Showing papers in "International Journal of Intelligent Systems and Applications in 2014"
TL;DR: A case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them.
Abstract: Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.
91 citations
TL;DR: The compendious survey on the GSA algorithm and its applications is presented as well as enlightens the applicability of GSA in data clustering & classification.
Abstract: Natural phenomenon's and swarms behavior are the warm area of research among the researchers. A large number of algorithms have been developed on the account of natural phenomenon's and swarms behavior. These algorithms have been implemented on the various computational problems for the sake of solutions and provided significant results than conventional methods but there is no such algorithm which can be applied for all of the computational problems. In 2009, a new algorithm was developed on the behalf of theory of gravity and was named gravitational search algorithm (GSA) for continuous optimization problems. In short span of time, GSA algorithm gain popularity among researchers and has been applied to large number of problems such as clustering, classification, parameter identification etc. This paper presents the compendious survey on the GSA algorithm and its applications as well as enlightens the applicability of GSA in data clustering & classification.
62 citations
TL;DR: This article provides the metrics list may stand to help the future study and also assessment within the field of Cloud service's evaluation, as well as describing metrics for assessing the QoS.
Abstract: Cloud systems are transforming the Information Technology trade by facultative the companies to provide admission to their structure and also software products to the membership foundation. Because of the vast range within the delivered Cloud solutions, from the customer's perspective of an aspect, it's emerged as troublesome to decide whose providers they need to utilize and then what's the thought of his or her option. Especially, employing suitable metrics is vital in assessing practices. Nevertheless, to the most popular of our knowledge, there's no methodical explanation relating to metrics for estimating Cloud products and services. QoS (Quality of Service) metrics playing an important role in selecting Cloud providers and also optimizing resource utilization efficiency. While many reports have got to devote to exploitation QoS metrics, relatively not much equipment supports the remark and investigation of QoS metrics of Cloud programs. To guarantee a specialized product is published, describing metrics for assessing the QoS might be an essential necessity. So, this text suggests various QoS metrics for service vendors, especially thinking about the consumer's worry. This article provides the metrics list may stand to help the future study and also assessment within the field of Cloud service's evaluation.
57 citations
TL;DR: A cloud task scheduling policy based on ant colony optimization algorithm for load balancing compared with different scheduling algorithms has been proposed and experimental results showed that, the proposed algorithm outperformed scheduling algorithms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).
Abstract: Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different computing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimization algorithm for load balancing compared with different scheduling algorithms has been proposed. Ant Colony Optimization (ACO) is random optimization search approach that will be used for allocating the incoming jobs to the virtual machines. The main contribution of our work is to balance the system load while trying to minimizing the make span of a given tasks set. The load balancing factor, related to the job finishing rate, is proposed to make the job finishing rate at different resource being similar and the ability of the load balancing will be improved. The proposed scheduling strategy was simulated using Cloudsim toolkit package. Experimental results showed that, the proposed algorithm outperformed scheduling algorithms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).
37 citations
TL;DR: The benefits of cooperative transmission than traditional non - cooperative communication are inscribe and the advantages, applications and different routing strategies for cooperative mesh networks, Ad hoc networks and wireless sensor networks are presented.
Abstract: Cooperative communication in wireless networks has become more and more attractive recently since it could mitigate the particularly severe channel impairments arising from multipath propagation. Here the greater benefits gained by exploiting spatial diversity in the channel. In this paper, an overview on cooperative communication in wireless networks is presented. We inscribe the benefits of cooperative transmission than traditional non - cooperative communication. Practical issues and challenges in cooperative communication are identified. In particular, we present a study on the advantages, applications and different routing strategies for cooperative mesh networks, Ad hoc networks and wireless sensor networks.
34 citations
TL;DR: The proposed method is tested on 8-bus power transmission systems test systems considering the influences of current relay under three-phases short-circuit with and without IFCL for different locality and the results show the effectiveness of the solution.
Abstract: This paper considers the impact of the Inductive Fault Current Limiter (IFCL) on directional overcurrent relays coordination. The coordination problem is formulated as a non-linear constrained mono-objective optimization problem. The objective function of this optimization problem is the minimization of the operation time of the associated relays in the systems, and the decision variables are: the time dial setting (TDS) and the pickup current setting (IP) of each relay. To solve this complex non linear optimization problem, a variant of optimization algorithms named Firefly Algorithm (FA) is used. The proposed method is tested on 8-bus power transmission systems test systems considering the influences of current relay under three-phases short-circuit with and without IFCL for different locality. The results show the effectiveness of the solution.
26 citations
TL;DR: It is suggested that GA can be a good option in conjunction with other e-mail filtering techniques can provide more robust solution and genetic algorithm based method for spam email filtering is discussed with its advantages and dis- disadvantages.
Abstract: Now a day's everybody email inbox is full with spam mails. The problem with spam mails is that they are not malicious in nature so generally don't get blocked with firewall or filters etc., however, they are unwanted mails received by any internet users. In 2012, more that 50% emails of the total emails were spam emails. In this paper, a genetic algorithm based method for spam email filtering is discussed with its advantages and dis-advantages. The results presented in the paper are promising and suggested that GA can be a good option in conjunction with other e-mail filtering techniques can provide more robust solution.
23 citations
TL;DR: Simulation result proves that APSO based speech enhancement algorithm is superior to the standard PSO based algorithm with an improved speech quality and intelligibility measures.
Abstract: This research paper proposes a recently developed new variant of Particle Swarm Optimization (PSO) called Accelerated Particle Swarm Optimization (APSO) in speech enhancement application. Accelerated Particle Swarm Optimization technique is developed by Xin she Yang in 2010. APSO is simpler to implement and it has faster convergence when compared to the standard PSO (SPSO) algorithm. Hence as an alternative to SPSO based speech enhancement algorithm, APSO is introduced to speech enhancement in the present paper. The present study aims to analyze the performance of APSO and to compare it with existing standard PSO algorithm, in the context of dual channel speech enhancement. Objective evaluation of the proposed method is carried out by using three objective measures of speech quality SNR, Improved SNR, PESQ and one objective measure of speech intelligibility FAI. The performance of the algorithm is studied under babble and factory noise environments. Simulation result proves that APSO based speech enhancement algorithm is superior to the standard PSO based algorithm with an improved speech quality and intelligibility measures.
22 citations
TL;DR: In this paper, a multidimensional walking aid was designed and implemented using a network of ultrasonic sensors, thereby capable of detecting the direction and position of obstacle(s), and the performance and functionality are also improved by the addition of alert light, and voice guidance signal which is relayed to a miniature headset.
Abstract: The application of engineering practices in medicine has immensely contributed to the recent findings in biomedical research areas. One of the products of this application is the development of sophisticated aids for physically challenged people. In this paper, visually impaired walking aid is designed and implemented using a network of ultrasonic sensors, thereby capable of detecting the direction and position of obstacle(s). The performance and functionality are also improved by the addition of alert light, and voice guidance signal which is relayed to a miniature headset. The recorded voice alerts the user of the presence and direction of the obstacle(s). The prototype of the multidimensional walking aid was able to detect obstacles within the range of 0m to 1m at the left, right and front of the stick with an appropriate voice alert. The test results of the prototype showed that the stick can efficiently guide its user.
21 citations
TL;DR: The main goal of this paper is design of the fuzzy logic controller in the model of DC- DC converter (boost converter) that allows the MPPT controller output (duty cycle) adjusts the voltage input to the converter to track the maximum power point of the wind generator.
Abstract: in this paper, a fuzzy logic control (FLC) is proposed for maximum power point tracking (MPPT) in wind turbine connection to Permanent Magnet Synchronous Generator (PMSG). The proposed fuzzy logic controller tracks the maximum power point (MPP) by measurements the load voltage and current. This controller calculates the load power and sent through the fuzzy logic system. The main goal of this paper is design of the fuzzy logic controller in the model of DC- DC converter (boost converter). This method allows the MPPT controller output (duty cycle) adjusts the voltage input to the converter to track the maximum power point of the wind generator.
20 citations
TL;DR: The results demonstrate that the error-based parallel fuzzy inverse dynamic plus gravity controller is a partly model-free controllers which works well in certain and partly uncertain system.
Abstract: Refer to this research, a position parallel error-based fuzzy inverse dynamic plus gravity controller is proposed for continuum robot manipulator. The main problem of the pure inverse dynamic controller was equivalent dynamic formulation in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using fuzzy logic theory. To estimate the continuum robot manipulator system's dynamic, 49 rules Mamdani inference system is design and applied to inverse dynamic plus gravity methodology. This methodology is based on applied fuzzy logic in equivalent nonlinear dynamic part to estimate unknown parameters. The results demonstrate that the error-based parallel fuzzy inverse dynamic plus gravity controller is a partly model-free controllers which works well in certain and partly uncertain system.
TL;DR: A novel decision making framework in which expert systems, geographic information systems–based multicriteria evaluation techniques, and GIS-based ANP-OWA are integrated systematically to facilitate the selection of suitable sites for building new tourism facilities is presented.
Abstract: —The selection of a tourism development site involves a complex array of decision criteria that may have interdependence relationships within and between them. In the process of finding the optimum location that meet desired conditions, the analyst is challenged by the tedious manipulation of spatial data and the management of multiple decision-making criteria. This paper presents a novel decision making framework in which expert systems (ES), and geographic information systems–based multicriteria evaluation techniques (Analytical Network Process and fuzzy quantifiers-guided ordered weighted averaging operators (GIS-based ANP-OWA)) are integrated systematically to facilitate the selection of suitable sites for building new tourism facilities. First, ES is used for recommending the proper site selection criteria and their interdependence relationships. Then, the GIS-based ANP-OWA is used to perform the spatial data analysis necessary to generate a wide range of possible candidate sites’ scenarios taking into accounts both the interdependence relationships between sitting criteria and the level of risk the decision-makers wish to assume in their multicriteria evaluation. A typical case study is presented to demonstrate the application of the proposed decision making framework.
TL;DR: A modified time series based weather Prediction model is proposed to eliminate the problems incurred in hybrid BP/GA technique and the results are very encouraging; the proposed temporal weather prediction model outperforms the previous models while performing for dynamic and chaotic weather conditions.
Abstract: Hybrid back propagation based genetic algorithm approach is a popular way to train neural networks for weather prediction. The major drawback of this method is that weather parameters were assumed to be independent of each other and their temporal relation with one another was not considered. So in the present research a modified time series based weather prediction model is proposed to eliminate the problems incurred in hybrid BP/GA technique. The results are very encouraging; the proposed temporal weather prediction model outperforms the previous models while performing for dynamic and chaotic weather conditions.
TL;DR: Performance comparison between artificial neural network controller and Perturb and Observe method has been carried out which has shown the effectiveness of artificial neural networks controller to draw much energy and fast response against change in working conditions.
Abstract: Photovoltaic generation is the technique which uses photovoltaic cell to convert solar energy to electric energy Nowadays, PV generation is developing increasingly fast as a renewable energy source However, the disadvantage is that PV generation is intermittent because it depends considerably on weather conditions This paper proposes an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and solar irradiation conditions In this paper, a simulation study of the maximum power point tracking (MPPT) for a photovoltaic system using an artificial neural network is presented The system simulation is elaborated by combining the models established of solar PV module and a DC/DC Boost converter Finally performance comparison between artificial neural network controller and Perturb and Observe method has been carried out which has shown the effectiveness of artificial neural networks controller to draw much energy and fast response against change in working conditions
TL;DR: The robust sliding mode controller proposed in this paper is used to further demonstrate the appealing features exhibited by the spherical motor and is designed in a robust stabilizing torque designed for the nominal spherical motor dynamics derived using the constrained Lagrangian formulation.
Abstract: The increasing demand for multi-degree-of- freedom (DOF) actuators in a number of industries has motivated a flurry of research in the development of non-conventional actuators, spherical motor. This motor is capable of providing smooth and isotropic three- dimensional motion in a single joint. Not only can the spherical motor combine 3-DOF motion in a single joint, it has a large range of motion with no singularities in its workspace. The spherical motor, however, exhibits coupled, nonlinear and very complex dynamics that make the design and implementation of feedback controllers very challenging. The orientation- varying torque generated by the spherical motor also contributes to the challenges in controller design. This paper contributes to the on-going research effort by exploring alternate methods for nonlinear and robust controlling the motor. The robust sliding mode controller proposed in this paper is used to further demonstrate the appealing features exhibited by the spherical motor. In opposition, sliding mode controller is used in many applications especially to control of highly uncertain systems; it has two significant drawbacks namely; chattering phenomenon and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system (e.g., spherical motor) can be solved by using artificial intelligence theorem and applied a modified linear controller to switching part of sliding mode controller. Using Lyapunov-type stability arguments, a robust modified linear fuzzy sliding mode controller is designed to achieve this objective. The controller developed in this paper is designed in a robust stabilizing torque is designed for the nominal spherical motor dynamics derived using the constrained Lagrangian formulation. The eventual stability of the controller depends on the torque generating capabilities of the spherical motor.
TL;DR: A fuzzy logic based hybrid performance metric comprising of link and node parameters is proposed and computed for each link based upon throughput, delay, jitter of the link and residual energy of the node and is used to compute shortest path between a given source- terminal node pair.
Abstract: Wireless Mesh Networks (WMNs) are the evolutionary self-organizing multi-hop wireless networks to promise last mile access. Due to the emergence of stochastically varying network environments, routing in WMNs is critically affected. In this paper, we first propose a fuzzy logic based hybrid performance metric comprising of link and node parameters. This Integrated Link Cost (ILC) is computed for each link based upon throughput, delay, jitter of the link and residual energy of the node and is used to compute shortest path between a given source- terminal node pair. Further to address the optimal routing path selection, two soft computing based approaches are proposed and analyzed along with a conventional approach. Extensive simulations are performed for various architectures of WMNs with varying network conditions. It was observed that the proposed approaches are far superior in dealing with dynamic nature of WMNs as compared to Adhoc On- demand Distance Vector (AODV) algorithm.
TL;DR: This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario using Ant Colony Optimization (ACO) technique which is based on ant's food finding behavior.
Abstract: This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO) technique which is based on ant's food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS) and Standard Train Scheduling (STS) algorithm has been performed.
TL;DR: This work dealt with the tracking of single object in a sequence of frames either from a live camera or a previously saved video, using Median approximation technique and dynamically generated templates for this purpose.
Abstract: In this work, we dealt with the tracking of single object in a sequence of frames either from a live camera or a previously saved video. A moving object is detected frame-by-frame with high accuracy and efficiency using Median approximation technique. As soon as the object has been detected, the same is tracked by kalman filter estimation technique along with a more accurate Template Matching algorithm. The templates are dynamically generated for this purpose. This guarantees any change in object pose which does not be hindered from tracking procedure. The system is capable of handling entry and exit of an object. Such a tracking scheme is cost effective and it can be used as an automated video conferencing system and also has application as a surveillance tool. Several trials of the tracking show that the approach is correct and extremely fast, and it's a more robust performance throughout the experiments.
TL;DR: This research is predicting qualitative bankruptcy using ant- miner algorithm, which yields better accuracy with lesser number of terms than previously applied qualitative bankruptcy prediction methodologies.
Abstract: Qualitative bankruptcy prediction rules represent experts' problem-solving knowledge to predict qualitative bankruptcy The objective of this research is predicting qualitative bankruptcy using ant- miner algorithm Qualitative data are subjective and more difficult to measure This approach uses qualitative risk factors which include fourteen internal risk factors and sixty eight external risk factors associated with it By using these factors qualitative prediction rules are generated using ant-miner algorithm and the influence of these factors in bankruptcy is also analyzed Ant-Miner algorithm is a application of ant colony optimization and data mining concepts Qualitative rules generated by ant miner algorithm are validated using measure of agreement These prediction rules yields better accuracy with lesser number of terms than previously applied qualitative bankruptcy prediction methodologies
TL;DR: A new method for analyzing the linkages among features and reducedsemantically using Non- Negative Matrix Factorization (NMF) is proposed, which achieves the problem of emotion detection which is proved by the result near ground truth.
Abstract: Emotion detection is an application that is widely used in social media for industrial environment, health, and security problems. Twitter is ashort text messageknown as tweet. Based on content and purposes, the tweet can describes as information about a user"s emotion. Emotion detection by means oftweet, is a challenging problem because only a few features can be extracted. Getting features related to emotion is important at the first phase of extraction, so the appropriate features such as a hashtag, emoji, emoticon, and adjective terms are needed. We propose a new method for analyzing the linkages among features and reducedsemantically using Non- Negative Matrix Factorization (NMF). The dataset is taken from a Twitter application using Indonesian language with normalization of informal terms in advance. There are 764 tweets in corpus which have five emotions, i.e. happy (senang), angry (marah), fear (takut), sad (sedih), and surprise(terkejut). Then, the percentage of user"s emotion is computed by k- Nearest Neighbor(kNN) approach. Our proposed model achieves the problem of emotion detectionwhich is proved by the result near ground truth.
TL;DR: The results show that keeping the stance foot flat at beginning of the DSP is necessary for balancing the biped robot.
Abstract: —This paper addresses three issues of motion planning for zero-moment point (ZMP)-based biped robots. First, three methods have been compared for smooth transition of biped locomotion from the single support phase (SSP) to the double support phase (DSP) and vice versa. All these methods depend on linear pendulum mode (LPM) to predict the trajectory of the center of gravity (COG) of the biped. It has been found that the three methods could give the same motion of the COG for the biped. The second issue is investigation of the foot trajectory with different walking patterns especially during the DSP. The characteristics of foot rotation can improve the stability performance with uniform configurations. Last, a simple algorithm has been proposed to compensate for ZMP deviations due to approximate model of the LPM. The results show that keeping the stance foot flat at beginning of the DSP is necessary for balancing the biped robot. Index Terms —Biped robot, Zero-moment point, Walking pattern generators, Gait cycle, Single support phase, Double support phase.
TL;DR: In this paper, parallel fuzzy logic theory is used to compensate the system dynamic uncertainty, and the nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem.
Abstract: Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applicat ions; it has two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic formulat ion in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic sys tems, but it cannot guarantee stability and robustness. In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.
TL;DR: This paper analysed the major considerations for sensors placements, typical sensors deployments in NIDS, and established an extended model for sensors deployment to further strengthen the network for intrusion detection which was based on the escape of some malicious activities through the firewall.
Abstract: Network Intrusion Detection Systems (NIDSs) can be composed of a potentially large number of sensors, which monitor the traffic flowing in the network. Deciding where sensors should be placed and what information they need in order to detect the desired attacks can be a demanding task for network administrators, one that should be made as automatic as possible. Some few works have been done on positioning sensors using attack graph analysis, formal logic-based approach and Network Simulator NS2 which were studied to determine a strategy for sensors placement on the network. This paper analysed the major considerations for sensors placements, typical sensors deployments in NIDS, and established an extended model for sensors deployment to further strengthen the network for intrusion detection which was based on the escape of some malicious activities through the firewall.
TL;DR: F fuzzy expert system is selected, this system is based on data and also extracts rules by using data and interpretability of rules is obtained and Validity of these rules could be confirmed or rejected by banking affair experts.
Abstract: Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.
TL;DR: This research is focused on proposed minimum rule base PID computed torque algorithms with application to continuum robot manipulator and the stability of the closed-loop system is proved mathematically based on the Lyapunov method.
Abstract: This research is focused on proposed minimum rule base PID computed torque algorithms with application to continuum robot manipulator. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Classical Computed Torque Controller (CTC) is robust to control model partly uncertainties and external disturbances. This controller is one of the significant nonlinear methodologies; according to the nonlinear dynamic formulation. One of the main targets in this research is increase the robustness based on the artificial intelligence methodology. Classical computed torque control has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a computed torque controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. To reduce the number of rule base this research is focused on the PD like fuzzy plus integral methodology. This method is applied to continuum robot manipulator to have the best performance.
TL;DR: The object of this paper is to provide an overview of speech enhancement algorithms so that enhance the noisy speech signal which is corrupted by additive noise.
Abstract: Speech enhancement is a long standing problem with various applications like hearing aids, automatic recognition and coding of speech signals. Single channel speech enhancement technique is used for enhancement of the speech degraded by additive background noises. The background noise can have an adverse impact on our ability to converse without hindrance or smoothly in very noisy environments, such as busy streets, in a car or cockpit of an airplane. Such type of noises can affect quality and intelligibility of speech. This is a survey paper and its object is to provide an overview of speech enhancement algorithms so that enhance the noisy speech signal which is corrupted by additive noise. The algorithms are mainly based on statistical based approaches. Different estimators are compared. Challenges and Opportunities of speech enhancement are also discussed. This paper helps in choosing the best statistical based technique for speech enhancement
TL;DR: This paper presents a Time Petri Net (TPN) based approach to solve the scheduling problem by mapping each entity (tasks, resources and constraints) to correspondent one in the TPN in order to find an optimal sequence of transitions leading from an initial marking to a final one.
Abstract: The optimal resources allocation to tasks was the primary objective of the research dealing with scheduling problems. These problems are characterized by their complexity, known as NP-hard in most cases. Currently with the evolution of technology, classical methods are inadequate because they degrade system performance (inflexibility, inefficient resources using policy, etc.). In the context of parallel and distributed systems, several computing units process multitasking applications in concurrent way. Main goal of such process is to schedule tasks and map them on the appropriate machines to achieve the optimal overall system performance (Minimize the Make-span and balance the load among the machines). In this paper we present a Time Petri Net (TPN) based approach to solve the scheduling problem by mapping each entity (tasks, resources and constraints) to correspondent one in the TPN. In this case, the scheduling problem can be reduced to finding an optimal sequence of transitions leading from an initial marking to a final one. Our approach improves the classical mapping algorithms by introducing a control over resources allocation and by taking into consideration the resource balancing aspect leading to an acceptable state of the system. The approach is applied to a specific class of problems where the machines are parallel and identical. This class is analyzed by using the TiNA (Time Net Analyzer) tool software developed in the LAAS laboratory (Toulouse, France).
TL;DR: An improved artificial bee colony (IABC) optimization method to solving practical economic dispatch taking into account the nonlinear generator characteristics such as valve-point loading effects is presented.
Abstract: This paper presents an improved artificial bee colony (IABC) optimization method to solving practical economic dispatch taking into account the nonlinear generator characteristics such as valve-point loading effects. In order to exploit the performance of this new variant based ABC method to solving practical economic dispatch, a new local search mechanism (LSM) associated to the original ABC algorithm; it allows exploiting effectively the promising region to locate the best solution. The proposed approach has been examined and applied to many practical electrical power systems, the 13 generating units, and to the large electrical system with 40 generating units considering valve point loading effects. From the different case studies, it is observed that the results compared with the other recent techniques demonstrate the potential of the proposed approach and show clearly its effectiveness to solve practical and large ED.
TL;DR: A rule based approach for classifying messages in Twitter which can also successfully reveal overlapping clusters is deployed and can be used along the temporal dimension forclassifying messages into topics and rank the most prominent topics of conversation at a particular instance of time.
Abstract: Rapid developments in information technology and Web 2.0 have provided a platform for the evolution of terrorist organizations, extremists from a traditional pyramidal structure to a technology enabled networked structure. Growing presence of these subversive groups on social networking sites has emerged as one of the prominent threats to the society, governments and law enforcement agencies across the world. Identifying messages relevant to the domain of security can serve as a stepping stone in criminal network analysis. In this paper, we deploy a rule based approach for classifying messages in Twitter which can also successfully reveal overlapping clusters. The approach incorporates dictionaries of enriched themes where each theme is categorized by semantically related words. The message is vectorized according to the security dictionaries and is termed as ‗Security Vector'. The documents are classified in categories on the basis of security associations. Further, the approach can also be used along the temporal dimension for classifying messages into topics and rank the most prominent topics of conversation at a particular instance of time. We further employ social network analysis techniques to visualize the hidden network at a particular time. Some of the results of our approach obtained through experiment with information network of Twitter are also discussed.
TL;DR: It is shown that the proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty.
Abstract: In this paper, a fuzzy predictive control scheme is proposed for controlling output voltage of a step-down DC-DC converter in presence of disturbance and uncertainty. The DC-DC converter is considered as a hybrid system and modeled by Mixed Logical Dynamical modeling approach. The main objective of the paper is to design a Fuzzy Predictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. A model predictive control is designed based on the hybrid model and applied to the system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from lack of robustness in presence of disturbance and uncertainty. So, to succeed in facing up to the problem a fuzzy supervisor is utilized to adjust the main predictive controller based on the measured states of the system. In this paper it is shown that the proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty. Through simulations effectiveness of the proposed method is shown.