Modeling of mechanical properties and bond relationship using data mining process

Reinforced concrete is a widely used construction material. Its properties depend on the bond between the reinforcing bar and concrete as much as the compressive strength or properties of the reinforcing bar because of component of construction expose to both flexural and bond together compressive loads. In this paper, the bond properties of concretes with different mix designs were investigated according to the results of compressive, flexural, bond, and flexural-bond tests. The data mining (DM) process was used to determine relationships among the test results and DM algorithms. Seventeen modeling techniques within WEKA were applied to the experimental data for the prediction of bond properties.The results show that the implemented models were good at predicting the bond properties. The best results were obtained from the RepTree algorithm for bond strength, the Multilayer Perceptron algorithm for flexural-bond strength, the MedSq algorithm for bond slippage, and the Pace Regression for flexural-bond deformation. Bond and flexural-bond can be easily predicted using the compressive strength, flexural strength and tensile stress of the rebar. Although a relationship is also existent between these and bond slippage and flexural-bond deformation, these relationships are weaker than the others.These results suggested that the DM algorithms can be used as an alternative approach to predict the bond strength using the results of compressive, flexural, bond, and flexural-bond tests as input parameters. The predictions of the bond slippage and flexural-bond deformation models poorly agreed with the experimental results. It can be obtained more successful results for these variables, when DM models with different inputs like the rebar-concrete interface stress together the measured parameters are used.     

Preliminary results of a novel enhancement method for high-frequency hearing loss

In this study, a software program was developed for high-frequency hearing loss subjects that includes a detailed audiogram and novel enhancement methods. The software performs enhancements of the audibility of high-frequency sounds according to the subject's detailed 31-point audiogram. This provides subject-specific gains in the entire frequency spectrum, and especially for high frequencies, of sounds. Amplification, compression, and transposition are the three main processing methods used to obtain the desired enhancements for the subjects. For low frequencies, only the amplification method was used according to the dB value of the input. For mid and high frequencies, the compression and transposition methods were used together. To obtain the preliminary results of the study, 10 subjects were enrolled in a detailed audiogram study for five weeks. In the study, environmental, music, and speech sounds were used. While the perceptual mean performances of the subjects were in the range of 25.33-63.77% in the first week, those values increased to 68.75-95.75% in the fifth week. In particular, all noisy and speech sounds were more significantly identified and understood by the subjects with the enhancement method (from 25.33% to 87.5% and from 42.33% to 90.5%, respectively). Three subjects had dropped out at the end of the study and small number of participants are the limitations of this study; however, as a preliminary result, some ideas can be inferred from the results for a smaller set of subjects obtained in the five weeks of the study. Thus, the reliability of the study needs to be tested with more subjects and a comparison with their own hearing aids should be conducted.     

Integration of Path/Maneuver Planning in Complex Environments for Agile Maneuvering UCAVs

In this work, we consider the problem of generating agile maneuver profiles for Unmanned Combat Aerial Vehicles in 3D Complex environments. This problem is complicated by the fact that, generation of the dynamically and geometrically feasible flight trajectories for agile maneuver profiles requires search of nonlinear state space of the aircraft dynamics. This work suggests a two layer feasible trajectory/maneuver generation system. Integrated Path planning (considers geometrical, velocity and acceleration constraints) and maneuver generation (considers saturation envelope and attitude continuity constraints) system enables each layer to solve its own reduced order dimensional feasibility problem, thus simplifies the problem and improves the real time implement ability. In Trajectory Planning layer, to solve the time depended path planning problem of an unmanned combat aerial vehicles, we suggest a two step planner. In the first step, the planner explores the environment through a randomized reachability tree search using an approximate line segment model. The resulting connecting path is converted into flight way points through a line-of-sight segmentation. In the second step, every consecutive way points are connected with B-Spline curves and these curves are repaired probabilistically to obtain a geometrically and dynamically feasible path. This generated feasible path is turned in to time depended trajectory with using time scale factor considering the velocity and acceleration limits of the aircraft. Maneuver planning layer is constructed upon multi modal control framework, where the flight trajectories are decomposed to sequences of maneuver modes and associated parameters. Maneuver generation algorithm, makes use of mode transition rules and agility metric graphs to derive feasible maneuver parameters for each mode and overall sequence. Resulting integrated system; tested on simulations for 3D complex environments, gives satisfactory results and promises successful real time implementation.     

Covering-based rough set classification system

Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

     

Multimodal soft biometrics: combining ear and face biometrics for age and gender classification

Multimedia Tools and Applications - In this paper, we present a multimodal, multitask deep convolutional neural network framework for age and gender classification. In the developed framework, we...     

Determining Rainwater Harvesting Storage Capacity with Particle Swarm Optimization

Water Resources Management - A reduction in the amount of available clean water is a universal problem, and the harvest of rainwater is one of the methods that can solve this issue. In this study,...     

Protective properties of polyaniline and poly(aniline-co-o-anisidine) films electrosynthesized on brass

Polyaniline and poly(aniline-co-o-anisidine) films were deposited on brass (Cu40Zn). The synthesis processes of homo and copolymer film were carried out under cyclic voltammetric condition from 0.12 M aniline and 0.06 M aniline + 0.06 M o-anisidine containing 0.2 M sodium oxalate solutions. Homo and copolymer films were characterized by scanning electron microscopy (SEM). SEM images clearly show that one of the brass electrodes was covered with a black copolymer film of strongly adherent homogeneous characteristic while the other one with a porous dark green homo polymer one. The corrosion performances of coated and uncoated electrodes in 3.5% NaCl were evaluated with the help of AC impedance spectroscopy, anodic polarization plots and open circuit potential–time curves. The protective effect of homo and copolymer films formed on brass grew in parallel with extended exposure time. It was only observed with copolymer-coated electrode that changes in the charge transfer resistance of copolymer-coated electrode were related to strong adsorption of copolymer film on the brass surface which led to the formation of a protective oxide layer due to its catalytic behaviour.     

Movement-Assisted Connectivity Restoration in Wireless Sensor and Actor Networks

Recent years have witnessed a growing interest in applications of wireless sensor and actor networks (WSANs). In these applications, a set of mobile actor nodes are deployed in addition to sensors in order to collect sensors' data and perform specific tasks in response to detected events/objects. In most scenarios, actors have to respond collectively, which requires interactor coordination. Therefore, maintaining a connected interactor network is critical to the effectiveness of WSANs. However, WSANs often operate unattended in harsh environments where actors can easily fail or get damaged. An actor failure may lead to partitioning the interactor network and thus hinder the fulfillment of the application requirements. In this paper, we present DARA, a Distributed Actor Recovery Algorithm, which opts to efficiently restore the connectivity of the interactor network that has been affected by the failure of an actor. Two variants of the algorithm are developed to address 1- and 2-connectivity requirements. The idea is to identify the least set of actors that should be repositioned in order to reestablish a particular level of connectivity. DARA strives to localize the scope of the recovery process and minimize the movement overhead imposed on the involved actors. The effectiveness of DARA is validated through simulation experiments.     

Modeling of radiation heat transport in complex ladder-like structures placed in rectangular enclosures

Complex ladder-like structures recently have been considered as the target design for accelerator applications. The decay heat, during a postulated beyond design-basis loss-of-coolant accident in the target where all normal and emergency cooling fails, is removed mainly by radiation heat transfer. Modeling of the radiation transport in complex ladder-like structures has several challenges and limitations when the standard net-radiation model is used. This paper proposes a simplified lumped, or ‘hot-rung’ model, that considers the worst elements and utilizes the standard net-radiation method. The net-radiation model would under-predict structure temperatures if surfaces were subject to non-uniform radiosity. The proposed model was assessed to suggest corrections to account for the non-uniform radiosity. The non-uniform radiosity effect causes the proposed hot-rung model to under-predict the center-rung temperatures by ≈4–74°C when all parametrics, including temperatures up to 1500°C, were considered. These temperatures are small. The proposed model predicted that an important effect of decreasing the emissivity was smoothing of non-isothermal effects. The radiosity effects are more pronounced when there are strong temperature gradients. Uniform rung temperatures tend to decrease the radiosity effects. We concluded that a relatively simple model that is conservative with respect to radiosity effects could be developed.     

Clustering of wireless sensor and actor networks based on sensor distribution and connectivity

Wireless Sensor and Actor Networks (WSANs) employ significantly more capable actor nodes that can collect data from sensors and perform application specific actions. To take these actions collaboratively at any spot in the monitored regions, maximal actor coverage along with inter-actor connectivity is desirable. In this paper, we propose a distributed actor positioning and clustering algorithm which employs actors as cluster-heads and places them in such a way that the coverage of actors is maximized and the data gathering and acting times are minimized. Such placement of actors is done by determining the k-hop Independent Dominating Set (IDS) of the underlying sensor network. Basically, before the actors are placed, the sensors pick the cluster-heads based on IDS. The actors are then placed at the locations of such cluster-heads. We further derive conditions to guarantee inter-actor connectivity after the clustering is performed. If inter-connectivity does not exist, the actors coordinate through the underlying sensors in their clusters to adjust their locations so that connectivity can be established. The performances of the proposed approaches are validated through simulations.