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.     

Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

One of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer’s spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.

     

Fuzzy axiomatic design extension for managing model selection paradigm in decision science

The model selection paradigm is one of the focused themes within decision science. This paper addresses the consistent solution of model selection issue on the basis of the fuzzy axiomatic design (FAD) methodology. Moreover, the developed FAD–based model selection interface (FAD–MSI) is performed over the critical ship management processes in order to assign suitable (multiple criteria decision-making) MCDM techniques even if commonly utilized hybrid approaches. The outcomes of this study encourage the maritime practitioners for the further researches towards analytical modelling of ship management processes.     

Extending boosting for large scale spoken language understanding

We propose three methods for extending the Boosting family of classifiers motivated by the real-life problems we have encountered. First, we propose a semisupervised learning method for exploiting the unlabeled data in Boosting. We then present a novel classification model adaptation method. The goal of adaptation is optimizing an existing model for a new target application, which is similar to the previous one but may have different classes or class distributions. Finally, we present an efficient and effective cost-sensitive classification method that extends Boosting to allow for weighted classes. We evaluated these methods for call classification in the AT&;T VoiceTone^® spoken language understanding system. Our results indicate that it is possible to obtain the same classification performance by using 30% less labeled data when the unlabeled data is utilized through semisupervised learning. Using model adaptation we can achieve the same classification accuracy using less than half of the labeled data from the new application. Finally, we present significant improvements in the “important” (i.e., higher weighted) classes without a significant loss in overall performance using the proposed cost-sensitive classification method.     

Hybrid simulation and optimization-based design and operation of integrated photovoltaic generation,storage units,and grid

Unlike fossil-fueled generation, solar energy resources are geographically distributed and highly intermittent, which makes their direct control extremely difficult and requires storage units as an additional concern. The goal of this research is to design and develop a flexible tool, which will allow us to obtain (1) an optimal capacity of an integrated photovoltaic (PV) system and storage units and (2) an optimal operational decision policy considering the current and future market prices of the electricity. The proposed tool is based on hybrid (system dynamics model and agent-based model) simulation and meta-heuristic optimization. In particular, this tool has been developed for three different scenarios (involving different geographical scales), where PV-based solar generators, storage units (compressed-air-energy-storage (CAES) and super-capacitors), and grid are used in an integrated manner to supply energy demands. Required data has been gathered from various sources, including NASA and TEP (utility company), US Energy Information Administration, National Renewable Energy Laboratory, commercial PV panel manufacturers, and publicly available reports. The constructed tool has been demonstrated to (1) test impacts of several factors (e.g. demand growth, efficiencies in PV panel and CAES system) on the total cost of the integrated generation and storage system and an optimal mixture of PV generation and storage capacity, and to (2) demonstrate an optimal operational policy.     

Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development

The development of alliance-based software requires the collaboration of many stakeholders. These different stakeholders across multiple organizations form a complex social network. The goal of this paper is to develop a novel modeling framework, which will help task managers devise optimal workforce assignments considering both short-term and long-term aspects of the software development process. The proposed framework is composed of an assignment module and a prediction module. For a given task, the assignment module first selects a candidate workforce mix. Based on the candidate workforce mix, the prediction module then predicts the short-term performance (productivity) as well as the long-term performance (workforce training and robustness of the organization) of the organization. Then, the assignment module selects another candidate mix, and this iteration continues until an optimal workforce mix is found. The prediction module and the assignment module are based on an agent-based simulation method and a multi-objective optimization model, respectively. The proposed modeling framework is illustrated with a software enhancement request process in Kuali, an alliance-based open source software development project involving 12 organizations. The constructed framework is executed with varying parameters to demonstrate its use and benefit in the software enhancement process.     

User-Driven Frequency Scaling

We propose and evaluate user-driven frequency scaling (UDFS) for improved power management on processors that support dynamic voltage and frequency scaling (DVFS), e.g, those used in current laptop and desktop computers. UDFS dynamically adapts CPU frequency to the individual user and the workload through a simple user feedback mechanism, unlike currently-used DVFS methods which rely only on CPU utilization. Our UDFS algorithms dramatically reduce typical operating frequencies while maintaining performance at satisfactory levels for each user. We evaluated our techniques through user studies conducted on a Pentium M laptop running Windows applications. The UDFS scheme reduces measured system power by 22.1%, averaged across all our users and applications, compared to the Windows XP DVFS scheme     

Epidemiology of enuresis in Turkish children

The treatment and favorable outcome of a bitch with uterine torsion and two retained fetuses are described. The condition was corrected surgically by ovariohysterectomy. Complications (i.e., septic shock, peritonitis, and hemostatic abnormalities) were managed with aggressive medical therapy. Torsion of the gravid uterus in dogs is a life-threatening condition which can have a successful outcome if medical complications encountered in the pre- and postoperative periods are treated quickly and effectively.     

Fabrication and characterization of anisotropic dielectrics for low-loss microwave applications

New magneto-photonic assembly designs for high-gain antennas require dielectrics with a significant anisotropy and low loss at GHz frequencies. This paper describes an approach to fabricate such dielectrics from ceramic laminates. These laminates consist of two ceramics with largely different permittivities and low dielectric losses. Alternating layers of commercially available α-Al₂O₃ and Nd-doped BaTiO₃ were laminated using organic adhesives. Equivalent permittivity tensors and loss tangents were characterized using a resonant cavity-based approach, which was coupled with a finite-element method full-wave solver. Measured permittivity values were in good agreement with mean field predictions; a minimum loss tangent 1.1 × 10^?3 was obtained when using one-component epoxy (Loctite^®-3982) adhesive. Application of two-component epoxy (M-bond 610) adhesive results in a slightly higher loss but better mechanical properties and machinability. These laminates were used to demonstrate high gain in a prototype antenna with 6 misaligned anisotropic dielectric layers.