Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Immersion and Invariance Adaptive Control of Linear Motor System

In this paper, a novel robust adaptive control law based on Immersion and Invariance (I&I) method for the uncertain linear motor system subjected to external disturbances is proposed to achieve satisfying motion control performance. To deal with the parametric uncertainties in the system, an I&I adaption law with smooth projection is designed to handle the unknown parameters in the system. Different from traditional adaptive control, the presented robust I&I adaptive control additionally introduces a tuning function in adaption law, which shapes the convergence dynamic of the parameter estimation vector. The stringent stability of the closed-loop linear motor system is proved in this paper. Finally, both numerical simulation and experimental results demonstrate that the proposed control law can guarantee prominent transient and steady-state performance of the linear motor system.
Cooperative Control of High-speed Trains with Transmission Delay via Active Compensation Approach

The cooperative control problem for high speed trains under transmission delay is discussed in this paper. The control law is constructed guaranteeing that all the trains can operate at the desired distance and speed profile cooperatively. An active compensation approach is used to handle the transmission delays when each train communicates with its neighbors and gets information. In addition, adaptive laws are designed to deal with parametric and environmental uncertainties. Simulation results are displayed to demonstrate the effectiveness of the proposed method.
Research on Soft Measurement Method of Temperature Field in Cement Rotary Kiln

In order to solve the problem that the temperature field in cement rotary kiln can not be monitored in real time,this paper proposes a novel soft-sensing method for temperature field in rotary kiln based on mechanism-and-data-driven.This paper firstly uses the CFD (Computational Fluid Dynamics) technology to establish a finite element model of the rotary kiln to simulate the combustion process in the kiln,and calculates the temperature field in the kiln under the different conditions of temperature of secondary wind,feed coal amount,and velocity of swirl and axis wind.Then, the BPNN (BP Neural Network)is trained using the data set obtained from the numerical simulation of the finite element model, thereby obtaining a soft-sensing model of the temperature field in the kiln.The method can predict the temperature distribution in the kiln in real time by several measurable variables in the field,and the visualization of temperature distribution in rotary kiln is given.
Convolutional Neural Network for Determining Sensitivity Matrix in ETI

Electrical Tomography imaging (ETI) is an advanced visualization technique, which can reconstruct all targets in an investigated field based on boundary measurements. The sensitivity matrix plays a critical role in ETI process, but cannot be effectively determined by the existing method. In the paper, a convolutional neural network (CNN) is used to train sensitivity matrix, where the boundary measurements in a frame of image are regarded as a two-dimensional input vector and selected error criterions of the image to real model as output. In view of main factors that affect effectiveness of sensitivity matrix, we establish a dataset to contain various images in the ETI processes. Experimental data from a 16-electrode industrial ETI system was used to compare the accuracy of the proposed model with the existing ETI methods. Results show that the used CNN can work out a more accurate sensitive matrix which leads to better ETI results and generalization ability.
BitProb: Probabilistic Bit Signatures for Accurate Application Identification

Network traffic classification finds its applications in a variety of network management tasks such as quality of service, security monitoring, traffic engineering, etc. Deep Packet Inspection is one of the methods to identify applications. With the number of proprietary protocols on the rise and network protocols using bit level information for encoding, recently it has been shown that bit level signatures are effective for identifying applications. In this paper, we propose BitProb which generates probabilistic bit signatures for traffic classification. It uses the probability of a bit at a particular position being either 0 or 1 and generates a space efficient signature represented as a state transition machine. Subsequently, it uses the overall probability of an ${n}$ bit binary string extracted from a network flow to identify which application generated the flow. We experiment with three datasets covering twenty protocols (text, binary and proprietary) and show that BitProb classifies network flows with high accuracy and has a minimum number of misclassifications.
Aerial Video Multi-target Detection with Memory Module *

Multi-target detection for aerial video is widely applied for both military and civilian. A multi-target detection algorithm on aerial video with memory module is proposed in this paper. This method uses memory module to connect time-scale information, so that the information of past frames can be used to improve the detection result of the current frame. ShuffleNet unit which is based on depthwise convolution and channel mixing was been used in the feature extraction network. The experimental results on VisDrone dataset show that the detection algorithm presented in this paper possesses fast detection speed and higher accuracy.
CRecSys: A Context-Based Recommender System Using Collaborative Filtering and LOD

Linked Open Data (LOD) is an emerging Web technology to store and publish structured data in the form of interlinked knowledgebases like <monospace>DBpedia</monospace>, Freebase, Wikidata, and Yago. It uses structured data from multiple domains, and it can be used to conceptualize a concept of interest. Recently, researchers have shown that incorporating contextual features in recommender systems improves rating prediction accuracy. However, identification of contextual features for building context-aware recommender systems is a major bottleneck. To this end, in this article, we present the development of a context-based recommender system, <monospace>CRecSys</monospace>, for item ratings prediction in movie domain. <monospace>CRecSys</monospace> extracts item-based contextual features from the underlying dataset and generates an <monospace>RDF</monospace> graph to model items and their contextual features for computing context-based items similarity using graph matching techniques and item-based collaborative filtering. It uses LOD and two well-known movie data sources – Rotten Tomatoes and IMDB for item profiling using a dataset of 1300 movies. <monospace>CRecSys</monospace> is experimentally evaluated over two movie datasets, one is generated by the authors and second is the <monospace>MovieLens-1M</monospace> benchmark dataset. <monospace>CRecSys</monospace> is also compared with ten baselines and two state-of-the-art recommendation methods, and performs significantly better. It is also empirically established that <monospace>CRecSys</monospace> is able to effectively deal with some of the open challenges like <italic>cold-start</italic> and <italic>limited content</italic> problems of the traditional recommender systems.
Distributed Leader-following Formation Control for Mobile Robots with Unknown Amplitudes of Leader’s Velocity

This paper studies the distributed control for leader-following formation of mobile robots. The follower robots do not have full access to the linear and angular velocities of the leader robot. The network among all robots is described by a directed communication graph. The leader’s velocity is assumed to be the linear combinations of a step signal and a finite number of sinusoidal signals. The follower robots are divided into two groups. Two forms of the dynamic control laws are proposed respectively, so that the controlled follower mobile robots and the leader can form the desired formation if the certain assumptions are satisfied. Finally, a simulation example is given to illustrate the effectiveness of the dynamic control laws.
Neuro-adaptive tracking control for uncertain Euler-Lagrange systems with time-varying output constraints and obstacle avoidance

This paper addresses the tracking control problem for uncertain Euler-Lagrange system with time-varying output constraints in an environment containing obstacles. First, a novel log-type attractive potential field is utilized to describe the trajectory tracking task with time-varying constraints, and a bounded artificial potential field is established to describe the obstacle avoidance task. Then, by incorporating the two artificial potential fields (APFs) into the dynamic surface control, a neuro-adaptive tracking control is designed for the uncertain Euler-Lagrange system, which can ensure the system to fulfill the trajectory track task within time-varying limit range while avoiding obstacles. Because the obstacle avoidance task has a higher priority, the proposed control scheme can also guarantee the obstacle avoiding task can be fulfilled first when it is conflicted with the trajectory tracking task. Numerical simulations are provided to demonstrate the efficacy of the control strategy.
Similarity Measure of Time Series Based on Siamese and Sequential Neural Networks

In order to improve the performance of time series similarity measure, a model combined Siamese and Sequential Neural Network(SSNN) is proposed. The model consists of three parts: siamese neural network, distance measurement and sequential neural network. Time series’ features are extracted through the siamese network, the distance measurement calculates the sequence distance in the feature space, and the sequential network calculates the similarity between two time series according to the result of distance mesurement. The siamese neural network and the sequential neural network are composed of multilayer perceptrons, and the distance measurement calculates the Euclidean distance between the corresponding dimensions of the time series. Experimental results on 22 time series datasets in UCRArchive2018 show that SSNN has better classification performance than several other methods, and has a good ability to measure similarity.
On Virtual Network Embedding: Paths and Cycles

Network virtualization provides a promising solution to overcome the ossification of current networks, allowing multiple Virtual Network Requests (VNRs) to be embedded on a common infrastructure. The major challenge in network virtualization is the Virtual Network Embedding (VNE) problem, which is to embed VNRs onto a shared substrate network and known to be $\mathcal {NP}$ -hard. The topological heterogeneity of VNRs is one important factor hampering the performance of the VNE. However, in many specialized applications and infrastructures, VNRs are of some common structural features, e.g., paths and cycles. To achieve better outcomes, it is thus critical to design dedicated algorithms for these applications and infrastructures by taking into accounting topological characteristics. Besides, paths and cycles are two of the most fundamental topologies that all network structures consist of. Exploiting the characteristics of path and cycle embeddings is vital to tackle the general VNE problem. In this paper, we investigate the path and cycle embedding problems. For path embedding, we prove its $\mathcal {NP}$ -hardness and inapproximability. Then, by utilizing Multiple Knapsack Problem (MKP) and Multi-Dimensional Knapsack Problem (MDKP), we propose an efficient and effective MKP-MDKP-based algorithm. For cycle embedding, we propose a Weighted Directed Auxiliary Graph (WDAG) to develop a polynomial-time algorithm to determine the least-resource-consuming embedding. Numerical results show our customized algorithms can boost the acceptance ratio and revenue compared to generic embedding algorithms in the literature.
An Automatic Stent Detection for Intravascular Optical Coherence Tomographic

Intravascular Optical Coherence Tomography (IVOCT) is an important imaging technique for diagnose of atherosclerosis due to its advantages of high spatial and temporal resolutions. In this paper, an automatic detection method is proposed for the evaluation of the stent placement in Percutaneous Coronary Intervention (PCI). The method mainly contains the flowing three steps. Firstly, the IVOCT images are de-noised by total variation model. Secondly, the ROI zone and the seed points of stents are detected. Finally, the region growing algorithm is used to segment the stents according to the selected seed points. More than one thousand clinical OCT images are collected from 10 post-PCI treatment patients for evaluating the performance of the proposed method. The results show that the proposed method could achieve 89.3% accuracy and 86.5% sensitivity.
Accelerating Virtual Network Functions With Fast-Slow Path Architecture Using eXpress Data Path

By decoupling network functions from dedicated, proprietary hardware network devices, Network Function Virtualization (NFV) allows building Virtual Network Functions (VNFs) that can run on standard, commodity servers to reduce cost and gain flexibility in network deployment, operation, and management. However, building VNFs with high-throughput and low-latency is a big challenge. In this paper, we propose eVNF - a hybrid fast-slow path architecture to build and accelerate VNFs with eXpress Data Path (XDP), which is a Linux kernel framework that enables high performance and programmable network processing. The programmability of XDP is limited to ensure kernel safety, thus causing difficulties when using XDP to accelerate VNFs. eVNF solves this problem by taking a hybrid approach: leave the simple but critical tasks inside the kernel with XDP, and let complex tasks be processed outside XDP, e.g., in user-space. With the hybrid architecture, eVNF allows building fast and flexible VNFs. We applied eVNF to build four prototype VNFs: Flow Monitoring (eFM), Firewall (eFW), Deep Packet Inspection (eDPI), and Load Balancer (eLB). These VNFs are evaluated individually and in service function chains (SFCs) using OpenStack. Our experiments showed that eVNF can significantly improve service throughput as well as reduce latency and CPU usage. eVNF-based VNFs also can scale out with the number of CPU cores and can combine with Open vSwitch - Data Plane Development Kit (OvS-DPDK) for better performance.
Reinforcement learning-based joint task offloading and migration schemes optimization in mobility-aware MEC network

Intelligent edge computing carries out edge devices of the Internet of things (IoT) for data collection, calculation and intelligent analysis, so as to proceed data analysis nearby and make feedback timely. Because of the mobility of mobile equipments (MEs), if MEs move among the reach of the small cell networks (SCNs), the offloaded tasks cannot be returned to MEs successfully. As a result, migration incurs additional costs. In this paper, joint task offloading and migration schemes in mobility-aware Mobile Edge Computing (MEC) network based on Reinforcement Learning (RL) are proposed to obtain the maximum system revenue. Firstly, the joint optimization problems of maximizing the total revenue of MEs are put forward, in view of the mobility-aware MEs. Secondly, considering time-varying computation tasks and resource conditions, the mixed integer non-linear programming (MINLP) problem is described as a Markov Decision Process (MDP). Then we propose a novel reinforcement learning-based optimization framework to work out the problem, instead traditional methods. Finally, it is shown that the proposed schemes can obviously raise the total revenue of MEs by giving simulation results.
Tracking Control of Quadrotor UAV with Input Delay

This paper aims to solve the position control problem for quadrotor unmanned aerial vehicle (UAV). In order to be closer to the real UAV system, we consider the UAV with input delay. A barrier Lyapunov function is introduced to ensure that the tracking error is limited within a predetermined range. Pade approximation and an intermediate variable are applied to compensate the effect of the input delay. By using the designed control method, the UAV can track the desired trajectory. Finally, the simulation example is given to verify the effectiveness of the proposed approach.
Chebyshev Center Computation on Probability Simplex With $\alpha$-Divergence Measure

Chebyshev center computation problem, i.e. finding the point which is at minimum distance to a set of given points, on the probability simplex with <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-divergence distance measure is studied. The proposed solution generalizes the Arimoto-Blahut (AB) algorithm utilizing Kullback-Leibler divergence to <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-divergence, and reduces to the AB method as <inline-formula><tex-math notation="LaTeX">$\alpha \rightarrow 1$</tex-math></inline-formula>. Similar to the AB algorithm, the method is an ascent method with a guarantee on the objective value (<inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-mutual information or Chebyshev radius) improvement at every iteration. A practical application area for the method is the fusion of probability mass functions lacking a joint probability description. Another application area is the error exponent calculation.
K-Means Clustering Method Based on Node Similarity in Traditional Chinese Medicine Efficacy

A typical traditional Chinese medicine (TCM) prescription (or formula) is composed of one or more kinds of herbs, which can play a certain role according to the appropriate dosage and production method. The number of possible TCM prescription is nearly as large as that of chemical structures, so the development of quantitative prescription-efficacy relationship models is as appealing as to build a quantitative structure-efficacy relationship model. In this paper, we first construct a binary network of prescriptions and herbs based on the real TCM data. Through the analysis of the network, we propose a method to calculate the expected value of herbs based on the node similarity, which represents the probability that two kinds of herbs exist in the same prescription. Moreover, we use the hierarchical clusters to classify the herb pairs whose expected value is greater than a certain value, and observe which herbs belong to the same category. In addition, we predict the efficacy of herbs by both classic K-means algorithm and improved K-means algorithm which is based on the node similarity. After verification, the results show that the improved algorithm can predict the efficacy of traditional Chinese medicine more accurately comparing with the classic K-means algorithm. Therefore the model can be used for predicting the potential efficacy of traditional Chinese medicine.
State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

The functional use of brain-machine interfaces (BMIs) in everyday tasks requires the accurate decoding of both movement and force information. In real-word tasks such as reach-to-grasp movements, a prosthetic hand should be switched between reaching and grasping modes, depending on the detection of the user intents in the decoder part of the BMI. Therefore, it is important to detect the rest or active states of different actions in the decoder to produce the corresponding continuous command output during the estimated state. In this study, we demonstrated that the resting and force-generating time-segments in a key pressing task could be accurately detected from local field potentials (LFPs) in rat’s primary motor cortex. Common spatial pattern (CSP) algorithm was applied on different spectral LFP sub-bands to maximize the difference between the two classes of force and rest. We also showed that combining a discrete state decoder with linear or non-linear continuous force variable decoders could lead to a higher force decoding performance compared with the case we use a continuous variable decoder only. Moreover, the results suggest that gamma LFP signals (50-100 Hz) could be used successfully for decoding the discrete rest/force states as well as continuous values of the force variable. The results of this study can offer substantial benefits for the implementation of a self-paced, force-related command generator in BMI experiments without the need for manual external signals to select the state of the decoder.
Management of collaborations in digital marketplaces

With everyone generating value out of data, our work focuses to distributed data trading platforms, Digital Market Places (DMPs), that can handle the intricacies of data sharing, e.g. how, where, and what can be done with the traded data. Here we represent collaborations among involving parities in DMPs in the form of archetypes and model them with numeric representations for easier manipulation with standard mathematical tools. We also develop a methodology which aims to select a best-fit infrastructure archetype with any customer-defined application request. In addition, we propose multiple metrics which allows evaluate and compare competing DMPs systemically from more dimensions: coverage, extensibility, precision and flexibility.
Monitoring Bond Wires Fatigue of Multichip IGBT Module Using Time Duration of the Gate Charge

Monitoring the defective multichip insulated gate bipolar transistor (IGBT) module is a cost-effective approach to improve the quality of customer service. A method using time duration of the gate charge is proposed to monitor bond wires fatigue of the multichip IGBT modules when the modules are in the off-state. It is based on the fact that failure of the chip branch due to bond wires fatigue changes the gate input capacitance of the multichip IGBT module. The health state of bond wires can be converted into the change of time duration of gate charge under the constant gate current. A driver containing the constant current source is proposed. The study results indicate that the time duration of the gate charge decreases significantly when the failure of the chip branch occurs, and the effect of junction temperature can be ignored within the special range of gate voltage. Condition monitoring can be implemented during the off-state of the multichip module and without considering the effects of junction temperature. The confirmatory experiment is carried out to verify the correctness of the proposed method.
Convolutional Domain Adaptation Network for Fault Diagnosis of Thermal System under Different Loading Conditions

The failure of thermal system will cause huge economic loss and waste of resources in a large-scale power plant, so the fault diagnosis of thermal system is of great significance. However, in practical applications, it is difficult to obtain sufficient labeled fault data to build diagnostic models under variable operating conditions. To utilize data obtained under different loading conditions to improve classification accuracy, this paper proposes a new convolutional domain adaptation network for fault diagnosis of thermal system in the power plant. In the proposed method, one-dimensional convolutional neural network is employed to extract discriminative features from raw operating data and Wasserstein distance is used to minimize the distribution gap in the feature space to learn domain-invariant features. Experiments are performed under different loading conditions on a 600-MW coal-fired power unit and the results validate the effectiveness of the proposed method.