We propose a novel algorithm for the navigation of agents based on reinforcement learning, using boredomas an element of intrinsic motivation. Improvements obtained with the inclusion of this element over classicstrategies are shown through simulations. Boredom is modeled through a chaotic element that generates conditionsfor the creation of routes when the environment does not offer any reward, allowing prompting the robotto navigate. Our proposal seeks to avoid what classical algorithms suffer in scenarios without rewards, generatinglosses of time in the resolution. We demonstrate experimentally that by adding the element of boredomit is possible to generate routes in scenarios in which rewards do not exist, allowing the use of these strategiesin real circumstances and facilitating the robot's navigation towards its objective. The most important contributionsustained by this work corresponds to the fact that it is possible to improve navigation in completelyadverse scenarios for a navigation algorithm based on rewards.

Data currency is a temporal reference of data, it reflects the degree to which the data is current with the worldit models. Currency rule is a formal rule extracted from the data set and reflecting the currency order of thedata tuples, it can be used for both data repairing and currency quality evaluation. Based on the research of datacurrency repairing, the basic form of currency rule is extended, and parallel rule extraction and update algorithmsare proposed to meet the requirement of running on dynamic data sets. Besides, four data currency qualityevaluation models are proposed and verified by experiments. The performance test show that the efficiencyof parallel algorithms is significantly improved, the rules compliance mean(CM2) model based on extendedcurrency rule has the highest average precision. The extended currency rules not only improve the efficiencyand adaptability, but also provide more valuable features for data quality evaluation.

This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

Communication protocols are used in telecommunication systems. These protocols are defined as rules, which enable the entities of communicating systems to transfer information usually as packages. Additionally, each standard communication protocol has uniquely-defined structure and consequently a special pattern of network packets. Here, it is worth mentioning that communication protocols are implemented in different ways, in the system software layer or in hardware layer (silicon chipsets). This paper presents the implementation of a special communication protocol called "Packet Analysis", which is used in the Hardware project "Minimax machine". This implementation is a software that can be written using a special simulator "Minimax simulator", which is the target execution environment. That simulator was successfully developed for micro programming and hardware simulations. In this regard, this study develops an algorithm that represents a step toward simulating communication protocols using micro-programming. The flow chart designed here gives an overview of how the "Packet Analysis" algorithm works (designed protocol), which in turn describes all steps in details. As a result, the entire system of this research paper was implemented and tested with various input values. Additionally, the implemented proposed solution (implemented protocol) was evaluated by two metrics (quantitative measures) using test-benches so that its statistics will be trustworthy for research. Other results of this study showed that there is a lot of scope for optimization in the solution presented in this research paper. This leads in turn to optimize the proposed implementation and to consider implementation alternatives.


 
 
 Recent cloud search engines lack a formal method in their service composition mechanisms to automatically build composite services realizing user requirements. This paper prescribes behavior composition framework as a formal tools for the search engines. The framework automatically synthesizes a controller that delegates the service operations requested by a cloud user to the proper available cloud services whose operations satisfy the request. Since most cloud search engines support semantic and ontology to discover similar service operations, the paper extends the framework to be more adaptable with such search engines through the use of resource reasoning. Several experiments are provided to demonstrate how the extended framework outperforms the original one in terms of realizing users’ requirements.
 
 
 

In this study, it is examined that simultaneous flight control system and lateral and longitudional state-space model of a Unmanned Aerial Vehicle (UAV) and real time application. For this purpose an UAV whose wing and tail unit can be assembled to fuselage from different points in a prescribed interval and whose wing and tail can move forward and backward independently in tail to nose direction is manufactured. Following this, an autopilot is purchased and it lets change of P, I, D coefficients in certain intervals. First, dynamic model, and longitudinal and lateral state space models of UAV are obtained and then simulation model of UAV is reached. At the same time block diagram of autopilot system and modeling of it in MATLAB/Simulink environment are found. After these, using these two models and also benefiting and adaptive stochastic optimization method namely SPSA, simultaneous design of UAV and autopilot is done in order to minimize a cost function consisting of rise time, settling time and maximum overshoot. Therefore, primarily autonomous performance is maximized in computer environment. Moreover, high performance is observed by looking at simulation responses and real-time flights.

Most of existing particle filtering-based video target tracking algorithms are in Euclidean space, when object posture and scale size changes, and to track high dimensional system, it is difficult to guarantee the tracking effect. This paper describes the covariance descriptor to represent the object image region, the geometric deformation of the object image region can be realized by an affine transformation, and the affine transformation matrix is one element of the Lie group. Then particle filter algorithm based on lie group of manifold is proposed , the video tracking system state lies directly on a low dimensional manifold, state samples are drawn moving on the manifold geodesics, thus state space of intrinsic geometrical characteristic can be in full use, which provides a new idea for improving the tracking efficiency and robustness. Simulation results show that object in the case of geometric deformation including scale size changes, rotating, etc. The proposed manifold particle filtering algorithm can still realize target tracking well and improve the real-time performance.

Computational intelligence search and optimization algorithms have been efficiently adopted and used for many types of complex problems. Optics Inspired Optimization (OIO) is one of the most recent physics inspired computational intelligence methods which treats the search space of the problem to be optimized as a wavy mirror in which each peak is assumed to reflect as a convex mirror and each valley to reflect as a concave one. Each candidate solution is treated as an artificial light point that its glittered ray is reflected back by the search space of the problem and the artificial image is formed based on mirror equations adopted from Optics, as a new candidate solution. In this study, OIO for the first time has been designed as solution search strategy for travelling tournament problem which is one of the current sports problems and aids to minimize transportation and total movement of teams. Furthermore, this problem has been firstly solved by League Championship Algorithm and obtained results from both synthetic and real datasets have been compared in this study for the first time. Obtained results show the superiority of OIO which is a novel algorithm and seems to efficiently solve many complex problems.

This study addresses a real-time experimental application of a novel modified delay-based control structure for a second order linear time-invariant system. The electromechanical system is identified with an interactive system identification tool. The controller parameters are tuned by using the integral of the absolute error criterion. Further, experimental results validate the theoretical background. From a practical point of view, an alternative control structure is proposed to obtain better control performance in the presence of measurement noise as well as uncertainties.