schemas significantly in the best performing configurations of the classifiers.
A Dual Stopband SIW Ka-V Band Filter

A novel dual stop-band filter operating at Ka-V band on substrate integrated waveguide (SIW) with dominant TE10 mode is presented. Air posts, defected-ground structure (DGS) and circular spiral resonator techniques have been implemented to improve the rejection in stopband. Results confirm that filter rejection in the stopband is better than 13dB. The filter is designed on Rogers RO4003 laminate with 12mil thickness, offering a low profile with an overall filter area of 45mm2. The stopband center frequencies are 36.5GHz and 44.3GHz with fractional bandwidth of 3.9% for both stopbands. The filter insertion loss is better than 1.2dB.
Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.
Multiscale Block Fusion Object Detection Method for Large-Scale High-Resolution Remote Sensing Imagery

Object detection in high-spatial-resolution remote sensing images (HSRIs) is an import part of the automatic extraction and understanding of image information in high-resolution earth observation systems. Regarding how to achieve optimal block object detection for large-scale HSRIs, this paper proposes a multiscale block fusion object detection method for large-scale HSRIs. First, the objects in large-scale HSRIs are detected using different block scales, and the average precision (AP) of the different object detection results is counted at different block scales. Then, according to the statistical information, the image block scales corresponding to the optimal AP value of the different objects are obtained. Finally, a soft non-maximum suppression algorithm is used to fuse the image block scale detection results corresponding to the optimal AP values of the different objects, to obtain the object detection results of the large-scale HSRIs. The experimental results confirm that the proposed method outperforms all other single-scale image block detection methods and provides acceptable object detection results in large-scale HSRIs.
Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

Recently, there is the widespread use of mobile devices and sensors, and rapid emergence of new wireless and networking technologies, such as wireless sensor network, device-to-device (D2D) communication, and vehicular ad hoc networks. These networks are expected to achieve a considerable increase in data rates, coverage, and the number of connected devices with a significant reduction in latency and energy consumption. Because there are energy resource constraints in user’s devices and sensors, the problem of wireless network resource allocation becomes much more challenging. This leads to the call for more advanced techniques in order to achieve a tradeoff between energy consumption and network performance. In this paper, we propose to use reinforcement learning, an efficient simulation-based optimization framework, to tackle this problem so that user experience is maximized. Our main contribution is to propose a novel non-cooperative and real-time approach based on deep reinforcement learning to deal with the energy-efficient power allocation problem while still satisfying the quality of service constraints in D2D communication.
A Way to Quickly Converge to The Destination for Multi-Address Packet in Information-Centric Networking

Multi-homed host could own multiple addresses because of the separation of the identifier and address of the host in Information-Centric Networking (ICN). The packet with multiple destination addresses could obtain multiple output ports at each hop after matching the routing table. Thus, the router could dynamically select the path of the multi-address packet at each hop to improve the throughput of the transmission. However, the packet may not be forwarded along the shortest path, which leads to slow converge to the destination host. Therefore, we need a way to balance the throughput and convergence of the transmission. The carrying of multiple destination addresses is the main reason of the slow convergence of the multi-address packet. So we need to dynamically delete the destination addresses of the packet to decrease the unnecessary hop. In this paper, we propose an address trimming method that increases the deletion probability hop-by-hop, and an edge network judgment method. The experimental results show that the method can increase the throughput by about 33.5% compared with MPTCP at the cost of an increase of 13% in the hop count, which achieves a good balance between the throughput and path convergence.
Attribute-Based Accountable Access Control for Multimedia Content with In-Network Caching

Nowadays, multimedia content retrieval has become the major service requirement of the Internet and the traffic of these contents has dominated the IP traffic. To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, because in-network content caching can be directly utilized to respond users' requests, multimedia content retrieval is beyond content providers' control and makes it hard for them to implement access control and service accounting. In this paper, we propose an attribute-based accountable access control scheme for multimedia content distribution while making the best of in-network caching, in which content providers can be fully offline. In our scheme, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure the secure access control, which is also efficient in both space and time. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users' request process. Through the informal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.
A Dual-Channel Enhanced Power Generation Architecture with Back-to-back Converter for MEA Application

This paper presents a new electricity power generation architecture for the engine system of more electric aircraft (MEA). A starter/generator (SG) is connected to high-pressure (HP) spool, and a generator is attached to low-pressure (LP) spool. Their outputs supply a common DC bus. A back-to-back (B2B) converter is connected between the AC sides of two generators. There are two main contributions of the proposed idea. First, some power can be transferred from LP shaft to HP shaft via the B2B converter, which will benefit to reduce the fuel consumption and increase compressor surge margin of the engine. Second, the HP starter/generator could operate in a high speed without flux weakening, hence the magnitude of stator current will largely decrease when output same active power, leading to the reduction of overall power losses. Modeling and control method design are illustrated. The effectiveness of proposed power generation architecture, engine performance improvement and power loss reduction are verified.
An Improved Cooperative Cell Outage Detection in Self-Healing Het Nets using Optimal Cooperative Range

Heterogeneous Networks (Het Nets) are introduced to fulfill the increasing demands of wireless communications. To be manageable, it is expected that these networks are self-organized and in particular, self-healing to detect and relief faults autonomously. In the Cooperative Cell Outage Detection (COD), the Macro-Base Station (MBS) and a group of Femto-Base Stations (FBSs) in a specific range are cooperatively communicating to find out if each FBS is working properly or not. In this paper, we discuss the impacts of the cooperation range on the detection delay and accuracy and then conclude that there is an optimal amount for cooperation range which maximizes detection accuracy. We then derive the optimal cooperative range that improves the detection accuracy by using network parameters such as FBS's transmission power, noise power, shadowing fading factor, and path-loss exponent and investigate the impacts of these parameters on the optimal cooperative range. The simulation results show the optimal cooperative range that we proposed maximizes the detection accuracy.
Extracting the Pairs of Opinion Target and Opinion Term From Reviews With Adaptive Crowd Labeling

Labeling samples manually is a laborious, error-prone, and cost-consuming process, which is a traditional approach to preparing training data for supervised learning. Crowdsourcing provides an effective way to acquire labeled training data. In this paper, we propose an adaptive crowd labeling method to construct a set of the pairs of opinion target and opinion term iteratively under certain budget constraint. First, we assess the workers’ reliabilities with a small number of labeled samples based on an EM process, since the worker’s expertise varies widely on an open crowdsourcing platform. And then, the tasks are assigned to the high reliable workers for labeling the pairs of opinion target and opinion term. Finally, the responds of a sample from multiple workers are integrated to generate a final result based on the labelers’ reliabilities as well as the dependency relation between opinion target and opinion term in this sample. A series of the experimental results show that the proposed method can achieve better extracting effectiveness compared with the baselines and the state-of-the-art methods.