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The purpose of this paper is to propose multisensory integration for train positioning application, to support recent automatic train control systems and also moving block signaling systems.

Reducing the cost and at the same time improving the reliability and accuracy of the overall positioning system, are primary goals of the researches going on in this field.

This paper designs and evaluates two different algorithms of Kalman filtering (KF) and particle filtering (PF), on a set of low cost positioning systems, as tachometers, Doppler radar and balises.

This paper's research outcomes introduce considerable improvements upon the results when compared to the current utilization of the stand‐alone tachometer and Doppler radar sensors, and slight improvements in comparison with the KF algorithm, and also upon results in recent publications on the subject.

Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides, most of learning to rank benchmark datasets do not include click-through data as a very rich source of information about the search behavior of users while dealing with the ranked lists of search results. To deal with these limitations, this paper aims to introduce a novel learning to rank algorithm by using a set of complex click-through features in a reinforcement learning (RL) model. These features are calculated from the existing click-through information in the data set or even from data sets without any explicit click-through information.

The proposed ranking algorithm (QRC-Rank) applies RL techniques on a set of calculated click-through features. QRC-Rank is as a two-steps process. In the first step, Transformation phase, a compact benchmark data set is created which contains a set of click-through features. These feature are calculated from the original click-through information available in the data set and constitute a compact representation of click-through information. To find most effective click-through feature, a number of scenarios are investigated. The second phase is Model-Generation, in which a RL model is built to rank the documents. This model is created by applying temporal difference learning methods such as Q-Learning and SARSA.

The proposed learning to rank method, QRC-rank, is evaluated on WCL2R and LETOR4.0 data sets. Experimental results demonstrate that QRC-Rank outperforms the state-of-the-art learning to rank methods such as SVMRank, RankBoost, ListNet and AdaRank based on the precision and normalized discount cumulative gain evaluation criteria. The use of the click-through features calculated from the training data set is a major contributor to the performance of the system.

In this paper, we have demonstrated the viability of the proposed features that provide a compact representation for the click through data in a learning to rank application. These compact click-through features are calculated from the original features of the learning to rank benchmark data set. In addition, a Markov Decision Process model is proposed for the learning to rank problem using RL, including the sets of states, actions, rewarding strategy and the transition function.

The purpose of this paper is to design and implement a landmine detection robot (Venus) equipped with three electromagnetic sensors and controlled by ordered weighted averaging (OWA) sensor fusion approach. Higher numbers of detected mines in a fixed time interval and lower total power consumption are the achieved goals of this research.

OWA sensor fusion is exploited for data combination in this paper. Unlike most other landmine detection robots, Venus has three electromagnetic sensors, the positions of which can be adjusted according to the environmental conditions. Also, a novel approach for OWA weight dedication using Gaussian distribution function is applied and the whole idea is evaluated practically in several randomly mined fields. Finally, for better evaluation, performance of Venus is compared with the other two landmine detection robots.

The simulation and experimental results proved that in a predetermined interval of time, not only total energy consumption is reduced, but also by expanding the surface and the depth of influence of electromagnetic waves, the number of detected mines is considerably raised.

In contrast to the regular demining process, which is relatively expensive and complicated, the landmine detection method proposed in this research is surprisingly simple, cost effective, and efficient. Therefore, it may be attractive for every company or organization in this field of research.

The paper describes research which implements and evaluates a novel control approach based on OWA sensor fusion method, a new way of using Gaussian distribution function for determining OWA weights, and also an adaptive physical configuration for sensors based on environmental conditions.

Intelligent control for unidentified systems with unstable equilibriums is not always a proper control strategy, which results in inferior performance in many cases. Because of the existing trial and error manner of the procedure in former duration of learning, this exploration for finding the appropriate control signals can lead to instability. However, the recent proposed emotional controllers are capable of learning swiftly; the use of these controllers is not an efficient solution for the mentioned instability problems. Therefore, a solution is needed to evade the instability in preliminary phase of learning. The purpose of this paper is to propose a novel approach for controlling unstable systems or systems with unstable equilibrium by model free controllers.

An existing controller (model‐based controller) with limited performance is used as a mentor for the emotional learning controller in the first step. This learning phase prepares the controller to control the plant as well as mentor, while it prevents any instability. When the emotional controller can imitate the behavior of model based one properly, the employed controller is gently switched from model based one to an emotional controller using a fuzzy inference system (FIS). Also, the emotional stress is softly switched from the mentor‐imitator output difference to the combination of the objectives. In this paper, the emotional stresses are generated once by using a nonlinear combination of objectives and once by employing different stresses to a FIS which attentionally modulated the stresses, and makes a subset of these objectives salient regarding the contemporary situation.

The proposed model free controller is employed to control an inverted pendulum system and an oscillator with unstable equilibrium. It is noticeable that the proposed controller is a model free one, and does not use any knowledge about the plant. The experimental results on two benchmarks show the superiority of proposed imitative and emotional controller with fuzzy stress generation mechanism in comparison with model based originally supplied controllers and emotional controller with nonlinear stress generation unit – in control of pendulum system – in all operating conditions.

There are two test beds for evaluating the proposed model free controller performance which are discussed in this paper: a laboratorial inverted pendulum system, which is a well‐known system with unstable equilibrium, and Chua's circuit, which is an oscillator with two stable and one unstable equilibrium point. The results show that the proposed controller with the mentioned strategy can control the systems with satisfactory performance.

In this paper, a novel approach for controlling unstable systems or systems with unstable equilibrium by model free controllers is proposed. This approach is based on imitative learning in preliminary phase of learning and soft switching to an interactive emotional learning. Moreover, FISs are used to model the linguistic knowledge of the ascendancy and situated importance of the objectives. These FISs are used to attentionally modulate the stress signals for the emotional controller. The results of proposed strategy on two benchmarks reveal the efficacy of this strategy of model free control.

The purpose of this paper is to propose a supervisory command‐to‐line‐of‐sight guidance law with lead angle which keeps the missile flight within the tracking beam.

A nonlinear supervisory controller is designed and coupled with the main sliding mode controller in the form of an additional control signal. The supervisory control signal is activated when the beam angle constraint goes to be violated. Initially a supervisory controller is designed using nonlinear control theory. Subsequently the main tracking controller is designed using sliding mode approach which forces the missile to fly along the desired line‐of‐sight. The stability of the supervisory controller coupled with the main controller is proved in the Lyapunov sense.

There exists a major drawback with the lead angle method of guidance, which is a high probability of flying out of the beam. The proposed supervisory controller has successfully overcome this deficiency. Thus, a better performance has been achieved.

The proposed guidance scheme can be applied to tactical surface to air missiles. Additionally the idea of supervisory controller can be applied to any similar control problem where there are some constrains over the states of the system.

The idea of supervisory controller has not been applied to the problem of command‐to‐line‐of‐sight guidance law. This paper utilizes and extends the idea of supervisory controller design to cases when a special state is to be supervised while considering the effect of external disturbances.

This paper aims to propose a 3D-map aided tightly coupled positioning solution for land vehicles to reduce the errors caused by non-line-of-sight (NLOS) and multipath interference in urban canyons.

First, a simple but efficient 3D-map is created by adding the building height information to the existing 2D-map. Then, through a designed effective satellite selection method, the distinct NLOS pseudo-range measurements can be excluded. Further, an enhanced extended Kalman particle filter algorithm is proposed to fuse the information from dual-constellation Global Navigation Satellite Systems and reduced inertial sensor system. The dependable degree of each selected satellite is adjusted through fuzzy logic to further mitigate the effect of misjudged LOS and multipath.

The proposed solution can improve positioning accuracy in urban canyons. The experimental results evaluate the effectiveness of the proposed solution and indicate that the proposed solution outperforms all the compared counterparts.

The effect of NLOS and multipath is addressed from both the observation level and fusion level. To the authors’ knowledge, mitigating the effect of misjudged LOS and multipath in the fusion algorithm of tightly coupled integration is seldom considered in existing literature.

Feature selection is an important step for data pre-processing specially in the case of high dimensional data set. Performance of the data model is reduced if the model is trained with high dimensional data set, and it results in poor classification accuracy. Therefore, before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy.

A novel optimization approach that hybridizes binary particle swarm optimization (BPSO) and differential evolution (DE) for fine tuning of SVM classifier is presented. The name of the implemented classifier is given as DEPSOSVM.

This approach is evaluated using 20 UCI benchmark text data classification data set. Further, the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images. From the results, it can be observed that the proposed DEPSOSVM techniques have significant improvement in performance over other algorithms in the literature for feature selection. The proposed technique shows better classification accuracy as well.

The proposed approach is different from the previous work, as in all the previous work DE/(rand/1) mutation strategy is used whereas in this study DE/(rand/2) is used and the mutation strategy with BPSO is updated. Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function. The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier (DEPSOSVM) to handle the feature selection problems.

The purpose of this study is the measuring of the human movement using printed wearable sensor. Human movement measurement is one of the usages for wearable sensors. This technology assists the researchers to collect data from the daily activities of individuals. In other words, the kinematics data of human motion will be extracted from this data and implemented in biomechanical aspects.

This study presents an innovative printed wearable sensor which can be used for measuring human movement orientations. In this paper, the manufacturing process, implementation, measurement setup and calibration procedure of this new sensor will be explained, and the results of calibration methods will be presented. The conductive flexible nylon/lycra fabric strain gauge was developed using polypyrrole (PPy)–1, 5-naphthalenedisulfonic acid by using a sophisticated method composed of screen printing followed by chemical vapor deposition at room temperature.

The morphological characterization using scanning electron microscopy shows the PPy-coated fabric exhibiting a homogenous and smooth surface. Based on the results, the linearity and hysteresis error are 98 and 8 per cent, respectively. Finally, the behavior of our sensor is evaluated in some cases, and the effects of relaxation and strain rate will be discussed.

The wearable sensor is one of the most advanced technologies in biomedical engineering. It can be used in several applications for prohibition, diagnosing and treatment of diseases.

The paper present original data acquired from a technical set-up in biomechanic labs. An innovative method was used for collecting the resistance changing of the sensor. A measurement setup was prepared as a transducer to convert the resistance into voltage.

The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing.

In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope.

Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor.

The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed.

Today, most of human–robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors.

Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human–robot interaction scenario show the performance of the system.

This paper seeks to disclose the important role of missing documents, broken links and duplicate items in the results merging process of a metasearch engine in detail. It aims to investigate some related practical challenges and proposes some solutions. The study also aims to employ these solutions to improve an existing model for results aggregation.

This research measures the amount of an increase in retrieval effectiveness of an existing results merging model that is obtained as a result of the proposed improvements. The 50 queries of the 2002 TREC web track were employed as a standard test collection based on a snapshot of the worldwide web to explore and evaluate the retrieval effectiveness of the suggested method. Three popular web search engines (Ask, Bing and Google) as the underlying resources of metasearch engines were selected. Each of the 50 queries was passed to all three search engines. For each query the top ten non‐sponsored results of each search engine were retrieved. The returned result lists of the search engines were aggregated using a proposed algorithm that takes the practical issues of the process into consideration. The effectiveness of the result lists generated was measured using a well‐known performance indicator called “TSAP” (TREC‐style average precision).

Experimental results demonstrate that the proposed model increases the performance of an existing results merging system by 14.39 percent on average.

The findings of this research would be helpful for metasearch engine designers as well as providing motivation to the vendors of web search engines to improve their technology.

This study provides some valuable concepts, practical challenges, solutions and experimental results in the field of web metasearching that have not been previously investigated.