Journal of Computer and Robotics
Volume:10 Issue: 1, Winter and Spring 2017

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, the NTN is modeled as a multi-layer perceptron (MLP) network and the tensor parameter can be distributed into the new network neurons.  Moreover, it is suggested that the inputs can be converted into one vector rather than the inputs of NTN are two correlated vectors at the same time. The results approve that the NTN does not indeed represent a new neural network and the implementation results easily confirm it can be considered as another representation of the MLP network. So, the first idea is representation of a neuron based mathematical model for the NTN through the ordinary and yet well-defined neural network concepts and next contribution will be equivalency proof of the two NTN and suggested MLP networks.

Due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the important fields of study in this domain. The main researches in the area of sentiment analysis have focused on English language and few works considered the sentiment analysis in Persian language due to the lack of resources. This paper aims to introduce a supervised method for creating a sentiment dictionary in Persian language with extracting linguistic features in reviews and statistical mutual information to determine the sentiment orientation and sentistrength of words. To evaluate the proposed method, a set of existing reviews in the online retail site is used in various domains and the present dictionary is compared with Sentiwordnet. The results show the proposed method achieves an accuracy of 80% in determining the orientation of sentiment word.

Multicast routing is one of the most important services in Multi Radio Multi Channel (MRMC) Wireless Mesh Networks (WMN). Multicast routing performance in WMNs could be improved by choosing the best routes and the routes that have minimum interference to reach multicast receivers. In this paper we want to address the multicast routing problem for a given channel assignment in WMNs. The channels that are assigned to the network graph are given to the algorithm as an input. To reduce the problem complexity and decrease the problem size, we partition the network to balanced clusters. Fuzzy logic is used as a tool for clustering in our method. After clustering and electing most suitable nodes as cluster head, we take a mathematical method to solve the multicast tree construction problem. We conducted several simulations to verify the performance of our method and the simulation results demonstrated that our proposed method outperforms CAMF algorithm in terms of throughput and end to end delay.

The air transport industry is seeking to manage risks in air travels. Its main objective is to detect abnormal behaviors in various flight conditions. The current methods have some limitations and are based on studying the risks and measuring the effective parameters. These parameters do not remove the dependency of a flight process on the time and human decisions. In this paper, we used an HMM-based method which is among the main methods of situation assessment in data fusion. This method includes two clustering levels based on data and model. The experiments were conducted with B_777 flight data and the variables considered in the next generation of ADS_B. According to the results of this study, our method has high speed and sensitivity in detection of abnormal changes which are effective in the flight parameters when landing. With the dynamic modelling, there is no dependency on time and conditions. The adaptation of this method to other air traffic control systems makes its extension possible to cover all flight conditions.

One of important problems in supply chains management is supplier selection. In a company, there are massive data from various departments so that extracting knowledge from the company’s data is too complicated. Many researchers have solved this problem by some methods like fuzzy set theory, goal programming, multi objective programming, the liner programming, mixed integer programming, analytic hierarchy process (AHP), analytic network process model, TOPSIS, etc. Past research gaps are lack of attention to enterprise historical data and extract knowledge from them, review the past performance of suppliers and use effect of the their past performance to their future work. The aim of this paper is to solve supplier selection problem based on historical data by a novel model. The proposed model has tried to uncover hidden relation in massive unstructured industrial data and has used them to extract knowledge for optimizing decision making and predicting in supply chain management by BI tools. The model is based on FP-Growth algorithm integrated with AHP. Moreover, the proposed model is a multi-criteria decision making model (MCDM) with four criteria: quality, priority, delay on delivery and cost that have chosen from literature review. The criteria have been weighed by AHP and finally the model has been validated by industrial group’s historical data.

Target coverage is one of the important problems in visual sensor networks. The coverage should be accompanied with an efficient use of energy in order to increase the network lifetime. In this paper, we address the maximum lifetime for visual sensor networks (MLV) problem by maximizing the network lifetime while covering all the targets. For this purpose, we develop a simulated annealing (SA) algorithm that divides the sensors’ Field-of-View (FoV) to a number of cover sets and then applies a sleep-wake schedule for cover sets. We also identify the best possible FoV of sensors according to the targets’ location using rotating cameras, to reduce the solution space and approaching to a near-optimal solution. Our proposed energy and neighbor generating functions of the SA result in a balanced distribution of energy consumption as well as escaping from local optima. We conduct some simulation experiments to evaluate the performance of our proposed method by comparing with some well-known solutions in the literature.

With the growth of technology, supervising systems are increasingly replacing humans in military, transportation, medical, spatial, and other industries. Among these systems are machine vision systems which are based on image processing and analysis. One of the important tasks of image processing is classification of images into desirable categories for the identification of objects or their specific areas. One of the common methods is using an edge finder in image classification. Due to the lack of definite edges in many images obtained from various sciences and industries such as textural images, the topic of textural image classification has recently become of interest in the science of machine vision. Thus, in this article, two methods are proposed to detect edges and eliminate blocks with non-connected classes based on fuzzy theory and weighted voting concepts in classifying textural images. In the proposed methods, the boundaries are corrected using fuzzy theory and weighted voting concepts. Using the proposed methods can help improve the definition of boundaries and classification accuracy.

Finding an optimal path for a robot in a soccer field involves different parameters such as the positions of the robot, positions of the obstacles, etc. Due to simplicity and smoothness of Ferguson Spline, it has been employed for path planning between arbitrary points on the field in many research teams. In order to optimize the parameters of Ferguson Spline some evolutionary or intelligent algorithms are proposed in the literature. In this paper, we present a comparative study on different evolutionary and swarm algorithms as solutions to the problem of robot path planning. We optimize the parameters of Ferguson Spline and find the best path between two arbitrary points, studying Differential Evaluation (DE), Genetic Algorithm (GA), Evolutionary Strategies (ES), Artificial Bee Colony (ABC), and Particle Swarm optimization (PSO) algorithms. Firstly, a path for robot movement is describe by Ferguson splines and then these algorithms are used to optimize the parameters of splines to find an optimal path between the starting and the goal point considering the obstacles between them. The experimental results show the performance and effectiveness of the studied solutions in comparison with other swarm intelligent algorithms.