Search results for: Parallel Algorithm

Authors: Rami N. Khushaba, Adel Al-Jumaily

Abstract:

The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.

Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.

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Authors: Rishabh Garg

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Traditionally, business parties build trust with a centralized operating mechanism, such as payment by letter of credit. However, the increase in cyber-attacks and malicious hacking has jeopardized business operations and finance practices. Emerging markets, due to their high banking risks and the large presence of digital financing, are looking for technology that enables transparency and traceability of any transaction in trade, finance or supply chain management. Blockchain systems, in the absence of any central authority, enable transactions across the globe with the help of decentralized applications. DApps consist of a front-end, a blockchain back-end, and middleware, that is, the code that connects the two. The front-end can be a sophisticated web app or mobile app, which is used to implement the functions/methods on the smart contract. Web apps can employ technologies such as HTML, CSS, React and Express. In this wake, fintech and blockchain products are popping up in brokerages, digital wallets, exchanges, post-trade clearance, settlement, middleware, infrastructure and base protocols. The present paper provides a technology driven solution, financial inclusion and innovative working paradigm for business and finance.

Keywords: Authentication, blockchain, channel, cryptography, DApps, data portability, Decentralized Public Key Infrastructure, Ethereum, hash function, Hashgraph, Privilege creep, Proof of Work algorithm, revocation, storage variables, Zero Knowledge Proof.

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: Decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle.

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Authors: H.-H. Doh, J.-M. Yu, Y.-J. Kwon, J.-H. Shin, H.-W. Kim, S.-H. Nam, D.-H. Lee

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This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness.

Keywords: Flexible job shop scheduling, Decision tree, Priority rules, Case study.

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Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

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With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: Curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering.

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Authors: Andrew Hwang

Abstract:

The average American worker sits for thirteen hours a day, often with poor posture and infrequent breaks, which can lead to health issues and back problems. The Smart Cushion was created to alert individuals of their poor postures, and may potentially alleviate back problems and correct poor posture. The Smart Cushion is a portable, rectangular, foam cushion, with five strategically placed pressure sensors, that utilizes an Arduino Uno circuit board and specifically designed software, allowing it to collect data from the five pressure sensors and store the data on an SD card. The data is then compiled into graphs and compared to controlled postures. Before volunteers sat on the cushion, their levels of back pain were recorded on a scale from 1-10. Data was recorded for an hour during sitting, and then a new, corrected posture was suggested. After using the suggested posture for an hour, the volunteers described their level of discomfort on a scale from 1-10. Different patterns of sitting postures were generated that were able to serve as early warnings of potential back problems. By using the Smart Cushion, the areas where different volunteers were applying the most pressure while sitting could be identified, and the sitting postures could be corrected. Further studies regarding the relationships between posture and specific regions of the body are necessary to better understand the origins of back pain; however, the Smart Cushion is sufficient for correcting sitting posture and preventing the development of additional back pain.

Keywords: Arduino Sketch Algorithm, biomedical technology, pressure sensors, Smart Cushion.

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Authors: Zohreh Orshesh

Abstract:

In this study, a 3D combustion chamber was simulated using FLUENT 6.32. Aims to obtain accurate information about the profile of the combustion in the furnace and also check the effect of oxygen enrichment on the combustion process. Oxygen enrichment is an effective way to reduce combustion pollutant. The flow rate of air to fuel ratio is varied as 1.3, 3.2 and 5.1 and the oxygen enriched flow rates are 28, 54 and 68 lit/min. Combustion simulations typically involve the solution of the turbulent flows with heat transfer, species transport and chemical reactions. It is common to use the Reynolds-averaged form of the governing equation in conjunction with a suitable turbulence model. The 3D Reynolds Averaged Navier Stokes (RANS) equations with standard k-ε turbulence model are solved together by Fluent 6.3 software. First order upwind scheme is used to model governing equations and the SIMPLE algorithm is used as pressure velocity coupling. Species mass fractions at the wall are assumed to have zero normal gradients.Results show that minimum mole fraction of CO2 happens when the flow rate ratio of air to fuel is 5.1. Additionally, in a fixed oxygen enrichment condition, increasing the air to fuel ratio will increase the temperature peak. As a result, oxygen-enrichment can reduce the CO2 emission at this kind of furnace in high air to fuel rates.

Keywords: Combustion chamber, Oxygen enrichment, Reynolds Averaged Navier- Stokes, CO2 emission

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Authors: Wayan F. Mahmudy, Romeo M. Marian, Lee H. S. Luong

Abstract:

This paper presents modeling and optimization of two NP-hard problems in flexible manufacturing system (FMS), part type selection problem and loading problem. Due to the complexity and extent of the problems, the paper was split into two parts. The first part of the papers has discussed the modeling of the problems and showed how the real coded genetic algorithms (RCGA) can be applied to solve the problems. This second part discusses the effectiveness of the RCGA which uses an array of real numbers as chromosome representation. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.

Keywords: Flexible manufacturing system, production planning, part type selection problem, loading problem, real-coded genetic algorithm

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Authors: Hyun-Koo Kim, Ju H. Park, Ho-Youl Jung

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This paper presents an effective traffic lights recognition method at the daytime. First, Potential Traffic Lights Detector (PTLD) use whole color source of YCbCr channel image and make each binary image of green and red traffic lights. After PTLD step, Shape Filter (SF) use to remove noise such as traffic sign, street tree, vehicle, and building. At this time, noise removal properties consist of information of blobs of binary image; length, area, area of boundary box, etc. Finally, after an intermediate association step witch goal is to define relevant candidates region from the previously detected traffic lights, Adaptive Multi-class Classifier (AMC) is executed. The classification method uses Haar-like feature and Adaboost algorithm. For simulation, we are implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and rural roads. Through the test, we are compared with our method and standard object-recognition learning processes and proved that it reached up to 94 % of detection rate which is better than the results achieved with cascade classifiers. Computation time of our proposed method is 15 ms.

Keywords: Traffic Light Detection, Multi-class Classification, Driving Assistance System, Haar-like Feature, Color SegmentationMethod, Shape Filter

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Authors: Mrs.K.Kavitha, S.Arivazhagan

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A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.

Keywords: Multi-class, Run Length features, PCA, ICA, classification and Support Vector Machines.

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Authors: R. Bremananth, A. Chitra

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Cryptography provides the secure manner of information transmission over the insecure channel. It authenticates messages based on the key but not on the user. It requires a lengthy key to encrypt and decrypt the sending and receiving the messages, respectively. But these keys can be guessed or cracked. Moreover, Maintaining and sharing lengthy, random keys in enciphering and deciphering process is the critical problem in the cryptography system. A new approach is described for generating a crypto key, which is acquired from a person-s iris pattern. In the biometric field, template created by the biometric algorithm can only be authenticated with the same person. Among the biometric templates, iris features can efficiently be distinguished with individuals and produces less false positives in the larger population. This type of iris code distribution provides merely less intra-class variability that aids the cryptosystem to confidently decrypt messages with an exact matching of iris pattern. In this proposed approach, the iris features are extracted using multi resolution wavelets. It produces 135-bit iris codes from each subject and is used for encrypting/decrypting the messages. The autocorrelators are used to recall original messages from the partially corrupted data produced by the decryption process. It intends to resolve the repudiation and key management problems. Results were analyzed in both conventional iris cryptography system (CIC) and non-repudiation iris cryptography system (NRIC). It shows that this new approach provides considerably high authentication in enciphering and deciphering processes.

Keywords: Autocorrelators, biometrics cryptography, irispatterns, wavelets.

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Authors: Ki Hyun Kim, Jang-Hee Yoo, Kyo Il Chung

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This paper proposes a VPN Accelerator Board (VPN-AB), a virtual private network (VPN) protocol designed for trust channel security system (TCSS). TCSS supports safety communication channel between security nodes in internet. It furnishes authentication, confidentiality, integrity, and access control to security node to transmit data packets with IPsec protocol. TCSS consists of internet key exchange block, security association block, and IPsec engine block. The internet key exchange block negotiates crypto algorithm and key used in IPsec engine block. Security Association blocks setting-up and manages security association information. IPsec engine block treats IPsec packets and consists of networking functions for communication. The IPsec engine block should be embodied by H/W and in-line mode transaction for high speed IPsec processing. Our VPN-AB is implemented with high speed security processor that supports many cryptographic algorithms and in-line mode. We evaluate a small TCSS communication environment, and measure a performance of VPN-AB in the environment. The experiment results show that VPN-AB gets a performance throughput of maximum 15.645Gbps when we set the IPsec protocol with 3DES-HMAC-MD5 tunnel mode.

Keywords: TCSS(Trust Channel Security System), VPN(VirtualPrivate Network), IPsec, SSL, Security Processor, Securitycommunication.

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity.

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Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: Social networks, community detection, modularity optimization, geographically dispersed communities.

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Authors: Wayan F. Mahmudy, Romeo M. Marian, Lee H. S. Luong

Abstract:

 This paper deals with modeling and optimization of two NP-hard problems in production planning of flexible manufacturing system (FMS), part type selection problem and loading problem. The part type selection problem and the loading problem are strongly related and heavily influence the system’s efficiency and productivity. These problems have been modeled and solved simultaneously by using real coded genetic algorithms (RCGA) which uses an array of real numbers as chromosome representation. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.

Keywords: Flexible manufacturing system, production planning, part type selection problem, loading problem, real-coded genetic algorithm.

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Authors: Rachid Ait daoud, Abdellah Amine, Belaid Bouikhalene, Rachid Lbibb

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Given the increase in the number of e-commerce sites, the number of competitors has become very important. This means that companies have to take appropriate decisions in order to meet the expectations of their customers and satisfy their needs. In this paper, we present a case study of applying LRFM (length, recency, frequency and monetary) model and clustering techniques in the sector of electronic commerce with a view to evaluating customers’ values of the Moroccan e-commerce websites and then developing effective marketing strategies. To achieve these objectives, we adopt LRFM model by applying a two-stage clustering method. In the first stage, the self-organizing maps method is used to determine the best number of clusters and the initial centroid. In the second stage, kmeans method is applied to segment 730 customers into nine clusters according to their L, R, F and M values. The results show that the cluster 6 is the most important cluster because the average values of L, R, F and M are higher than the overall average value. In addition, this study has considered another variable that describes the mode of payment used by customers to improve and strengthen clusters’ analysis. The clusters’ analysis demonstrates that the payment method is one of the key indicators of a new index which allows to assess the level of customers’ confidence in the company's Website.

Keywords: Customer value, LRFM model, Cluster analysis, Self-Organizing Maps method (SOM), K-means algorithm, loyalty.

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Authors: Taylan Kabbani, Ekrem Duman

Abstract:

Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining  the financial assets price ”prediction” step and the ”allocation” step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solved the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and achieved a 2.68 Sharpe ratio on the test dataset. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.

Keywords: Autonomous agent, deep reinforcement learning, MDP, sentiment analysis, stock market, technical indicators, twin delayed deep deterministic policy gradient.

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Authors: Nasim Fallahi, Erasmo Carrera, Alfonso Pagani

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Variable angle tow describes the fibres which are curvilinearly steered in a composite lamina. Significantly, stiffness tailoring freedom of VAT composite laminate can be enlarged and enabled. Composite structures with curvilinear fibres have been shown to improve the buckling load carrying capability in contrast with the straight laminate composites. However, the optimal design and analysis of VAT are faced with high computational efforts due to the increasing number of variables. In this article, an efficient optimum solution has been used in combination with 1D Carrera’s Unified Formulation (CUF) to investigate the optimum fibre orientation angles for buckling analysis. The particular emphasis is on the LE-based CUF models, which provide a Lagrange Expansions to address a layerwise description of the problem unknowns. The first critical buckling load has been considered under simply supported boundary conditions. Special attention is lead to the sensitivity of buckling load corresponding to the fibre orientation angle in comparison with the results which obtain through the Genetic Algorithm (GA) optimization frame and then Artificial Neural Network (ANN) is applied to investigate the accuracy of the optimized model. As a result, numerical CUF approach with an optimal solution demonstrates the robustness and computational efficiency of proposed optimum methodology.

Keywords: Beam structures, layerwise, optimization, variable angle tow, neural network

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Authors: P. Kaladevi, N. Giridharan

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The system for analyzing and eliciting public grievances serves its main purpose to receive and process all sorts of complaints from the public and respond to users. Due to the more number of complaint data becomes big data which is difficult to store and process. The proposed system uses HDFS to store the big data and uses MapReduce to process the big data. The concept of cache was applied in the system to provide immediate response and timely action using big data analytics. Cache enabled big data increases the response time of the system. The unstructured data provided by the users are efficiently handled through map reduce algorithm. The processing of complaints takes place in the order of the hierarchy of the authority. The drawbacks of the traditional database system used in the existing system are set forth by our system by using Cache enabled Hadoop Distributed File System. MapReduce framework codes have the possible to leak the sensitive data through computation process. We propose a system that add noise to the output of the reduce phase to avoid signaling the presence of sensitive data. If the complaints are not processed in the ample time, then automatically it is forwarded to the higher authority. Hence it ensures assurance in processing. A copy of the filed complaint is sent as a digitally signed PDF document to the user mail id which serves as a proof. The system report serves to be an essential data while making important decisions based on legislation.

Keywords: Big Data, Hadoop, HDFS, Caching, MapReduce, web personalization, e-governance.

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Authors: Ehsan Hassankhan, Davood A. Khaburi

Abstract:

Direct Torque Control is a control technique in AC drive systems to obtain high performance torque control. The conventional DTC drive contains a pair of hysteresis comparators. DTC drives utilizing hysteresis comparators suffer from high torque ripple and variable switching frequency. The most common solution to those problems is to use the space vector depends on the reference torque and flux. In this Paper The space vector modulation technique (SVPWM) is applied to 2 level inverter control in the proposed DTC-based induction motor drive system, thereby dramatically reducing the torque ripple. Then the controller based on space vector modulation is designed to be applied in the control of Induction Motor (IM) with a three-level Inverter. This type of Inverter has several advantages over the standard two-level VSI, such as a greater number of levels in the output voltage waveforms, Lower dV/dt, less harmonic distortion in voltage and current waveforms and lower switching frequencies. This paper proposes a general SVPWM algorithm for three-level based on standard two-level SVPWM. The proposed scheme is described clearly and simulation results are reported to demonstrate its effectiveness. The entire control scheme is implemented with Matlab/Simulink.

Keywords: Direct torque control, space vector Pulsewidthmodulation(SVPWM), neutral point clamped(NPC), two-levelinverter.

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Authors: C. Lodato, S. Lopes

Abstract:

The image segmentation method described in this paper has been developed as a pre-processing stage to be used in methodologies and tools for video/image indexing and retrieval by content. This method solves the problem of whole objects extraction from background and it produces images of single complete objects from videos or photos. The extracted images are used for calculating the object visual features necessary for both indexing and retrieval processes. The segmentation algorithm is based on the cooperation among an optical flow evaluation method, edge detection and region growing procedures. The optical flow estimator belongs to the class of differential methods. It permits to detect motions ranging from a fraction of a pixel to a few pixels per frame, achieving good results in presence of noise without the need of a filtering pre-processing stage and includes a specialised model for moving object detection. The first task of the presented method exploits the cues from motion analysis for moving areas detection. Objects and background are then refined using respectively edge detection and seeded region growing procedures. All the tasks are iteratively performed until objects and background are completely resolved. The method has been applied to a variety of indoor and outdoor scenes where objects of different type and shape are represented on variously textured background.

Keywords: Image Segmentation, Motion Detection, Object Extraction, Optical Flow

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Authors: A. Joseph, D.B.L. Bong, D.A.A. Mat

Abstract:

Security has been an important issue and concern in the smart home systems. Smart home networks consist of a wide range of wired or wireless devices, there is possibility that illegal access to some restricted data or devices may happen. Password-based authentication is widely used to identify authorize users, because this method is cheap, easy and quite accurate. In this paper, a neural network is trained to store the passwords instead of using verification table. This method is useful in solving security problems that happened in some authentication system. The conventional way to train the network using Backpropagation (BPN) requires a long training time. Hence, a faster training algorithm, Resilient Backpropagation (RPROP) is embedded to the MLPs Neural Network to accelerate the training process. For the Data Part, 200 sets of UserID and Passwords were created and encoded into binary as the input. The simulation had been carried out to evaluate the performance for different number of hidden neurons and combination of transfer functions. Mean Square Error (MSE), training time and number of epochs are used to determine the network performance. From the results obtained, using Tansig and Purelin in hidden and output layer and 250 hidden neurons gave the better performance. As a result, a password-based user authentication system for smart home by using neural network had been developed successfully.

Keywords: Neural Network, User Authentication, Smart Home, Security

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Authors: Leila Vafajoo, Farhad Khorasheh, Mehrnoosh Hamzezadeh Nakhjavani, Moslem Fattahi

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In the present study, a procedure was developed to determine the optimum reaction rate constants in generalized Arrhenius form and optimized through the Nelder-Mead method. For this purpose, a comprehensive mathematical model of a fixed bed reactor for dehydrogenation of heavy paraffins over Pt–Sn/Al2O3 catalyst was developed. Utilizing appropriate kinetic rate expressions for the main dehydrogenation reaction as well as side reactions and catalyst deactivation, a detailed model for the radial flow reactor was obtained. The reactor model composed of a set of partial differential equations (PDE), ordinary differential equations (ODE) as well as algebraic equations all of which were solved numerically to determine variations in components- concentrations in term of mole percents as a function of time and reactor radius. It was demonstrated that most significant variations observed at the entrance of the bed and the initial olefin production obtained was rather high. The aforementioned method utilized a direct-search optimization algorithm along with the numerical solution of the governing differential equations. The usefulness and validity of the method was demonstrated by comparing the predicted values of the kinetic constants using the proposed method with a series of experimental values reported in the literature for different systems.

Keywords: Dehydrogenation, Pt-Sn/Al2O3 Catalyst, Modeling, Nelder-Mead, Optimization

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Authors: T. Hirner, J. Polec

Abstract:

This paper presents an approach for an unequal error protection of facial features of personal ID images coding. We consider unequal error protection (UEP) strategies for the efficient progressive transmission of embedded image codes over noisy channels. This new method is based on the progressive image compression embedded zerotree wavelet (EZW) algorithm and UEP technique with defined region of interest (ROI). In this case is ROI equal facial features within personal ID image. ROI technique is important in applications with different parts of importance. In ROI coding, a chosen ROI is encoded with higher quality than the background (BG). Unequal error protection of image is provided by different coding techniques and encoding LL band separately. In our proposed method, image is divided into two parts (ROI, BG) that consist of more important bytes (MIB) and less important bytes (LIB). The proposed unequal error protection of image transmission has shown to be more appropriate to low bit rate applications, producing better quality output for ROI of the compresses image. The experimental results verify effectiveness of the design. The results of our method demonstrate the comparison of the UEP of image transmission with defined ROI with facial features and the equal error protection (EEP) over additive white gaussian noise (AWGN) channel.

Keywords: Embedded zerotree wavelet (EZW), equal error protection (EEP), facial features, personal ID images, region of interest (ROI), unequal error protection (UEP)

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Authors: Loai AbdAllah, Mahmoud Kaiyal

Abstract:

Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: Missing values, distance metric, Bhattacharyya distance.

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Authors: Mohsen A. Bakouri, Andrey V. Savkin, Abdul-Hakeem H. Alomari, Robert F. Salamonsen, Einly Lim, Nigel H. Lovell

Abstract:

Physiological control of a left ventricle assist device (LVAD) is generally a complicated task due to diverse operating environments and patient variability. In this work, a tracking control algorithm based on sliding mode and feed forward control for a class of discrete-time single input single output (SISO) nonlinear uncertain systems is presented. The controller was developed to track the reference trajectory to a set operating point without inducing suction in the ventricle. The controller regulates the estimated mean pulsatile flow Qp and mean pulsatility index of pump rotational speed PIω that was generated from a model of the assist device. We recall the principle of the sliding mode control theory then we combine the feed-forward control design with the sliding mode control technique to follow the reference trajectory. The uncertainty is replaced by its upper and lower boundary. The controller was tested in a computer simulation covering two scenarios (preload and ventricular contractility). The simulation results prove the effectiveness and the robustness of the proposed controller

Keywords: robust control system, discrete-sliding mode, left ventricularle assist devicse, pulsatility index.

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Authors: Kemal Polat

Abstract:

In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: Fuzzy C-means clustering, Fuzzy C-means clustering based attribute weighting, Pima Indians diabetes dataset, SVM.

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Authors: Md. Zahangir Alom, Mei-Lan Piao, Md. Shariful Islam, Nam Kim, Jae-Hyeung Park

Abstract:

The evaluation and measurement of human body dimensions are achieved by physical anthropometry. This research was conducted in view of the importance of anthropometric indices of the face in forensic medicine, surgery, and medical imaging. The main goal of this research is to optimization of facial feature point by establishing a mathematical relationship among facial features and used optimize feature points for age classification. Since selected facial feature points are located to the area of mouth, nose, eyes and eyebrow on facial images, all desire facial feature points are extracted accurately. According this proposes method; sixteen Euclidean distances are calculated from the eighteen selected facial feature points vertically as well as horizontally. The mathematical relationships among horizontal and vertical distances are established. Moreover, it is also discovered that distances of the facial feature follows a constant ratio due to age progression. The distances between the specified features points increase with respect the age progression of a human from his or her childhood but the ratio of the distances does not change (d = 1 .618 ) . Finally, according to the proposed mathematical relationship four independent feature distances related to eight feature points are selected from sixteen distances and eighteen feature point-s respectively. These four feature distances are used for classification of age using Support Vector Machine (SVM)-Sequential Minimal Optimization (SMO) algorithm and shown around 96 % accuracy. Experiment result shows the proposed system is effective and accurate for age classification.

Keywords: 3D Face Model, Face Anthropometrics, Facial Features Extraction, Feature distances, SVM-SMO

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Authors: T. Sattarpour, D. Nazarpour

Abstract:

This paper investigates the effect of simultaneous placement of DGs and smart meters (SMs), on voltage profile improvement in active distribution networks (ADNs). A substantial center of attention has recently been on responsive loads initiated in power system problem studies such as distributed generations (DGs). Existence of responsive loads in active distribution networks (ADNs) would have undeniable effect on sizing and siting of DGs. For this reason, an optimal framework is proposed for sizing and siting of DGs and SMs in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Looking for voltage profile improvement, the optimization procedure is solved by genetic algorithm (GA) and tested on IEEE 33-bus distribution test system. Different scenarios with variations in the number of DG units, individual or simultaneous placing of DGs and SMs, and adaptive power factor (APF) mode for DGs to support reactive power have been established. The obtained results confirm the significant effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the improvement of voltage profile as well.

Keywords: Active distribution network (ADN), distributed generations (DGs), smart meters (SMs), demand response programs (DRPs), adaptive power factor (APF).

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Authors: J. K. Adedeji, S. T. Ijatuyi

Abstract:

The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: Neural network, gravitational resistance, pattern recognition, non-linear.

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