Development of an Autonomous Friction Gripper for Industrial Robots

Industrial robots become useless without end-effectors that for many instances are in the form of friction grippers. Commonly friction grippers apply frictional forces to different objects on the basis of programmers- experiences. This puts a limitation on the effectiveness of gripping force that may result in damaging the object. This paper describes various stages of design and development of a low cost sensor-based robotic gripper that would facilitate the task of applying right gripping forces to different objects. The gripper is also equipped with range sensors in order to avoid collisions of the gripper with objects. It is a fully functional automated pick and place gripper which can be used in many industrial applications. Yet it can also be altered or further developed in order to suit a larger number of industrial activities. The current design of gripper could lead to designing completely automated robot grippers able to improve the efficiency and productivity of industrial robots.

The Comparison of Form Drag and Profile Dragof a Wind Turbine Blade Section in Pitching Oscillation

Extensive wind tunnel tests have been conducted to investigate the unsteady flow field over and behind a 2D model of a 660 kW wind turbine blade section in pitching motion. The surface pressure and wake dynamic pressure variation at a distance of 1.5 chord length from trailing edge were measured by pressure transducers during several oscillating cycles at 3 reduced frequencies and oscillating amplitudes. Moreover, form drag and linear momentum deficit are extracted and compared at various conditions. The results show that the wake velocity field and surface pressure of the model have similar behavior before and after the airfoil beyond the static stall angle of attack. In addition, the effects of reduced frequency and oscillation amplitudes are discussed.

Optimum Neural Network Architecture for Precipitation Prediction of Myanmar

Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.

Influence of Sire Breed, Protein Supplementation and Gender on Wool Spinning Fineness in First-Cross Merino Lambs

Our objectives were to evaluate the effects of sire breed, type of protein supplement, level of supplementation and sex on wool spinning fineness (SF), its correlations with other wool characteristics and prediction accuracy in F1 Merino crossbred lambs. Texel, Coopworth, White Suffolk, East Friesian and Dorset rams were mated with 500 purebred Merino dams at a ratio of 1:100 in separate paddocks within a single management system. The F1 progeny were raised on ryegrass pasture until weaning, before forty lambs were randomly allocated to treatments in a 5 x 2 x 2 x 2 factorial experimental design representing 5 sire breeds, 2 supplementary feeds (canola or lupins), 2 levels of supplementation (1% or 2% of liveweight) and sex (wethers or ewes). Lambs were supplemented for six weeks after an initial three weeks of adjustment, wool sampled at the commencement and conclusion of the feeding trial and analyzed for SF, mean fibre diameter (FD), coefficient of variation (CV), standard deviation, comfort factor (CF), fibre curvature (CURV), and clean fleece yield. Data were analyzed using mixed linear model procedures with sire fitted as a random effect, and sire breed, sex, supplementary feed type, level of supplementation and their second-order interactions as fixed effects. Sire breed (P

Dimension Reduction of Microarray Data Based on Local Principal Component

Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.

Influence of Ammonium Concentration on the Performance of an Inorganic Biofilter Treating Methane

Among the technologies available to reduce methane emitted from the pig industry, biofiltration seems to be an effective and inexpensive solution. In methane (CH4) biofiltration, nitrogen is an important macronutrient for the microorganisms growth. The objective of this research project was to study the effect of ammonium (NH4 +) on the performance, the biomass production and the nitrogen conversion of a biofilter treating methane. For NH4 + concentrations ranging from 0.05 to 0.5 gN-NH4 +/L, the CH4 removal efficiency and the dioxide carbon production rate decreased linearly from 68 to 11.8 % and from 7.1 to 0.5 g/(m3-h), respectively. The dry biomass content varied from 4.1 to 5.8 kg/(m3 filter bed). For the same range of concentrations, the ammonium conversion decreased while the specific nitrate production rate increased. The specific nitrate production rate presented negative values indicating denitrification in the biofilter.

An Experimental Investigation of Heating in Induction Motors

The ability to predict an accurate temperature distribution requires the knowledge of the losses, the thermal characteristics of the materials, and the cooling conditions, all of which are very difficult to quantify. In this paper, the impact of the effects of iron and copper losses are investigated separately and their effects on the heating in various points of the stator of an induction motor, is highlighted by using two simple tests. In addition, the effect of a defect, such as an open circuit in a phase of the stator, on the heating is also obtained by a no-load test. The squirrel cage induction motor is rated at 2.2 kW; 380 V; 5.2 A; Δ connected; 50 Hz; 1420 rpm and the class of insulation F, has been thermally tested under several load conditions. Several thermocouples were placed in strategic points of the stator.

A General Segmentation Scheme for Contouring Kidney Region in Ultrasound Kidney Images using Improved Higher Order Spline Interpolation

A higher order spline interpolated contour obtained with up-sampling of homogenously distributed coordinates for segmentation of kidney region in different classes of ultrasound kidney images has been developed and presented in this paper. The performance of the proposed method is measured and compared with modified snake model contour, Markov random field contour and expert outlined contour. The validation of the method is made in correspondence with expert outlined contour using maximum coordinate distance, Hausdorff distance and mean radial distance metrics. The results obtained reveal that proposed scheme provides optimum contour that agrees well with expert outlined contour. Moreover this technique helps to preserve the pixels-of-interest which in specific defines the functional characteristic of kidney. This explores various possibilities in implementing computer-aided diagnosis system exclusively for US kidney images.

Lateral Behavior of Concrete

Lateral expansion is a factor defining the level of confinement in reinforced concrete columns. Therefore, predicting the lateral strain relationship with axial strain becomes an important issue. Measuring lateral strains in experiments is difficult and only few report experimental lateral strains. Among the existing analytical formulations, two recent models are compared with available test results in this paper with shortcomings highlighted. A new analytical model is proposed here for lateral strain axial strain relationship and is based on the supposition that the concrete behaves linear elastic in the early stages of loading and then nonlinear hardening up to the peak stress and then volumetric expansion. The proposal for the lateral strain axial strain relationship after the peak stress is mainly based on the hypothesis that the plastic lateral strain varies linearly with the plastic axial strain and it is shown that this is related to the lateral confinement level.

Perturbation Based Search Method for Solving Unconstrained Binary Quadratic Programming Problem

This paper presents a perturbation based search method to solve the unconstrained binary quadratic programming problem. The proposed algorithm was tested with some of the standard test problems and the results are reported for 10 instances of 50, 100, 250, & 500 variable problems. A comparison of the performance of the proposed algorithm with other heuristics and optimization software is made. Based on the results, it was found that the proposed algorithm is computationally inexpensive and the solutions obtained match the best known solutions for smaller sized problems. For larger instances, the algorithm is capable of finding a solution within 0.11% of the best known solution. Apart from being used as a stand-alone method, this algorithm could also be incorporated with other heuristics to find better solutions.

Information Retrieval: Improving Question Answering Systems by Query Reformulation and Answer Validation

Question answering (QA) aims at retrieving precise information from a large collection of documents. Most of the Question Answering systems composed of three main modules: question processing, document processing and answer processing. Question processing module plays an important role in QA systems to reformulate questions. Moreover answer processing module is an emerging topic in QA systems, where these systems are often required to rank and validate candidate answers. These techniques aiming at finding short and precise answers are often based on the semantic relations and co-occurrence keywords. This paper discussed about a new model for question answering which improved two main modules, question processing and answer processing which both affect on the evaluation of the system operations. There are two important components which are the bases of the question processing. First component is question classification that specifies types of question and answer. Second one is reformulation which converts the user's question into an understandable question by QA system in a specific domain. The objective of an Answer Validation task is thus to judge the correctness of an answer returned by a QA system, according to the text snippet given to support it. For validating answers we apply candidate answer filtering, candidate answer ranking and also it has a final validation section by user voting. Also this paper described new architecture of question and answer processing modules with modeling, implementing and evaluating the system. The system differs from most question answering systems in its answer validation model. This module makes it more suitable to find exact answer. Results show that, from total 50 asked questions, evaluation of the model, show 92% improving the decision of the system.

Analysis of Chatter in Ball End Milling by Wavelet Transform

The chatter is one of the major limitations of the productivity in the ball end milling process. It affects the surface roughness, the dimensional accuracy and the tool life. The aim of this research is to propose the new system to detect the chatter during the ball end milling process by using the wavelet transform. The proposed method is implemented on the 5-axis CNC machining center and the new three parameters are introduced from three dynamic cutting forces, which are calculated by taking the ratio of the average variances of dynamic cutting forces to the absolute variances of themselves. It had been proved that the chatter can be easier to detect during the in-process cutting by using the new parameters which are proposed in this research. The experimentally obtained results showed that the wavelet transform can provide the reliable results to detect the chatter under various cutting conditions.

Approximations to the Distribution of the Sample Correlation Coefficient

Given a bivariate normal sample of correlated variables, (Xi, Yi), i = 1, . . . , n, an alternative estimator of Pearson’s correlation coefficient is obtained in terms of the ranges, |Xi − Yi|. An approximate confidence interval for ρX,Y is then derived, and a simulation study reveals that the resulting coverage probabilities are in close agreement with the set confidence levels. As well, a new approximant is provided for the density function of R, the sample correlation coefficient. A mixture involving the proposed approximate density of R, denoted by hR(r), and a density function determined from a known approximation due to R. A. Fisher is shown to accurately approximate the distribution of R. Finally, nearly exact density approximants are obtained on adjusting hR(r) by a 7th degree polynomial.

Statistical Approach to Basis Function Truncation in Digital Interpolation Filters

In this paper an alternative analysis in the time domain is described and the results of the interpolation process are presented by means of functions that are based on the rule of conditional mathematical expectation and the covariance function. A comparison between the interpolation error caused by low order filters and the classic sinc(t) truncated function is also presented. When fewer samples are used, low-order filters have less error. If the number of samples increases, the sinc(t) type functions are a better alternative. Generally speaking there is an optimal filter for each input signal which depends on the filter length and covariance function of the signal. A novel scheme of work for adaptive interpolation filters is also presented.

Performance Analysis of an Adaptive Threshold Hybrid Double-Dwell System with Antenna Diversity for Acquisition in DS-CDMA Systems

In this paper, we consider the analysis of the acquisition process for a hybrid double-dwell system with antenna diversity for DS-CDMA (direct sequence-code division multiple access) using an adaptive threshold. Acquisition systems with a fixed threshold value are unable to adapt to fast varying mobile communications environments and may result in a high false alarm rate, and/or low detection probability. Therefore, we propose an adaptively varying threshold scheme through the use of a cellaveraging constant false alarm rate (CA-CFAR) algorithm, which is well known in the field of radar detection. We derive exact expressions for the probabilities of detection and false alarm in Rayleigh fading channels. The mean acquisition time of the system under consideration is also derived. The performance of the system is analyzed and compared to that of a hybrid single dwell system.

Performance Evaluation of ROI Extraction Models from Stationary Images

In this paper three basic approaches and different methods under each of them for extracting region of interest (ROI) from stationary images are explored. The results obtained for each of the proposed methods are shown, and it is demonstrated where each method outperforms the other. Two main problems in ROI extraction: the channel selection problem and the saliency reversal problem are discussed and how best these two are addressed by various methods is also seen. The basic approaches are 1) Saliency based approach 2) Wavelet based approach 3) Clustering based approach. The saliency approach performs well on images containing objects of high saturation and brightness. The wavelet based approach performs well on natural scene images that contain regions of distinct textures. The mean shift clustering approach partitions the image into regions according to the density distribution of pixel intensities. The experimental results of various methodologies show that each technique performs at different acceptable levels for various types of images.

Optimizing the Design of Radial/Axial PMSM and SRM used for Powered Wheel-Chairs

the paper presents the optimization results for several electrical machines dedicated for powered electric wheel-chairs. The optimization, using the Hook-Jeeves algorithm, was employed based on a design approach which takes into consideration the road conditions. Also, through numerical simulations (based on finite element method), the analytical approach was validated. The optimization approach gave satisfactory results and the best suited variant was chosen for the motorization of the wheel-chair.

Design a Biodegradable Hydrogel for Drug Delivery System

In this article, we synthesize a novel chitosan -based superabsorbent hydrogel via graft copolymerization of mixtures acrylic acid (AA) and N-vinyl pyrollidon onto chitosan backbones. The polymerization reaction was carried out in an aqueous medium and in the presence of ammonium persulfate (APS) as an initiator and N,N'-methylene bisacrylamide (MBA) as a crosslinker.The hydrogel structures were confirmed by FTIR spectroscopy. The swelling behavior of these absorbent polymers was also investigated in various salt solutions. Results indicated that the swelling capacity decreased with an increase in the ionic strength of the swelling medium. Furthermore, the swelling of superabsorbing hydrogels was examined in solutions with pH values ranging between 1.0 and 13.0. It showed a reversible pH-responsive behavior at pHs 2.0 and 8.0. This on-off switching behavior makes the synthesized hydrogels as an excellent candidate for controlled delivery of bioactive agents.

Computational Identification of Bacterial Communities

Stable bacterial polymorphism on a single limiting resource may appear if between the evolved strains metabolic interactions take place that allow the exchange of essential nutrients [8]. Towards an attempt to predict the possible outcome of longrunning evolution experiments, a network based on the metabolic capabilities of homogeneous populations of every single gene knockout strain (nodes) of the bacterium E. coli is reconstructed. Potential metabolic interactions (edges) are allowed only between strains of different metabolic capabilities. Bacterial communities are determined by finding cliques in this network. Growth of the emerged hypothetical bacterial communities is simulated by extending the metabolic flux balance analysis model of Varma et al [2] to embody heterogeneous cell population growth in a mutual environment. Results from aerobic growth on 10 different carbon sources are presented. The upper bounds of the diversity that can emerge from single-cloned populations of E. coli such as the number of strains that appears to metabolically differ from most strains (highly connected nodes), the maximum clique size as well as the number of all the possible communities are determined. Certain single gene deletions are identified to consistently participate in our hypothetical bacterial communities under most environmental conditions implying a pattern of growth-condition- invariant strains with similar metabolic effects. Moreover, evaluation of all the hypothetical bacterial communities under growth on pyruvate reveals heterogeneous populations that can exhibit superior growth performance when compared to the performance of the homogeneous wild-type population.

Maximum Water Hammer Sensitivity Analysis

Pressure waves and Water Hammer occur in a pumping system when valves are closed or opened suddenly or in the case of sudden failure of pumps. Determination of maximum water hammer is considered one of the most important technical and economical items of which engineers and designers of pumping stations and conveyance pipelines should take care. Hammer Software is a recent application used to simulate water hammer. The present study focuses on determining significance of each input parameter of the application relative to the maximum amount of water hammer estimated by the software. The study determines estimated maximum water hammer variations due to variations of input parameters including water temperature, pipe type, thickness and diameter, electromotor rpm and power, and moment of inertia of electromotor and pump. In our study, Kuhrang Pumping Station was modeled using WaterGEMS Software. The pumping station is characterized by total discharge of 200 liters per second, dynamic height of 194 meters and 1.5 kilometers of steel conveyance pipeline and transports water to Cheshme Morvarid for farmland irrigation. The model was run in steady hydraulic condition and transferred to Hammer Software. Then, the model was run in several unsteady hydraulic conditions and sensitivity of maximum water hammer to each input parameter was calculated. It is shown that parameters to which maximum water hammer is most sensitive are moment of inertia of pump and electromotor, diameter, type and thickness of pipe and water temperature, respectively.