Search results for: forecast accuracy unemployment rate.

Authors: M. H. Chan

Abstract:

This paper describes an application of a dual satellite geolocation (DSG) system on identifying and locating the unknown source of uplink sweeping interference. The geolocation system integrates the method of joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) with ephemeris correction technique which successfully demonstrated high accuracy in interference source location. The factors affecting the location error were also discussed.

Keywords: Dual satellite geolocation system, DGS, geolocation, TDOA/FDOA, and sweeping interference

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Authors: S. I. Abu Alasal, M. M. Esbeih, E. R. Fayyad, R. S. Gharaibeh, M. Z. Ali, A. A. Freewan, M. M. Jamhawi

Abstract:

This research proposes a novel reconstruction protocol for restoring missing surfaces and low-quality edges and shapes in photos of artifacts at historical sites. The protocol starts with the extraction of a cloud of points. This extraction process is based on four subordinate algorithms, which differ in the robustness and amount of resultant. Moreover, they use different -but complementary- accuracy to some related features and to the way they build a quality mesh. The performance of our proposed protocol is compared with other state-of-the-art algorithms and toolkits. The statistical analysis shows that our algorithm significantly outperforms its rivals in the resultant quality of its object files used to reconstruct the desired model.

Keywords: Meshes, Point Clouds, Surface Reconstruction Protocols, 3D Reconstruction.

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

Abstract:

Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision making has not been far-fetched. Proper classification of these textual information in a given context has also been very difficult. As a result, a systematic review was conducted from previous literature on sentiment classification and AI-based techniques. The study was done in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that could correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy using the knowledge gain from the evaluation of different artificial intelligence techniques reviewed. The study evaluated over 250 articles from digital sources like ACM digital library, Google Scholar, and IEEE Xplore; and whittled down the number of research to 52 articles. Findings revealed that deep learning approaches such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Encoder Representations from Transformer (BERT), and Long Short-Term Memory (LSTM) outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also required to develop a robust sentiment classifier. Results also revealed that data can be obtained from places like Twitter, movie reviews, Kaggle, Stanford Sentiment Treebank (SST), and SemEval Task4 based on the required domain. The hybrid deep learning techniques like CNN+LSTM, CNN+ Gated Recurrent Unit (GRU), CNN+BERT outperformed single deep learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of development simplicity and AI-based library functionalities. Finally, the study recommended the findings obtained for building robust sentiment classifier in the future.

Keywords: Artificial Intelligence, Natural Language Processing, Sentiment Analysis, Social Network, Text.

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Authors: Sansani Rungrattawatchai

Abstract:

The White Kwao (Pueraria mirifica), a potent phytoestrogenic medicinal plant, has long been use in Thailand as a traditional folkmedicine. However, no scientific information of the direct effect of White Kwao on the development of mammalian embryo was available. Therefore, the purpose of this study was to investigate the effect of White Kwao extract on the in vitro development and implantation rate of mouse embryos. This study was designed into two experiments. In the first experiment, the two-cell stage mouse embryos were collected from the oviduct of superovulated mature female mice, and randomly cultured in three different media, the M16, M16 supplemented with 0.52μg esthinylestradiol-17β, and M16 supplemented with 10 mg/ml White Kwao extract. The culture was incubated in CO2 incubator at 37 oC . After the embryos were cultivated, the developments of embryos were observed every 24 hours for 5 days. The development rate of embryos from the two-cell stage to blastocyst stage in the media was with White Kwao was significantly higher (p<0.05) than those of the control group (68.50% versus 43.50%) but did not differ from the positive control group (68.50% versus 57.66%). In the second experiment, hatched blastocysts, which obtained from three different media, were differently labeled the nuclei with two polynucleotide-specific fluorochromes, the propidium iodide (PI) and the bisbenzimide. The results showed that the number of trophectoderm cells in the blastocysts that cultivated in the media with White Kwao did not significantly differ from the control (80.00 versus 70 cells) and the positive control group (80.00 versus 112.50 cells). The average number of inner cell mass in the White Kwao treated group did not significantly differ from the control group (20.50 versus 16.00 cells) and the positive control group (20.50 versus 20.50 cells). The total cell number including the trophectoderm and the inner cell mass of the individual hatched blastocyst was evaluated. The cell number in the blastocysts obtained from the media with the White Kwao did not significantly differ from the control (94.25 + 9.50 versus 92.33 + 4.05) and the positive control group (94.25 + 9.50 versus 110.33 + 9.16). The results demonstrated that the White Kwao treatment group did have a stimulating effect on the in vitro development of mouse embryos. The exact mechanism that White Kwao stimulated mouse embryo development is not known. The suspect mechanism may in a manner similar to the mechanism that of estrogen stimulated the development of the mouse embryos. Futher studies are needed to transfer the blastocyst into the endometrium of pseudopreagnancy mice to evaluate the effect of White Kwao on implantation

Keywords: White Kwao (Pueraria mirifica), blastocyst.

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Authors: Girija Chetty, Monica Singh

Abstract:

In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..

Keywords: Biometrics, gait recognition, PCA, LDA, Eigenface, Fisherface, Multivariate Gaussian Classifier

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Authors: Ava Dodd, Jonathan Adams

Abstract:

The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represent another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

Abstract:

In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.

Keywords: Brain balancing, kNN, power spectral density, 3D EEG model.

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Authors: E. Nemati Lay, M. Peymani, E. Sanjari

Abstract:

Prediction of viscosity of natural gas is an important parameter in the energy industries such as natural gas storage and transportation. In this study viscosity of different compositions of natural gas is modeled by using an artificial neural network (ANN) based on back-propagation method. A reliable database including more than 3841 experimental data of viscosity for testing and training of ANN is used. The designed neural network can predict the natural gas viscosity using pseudo-reduced pressure and pseudo-reduced temperature with AARD% of 0.221. The accuracy of designed ANN has been compared to other published empirical models. The comparison indicates that the proposed method can provide accurate results.

Keywords: Artificial neural network, Empirical correlation, Natural gas, Viscosity

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Authors: Hoda A. Abdel Hafez

Abstract:

Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: Mining Big Data, Big Data, Machine learning, Data Streams, Telecommunication.

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Authors: Esma Gizem Daskiran, Mehmet Mustafa Daskiran, Mustafa Gencoglu

Abstract:

Textile reinforced cementitious composite (TRCC) is a development of a composite material where textile and fine-grained concrete (matrix) materials are used in combination. These matrices offer high performance properties in many aspects. To achieve high performance, polymer modified fine-grained concretes were used as matrix material which have high flexural strength. In this study, ten latex polymers and ten powder polymers were added to fine-grained concrete mixtures. These latex and powder polymers were added to the mixtures at different rates related to binder weight. Mechanical properties such as compressive and flexural strength were studied. Results showed that latex polymer and redispersible polymer modified fine-grained concretes showed different mechanical performance. A wide range of both latex and redispersible powder polymers were studied. As the addition rate increased compressive strength decreased for all mixtures. Flexural strength increased as the addition rate increased but significant enhancement was not observed through all mixtures.

Keywords: Textile reinforced composite, cement, fine grained concrete, latex, redispersible powder.

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Authors: Nebi Gedik

Abstract:

The aim of the brain-computer interface studies on electroencephalogram (EEG) signals containing motor imagery is to extract the effective features that will provide the highest possible classification accuracy for the detection of the desired motor movement. However, achieving this goal is difficult as the most suitable frequency band and time frame vary from subject to subject. In this study, the classification success of the two-feature data obtained from raw EEG signals and the coefficients of the multi-resolution analysis method applied to the EEG signals were analyzed comparatively. The method was applied to several EEG channels (C3, Cz and C4) signals obtained from the EEG data set belonging to the publicly available BCI competition III.

Keywords: Motor imagery, EEG, wave atom transform, k-NN.

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Authors: Archit Yajnik

Abstract:

Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: Hidden Markov model, Viterbi algorithm, POS tagging, natural language processing.

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Authors: Neeraj K. Chasta

Abstract:

This paper presents an idea for analog current comparison which compares input signal and reference currents with high speed and accuracy. Proposed circuit utilizes amplification properties of common gate configuration, where voltage variations of input current are amplified and a compared output voltage is developed. Cascaded inverter stages are used to generate final CMOS compatible output voltage. Power consumption of circuit can be controlled by the applied gate bias voltage. The comparator is designed and studied at 180nm CMOS process technology for a supply voltage of 3V.

Keywords: Current Mode, Comparator, High Resolution, High Speed.

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Authors: Seyedvahid Najafi, Viliam Makis

Abstract:

In recent years, maintenance optimization has attracted special attention due to the growth of industrial systems complexity. Maintenance costs are high for many systems, and preventive maintenance is effective when it increases operations' reliability and safety at a reduced cost. The novelty of this research is to consider general repair in the modeling of multi-unit series systems and solve the maintenance problem for such systems using the semi-Markov decision process (SMDP) framework. We propose an opportunistic maintenance policy for a series system composed of two main units. Unit 1, which is more expensive than unit 2, is subjected to condition monitoring, and its deterioration is modeled using a gamma process. Unit 1 hazard rate is estimated by the proportional hazards model (PHM), and two hazard rate control limits are considered as the thresholds of maintenance interventions for unit 1. Maintenance is performed on unit 2, considering an age control limit. The objective is to find the optimal control limits and minimize the long-run expected average cost per unit time. The proposed algorithm is applied to a numerical example to compare the effectiveness of the proposed policy (policy Ⅰ) with policy Ⅱ, which is similar to policy Ⅰ, but instead of general repair, replacement is performed. Results show that policy Ⅰ leads to lower average cost compared with policy Ⅱ. 

Keywords: Condition-based maintenance, proportional hazards model, semi-Markov decision process, two-unit series systems.

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Authors: Taiwo. M. Akinmuyisitan, John Cosmas

Abstract:

This paper practically demonstrates the theories and concepts of an Advanced Convolutional Neural Network in the design and development of a scalable artificial intelligence model for the detection of criminal masterminds. The technique uses machine vision algorithms to compute the facial characteristics of suspects and classify actors as criminal or non-criminal faces. The paper proceeds further to compare the results of the error accuracy of two popular custom convolutional pre-trained networks, VGG16 and Resnet50. The result shows that VGG16 is probably more efficient than ResNet50 for the dataset we used.

Keywords: Artificial intelligence, convolutional neural networks, Resnet50, VGG16.

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Authors: Hui-Yu Huang, Shih-Hsu Chang

Abstract:

In this paper, we propose a high capacity image hiding technology based on pixel prediction and the difference of modified histogram. This approach is used the pixel prediction and the difference of modified histogram to calculate the best embedding point. This approach can improve the predictive accuracy and increase the pixel difference to advance the hiding capacity. We also use the histogram modification to prevent the overflow and underflow. Experimental results demonstrate that our proposed method within the same average hiding capacity can still keep high quality of image and low distortion

Keywords: data hiding, predictor

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Authors: Jennifer Leach, Umashanger Thayasivam

Abstract:

The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate others. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease these advancements. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent datasets, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which split and technique would lead to the most optimal results.

Keywords: Data science, fraud detection, machine learning, supervised learning.

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Authors: Salsabil Trabelsi, Zied Elouedi, Khaled Mellouli

Abstract:

The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. In this paper, we develop a post-pruning method of belief decision trees in order to reduce size and improve classification accuracy on unseen cases. The pruning of decision tree has a considerable intention in the areas of machine learning.

Keywords: machine learning, uncertainty, belief function theory, belief decision tree, pruning.

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Authors: Luisa Mesquita Oliveira Ribeiro, Alexei Manso Correa Machado

Abstract:

Handwritten signature is a unique form for recognizing an individual, used to discern documents, carry out investigations in the criminal, legal, banking areas and other applications. Signature verification is based on large amounts of biometric data, as they are simple and easy to acquire, among other characteristics. Given this scenario, signature forgery is a worldwide recurring problem and fast and precise techniques are needed to prevent crimes of this nature from occurring. This article carried out a study on the efficiency of the Capsule Network in analyzing and recognizing signatures. The chosen architecture achieved an accuracy of 98.11% and 80.15% for the CEDAR and GPDS databases, respectively.

Keywords: Biometrics, deep learning, handwriting, signature forgery.

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Authors: L. Al Ahmad, C. Latrille, D. Hainos, D. Blanc, M. Clausse

Abstract:

A two-dimensional finite volume axisymmetric model is developed to predict the simultaneous heat and mass transfers during the drying of industrial sludge. The simulations were run using COMSOL-Multiphysics 3.5a. The input parameters of the numerical model were acquired from a preliminary experimental work. Results permit to establish correlations describing the evolution of the various parameters as a function of the drying temperature and the sludge water content. The selection and coupling of the equation are validated based on the drying kinetics acquired experimentally at a temperature range of 45-65 °C and absolute pressure range of 200-1000 mbar. The model, incorporating the heat and mass transfer mechanisms at different operating conditions, shows simulated values of temperature and water content. Simulated results are found concordant with the experimental values, only at the first and last drying stages where sludge shrinkage is insignificant. Simulated and experimental results show that sludge drying is favored at high temperatures and low pressure. As experimentally observed, the drying time is reduced by 68% for drying at 65 °C compared to 45 °C under 1 atm. At 65 °C, a 200-mbar absolute pressure vacuum leads to an additional reduction in drying time estimated by 61%. However, the drying rate is underestimated in the intermediate stage. This rate underestimation could be improved in the model by considering the shrinkage phenomena that occurs during sludge drying.

Keywords: Industrial sludge drying, heat transfer, mass transfer, mathematical modelling.

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Authors: Hongkui Li, Tongli Lu , Jianwu Zhang

Abstract:

This paper focuses on developing an estimation method of clutch drag torque in wet DCT. The modelling of clutch drag torque is investigated. As the main factor affecting the clutch drag torque, dynamic viscosity of oil is discussed. The paper proposes an estimation method of clutch drag torque based on recursive least squares by utilizing the dynamic equations of gear shifting synchronization process. The results demonstrate that the estimation method has good accuracy and efficiency.

Keywords: Clutch drag torque, wet DCT, dynamic viscosity, recursive least squares.

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Authors: Papon Thamvasupong, Kwanchanok Viravaidya-Pasuwat

Abstract:

Osteoarthritis affects a lot of people worldwide. Local injection of glucosamine is one of the alternative treatment methods to replenish the natural lubrication of cartilage. However, multiple injections can potentially lead to possible bacterial infection. Therefore, a drug delivery system is desired to reduce the frequencies of injections. A hydrogel is one of the delivery systems that can control the release of drugs. Thermo-reversible hydrogels can be beneficial to the drug delivery system especially in the local injection route because this formulation can change from liquid to gel after getting into human body. Once the gel is in the body, it will slowly release the drug in a controlled manner. In this study, various formulations of Pluronic-based hydrogels were synthesized for the controlled release of glucosamine. One of the challenges of the Pluronic controlled release system is its fast dissolution rate. To overcome this problem, alginate and calcium sulfate (CaSO₄) were added to the polymer solution. The characteristics of the hydrogels were investigated including the gelation temperature, gelation time, hydrogel dissolution and glucosamine release mechanism. Finally, a mathematical model of glucosamine release from Pluronic-alginate-hyaluronic acid hydrogel was developed. Our results have shown that crosslinking Pluronic gel with alginate did not significantly extend the dissolution rate of the gel. Moreover, the gel dissolution profiles and the glucosamine release mechanisms were best described using the zeroth-order kinetic model, indicating that the release of glucosamine was primarily governed by the gel dissolution.

Keywords: Controlled release, drug delivery system, glucosamine, Pluronic® F-127, thermoreversible hydrogel.

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Authors: P. Niranatlumpong, H. Koiprasert, C. Sukhonket, K. Ninon, N. Coompreedee

Abstract:

The use of a conventional air plasma-sprayed thermal barrier coating (TBC) and a porous, functionally graded TBC as a thermal insulator for Al7075 alloy was explored. A quench test at 1200°C employing fast heating and cooling rates was setup to represent a dynamic thermal condition of an aerospace component. During the test, coated samples were subjected the ambient temperature of 1200°C for a very short time. This was followed by a rapid drop in temperature resulting in cracking of the coatings. For the conventional TBC, it was found that the temperature of the Al7075 substrate decreases with the increase in the ZrO₂ topcoat thickness. However, at the topcoat thickness of 1100 µm, large horizontal cracks can be observed in the topcoat and at the topcoat thickness of 1600 µm, the topcoat delaminate during cooling after the quench test. The porous, functionally graded TBC with 600 µm thick topcoat, on the other hand, was found to be as effective at reducing the substrate temperature as the conventional TBC with 1100 µm thick topcoat. The maximum substrate temperature is about 213°C for the former and 208°C for the latter when a heating rate of 38°C/s was used. When the quench tests were conducted with a faster heating rate of 128°C/s, the Al7075 substrate heat up faster with a reduction in the maximum substrate temperatures. The substrate temperatures dropped from 297 to 212°C for the conventional TBC and from 213 to 155°C for the porous TBC, both with 600 µm thick topcoat. Segmentation cracks were observed in both coating after the quench test.

Keywords: Thermal barrier coating, Al7075, porous TBC, Quenching.

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Authors: S. Sowmyayani, V. Murugan

Abstract:

With the advent of three-dimension (3D) technology, there are lots of research in converting 2D images to 3D images. The main difference between 2D and 3D is the visual illusion of depth in 3D images. In the recent era, there are more depth estimation techniques. The objective of this paper is to convert 2D images to 3D images with less computation time. For this, the input image is divided into blocks from which the depth information is obtained. Having the depth information, a depth map is generated. Then the 3D image is warped using the original image and the depth map. The proposed method is tested on Make3D dataset and NYU-V2 dataset. The experimental results are compared with other recent methods. The proposed method proved to work with less computation time and good accuracy.

Keywords: Depth map, 3D image warping, image rendering, bilateral filter, minimum spanning tree.

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Authors: Y. Duvarci, S. Mizokami

Abstract:

Today, the preferences and participation of the TD groups such as the elderly and disabled is still lacking in decision-making of transportation planning, and their reactions to certain type of policies are not well known. Thus, a clear methodology is needed. This study aimed to develop a method to extract the preferences of the disabled to be used in the policy-making stage that can also guide to future estimations. The method utilizes the combination of cluster analysis and data filtering using the data of the Arao city (Japan). The method is a process that follows: defining the TD group by the cluster analysis tool, their travel preferences in tabular form from the household surveys by policy variableimpact pairs, zones, and by trip purposes, and the final outcome is the preference probabilities of the disabled. The preferences vary by trip purpose; for the work trips, accessibility and transit system quality policies with the accompanying impacts of modal shifts towards public mode use as well as the decreasing travel costs, and the trip rate increase; for the social trips, the same accessibility and transit system policies leading to the same mode shift impact, together with the travel quality policy area leading to trip rate increase. These results explain the policies to focus and can be used in scenario generation in models, or any other planning purpose as decision support tool.

Keywords: Transportation Disadvantaged, Disabled, Mixed Preference, Stated Preference Data.

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Authors: Dipankaj G Medhi, Juri Dakua

Abstract:

Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with no emphasis on the trustworthiness of the user. RS depends on information provided by different users to gather its knowledge. We believe, if a large group of users provide wrong information it will not be possible for the RS to arrive in an accurate conclusion. The system described in this paper introduce the concept of Testing the knowledge of user to filter out these “bad users". This paper emphasizes on the mechanism used to provide robust and effective recommendation.

Keywords: Collaborative Filtering, Content Based Filtering, Intelligent Agent, Level of Interest, Recommendation System.

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Authors: Mehmet Hacibeyoglu, Ahmet Arslan, Sirzat Kahramanli

Abstract:

This study analyzes the effect of discretization on classification of datasets including continuous valued features. Six datasets from UCI which containing continuous valued features are discretized with entropy-based discretization method. The performance improvement between the dataset with original features and the dataset with discretized features is compared with k-nearest neighbors, Naive Bayes, C4.5 and CN2 data mining classification algorithms. As the result the classification accuracies of the six datasets are improved averagely by 1.71% to 12.31%.

Keywords: Data mining classification algorithms, entropy-baseddiscretization method

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Authors: Sakshi Kausha, R.K Sharma

Abstract:

High speed networks provide realtime variable bit rate service with diversified traffic flow characteristics and quality requirements. The variable bit rate traffic has stringent delay and packet loss requirements. The burstiness of the correlated traffic makes dynamic buffer management highly desirable to satisfy the Quality of Service (QoS) requirements. This paper presents an algorithm for optimization of adaptive buffer allocation scheme for traffic based on loss of consecutive packets in data-stream and buffer occupancy level. Buffer is designed to allow the input traffic to be partitioned into different priority classes and based on the input traffic behavior it controls the threshold dynamically. This algorithm allows input packets to enter into buffer if its occupancy level is less than the threshold value for priority of that packet. The threshold is dynamically varied in runtime based on packet loss behavior. The simulation is run for two priority classes of the input traffic – realtime and non-realtime classes. The simulation results show that Adaptive Partial Buffer Sharing (ADPBS) has better performance than Static Partial Buffer Sharing (SPBS) and First In First Out (FIFO) queue under the same traffic conditions.

Keywords: Buffer Management, Consecutive packet loss, Quality-of-Service, Priority based packet discarding, partial buffersharing.

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Authors: S. Maheswari, P. Arockia Jansi Rani

Abstract:

Human action is recognized directly from the video sequences. The objective of this work is to recognize various human actions like run, jump, walk etc. Human action recognition requires some prior knowledge about actions namely, the motion estimation, foreground and background estimation. Region of interest (ROI) is extracted to identify the human in the frame. Then, optical flow technique is used to extract the motion vectors. Using the extracted features similarity measure based classification is done to recognize the action. From experimentations upon the Weizmann database, it is found that the proposed method offers a high accuracy.

Keywords: Background subtraction, human silhouette, optical flow, classification.

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Authors: S. A. H. Feghhi, Z. Gholamzadeh, Z. Alipoor, C. Tenreiro

Abstract:

Currently, thorium fuel has been especially noticed because of its proliferation resistance than long half-life alpha emitter minor actinides, breeding capability in fast and thermal neutron flux and mono-isotopic naturally abundant. In recent years, efficiency of minor actinide burning up in PWRs has been investigated. Hence, a minor actinide-contained thorium based fuel matrix can confront both proliferation resistance and nuclear waste depletion aims. In the present work, minor actinide depletion rate in a CANDU-type nuclear core modeled using MCNP code has been investigated. The obtained effects of minor actinide load as mixture of thorium fuel matrix on the core neutronics has been studied with comparing presence and non-presence of minor actinide component in the fuel matrix. Depletion rate of minor actinides in the MA-contained fuel has been calculated using different power loads. According to the obtained computational data, minor actinide loading in the modeled core results in more negative reactivity coefficients. The MA-contained fuel achieves less radial peaking factor in the modeled core. The obtained computational results showed 140 kg of 464 kg initial load of minor actinide has been depleted in during a 6-year burn up in 10 MW power.

Keywords: Minor actinide burning, CANDU-type reactor, MCNPX code, Neutronic parameters.

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