Regionalization of Rainfall Intensity-Duration-Frequency using a Simple Scaling Model

Design storm is one of the most important tools to design hydraulic structures, hydrologic system and watershed management, mostly extracted by intensity- duration - frequency (IDF) curves for a given specific duration and return period. As for conventional methods to calculate IDF curves, the precipitation should be recorded for different durations so that foregoing curves can be extracted. Such data can be collected from rain gauge stations. In many areas, just daily precipitation data are available by which IDF curves cannot be extracted as per conventional methods. The aim of this research is to make IDF curves for short-term durations according to time scaling model as well as daily rainfalls. The relationships of this method are characterized with three variables including mean (μ ₂₄) and standard deviation (σ ₂₄) of daily rainfall intensity, and scaling exponent (H) by which all IDF curves might be drawn. The method used in present paper entails for less computational steps than conventional methods and by far has low parameters considerably than others in turn increases reliability. Scaling method is used to extract the IDF curves in rain-gauge stations in Khuzestan province located in southwest Iran and results proved the efficiency and robustness of the scaling method. Also ability of scaling concept method was examined in constructing of regional IDF.     

Evaluation of changes in RDI<Subscript>st</Subscript> index effected by different Potential Evapotranspiration calculation methods

Investigation of drought event has a great importance in the natural erctt management and water resources management planning. One of the important indexes of drought severity assessment is RDIst index, this index is B35based on ratio of precipitation (P) and potential evapotranspiration (PET) factors. Many different methods have been introduced for PET calculation. In this study results of monthly RDIst index (1 month) based on some popular PET calculation methods include: Jensen-Haise, Modified Jensen-Haise, Thornthwaite, Hargreaves-Samani, Blaney-Criddle and FAO Penman-Monteith (this method was used as reference method) with minimum data requirements were compared. In this research climatic data of 17 synoptic stations (with different climate conditions) in Iran during 1967–2014 were used to estimate PET and monthly RDIst index. Results of this paper showed that different methods of PET calculation had significant impact on RDIst index. According to results based on RMSE index Hargreaves- Samani had the most similarities with FAO Penman-Monteith method. Based on means comparison test, Blaney-Criddle and Modified Jensen-Haise methods had the most similarities with FAO Penman-Monteith (mean of RDIst index in 58.8% of stations in Thornthwaite method, 52.9% of stations in Jensen-Haise and Hargreaves-Samani methods and 71% of stations in Blaney-Criddle and Modified Jensen- Haise methods had not different by FAO Penman-Monteith).     

Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems

An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.     

Combining classifiers using nearest decision prototypes

We present a new classifier fusion method to combine soft-level classifiers with a new approach, which can be considered as a generalized decision templates method. Previous combining methods based on decision templates employ a single prototype for each class, but this global point of view mostly fails to properly represent the decision space. This drawback extremely affects the classification rate in such cases: insufficient number of training samples, island-shaped decision space distribution, and classes with highly overlapped decision spaces. To better represent the decision space, we utilize a prototype selection method to obtain a set of local decision prototypes for each class. Afterward, to determine the class of a test pattern, its decision profile is computed and then compared to all decision prototypes. In other words, for each class, the larger the numbers of decision prototypes near to the decision profile of a given pattern, the higher the chance for that class. The efficiency of our proposed method is evaluated over some well-known classification datasets suggesting superiority of our method in comparison with other proposed techniques.     

A teleoperation system for micro positioning with haptic feedback

This paper presents the research work on a 1 Degree of Freedom (DOF) macro-micro teleoperation system which enables human operator to perform complex task in micro environment such as cell insertion with the capability of haptic feedback. To reach submicron resolution, a nano-motion piezo actuator was used as the slave robot and a servo DC motor was used as the master robot. Force sensors were implemented at both ends for haptic feedback and a microscope equipped with camera was employed for real-time visual feedback. The hysteresis nonlinearity of the piezo motor was modeled using LuGre friction model and compensated for. A Sliding Mode Based Impedance Controller (SMBIC) was designed at the slave side to ensure position tracking while an impedance force controller was designed at the master side to ascertain tracking of the force. Control parameters were chosen based on Llewellyn stability criteria such that the entire system stays stable against parameter uncertainties and constant time delay. The experimental results demonstrated capability of the proposed control frameworks in desirable tracking of the position and force signals while the entire system remained stable. The results of this study can be used for complex tasks in micron environment such as cell insertion.     

Perceptual pain classification using ANFIS adapted RBF kernel support vector machine for therapeutic usage

Diagnosing pain mechanisms is one of the main approaches to improve clinical treatments. Especially, the detection of existence and/or level of pain can be vital when verbal information is not present for instant for neonates, disabled persons, anesthetized patients and also animals. Various researches have been performed to locate and classify pain, however, no consistent result has been achieved. The aim of this study is to show a strict relation between electroencephalogram (EEG) signal features and perceptual pain levels and to clarify the relation of classified signal to pain origin. Cortical regions on scalp were assigned based on an evolutional method for optimized alignment of electrodes that improve the clinical monitoring results. The EEG signals were recorded during relax condition and variety of pain conditions. Specific spectral features which are studied to show consistency with dynamical characteristic of EEG signals were combined with non-linear features including approximate entropy and Lyapunov exponent to provide the feature vector. Evolutionary optimization method was used to reduce the features space dimension and computational costs. A hybrid adaptive network fuzzy inference system (ANFIS) and support vector machine (SVM) scheme was used for classification of pain levels. ANFIS optimizer is used to fine tune the non-linear alignment of kernels of SVM. The results show that pain levels can be differentiated with high accuracy and robustness even for few recording electrodes. This research verifies the hypothesis that electrical variations of brain patterns can be used for determination of pain levels. The proposed classification method provided up to 95% accuracy.     

LLACA: An adaptive localized clustering algorithm for wireless ad hoc networks

Performance of ad hoc networks dramatically declines as network grows. Cluster formation in which the network hosts are hierarchically partitioned into several autonomous non-overlapping groups, based on proximity, is a promising approach to alleviate the scalability problem of ad hoc networks. In this paper, we propose a localized learning automata-based clustering algorithm for wireless ad hoc networks. The proposed clustering method is a fully distributed algorithm in which each host chooses its cluster-head based solely on local information received from neighboring hosts. The proposed algorithm can be independently localized at each host. This results in a significantly reduction in message overhead of algorithm, and allows cluster maintenance can be locally performed only where it is required. To show the performance of proposed algorithm, obtained results are compared with those of several existing clustering methods in terms of the number of clusters, control message overhead, clustering time, and load standard deviation.     

A bootstrap test for symmetry based on ranked set samples

To test the hypothesis of symmetry about an unknown median we propose the maximum of a partial sum process based on ranked set samples. We discuss the properties of the test statistic and investigate a modified bootstrap ranked set sample bootstrap procedure to obtain its sampling distribution. The power of the new test statistic is compared with two existing tests in a simulation study.     

Non-local adaptive structure tensors : Application to anisotropic diffusion and shock filtering

Structure tensors are used in several PDE-based methods to estimate information on the local structure in the image, such as edge orientation. They have become a common tool in many image processing applications. To integrate the local data information, the structure tensor is based on a local regularization of a tensorial product. In this paper, we propose a new regularization model based on the non-local properties of the tensor product. The resulting non-local structure tensor is effective in the restitution of the non homogeneity of the local orientation of the structures. It is particularly efficient in texture regions where patches repeat non locally. The new tensor regularization also offers the advantage of automatically adapting the smoothing parameter to the local structures of the tensor product. Finally, we explain how this new adaptive structure tensor can be plugged into two PDEs: an anisotropic diffusion and a shock filter. Comparisons with other tensor regularization methods and other PDEs demonstrate the clear advantage of using the non-local structure tensor.     

A review: accuracy optimization in clustering ensembles using genetic algorithms

The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper describes a number of suggested genetic-guided clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research.