Two-layered Framework Research Articles

Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks, including denial-of-services (DoS) attacks, false-data injection attacks, camouflage attacks, and actuation attacks (AAs). Inspired by the concept of digital twin, a new two-layered protocol equipped with a safe and private twin layer (TL) is proposed, which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer. First, a topology-repairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme, which saves communication resources considerably. The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated. Second, a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed. Moreover, this novel adaptive controller can achieve uniformly ultimately bounded convergence, whose error bound can be given explicitly. The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.

Distributed Trajectory Optimization and Fixed-Time Tracking Control of a Group of Connected Vehicles

This paper investigates a distributed trajectory optimization and trajectory tracking control problem for a collection of vehicles. A two-layered framework is established for distributed trajectory optimization and fixed-time tracking control. The trajectory optimization problem is solved using distributed convex optimization based on spacing errors minimization, resulting in an algorithm to provide the optimal trajectory for all following vehicles. The lower layer plays the role of tracking the designated trajectory and robustly guarantee zero steady-state spacing errors for the vehicular platoon. The controller is derived based on the tracking error system in the context of fixed-time stability and terminal sliding mode control theory. The methodology can guarantee the internal stability and string stability, as demonstrated by numerical simulations.

Qualitative reasoning in a two-layered framework

The reasoning with qualitative uncertainty measures involves comparative statements about events in terms of their likeliness without necessarily assigning an exact numerical value to these events.The paper is divided into two parts. In the first part, we formalise reasoning with the qualitative counterparts of capacities, belief functions, and probabilities, within the framework of two-layered logics. Namely, we provide two-layered logics built over the classical propositional logic using a unary belief modality B that connects the inner layer to the outer one where the reasoning is formalised by means of Gödel logic. We design their Hilbert-style axiomatisations and prove their completeness. In the second part, we discuss the paraconsistent generalisations of the logics for qualitative uncertainty that take into account the case of the available information being contradictory or inconclusive.

Path following control for miniature fixed-wing unmanned aerial vehicles under uncertainties and disturbances: a two-layered framework

Path following control for miniature fixed-wing unmanned aerial vehicles under uncertainties and disturbances: a two-layered framework

A Novelty Two-Layered Ensemble Framework with Hybrid Filter-Based Feature Selection for Prediction of Coronary Artery Disease

A Novelty Two-Layered Ensemble Framework with Hybrid Filter-Based Feature Selection for Prediction of Coronary Artery Disease

Responsible Resource Management in Remanufacturing—Framework for Qualitative Assessment in Small and Medium-Sized Enterprises

Through the remanufacturing process, obsolete, broken, and end-of-use products are brought to “a like new condition”. Remanufacturing is an example of implementation of circular economy at a company level. There are few studies on responsible resource management in a remanufacturing process. This paper contributes to this research gap by presenting a two-layered framework, which uses the maturity model theory, and it allows for a quick scan of a remanufacturing process. First, in the descriptive layer of the framework we define five maturity levels with regard to responsible resource management. We analyze water, emissions, energy, and materials, and describe relevant responsible resource management practices, which we link with maturity levels. We also design the relevant self-assessment tool which utilizes the existing expert’s knowledge of a company. Then, in the prescriptive layer of the framework, we propose a method for the identification of the maturity gap, and areas for improvement. We develop a procedure for prioritizing the measures, which shall be implemented in order to achieve a higher level of responsible resource management in a remanufacturing company. The framework is tested in small and medium-sized enterprises from the automotive industry.

Two Effective Strategies to Support Cross-Organization Emergency Resource Allocation Optimization

Cross-organization emergency resource allocation optimization problem is essential to guarantee a successful emergency disposal, and it has become a research focus of modern emergency management. Generally speaking, there are two possible types of resource allocation scenarios: (1) if the emergency resources are overallocated, on the one hand, parallel execution of independent emergency activities can be supported and the emergency disposal time is reduced; on the other hand, too many idle resources may cause low resource utilization rate, high scheduling overhead, and high cost; and (2) if emergency resources are underallocated, this may lead to resource conflicts and the need for some emergency activities to wait for others to complete, and finally the emergency disposal time may increase. Therefore, reasonable emergency resource allocation strategies are highly desired. To the best of our knowledge, there is no formal approach to support the cross-organization emergency resource allocation issue. To handle this problem, we propose a two-layered framework to facilitate the allocation of limited emergency resources to meet its time constraints with high efficiency. More specifically, a kind of Petri net extended with time, resource, and message information, denoted as CE-net, is presented to model cross-organization emergency response processes. Based on the obtained CE-net, the minimum resource requirements are obtained with corresponding algorithms. Then, Minimum Execution Time (MET) strategy and Minimum Resource Consumption (MRC) strategy with their corresponding estimated execution intervals are introduced to facilitate the stakeholder to determine which strategy is suitable according to the timing requirements. A cross-organization fire emergency case is applied to validate the proposed approaches throughout the whole paper.

A self-adapting hierarchical actions and structures joint optimization framework for automatic design of robotic and animation skeletons

Skeleton designs are widely seen in the robotics industry and multimedia applications such as animated films and computer games. The design of skeletons is mental-labor intensive, especially axes directions of joints are difficult even for the most experienced designers to select. In the existing works, there are auto creation of skeletons from meshes, and skeleton axes optimizations from a predefined set of actions. In this work, we extend automatic construction of skeletons by proposing skeleton axes design from an objective task. A two-layered framework is proposed to optimize randomly initialized axes based on auto generated actions. First, the limit of skeleton scales that can be automatically designed is discussed. Second, a self-adaptive actions discretizer implemented by neural networks is proposed to reduce the optimization complexity. Third, actions and axes are scored by physics engine simulation, and optimizations on the score generate axes that have the best performance. Experiments include robotic designs and an animation application. Comparisons show that the proposed framework outperforms other mainstream optimizers both in speed and effectiveness.

A Two-Layered Framework for the Discovery of Software Behavior: A Case Study

During the execution of software, tremendous amounts of data can be recorded. By exploiting the execution data, one can discover behavioral models to describe the actual software execution. As a well-known open-source process mining toolkit, ProM integrates quantities of process mining techniques and enjoys a variety of applications in a broad range of areas. How to develop a better ProM software, both from user experience and software performance perspective, are of vital importance. To achieve this goal, we need to investigate the real execution behavior of ProM which can provide useful insights on its usage and how it responds to user operations. This paper aims to propose an effective approach to solve this problem. To this end, we first instrument existing ProM framework to capture execution logs without changing its architecture. Then a two-layered framework is introduced to support accurate ProM behavior discovery by characterizing both user interaction behavior and plug-in calling behavior separately. Next, detailed discovery techniques to obtain user interaction behavior model and plug-in calling behavior model are proposed. All proposed approaches have been implemented.

Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties

This paper addresses a three-dimensional (3D) path following control problem for underactuated autonomous underwater vehicle (AUV) subject to both internal and external uncertainties. A two-layered framework synthesizing the 3D guidance law and heuristic fuzzy control is proposed to achieve robust adaptive following along a predefined path. In the first layer, a 3D guidance controller for underactuated AUV is presented to guarantee the stability of path following in the kinematics stage. In the second layer, a heuristic adaptive fuzzy algorithm based on the guidance command and feedback linearization Proportional-Integral-Derivative (PID) controller is developed in the dynamics stage to account for the nonlinear dynamics and system uncertainties, including inaccuracy modelling parameters and time-varying environmental disturbances. Furthermore, the sensitivity analysis of the heuristic fuzzy controller is presented. Against most existing methods for 3D path following, the proposed robust fuzzy control scheme reduces the design and implementation costs of complicated dynamics controller, and relaxes the knowledge of the accuracy dynamics modelling and environmental disturbances. Finally, numerical simulation results validate the effectiveness of the proposed control framework and illustrate the outperformance of the proposed controller as well.

Two-Layered Framework for Distributed Multiagent Formation Following

This brief addresses a distributed region formation following problem for multiagent systems with the Euler–Lagrange dynamics. In the region control method, specific orders or positions of the agents are not required, but the main drawback is that a desired reference of the whole group is preplanned and made available to all agents. Here, we present a two-layered framework to achieve the distributed region formation following. In the first layer, we develop a smooth estimator to estimate the global reference information. In the second layer, we propose a control methodology using the estimated desired reference to achieve region formation following in a distributed manner. Unlike most existing methods for distributed tracking, which suffer from discontinuity in the controller, our proposed method is continuous and therefore chattering free. A rigorous Lyapunov-based analysis is utilized to show the stability of the overall system. It is shown that the stability of the overall system can be ensured if the estimation error converges at least exponentially. Finally, the experimental result is presented to illustrate the performance of the proposed methodology.

Oil, centralised power and their shaping effects on tourism in Gabon

The paper looks into tourism non-growth at the early stage of destination development, an under-researched phenomenon. More specifically, it examines the constraining effects of structural environment on tourism growth, with a focus on the leisure travel industry. The study involves the designing of a two-layered analytical framework, drawing on destination development theory and Jessop's strategic-relational approach to structure-agency. The framework is applied to the case of Gabon, using primary and secondary data collected through various techniques. The results highlight how the Dutch disease and institutional behaviours have determined most variables that directly affect destination development in the country. Although tourism is able to emerge in variety of forms in an unfavourable structural environment it cannot grow further. They also suggest that concentrating efforts on operational obstacles in such circumstances is unlikely to be fruitful unless power relationships and ruling elites' interests are directly addressed in business development strategies.

Identity and desecuritisation: the pitfalls of conflating ontological and physical security

How can the Self move from a securitised to a non-securitised relation with the Other while its very identity depends on its relation to the Other? Within the existing critical approaches to security, this question, which encapsulates the complex interrelationship between identity and desecuritisation, has not been explored in a systematic manner. This article builds on the emerging literature on ontological security to develop a two-layered framework of security as both ontological and physical, wherein the relationship between identity and desecuritisation can be better analysed. I argue that the conflation of ontological and physical security within the existing critical approaches to security has generated an insufficient appreciation of how identity expands the possibilities for desecuritisation while imposing new limits. In particular, the framework offered in this article highlights the possibilities for achieving ontological security in the absence of securitisation and limits to desecuritisation that stem from ontological insecurity.

A stratified framework for handling conditional preferences: An extension of the analytic hierarchy process

Representing and reasoning over different forms of preferences is of crucial importance to many different fields, especially where numerical comparisons need to be made between critical options. Focusing on the well-known Analytical Hierarchical Process (AHP) method, we propose a two-layered framework for addressing different kinds of conditional preferences which include partial information over preferences and preferences of a lexicographic kind. The proposed formal two-layered framework, called CS-AHP, provides the means for representing and reasoning over conditional preferences. The framework can also effectively order decision outcomes based on conditional preferences in a way that is consistent with well-formed preferences. Finally, the framework provides an estimation of the potential number of violations and inconsistencies within the preferences. We provide and report extensive performance analysis for the proposed framework from three different perspectives, namely time-complexity, simulated decision making scenarios, and handling cyclic and partially defined preferences.

Two-layered Blogger identification model integrating profile and instance-based methods

This paper introduces a two-layered framework that improves the result of authorship identification within larger sample numbers of bloggers as compared with earlier work. Previous studies are mainly divided into two categories: profile-based and instance-based methods. Each of these approaches has its advantages and limitations. The two-layered framework presented here integrates the two previous approaches and presents a new solution to a key problem in authorship identification, namely the drop in accuracy experienced as the number of authors increases. The paper begins by illustrating the regular instance-based core model and the investigated features. It then introduces a new psycholinguistic profile representation of authors, presents similarity grouping extraction over profiles, and applies blogger identification utilizing the two-layered approach. The results confirm the improvement introduced by the proposed two-layered approach against our regular classifier, as well as a selected baseline, for an extended number of users.

Fuzzy Neural Control for Economic-Driven Radio Resource Management in Beyond 3G Networks

Joint radio resource management (JRRM) is the envisaged process aimed at optimizing the radio resource usage of wireless systems to satisfy the requirements of both the network operators and the users in the context of future generation wireless networks. In particular, this paper proposes a two-layered JRRM framework to improve the efficiency of multiradio and multioperator cellular scenarios. On the one hand, the intraoperator JRRM relies on fuzzy neural mechanisms with economic-driven reinforcement learning techniques to exploit radio resources within a single-operator domain. Microeconomic concepts are included in the proposed approach so that user profile differentiation can be considered when making a JRRM decision. On the other hand, interoperator JRRM enables subscribers to obtain service through other operators, if the home operator network is blocked. Simulation results in a number of different scenarios show that interoperator agreements established in a cooperative scenario benefit both the operators and users, which enables efficient load management and increased operator revenue.

Modeling and Detection of Geospatial Objects Using Texture Motifs

We propose the use of motifs, or characteristic spatially recurrent patterns, for modeling and detecting geospatial objects. A method is proposed for learning a texture-motif model from object examples and detecting objects based on the learned model. The model is learned in a two-layered framework: the first learns the constituent texture elements of the motif and, the second, the spatial distribution of the elements. In the experimental session, we demonstrate the model training and selection methodology for objects given a set of training examples. The utility of such models for detecting the presence or absence of geospatial objects in large aerial image datasets comprising tens of thousands of image tiles is then emphasized