Journal of Artificial Intelligence and Data Mining
Volume:5 Issue: 1, Winter-Spring 2017

Quad rotor is a renowned underactuated Unmanned Aerial Vehicle (UAV) with widespread military and civilian applications. Despite its simple structure, the vehicle suffers from inherent instability. Therefore, control designers always face formidable challenge in stabilization and control goal. In this paper fuzzy membership functions of the quad rotor’s fuzzy controllers are optimized using nature-inspired algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Finally, the results of the proposed methods are compared and a trajectory is defined to verify the effectiveness of the designed fuzzy controllers based on the algorithm with better results.

In recent years, significant research efforts have been devoted to the optimal planning of power systems. Substation Expansion Planning (SEP) as a sub-system of power system planning consists of finding the most economical solution with the optimal location and size of future substations and/or feeders to meet the future load demand. The large number of design variables and combination of discrete and continuous variables make the substation expansion planning a very challenging problem. So far, various methods have been presented to solve such a complicated problem. Since the Bacterial Foraging Optimization Algorithm (BFOA) yield to proper results in power system studies, and it has not been applied to SEP in sub-transmission voltage level problems yet, this paper develops a new BFO-based method to solve the Sub-Transmission Substation Expansion Planning (STSEP) problem. The technique discussed in this paper uses BFOA to simultaneously optimize the sizes and locations of both the existing and new installed substations and feeders by considering reliability constraints. To clarify the capabilities of the presented method, two test systems (a typical network and a real ones) are considered, and the results of applying GA and BFOA on these networks are compared. The simulation results demonstrate that the BFOA has the potential to find more optimal results than the other algorithm under the same conditions. Also, the fast convergence, consideration of real-world networks limitations as problem constraints, and the simplicity in applying it to real networks are the main features of the proposed method.

In this paper, a new iris localization method for mobile devices is presented. Our system uses both intensity and saturation threshold on the captured eye images to determine iris boundary and sclera area, respectively. Estimated iris boundary pixels which have been placed outside the sclera will be removed. The remaining pixels are mainly the boundary of iris inside the sclera. Then, circular Hough transform is applied to such iris boundary pixels in order to localize the iris. Experiments were done on 60 iris images taken by a HTC mobile device from 10 different persons with both left and right eyes images available per person. Also, we evaluate the proposed algorithm on MICHE datasets include iphone5, Samsung Galaxy S4 and Samsung Galaxy Tab2. Experimental evaluation shows that the proposed system can successfully localize iris on tested images.

In this paper, a new method is presented for encryption of colored images. This method is based on using stack data structure and chaos which make the image encryption algorithm more efficient and robust. In the proposed algorithm, a series of data whose range is between 0 and 3 is generated using chaotic logistic system. Then, the original image is divided into four subimages, and these four images are respectively pushed into the stack based on next number in the series. In the next step, the first element of the stack (which includes one of the four sub-images) is popped, and this image is divided into four other parts. Then, based on the next number in the series, four sub-images are pushed into the stack again. This procedure is repeated until the stack is empty. Therefore, during this process, each pixel unit is encrypted using another series of chaotic numbers (generated by Chen chaotic system). This method is repeated until all pixels of the plain image are encrypted. Finally, several extensive simulations on well-known USC datasets have been conducted to show the efficiency of this encryption algorithm. The tests performed show that the proposed method has a really large key space and possesses high-entropic distribution. Consequently, it outperforms the other competing algorithms in the case of security .

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new feature extraction method in this paper, which uses the boundary semi-labeled samples for solving small sample size problem. The proposed method, which called hybrid feature extraction based on boundary semi-labeled samples (HFE-BSL), uses a hybrid criterion that integrates both the local and global criteria for feature extraction. Thus, it is robust and flexible. The experimental results with three real hyperspectral images show the good efficiency of HFE-BSL compared to some popular and state-of-the-art feature extraction methods.

This paper uses nonlinear regression, Artificial Neural Network (ANN) and Genetic Programming (GP) approaches for predicting an important tangible issue i.e. scours dimensions downstream of inverted siphon structures. Dimensional analysis and nonlinear regression-based equations was proposed for estimation of maximum scour depth, location of the scour hole, location and height of the dune downstream of the structures. In addition, The GP-based formulation results are compared with experimental results and other accurate equations. The results analysis showed that the equations derived from Forward Stepwise nonlinear regression method have correlation coefficient of R2=0.962 , 0.971 and 0.991 respectively. This correlates the relative parameter of maximum scour depth (s/z) in comparison with the genetic programming (GP) model and artificial neural network (ANN) model. Furthermore, the slope of the fitted line extracted from computations and observations for dimensionless parameters generally presents a new achievement for sediment engineering and scientific community, indicating the superiority of artificial neural network (ANN) model

Predicting financially false statements to detect frauds in companies has an increasing trend in recent studies. The manipulations in financial statements can be discovered by auditors when related financial records and indicators are analyzed in depth together with the experience of auditors in order to create knowledge to develop a decision support system to classify firms. Auditors may annotate the firms’ statements as “correct” or “incorrect” to add their experience, and then these annotations with related indicators can be used for the learning process to generate a model. Once the model is learned and tested for validation, it can be used for new firms to predict their class values. In this research, we attempted to reveal this benefit in the framework of Turkish firms. In this regard, the study aims at classifying financially correct and false statements of Turkish firms listed on Borsa İstanbul, using their particular financial ratios as indicators of a success or a manipulation. The dataset was selected from a particular period after the crisis (2009 to 2013). Commonly used three classification methods in data mining were employed for the classification: decision tree, logistic regression, and artificial neural network, respectively. According to the results, although all three methods are performed well, the latter had the best performance, and it outperforms other two classical methods. The common ground of the selected methods is that they pointed out the Z-score as the first distinctive indicator for classifying financial statements under consideration.

In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated according to the statistical source, balance-sheets and the method proposed in this paper. It can be seen that diesel and liquefied petroleum gas have respectively the highest and lowest shares of energy consumption compared with the other types of carriers. For each of the evaluated energy carriers (diesel, kerosene, fuel oil, natural gas, electricity, liquefied petroleum gas and gasoline), the best fitting model was selected after taking the average of runs of the developed models. At last, the developed models, representing the energy consumption of food and beverage industries by each energy carrier, were put into a finalized model using Simulink toolbox of Matlab software. Results of data analysis indicated that consumption of natural gas is being increased in Iran food and beverage industries, while in the case of fuel oil and liquefied petroleum gas a decreasing trend was estimated.

The optimal reactive power dispatch (ORPD) is a very important problem aspect of power system planning and is a highly nonlinear, non-convex optimization problem because consist of both continuous and discrete control variables. Since the power system has inherent uncertainty, hereby, this paper presents both of the deterministic and stochastic models for ORPD problem in multi objective and single objective formulation, respectively. The deterministic model consider three main issues in ORPD problem as real power loss, voltage deviation and voltage stability index, but, in the stochastic model the uncertainty on the demand and the equivalent availability of shunt reactive power compensators have been investigated. To solve them, propose a new modified harmony search algorithm (HSA) which implemented in single and multi objective forms. Since, like many other general purpose optimization methods, the original HSA often traps into local optima, to aim with this cope, an efficient local search method called chaotic local search (CLS) and global search operator are proposed in the internal architecture of the original HSA algorithm to improve its ability in finding of best solution because ORPD problem is very complex problem with different types of continuous and discrete constrains i.e. excitation settings of generators, sizes of fixed capacitors, tap positions of tap changing transformers and the amount of reactive compensation devices. Moreover, fuzzy decision-making method is employed to select the best solution from the set of Pareto solutions.

Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, such as tracking and locating services, GIS and data mining, it is possible both of them are imprecise. So, in this situation, a natural way to handle the issue is to report the data have a nonzero probability —called nonzero nearest neighbor— to be the nearest neighbor of a given query. Formally, let P be a set of n uncertain points modeled by some regions. We first consider the following variation of NN searching problem under uncertainty. If both the query and the data are uncertain points modeled by distinct unit segments parallel to the x-axis, we propose an efficient algorithm that reports nonzero nearest neighbors under Manhattan metric in O(n^2 α(n^2 )) preprocessing and O(log⁡n) query time, where α(.) is the extremely slowly growing functional inverse of Ackermann’s function. Finally, for the arbitrarily length segments parallel to the x-axis, we propose an approximation algorithm that reports nonzero nearest neighbor with maximum error L in O(n^2 α(n^2 )) preprocessing and O(log⁡n) query time, where L is the length of the query.

State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual calculation. The estimator is equipped with a filter formed in different times according to Principal Component Analysis (PCA) of measurement data. In addition, the proposed estimator employs the dynamic relationships of the system and the prediction property of the Extended Kalman Filter (EKF) to estimate the states of network fast and precisely. Therefore, it makes real-time monitoring of the power network possible. The proposed dynamic model also enables the estimator to estimate the states of a large scale system online. Results of state estimation of the proposed algorithm for an IEEE 9 bus system shows that even with the presence of bad data, the estimator provides a valid and precise estimation of system states and tracks the network with appropriate speed.

Due to urbanization and population increase, need for metro tunnels, has been considerably increased in urban areas. Estimating the surface settlement caused by tunnel excavation is an important task especially where the tunnels are excavated in urban areas or beneath important structures. Many models have been established for this purpose by extracting the relationship between the settlement and the factors that influence it. In this paper, Random Forest (RF) is introduced and investigated for the prediction of maximum surface settlement caused by EPB shield tunneling. Various factors that affect this settlement, including geometrical, geological and shield operational parameters were considered. The results of RF model has been compared with the available artificial neural network (ANN) model. It is shown that the proposed RF model provides more accurate results than the ANN model proposed in the literature.

In this paper a novel direct adaptive fuzzy system is proposed to control flexible-joints robot including actuator dynamics. The design includes two interior loops: the inner loop controls the motor position using proposed approach while the outer loop controls the joint angle of the robot using a PID control law. One novelty of this paper is the use of a PSO algorithm for optimizing the control design parameters to achieve a desired performance. It is worthy of note that to form control law by considering practical considerations just the available feedbacks are used. It is beneficial for industrial applications where the real-time computation is costly. The proposed control approach has a fast response with a good tracking performance under the well-behaved control efforts. The stability is guaranteed in the presence of both structured and unstructured uncertainties. As a result, all system states are remained bounded. Simulation results on a two-link flexible-joint robot show the efficiency of the proposed scheme.