Miracle-dz/UniFAR-FTUs huggingface dataset:
Recursive State Estimation for Stochastic Complex Networks Under Round-Robin Communication Protocol: Handling Packet Disorders

This paper investigates the recursive state estimation problemfor a class of discrete-time stochastic complex networks with packet disorders under Round-Robin (RR) communication protocols. The phenomenon of packet disorders results from the random transmission delays during the signal propagation process due to the unpredictable fluctuations of the network load, and such random delays are modeled by a set of random variables satisfying certain known probability distributions. For the sake of lessening the communication burden and abating the data collisions, the RR protocol is introduced to govern the order of the nodes for data transmission. Under the scheduling of the RR protocol, only one node is allowed to gain the access to the network at each time instant. Then, a recursive estimator is devised to guarantee an upper bound for the estimation error covariance, and then the obtained upper bound is locally minimized by adequately choosing the estimator parameters. Furthermore, the boundedness of estimation error is analyzed in the sense of mean square with the help of stochastic analysis techniques. At last, a simulation example is presented to show the applicability of the proposed estimator design scheme.
Balancing Supercapacitor Voltages in Modular Bidirectional DC–DC Converter Circuits

At present, passive balancing methods dominate energy storage applications, however, they suffer from a long balancing duration. In this article, we took advantage of a modular architecture, where several modular power converters replace a central dc–dc converter for fast charging and balancing of a supercapacitor stack. A strategy has been proposed to control how power is shared among the converters during the charging period in order to balance the supercapacitors. However, some converters enter control saturation due to voltage differences between supercapacitors caused by their nonuniform conditions and characteristics. The originality of this article lies in taking into account the saturation by modifying an energy-based strategy to correct the power shares and make balancing the supercapacitors possible. Simulation and experimental case studies were used to demonstrate the strategy's performance and limitations.
Aircraft–Receiver Distance Estimation Using ADS-B Signal Strength for Position Verification Application

Estimation of aircraft–receiver distance based on the received signal strength (RSS) of a civil aviation system called the Automatic Dependent Surveillance—Broadcast (ADS-B) was investigated. The estimated distance was intended to be used for position verification, which is a countermeasure against spoofing. A two-step method was proposed in order to mitigate the uncertainty issue regarding the aircraft’s equivalent isotropically radiated power (EIRP), leveraging the regularity of flight schedule. The first step is calibration, where the bias of the EIRP from a nominal value is obtained under the assumption that the ADS-B position is true. The obtained value is stored to be used in the second step, where the distance is estimated when the same aircraft reappears. The proposed method was evaluated with measurement data. As a result, distance errors with a mean of 0.8 km and a standard deviation of 18.6 km were obtained. Also, the resulting error distribution was approximated by a normal distribution.
Managing Distributed Flexibility Under Uncertainty by Combining Deep Learning With Duality

In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty. A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.
Synchronous Rectification Based on Resonant Inductor Voltage for CLLC Bidirectional Converter

Synchronous rectification (SR) is an effective method to reduce the secondary side conduction loss of CLLC bidirectional resonant converter and improve its efficiency, but the SR driving signal in CLLC converter is hard to determine. Existing SR signal generating methods can be divided into three categories: theoretical calculation, voltage detection, and current detection. Compared with the other two methods, the current detection method has wider applicability and is insensitive to parasitic parameters, but usually requires lossy, bulky and costly current sensors, increasing the cost and size of the converter. This article proposed a SR scheme based on resonant inductor voltage. The polarity of the resonant current is obtained indirectly through the inductor voltage, thereby avoiding the use of current sensors. To handle the false pulses problem caused by parasitic oscillation, a corresponding SR control strategy is proposed. The SR driving signal is then adjusted according to the delay and transient requirements. Experimental results on a 380V/270V 3kW CLLC converter prototype were presented to verify the effectiveness of the proposed SR method.
Horizontal Integrated Framework for Mobile Crowdsensing

Mobile crowdsensing is a promising paradigm to leverage the power of people to collect large-scale spatially distributed data. This concept has been intensely studied to efficiently and securely complete sensing tasks at lower cost. The development of a unified platform designed to provide various types of sensing applications is among the major approaches to economical crowdsourcing. However, existing previous frameworks were not optimized for shared use among multiple organizers because they were largely vertically integrated systems. Security and user trust and confidence is also a significant issue a crowdsensing frameworks, given the potential security concerns. Therefore, in this study, we propose a network-side task allocation (NeSTA) framework to address the existing problems in mobile crowdsensing. The proposed framework enables the horizontal integration of sensing applications, in which mobile networks mediate communication among organizers and participants, significantly reducing the installation cost of individual applications. Privacy preservation is achieved by task distribution and allocation procedures, where the participants were obscured by organizers. The validity of the proposed NeSTA was confirmed by simulations with an analytical model using an open dataset. The results show that the proposed method exhibited computational efficiency over two orders of magnitude greater than the conventional approach. This advantage originates from the reduction of problem size by dividing the original problem into subproblems.
Photodiodes for Terahertz Applications

Terahertz generation using high-speed photodiodes has found commercial application in many areas ranging across spectroscopy, imaging and communications. In this paper we discuss the optimization of high-speed photodiodes in terms of bandwidth and output power. We identify some of the main limitations in the generation of high output power in the Terahertz frequency band. We present a modelling tool for the numerical evaluation of antenna coupled uni-travelling carrier photodiodes and experimental evaluation of the fabricated designs. We also present a thermal analysis of the photodiodes alongside pulsed measurements of the output power saturation.
Integrating General and Specific Priors into Deep Convolutional Neural Networks for Bladder Tumor Segmentation

Segmenting bladder tumors from MR images is important for the early detection and auxiliary diagnosis of bladder cancer. In recent years, Deep Convolutional Neural Networks (DCNNs) have been widely used for bladder tumor segmentation. However, the DCNN-based tumor segmentation methods generally over-depend on the model training with labeled data, and neglect the priors that can be learned from data or domain experts. The priors of tumor size and location are very helpful to improve the segmentation precision of bladder tumors, especially for the situation when the labeled images are insufficient to cover the diversity. To tackle the problem, we proposed a method to integrate priors into DCNN-based bladder tumor segmentation in this paper. The priors consist of both specific prior from data and general prior from domain knowledge. Specifically, we add a decoding path into the segmentation network to generate the specific prior, and formulate the general prior of domain knowledge to guide the generation of specific prior. Comprehensive experiments on massive bladder MR images clearly validate the effectiveness of the proposed bladder tumor segmentation method with priors.
A Unified Binary Embedding Framework for Image Retrieval

Hashing, as an efficient retrieval strategy, has been extensively studied in the image retrieval community. It aims to map the original high-dimensional features into compact binary codes, which dramatically reduces the cost of computing similarity with economic memory consumption. In general, most existing algorithms contain two stages: projecting high-dimensional descriptors into low-dimensional features, and encoding the low-dimensional features as binary strings. Although these two-stage approaches are intuitive and effective, certain beneficial information to binary encoding is inevitably discarded in the dimensionality reduction, which tends to lossy encoding. In this paper, we propose a novel hash algorithm where integrate subspace reconstruction and binary quantization into a unified framework and compact hash codes can be generated directly from high-dimensional features. More specifically, the proposed method discovers a latent subspace from the original feature space which is suitable for hash learning, thus alleviating the heavy information loss caused by traditional dimensionality reduction strategy. Meanwhile, thanks to the excellent property of rotational invariance in Euclidean space, the proposed method avoid the challenge of directly optimizing the binary matrix. Specially, our approach essentially is built on l2 estimator obeying the unsupervised paradigm, without involving any labeled data. Furthermore, we design an effective iterative alternating strategy to optimize the proposed model, so as to generate compact binary codes for the subsequent image matching. Extensive experiments on public benchmarks demonstrate that our method outperforms the compared method by a significant margin.
Multi-scale anatomical awareness improves the accuracy of the real-time electric field estimation

Induced electric fields (E-field) of a coil within target areas are substantial for precise trans cranial therapy. The fast and precise estimation of a stimulation is essential for a navigation system. However, high accuracy and low time consumption are rarely satisfied at the same time in previous models. In this paper, we present an anatomical-awareness model to integrate binary, explicit anatomical structures as mediate variables. Multi-scale attention blocks are also introduced to capture the anatomical variations. The presented model mitigates the anatomy-related errors. The presented architecture not only reduces the mean relative errors of E-field to about 7%, but also has the characteristics of low time consumption, which makes it suitable for a real-time navigation system.
Research on Object Detection Method Based on FF-YOLO for Complex Scenes

YOLO v3 has poor accuracy in target location recognition, and the detection effect needs to be improved in complex scenes with dense target distribution and large size differences. To solve this problem, an improved multi-scale target detection algorithm based on feature fusion (FF-YOLO) is proposed in this paper. Firstly, the residual structure in Darknet53 backbone of YOLO v3 is replaced by the optimized dense connection network FCN-DenseNet, and features are extracted effectively through feature reuse, and the problem of vanishing gradient is further alleviated. Secondly, based on the three-scale prediction mechanism of YOLO v3, we added a fourth detection scale to make the network learn more shallower location information. Finally, Spatial Pyramid Pooling (SPP) module is added before each detection layer to make the local feature information deeply fused. It increases the receptive field of the backbone network and significantly isolates the most important contextual feature receptive. Experiments show that FF-YOLO can effectively improve the detection accuracy of multi-scale targets in complex scenes. On Pascal VOC2007 data set, the mAP of FF-YOLO is 5.8% higher than that of YOLO v3. At the same time, the mAP of FF-YOLO for medium and small targets are 1.5% and 2.2% higher than that of YOLO v3 on MS COCO data set.
Efficient Subgraph Pruning & Embedding for Multi-Relation QA over Knowledge Graph

The intelligent question answering over the knowledge graph aims to automatically answer natural language questions via locating the correct entities in the knowledge graph. Aside from the former progresses, it is still challenging to answer the multi-relation questions because of the variety and complexity of the natural language, as well as the combinatorial explosion on possible candidates. In this paper, we propose a novel embedding-based approach named SPE-QA to address these issues. It answers a question by identifying its most semantic like question-answer path from the candidate topic-entity-centric subgraph, and locating this path's tail entity as the final answer. In order to limit the scale of the candidate sub graph and thus reduce the neural network's training complexity, it is essential to filter the explicit noises as much as possible. Therefore, we employ a sub graph separation method to decide the scale of the sub graph, and remove the false question-answer paths with two different pruning policies. The experimental results on two widely used benchmarks approve that, our mechanism can not only reduce the subgraph's scale dramatically, but also obtain better performance on the multi-relation questions, compare to the stat-of-the-art approaches.
Energy Efficient UAV-Enabled Mobile Edge Computing for IoT Devices: A Review

With the emergence of computation-intensive and delay-sensitive applications, such as face recognition, virtual reality, augmented reality, and Internet of Things (IoT) devices; Mobile Edge Computing (MEC) allows the IoT devices to offload their heavy computation tasks to nearby edge cloud network rather than to compute the tasks locally. Therefore, it helps to reduce the energy consumption and execution delay in the ground mobile users. Flying Unmanned Aerial Vehicles (UAVs) integrated with the MEC server play a key role in 5G and future wireless communication networks to provide spatial coverage and further computational services to the small, battery-powered and energy-constrained devices. The UAV-enabled MEC (U-MEC) system has flexible mobility and more computational capability compared to the terrestrial MEC network. They support line-of-sight (LoS) links with the users offloading their tasks to the UAVs. Hence, users can transmit more data without interference by mitigating small-scale fading and shadowing effects. UAVs resources and flight time are very limited due to size, weight, and power (SWaP) constraints. Therefore, energy-aware communication and computation resources are allocated in order to minimize energy consumption.In this paper, a brief survey on U-MEC networks is presented. It includes the brief introduction regarding UAVs and MEC technology. The basic terminologies and architectures used in U-MEC networks are also defined. Moreover, mobile edge computation offloading working, different access schemes used during computation offloading technique are explained. Resources that are needed to be optimized in U-MEC systems are depicted with different optimization problem, and solution types. Furthermore, to guide future work in this area of research, future research directions are outlined. At the end, challenges and open issues in this domain are also summarized.
An Adaptive Thresholding Based Method to Locate and Segment Defects on LCD Panels

Real time defect detection plays a vital role in the liquid crystal display (LCD) manufacturing industry. The different shapes and types of defects poses a great challenge to accurately segment the defects. To achieve the objective this paper presented a LCD panel defect segmentation method. The proposed method constituted three parts: (1) preprocessing of the defective images was necessary to normalize and minimize the noise presented in the images. (2) Then, an image was represented in gray levels whose frequencies were computed using histogram function. But inconsistency was present in the gray levels due to strong illumination gradients. Therefore, the probability of intensity value at all locations of the image was computed to obtain the total variance. The obtained total variance as a thresholding factor in the image was used to separate the image into background and foreground. A thresholding factor was computed adaptively by averaging all the pixel values for the segmentation purpose. (3) Lastly, a binarized defect image was obtained from matching the ground truth image to the obtained threshold image by using pixel by pixel multiplications. Experimental results and comparative study showed that the presented approach was able to correctly locate and segment various shapes of defects with better metrics values.
Attention-based Spatio-Temporal Graphic LSTM for EEG Emotion Recognition

Automatic emotion recognition based on electroencephalogram (EEG) is a challenging task in Brain Machine Interfaces (BMI). Since it is still not very clear about the intrinsic connection relationship among the various EEG channels, it is still a challenging task of how to better represent the topology of EEG channels for emotion recognition. On the other hand, the intensity of the emotion may vary along the different time instants, which would affect the recognition accuracy of emotion. To tackle the above issues, in this paper, we propose a novel multichannel EEG emotion recognition method called attention-based spatiotemporal graphic long short-term memory (ASTG-LSTM), in which a Dynamic Structured Learning (DSL) branch that focuses on the most emotion-relevant connectivity of the brain is incorporated to represent inter-channel connections of the EEG signals. In addition, a specific spatio-temporal attention is embedded into the DSL branch to improve the invariance ability against the emotional intensity fluctuation. Extensive experiments on: DEAP and DREAMER are conducted and the experimental results indicate that the proposed ASTG-LSTM model improves the EEG emotion recognition performance compared with many state-of-the-art approaches.
Author Name Disambiguation Using Multiple Graph Attention Networks

The ambiguity of name entities is a common problem in information retrieval, which leads to the decline of retrieval quality. This makes name disambiguation particularly important. In academic field, the rapidly increasing large-scale of publications has imposed more challenges to the name disambiguation problem. Existing works mainly focus on leveraging content information to distinguish different name entities. In this paper, we consider jointly utilizing both content information and relational information to disambiguate the same name. Firstly, we construct a Heterogeneous Academic Network based on meta information of publications such as collaborators, institutions and venues. Then, we transform the network into separate homogeneous graphs. After that, we propose Graph Attention Networks to jointly learn content and relational information by optimizing an embedding vector. Finally, a clustering algorithm is presented to gather author names most likely representing the same person. The experiments show that our method is effective and outperforms the state-of-the-art methods in both precision and recall metrics.
Model Predictive Control as an Industrially Applicable Approach for Power Control of Solid Oxide Fuel Cells

In this paper, a model predictive control (MPC) combined with a discrete-time stationary Kalman filter as an observer for non-measureable states and input disturbances is presented as a simple and industrially applicable approach for controlling the electric power of a solid oxide fuel cell (SOFC). The developed controller was tested in a simulation in terms of its robustness under consideration of model uncertainties and measurement noise. The results were compared with a PI output-feedback controller combined with a feedforward control and an internal model control (IMC). For the MPC the framework conditions are equal to the PI controller and the IMC. Since these conditions can be reproduced by simulations, we can omit a rerun of the experiments. As a result, the MPC provides comparable results and presents as the better of the two alternative controllers.
AttrHIN: Network Representation Learning Method for Heterogeneous Information Network

Network representation learning can map complex network to the low dimensional vector space, capture the topological properties of networks, and reduce the time complexity and space complexity of the algorithm. However, most of the existing network representation learning (NRL) methods are for the homogeneous networks, while the real-world networks are usually heterogeneous, therefore, it is more practical to provide an intelligent insight into the evolution of heterogeneous networks. In this paper, we propose a novel heterogeneous network embedding method, called AttrHIN, which adopts weighted meta-path-based random walks strategy, and can make full use of the attribute information to capture the latent features. AttrHIN is suitable for the different types of nodes in heterogeneous networks. Extensive experimental results show that compared with the state-of-art algorithms, AttrHIN achieves better results in Macro-F1 and Micro-F1 for multi-class node classification and Link Prediction on several real-world datasets.