Fuzzy Decision in Airplane Speed Control

Lovato, Agnaldo Volpe; Araújo, Ernesto; Silva, J.D.S. da

doi:10.1109/fuzzy.2006.1681918

Cited by 12 publications

(10 citation statements)

References 18 publications

(15 reference statements)

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“…Equation (25) causes A i to minimize the global positive conflict level, according to (18), and at the same time, it prevents its conflict level from rising. Equations (24) and (25) are hard constraints. The white cells in the column defined by FL A i (Table 4) present the combinations of actions between A i and A 0 i∓1 leading to the behavior defined in (24).…”

Section: ) Objective Function Extended With Penaltiesmentioning

confidence: 99%

“…Equations (24) and (25) are hard constraints. The white cells in the column defined by FL A i (Table 4) present the combinations of actions between A i and A 0 i∓1 leading to the behavior defined in (24). The orange cells shown in Table 4 represent actions performed by A ∓10 i∓1 that, in combination with the actions of A i , lead both to occupy the same flight level in t + 1.…”

Section: ) Objective Function Extended With Penaltiesmentioning

confidence: 99%

“…The use of fuzzy logic in air traffic control allows the uncertainty of the information inherent in this type of problem to be considered in a systematic way. Takeoff and landing [23], flight level changes and speed control [24], [25], altitude control [26], the setting of flight routes ( [27], [28]) and conflict detection and resolution [29] are examples of tasks that are well suited to the use of fuzzy logic (types I and II).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Approach for Detecting and Resolving Conflicts in Air Traffic Routes

et al. 2019

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This paper presents a hybrid model for the detection and resolution of conflicts in air traffic routes involving flight level change actions and adjustment of the longitudinal acceleration of aircraft. The strategy comprises an integrated approach that uses a fuzzy model to quantify the level of longitudinal conflict between two aircraft on the same airway. In addition, optimum flight level change actions between aircraft are calculated through a global and dynamic analysis involving the recognition of clusters of aircraft in conflict and the search for the best scenario by means of a genetic algorithm that minimizes the sum of positive conflicts. The results show that the proposed approach is able to detect and remove longitudinal conflicts in advance, providing a potential tool to support decision-making, improve safety and optimize the use of airspace.

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Section: ) Objective Function Extended With Penaltiesmentioning

confidence: 99%

Section: ) Objective Function Extended With Penaltiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach for Detecting and Resolving Conflicts in Air Traffic Routes

et al. 2019

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“…Digital systems are inseparable parts of modern life style, one category of them are embedded systems which are embodied in other ones for controlling and management purposes, such as traffic controllers [1], [2], high speed network switches, airplanes and spacecraft controllers and medical devices [3]. The characteristic of safety is vital in embedded systems, because any anomaly or fault can cause severe financial and physical damages.…”

Section: Introductionmentioning

confidence: 99%

Markov-Based Anomaly Correction in Embedded Systems

Mojarad¹,

Zarandi²

2016

IJCTE

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Abstract-In this paper, an anomaly correction method is proposed which is based on Markov anomaly detection method. The proposed method employs the probability of transitions between events to evaluate the behavior of a system. This method consists of three steps: 1) Construction of transition matrix by probability of transitions between events and list of known events are generated in training phase; 2) Detection of anomaly based on Markov detection method will be done. In test data when the probability of transition previous event to current event does not reach a predefined threshold, an anomaly is detected. Threshold is determined based on constructed transition matrix in step 1; 3) Check the defined constraints for each anomalous event to find source of anomaly and the suitable way to correct the anomalous event. Next, an event with the highest compliance with the constraints is selected. Evaluation of the proposed method is done using a total of 7000 data sets. The operational scope of corrector and the number of injected anomalies varied between 3 and 5, 1 and 7, respectively. The simulation experiments have been done to measure the correction coverage rate which is between 53.5% and 97.2% with average of 77.66%. For evaluation of hardware consumptions of the proposed method, this method is implemented by VHDL. Power, area and time consumptions are on average 87.43 w, 415.48 m 2 , and 4.12ns, respectively.

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“…Their advantages over superscalar processor architectures are [1,2]: low power consumption, high performance, and low design complexity. The spread pace of smart systems calls out for a focus on embedded systems, de ned as a controlling and management unit embodied in a larger system [1,2]. Modern vehicles, home appliance, and medical instrument are mere examples of the vast application of embedded systems [3].…”

Section: Introductionmentioning

confidence: 99%

Comparison and Analysis of Three Anomaly Correction Methods in Embedded Systems

Mojarad

Zarandi

2017

Scientia Iranica

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Abstract. This paper proposes and compares three anomaly correction methods in embedded systems: 1) Markov-based; 2) Stide-based (sequence time-delay embedding); 3) Cluster-based correction methods. All these methods work online on data streams coming from sensors of embedded systems. In these methods, detection is rst obtained using training on normal data, and next in runtime, the correction mechanisms can be applied. Markov-based method is based on probabilities between states, Stide-based method is based on storing common events, and Cluster-based one is based on clustering similar members. In the detection phase, these methods check normal behavior of input data based on what is learned at the training phase. Evaluation is performed using 7000 data sets. The window size and number of injected anomalies vary between 3 and 5, 1 and 7, respectively. Correction coverage for Markov-based, Stide-based, and Cluster-based methods is on average 77.66%, 60.9%, and 70.36%, respectively. Therefore, Markov-based method is the best in terms of correction coverage. Moreover, area overheads of these methods are 249.64, 63.35, and 2.08 m 2 , respectively. As a trade-o , Cluster-based method shows the best correction coverage compared to area, power, and delay overheads.

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Fuzzy Decision in Airplane Speed Control

Cited by 12 publications

References 18 publications

A Hybrid Approach for Detecting and Resolving Conflicts in Air Traffic Routes

A Hybrid Approach for Detecting and Resolving Conflicts in Air Traffic Routes

Markov-Based Anomaly Correction in Embedded Systems

Comparison and Analysis of Three Anomaly Correction Methods in Embedded Systems

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