A new fault tolerant control approach for the three-tank system using data mining

In this study, we propose a knowledge-based approach for detection and isolation of sensor faults in fault tolerant control (FTC) of the three-tank system. Farthest first traversal algorithm (FFTA) of data mining is used first-time for the classification of faults in an FTC system. The sliding window is used to detect signal changes, which contain possible transients due to faults. The variance-changing ratio is calculated to extract the features of the sensor signal in each window. Then, FFTA is utilized for the isolation of sensor faults. In order to demonstrate the efficiency of the proposed method, seven types of artificial faults were applied to closed-loop fault tolerant control system in certain periods. All faults were detected and isolated immediately after they occurred. Moreover, fault isolation was achieved when multiple faults occurred simultaneously.     

Web++ architecture,design and performance

We describe the design of a system for fast and reliable HTTP service which we call Web++. Web++ achieves high reliability by dynamically replicating web data among multiple web servers. Web++ selects the available server that is expected to provide the fastest response time. Furthermore, Web++ guarantees data delivery given that at least one server containing the requested data is available. After detecting a server failure, Web++ client requests are satisfied transparently to the user by another server. Furthermore, the Web++ architecture is flexible enough for implementing additional performance optimizations. We describe implementation of one such optimization, batch resource transmission, whereby all resources embedded in an HTML page that are not cached by the client are sent to the client in a single response. Web++ is built on top of the standard HTTP protocol and does not require any changes either in existing web browsers or the installation of any software on the client side. In particular, Web++ clients are dynamically downloaded to web browsers as signed Java applets. We implemented a Web++ prototype; performance experiments indicate that the Web++ system with 3 servers improves the response time perceived by clients on average by 36.6%, and in many cases by as much as 59%, when compared with the current web performance. In addition, we show that batch resource transmission can improve the response time on average by 39% for clients with fast network connections and 21% for the clients with 56 Kb modem connections. This revised version was published online in August 2006 with corrections to the Cover Date.     

ANFIS and statistical based approach to prediction the peak pressure load of concrete pipes including glass fiber

In this paper, Adaptive Neural Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) models are discussed to determine peak pressure load measurements of the 0, 0.2, 0.4 and 0.6% glass fibers (by weight) reinforced concrete pipes having 200, 300, 400, 500 and 600 mm diameters. For comparing the ANFIS, MLR and experimental results, determination coefficient (R²), root mean square error (RMSE) and standard error of estimates (SEE) statistics were used as evaluation criteria. It is concluded that ANFIS and MLR are practical methods for predicting the peak pressure load (PPL) values of the concrete pipes containing glass fibers and PPL values can be predicted using ANFIS and MLR without attempting any experiments in a quite short period of time with tiny error rates. Furthermore ANFIS model has the predicting potential better than MLR.     

Estimation of global solar radiation using ANN over Turkey

The main objective of the present study is to develop an artificial neural network (ANN) model based on multi-nonlinear regression (MNLR) method for estimating the monthly mean daily sum global solar radiation at any place of Turkey. For this purpose, the meteorological data of 31 stations spread over Turkey along the years 2000-2006 were used as training (27 stations) and testing (4 stations) data. Firstly, all independent variables (latitude, longitude, altitude, month, monthly minimum atmospheric temperature, maximum atmospheric temperature, mean atmospheric temperature, soil temperature, relative humidity, wind speed, rainfall, atmospheric pressure, vapor pressure, cloudiness and sunshine duration) were added to the Enter regression model. Then, the Stepwise MNLR method was applied to determine the most suitable independent (input) variables. With the use of these input variables, the results obtained by the ANN model were compared with the actual data, and error values were found within acceptable limits. The mean absolute percentage error (MAPE) was found to be 5.34% and correlation coefficient (R) value was obtained to be about 0.9936 for the testing data set.     

Semantic analysis on social networks: A survey

As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.     

A Patch of Detachable Hybrid Microneedle Depot for Localized Delivery of Mesenchymal Stem Cells in Regeneration Therapy

Mesenchymal stem cells (MSCs) have been widely used for regenerative therapy. In most current clinical applications, MSCs are delivered by injection but face significant issues with cell viability and penetration into the target tissue due to a limited migration capacity. Some therapies have attempted to improve MSC stability by their encapsulation within biomaterials; however, these treatments still require an enormous number of cells to achieve therapeutic efficacy due to low efficiency. Additionally, while local injection allows for targeted delivery, injections with conventional syringes are highly invasive. Due to the challenges associated with stem cell delivery, a local and minimally invasive approach with high efficiency and improved cell viability is highly desired. In this study, a detachable hybrid microneedle depot (d‐HMND) for cell delivery is presented. The system consists of an array of microneedles with an outer poly(lactic‐co‐glycolic) acid shell and an internal gelatin methacryloyl (GelMA)‐MSC mixture (GMM). The GMM is characterized and optimized for cell viability and mechanical strength of the d‐HMND required to penetrate mouse skin tissue is also determined. MSC viability and function within the d‐HMND is characterized in vitro and the regenerative efficacy of the d‐HMND is demonstrated in vivo using a mouse skin wound model.     

A new student performance analysing system using knowledge discovery in higher educational databases

Knowledge discovery is a wide ranged process including data mining, which is used to find out meaningful and useful patterns in large amounts of data. In order to explore the factors having impact on the success of university students, knowledge discovery software, called MUSKUP, has been developed and tested on student data. In this system a decision tree classification is employed as a data mining technique. With this software system all the tasks involved in the knowledge discovery process are kept together. The advantage of this approach is to have access to all the functionalities of SQL server and Analysis Services through single software. The study was carried out on the data from university students. According to results of the study, the types of registration to the university and the income levels of the students’ family were found to be associated with student success.     

Structural optimization with CADO method for a three-dimensional sheet-metal vehicle body

The aim of this study is to increase power/weight ratio of a steel-alloyed vehicle body without any structural weakness and to use an integrated engineering solution of “computer-aided design, engineering and optimization (CADO)”. In this optimization study, primarily the body's “computer-aided design (CAD)” parametric model has been prepared for some static analyses are essential for the design study. In the following step, some critical dimensions of the structure's parts have been defined as design parameters. The goal of the optimization study is a minimization of critical equivalent stress value is under the yield limit. In addition, a sensitivity study has been made on the body model with stress measures for an in-depth analysis. In various steps, Pro/Engineer CAD and Pro/MECHANICA computer-aided engineering (CAE) software has been used. Finally, the obtained results have been presented as both visually and in diagrams or tables. In other words, this study can be defined as a computer-aided design and optimization application of a sophisticated three-dimensional (3D) sheet-metal structural model. Consequently, for the solution of a sophisticated structural design problem, it has been seen that integrated CAD/CAE programs supported optimization techniques are vital much more proper to provide the time, error and cost reduction compared to classical design processes, can be given as contributes of this study to previous literature.     

Class-E self-oscillation for the transmission of wireless power to implants

This paper develops the concept of Class-E self oscillation for wireless power delivery to implantable sensors with the comparison of several topologies. Power amplifiers and oscillators are considered as two separate blocks in wireless power transmission. By combining these topologies into a self-oscillating power transmitter, greater efficiency can be achieved. Various topologies are compared with measured hardware results, determining that a crystal feedback network provides both accuracy and high power output. A new crystal feedback Class-E self oscillator has been implemented by transmitting power through 2 cm-thick biological tissue. The paper includes a second order modelling and design process that can be used to design a Class-E self oscillator as an inductive power transmitter as well as measured results.