Search results for: Estimation algorithms

Authors: Varun Singh, Mainak Bandyopadhyay, Maharana Pratap Singh

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The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS.

Keywords: GIS, Outliers, PSO, Traffic Data.

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Authors: Wong Poh Lee, Mohd. Azam Osman, Abdullah Zawawi Talib, Ahmad Izani Md. Ismail

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Traffic congestion has become a major problem in many countries. One of the main causes of traffic congestion is due to road merges. Vehicles tend to move slower when they reach the merging point. In this paper, an enhanced algorithm for traffic simulation based on the fluid-dynamic algorithm and kinematic wave theory is proposed. The enhanced algorithm is used to study traffic congestion at a road merge. This paper also describes the development of a dynamic traffic simulation tool which is used as a scenario planning and to forecast traffic congestion level in a certain time based on defined parameter values. The tool incorporates the enhanced algorithm as well as the two original algorithms. Output from the three above mentioned algorithms are measured in terms of traffic queue length, travel time and the total number of vehicles passing through the merging point. This paper also suggests an efficient way of reducing traffic congestion at a road merge by analyzing the traffic queue length and travel time.

Keywords: Dynamic, fluid-dynamic, kinematic wave theory, simulation, traffic congestion.

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Authors: Rafic Ayoubi, Jean-Pierre Dubois, Rania Minkara

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In this paper, we study FPGA implementation of a novel supra-optimal receiver diversity combining technique, generalized maximal ratio combining (GMRC), for wireless transmission over fading channels in SIMO systems. Prior published results using ML-detected GMRC diversity signal driven by BPSK showed superior bit error rate performance to the widely used MRC combining scheme in an imperfect channel estimation (ICE) environment. Under perfect channel estimation conditions, the performance of GMRC and MRC were identical. The main drawback of the GMRC study was that it was theoretical, thus successful FPGA implementation of it using pipeline techniques is needed as a wireless communication test-bed for practical real-life situations. Simulation results showed that the hardware implementation was efficient both in terms of speed and area. Since diversity combining is especially effective in small femto- and picocells, internet-associated wireless peripheral systems are to benefit most from GMRC. As a result, many spinoff applications can be made to the hardware of IP-based 4th generation networks.

Keywords: Femto-internet cells, field-programmable gate array, generalized maximal-ratio combining, Lyapunov fractal dimension, pipelining technique, wireless SIMO channels.

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Authors: V Krishnaveni, S Jayaraman, K Ramadoss

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The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.

Keywords: Electroencephalogram, Ocular Artifacts (OA), Independent Component Analysis (ICA), Mutual Information (MI), Mutual Information based Least dependent Component Analysis(MILCA)

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Authors: Oluwaseun K. Oyebode, Josiah A. Adeyemo

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Hydrological modelling plays a crucial role in the planning and management of water resources, most especially in water stressed regions where the need to effectively manage the available water resources is of critical importance. However, due to the complex, nonlinear and dynamic behaviour of hydro-climatic interactions, achieving reliable modelling of water resource systems and accurate projection of hydrological parameters are extremely challenging. Although a significant number of modelling techniques (process-based and data-driven) have been developed and adopted in that regard, the field of hydrological modelling is still considered as one that has sluggishly progressed over the past decades. This is majorly as a result of the identification of some degree of uncertainty in the methodologies and results of techniques adopted. In recent times, evolutionary computation (EC) techniques have been developed and introduced in response to the search for efficient and reliable means of providing accurate solutions to hydrological related problems. This paper presents a comprehensive review of the underlying principles, methodological needs and applications of a promising evolutionary computation modelling technique – genetic programming (GP). It examines the specific characteristics of the technique which makes it suitable to solving hydrological modelling problems. It discusses the opportunities inherent in the application of GP in water related-studies such as rainfall estimation, rainfall-runoff modelling, streamflow forecasting, sediment transport modelling, water quality modelling and groundwater modelling among others. Furthermore, the means by which such opportunities could be harnessed in the near future are discussed. In all, a case for total embracement of GP and its variants in hydrological modelling studies is made so as to put in place strategies that would translate into achieving meaningful progress as it relates to modelling of water resource systems, and also positively influence decision-making by relevant stakeholders.

Keywords: Computational modelling, evolutionary algorithms, genetic programming, hydrological modelling.

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Authors: Li Shoutao, Gordon Lee

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Evolutionary robotics is concerned with the design of intelligent systems with life-like properties by means of simulated evolution. Approaches in evolutionary robotics can be categorized according to the control structures that represent the behavior and the parameters of the controller that undergo adaptation. The basic idea is to automatically synthesize behaviors that enable the robot to perform useful tasks in complex environments. The evolutionary algorithm searches through the space of parameterized controllers that map sensory perceptions to control actions, thus realizing a specific robotic behavior. Further, the evolutionary algorithm maintains and improves a population of candidate behaviors by means of selection, recombination and mutation. A fitness function evaluates the performance of the resulting behavior according to the robot-s task or mission. In this paper, the focus is in the use of genetic algorithms to solve a multi-objective optimization problem representing robot behaviors; in particular, the A-Compander Law is employed in selecting the weight of each objective during the optimization process. Results using an adaptive fitness function show that this approach can efficiently react to complex tasks under variable environments.

Keywords: adaptive fuzzy neural inference, evolutionary tuning

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Authors: S. Farid F. Mojtahedi, Ali Khosravi, Behnaz Naeimian, S. Adel A. Hosseini

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Land subsidence is a gradual settling or sudden sinking of the land surface from changes that take place underground. There are different causes of land subsidence; most notably, ground-water overdraft and severe weather conditions. Subsidence of the land surface due to ground water overdraft is caused by an increase in the intergranular pressure in unconsolidated aquifers, which results in a loss of buoyancy of solid particles in the zone dewatered by the falling water table and accordingly compaction of the aquifer. On the other hand, exploitation of underground water may result in significant changes in degree of saturation of soil layers above the water table, increasing the effective stress in these layers, and considerable soil settlements. This study focuses on estimation of soil moisture at surface using different methods. Specifically, different methods for the estimation of moisture content at the soil surface, as an important term to solve Richard’s equation and estimate soil moisture profile are presented, and their results are discussed through comparison with field measurements obtained from Yanco1 station in south-eastern Australia. Surface soil moisture is not easy to measure at the spatial scale of a catchment. Due to the heterogeneity of soil type, land use, and topography, surface soil moisture may change considerably in space and time.

Keywords: Artificial neural network, empirical method, remote sensing, surface soil moisture, unsaturated soil.

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Authors: Hanieh Tarbiat Khosrowshahi, Mojtaba Shakeri

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Wireless Sensor Networks (WSNs) consist of a set of sensor nodes with limited capability. WSNs may suffer from multiple node failures when they are exposed to harsh environments such as military zones or disaster locations and lose connectivity by getting partitioned into disjoint segments. Relay nodes (RNs) are alternatively introduced to restore connectivity. They cost more than sensors as they benefit from mobility, more power and more transmission range, enforcing a minimum number of them to be used. This paper addresses the problem of RN placement in a multiple disjoint network by developing a genetic algorithm (GA). The problem is reintroduced as the Steiner tree problem (which is known to be an NP-hard problem) by the aim of finding the minimum number of Steiner points where RNs are to be placed for restoring connectivity. An upper bound to the number of RNs is first computed to set up the length of initial chromosomes. The GA algorithm then iteratively reduces the number of RNs and determines their location at the same time. Experimental results indicate that the proposed GA is capable of establishing network connectivity using a reasonable number of RNs compared to the best existing work.

Keywords: Connectivity restoration, genetic algorithms, multiple-node failure, relay nodes, wireless sensor networks.

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Authors: Roza Dastres, Mohsen Soori

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In order to electronically exchange information between network users in the web of data, different software such as outlook is presented. So, the traffic of users on a site or even the floors of a building can be decreased as a result of applying a secure and reliable data sharing software. It is essential to provide a fast, secure and reliable network system in the data sharing webs to create an advanced communication systems in the users of network. In the present research work, different encoding methods and algorithms in data sharing systems is studied in order to increase security of data sharing systems by preventing the access of hackers to the transferred data. To increase security in the networks, the possibility of textual conversation between customers of a local network is studied. Application of the encryption and decryption algorithms is studied in order to increase security in networks by preventing hackers from infiltrating. As a result, a reliable and secure communication system between members of a network can be provided by preventing additional traffic in the website environment in order to increase speed, accuracy and security in the network and web systems of data sharing.

Keywords: Secure Socket Layer, Security of networks.

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Authors: Axay J Mehta, Hema A Mehta, T.C.Manjunath, C. Ardil

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In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.

Keywords: Power system, Load forecasting, Neural Network, Neuron, Stabilization, Network structure, Load.

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Authors: Amira Elsonbaty, Rawya Rizk, Mohamed Elksas, Mofreh Salem

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In this paper, a Cooperative Multi-robot for Carrying Targets (CMCT) algorithm is proposed. The multi-robot team consists of three robots, one is a supervisor and the others are workers for carrying boxes in a store of 100×100 m2. Each robot has a self recharging mechanism. The CMCT minimizes robot-s worked time for carrying many boxes during day by working in parallel. That is, the supervisor detects the required variables in the same time another robots work with previous variables. It works with straightforward mechanical models by using simple cosine laws. It detects the robot-s shortest path for reaching the target position avoiding obstacles by using a proposed CMCT path planning (CMCT-PP) algorithm. It prevents the collision between robots during moving. The robots interact in an ad hoc wireless network. Simulation results show that the proposed system that consists of CMCT algorithm and its accomplished CMCT-PP algorithm achieves a high improvement in time and distance while performing the required tasks over the already existed algorithms.

Keywords: Ad hoc network, Computer vision based positioning, Dynamic collision avoidance, Multi-robot, Path planning algorithms, Self recharging.

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Authors: Mustafa Elfituri, Jonathan Cook

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Recently, graph-based computations have become more important in large-scale scientific computing as they can provide a methodology to model many types of relations between independent objects. They are being actively used in fields as varied as biology, social networks, cybersecurity, and computer networks. At the same time, graph problems have some properties such as irregularity and poor locality that make their performance different than regular applications performance. Therefore, parallelizing graph algorithms is a hard and challenging task. Initial evidence is that standard computer architectures do not perform very well on graph algorithms. Little is known exactly what causes this. The Graph500 benchmark is a representative application for parallel graph-based computations, which have highly irregular data access and are driven more by traversing connected data than by computation. In this paper, we present results from analyzing the performance of various example implementations of Graph500, including a shared memory (OpenMP) version, a distributed (MPI) version, and a hybrid version. We measured and analyzed all the factors that affect its performance in order to identify possible changes that would improve its performance. Results are discussed in relation to what factors contribute to performance degradation.

Keywords: Graph computation, Graph500 benchmark, parallel architectures, parallel programming, workload characterization.

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Authors: Manisha Dubey, Aalok Dubey

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This paper presents an approach for the design of fuzzy logic power system stabilizers using genetic algorithms. In the proposed fuzzy expert system, speed deviation and its derivative have been selected as fuzzy inputs. In this approach the parameters of the fuzzy logic controllers have been tuned using genetic algorithm. Incorporation of GA in the design of fuzzy logic power system stabilizer will add an intelligent dimension to the stabilizer and significantly reduces computational time in the design process. It is shown in this paper that the system dynamic performance can be improved significantly by incorporating a genetic-based searching mechanism. To demonstrate the robustness of the genetic based fuzzy logic power system stabilizer (GFLPSS), simulation studies on multimachine system subjected to small perturbation and three-phase fault have been carried out. Simulation results show the superiority and robustness of GA based power system stabilizer as compare to conventionally tuned controller to enhance system dynamic performance over a wide range of operating conditions.

Keywords: Dynamic stability, Fuzzy logic power systemstabilizer, Genetic Algorithms, Genetic based power systemstabilizer

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Authors: Yan Li, Ronald Briggs

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Aerial and satellite images are information rich. They are also complex to analyze. For GIS systems, many features require fast and reliable extraction of roads and intersections. In this paper, we study efficient and reliable automatic extraction algorithms to address some difficult issues that are commonly seen in high resolution aerial and satellite images, nonetheless not well addressed in existing solutions, such as blurring, broken or missing road boundaries, lack of road profiles, heavy shadows, and interfering surrounding objects. The new scheme is based on a new method, namely reference circle, to properly identify the pixels that belong to the same road and use this information to recover the whole road network. This feature is invariable to the shape and direction of roads and tolerates heavy noise and disturbances. Road extraction based on reference circles is much more noise tolerant and flexible than the previous edge-detection based algorithms. The scheme is able to extract roads reliably from images with complex contents and heavy obstructions, such as the high resolution aerial/satellite images available from Google maps.

Keywords: Automatic road extraction, Image processing, Feature extraction, GIS update, Remote sensing, Geo-referencing

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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Paper presents an comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in speaker dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signal to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients gives best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfy the real-time requirements and is suitable for applications in embedded systems.

Keywords: Isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW.

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Authors: Parvinder S. Sandhu, Manisha Prashar, Pourush Bassi, Atul Bisht

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Software effort estimation is the process of predicting the most realistic use of effort required to develop or maintain software based on incomplete, uncertain and/or noisy input. Effort estimates may be used as input to project plans, iteration plans, budgets. There are various models like Halstead, Walston-Felix, Bailey-Basili, Doty and GA Based models which have already used to estimate the software effort for projects. In this study Statistical Models, Fuzzy-GA and Neuro-Fuzzy (NF) Inference Systems are experimented to estimate the software effort for projects. The performances of the developed models were tested on NASA software project datasets and results are compared with the Halstead, Walston-Felix, Bailey-Basili, Doty and Genetic Algorithm Based models mentioned in the literature. The result shows that the NF Model has the lowest MMRE and RMSE values. The NF Model shows the best results as compared with the Fuzzy-GA based hybrid Inference System and other existing Models that are being used for the Effort Prediction with lowest MMRE and RMSE values.

Keywords: Neuro-Fuzzy Model, Halstead Model, Walston-Felix Model, Bailey-Basili Model, Doty Model, GA Based Model, Genetic Algorithm.

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Authors: Melvin A. Ballera

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Prior literature in the field of adaptive and personalized learning sequence in e-learning have proposed and implemented various mechanisms to improve the learning process such as individualization and personalization, but complex to implement due to expensive algorithmic programming and need of extensive and prior data. The main objective of personalizing learning sequence is to maximize learning by dynamically selecting the closest teaching operation in order to achieve the learning competency of learner. In this paper, a revolutionary technique has been proposed and tested to perform individualization and personalization using modified reversed roulette wheel selection algorithm that runs at O(n). The technique is simpler to implement and is algorithmically less expensive compared to other revolutionary algorithms since it collects the dynamic real time performance matrix such as examinations, reviews, and study to form the RWSA single numerical fitness value. Results show that the implemented system is capable of recommending new learning sequences that lessens time of study based on student's prior knowledge and real performance matrix.

Keywords: E-learning, fitness value, personalized learning sequence, reversed roulette wheel selection algorithms.

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Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin

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The automatic extraction of shipping routes is advantageous for intelligent traffic management systems to identify events and support decision-making in maritime surveillance. At present, there is a high demand for the extraction of maritime traffic networks that resemble the real traffic of vessels accurately, which is valuable for further analytical processing tasks for vessels trajectories (e.g., naval routing and voyage planning, anomaly detection, destination prediction, time of arrival estimation). With the help of big data and processing huge amounts of vessels’ trajectory data, it is possible to learn these shipping routes from the navigation history of past behaviour of other, similar ships that were travelling in a given area. In this paper, we propose a spatial clustering model of vessels’ trajectories (SPTCLUST) to extract spatial representations of sailing routes from historical Automatic Identification System (AIS) data. The whole model consists of three main parts: data preprocessing, path finding, and route extraction, which consists of clustering and representative trajectory extraction. The proposed clustering method provides techniques to overcome the problems of: (i) optimal input parameters selection; (ii) the high complexity of processing a huge volume of multidimensional data; (iii) and the spatial representation of complete representative trajectory detection in the context of trajectory clustering algorithms. The experimental evaluation showed the effectiveness of the proposed model by using a real-world AIS dataset from the Port of Halifax. The results contribute to further understanding of shipping route patterns. This could aid surveillance authorities in stable and sustainable vessel traffic management.

Keywords: Vessel trajectory clustering, trajectory mining, Spatial Clustering, marine intelligent navigation, maritime traffic network extraction, sdailing routes extraction.

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

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This paper and its companion (Part 2) deal 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. The complexity of the problems is harder when flexibilities of operations such as the possibility of operation processed on alternative machines with alternative tools are considered. 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. These real numbers can be converted into part type sequence and machines that are used to process the part types. This first part of the papers focuses on the modeling of the problems and discussing how the novel chromosome representation can be applied to solve the problems. The second part will discuss the effectiveness of the RCGA to solve various test bed problems.

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

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Authors: John S. Anagnostopoulos, Dimitrios E. Papantonis

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The incorporation of computational fluid dynamics in the design of modern hydraulic turbines appears to be necessary in order to improve their efficiency and cost-effectiveness beyond the traditional design practices. A numerical optimization methodology is developed and applied in the present work to a Turgo water turbine. The fluid is simulated by a Lagrangian mesh-free approach that can provide detailed information on the energy transfer and enhance the understanding of the complex, unsteady flow field, at very small computing cost. The runner blades are initially shaped according to hydrodynamics theory, and parameterized using Bezier polynomials and interpolation techniques. The use of a limited number of free design variables allows for various modifications of the standard blade shape, while stochastic optimization using evolutionary algorithms is implemented to find the best blade that maximizes the attainable hydraulic efficiency of the runner. The obtained optimal runner design achieves considerably higher efficiency than the standard one, and its numerically predicted performance is comparable to a real Turgo turbine, verifying the reliability and the prospects of the new methodology.

Keywords: Turgo turbine, Lagrangian flow modeling, Surface parameterization, Design optimization, Evolutionary algorithms.

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Authors: Yang Song, Yifan Guo, Edward F. Gehringer

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Fifty years of research has found great potential for peer assessment as a pedagogical approach. With peer assessment, not only do students receive more copious assessments; they also learn to become assessors. In recent decades, more educational peer assessments have been facilitated by online systems. Those online systems are designed differently to suit different class settings and student groups, but they basically fall into two categories: rating-based and ranking-based. The rating-based systems ask assessors to rate the artifacts one by one following some review rubrics. The ranking-based systems allow assessors to review a set of artifacts and give a rank for each of them. Though there are different systems and a large number of users of each category, there is no comprehensive comparison on which design leads to higher reliability. In this paper, we designed algorithms to evaluate assessors' reliabilities based on their rating/ranking against the global ranks of the artifacts they have reviewed. These algorithms are suitable for data from both rating-based and ranking-based peer assessment systems. The experiments were done based on more than 15,000 peer assessments from multiple peer assessment systems. We found that the assessors in ranking-based peer assessments are at least 10% more reliable than the assessors in rating-based peer assessments. Further analysis also demonstrated that the assessors in ranking-based assessments tend to assess the more differentiable artifacts correctly, but there is no such pattern for rating-based assessors.

Keywords: Peer assessment, peer rating, peer ranking, reliability.

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Authors: G. Kumaravelan, R. Sivakumar

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Modeling the behavior of the dialogue management in the design of a spoken dialogue system using statistical methodologies is currently a growing research area. This paper presents a work on developing an adaptive learning approach to optimize dialogue strategy. At the core of our system is a method formalizing dialogue management as a sequential decision making under uncertainty whose underlying probabilistic structure has a Markov Chain. Researchers have mostly focused on model-free algorithms for automating the design of dialogue management using machine learning techniques such as reinforcement learning. But in model-free algorithms there exist a dilemma in engaging the type of exploration versus exploitation. Hence we present a model-based online policy learning algorithm using interconnected learning automata for optimizing dialogue strategy. The proposed algorithm is capable of deriving an optimal policy that prescribes what action should be taken in various states of conversation so as to maximize the expected total reward to attain the goal and incorporates good exploration and exploitation in its updates to improve the naturalness of humancomputer interaction. We test the proposed approach using the most sophisticated evaluation framework PARADISE for accessing to the railway information system.

Keywords: Dialogue management, Learning automata, Reinforcement learning, Spoken dialogue system

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Authors: R. Bremananth

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Several computationally challenging issues are encountered while classifying complex natural scenes. In this paper, we address the problems that are encountered in rotation invariance with multi-intensity analysis for multi-scene overlapping. In the present literature, various algorithms proposed techniques for multi-intensity analysis, but there are several restrictions in these algorithms while deploying them in multi-scene overlapping classifications. In order to resolve the problem of multi-scenery overlapping classifications, we present a framework that is based on macro and micro basis functions. This algorithm conquers the minimum classification false alarm while pigeonholing multi-scene overlapping. Furthermore, a quadrangle multi-intensity decay is invoked. Several parameters are utilized to analyze invariance for multi-scenery classifications such as rotation, classification, correlation, contrast, homogeneity, and energy. Benchmark datasets were collected for complex natural scenes and experimented for the framework. The results depict that the framework achieves a significant improvement on gray-level matrix of co-occurrence features for overlapping in diverse degree of orientations while pigeonholing multi-scene overlapping.

Keywords: Automatic classification, contrast, homogeneity, invariant analysis, multi-scene analysis, overlapping.

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Authors: S. Chaisit, H.Y. Kung, N.T. Phuong

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Radio frequency identification (RFID) applications have grown rapidly in many industries, especially in indoor location identification. The advantage of using received signal strength indicator (RSSI) values as an indoor location measurement method is a cost-effective approach without installing extra hardware. Because the accuracy of many positioning schemes using RSSI values is limited by interference factors and the environment, thus it is challenging to use RFID location techniques based on integrating positioning algorithm design. This study proposes the location estimation approach and analyzes a scheme relying on RSSI values to minimize location errors. In addition, this paper examines different factors that affect location accuracy by integrating the backpropagation neural network (BPN) with the LANDMARC algorithm in a training phase and an online phase. First, the training phase computes coordinates obtained from the LANDMARC algorithm, which uses RSSI values and the real coordinates of reference tags as training data for constructing an appropriate BPN architecture and training length. Second, in the online phase, the LANDMARC algorithm calculates the coordinates of tracking tags, which are then used as BPN inputs to obtain location estimates. The results show that the proposed scheme can estimate locations more accurately compared to LANDMARC without extra devices.

Keywords: BPNs, indoor location, location estimation, intelligent location identification.

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Authors: Leong Lee, Cyriac Kandoth, Jennifer L. Leopold, Ronald L. Frank

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Protein 3D structure prediction has always been an important research area in bioinformatics. In particular, the prediction of secondary structure has been a well-studied research topic. Despite the recent breakthrough of combining multiple sequence alignment information and artificial intelligence algorithms to predict protein secondary structure, the Q3 accuracy of various computational prediction algorithms rarely has exceeded 75%. In a previous paper [1], this research team presented a rule-based method called RT-RICO (Relaxed Threshold Rule Induction from Coverings) to predict protein secondary structure. The average Q3 accuracy on the sample datasets using RT-RICO was 80.3%, an improvement over comparable computational methods. Although this demonstrated that RT-RICO might be a promising approach for predicting secondary structure, the algorithm-s computational complexity and program running time limited its use. Herein a parallelized implementation of a slightly modified RT-RICO approach is presented. This new version of the algorithm facilitated the testing of a much larger dataset of 396 protein domains [2]. Parallelized RTRICO achieved a Q3 score of 74.6%, which is higher than the consensus prediction accuracy of 72.9% that was achieved for the same test dataset by a combination of four secondary structure prediction methods [2].

Keywords: data mining, protein secondary structure prediction, parallelization.

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Authors: Anosh Arshad Sundhu, Francesco Giordano, Giacomo Della Croce, Maurizio Arnone

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Digital Twin (DT) is a technology that generates a virtual representation of a physical system or process, enabling real-time monitoring, analysis, and simulation. DT of an Electrical Distribution System (EDS) can perform online analysis by integrating the static and real-time data in order to show the current grid status and predictions about the future status to the Distribution System Operator (DSO), producers and consumers. DT technology for EDS also offers the opportunity to DSO to test hypothetical scenarios. This paper discusses the development of a DT of an EDS by Smart Grid Controller (SGC) application, which is developed using open-source libraries and languages. The developed application can be integrated with Supervisory Control and Data Acquisition System (SCADA) of any EDS for creating the DT. The paper shows the performance of developed tools inside the application, tested on real EDS for grid observability, Smart Recursive Load Flow (SRLF) calculation and state estimation of loads in MV feeders.

Keywords: Digital Twin, Distribution System Operator, Electrical Distribution System, Smart Grid Controller, Supervisory Control and Data Acquisition System, Smart Recursive Load Flow.

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Authors: Mukesh Kumar Kirar, Ganga Agnihotri

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This paper investigates the preliminary sizing of generator set to design electrical system at the early phase of a project, dynamic behavior of generator-unit, as well as induction motors, during start-up of the induction motor drives fed from emergency generator unit. The information in this paper simplifies generator set selection and eliminates common errors in selection. It covers load estimation, step loading capacity test, transient analysis for the emergency generator set. The dynamic behavior of the generator-unit, power, power factor, voltage, during Direct-on-Line start-up of the induction motor drives fed from stand alone gene-set is also discussed. It is important to ensure that plant generators operate safely and consistently, power system studies are required at the planning and conceptual design stage of the project. The most widely recognized and studied effect of motor starting is the voltage dip that is experienced throughout an industrial power system as the direct online result of starting large motors. Generator step loading capability and transient voltage dip during starting of largest motor is ensured with the help of Electrical Transient Analyzer Program (ETAP).

Keywords: Sizing, induction motor starting, load estimation, Transient Analyzer Program (ETAP).

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Authors: Varsha Shah, Patel Pritesh, Patel Sagar, PrasantaKundu, RanjanMaheshwari

Abstract:

Performance of vehicle depends on driving patterns and vehicle drive train configuration. Driving patterns depends on traffic condition, road condition and driver behavior. HEV design is carried out under certain constrain like vehicle operating range, acceleration, decelerations, maximum speed and road grades which are directly related to the driving patterns. Therefore the detailed study on HEV performance over a different drive cycle is required for selection and sizing of HEV components. A simple hardware is design to measured velocity v/s time profile of the vehicle by operating vehicle on Indian roads under real traffic conditions. To size the HEV components, a detailed dynamic model of the vehicle is developed considering the effect of inertia of rotating components like wheels, drive chain, engine and electric motor. Using vehicle model and different Indian drive cycles data, total tractive power demanded by vehicle and power supplied by individual components has been calculated.Using above information selection and estimation of component sizing for HEV is carried out so that HEV performs efficiently under hostile driving condition. Complete analysis is carried out in LABVIEW.

Keywords: BLDC motor, Driving cycle, LABVIEW Ultracapacitors, Vehicle Dynamics,

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Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer

Abstract:

This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.

Keywords: Non-stationary, BINARMA(1, 1) model, Poisson Innovations, CML

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

Abstract:

The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning (ML) archetypal that could forecast the COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID-19 cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organization (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data are split into 8:2 ratio for training and testing purposes to forecast future new COVID-19 cases. Support Vector Machine (SVM), Random Forest (RF), and linear regression (LR) algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID-19 cases is evaluated. RF outperformed the other two ML algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n = 30. The mean square error obtained for RF is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis, RF algorithm can perform more effectively and efficiently in predicting the new COVID-19 cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest.

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