Toughening of unmodified polyvinylchloride through the addition of nanoparticulate calcium carbonate

PVC/CaCO₃ polymer nanocomposites of differing compositions were produced using a two-roll mill and compression molding. The morphology was observed using transmission electron microscopy, and the static and dynamic mechanical and fracture properties determined. The presence of nanometer-sized CaCO₃ particles led to a slight decrease in the tensile strength but improved the impact energy, the storage modulus and the fracture toughness. Fracture surface examination by scanning electron microscopy indicated that the enhanced fracture properties in the nanocomposites were caused by the assisted void formation at the particles. This hypothesis is supported by a microstructure-based finite element modeling based upon elastic-plastic deformation around a weakly bonded particle. Hence, this provides an explanation of both the uniaxial tensile behavior and enhanced toughness of the nanocomposites.     

Efficiency assessment of Turkish power plants using data envelopment analysis

The performance of electricity generation plants in Turkey are analyzed and compared. The data set contains inputs from 65 thermal, hydro and wind power plants, owned by private and public sectors. Data envelopment analysis (DEA) is used as the primary mathematical tool. Two efficiency indexes, reflecting operational and investment performance, are defined and pursued. Constant returns to scale, variable returns to scale and assurance region type DEA models are used in the analysis. Scale efficiency is also considered. Performance comparisons include public versus private sector plants, and natural gas versus coal versus oil fired plants. Also, relationships between efficiency scores and various input/output factors are investigated and some interesting trends are identified.     

An overview of Turkish lignite qualities by logistic analysis

As an indispensable energy source, lignite is almost exclusively used in power generation in Turkey. To assess the quality level of Turkish lignite, a multivariate statistical analysis was conducted. The relationship among the lignite quality parameters has been investigated using a response method that is the logistic regression method. The analysis determines the effect of multiple predictor variables such as moisture, ash and sulphur presented simultaneously to predict membership of the two calorific value categories. By this way, a reliable binary response regression structure was constructed considering all the lignite fields in Turkey. Both the experiments on identifying the influential measurements and the measure of goodness of fit indicated that the overall model has a big capability to exhibit the relationship among the parameters of the Turkish lignite.     

Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing

This paper presents a novel method for diagnosis of heart disease. The proposed method is based on a hybrid method that uses fuzzy weighted pre-processing and artificial immune recognition system (AIRS). Artificial immune recognition system has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, liver disorders classification. The robustness of the proposed method is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 96.30% and it is very promising compared to the previously reported classification techniques.     

The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases

This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively.     

Testing the robustness of predictive models for chlorophyll generated from spaceborne imaging spectroscopy data for a mixedwood boreal forest canopy

The amount of chlorophyll in a leaf influences photosynthetic potential and can be an indicator of the overall condition of a plant, including its stress level and nutritional status. Hence, it is important to understand the spatial and temporal variation of chlorophyll concentration. Imaging spectroscopy (IS) has made it possible to estimate chlorophyll at leaf and canopy levels. Spaceborne imaging spectrometers offer the possibility of estimating chlorophyll concentration at larger spatial scales and at lower cost than from direct sampling. We undertook this study in a mixedwood boreal forest to test the robustness of predictive models generated using Hyperion data for predicting chlorophyll concentration of data sets from different locations collected in different years. Among the group of indices tested, the derivative chlorophyll index (DCI) (DCI = D₇₀₅/D₇₂₂) and the maximum derivative of the red-edge divided by the derivative of 703 nm (D_{max(680–750)})/D₇₀₃) emerged as the best predictors of chlorophyll concentration across space and through time. When the canopy level chlorophyll predictive models of DCI and D_{max(680–750)})/D₇₀₃ derived from Hyperion data were applied to other years’ remote-sensing data acquired by airborne and spaceborne sensors, DCI explained 71%, 63%, and 6% and D_{max(680–750)})/D₇₀₃ explained 61%, 54%, and 8% of the variation in chlorophyll in 2002, 2004, and 2008, respectively, with prediction errors ranging from 11.7% to 14.6%. Two-variable models generated using 2005 Hyperion data were not as robust for predicting chlorophyll concentration from other years. Two models were found to explain more than half of the variance in chlorophyll concentration for the 2004 data only. Single and two-variable models applied to 2008 chlorophyll data provided poor predictions. The presence of multiple species creates a gradient in the chlorophyll concentration, which makes it possible to predict chlorophyll concentration. The gradient also affects the performance of predictive models generated using data from a different year. However, differences in sensors may also affect model performance. Our results suggest that predictive models obtained from Hyperion data are robust in predicting chlorophyll concentration within the same site through time and also at different sites across sensors.     

Multimodal identity tracking in a smart room

The automatic detection, tracking, and identification of multiple people in intelligent environments are important building blocks on which smart interaction systems can be designed. Those could be, e.g., gesture recognizers, head pose estimators or far-field speech recognizers and dialog systems. In this paper, we present a system which is capable of tracking multiple people in a smart room environment while inferring their identities in a completely automatic and unobtrusive way. It relies on a set of fixed and active cameras to track the users and get close-ups of their faces for identification, and on several microphone arrays to determine active speakers and steer the attention of the system. Information coming asynchronously from several sources, such as position updates from audio or visual trackers and identification events from identification modules, is fused at higher level to gradually refine the room’s situation model. The system has been trained on a small set of users and showed good performance at acquiring and keeping their identities in a smart room environment. An erratum to this article can be found at     

Person re-identification in TV series using robust face recognition and user feedback

In this paper, we present a system for person re-identification in TV series. In the context of video retrieval, person re-identification refers to the task where a user clicks on a person in a video frame and the system then finds other occurrences of the same person in the same or different videos. The main characteristic of this scenario is that no previously collected training data is available, so no person-specific models can be trained in advance. Additionally, the query data is limited to the image that the user clicks on. These conditions pose a great challenge to the re-identification system, which has to find the same person in other shots despite large variations in the person’s appearance. In the study, facial appearance is used as the re-identification cue, since, in contrast to surveillance-oriented re-identification studies, the person can have different clothing in different shots. In order to increase the amount of available face data, the proposed system employs a face tracker that can track faces up to full profile views. This makes it possible to use a profile face image as query image and also to retrieve images with non-frontal poses. It also provides temporal association of the face images in the video, so that instead of using single images for query or target, whole tracks can be used. A fast and robust face recognition algorithm is used to find matching faces. If the match result is highly confident, our system adds the matching face track to the query set. Finally, if the user is not satisfied with the number of returned results, the system can present a small number of candidate face images and lets the user confirm the ones that belong to the queried person. These features help to increase the variation in the query set, making it possible to retrieve results with different poses, illumination conditions, etc. The system is extensively evaluated on two episodes of the TV series Coupling, showing very promising results.