Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Learning from data streams is a challenging task which demands a learning algorithm with several high quality features. In addition to space complexity and speed requirements needed for processing the huge volume of data which arrives at high speed, the learning algorithm must have a good balance between stability and plasticity. This paper presents a new approach to induce incremental decision trees on streaming data. In this approach, the internal nodes contain trainable split tests. In contrast with traditional decision trees in which a single attribute is selected as the split test, each internal node of the proposed approach contains a trainable function based on multiple attributes, which not only provides the flexibility needed in the stream context, but also improves stability. Based on this approach, we propose evolving fuzzy min–max decision tree (EFMMDT) learning algorithm in which each internal node of the decision tree contains an evolving fuzzy min–max neural network. EFMMDT splits the instance space non-linearly based on multiple attributes which results in much smaller and shallower decision trees. The extensive experiments reveal that the proposed algorithm achieves much better precision in comparison with the state-of-the-art decision tree learning algorithms on the benchmark data streams, especially in the presence of concept drift.     

Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering

The text clustering technique is an appropriate method used to partition a huge amount of text documents into groups. The documents size affects the text clustering by decreasing its performance. Subsequently, text documents contain sparse and uninformative features, which reduce the performance of the underlying text clustering algorithm and increase the computational time. Feature selection is a fundamental unsupervised learning technique used to select a new subset of informative text features to improve the performance of the text clustering and reduce the computational time. This paper proposes a hybrid of particle swarm optimization algorithm with genetic operators for the feature selection problem. The k-means clustering is used to evaluate the effectiveness of the obtained features subsets. The experiments were conducted using eight common text datasets with variant characteristics. The results show that the proposed algorithm hybrid algorithm (H-FSPSOTC) improved the performance of the clustering algorithm by generating a new subset of more informative features. The proposed algorithm is compared with the other comparative algorithms published in the literature. Finally, the feature selection technique encourages the clustering algorithm to obtain accurate clusters.     

Structural Properties of Networked Control Systems With Bandwidth Limitations and Delays

The structural properties of networked control systems with both bandwidth limitations and delays are investigated. Sufficient conditions are given for controllability (stabilizability) and reconstructibility (detectability). Our results enhance previous works by capturing bandwidth limitations and delays simultaneously. The adopted modeling framework could be readily used in control and estimation methods, including optimal and predictive schemes. It also facilitates the use of scheduling optimization algorithms in conjunction with the control scheme presented.     

Development of broadband forward‐wave directional couplers loaded by periodic patterned ground structure

In this paper two wideband Forward‐Wave Directional Couplers (FWDCs) with 0 dB and 3 dB coupling level are proposed. Using periodic patterned ground structure in a microstrip coupled lines by a new unit cell; even‐ and odd‐mode characteristic impedances of the couplers are equal over a wide frequency range. Moreover, it provides a constant phase difference between even and odd‐modes. The proposed cell is modeled using the equivalent circuit model and a design procedure is introduced for designing FWDCs for an arbitrary value of coupling level. The introduced couplers are numerically investigated and a prototype of both couplers is made. It is shown that for 0 dB coupling level, the measured coupling is 0.85 dB with 1 dB flatness over fractional bandwidth of 96% bandwidth. In case of 3 dB coupling, the measured coupling level is 3.5 dB at 7.42 GHz with 1 dB flatness over fractional bandwidth of 67.1%.     

Laser micro-welding of aluminum and copper with and without tin foil alloy

In this paper continuous laser welding of two dissimilar materials, aluminum and copper, was investigated. The aluminum and the copper utilized were Al3003-H14 and Cu110-H00, respectively. Two different sets of samples were laser welded; one in which a filler material, tin foil alloy (S-bond 220), was sandwiched between the aluminum and the copper and another set in which the aluminum and copper were directly welded without any filler. The foil alloy was utilized to enhance the compatibility of the two metals; aluminum and copper, reducing the brittleness of the intermetallic compound that may form and, subsequently, enhance the mechanical properties. The welding was carried out using an IPG 500 SM fiber laser. The length of the laser joint produced was 20 mm and the width was about 200 μm. The strength of the joint was evaluated by conducting the lap shear stress test. Samples in which filler foil was used exhibited a better performance in the lap shear stress test (an average of 780 N) than the samples without tin foil (an average of 650 N). The improvement in the lap shear test could be attributed to the positive effects of the filler on enhancing the compatibility of the intermetallic compound formed via diffusion. The fracture surface of both types of joints (with and without filler) was characterized using scanning electron microscope equipped with energy-dispersive X-ray (EDAX). To understand the failure initiation and propagation of the samples under tension, a finite element (FE) model was developed for the samples created with no filler material. The failure mechanism predicted from the FE model matches reasonably well with the experimental observations from EDAX analysis.     

An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem

Multiprocessor task scheduling is an important problem in parallel applications and distributed systems. In this way, solving the multiprocessor task scheduling problem (MTSP) by heuristic, meta-heuristic, and hybrid algorithms have been proposed in literature. Although the problem has been addressed by many researchers, challenges to improve the convergence speed and the reliability of methods for solving the problem are still continued especially in the case that the communication cost is added to the problem frame work. In this paper, an Immune-based Genetic algorithm (IGA), a meta-heuristic approach, with a new coding scheme is proposed to solve MTSP. It is shown that the proposed coding reduces the search space of MTSP in many practical problems, which effectively influences the convergence speed of the optimization process. In addition to the reduced search space offered by the proposed coding that eventuate in exploring better solutions at a shorter time frame, it guarantees the validity of solutions by using any crossover and mutation operators. Furthermore, to overcome the regeneration phenomena in the proposed GA (generating similar chromosomes) which leads to premature convergence, an affinity based approach inspired from Artificial Immune system is employed which results in better exploration in the searching process. Experimental results showed that the proposed IGA surpasses related works in terms of found makespan (20% improvement in average) while it needs less iterations to find the solutions (90% improvement in average) when it is applied to standard test benches.     

Soil moisture estimation using land surface temperature and soil temperature at 5 cm depth

Soil Temperature (ST) data, obtained from either field works or satellite imagery, has frequently been studied for Soil Moisture (SM) estimation. However, a combination of ST data at different depths and soil surface temperature, i.e., Surface Radiometric Temperature (SRT) or Land Surface Temperature (LST), has not yet been well investigated for accurate SM prediction. In this study, an empirical model was first developed to estimate SM at 5 cm Depth (SM_5D) over areas with no or sparse vegetation cover using the field SRT and field ST data at 5 cm Depth (ST_5D). A Root Mean Square Error (RMSE) and a correlation coefficient (r) of 0.037 m³ m^?3 and 0.8 were obtained using this model, respectively. Then, the SRT was substituted by the LST obtained from Landsat thermal bands and ST_5D was estimated using the ST data collected at the nearest weather station to the study area by developing a regression equation. The second model demonstrated an RMSE and r of 0.035 m³ m^?3 and 0.71, respectively. Overall, it was concluded that the proposed models had high potential for SM estimation using the ST data at different depths collected in the field or acquired by optical satellites.     

A local view on the Hudetz correction of the Yager entropy of dynamical systems

In this paper, we define a local version of Hudetz correction for the Yager entropy of dynamical systems. It is proved that the introduced function is indeed a local entropy map for the Hudetz correction of the Yager entropy, in the sense that, the Hudetz correction of the Yager entropy of a continuous map on a compact metric space is attained by integrating the introduced local entropy.     

A globally convergent BFGS method for pseudo-monotone variational inequality problems

In this paper, we propose a globally convergent BFGS method to solve Variational Inequality Problems (VIPs). In fact, a globalization technique on the basis of the hyperplane projection method is applied to the BFGS method. The technique, which is independent of any merit function, is applicable for pseudo-monotone problems. The proposed method applies the BFGS direction and tries to reduce the distance of iterates to the solution set. This property, called Fejer monotonicity of iterates with respect to the solution set, is the basis of the convergence analysis. The method applied to pseudo-monotone VIP is globally convergent in the sense that subproblems always have unique solutions, and the sequence of iterates converges to a solution to the problem without any regularity assumption. Finally, some numerical simulations are included to evaluate the efficiency of the proposed algorithm.     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.