Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Tolerant Control for a Class of Evolutionary Matrix Games

This paper investigates the fault tolerant control (FTC) problem of a class of evolutionary matrix games modeled by replicator dynamics (RD). The faults are malicious rather than accidental, which change the payoffs of certain strategies for all agents, thus may affect the stability of the equilibrium and the domain of attraction (DOA). To realize the purpose of FTC, an incentive-based method is proposed. Finally, the case of population with mutation is also mentioned.
Event-triggered Fixed-time Phase Agreement of Kuramoto-oscillators with Switching Topologies

This paper investigates the synchronization problem for a class of Kuramoto-oscillator network model, which has nonidentical oscillators coupled by a time-varying topology. The distributed fixed-time event-triggered control strategies are adopted to achieve phase agreement. To deal with the time-varying problem of the connection topology of oscillators, we propose a multilayered control structure, which makes it possible to design the upper bound of the settling time independent of the initial phase diameter. Moreover, the fixed-time global stability of the proposed method is guaranteed by the Lyapunov analysis. And we also prove that there is no Zeno behavior in the settling progress. Finally, numerical simulations validate the effectiveness and correctness of the proposed approach.
Event-triggered Control for Stochastic Networked Control Systems under DoS Attacks

This paper investigates the event-triggered control (ETC) problem for stochastic networked control systems (NCSs) with exogenous disturbances and Denial-of-Service (DoS) attacks. The ETC strategy is proposed to reduce the utilization of network resource while defending the DoS attacks. Based on the introduced ETC strategy, sufficient conditions, which rely on the frequency and duration properties of DoS attacks, are obtained to achieve the stochastic input-to-state stability and Zeno-freeness of the ETC stochastic NCSs. An example of air vehicle system is given to explain the effectiveness of proposed ETC strategy.
Deep Neural-Gas Clustering for Instance Segmentation across Imaging Experiments

CNNs are characterized in particular by the ability to independently learn suitable features from a given data set. However, the resulting latent space is optimized for the given training data. Especially for tasks that require a high generalization ability, like e.g. the segmentation of single cells in a microscopic image across various experiments, these specific solutions might not offer optimal results. In this work, we improve generalization with an additional unsupervised training step that operates in the latent space. First experiments with the Kaggle cell segmentation competition data show a strong improvement in the generalization of acquired knowledge when using a soft- and hard-competitive Neural-Gas algorithm for deep clustering with a standard CNN architecture.
Survey on 6D Pose Estimation of Rigid Object

Estimating 6D pose of rigid objects has gained increasing attention as it has become an curcial problem in rapidly growing technology related to robotics, augmented reality and autonomous driving. Therefore, the research on 6D pose estimation technology is of great significance. In this paper, firstly, current position of the field is summarized regarding object pose estimation. We found that deep learning combined with traditional methods can produce better results. Then, pose ambiguity which is an open problem needed further study is raised. Finally, the main problems of the current research and possible development directions are identified.
A Nonlinear Model Predictive Controller for Remotely Operated Underwater Vehicles With Disturbance Rejection

Remotely Operated underwater Vehicles (ROVs) are growing in importance in the ocean environment for observation and manipulation tasks, particularly when used to maintain offshore energy and offshore renewable energy assets. Many such tasks require the dynamic positioning of ROV in challenging sea conditions with multiple disturbances resulting from the effects of waves, currents and turbulence. This work presents a novel, nonlinear, model predictive dynamic positioning controller that accounts for such complex stochastic disturbances. These external disturbances are modelled as 6-degree of freedom forces and moments within the nonlinear ROV dynamic and propulsion model. A nonlinear model predictive dynamic positioning strategy based on the nonlinear model predictive control (NMPC) is proposed for the disturbance rejection in this work. A numerical water tank model is used to test the performance of the strategy using hardware in-the-loop simulation. The results of the simulation have been compared against baseline proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers tested under wave and current conditions in the FloWave basin. A quantitative comparison of the controllers is presented. The resulting controller is shown to maintain a small root mean squared error (RMSE) in position when subjected to multiple directional disturbance, with minimal control effort. This study contributes an important insight on future theoretical design of model predictive disturbance rejection controllers and illustrates their practical implementation on real hardware.
The Surface Damage Identifications of Wind Turbine Blades Based on ResNet50 Algorithm

Deep learning was widely used in the field of image recognitions to solve a variety of engineering problems. Aiming at the problems of high cost and low efficiency caused by traditional detection of wind turbine blades, this paper applied a deep learning classification method, ResNet50 algorithm, to identifying the surface damage of wind turbine blades based on UAV machine vision. In the study, DJI UAV were first used to collect image data, and the training set and test set were constructed after segmentations. The performance of both ResNet50 classifier and AlexNet classifier are compared to show the advantages of the ResNet50 classifier. With the same sample data and training parameters, the ResNet50 classifier achieves an average classification accuracy rate of 95.58%, which is higher than the average classification accuracy rate of the AlexNet classifier of 94.19%, which verifies the effectiveness of this method.
Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms

With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently taking a major part of the ongoing research, and expected to be the key player in today’s technologies. This paper introduces and investigates the applications of machine learning in antenna design. It covers the major aspects of machine learning, including its basic concept, differentiation with artificial intelligence and deep learning, learning algorithms, its wide applications in various technologies, with a main focus on its usage in antenna design. The review also includes a comparison of the results using machine learning in antenna design, compared to the conventional design methods.
Global finite-time chaos synchronization between the Loren system and the Chen system via a simple controller

The global finite-time synchronization between the Loren system and the Chen system via a simple controller is investigated in this paper. Firstly, a simple controller is designed to establish the master-slave synchronization scheme of the chaotic Lorenz system and the chaotic Chen system. And then, the finite-time stability theory is applied to verify the synchronization criterion of the chaotic Lorenz system and the chaotic Chen system and the corresponding synchronization time is analytically estimated in a mathematical formula. Finally, the effectiveness of the result obtained in this paper is confirmed by a numerical example.
Modular Design for a Stacked SIW Antenna Array at Ka-Band

This article presents a modular substrate-integrated waveguide (SIW) antenna array based on H-plane aperture structures for Ka-band. The unitary antenna is based on a SIW aperture antenna with an improved H-plane radiation pattern by means of the implementation of metallic vias in the wave propagation along the H-plane antenna. The inner metallic vias are introduced to form four different sub-apertures at the end of the H-plane aperture antenna, dividing the field from the feeding into four in-phase wavefronts. In that manner, a flatter wavefront is generated to achieve high directivity. Additionally, some periodic parallel strips are printed at the end of the antenna aperture to improve the impedance matching with the air. The H-plane antenna is used as the constituting element for an E-plane array antenna, using four H-plane elements. The E-plane array antenna increases the antenna directivity, providing a pencil-shape beam, based on a series coaxial feeding structure. This feeding strategy favours the antenna modularity at the expense of suffering from a slight beam steering with frequency in the working bandwidth. The proposed antenna has an impedance matching below −10 dB from 32.9 to 37.0 GHz (equivalent to 11.73% bandwidth) with a nearly stable gain of almost 10 dBi for the H-plane unitary element and 14 dBi for the E-plane array. Prototypes of both antennas are manufactured to validate the proposed unitary antenna and array designs.
Split'n Trace NVM: Leveraging Library OSes for Semantic Memory Tracing

With the rise of non-volatile memory (NVM) as a replacement for traditional main memories (e.g. DRAM), memory access analysis is becoming an increasingly important topic. NVMs suffer from technical shortcomings as such as reduced cell endurance which call for precise memory access analysis in order to design maintenance strategies that can extend the memory's lifetime. While existing memory access analyzers trace memory accesses at various levels, from the application level with code instrumentation, down to the hardware level where software is executed on special analysis hardware, they usually interpret main memory as a consecutive area, without investigating the application semantics of different memory regions. In contrast, this paper presents a memory access simulator, which splits the main memory into semantic regions and enriches the simulation result with semantics from the analyzed application. We leverage a library-based operating system called Unikraft by ascribing memory regions of the simulation to the relevant OS libraries. This novel approach allows us to derive a detailed analysis of which libraries (and thus functionalities) are responsible for which memory access patterns. Through offline profiling with our simulator, we provide a fine-granularity analysis of memory access patterns that provide insights for the design of efficient NVM maintenance strategies.
Enhanced Autonomous Resource Selection Algorithm for Cooperative Awareness in Vehicular Communication

With rapid development in wireless communication, Intelligent Transportations System (ITS) has received significant attention. This system delivers various social and economic benefits including efficient traffic management, lesser prone to accidents, reduced air pollution and enabling of unmanned driving for enhanced leisure. ITS is enabled through real-time communication either Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) modes of intra-infrastructure communication. LTE-V2V is seen as a major technology towards ITS implementation. In this paper, we discuss various autonomous resource selection techniques available for Mode-4 V2V communication under LTE-A network. An enhanced resource selection scheme based on exponential averaging in time domain and normalized scaling in frequency domain is being proposed. Simulation results show the efficacy of the proposed algorithm in terms of packet reception ratio (PRR), error rate and update delay. The proposed scheme enables low latency transmission of cooperative awareness messages while maintaining high PRR and low error rate.
Complex Properness Inspired Blind Adaptive Frequency-Dependent I/Q Imbalance Compensation for Wideband Direct-Conversion Receivers

Direct-conversion receivers (DCRs) have been adopted in wideband communication systems owing to their simple structure and low cost, however, their operation is affected by amplitude and phase mismatches between their analog inphase (I) and quadrature (Q) branches, as well as the discrepancy of low-pass filter coefficients between these two channels. In this paper, a blind adaptive frequency-dependent I/Q imbalance compensator is proposed, which exploits the complex properness (second-order circularity) of ideal constellation mappings to provide more enhanced insight into the problem setting within the proposed compensator. This serves as a basis for a novel full second-order performance assessment framework, which is established through a joint consideration of the weight error covariance and complementary covariance in both the transient and steady-state stages. This conjoint analysis is further shown to facilitate accurate quantification of the overall mirror-frequency interference attenuation capability of the proposed compensator. Simulation results in an orthogonal frequency division multiplexing (OFDM) transmission system demonstrate the excellent performance of the proposed compensator.
A General Framework of Knowledge-Based Coaching System with Application in Table Tennis Training

As the growth of people’s awareness in health and fitness, high-level sports exercise coaching resources become scarce comparing to the fast increasing demands. In traditional sports training, coaches are responsible for scheduling training sessions and giving instructions based on the performance of the trainee. This paper proposes a general knowledge-based coaching system (KBCS) for the purpose of automatic and intelligent sports training. Through systematically modeling the interactive training process between the coach and the trainee as a sequential decision problem, we show that the knowledge-based training controller can be properly designed to help the trainee to make effective progress towards the preset training goal. Partial implementation of KBCS in the table tennis training has been realized to demonstrate and verify the effectiveness of the proposed method.
Localization Platform Design of Wireless Sensor Network

Affected by terrain limitation, energy consumption and computation complexity, it is difficult for wireless sensor networks to achieve self-localization in GPS-denied environment. In order to locate sensor nodes in engineering, a localization platform of wireless sensor network is designed and developed. As the control chip of localization platform, CC2530 is used to collect location-related and physical information of all sensors by wireless transmission of ZigBee. Additionally, we design and develop a localization software of wireless sensor network. In the software, a two-dimensional and three-dimensional cooperative localization method is proposed and implemented to locate sensors practically. The system can also realize diverse functions such as localization result display, historical data query and network topology visualization. Finally, the platform has been verified in effectiveness and efficiency. Its design and development promote wireless sensor network technology and provide good reference for engineering.
Cognition-based Evaluation of Commercial Advertisement Videos using Functional Near-Infrared Spectroscopy

Objective. This study is to investigate a cognition-based evaluation method of commercial advertisement videos (CAV) using neuroscientific technologies. The videos include three types of duration (15, 30, and 60 sec) and two narrative structures (linear and nonlinear). In the neuro-marketing area, the brain imaging tools have become more prevalent in recent years, and it can improve product-marketing competition as a kind of novel approach. Approach. In this study, the hemodynamic responses (HRs) in the prefrontal cortex using functional near-infrared spectroscopy were measured from 8 participants while exposing them to six different types of videos. In examining the HRs, the paired sample t-test and t-value were utilized. The oxyhemoglobin (HbO) responses of three durations and two structures showed that the HRs between different structures and durations were distinguishable. Main results. For the same duration CAVs, the averaged HbO concentration level of linear structure was higher than that of nonlinear structure. The HRs of 30 s and 15 s CAVs were the higher than that of 60 s CAVS of linear and nonlinear structures. Significance. This finding suggests that the brain cortex HRs involve during the evaluation of CAVs that can be related to the structure and duration of the video. A cognition-based CAV assessment can be utilized in the digital advertisement industry. Conclusively, to persuade the consumers, a 30 s video is recommended if the structure is linear, and a 15 s video is suggested if the structure is nonlinear. Therefore, considering the production budget in advance, a wise decision can be made in developing consumer market strategies.
An integrated model of clinical information and gene expression for prediction of survival in breast cancer patients

A model that combines clinical data and genetic characteristics to predict patients with breast cancer survival is investigated in this paper. Accumulating evidence shows that clinical factors alone are not adequate for predicting the survival of patients with breast cancer, and many genes have been found to be associated with breast cancer prognosis. The objective of this study was to develop a model that integrates clinical information and genes to predict the survival durations of patients diagnosed with breast cancer. The clinical data of 531 breast cancer patients and 63 normal control groups were collected. Meanwhile, the mRNAs expression profile was constructed. Univariate Cox regression, the 2-sided log-rank test, multivariate Cox regression and ridge regression were used to obtain a prognostic index consisting of 4 clinical variables and 35 mRNAs. The area under the curve of the receiver operating characteristic of the integrated clinical and gene model was 0.67322, larger than that of the clinical alone model (0.62535) or the gene alone model (0.61989). Breast cancer patients in the high-risk group had a significantly shorter overall survival time compared with patients in the low-risk group.
Joint Computation Offloading and Resource Allocation for NOMA-Enabled Industrial Internet of Things

In Industrial Internet of Things (IIoT), in order to satisfy the requirements of delay-sensitive service, devices with limited computing resources could offload computing task to edge server via wireless network. However, limited spectrum resource restricts the computation offloading of massive devices and may cause high transmission delay. Non-orthogonal multiple access (NOMA) is a promising technique to handle the issue which allows multiple devices to share the same frequency resource simultaneously. Thus in order to improve spectrum efficiency, this paper investigates computation offloading in NOMA-enabled IIoT. Specially, we consider co-channel interference in our model which complicates network spectrum resource allocation. A computation offloading scheme is designed by solving the minimum total completion delay problem which jointly optimizes computation resources allocation, transmission power allocation and subchannel assignment. In order to reduce the computational complexity, we decompose the original problem into three subproblems and propose an iterative algorithm based on optimization theory and many-to-one matching. Simulation results show that compared with orthogonal multiple access (OMA) based method, our method outperforms in total completion delay and network transmission rate.
Method of Arrival Process Description for Packet Switch

The arrival process description is important problem for analysis of the packet switches that can be considered as queueing systems. Such queueing systems are the appropriate models for the study of the stochastic characteristics. The accuracy of the results of the model study is largely determined by the correctness of the description of the arrival process. This paper proposes a method to solve the problem of choosing a distribution type to describe arrival process at the teletraffic system input. The authors introduce a criterion for choosing the distribution type based on the error minimization in the estimates of the mean value and coefficient of variation in the delay times in the teletraffic system. Sometimes measurement results characterizing the arrival process are available. In this case, the correctness of the proposed distribution is also checked using the goodness-of-fit test. The paper provides case studies on how the proposed method can be applied to the process of choosing the distribution type in question.
Survey of sequential convex programming and generalized Gauss-Newton methods

We provide an overview of a class of iterative convex approximation methods for nonlinear optimization problems with convex-over-nonlinear substructure. These problems are characterized by outer convexities on the one hand, and nonlinear, generally nonconvex, but differentiable functions on the other hand. All methods from this class use only first order derivatives of the nonlinear functions and sequentially solve convex optimization problems. All of them are different generalizations of the classical Gauss-Newton (GN) method. We focus on the smooth constrained case and on three methods to address it: Sequential Convex Programming (SCP), Sequential Convex Quadratic Programming (SCQP), and Sequential Quadratically Constrained Quadratic Programming (SQCQP). While the first two methods were previously known, the last is newly proposed and investigated in this paper. We show under mild assumptions that SCP, SCQP and SQCQP have exactly the same local linear convergence – or divergence – rate. We then discuss the special case in which the solution is fully determined by the active constraints, and show that for this case the KKT conditions are sufficient for local optimality and that SCP, SCQP and SQCQP even converge quadratically. In the context of parameter estimation with symmetric convex loss functions, the possible divergence of the methods can in fact be an advantage that helps them to avoid some undesirable local minima: generalizing existing results, we show that the presented methods converge to a local minimum if and only if this local minimum is stable against a mirroring operation applied to the measurement data of the estimation problem. All results are illustrated by numerical experiments on a tutorial example.
Many-Body effects in Semiconductor Nanostructures

Faculty of Science Department of Physics Teknologie Licentiat Many-Body effects in Semiconductor Nanostructures by Carl Wesslén Low dimensional semiconductor structures are modeled using techniques from the field of many-body atomic physics. B-splines are used to create a one-particle basis, used to solve the more complex many-body problems. Details on methods such as the Configuration Interaction (CI), Many-Body Perturbation Theory (MBPT) and Coupled Cluster (CC) are discussed. Results from the CC singles and doubles method are compared to other high-precision methods for the circular harmonic oscillator quantum dot. The results show a good agreement for the energy of many-body states of up to 12 electrons. Properties of elliptical quantum dots, circular quantum dots, quantum rings and concentric quantum rings are all reviewed. The effects of tilted external magnetic fields applied to the elliptical dot are discussed, and the energy splitting between the lowest singlet and triplet states is explored for varying geometrical properties. Results are compared to experimental energy splittings for the same system containing 2 electrons.