Evolutionary Training of Hybrid Systems of Recurrent Neural Networks and Hidden Markov Models

We present a hybrid architecture of recurrent neural networks (RNNs) inspired by hidden Markov models (HMMs). We train the hybrid architecture using genetic algorithms to learn and represent dynamical systems. We train the hybrid architecture on a set of deterministic finite-state automata strings and observe the generalization performance of the hybrid architecture when presented with a new set of strings which were not present in the training data set. In this way, we show that the hybrid system of HMM and RNN can learn and represent deterministic finite-state automata. We ran experiments with different sets of population sizes in the genetic algorithm; we also ran experiments to find out which weight initializations were best for training the hybrid architecture. The results show that the hybrid architecture of recurrent neural networks inspired by hidden Markov models can train and represent dynamical systems. The best training and generalization performance is achieved when the hybrid architecture is initialized with random real weight values of range -15 to 15.

The Evaluation and the Comparison of the Effect of Without Engine Power and Power Mechanical Systems on Rice Weed

In order to study the influence of different methods of controlling weeds such as mechanical weeding and mechanical weeder efficiency analysis in mechanical cultivation conditions, in farming year of 2011 an experiment was done in a farm in coupling and development of technology center in Haraz,Iran. The treatments consisted of (I) control treatment: where no weeding was done, (II) use of mechanical weeding without engine and (III) power mechanical weeding. Results showed that experimental treatments had significantly different effects (p=0.05) on yield traits and number of filled grains per panicle, while treatments had the significant effects on grain weight and dry weight of weeds in the first, second and third weeding methods at 1% of confidence level. Treatment (II) had its most significant effect on number of filled grains per panicle and yield performance standpoint, which was 3705.97 kg ha-1 in its highest peak. Treatment (III) was ranked as second influential with 3559.8 kg ha-1. In addition, under (I) treatments, 2364.73 kg ha-1 of yield produced. The minimum dry weights of weeds in all weeding methods were related to the treatment (II), (III) and (I), respectively. The correlation coefficient analysis showed that total yield had a significant positive correlation with the panicle grain yield per plant (r= 0.55*) and the number of grains per panicle-1 (r= 0.57*) and the number of filled grains (r= 0.63*). Total rice yield also had negative correlation of r= -0. 64* with weed dry weight at second weed sampling time (17 DAT). The weed dry weight at third and fourth sampling times (24 and 40 DAT) had negative correlations of -0.65** and r=-0.61* with rice yield, respectively.

Retarding Potential Analyzer Design and Result Analysis for Ion Energy Distribution Measurement of the Thruster Plume in the Laboratory

Plasma plume will be produced and arrive at spacecraft when the electric thruster operates on orbit. It-s important to characterize the thruster plasma parameters because the plume has significant effects or hazards on spacecraft sub-systems and parts. Through the ground test data of the desired parameters, the major characteristics of the thruster plume will be achieved. Also it is very important for optimizing design of Ion thruster. Retarding Potential Analyzer (RPA) is an effective instrument for plasma ion energy per unit charge distribution measurement. Special RPA should be designed according to certain plume plasma parameters range and feature. In this paper, major principles usable for good RPA design are discussed carefully. Conform to these principles, a four-grid planar electrostatic energy analyzer RPA was designed to avoid false data, and details were discussed including construction, materials, aperture diameter and so on. At the same time, it was designed more suitable for credible and long-duration measurements in the laboratory. In the end, RPA measurement results in the laboratory were given and discussed.

Comparative Analysis of Farm Enterprises Performance in Two Agro-Ecological Feuding Zone of Nigeria

The two agro-ecological zones became the focus of the study because of violent nature of the incessant conflict in the zones. The available register of farmers association was the sampling frame work where ten percent (61) farmers per state were randomly sampled. Data were collected and analysed using z-test. The research findings revealed tree crops and grains production enterprises ranked higher in Osun (rain fed zones) and Taraba states (savannah zones) respectively. Osun state entrepreneur felt the effect of the conflict on their enterprises more than Tarba state. The reasons adduced for severity of the conflict on enterprises are majority (77.0%) migrated and (75.5%) of them were not allowed to enter their farms during and when conflict deescalated unlike situation in Taraba state. The different in enterprises production level between the two agroecological zone was statistically significant at p

Overriding Moral Intuitions – Does It Make Us Immoral? Dual-Process Theory of Higher Cognition Account for Moral Reasoning

Moral decisions are considered as an intuitive process, while conscious reasoning is mostly used only to justify those intuitions. This problem is described in few different dual-process theories of mind, that are being developed e.g. by Frederick and Kahneman, Stanovich and Evans. Those theories recently evolved into tri-process theories with a proposed process that makes ultimate decision or allows to paraformal processing with focal bias.. Presented experiment compares the decision patterns to the implications of those models. In presented study participants (n=179) considered different aspects of trolley dilemma or its footbridge version and decided after that. Results show that in the control group 70% of people decided to use the lever to change tracks for the running trolley, and 20% chose to push the fat man down the tracks. In contrast, after experimental manipulation almost no one decided to act. Also the decision time difference between dilemmas disappeared after experimental manipulation. The result supports the idea of three co-working processes: intuitive (TASS), paraformal (reflective mind) and algorithmic process.

Wavelet based Image Registration Technique for Matching Dental x-rays

Image registration plays an important role in the diagnosis of dental pathologies such as dental caries, alveolar bone loss and periapical lesions etc. This paper presents a new wavelet based algorithm for registering noisy and poor contrast dental x-rays. Proposed algorithm has two stages. First stage is a preprocessing stage, removes the noise from the x-ray images. Gaussian filter has been used. Second stage is a geometric transformation stage. Proposed work uses two levels of affine transformation. Wavelet coefficients are correlated instead of gray values. Algorithm has been applied on number of pre and post RCT (Root canal treatment) periapical radiographs. Root Mean Square Error (RMSE) and Correlation coefficients (CC) are used for quantitative evaluation. Proposed technique outperforms conventional Multiresolution strategy based image registration technique and manual registration technique.

Mass Transfer Modeling in a Packed Bed of Palm Kernels under Supercritical Conditions

Studies on gas solid mass transfer using Supercritical fluid CO2 (SC-CO2) in a packed bed of palm kernels was investigated at operating conditions of temperature 50 °C and 70 °C and pressures ranges from 27.6 MPa, 34.5 MPa, 41.4 MPa and 48.3 MPa. The development of mass transfer models requires knowledge of three properties: the diffusion coefficient of the solute, the viscosity and density of the Supercritical fluids (SCF). Matematical model with respect to the dimensionless number of Sherwood (Sh), Schmidt (Sc) and Reynolds (Re) was developed. It was found that the model developed was found to be in good agreement with the experimental data within the system studied.

A Novel Approach for Tracking of a Mobile Node Based on Particle Filter and Trilateration

This paper evaluates the performance of a novel algorithm for tracking of a mobile node, interms of execution time and root mean square error (RMSE). Particle Filter algorithm is used to track the mobile node, however a new technique in particle filter algorithm is also proposed to reduce the execution time. The stationary points were calculated through trilateration and finally by averaging the number of points collected for a specific time, whereas tracking is done through trilateration as well as particle filter algorithm. Wi-Fi signal is used to get initial guess of the position of mobile node in x-y coordinates system. Commercially available software “Wireless Mon" was used to read the WiFi signal strength from the WiFi card. Visual Cµ version 6 was used to interact with this software to read only the required data from the log-file generated by “Wireless Mon" software. Results are evaluated through mathematical modeling and MATLAB simulation.

Stereoselective Reduction of Amino Ketone with Sodium Borohydride in the Presence of Metal Chloride. A Simple Pathway to S-Propranolol

Propranolol is worldwide hypertension drug that is active in S-isomer. Patients must use this drug throughout their lives, and this action employsa significant level of expenditure. A simpler synthesis and lower cost can reduce the price for the patient. A sis pathway of S-propranolol starting from protection of (R,S)-propranolol with di-t-butyldicarbonate and then the product is oxidized with pyridiniumchlorochromate. The selective reduction of ketone occurrs with sodiumborohydride in the presence of metal chloride provided S-propranolol.

Marketing Segmentation of Students Willing to Study Abroad based on Cluster Analysis

Market segmentation is one of the most fundamental strategic marketing concepts. The better the segment which is chosen for targeting by a particular organisation, the more successful the organisation is assumed to be in the marketplace. Also higher education institutions have to improve their marketing tools for attracting foreign students, particularly when demanding tuition fees. This contribution aims at demonstrating the proper usage of the cluster analysis for segmentation (represented by students' willingness to study abroad) and also, based on large international survey, offers some practical marketing implications.

Using LabVIEW Software in an Introductory Residual Current Device Course

Laboratory classes in Electrical Engineering are often hampered by safety issues, as students have to work on high voltage lines. One solution is to make use of virtual laboratory simulations, to help students understand the concepts taught in their coursework. In this context, we have conceived and implemented virtual lab experiments in connection with the study of earthing arrangements. In this work, software was developed, which aid student in understanding the working of a residual current device (RCD) in a TT earthing system. Various parameters, such as the earthing resistances, leakage currents and harmonics were included for a TT system with RCD connection.

The Effect of Cyclone Shape and Dust Collector on Gas-Solid Flow and Performance

Numerical analysis of flow characteristics and separation efficiency in a high-efficiency cyclone has been performed. Several models based on the experimental observation for a design purpose were proposed. However, the model is only estimated the cyclone's performance under the limited environments; it is difficult to obtain a general model for all types of cyclones. The purpose of this study is to find out the flow characteristics and separation efficiency numerically. The Reynolds stress model (RSM) was employed instead of a standard k-ε or a k-ω model which was suitable for isotropic turbulence and it could predict the pressure drop and the Rankine vortex very well. For small particles, there were three significant components (entrance of vortex finder, cone, and dust collector) for the particle separation. In the present work, the particle re-entraining phenomenon from the dust collector to the cyclone body was observed after considerable time. This re-entrainment degraded the separation efficiency and was one of the significant factors for the separation efficiency of the cyclone.

Using Combination of Optimized Recurrent Neural Network with Design of Experiments and Regression for Control Chart Forecasting

recurrent neural network (RNN) is an efficient tool for modeling production control process as well as modeling services. In this paper one RNN was combined with regression model and were employed in order to be checked whether the obtained data by the model in comparison with actual data, are valid for variable process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of models. First, the regression was made for predicting the response time of process based upon determined factors, and then the error between actual and predicted response time as output and also the same factors as input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, in training process of RNN, design of experiments was set so as to optimize the RNN.

Comparative Life Cycle Assessment of Rapeseed Oil and Biodiesel from Winter Rape Produced in Romania

The environmental performance of rapeseed oil (RO) and rapeseed methyl ester(RME) from winter rape as fuels produced in Romanian agroclimate is analyzed in this paper. The proposed methodology is life cycle assessment (LCA) and takes into consideration the influence of grain production and agroclimatic conditions. This study shows favorable results first for RO and then for RME. When compared to diesel fuel, both studied biofuels show better results in the following impact categories: Abiotic depletion potential (ADP), Ozone layer depletion (ODP) and Photochemical ozone creation potential (POCP).Furthermore, the environmental performance of the two biofuels studied can be improved by changing the type of fertilizer used and also by using biofuels instead of diesel in the field works.

Prediction of Soil Exchangeable Sodium Ratio Based on Soil Sodium Adsorption Ratio

Researchers have long had trouble in measurement of Exchangeable Sodium Ratio (ESR) at salt-affected soils. this parameter are often determined using laborious and time consuming laboratory tests, but it may be more appropriate and economical to develop a method which uses a more simple soil salinity index. The aim of this study was to determine the relationship between exchangeable sodium ratio (ESR) and sodium adsorption ratio (SAR) in some salt-affected soils of Khuzestan plain. To this purpose, two experimental areas (S1, S2) of Khuzestan province-IRAN were selected and four treatments with three replications by series of double rings were applied. The treatments were included 25cm, 50cm, 75cm and 100cm water application. The statistical results of the study indicated that in order to predict soil ESR based on soil SAR the linear regression model ESR=0.2048+0.0066 SAR (R2=0.53) & ESR=0.0564+0.0171 SAR (R2=0.76) can be recommended in Pilot S1 and S2 respectively.

Phosphine Mortality Estimation for Simulation of Controlling Pest of Stored Grain: Lesser Grain Borer (Rhyzopertha dominica)

There is a world-wide need for the development of sustainable management strategies to control pest infestation and the development of phosphine (PH3) resistance in lesser grain borer (Rhyzopertha dominica). Computer simulation models can provide a relatively fast, safe and inexpensive way to weigh the merits of various management options. However, the usefulness of simulation models relies on the accurate estimation of important model parameters, such as mortality. Concentration and time of exposure are both important in determining mortality in response to a toxic agent. Recent research indicated the existence of two resistance phenotypes in R. dominica in Australia, weak and strong, and revealed that the presence of resistance alleles at two loci confers strong resistance, thus motivating the construction of a two-locus model of resistance. Experimental data sets on purified pest strains, each corresponding to a single genotype of our two-locus model, were also available. Hence it became possible to explicitly include mortalities of the different genotypes in the model. In this paper we described how we used two generalized linear models (GLM), probit and logistic models, to fit the available experimental data sets. We used a direct algebraic approach generalized inverse matrix technique, rather than the traditional maximum likelihood estimation, to estimate the model parameters. The results show that both probit and logistic models fit the data sets well but the former is much better in terms of small least squares (numerical) errors. Meanwhile, the generalized inverse matrix technique achieved similar accuracy results to those from the maximum likelihood estimation, but is less time consuming and computationally demanding.

A Study on Physicochemical Analysis of Road and Railway Track Side Soil Samples of Amritsar (Punjab) and Their Genotoxic Effects

Considering the serious health hazards of air pollutants from automobiles, the present study was aimed to estimate the genotoxic/tumor inducing potential of three soil samples collected from junctions of Bus stand (BS), Crystal (CT) and Railway station (RS) of Amritsar, Punjab (India) using Allium cepa root chromosomal aberration assay (AlRCAA) and potato disc tumor assay (PDTA). The genotoxic potential in AlRCAA was 41.27% and 41.26% for BS; 37.89% and 43.38% for RS and 33.76% and 37.83% for CT during in situ and root dip treatments, respectively. The maximum number of tumors were induced in RS sample (64) followed by BS (21) and CT (9) during PDTA. The physicochemical parameters of soil sample were also studied and the concentration of lead was found to be 95.21 mg/Kg in RS, 35.30 mg/Kg in BS and 24.59 mg/Kg in CT samples.

Effective Image and Video Error Concealment using RST-Invariant Partial Patch Matching Model and Exemplar-based Inpainting

An effective visual error concealment method has been presented by employing a robust rotation, scale, and translation (RST) invariant partial patch matching model (RSTI-PPMM) and exemplar-based inpainting. While the proposed robust and inherently feature-enhanced texture synthesis approach ensures the generation of excellent and perceptually plausible visual error concealment results, the outlier pruning property guarantees the significant quality improvements, both quantitatively and qualitatively. No intermediate user-interaction is required for the pre-segmented media and the presented method follows a bootstrapping approach for an automatic visual loss recovery and the image and video error concealment.

Integrated Reasoning Approach for Car Faulty Diagnosis

This paper presents an integrated case based and rule based reasoning method for car faulty diagnosis. The reasoning method is done through extracting the past cases from the Proton Service Center while comparing with the preset rules to deduce a diagnosis/solution to a car service case. New cases will be stored to the knowledge base. The test cases examples illustrate the effectiveness of the proposed integrated reasoning. It has proven accuracy of similar reasoning if carried out by a service advisor from the service center.

Motivated Support Vector Regression with Structural Prior Knowledge

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studies with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.