Miracle-dz/UniFAR-FTUs huggingface dataset:
Average-consensus Tracking for First-order Multi-agent Systems with

This paper addresses the average-consensus tracking problem of first-order multi-agent systems with constant reference signals via exchanging quantized data, where the topology is balanced and strongly connected. By transforming the quantized consensus problem into the stability problem, a necessary and sufficient condition is obtained by using matrix analysis theory to guarantee that the multi-agent systems achieve the quantized consensus, and the final consensus state is the average value of constant reference signals. Numerical simulation examples are provided to illustrate the correctness of the results.
L2-gain Stabilized Attitude Control for Combined Spacecraft

In this paper, an attitude controller based on L2-gain stabilization is proposed for a combined spacecraft, consisting of a server spacecraft, non-cooperative target and manipulators as the connection. Firstly, consuming that the connection between the server and target spacecraft is fixed, a novel modeling method based on interaction torque for the attitude dynamics of combination system is investigated. Then, a robust L2-gain stabilized attitude controller without spacecraft inertia parameters is proposed. Finally, numerical simulations verifies the effectiveness and superior performance of the resulting controller.
Research on fault diagnosis method of rolling bearing based on 2DCNN

The traditional fault diagnosis method of rolling bearing relies on signal analysis and processing technology, and the accuracy of fault identification is low; artificial neural network(ANN) and support vector machine(SVM) need to extract features manually, and the accuracy rate can't meet people's needs. With the arrival of the era of big data, those methods more and more can not meet the needs of practical problems, and deep learning plays an increasingly important role in the field of fault diagnosis. In this study, a method of rolling bearing fault diagnosis based on Two-Dimensional Convolutional Neural Network(2DCNN) is proposed, 2DCNN architecture model is established, the network parameters are optimized, the experimental scheme is designed, and the classification accuracy of 2DCNN for rolling bearing fault is explored. The experimental results show that in the process of identifying the fault mode of rolling bearing, the 2DCNN can distinguish the fault and normal state of rolling bearing accurately and classify the fault accurately.
Robust exponential stability for switched systems with interval time-varying delay

Stability criteria and design of hybrid controllers for a class of switched systems with interval time-varying delay are considered in this paper. Based on taking both the lower bound and upper bound of delay into consideration in the chosen piecewise Lyapunov-Krasovskii functional, we investigate the exponential stability by the method of average dwell time. A new delay-rang-dependent stability criteria and stabilization conditions are derived in terms of linear matrix inequalities. Then based on this condition, the hybrid state feedback controller is designed and the robust exponential stability is also proposed. Moreover, in order to obtain much less conservative results, the integral inequality and reciprocally convex approach are introduced. Finally, the numerical example is given to demonstrate the applicability and the effectiveness of the proposed method.
Distributionally Robust Deviation Flow Refueling Location for Electric Vehicle Swapping Stations

Battery swapping has been recognized as an effective strategy to reduce charging time and enhance mobility for electric vehicles (EVs). The deployment of EV battery swapping stations (BSSs) is critical to provide effective battery swapping service and thus promote EV adoption. This paper proposes a distributionally robust deviation flow refueling location (DR-DFRL) model considering both uncertain EV traffic flow and driver routing choice behavior. Specially, the EV traffic flow is unknown at the planning stage, and characterized by a distributional set based on mean-covariance information. For given location decisions, drivers may have to deviate from their shortest paths to travel to available swapping stations, which also influence the captured EV traffic flow. We show that the DR-DFRL model can be equivalently reformulated into a mixed integer nonlinear optimization problem, and further present an outer approximation algorithm. Numerical experiments over real-world transportation networks are conducted to validate the effectiveness of the proposed model and algorithm, and provide useful managerial insights.
Face Semantic Segmentation and Enhanced Attribute Description on Portrait Robot

Traditionally, image drawing robot draws a portrait only by generated image, without concerning the certain order of people’s facial features. In this paper, a method of attribute enhanced description based on semantic segmentation of face image is proposed. The semantics of face image is segmented by BiSeNet, and the data sets of different feature organs are constructed. We use the enhanced attribute module to generate the description of the relevant attributes for each feature organ image. Unlike the previous soft biological features of human face, this method can more intelligently realize the description of feature attributes. The experimental results show that our method can obtain better organ localization and enhanced attribute description. Combining our existing drawing robot platform with our drawing strategy based on enhanced attribute description, it has a good promotion effect on humanoid drawing of robots.
Full Adder Circuit using Multi-Input MRL

this paper proposes the Full Adder Circuit using Multi-Input MRL without CMOS. It is designed by Multi-Input MRL which component and delay time less than 1-bit full adder circuit using Hybrid Memristor-CMOS logic. Structure of Purpose circuit is most simple. The performances of Purpose circuit are compared with another full adder circuits are carried out by using LTspice simulator
Secure and Private Smart Grid: The SPEAR Architecture

Information and Communication Technology (ICT) is an integral part of Critical Infrastructures (CIs), bringing both significant pros and cons. Focusing our attention on the energy sector, ICT converts the conventional electrical grid into a new paradigm called Smart Grid (SG), providing crucial benefits such as pervasive control, better utilisation of the existing resources, self-healing, etc. However, in parallel, ICT increases the attack surface of this domain, generating new potential cyberthreats. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) architecture which constitutes an overall solution aiming at protecting SG, by enhancing situational awareness, detecting timely cyberattacks, collecting appropriate forensic evidence and providing an anonymous cybersecurity information-sharing mechanism. Operational characteristics and technical specifications details are analysed for each component, while also the communication interfaces among them are described in detail.
Varying Balancing Transfer Learning for BN Parameter Estimation

To solve the problem of Bayesian network (BN) parameter estimation accuracy under small dataset conditions, this paper proposes a parameter Varying Balancing Transfer Learning algorithm (VBTL) based on varying weight transfer learning. Firstly, the MAP method and the MLE method are used to learn the initial parameters of the target domain and the parameters of each source domain. Then, the source weight factors of the source domain are obtained according to the different data source contributions. Based on the sample statistic the data size threshold values, the balance coefficients for the target initial parameters and the source domain parameters are calculated to obtain the final target parameters. The experimental results show that under the condition of the small data set, the learning accuracy of VBTL algorithm is better than MLE algorithm, MAP algorithm or classical transfer learning algorithm (LoLP). Under the condition of sufficient data set, the learning accuracy of VBTL algorithm approaches the classical MLE algorithm, and the correctness of the algorithm is verified. Moreover, we demonstrate the successful application to real-world bearing fault diagnosis case studies. Compared with the LoLP algorithm, the VBTL algorithm achieves about 10% enhancement for the average diagnosis precision.
Multiagent Reinforcement Learning With Learning Automata for Microgrid Energy Management and Decision Optimization

As the increasing willingness of electric users to actively participate in power scheduling and to pursue self-interest, the management and optimization of residential microgrid confront higher requirements to balance the tradeoff between overall operational objectives and individual rights; and to resolve the influence of various uncertainties. Therefore, a multiagent reinforcement learning (MARL) approach is proposed in this paper for auction-based microgrid market. Distributed model-free reinforcement learning is used for each supplier and user to make reasonable market strategies; on the other hand, equilibrium-based game theory is combined in the learning process to ensure utility balance and supply-demand balance of the whole microgrid. Besides, to guarantee the efficiency of MARL, a learning automata (LA) is introduced to improve the strategy selection procedure which plays an essential role in algorithm optimization. A case study about microgrid market operation is conducted to verify the performance of the proposed approach.
Design and Analysis of a Spherical Robot with Two Degrees of Freedom Swing

Aiming at the problem that the mechanical structure of the ordinary pendulum spherical robot is improperly designed to cause a certain degree of freedom to swing the range, this paper proposes a two-degree-of-freedom spherical robot XK-I with a swing around the X and Y axes. The design concept, mechanical principle and rolling and rotating motion characteristics of the XK-I robot are introduced. According to the characteristics of XK-I robot motion, the dynamic model of linear motion is established by Lagrangian equation and simulated. According to the kinetic model, the XK-I robot steering is implemented by controlling the number of steps of the stepping motor by the pulse signal, so that the XK-I robot can move in any direction. The linear and steering tests of the XK-I robot were carried out through real experiments to verify the feasibility of the design.
State Estimation for Second-order Linear Systems: A Sructure-improved First-order Observer

In this paper, a structure-improved first-order observer is proposed to more accurately estimate the state and its derivative in the second-order system. Firstly, under the time-invariant change of coordinates, the novel structure of an improved first-order observer is given to deal with the problem where the derivative of the position estimation is not equal to the estimation of state vector for all time, and further, the sufficient conditions for the existence of such an observer is obtained as a type of the full-actuated second-order Sylvester equations, directly related to the second-order system coefficient matrices. Secondly, on the basis of the proposed existence conditions, the parametric method of the improved first-order observer is presented by adopting the solutions of the Sylvester equation. Finally, the correctness of the proposed method is illustrated through numerical simulation about a simple flexible-joint mechanism.
Network Flow based IoT Botnet Attack Detection using Deep Learning

Governments around the globe are promoting smart city applications to enhance the quality of daily-life activities in urban areas. Smart cities include internet-enabled devices that are used by applications like health care, power grid, water treatment, traffic control, etc to enhance its effectiveness. The expansion in the quantity of Internet-of-things (IoT) based botnet attacks is due to the growing trend of Internet-enabled devices. To provide advanced cyber security solutions to IoT devices and smart city applications, this paper proposes a deep learning (DL) based botnet detection system that works on network traffic flows. The botnet detection framework collects the network traffic flows, converts them into connection records and uses a DL model to detect attacks emanating from the compromised IoT devices. To determine an optimal DL model, many experiments are conducted on well-known and recently released benchmark data sets. Further, the datasets are visualized to understand its characteristics. The proposed DL model outperformed the conventional machine learning (ML) models.
Type-II QC-LDPC Codes From Multiplicative Subgroup of Prime Field

A quasi-cyclic (QC) low-density parity-check (LDPC) code is called type-II, if the maximum weight over all circulants appearing in the parity-check matrix has the value of two. On the basis of multiplicative subgroup analysis for the prime field, a novel algebraic approach for type-II QC-LDPC codes is proposed from Tanner’s method. For column weight of four, the new type-II codes possess girth at least six and include a subset with very small circulant sizes almost attaining the theoretical lower bound. The new approach can yield type-II codes with two times smaller circulant sizes, in comparison with the state-of-the-art method. To enhance the flexibility of circulant sizes, a generalized Chinese-remainder-theorem (gCRT) method is proposed as well for type-II codes. Simulation results show that combining gCRT with the proposed short code yields compound type-II codes with a very promising decoding performance and flexible circulant sizes.
An LSTM-Based Model for Person Identification Using ECG Signal

Electrocardiogram (ECG)-based biometrics are gaining popularity because of its robustness against falsification. In this letter, we have designed a new long short-term memory (LSTM) based framework for person identification using ECG signal. Our model learns the underlying temporal representation of an ECG signal by considering both intra-beat and inter-beat variations. We have shown that the LSTM model captures the intra-beat variations better for smaller ECG segments. Unlike most of the models in the literature, our model doesn't require the detection of any fiducial points. This is achieved by training the LSTM network with smaller segments of ECG signals, which are extracted by sliding a rectangular window. The effect of different window length on person identification accuracy is studied. The efficiency of the model is extensively tested on four databases; PTB, MIT-BIH arrhythmia database, ECG-ID, and CYBHi. An accuracy of 97.3% is obtained for 290 subjects of the PTB database. Similar results are also obtained for other databases, as well as 79.37% accuracy for CYBHi.
A Visual/Inertial Relative Navigation Method for UAV Formation

Nowadays, the task of determining positions for Unmanned Aerial Vehicles (UAVs) has been mainly handled by combining Inertial Navigation System (INS) with Global Positioning System (GPS). This paper presents a visual/inertial relative navigation method for UAV formation to overcome the disadvantages of GPS such as disturbed easily. Firstly, INS and visual navigation system are respectively studied. Secondly, the state and observation equations of the visual/inertial navigation system are established. And Muti-rate Kalman Filter (MKF) is designed to estimate the position errors. Finally, the performance of this method is analyzed via a simulation.
Assessment of Volume and Above-Ground Biomass in Araucaria Forest Through Satellite Images, Comparing Different Methods in the South of Chile

Initial results of biomass estimation in the La Fusta area from existing equations found in literature are presented. As expected, accuracy of general equations suffer from the equation coefficients being obtained from fitting training data from different sites. It is also clear from the results that there is a high variance between different methods, in particular when complex data mixture is applied. Biomass is difficult to assess for dense forests, as pixels are saturated. This must be considered when planning field-data collection, with more samples in dense forest to provide more robust estimators from the training phase. The SAR-only (PALSAR) method from eq. 4 provided the most bias in results, overestimating with respect to the other methods.
Occlusion-Aware GAN for Face De-Occlusion in the Wild

Occluded faces–as a common scene in real life–have a significant negative impact on most face recognition systems. Existing methods try to remove the occlusions by a single-stage generative adversarial network (GAN), which is unaware of the occlusion and thus has difficulties in generalizing to a large variety of occlusion types, e.g., different objects at various positions. To this end, we propose the two-stage Occlusion-Aware GAN (OA-GAN), where the first GAN is for disentangling the occlusions, which will be served as the additional input of the second GAN for synthesizing the final de-occluded faces. In this way, our two-stage model can handle diverse occlusions in the wild and is naturally more explainable because of its awareness of the occluded objects. Extensive experiments on both synthetic and real-world datasets validate the superiority of the two-stage OAGAN design. Furthermore, by applying the generated de-occluded faces to facial expression recognition (FER) systems, we find that our two-stage de-occlusion process significantly increases the accuracy of FER under occlusion.
Disturbance-observer-based Tracking Control for A Flexible-Joint Robotic Manipulator with External Disturbance

This paper reports our study on tracking control for one-flexible-joint robotic manipulator with external disturbance. First, to deal with the mismatching disturbance, a nonlinear disturbance observer (NDO) is introduced which guarantees the existing disturbance can be estimated by disturbance observer asymptotically. Then, by using integrator backstepping, method, a NDO-based nonlinear tracking controller is presented to achieve the output tracking control. And the designed control law also ensures that the other closed-loop states converge to zero asymptotically.
Exponential Stabilization for Networked Switched Positive Systems with Mixed Time-Varying Delays via an Event-Triggered Scheme

This paper deals with exponential stabilization issue for a family of discrete-time networked switched positive systems with mixed time-varying delays. The mixed time-varying delays lie in not only state but the switching information, which increases the difficulty of designing controller. An event-triggered mechanism is adopted in the paper, which can reduce communication resources and save the cost. Different from the triggering conditions expressed as quadratic forms in previous results, a mode-dependent linear form triggering condition is given, which is more efficient to resolve the positive stabilization problem. With the aid of a copositive type Lyapunov-Krasovskii functional and the average dwell time approach, a desired delay controller by means of a linear programming method is designed to guarantee the positivity and exponential stability of the event-triggered control closed loop system. Finally, the validity of our main results is demonstrated by a simulation example.
Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks. As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN). By providing computational and storage resources at the edge, MEC can reduce latency for end users. Hence, this article investigates MEC for 5G and IoT comprehensively. It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading. In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT. Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT. Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios.
Experimental and Theoretical Analysis of the Fast Charging Polymer Lithium-Ion Battery Based on Cuckoo Optimization Algorithm (COA)

Fast charging of the electric-vehicles is one of the paramount challenges in solar smart cities. This paper investigates intelligent optimization methodology to improvise the existing approaches in order to speed up the charging process whilst reducing the energy consumption without degradation in the light of the outrageous demand for lithium-ion battery in the electric vehicles (EVs). Two fitness functions are combined as the targeted objective function: energy losses (EL) and charging interval time (CIT). An intelligent optimization methodology based on Cuckoo Optimization Algorithm (COA) is implemented to the objective function for improving the charging performance of the lithium-ion battery. COA is applied through two main techniques: The Hierarchical technique (HT) and the Conditional random technique (CRT). The experimental results show that the proposed techniques permit a full charging capacity of the polymer lithium-ion battery (0 to 100% SOC) within 91 mins. Compared with the constant current-constant voltage (CCCV) technique, an improvement in the efficiency of 8% and 14.1% was obtained by the Hierarchical technique (HT) and the Conditional random technique (CRT) respectively, in addition to a reduction in energy losses of 7.783% and 10.408% respectively and a reduction in charging interval time of 18.1% and 22.45% respectively. Experimental and theoretical analyses are performed and are in good agreement on the polymer lithium-ion battery fast charging method.
Taxonomy versus cladonomy, a fundamental controversy in biological systematics

In an article in the February issue of this journal, Welzen (1997) has expressed clearly his reservations about my paper 'In defence of paraphyletic taxa' (Brummitt, 1996a) which was originally read at an AETFAT (Association pour l'Etude Taxonomique de la Flore d'Afrique Tropicale) Congress in 1994. In the same issue of this journal, however, Sosef (1997) has come to very much the same conclusions as I had, emphasizing the inevitability of paraphyletic taxa. Essentially the same conflict of views was addressed in a formal debate at the Linnean Society of London on 6 March 1997, when a motion "that this house believes that Linnaean classification without paraphyletic taxa is nonsensical" was proposed by myself and seconded by my colleague Alan Paton. The motion was approved by 69 votes to 43. While this vote itself proves nothing, it does apparently indicate that there is a significant body of opinion in the systematic community (in the U.K. at least) which does not accept the view widely promoted today among followers of 'phylogenetic systematics' that paraphyletic taxa should have no place in biological classification. This is indeed now a matter of major controversy in systematics, and it is timely to review the arguments and further prospects.