A fault-tolerant strategy based on SMC for current-controlled converters

The sliding mode control (SMC) is used to control variable structure systems such as power electronics converters. This paper presents a fault-tolerant strategy based on the SMC for current-controlled AC–DC converters. The proposed SMC is based on three sliding surfaces for the three legs of the AC–DC converter. Two sliding surfaces are assigned to control the phase currents since the input three-phase currents are balanced. Hence, the third sliding surface is considered as an extra degree of freedom which is utilised to control the neutral voltage. This action is utilised to enhance the performance of the converter during open-switch faults. The proposed fault-tolerant strategy is based on allocating the sliding surface of the faulty leg to control the neutral voltage. Consequently, the current waveform is improved. The behaviour of the current-controlled converter during different types of open-switch faults is analysed. Double switch faults include three cases: two upper switch fault; upper and lower switch fault at different legs; and two switches of the same leg. The dynamic performance of the proposed system is evaluated during healthy and open-switch fault operations. Simulation results exhibit the various merits of the proposed SMC-based fault-tolerant strategy.     

Probabilistic Characteristics of Drought Propagation from Meteorological to Hydrological Drought in South Korea

Water Resources Management - A reliable understanding of propagation from meteorological to hydrological drought is necessary for accurate forecasting of hydrological droughts. Our current...     

1-D coupled non-equilibrium sediment transport modeling for unsteady flows in the discontinuous Galerkin framework

A high-resolution,1-D numerical model has been developed in the discontinuous Galerkin framework to simulate 1-D flow behavior,sediment transport,and morphological evaluation under unsteady flow conditions.The flow and sediment concentration variables are computed based on the one-dimensional shallow water flow equations,while empirical equations are used for entrainment and deposition processes.The sediment transport model includes the bed load and suspended load components.New formulations for Harten-Lax-van Leer(HLL) and Harten-Lax-van Contact(HLLC) are presented for shallow water flow equations that include the bed load and suspended load fluxes.The computational results for the flow and morphological changes after two dam break events are compared with the physical model tests.Results show that the modified HLL and HLLC formulations are robust and can accurately predict morphological changes in highly unsteady flows.     

Breaking a novel image encryption scheme based on an improper fractional order chaotic system

In this paper, we analyze the security of a recent image encryption algorithm based on an improper fractional-order chaotic system suggested by Zhao et al. The fatal flaw in the cryptosystem is that the keystream generated depends on neither the plain-image nor the cipher-image. Another main issue with this algorithm is using the same key (the last key in the keystream) in all encryption equations. Based on these points, it is easy to recover the plain-image and the keystream by applying chosen plaintext attack in only one plain-image. Both mathematical analysis and experimental results confirm the feasibility of this attack. As a result, the cryptosystem under study is not suitable for cryptography.     

Implication of Remote Sensing Data under GIS Environment for Appraisal of Irrigation System Performance

Water Resources Management - Poor irrigation management is a common issue of irrigated agriculture. Assessment of irrigation system performance is essential to improve the irrigation system, which...     

Detecting intrusion transactions in database systems:a novel approach

The security of computers and their networks is of crucial concern in the world today. One mechanism to safeguard information stored in database systems is an Intrusion Detection System (IDS). The purpose of intrusion detection in database systems is to detect malicious transactions that corrupt data. Recently researchers are working on using data mining techniques for detecting such malicious transactions in database systems. Their approach concentrates on mining data dependencies among data items. However, the transactions not compliant with these data dependencies are identified as malicious transactions. Algorithms that these approaches use for designing their data dependency miner have limitations. For instance, they need to experimentally determine appropriate settings for minimum support and related constraints, which does not necessarily lead to strong data dependencies. In this paper we propose a new data mining algorithm, called the Optimal Data Access Dependency Rule Mining (ODADRM), for designing a data dependency miner for our database IDS. ODADRM is an extension of k-optimal rule discovery algorithm, which has been improved to be suitable in database intrusion detection domain. ODADRM avoids many limitations of previous data dependency miner algorithms. As a result, our approach is able to track normal transactions and detect malicious ones more effectively than existing approaches.     

CBDL: Context‐based distance learning for categorical attributes

Distance learning is an important notion and has played a critical role in success of various machine learning algorithms. Any learning algorithm that requires dissimilarity/similarity measures has to assume some forms of distance functions, either explicitly or implicitly. Hence, in recent years a considerable amount of research has been devoted to distance learning. Despite great achievements in this field, a number of important issues need to be further explored for real world datasets mainly containing categorical attributes. Based on these considerations, the current research presents a Context‐Based Distance Learning approach (CBDL) to advance the state of the art existing researches on distance metric learning for categorical datasets. CBDL is designed and developed based on the idea that distance between two values of a given categorical attribute can be estimated by using information inherently exists within subset of attributes called context. CBDL composes of two main components: context extraction component and distance learning component. Context extraction component is responsible for extracting the relevant subset of feature set for a given attribute, while distance learning component tries to learn distance between each pair of values based on the extracted context. To have a comprehensive analysis, we conduct wide range of experiments in both supervised and unsupervised environments in the presence of noise. Our experimental results reveal that CBDL is the method of choice distance learning approach by offering a comparable or better performance compared to the state of the art existing distance learning schemes according to studied evaluation measures. © 2011 Wiley Periodicals, Inc.     

An entropy-based distance measure for analyzing and detecting metamorphic malware

Metamorphic malware is a kind of malware which evades signature-based anti-viruses by changing its internal structure in each infection. This paper, firstly, introduces a new measure of distance between two computer programs called program dissimilarity measure based on entropy (PDME). Then, it suggests a measure for the degree of metamorphism, based on the suggested distance measure. The distance measure is defined based on the Entropy of the two malware programs. Moreover, the paper shows that the distance measure can be used for classifying metamorphic malware via K-Nearest Neighbors (KNN) method. The method is evaluated by four metamorphic malware families. The results demonstrate that the measure can indicate the degree of metamorphism efficiently, and the KNN classification method using PDME can classify the metamorphic malware with a high precision.     

X-CleLo: intelligent deterministic RFID data and event transformer

Recently, passive Radio Frequency Identification (RFID) systems have received an increased amount of attention as researchers have worked to implement a stable and reliable system. Unfortunately, despite vast improvements in the quality of RFID technology, a significant amount of erroneous data is still captured in the system. Currently, the problems associated with RFID have been addressed by cleaning algorithms to enhance the data quality. In this paper, we present X-CleLo, a means to intelligently clean and transform the dirty data into high-level events using Clausal Defeasible Logic. The extensive experimental study we have conducted has shown that the X-CleLo method has several advantages over currently utilised cleaning techniques and achieves a higher cleaning and event discovery rate.