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The purpose of this paper is to present the development of a Matlab/Simulink‐based simulation environment for the design and testing of indirect and direct adaptive flight control laws with fault tolerant capabilities to deal with the occurrence of actuator and sensor failures.

The simulation environment features a modular architecture and a detailed graphical user interface for simulation scenario set‐up. Indirect adaptive flight control laws are implemented based on an optimal control design and frequency domain‐based online parameter estimation. Direct adaptive flight control laws consist of non‐linear dynamic inversion performed at a reference nominal flight condition augmented with artificial neural networks (NNs) to compensate for inversion errors and abnormal flight conditions following the occurrence of actuator or sensor failures. Failure detection, identification, and accommodation schemes relying on neural estimators are developed and implemented.

The simulation environment provides a valuable platform for the evaluation and validation of fault‐tolerant flight control laws.

The modularity of the simulation package allows rapid reconfiguration of control laws, aircraft model, and detection schemes. This flexibility allows the investigation of various design issues such as: the selection of control laws architecture (including the type of the neural augmentation), the tuning of NN parameters, the selection of parameter identification techniques, the effects of anti‐control saturation techniques, the selection and the tuning of the control allocation scheme, as well as the selection and tuning of the failure detection and identification schemes.

The novelty of this research efforts resides in the development and the integration of a comprehensive simulation environment allowing a very detailed validation of a number of control laws for the purpose of verifying the performance of actuator and sensor failure detection, identification, and accommodation schemes.

The purpose of this paper is to develop and implement a general sensor model under normal and abnormal operational conditions including nine functional categories (FCs) to provide additional tools for the design, testing and evaluation of unmanned aerial systems within the West Virginia University unmanned air systems (UAS) simulation environment.

The characteristics under normal and abnormal operation of various types of sensors typically used for UAS control are classified within nine FCs. A general and comprehensive framework for sensor modeling is defined as a sequential alteration of the exact value of the measurand corresponding to each FC. Simple mathematical and logical algorithms are used in this process. Each FC is characterized by several parameters, which may be maintained constant or may vary during simulation. The user has maximum flexibility in selecting values for the parameters within and outside sensor design ranges. These values can be set to change at pre-defined moments, such that permanent and intermittent scenarios can be simulated. Sensor outputs are integrated with the autonomous flight simulation allowing for evaluation and analysis of control laws.

The developed sensor model can provide the desirable levels of realism necessary for assessing UAS behavior and dynamic response under sensor failure conditions, as well as evaluating the performance of autonomous flight control laws.

Due to its generality and flexibility, the proposed sensor model allows detailed insight into the dynamic implications of sensor functionality on the performance of control algorithms. It may open new directions for investigating the synergistic interactions between sensors and control systems and lead to improvements in both areas.

The implementation of the proposed sensor model provides a valuable and flexible simulation tool that can support system design for safety purposes. Specifically, it can address directly the analysis and design of fault tolerant flight control laws for autonomous UASs. The proposed model can be easily customized to be used for different complex dynamic systems.

In this paper, information on sensor functionality is fused and organized to develop a general and comprehensive framework for sensor modeling at normal and abnormal operational conditions. The implementation of the proposed approach enhances significantly the capability of the UAS simulation environment to address important issues related to the design of control laws with high performance and desirable robustness for safety purposes.

We discuss recent developments in real options theory and its applications to strategic management research, examine the potential difficulties in implementing real options in theory and practice, and propose several areas for future research. Our review shows that real options theory has provided substantial insights into investment and exit decisions as well as into the choice of investment modes. In addition, extant research studies have contributed significantly to our understanding of whether and how organizations can benefit from real options. Future research that addresses difficulties in applications will further advance both real options theory and practice in strategic management. We call for future generations of research to enhance the impact of real options as an emerging dominant conceptual lens in strategic management.

Internal capital markets of diversified firms have been associated with inefficient allocation of investment funds across divisions, leading to value losses. Utilizing a sample of diversified firms that adopted or eliminated Residual Income (RI) plans between 1990 and 2009, we show that adoptions of these plans mitigate investment distortions and lead to value gains. Following the adoption of RI plans, diversified firms start allocating investment funds based on growth opportunities of their divisions. RI plan adopters lower their divisional investment levels, especially in segments with below-average growth opportunities. The overall investment allocation efficiency improves, and the diversification discount diminishes after the adoption of RI plans. However, RI plans appear to be used only as temporary tools for assessing corporate performance. The plans are adopted primarily by firms expected to immediately generate plan bonuses for management, and they are frequently eliminated by firms with bad accounting performance and low managerial bonuses. The study contributes to the literature on organizational efficiency, internal capital markets, and on the importance of measures based on economic profits or RI.

The purpose of this paper is to analyze and compare the performance of several different UAV trajectory tracking algorithms in normal and abnormal flight conditions to investigate the fault‐tolerant capabilities of a novel immunity‐based adaptive mechanism.

The evaluation of these algorithms is performed using the West Virginia University (WVU) UAV simulation environment. Three types of fixed‐parameter algorithms are considered as well as their adaptive versions obtained by adding an immunity‐based mechanism. The types of control laws investigated are: position proportional, integral, and derivative control, outer‐loop nonlinear dynamic inversion (NLDI), and extended NLDI. Actuator failures on the three channels and increased turbulence conditions are considered for several different flight paths. Specific and global performance metrics are defined based on trajectory tracking errors and control surface activity.

The performance of all of the adaptive controllers proves to be better than their fixed parameter counterparts during the presence of a failure in all cases considered.

The immunity inspired adaptation mechanism has promising potential to enhance the fault‐tolerant capabilities of autonomous flight control algorithms and the extension of its use at all levels within the control laws considered and in conjunction with other control architectures is worth investigating.

The WVU UAV simulation environment has been proved to be a valuable tool for autonomous flight algorithm development, testing, and evaluation in normal and abnormal flight conditions.

A novel adaptation mechanism is investigated for UAV control algorithms with fault‐tolerant capabilities. The issue of fault tolerance of UAV control laws has only been addressed in a limited manner in the literature, although it becomes critical in the context of imminent integration of UAVs within the commercial airspace.

In this paper, we conduct a conceptual and bibliographic analysis of the literature that deals with the international strategy of state-owned enterprises (SOEs), with particular attention to SOEs from emerging economies (EEs). We first review the state of the art in defining the concepts of EEs and SOEs. We then conduct a detailed bibliographic analysis of the literature pertaining to SOEs’ involvement in international activities, whether as outward foreign investors or as potential local partners of inward-investing multinational enterprises. The analysis covers general trends in the literature, prominent research questions and outcome variables, use of theories, and choices pertaining to methodology (type of research and effects, empirical contexts). We document a literature that is fast-growing and well balanced in some respects. In other respects, we advance recommendations pertaining to (a) consistency and precision in the use of the concepts of “state-owned enterprise” and “emerging economy”; (b) search for specific evidence on the outward activities of EE SOEs in less-developed economies and even in other EEs, and on their performance; (c) understanding of relative propensities of local SOEs and inward investors to collaborate, and what happens when SOEs encounter each other across borders; (d) opportunities to strengthen the theoretical foundations and contributions of this research; and (e) minding the mix of home and host countries in studies and avoiding undue generalization from what has become a predominantly China-centric literature.

The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA).

The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible control laws design environment is developed, which can be used to easily optimize the gains for a variety of unmanned aerial vehicle (UAV) control laws architectures.

The performance of the aircraft trajectory-tracking controllers was shown to improve significantly through the GA optimization. Additionally, the novel normalization modification was shown to encourage more rapid convergence to an optimal solution.

The GA paradigm shows much promise in the optimization of highly non-linear aircraft trajectory-tracking controllers. The proposed optimization tool facilitates the investigation of novel control architectures regardless of complexity and dimensionality.

The addition of the evolutionary optimization to the WVU UAV simulation environment enhances significantly its capabilities for autonomous flight algorithm development, testing, and evaluation. The normalization methodology proposed in this paper has been shown to appreciably speed up the convergence of GAs.

The paper provides a flexible generalized framework for UAV control system evolutionary optimization. It includes specific novel structural elements and mechanisms for improved convergence as well as a comprehensive PI for trajectory tracking.

The purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling (AAR) for unmanned aerial vehicle, which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.

Methods and techniques for marker detection, feature matching and pose estimation have been designed and implemented in the visual measurement system.

The simple blob detection (SBD) method is adopted, which outperforms the Laplacian of Gaussian method. And a novel noise-elimination algorithm is proposed for excluding the noise points. Besides, a novel feature matching algorithm based on perspective transformation is proposed. Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.

The visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.

The SBD method is used to detect the features and a novel noise-elimination algorithm is proposed. Besides, a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

The purpose of this paper is to examine the degree of the exchange rate pass-through (ERPT) to the consumer price index (CPI) at both aggregated and disaggregated levels in Vietnam. Updated data of the nominal effective exchange rate (NEER) and bilateral exchange rate (BiER) have been utilized in this study for the comparison purposes.

Advanced time-series approaches such as a structural vector autoregressive framework, structural impulse response functions (SIRFs), and structural forecast-error variance decomposition (SFEVD) are utilized in this paper.

Empirical findings from this paper present an incomplete degree of the ERPT to the aggregated CPI. The ERPT based on the BiER is observed to have substantially larger magnitude than the NEER-based pass-through. For the disaggregated level, the degree of the ERPT varies considerably across sub-components of the CPI, with a higher magnitude of the ERPT elasticity being found from the BiER estimations. The index of housing and construction materials has the largest ERPT based on the BiER, followed by the food and foodstuffs (1.00 and 0.56, respectively). The macroeconomic and financial environments as well as an economic integration into the global market may be the main causes of a higher ERPT in Vietnam in comparison with other ASEAN countries.

The significant and incomplete pass-through of the exchange rate in Vietnam can affect firms’ and households’ budget planning, savings and profits. This finding generally implies that the cost of devaluation of the domestic currency affects the society as the whole in terms of welfare. The State Bank of Vietnam should carefully consider the overall effect of welfares when formulating and implementing strategies of currency devaluation. In addition, the Vietnamese economy becomes more sensitive to external vulnerabilities via changes of the exchange rate during an increasingly economic integration into the global market. In order to maintain inflation stability, it is vitally important to reduce the impact of exchange rate movements on the domestic prices, both aggregated and disaggregated levels, by pursuing either monetary policy credibility or inflation targeting.

Previous studies on the ERPT literature in the Asia region or for emerging countries focus mainly on the aggregated data of the CPI. Previous studies were conducted before the global financial crisis in 2008/2009. The current paper is the first of its kind to examine the pass-through from exchange rates to consumer prices in Vietnam using both aggregated and disaggregated data.

The purpose of this paper is to maintain safe flight and to improve existing deicing (in‐flight removal of ice) and anti‐icing (prevention of ice accretion) systems under in‐flight icing conditions.Design/methodology/approach – A recent academic research on aircraft icing phenomenon is presented. Several wind tunnel tests of an experimental aircraft provided by NASA are used in the neural network training. Five ice‐affected parameters are chosen in the light of these experiments and researches. An offline artificial neural network is used as an identification technique. The Kalman filter is used to increase the state measurement's accuracy such that neural network training performance gets better. A linear A340 dynamic model is selected in cruise conditions. This linear model is simulated in time varying manner in terms of changing icing parameters in a system dynamic matrix. The obtained data are used in neural network training and testing.Findings – Airframe icing can grow in many ways and many points on aircraft. In this research, wing leading edge ice occurrence is only considered at the same level in both left and right wings. During ice growth other faults or anomalies are ignored.Originality/value – Existing icing sensors can only provide an indication about possible ice presence. They cannot give information of the exact level of ice. However, the efficiency of current control system of changed model decreases. The proposed technique offers a method to find out the model changes under icing conditions.