Search

In recent years, the number of people with sub-health is increasing, so sub-health is widely concerned by people from all walks of life. In this paper, according to the characteristics of the sub-health, and the data, set up a differential equation model, which based on the multiple subhealthy district for the data as a whole, at the same time set up on the mathematical model of health system in different populations, according to an area or a particular population health, the entire population was divided into healthy people, healthy people, with light patient group, become seriously ill crowd four types, assume that each type of people at a certain probability to other types, and meet certain transformation relations, on this basis, established the basis points by random phenomenon, The mathematical model of health status system of specific population based on random change and evolution over time is used to explore how to transform sub-health into health and what factors form sub-health so as to better eliminate it.

The last few years have witnessed a vast evolution in the communication and computational fields. The presence of social media makes data transferring more easier, which raises a flag of unauthorized usage and redistribution of digital contents. This copyright scenario is hardly affecting the publishing rights of authors and publishers. This paper presents a robust framework that protects the copyright property where only the authorized users legally use the data and prevents the illegal using or copying of data. The proposed framework is based on Digital Right Management (DRM) and includes two robust cryptosystems; Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) algorithms. The multimedia contents are encrypted using the AES-256 algorithm, while the shared key is encrypted using the ECC algorithm. The authorized user can utilize the copyrighted contents if the authorization is not expired. The license generation is done by a lightweight data-interchange format JavaScript Object Notation (JSON), and the license is validated using the Elliptic Curve Digital Signature Algorithm. The evaluation of the system has shown that it is fulfilling all the requirements of DRM technology with low cost, availability, platform independently, reliability and scalability since it is implemented on a cloud platform.

Inc, Mustafa/0000-0003-4996-8373 In this paper, the new series solutions of some fractional cancer tumor models are investigated by using residual power series method (RPSM). The RPSM is explained with Maclaurin expansion for the solution. One of the advantages of this method is quick and easy calculation to find series solutions by using mathematica software package. Graphical presentations for series solutions are given to explanation of the method. The obtained outcomes explain that process is applicable and reliable method to obtain numerical solutions of fractional equations. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

This paper investigates the effects of tire characteristics on vehicular rollover and lateral stability. Two tire types with different adhesion coefficients were selected to evaluate the relation between vehicular rollover propensity and lateral stability. Simulations were used to calculate the critical rollover factor and to analyze the effects of vehicular parameters on handling, including the center of gravity, payload condition, and vehicle speed, with the two proposed types of tires. To replicate an actual vehicular response, particularly during extreme driving operations, a two-degrees of freedom (DOF) planar two-track model with nonlinear Pacejka’s Magic Tire Formula was applied. Subsequently, a 7-DOF vehicle vibration and roll model was developed to consider the effects of suspension and road excitation. The tire, steering, and vehicle vibration models were implemented in MATLAB/Simulink by subjecting them to the Fishhook maneuver steering input defined by the National Highway Traffic Safety Administration. The results confirm that the adhesion capacities of tires have an opposite effect on lateral vehicle stability and rollover propensity, while both suspension parameters and road excitation inputs significantly influence vehicle rollover and lateral stability. Additionally, we identified a positive correlation between vehicle properties and lateral handling, especially when tire characteristics are considered.

The deck pavement, as an important structure of the bridge, cushions the wheel actions on the bridge, prevents the main girder from rain erosion, and ensures the flatness and slip resistance for vehicles. The asphalt concrete has been widely used in bridge decks. However, the conventional crack detection methods cannot identify the defects on asphalt concrete bridge deck accurately and efficiently, due to the dark color of the deck and the complexity, different types of defects. The objective of this paper is to develop a weakly supervised network for the segmentation and detection of cracks in asphalt concrete deck. Firstly, the data were differentiated by the autoencoder, and the unlabeled data features were highlighted, so that the original data autonomously generate a weakly supervised start point for convergence. Secondly, the features were classified by k-means clustering (KMC). Thirdly, the cracks in the bridge deck defects images were subjected to semantic segmentation under weak supervision. A dataset of six types of defects on asphalt concrete bridge deck which was set up the defects in the dataset were labeled manually. The experimental results show that the proposed achieved outstanding segmentation effects on all six types of defects was better than the other existed methods reported in the references.

In this paper, a novel supervised cross-modal retrieval method, combination subspace graph learning (CSGL), is proposed, which primarily concentrates on the cross retrieval between images and texts, i.e., image retrieving texts (I2T) and text retrieving images (T2I). To project multimodal data from a low-level feature space into a latent common subspace, most classical methods would learn the projective matrix separately for each mode, ignoring the consistency between different modes. Graph regularization is added to our objective function to preserve the structure of the original data in the projective space. Furthermore, to avoid the suboptimal solution during optimization, we use the collaborative learning strategy to obtain the projective matrix directly, which unites all the modes for better projection. Generally, the CSGL method takes the advantage of the semantic information and the original distribution of the image and text to obtain a more discriminative projection, which is learned in combination, rather than learning individually. Experimental results on three benchmark datasets, Wikipedia, Pascal Sentence, and INRIA-Websearch, show that the proposed method outperforms the state-of-the-art methods.

In this paper, the consumers' participation in the demand response program for virtual power plant (VPP) is proposed. A proposed price-based scheduling strategy for optimal day-ahead scheduling of VPP is investigated to maximize the profit of the VPP composed of a set of distributed generation, dispatchable load, and wind turbines. For solving the optimal scheduling problem, a modified differential evolution algorithm (MDEA) is proposed. To achieve the work target, 33-kV, 18-bus test system is selected as a test system. The proposed algorithm leads to effective scheduling of VPP resources, especially for reserve power. To investigate the proposed methodology, the load is increased and the scheduling of VPP is investigated at the best and worst boundaries of VPP resources uncertainty. The best case happens at maximum load, high prices and maximum energy from renewables, while the worst scenario occurs at minimum load, low prices and minimum energy from renewables. In addition, the algorithm ensures the importance of VPP in playing a dual role as a demand/generation unit from the perspective of the upstream network in some time periods and highlights the vital effect of dispatchable loads on the management process at different conditions of the dispatchable load. Keywords: Adaptive differential evolution, Demand response program (DRP), Distributed energy resources (DERs), Uncertainty and Virtual power plant (VPP)

Due to the rapid increase in electricity consumption and the need to increase the share of renewable sources of energy, there is a tendency to install distributed generations (DGs) at the level of distribution systems. The allocation of DGs has to be optimally determined to reduce system power losses. In this paper, an analytical formula (AF) is developed for loss reduction due to inserting multiple DG units. This analytical formula achieves the optimal placement of multiple DGs. The derived analytical formula considers the effects of the DGs during the placement of each unit. Based on this analytical formula, an iterative method is proposed for the sizing of the multiple DGs. In addition, mathematical proofs are provided to estimate approximately the effects of the load level, along with branch resistance, substation transformer tapping and DG power factor on the DG allocation. Applications on IEEE 33 and 69-bus distribution test systems are implemented for their original and simultaneous reconfiguration with DGs. The results of AF are compared with that given in the literature. Accordingly, the superiority of achieving the proved optimal solution is validated along with reducing the computational steps. Keywords: Distribution system, Distributed generation, Loss reduction, Analytical solution

In this paper, an alternative method to predict the noise of a submersible Axial Piston Pump (APP) for different valve seat materials is presented. The proposed method is composed of an Artificial Neural Network (ANN) model trained using experimental data and integrated with a hybrid algorithm consists of Cat Swarm Optimization (CSO) and Firefly Algorithm (FA) algorithms. The hybrid CSFA algorithm is used as a subroutine in the ANN model to estimate the ANN weights. The FA is used as local operator to improve the exploitation ability of CSO. The obtained results prove the excellence of the proposed method in predicting the noise of APP considering four different valve seat materials (Polytetrafluoroethylene (PTFE), Polyetheretherketone (PEEK), Aliphatic polyamides (NYLON), and stainless steel (316 L)), five speed levels, and six system pressures. Moreover, the effects of different mechanical properties of the valve seat materials as well as operating conditions (speed and system pressure) have been investigated. Keywords: Axial piston pump, Valve seat materials, Noise prediction, Artificial neural network

Each stylistic movement in building design and within its context throughout history is meant to possess a distinct visual character that is measurable in some way. From another perspective, building aesthetic measures in architecture have regularity included both experience and theories. Current styles of architecture are full of irregularity using a series of geometric construction which parametrically repeat themselves to produce complex images that could be defined as fractal. Recently, studying contemporary fractal dimensions in architecture has become significant as has been the metallic ratios in the past. In this study, fractal analysis is used to interpret current building elevation composition in a mathematical quantifiable way (boxcounting method). These measures are then verified by comparing results of questionnaires focused on habitats' perception of visual aesthetics of building elevation composition. This paper attempts to try to understand how fractal geometry contributes to the perception of buildings in their context and its participation in aesthetics measures. Findings confirm that highly dense fractals are not necessarily visually unpleasant, and that visual aesthetics are not correlated to fractal dimensions. This is due to the too many variables and conditions that are involved. Keywords: Fractals, Design analysis, Box-counting method, Visual aesthetics, Building composition, Elevations

Being smart has become a feature of almost all the electronic devices that we use every day. Recently, deep neural network based speech enhancement has made a breakthrough to speech de-noising process; however, in some situations, noise could be more important than speech, like talking while a fire alarm is activated. Hearing impaired people are the ones that could be most hurt in those situations, because of their lower ability to hear, especially in noisy environment. Available hearing aids lack the smart feature that distinguishes between desired and undesired noise types, so their users have to rely on separate alerting systems to ensure their safety. The idea that takes our concern in this work is to develop a smart hearing aid that has the ability to detect important noise and make it audible. In this paper, we will present three noise of interest aware speech enhancement networks for hearing aids application using deep learning, so as to introduce the idea of smart hearing aid. Keywords: Deep learning, Dropout, Noise of interest awareness, Smart hearing aid, Speech enhancement

The paper presents the results of numerical simulations of propeller open water characteristics by using RANSE method with rotating reference frame approach. The effects of grid type, mesh density and turbulence models on simulation results are analyzed. The well-known Potsdam Propeller PPTC test case is used to verify and validate the accuracy of case studies. At the end, the assessment and recommendation for choosing grid type and turbulence model from the point of view of the level of accuracy are given. The solver used in this study is the commercial package Star-CCM+from SIEMENS. Keywords: Propeller, Open water, Turbulence model, RANSE, Grid type

This paper presents a study of accuracy of a digital technique that uses a computer photogrammetric software which is PhotoModeler, this one based mainly on two mathematical theories to determine some measurement quantification, Direct linear Transformation (DLT) and Scale Invariant Feature Transform (SIFT) will be discussed as an explanation of the software technology. A set of laboratory experiments were prepared as a comparison with the traditional techniques, a try to assess the highest possible accuracy of the software and to demonstrate if it matches the proposed standard values, The pilot recommendations were also taken into account in the conduct of the tests as well as in the creation of projects by the software. These experiments focused on study of the accuracy of length monitoring and size measurement, the observation of the ground cartesian coordinates as well as the camera format size calculation. The results show that this technique could be relied upon effectively in some different measurements rather than traditional methods. Keywords: Digital Close Range Photogrammetry (DCRP), PhotoModeler, Laboratory experiments, Sofware, (SIFT), (DLT)

Traditional observation analysis techniques are widely used by researchers to analyze public spaces, in terms of visibility, and movement patterns. This method enables the researcher to have realistic results, however it depends mainly on the researcher’s views. The main aim of the research is to reach a more reliable method for evaluating and analyzing connectivity by integrating traditional observation techniques with a more accurate analysis technique, like Depthmap software analysis. The study highlights the importance of applying this technique along with observation method to assist and validate the results. The research was conducted by applying the two analysis techniques on two atria, inside “City Stars Mall” shopping center, located in Cairo city. Both atria are similar, however, the first atrium has a larger scale, while the second has a smaller scale. Results show that Depthmap analysis technique is better in smaller scaled atrium spaces, while observation is more realistic in larger scaled spaces. Each technique has its advantages over the other. However, the paper proves that in order to reach a clear and accurate analysis, both techniques must be applied together for accurate and reliable results. Keywords: Atrium, Observation, Depthmap, Connectivity, Visibility, Accessibility

Architectural design has always been a collaborative process that involves participatory practice. It requires experienced individuals working together; this includes architects, engineers and clients to achieve a singular goal. Therefore, there has been increased interest in achieving a collaborative design studio and developing architecture students’ skills.Architecture students need to interact, communicate and debate in design studios with their peers and their instructors. Moreover, they need to co-operate to achieve the same goal in a manner to be prepared to the highly collaborative nature of the architectural practice they will enter (Crosbie, 1995; Daniels, 2002) [9,10]. This leads to raising the question of “How can collaborative techniques be addressed in the design studio to enhance the skills of the students and prepare them to profession challenges?”The purpose of this paper is to discover how collaborative design studio can be improved to enhance each participant’s contribution in the design process. The study was conducted as a ten days workshop that includes ninety-two students from the third year from the Faculty of Engineering, Alexandria University, Egypt. The researcher used quantitative analysis in forms of questionnaires along the workshop and used tools like “time and task schedule” and “peer assessment” to assess their effectiveness. Keywords: Architectural education, Design studio, Collaborative learning, Coordination, Participatory design

Selected mapping (SLM) algorithm has been widely used for reducing high peak-toaverage power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signal. One of the factors affecting SLM performance is high computational complexity. To overcome this effect, in this paper, the time domain signals available at the output of IFFT blocks are separated into real odd, real even, imaginary odd and imaginary even subsequences using Fourier transform properties. To generate more candidates, the subsequences corresponding to each time-domain signals are swapped to get M4 candidates from M IFFT operation as swapped SLM. Then the real subsequences are retained and the imaginary subsequences are transformed into time reversed, circularly shifted and time-reversal of circularly shifted subsequences. Therefore, the proposed scheme generates 4M4 candidates by combining different real subsequences and imaginary subsequences using only M IFFT operations. The simulation results illustrate that the proposed SLM scheme offers an improved PAPR reduction performance than other existing algorithms. Also, the proposed algorithm achieves 97% computational complexity reduction in terms of multiplications and 67% in terms of additions when compared to CSLM. Similarly, it provides 75% complexity reduction in terms of multiplications and 20% in terms additions when compared to swapped SLM. 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

Vehicular Ad hoc Networks (VANETs) are a subdivision of Mobile Ad hoc Networks (MANETs), which take significant responsibility in the Intelligent Transportation System (ITS) domain for providing reliable road safety. Various researchers have dealt with the development under VANET for better routing. Yet, they found great difficulty in providing multi-constrained Quality of Service (QoS) to the network. To tackle the difficulty, the routing cost is determined by considering the congestion cost, travel cost, QoS awareness cost and collision cost, wherein the QoS awareness cost is estimated using fuzzification. In this paper, a renowned optimization algorithm, called Lion Algorithm (LA), is modified to adopt the minimized routing cost under the VANET. Further, the performance of the proposed LA is compared with the existing algorithms like, Genetic algorithm (GA) and LA by analyzing the convergence, routing cost and computational complexity. The proposed LA provides reliable routing with reduced cost and computational complexity. Keywords: VANET, MANET, Routing, Routing cost, LA, GA

Nowadays, Quantum-dot Cellular Automata (QCA) is a well-known technology for designing digital systems. Actually, QCA is the most possible alternative for semiconductor transistor based technology. In this paper, an improved design of QCA based convolution encoder has been proposed. The proposed encoder outperforms previous QCA based design from the view of size, speed, and power consumption. A popular simulator, QCADesigner has been used for simulating and verifying the proposed encoder. Simulation result shows proposed encoder’s dominance against previous designs from the context of cell number, area, latency, and energy consumption. The proposed 1/2 rate and 1/3 rate encoder reveal 89% and 85% less energy consumption at 0.5Ek tunneling energy level as compared to the previous design. It is worth mentioning that 90% and 86% optimizations in the number of cells in addition to 95% and 96% optimizations in the area are achieved for the proposed 1/2 rate and 1/3 rate convolution encoder, respectively, in comparison with the previous design. Keywords: Quantum-dot cellular automata, Convolution encoder, Power dissipation, QCADesigner, QCAPro

Active Magnetic Bearings (AMBs) are broadly utilized for high angular speed machines such as turbo-machinery, compressors and high speed motors. In AMBs, the rotating parts run without physical contacts with the moving parts. This reduces maintenance costs and minimizes friction. Generally, the applied loads cause extra gyroscopic effects on the rotating parts especially under high-speed operation. Although conventional PID controllers are widely employed in these systems, they experience some stability problems under high dynamic operations. In this paper, the design of an active magnetic bearing system based on fractional order PID (FOPID) controllers to enhance system dynamics and stability is introduced. The suggested controller gains are optimized utilizing particle swarm optimization (PSO) approach. An ordinary AMB system framework with four radial bearings is used to assess the suggested FOPID controller against routine PID controllers. The system current limitation, overshoot constraint, and time specifications requirements are deemed in the optimization technique. Keywords: Active magnetic bearings, PID, Fractional PID, Gyroscopic effects, Particle swarm optimization

In this paper, authors proposed a new methodology based on bio-inspired Cuckoo Search Algorithm (CSA), which can significantly be envisaged with simultaneous allocation of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) in the radial distribution systems (RDS). In the proposed method, optimal locations of the DG and DSTATCOM are determined by using Voltage Stability Index (VSI) and Loss Sensitivity Factor (LSF) respectively. The optimal size of DG and DSTATCOM are found by using a newly developed nature-inspired CSA in the RDS. Further, in the proposed method load flow calculations are carried out by using a fast and efficient Backward/Forward Sweep (BFS) algorithm. The objective function of the proposed method is derived in such a way that to minimize the system total power losses and enhancing the bus voltages. The effectiveness of the proposed method is tested by considering a well-known IEEE 33-bus and large 136-bus RDS. The attained results of the proposed method and comparison of different cases for allocation of DG and DSTATCOM are presented in result section. From the presented results, it can be clearly understood that the proposed method is able to significantly reduce the power losses and enhance the voltage profile of the RDS. Keywords: Distribution STATic COMpensator, Distributed generation, Cuckoo search algorithm, Voltage stability index, Loss sensitivity factor, Radial distribution system

This paper presents a proposed design of a mechatronics system for autonomous vehicles. The proposed design is able to memorize a route based on Global Positioning System (GPS) rather than using pre-saved maps that are infrequently updated and do not include all roads of all countries. Moreover, it can autonomously avoid obstacles and detect bumps. Experimental tests are conducted using a small-scale car equipped with the proposed mechatronics system. The results show that the proposed system operates with minor errors and slips. The proposed autonomous vehicle can serve normal, disabled, and elderly people. It can be used on roads and even inside facilities like campuses, airports, and factories to transport passengers or loads thus reducing workmanship and costs. Keywords: Autonomous vehicles, GPS, Collision avoidance, Tracking system

Wireless sensor networks (WSN) construct the infrastructure for a wide collection of applications concerning security, military services, and monitoring. Fault control and tolerance in WSN is a complicated issue since nodes in WSNs are susceptible to come to be a failure due to energy deterioration, hardware malfunction, communication link mistakes, and harmful attacks. As a result, fault tolerance can be one of the vital aspects in WSNs. One of the successful strategies to regulate fault tolerate is with the aid of clustering. In this paper, we propose a novel idea of an Irregular Cellular Automata (ICA) model in WSN to identify the faulty nodes based on clustering and cluster-head selection without consuming massive computational overhead and bandwidth. The suggested system is based on a node’s data structure that contains many parameters such as node energy level, a number of neighbors, coverage and connectivity variables. This data structure contributes to the selection of cluster head based on a set of rules inside ICA so that when the cluster head breaks down, it is going to be replaced by one more supplementary cluster head with the most affordable cost, and the route will come to be re-organized. Compared with existing approaches in which the re-clustering step can be commonly worked regularly in the full network, re-clustering step in the recommended approach can be ran locally anytime it is actually projected. Simulation outcomes illustrate that this kind of schemes possesses a substantial energy preservation, a low end-to-end packet transport latency, and a confident network lifetime ratio. Keywords: Wireless sensor networks (WSN), Fault control and tolerance, Irregular Cellular Automata (ICA), Adaptive clustering

This paper presents an exploratory review on various attempts made in the literature for improving the performance of diesel engine, since last decade. The review explains the evolution of various performance improvement methods followed by explaining the modeling techniques, state-of-the-art metrics that define the performance of the diesel engines. Subsequently, the review is confined to artificial intelligence methodologies for improving the performance improvement. This review addresses an important challenge for enhancing the performance of the diesel engine by exploiting the optimization algorithms. The significant challenges are the noisy experimental scenario, robustness, imprecise and temporal variations of approximations of fitness models. Since these challenges still exist in the optimization algorithms and diesel engine modeling, there is an extensive scope for researchers who are working on a diesel engine. This contemplates the reporting of various advancements in the optimization models for further enhancement of engine performance. Keywords: Engine, Modeling, Artificial Intelligence, Control, Diesel

Undesired roll motions of surface vessels under sea way have negative influence on passengers, crew members, equipment and cargo. Damping roll motions is a complex problem due to unpredictable and irregular nature of marine environmental disturbances that induce roll motions. There are several stabilizing systems regarding roll motions such as gyrostabilizers, anti-roll tanks, active fins and rudder roll stabilizers. Among all types of roll stabilizers or roll damping systems active fins practically represents the most effective roll damping technique and yet the most commonly used roll stabilizer. Assuming that the longitudinal location of fins corresponds to the vessel center of gravity a one degree of freedom nonlinear model can be used to identify ship dynamics. In this paper the performance and robustness of a proposed control system based on neural network approach applied to active fins that takes into account roll response spectral characteristics transitions due to not only varying sea state but also changing sailing conditions is assessed. The suggested control system achieves robust performance with adequate roll damping effect that ranges from 50 to 65% roll reduction according to severity of weather conditions and sailing status. Keywords: Ship roll damping, Active fins, Direct inverse control, Neural network, Marine environmental disturbances, Ship mathematical modeling

Spark has gained growing attention in the past couple of years as an in-memory cloud computing platform. It supports execution of various types of workloads such as SQL queries and machine learning applications. Currently, many enterprises use Spark to exploit its fast in-memory processing of large scale data. Additionally, speeding up the execution in Spark is an important problem for many real-time applications. This can be achieved by improving the scheduling approaches employed by Spark, optimizing the execution plans generated by Spark for various applications, and selecting the best cluster configuration to run an input workload. A first step for all these optimization approaches is to predict the execution time of an input Spark application. In this paper, we present a new platform that predicts with high accuracy the execution time of SQL queries and machine learning applications executed by Spark. We evaluate our proposed platform by measuring the accuracy of predicting execution time of various types of Spark jobs including TPC-H queries and machine learning classification/clustering applications. The evaluation experiments show that we are able to predict the execution time of Spark jobs using our proposed platform with accuracy greater than 90% for SQL queries and greater than 75% for machine learning jobs. Keywords: Spark, Execution Time Prediction, Machine Learning

Mining software repositories is a growing research field where rich data available in the different development software repositories, are analyzed and cross-linked to uncover useful information. Bug prediction is one of the potential benefits that can be gained through mining software repositories. Predicting potential defects early as they are introduced to the version control system would definitely help in saving time and effort during testing or maintenance phases. In this paper, defect prediction models that uses ensemble classification techniques have been proposed. The proposed models have been applied using different sets of software metrics as attributes of the classification techniques and tested on datasets of different sizes. The results show that change metrics outperform static code metrics and the combined model of change and static code metrics. Ensembles tend to be more accurate than their base classifiers. Defect prediction models using change metrics and ensemble classifiers have revealed the best performance, especially when the datasets used have imbalanced class distribution. Keywords: Modeling and prediction, Product metrics, Process metrics, Classifier design and evaluation

Till date, many Indian government organizations do not have robust software to search for words from scanned office documents having complex multilingual Indian scripts. Manual search of such a multilingual Indian document will take few minutes and there will be tens of thousands of documents to be searched for the desired content. Manual search of such a huge number of scanned Indian documents will be tedious, which requires robust automatic searching software. This led us to work toward indexing of aged printed multilingual Indian office documents. This paper presents a novel geometrical technique to group the components which belong to a text line of a document having multi-orientations and a novel approach to find the local skew of Devanagari word. The performance of the proposed technique was evaluated using 280 printed Indian documents with around 6000 text lines having English, Devanagari, and Marathi scripts and achieved 99% success rate for line segmentation indicates the legitimacy of the proposed method. To further assess the performance of the proposed method, we have considered publicly available Tobacco800 document image database and achieved significant performance results as compared with few of the prominent methods from the literature. Keywords: Character recognition, Character segmentation, Document analysis, Skew correction, Rough skew, Text line extraction, Word segmentation

Clustering is the technique used to partition the homogenous data, where the data are grouped together. In order to improve the clustering accuracy, the adaptive dynamic directive operative fractional lion algorithm is proposed using multi kernel function. Also, we intend to develop a new mathematical function for fitness evaluation. We utilize multi kernels, such as Gaussian, tangential, rational quadratic and Inverse multiquadratic to design the new fitness function. Consequently, the WLI fuzzy clustering mechanism is employed in this paper to determine the distance measurement based on new fitness function, named Multi kernel WLI (MKWLI). Then, we design a novel algorithm with the aid of dynamic directive operative searching strategy and adaptive fractional lion algorithm, termed Adaptive Dynamic Directive Operative Fractional Lion (ADDOFL) algorithm. Initially in this proposed algorithm, the solutions are generated based on the fractional lion algorithm. It also exploits the new MKWLI fitness function to evaluate the optimal value. Finally, the updation of female lion is performed through dynamic directive operative searching algorithm. Thus, the proposed ADDOFL algorithm is used to find out the optimal cluster center iteratively. The simulation results are validated and performance is analyzed using metrics such as clustering accuracy, Jaccard coefficient and rand coefficient. The outcome of the proposed algorithm attains the clustering accuracy of 89.6% for both Iris and Wine databases which ensures the better clustering performance. Keywords: Data clustering, Multi kernel function, Fractional lion optimization, Directive operative searching strategy, Clustering accuracy

Design phase of any construction project involves several designers who exchange information with each other most often in an unstructured manner throughout the design phase. When these information exchanges happen to occur in cycles/loops, it is termed as design iteration. Iteration is an inherent and unavoidable aspect of any design phase which requires proper planning. Till date, very few researchers have explored the design iteration (“complexity”) in construction sector. Hence, the objective of this paper was to document and review the complexities of iteration during design phase of construction projects for efficient design planning. To achieve this objective, exhaustive literature review on design iteration was done for four sectors – construction, manufacturing, aerospace, and software development. In addition, semi-structured interviews and discussions were done with a few design experts to verify the different dimensions of iteration. Finally, a design iteration framework was presented in this study that facilitates successful planning. Keywords: Design iteration, Types of iteration, Causes and impact of iteration, Models of iteration, Execution strategies of iteration

Allocation of distributed generation (DG) units is commonly formulated as a constrained nonlinear optimization problem solved by complex iterative mathematical or heuristic techniques. Heavy computational burden, very long solution time, probable divergence and possibility of getting only a sub-optimal solution are some serious drawbacks. In this paper, a systematic simple approach to allocate multiple DG units in radial/meshed distribution network is proposed. The concept of equivalent load is introduced and extended to identify the load centroid precisely with two methods. A performance index that combines the power system real power loss and average node voltage is defined. Based on load centroid and performance index, a straightforward algorithm for sizing and locating multiple DG units is developed. The proposed technique is applied to radial and meshed test systems. Results confirm stability, integrity and efficacy of the proposed approach.

The global positioning system (GPS) has been used to support a wide variety of applications, such as high-accuracy positioning and navigation. Differential GPS techniques can largely eliminate common-mode errors between the reference and the rover GPS stations resulting from ionospheric and tropospheric refraction and delays, satellite and receiver clock biases, and orbital errors [1] . The ionospheric delay in the propagation of global positioning system (GPS) signals is one of the main sources of error in GPS precise positioning and navigation. A dual-frequency GPS receiver can eliminate (to the first order) the ionospheric delay through a linear combination of the L1 and L2 observations [2] . The most significant effect of ionospheric delay appear in case of using single frequency data. In this paper the single frequency data of concerned station are converted to dual frequency data by employing dual frequency data from 11 regional GPS stations distributed around it. Total electron content (TEC) was calculated at every GPS station to produce the mathematical model of TEC which is a function of latitude (Φ) and longitude ( λ ). By using this mathematical model the values of TEC and L2 can be predicted at the single frequency GPS station for each satellite, after that the comparison between predicted and observation values of TEC and L2 was performed. The estimation method and test results of the proposed method indicates that the difference between predicted and observation values is very small.

Brain Computer Interface (BCI) is a communication and control mechanism, which does not rely on any kind of muscular response to send a message to the external world. This technique is used to help the paralyzed people with spinal cord injury to have the ability to communicate with the external world. In this paper we emphasize to increase the BCI System bit rate for controlling a virtual telephone keypad. To achieve the proposed algorithm, a simulated virtual telephone keypad based on Steady State Visual Evoked Potential (SSVEP) BCI system is developed. Dynamic programming technique with specifically modified Longest Common Subsequence (LCS) algorithm is used. By comparing the paralyzed user selection with the recent, and then the rest, of the stored records in the file of the telephone, the user can save the rest of his choices for controlling the keypad and thence improving the overall performance of the BCI system. This axiomatic approach, which is used in searching the web pages for increasing the performance of the searching, is urgent to be used for the paralyzed people rather than the normal user.