Robust Finite-Time Control Algorithm Based on Dynamic Sliding Mode for Satellite Attitude Maneuver

Robust finite-time control algorithms for satellite attitude maneuvers are proposed in this paper. The standard sliding mode is modified, hence the inherent robustness could be maintained, and this fixed sliding mode is modified to dynamic, therefore the finite-time stability could be achieved. First, the finite -time sliding mode based on attitude quaternion is proposed and the loose finite-time stability is achieved by enlarging the sliding mode parameter. In order to get the strict finite-time stability, a sliding mode based on the Euler axis is then given. The fixed norm property of the Euler axis is used, and a sliding mode parameter without singularity issue is achieved. System performance near the equilibrium point is largely improved by the proposed sliding modes. The singularity issue of finite-time control is solved by the property of rotation around a fixed axis. System finite-time stability and robustness are analyzed by the Lyapunov method. The superiority of proposed controllers and system robustness to some typical perturbations such as disturbance torque, model uncertainty and actuator error are demonstrated by simulation results.

A Briefing Survey on Advances of Coupled Networks With Various Patterns

In real-world scenarios, networks do not exist in isolation but coupled together in different ways, including dependent, multi-support, and inter-connected patterns. And, when a coupled network suffers from structural instability or dynamic perturbations, the system with different coupling patterns shows rich phase transition behaviors. In this review, we present coupled network models with different coupling patterns developed from real scenarios in recent years for studying the system robustness. For the coupled networks with different coupling patterns, based on the network percolation theory, this paper mainly describes the influence of coupling patterns on network robustness. Moreover, for different coupling patterns, we here show readers the research background, research context, and the latest research results and applications. Furthermore, different approaches to improve system robustness with various coupling patterns and future possible research directions for coupled networks are explained and considered.

Opportunistic Use of Crowdsourced Workers for Online Relabeling of Potential Adversarial Examples

<div>Deep Neural Networks (DDNs) have achieved tremendous success in handling various Machine Learning (ML) tasks, such as speech recognition, Natural Language Processing, and image classification. However, they have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, none can provide complete robustness. The cutting-edge defense techniques offer partial reliability. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This paper proposes a novel Online Selection and Relabeling Algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers (budget-constraint crowdsourcing) to maximize the ML system’s robustness. OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs (the potential adversarial examples) and moving them to the crowdsourced workers to be validated and corrected (relabeled). As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system’s reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-ofthe- art black-box (transfer-based) defense technique, resulting in a more robust system. Simulation results show that OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA’s performance has a positive correlation with system robustness.<br></div>

Opportunistic Use of Crowdsourced Workers for Online Relabeling of Potential Adversarial Examples

<div>Deep Neural Networks (DDNs) have achieved tremendous success in handling various Machine Learning (ML) tasks, such as speech recognition, Natural Language Processing, and image classification. However, they have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, none can provide complete robustness. The cutting-edge defense techniques offer partial reliability. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This paper proposes a novel Online Selection and Relabeling Algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers (budget-constraint crowdsourcing) to maximize the ML system’s robustness. OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs (the potential adversarial examples) and moving them to the crowdsourced workers to be validated and corrected (relabeled). As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system’s reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-ofthe- art black-box (transfer-based) defense technique, resulting in a more robust system. Simulation results show that OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA’s performance has a positive correlation with system robustness.<br></div>

Last Teen Pixels for Arabic Font Size and Style Recognition

Arabic printed script segmentation and recognition techniques change from font to other i.e. each font has particular properties calligraphic and structural which differ with other. Majority of segmentation system suffer in word or sub word segmentation into characters because they consider one algorithm to segment all kind of Arabic printed font, style and size. The goal of this work is to prepare a system of word or sub word Optical Font Arabic Recognition (OFAR) for different font size and style of Arabic printed script, in order to integrate it in global Arabic Optical Character Recognition (AOCR) to choose preferred and good segmentation algorithm. APTI database was used to extract last ten pixels for each word or sub word to build new database of last 10 pixels for each word; OFAR is based upon this new database and our extraction approach called Pixels Continuity (PC) algorithm in different matrix direction and some histogram statistics to extract 20 features. Three KNN classifiers with K=5 and three different distances using Cityblock, Euclidean and Correlation based upon majority-vote are used to evaluate the system robustness. This classifier is compared in the first time with Back propagation Neural Network and Steerable Pyramid (SP) algorithm to re cognize three font families, then in the second time with Gaussian Mixture Models (GMMs) to recognize font and size. The average recognition results obtained was 99.55% about font and size and 98.17% for font, size and style recognition.

A Digital Twin-Based Platform towards Intelligent Automation with Virtual Counterparts of Flight and Air Traffic Control Operations

Automation technologies have been deployed widely to boost the efficiency of production and operations, to trim the complicated process, and to reduce the human error involved. Nevertheless, aviation remains human-centred and requires collaboration between different parties. Given the lack of a collaborative decision-making training platform for air traffic operations in the industry, this study utilises the concept of cyber-physical systems (CPS) to formulate a system architecture for pilots and air traffic control officers training in collaborative decision making by linking and integrating the virtual counterparts of flights and air traffic control operations. Collaborative decision-making training and the corresponding intelligent automation aids could be realised and supported. A performance analysis via a flight task undertaken with different computational load settings was prepared to evaluate the platform’s latency and integrity. The latency is presented using its 95% confidence interval, and integrity is presented using the percentage of data loss during wireless transmission. The results demonstrated convincing performance and a promising system robustness in both domains.

Robust design of a multirotor aerial vehicle

This paper introduces the methodology of systematic robust design of a multirotor vehicle as an example of how to carry out the robust design of a physical system. Robustness in aerial vehicles is highly desirable as it guarantees a desired level of performance even under environmental uncertainties. Thus far, robustness has been considered in terms of active control in the space of multirotor vehicles, but exploration of the design space itself is lacking. In this work, a conceptual design followed by a robust design is performed to come up with the specifications that lead to least uncertain performance of the multirotor vehicle with respect to stochastic wind disturbances.

BIDIRECTIONAL CONTROLLED MULTIPLE SYNCHRONIZATION OF UNBALANCED ROTORS AND ITS EXPERIMENTAL EVALUATION

In the paper the problem of multiple controlled synchronization of a pair of unbalanced rotors is considered. A new bidirectional control law is proposed for multiple synchronization of rotors. A linear analysis of the dynamics of a simplified system model is presented, demonstrating the control system robustness with respect to the parameters of the vibration machine and controller. The main part of the paper is devoted to the description of the experimental results obtained at the Multiresonance Mechatronic Laboratory Setup, demonstrating the efficiency of the proposed approach and revealing its application scope.