Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis and additionally considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries, and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. It allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 0.989~84.995 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure

Improving Departure Throughput by Dynamically Adjusting Inter-Arrival Spacing

LaGuardia Airport (LGA) in New York has many unique challenges that create excess taxi-out delays. The purpose of this paper is to investigate the potential benefit that could be gained by tactically adjusting the Terminal Sequencing and Spacing (TSS) schedule to precisely manage inter-arrival spacing to maximize the number of departures per arrival pair. Three strategies for dynamically adjusting arrival schedules are proposed in this paper: Delay Control, Delay and Advance, and No Slack Capacity. The benefits of these strategies were examined on actual traffic data at LGA. The results showed that by applying these strategies, a 10 to 60 increase in departures and a reduction in un-utilized departure capacity (gaps) could be achieved during the airports busiest six-hour period. Significant increases in departure throughput would improve air traffic operations by reducing departure delay time. Furthermore, the concept could be used to resolve temporal mismatches between departure capacity and demand which also cause excessive departure delays

Concurrent learning adaptive control with directional forgetting

This paper proposes a new concurrent learning-based adaptive control algorithm. The main objective behind our proposition is to relax the persistent excitation requirement for the stability guarantee, while providing the ability to identify time-varying parameters. To achieve the objective, this paper designs a directional forgetting algorithm, which is then integrated with the adaptive law. The theoretical stability analysis shows that the tracking and parameter estimation error is exponentially stable with the signal only finitely excited, not persistently excited. The analysis also shows that the proposed algorithm can guarantee the stability under time-varying parameters. Moreover, the necessary and sufficient conditions for the stability given the time-varying parameters are derived. The results of numerical simulations confirm the validity of the theoretical analysis results and demonstrate the performance of the proposed algorithm

A new command shaping guidance law using Lagrange multiplier

This article presents a new command shaping guidance law by change of Lagrange multiplier (LM), called CSGL-LM. The Schwarz inequality approach is used to solve the optimal guidance problems considering both terminal constraints on interception and impact angle control. LM is introduced to combine two terminal constraints into a single equation. The main idea of this paper is to use LM as a design parameter for shaping the guidance command as well as controlling the terminal constraints. The guidance command of CSGL-LM is given a unified functional form of the time-to-go, the state variables, and LM. Therefore, through an appropriate choice of LM, we can achieve various shapes of the guidance commands for the interception case, as well as the impact angle control case. As illustrative examples, this paper also shows that a class of previous guidance laws is just one of particular solutions of CSGL-LM. Numerical simulations are performed to validate the properties of CSGL-LM, compared with the conventional guidance law

Are Histrionic Personality Traits Associated with Irritability during Conscious Sedation Endoscopy?

Aim. We aimed to evaluate whether histrionic personality traits are associated with irritability during conscious sedation endoscopy (CSE). Materials and Methods. A prospective cross-sectional study was planned. Irritability during CSE was classified into five grades: 0, no response; I, minimal movement; II, moderate movement; III, severe movement; IV, fighting against procedure. Patients in grades III and IV were defined as the irritable group. Participants were required to complete questionnaire sheet assessing the extent of histrionic personality traits, extraversion-introversion, and current psychological status. The present authors also collected basic sociodemographic data including alcohol use history. Results. A total of 32 irritable patients and 32 stable patients were analyzed. The histrionic personality trait score of the irritable group was higher than that of the stable group (9.5 ± 3.1 versus 6.9 ± 2.9; P = 0.001), as was the anxiety score (52.8 ± 8.6 versus 46.1 ± 9.6; P = 0.004). Heavy alcohol use was more frequently observed in the irritable group (65.6% versus 28.1%; P = 0.003). In multivariate analysis, all these three factors were independently correlated with irritability during CSE. Conclusion. This study revealed that histrionic personality traits, anxiety, and heavy alcohol use can affect irritability during CSE

Energy-optimal waypoint-following guidance considering autopilot dynamics

This paper addresses the problem of energy-optimal waypoint-following guidance for an Unmanned Aerial Vehicle with the consideration of a general autopilot dynamics model. The proposed guidance law is derived as a solution of a linear quadratic optimal control problem in conjunction with a linearized kinematics model. The algorithm developed integrates path planning and following into a single step and is able to be applied to a general waypoint-following mission. Theoretical analysis reveals that previously suggested optimal point-to-point guidance laws are special cases of the proposed approach. Nonlinear numerical simulations clearly demonstrate the effectiveness of the proposed formulations

Detection of Operator Performance Breakdown as an Automation Triggering Mechanism

Performance breakdown (PB) has been anecdotally described as a state where the human operator "loses control of context" and "cannot maintain required task performance." Preventing such a decline in performance is critical to assure the safety and reliability of human-integrated systems, and therefore PB could be useful as a point at which automation can be applied to support human performance. However, PB has never been scientifically defined or empirically demonstrated. Moreover, there is no validated objective way of detecting such a state or the transition to that state. The purpose of this work is: 1) to empirically demonstrate a PB state, and 2) to develop an objective way of detecting such a state. This paper defines PB and proposes an objective method for its detection. A human-in-the-loop study was conducted: 1) to demonstrate PB by increasing workload until the subject reported being in a state of PB, and 2) to identify possible parameters of a detection method for objectively identifying the subjectively-reported PB point, and 3) to determine if the parameters are idiosyncratic to an individual/context or are more generally applicable. In the experiment, fifteen participants were asked to manage three concurrent tasks (one primary and two secondary) for 18 minutes. The difficulty of the primary task was manipulated over time to induce PB while the difficulty of the secondary tasks remained static. The participants' task performance data was collected. Three hypotheses were constructed: 1) increasing workload will induce subjectively-identified PB, 2) there exists criteria that identifies the threshold parameters that best matches the subjectively-identified PB point, and 3) the criteria for choosing the threshold parameters is consistent across individuals. The results show that increasing workload can induce subjectively-identified PB, although it might not be generalizable-only 12 out of 15 participants declared PB. The PB detection method based on signal detection analysis was applied to the performance data and the results showed that PB can be identified using the method, particularly when the values of the parameters for the detection method were calibrated individually

Anomaly detection of aircraft engine in FDR (flight data recorder) data

This paper deals with detection of anomalous behaviour of aircraft engines in FDR (flight data recorder) data to improve airline maintenance operations. To this end, each FDR data that records different flight patterns is first sampled at a fixed time interval starting at the take-off phase, in order to map each FDR data into comparable data space. Next, the parameters related to the aircraft engine are only selected from the sampled FDR data. In this analysis, the feature points are chosen as the mean value of each parameter within the sampling interval. For each FDR data, the feature vector is then formed by arranging all feature points. The proposed method compares the feature vectors of all FDR data and detects an FDR data in which the abnormal behaviour of the aircraft engine is recorded. The clustering algorithm called DBSCAN (density-based spatial clustering of applications with noise) is applied for this purpose. In this paper, the proposed method is tested using realistic FDR data provided by NASA's open database. The results indicate that the proposed method can be used to automatically identify an FDR data in which the abnormal behaviour of the aircraft engine is recorded from a large amount of FDR data. Accordingly, it can be utilized for a high-level diagnosis of engine failure in airline maintenance operations