A multivariate fuzzy c-means method

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.     

Experience with model-based performance, reliability, and adaptability assessment of a complex industrial architecture

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of task execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system’s architect. Finally, we have developed models of hardware and software reliability. We report on hardware and software reliability results based on the evaluation of the system architecture.     

Interaction design for supporting communication between Chinese sojourners

In our global village, distance is not a barrier anymore for traveling. People experience new cultures and face accompanying difficulties in order to live anywhere. Social support can help these sojourners to cope with difficulties, such as culture shock. In this paper, we investigate how computer-mediated communication (CMC) tools can facilitate social support when living physically separated from loved-ones in different cultures. The goal is to understand the design considerations necessary to design new CMC tools. We studied communication practices of Chinese sojourners living in the Netherlands and the use of a technology probe with a novel video communication system. These results led to recommendations which can help designers to design interactive communication tools that facilitate communication across cultures. We conclude the paper with an interactive communication device called Circadian, which was designed based on these recommendations. We experienced the design recommendations to be abstract enough to leave space for creativity while providing a set of clear requirements which we used to base design decisions upon.     

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.     

Effects of a feedback signal in a computer mouse on movement behaviour, muscle load, productivity, comfort and user friendliness

To study the effects of a tactile feedback signal in a computer mouse on reduction of hovering behaviour and consequently on changes in muscle load, productivity, comfort and user friendliness, a comparative, experimental study with repeated measures was conducted. Fifteen subjects performed five trials with different mouse actions and a standardised task, once with a mouse with the feedback signal and once with a mouse without the feedback signal. Holding the hand just above the mouse caused higher muscle loading than clicking and scrolling. Holding the hand on the mouse caused higher muscle loading than resting the hand on the desk. The feedback signal effectively decreased hovering behaviour. It also led to a more dynamic activation pattern of the extensor muscles of the forearm. The overall opinion of the feedback signal for future use was rated as somewhat variable. No effects on discomfort or productivity were found. The use of a mouse with a tactile vibrating feedback signal seems promising for preventing arm complaints, although more research is needed to establish the clinical relevance.     

Fractal dimension applied to plant identification

This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants.     

Lyapunov-Based Model Predictive Control of Nonlinear Systems Subject to Data Losses

In this work, we focus on model predictive control of nonlinear systems subject to data losses. The motivation for considering this problem is provided by wireless networked control systems and control of nonlinear systems under asynchronous measurement sampling. In order to regulate the state of the system towards an equilibrium point while minimizing a given performance index, we propose a Lyapunov-based model predictive controller which is designed taking data losses explicitly into account, both in the optimization problem formulation and in the controller implementation. The proposed controller allows for an explicit characterization of the stability region and guarantees that this region is an invariant set for the closed-loop system under data losses, if the maximum time in which the loop is open is shorter than a given constant that depends on the parameters of the system and the Lyapunov-based controller that is used to formulate the optimization problem. The theoretical results are demonstrated through a chemical process example.     

Ready to preorder: The case of weak process semantics

Recently, Aceto, Fokkink and Ingólfsdóttir proposed an algorithm to turn any sound and ground-complete axiomatisation of any preorder listed in the linear time-branching time spectrum at least as coarse as the ready simulation preorder, into a sound and ground-complete axiomatisation of the corresponding equivalence—its kernel. Moreover, if the former axiomatisation is ω-complete, so is the latter. Subsequently, de Frutos Escrig, Gregorio Rodríguez and Palomino generalised this result, so that the algorithm is applicable to any preorder at least as coarse as the ready simulation preorder, provided it is initials preserving. The current paper shows that the same algorithm applies equally well to weak semantics: the proviso of initials preserving can be replaced by other conditions, such as weak initials preserving and satisfying the second τ-law. This makes it applicable to all 87 preorders surveyed in “the linear time-branching time spectrum II” that are at least as coarse as the ready simulation preorder. We also extend the scope of the algorithm to infinite processes, by adding recursion constants. As an application of both extensions, we provide a ground-complete axiomatisation of the CSP failures equivalence for BCCS processes with divergence.