Design of a Commercial Hybrid VTOL UAV System

For the last four decades Unmanned Air Vehicles (UAVs) have been extensively used for military operations that include tracking, surveillance, active engagement with weapons and airborne data acquisition. UAVs are also in demand commercially due to their advantages in comparison to manned vehicles. These advantages include lower manufacturing and operating costs, flexibility in configuration depending on customer request and not risking the pilot on demanding missions. Even though civilian UAVs currently constitute 3 % of the UAV market, it is estimated that their numbers will reach up to 10 % of the UAV market within the next 5 years. Most of the civilian UAV applications require UAVs that are capable of doing a wide range of different and complementary operations within a composite mission. These operations include taking off and landing from limited runway space, while traversing the operation region in considerable cruise speed for mobile tracking applications. This is in addition to being able traverse in low cruise speeds or being able to hover for stationary measurement and tracking. All of these complementary and but different operational capabilities point to a hybrid unmanned vehicle concept, namely the Vertical Take-Off and Landing (VTOL) UAVs. In addition, the desired UAV system needs to be cost-efficient while providing easy payload conversion for different civilian applications. In this paper, we review the preliminary design process of such a capable civilian UAV system, namely the TURAC VTOL UAV. TURAC UAV is aimed to have both vertical take-off and landing and Conventional Take-off and Landing (CTOL) capability. TURAC interchangeable payload pod and detachable wing (with potential different size variants) provides capability to perform different mission types, including long endurance and high cruise speed operations. In addition, the TURAC concept is to have two different variants. The TURAC A variant is an eco-friendly and low-noise fully electrical platform which includes 2 tilt electric motors in the front, and a fixed electric motor and ducted fan in the rear, where as the TURAC B variant is envisioned to use high energy density fuel cells for extended hovering time. In this paper, we provide the TURAC UAV’s iterative design and trade-off studies which also include detailed aerodynamic and structural configuration analysis. For the aerodynamic analysis, an in-house software including graphical user interface has been developed to calculate the aerodynamic forces and moments by using the Vortex Lattice Method (VLM). Computational Fluid Dynamics (CFD) studies are performed to determine the aerodynamic effects for various configurations For structural analysis, a Finite Element Model (FEM) of the TURAC has been prepared and its modal analysis is carried out. Maximum displacements and maximal principal stresses are calculated and used for streamlining a weight efficient fuselage design. Prototypes have been built to show success of the design at both hover and forward flight regime. In this paper, we also provide the flight management and autopilot architecture of the TURAC. The testing of the controller performance has been initiated with the prototype of TURAC. Current work focuses on the building of the full fight test prototype of the TURAC UAV and aerodynamic modeling of the transition flight.     

A comparative study of surface integrity of Ti–6Al–4V alloy machined by EDM and AECG

The electrical discharge machining (EDM) process produces the recast layer with or without cracks on the surface that requires a remedial post-treatment in the manufacture of critical or highly stressed surfaces. One of the frequently used post-treatment processes is also the abrasive electrochemical grinding (AECG) and it has been widely used in the precision machining of difficult-to-cut materials due to an enhanced surface integrity and productivity. The aim of this study is to investigate improvability of surface integrity in terms of machining voltage, electrolyte flow rate and table feed rate parameters of AECG in EDMed Ti6Al4V alloy. Scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive spectrograph (EDS) and surface roughness measurement were performed to study the surface characteristics of the machined samples. Experimental results indicate that the AECG process effectively improves the surface roughness and eliminates the EDM damages completely by setting suitable grinding parameters.     

SONIC: streaming overlapping community detection

A community within a graph can be broadly defined as a set of vertices that exhibit high cohesiveness (relatively high number of edges within the set) and low conductance (relatively low number of edges leaving the set). Community detection is a fundamental graph processing analytic that can be applied to several application domains, including social networks. In this context, communities are often overlapping, as a person can be involved in more than one community (e.g., friends, and family); and evolving, since the structure of the network changes. We address the problem of streaming overlapping community detection, where the goal is to maintain communities in the presence of streaming updates. This way, the communities can be updated more efficiently. To this end, we introduce SONIC—a find-and-merge type of community detection algorithm that can efficiently handle streaming updates. SONIC first detects when graph updates yield significant community changes. Upon the detection, it updates the communities via an incremental merge procedure. The SONIC algorithm incorporates two additional techniques to speed-up the incremental merge; min-hashing and inverted indexes. Results show that SONIC can provide high quality overlapping communities, while handling streaming updates several orders of magnitude faster than the alternatives performing from-scratch computation.     

A new approach based on a discrete hidden Markov model using the Rocchio algorithm for the diagnosis of heart valve diseases

Abstract: Application of the Doppler ultrasound technique in the diagnosis of heart diseases has been increasing in the last decade since it is non‐invasive, practicable and reliable. In this study, a new approach based on the discrete hidden Markov model (DHMM) is proposed for the diagnosis of heart valve disorders. For the calculation of hidden Markov model (HMM) parameters according to the maximum likelihood approach, HMM parameters belonging to each class are calculated by using training samples that only belong to their own classes. In order to calculate the parameters of DHMMs, not only training samples of the related class but also training samples of other classes are included in the calculation. Therefore HMM parameters that reflect a class's characteristics are more represented than other class parameters. For this aim, the approach was to use a hybrid method by adapting the Rocchio algorithm. The proposed system was used in the classification of the Doppler signals obtained from aortic and mitral heart valves of 215 subjects. The performance of this classification approach was compared with the classification performances in previous studies which used the same data set and the efficiency of the new approach was tested. The total classification accuracy of the proposed approach (95.12%) is higher than the total accuracy rate of standard DHMM (94.31%), continuous HMM (93.5%) and support vector machine (92.67%) classifiers employed in our previous studies and comparable with the performance levels of classifications using artificial neural networks (95.12%) and fuzzy‐C‐means/CHMM (95.12%).     

Just-in-time connectivity for large spiking networks

The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.