Description:
This paper presents a proposed adaptive technique for nonholonomic wheeled mobile robot (NWMR) using the sliding-mode control (SMC) method. The proposed control system based on the backstepping kinematic controller and PI sliding mode dynamic control. With an adaptive fuzzy logic to adjust adaptation gain of SMC for trajectory tracking control of nonholonomic mobile robot. Parametric and nonparametric uncertainties of mobile robot can be solved by using the proposed control which take advantages of stability and robustness in sliding mode control. The adaptation gain of SMC is adjusted by using Mamdani type inference system with adaptive tuning algorithm, which improves the adaptability for uncertainness and eliminate input chattering of the SMC. The stability and convergence of the control system are proved using Lypanouve criteria, and the comparison of the proposed controller with the other controllers ensures the validity and superiority of my own controller
Description:
In a distributed, dynamic and volatile operating environment, runtime anomalies occurring in service-oriented systems (SOSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly and inspecting the entire SOS upon a runtime anomaly are impractical due to excessive resource and time consumption required, especially in large-scale scenarios. We present a spectrum-based approach that goes through a five-phase process to quickly localize runtime anomalies occurring in SOSs based on end-to-end system delays. Upon runtime anomalies, our approach calculates the similarity coefficient for each basic component (BC) of the SOS to evaluate their suspiciousness of being faulty. Our approach also calculates the delay coefficients to evaluate each BC's contribution to the severity of the end-to-end system delays. Finally, the BCs are ranked by their similarity coefficient scores and delay coefficient scores to determine the order of them being inspected. Extensive experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach. The results indicate that our approach significantly outperforms random inspection and the popular Ochiai-based inspection in localizing single and multiple runtime anomalies effectively. Thus, our approach can help save time and effort for localizing runtime anomalies occuring in SOSs.
Description:
Malware authors are known to reuse existing code, this development process results in software evolution and a sequence of versions of a malware family containing functions that show a divergence from the initial version. This paper proposes the term evolved similarity to account for this gradual divergence of similarity across the version history of a malware family. While existing techniques are able to match functions in different versions of malware, these techniques work best when the version changes are relatively small. This paper introduces the concept of evolved similarity and presents automated Evolved Similarity Techniques (EST). EST differs from existing malware function similarity techniques by focusing on the identification of significantly modified functions in adjacent malware versions and may also be used to identify function similarity in malware samples that differ by several versions. The challenge in identifying evolved malware function pairs lies in identifying features that are relatively invariant across evolved code. The research in this paper makes use of the function call graph to establish these features and then demonstrates the use of these techniques using Zeus malware.
Description:
The unbalanced conditions are taken into account in the Reactive Power/Voltage control of distribution system. The aim of the control is to simultaneously minimize energy loss and improve voltage profile. The control is carried out by optimal dispatch of load tap changers (LTC) and shunt capacitors considering unbalanced conditions. A genetic algorithm (GA) is developed to determine the load curve partition for effective LTC scheduling and switching constraint satisfaction. GA is also appointed to determine the optimal dispatch schedule of the devices and to check the fulfillment of switching constraints prior to performing calculations. For power flow analyses under unbalanced conditions, a forward/backward propagation algorithm is developed. The optimization is implemented on the IEEE 34-bus unbalanced distribution system, and the presented system improvements are highlighted. The main contribution is inclusion of unbalanced system conditions into the optimal dispatch problem considering different daily load curves for the three phases of distribution system.
Description:
Generally, an industrial robot is named the Single-Function Robot (SFR) if it is only able to perform one task like material handling. However, a Multi-Function Robot (MFR) predominantly performs two tasks concurrently: material handling and a special operation. This type of recent robot with this ability can raise production rate. A robot equipped by a special kind of gripper namely Grip-Gage-Go is a real-life applications of MFRs. This gripper makes MFR competent to measure the diameter of the part in transit. These MFRs are widely employed in the inspection of automotive products including crankshaft, gears, and engine valves [1]
Description:
In order to save time and cost, many disease gene prediction methods have been proposed in recent years. However, the traditional network model uses a binary relationship to represent the relationship between different proteins or gene molecules and phenotypes, which leads to the loss of information. Recently, hypergraph shows that it can overcome this loss of information to some extent and preserve the multivariate relationship, so we transformed the disease gene prediction problem into the problem of ranking the multivariate-relationship object. In this paper, we propose a method of Heterogeneous Probabilistic Hypergraph Ranking (HPHR) to predict disease genes. Firstly, fix a graph centroid for each hyperedge and according to different associations, and add other nodes related to the graph centroid to hyperedges with a certain probability. Then transform the problem of predicting disease genes into the problem of ranking heterogeneous objects, and the candidate genes are sorted by hypergraph ranking. The method is then applied to the integrated disease gene network. Compared with other prediction methods achieved better results, which was verified by this experiment.
Description:
Intentional Controlled Islanding (ICI) is the last protective measure that must be adopted in power systems to prevent forming unstable islands with load-generation imbalance power. If conventional protective schemes fail to save the power system against wide-area blackout, some cutsets are selected to keep the system operating while feeding essential loads. This paper investigates the effect of the number of partitions on the dynamic performance of controlled islanding and proposes a unified algorithm to identify the coherent generators, optimal cutsets, and timing of islanding in online applications. The algorithm provides an islanding solution with minimum power flow disruption while each island contains coherent groups of generators. The concept of Centre of Inertia (COI) referred angles of generators has been used to determine the critical timing of islanding. Different scenarios in NE 39-bus and IEEE 118-bus test systems were simulated in PowerFactory that led to instability in the power system, and then controlled islanding schemes with a different number of partitions were applied to create stable islands. To better demonstrate the shortcomings of the existing clustering techniques, the effect of the number of required islands on transient stability of controlled islanding and the necessity of devising an automatic method to recognise the number of islands is discussed.
Description:
Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising.
Description:
This paper focuses on the behavior of maximum power point tracking (MPPT) on photovoltaic (PV) cell systems using MATLAB/Simulink software. The PV cells can offer better simulation results when a double-exponential type, i.e., double-diode model is used. The reason, it is more accurate with the real testing condition even though the efficiency is a bit less. This confirms that theoretical single-diode model still require more improvements. In addition, multijunction solar cell also investigated to obtain its maximum performance compared with the conventional silicon PV cell. The obtained results show that the tandem (or multijunction) cell can provide almost 3-times maximum power compared to the conventional PV cell. Therefore, the multijunction/ tandem PV cells can produce higher power throughout the sunny day due to the higher MPPT capacity. Furthermore, perturb and observe method seems to be very useful because of its simplicity despite the unresolved algorithm for different methods, such as constant voltage tracking technique.
Description:
Large remote area networks normally have self-suffi-cient electricity systems. These systems also rely on non-dispatchable DGs (N-DGs) for overall reduction in cost of electricity production. It is a fact that uncertainties included in the nature of N-DGs as well as load demand can cause cost burden on islanded microgrids (MGs). This paper proposes development of power exchange strategy for an interconnected MGs (IMG) system as part of large remote area network with optimized controls of dispatchable (D-DGs) which are members of master control unit (MCU). MCU analysis includes equal cost increment principle to give idea about the amount of power exchange which could take place with neighbor MGs in case of overloading situation. Sudden changes in N-DGs and load are defined as interruptions and are part of analysis too. Optimization problem is formulated on the basis of MCU adjustment for overloading or under loading situation and suitability of support MG (S-MG) in IMG system for power exchange along with key features of low cost and minimum technical impacts. Mixed integer linear programming (MILP) technique is applied to solve the formulated problem. The impact of proposed strategy is assessed by numerical analysis in MATLAB programming under stochastic environment.
Description:
Large remote area networks normally have self-suffi-cient electricity systems. These systems also rely on non-dispatchable DGs (N-DGs) for overall reduction in cost of electricity production. It is a fact that uncertainties included in the nature of N-DGs as well as load demand can cause cost burden on islanded microgrids (MGs). This paper proposes development of power exchange strategy for an interconnected MGs (IMG) system as part of large remote area network with optimized controls of dispatchable (D-DGs) which are members of master control unit (MCU). MCU analysis includes equal cost increment principle to give idea about the amount of power exchange which could take place with neighbor MGs in case of overloading situation. Sudden changes in N-DGs and load are defined as interruptions and are part of analysis too. Optimization problem is formulated on the basis of MCU adjustment for overloading or under loading situation and suitability of support MG (S-MG) in IMG system for power exchange along with key features of low cost and minimum technical impacts. Mixed integer linear programming (MILP) technique is applied to solve the formulated problem. The impact of proposed strategy is assessed by numerical analysis in MATLAB programming under stochastic environment.
Description:
While Grid environment has developed increasingly, unfortunately the importance of fault tolerance has not been remarkable in Grid resource management. On the other hand, the cost of computing by grid is important because grid is an economy-based system. Most organizations intend to spend little on their own computations by grid. Therefore, using a better approach to resource scheduling to avoid fault is necessary. This paper presents a new approach on fault tolerance mechanisms for the resource scheduling on grid by using Case-Based Reasoning technique in a local fashion. This approach applies a specific structure in order to prepare fault tolerance between executer nodes to retain system in a safe state with minimum data transferring. Certainly, this algorithm increases fault tolerant confidence therefore, performance of grid will be high.
Description:
2009 5th IEEE GCC Conference and Exhibition, GCC 2009
Description:
In a social network, the topology of the network grows through the formation of the link. the connection between two nodes in a social network indicates a confidence in terms of the similarity of some activities. Generally, a new link in the social network is created from different perspectives such as familiarity, cohesiveness, geographical locations etc. The concept of the link in the social network has been utilized to discover the hidden meaning of different fields such as e-commerce, bioinformatics and information retrieval. The prediction of a new link between two nodes in the social network is normally accomplished based on the nature of the topology and the similarity function among the nodes is defined with the help of the number of common friends. In this paper, we propose two link prediction algorithms: Local Link Prediction Algorithm and Global Link prediction by taking into consideration of user's activities as well as the common friends. We apply two formulas called correlation based cScore and influential score based iScore to measure the similarity between the two predicted nodes. Finally, we analyze the performance of the proposed algorithms by using DBLP, PPI, PB, and USAir data sets and the experimental result attests that our link predicted algorithm outperforms over the existing algorithms.
Description:
Sensors play a vital role in monitoring the important parameters of critical infrastructure. Failure of such sensors causes destabilization to the entire system. In this regard, this paper proposes a predictive analytics solution for detecting the failure of a sensor that measures surface temperature from an urban sewer. The proposed approach incorporates a forecasting technique based on the past time series of sparse data using an autoregressive integrated moving average (ARIMA) model. Based on the 95% forecast interval and continuity of faulty data, a criterion was set to detect anomalies and to issue a warning for sensor failure. The forecasted and faulty data were assumed Gaussian distributed. By using the probability density of the distribution, the mean and variance were computed for faulty data to examine the abnormality in the variance value of each day to detect the sensor failure. The experimental results on the sewer temperature data are appealing.
Description:
There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.
Description:
We consider the problem of security in wireless multicasting for a multiple-input multiple-output (MIMO) relay-aided system. The network suffers from a group of adaptive eavesdroppers who can act as both simple eavesdroppers and hostile jammers. This paper formulates the impact of friendly jammers to improve secured communication. We derived the expressions for secrecy multicast capacities considering the absence and presence of friendly jammers. The best relay for transmission is chosen from a group of relays that aids to achieve the maximum secrecy capacity while the best jammer is selected based on competitive interference price. Numerical results show that the achievable secrecy multicast capacity increases significantly in the presence of jammer to nullify the effect of adversaries. Results under different scenarios of varying jamming and relay powers demonstrate the efficacy of friendly jammers in providing physical layer security.
Description:
We consider the problem of security in wireless multicasting for a
Description:
Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.
Description:
Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.
Description:
Various statistical methods such as co-occurrence matrix, local binary patterns and spectral approaches such as Gabor filters have been used for generating global features for image classification. However, global image features fail to distinguish between local variations within an image. Bag-of-visual-words (BoVW) model do capture local variations in an image, but typically do not consider spatial relationships between the visual words. Here, a novel image representation ‘Pixel N-grams’, inspired from the character N-gram concept in text retrieval has been applied for texture classification purpose. Texture is an important property for image classification. Experiments on the benchmark texture database (UIUC) demonstrates that the overall classification accuracy resulting from Pixel N-gram approach (89.5%) is comparable with that achieved using BoVW approach (84.4%) with the added advantage of simplicity and reduced computational cost.
Description:
Large remote areas normally have isolated and self-sufficient electricity supply systems, often referred to as microgrids. These systems also rely on a mix of dispatchable and non-dispatcha- ble distributed energy resources to reduce the overall cost of electricity production. Emergencies such as shortfalls, overloading, and faults can cause problems in the operation of these remote area microgrids. This paper presents a power transaction management scheme amongst a few such microgrids when they are coupled provisionally during emergencies. By definition, power transaction is an instance of buying and selling of electricity amongst problem and healthy microgrids. The developed technique aims to define the suitable power generation from all dispatchable sources and regulate the power transaction amongst the coupled microgrids. To this end, an optimization problem is formulated that aims to define the above parameters while minimizing the costs and technical impacts. A mixed- integer linear programming technique is used to solve the formulated problem. The performance of the proposed management strategy is evaluated by numerical analysis in MATLAB.
Description:
Large remote areas normally have isolated and self-sufficient electricity supply systems, often referred to as microgrids. These systems also rely on a mix of dispatchable and non-dispatcha- ble distributed energy resources to reduce the overall cost of electricity production. Emergencies such as shortfalls, overloading, and faults can cause problems in the operation of these remote area microgrids. This paper presents a power transaction management scheme amongst a few such microgrids when they are coupled provisionally during emergencies. By definition, power transaction is an instance of buying and selling of electricity amongst problem and healthy microgrids. The developed technique aims to define the suitable power generation from all dispatchable sources and regulate the power transaction amongst the coupled microgrids. To this end, an optimization problem is formulated that aims to define the above parameters while minimizing the costs and technical impacts. A mixed- integer linear programming technique is used to solve the formulated problem. The performance of the proposed management strategy is evaluated by numerical analysis in MATLAB.
Description:
A Wireless Power Transfer (WPT) method is proposed by using a novel magnetic core structure. Similar to those transformers, magnetic cores are utilized as main object that conducts magnetic flux in the proposed cell. Power is transferred through near magnetic field and the distance between the receiver and the transmitter is thus relatively short. Every basic cell has only one primary side, where the magnetic cores of different permeabilities are combined to build a multi-transmitter structure for multiple receivers. By arranging and guiding the flux via a specific magnetic path, it offers some space of freedom for the locations of the multiple receivers. The usage of the magnetic core also increases the coupling coefficients between the primary-side transmitter and secondary-side receivers compared to core-less WPT using spiral coils. Analysis of the three working modes of the proposed WPT cell is presented. Experimental results show that the proposed basic cell with three receivers achieves 85% efficiency at 100W.
Description:
Most existing data models and transmission policies for sensor network assume uniform periodic data generation and unconstrained transmission range for sensor nodes, both assumptions being too restrictive to capture and analyze real- world operation for practical deployment. In this paper, we consider these two practical aspects and present a new transmission policy formulated after (i) stochastic data model where a set of events occur with certain probabilities and rate of data generation by a sensor varies based on sensed event and (ii) limited transmission range of sensors. Assuming co-centric rings around the base station, located at a generic location (internal or external to the network area), ring thickness and hope sizes over lifetime is determined by formatting an optimization problem where nodes in each ring may transmit data at different hop sizes at a given instant and also vary hop sizes over lifetime. Performance analysis shows significant improvement in network lifetime and better uniformity in energy usage distribution in the proposed policy irrespective of network size and maximum allowable transmission range of nodes.