Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

Wide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power system by Phasor Measurement Units (PMUs) are presented. The obtained results show that the temporal patterns can be used for extracting and identifying the low-frequency oscillation modes and the spatial patterns can be used for identifying modes with the most contribution in original data. Compared to other BSS approaches, the proposed method has shown to be better for the analysis of real ambient data.     

Processing and Properties of Textured Potassium Sodium Niobate [K,Na]NbO3 Ceramic Ribbons by Alginate Gelation Method

Random and <001> textured potassium sodium niobate – [K,Na]NbO₃ (KNN) ceramics with 1 mole% CuO sintering aid were fabricated in ribbon form through a combination of novel alginate gelation process and templated grain growth methods using platelike sodium niobate ‐ NaNbO₃ (NN) template particles. The platelike NN template particles were prepared by a two‐step molten salt synthesis method. Ribbons were drawn from alginate‐based slurries without or with 10 wt% NN template particles using 50 mm long slit nozzle with a rectangular orifice of 10 mm × 1 mm. Development of crystallographic texture as a result of varying sintering time and temperature was evaluated through the calculation of the degree of orientation as measured by the Lotgering factor (?₍₀₀₁₎) and an ?₍₀₀₁₎ of 0.81 was achieved. The electrical properties of textured ribbons were evaluated with polarization and strain versus electric field measurements.     

Partial purification of <Emphasis Type="Italic">nigella sativa</Emphasis> L. Seed lipase and its application in hydrolytic reactions. Enrichment of γ-linolenic acid from borage oil

Selective hydrolysis of borage (Borago officinalis L.) oil was catalyzed by two lipase preparations of Nigella sativa L. seeds at 40°C in a mixture of borage oil, water, and hexane. Ammonium sulfate-precipitated lipase (Nigella PL) and lipase partially purified by DEAE-ion exchange chromatography (Nigella CPL) exhibited a negative specificity toward γ-linolenic acid (GLA). Best results were obtained in the experiments conducted with 330 U/g oil of Nigella PL and 200 U/g oil of nigella CPL. When 330 U/g oil of Nigella PL was used, after 8 h the GLA level rose from 21.9% in the starting oil to 29.6 and 41.8% in TAG and DAG fractions of the product mixtures, respectively (1.5-fold enrichment of GLA in the total unhydrolyzed acylglycerol fraction). At 200 U/g oil enzyme concentration of Nigella CPL, after 77 h maximum GLA enrichment was observed in the DAG fraction. The GLA content of the DAG increased to 34.6%, corresponding to almost 1.6-fold enrichment. The relative inability of Nigella sativa lipase(s) to hydrolyze γ-linolenoyl moieties of TAG can be used for the enrichment of this acid in the unhydrolyzed acylglycerol fractions of GLA-containing oils.     

Synthesis and film properties of epoxy esters modified with amino resins from glycolysis products of postconsumer PET bottles

Glycolysis of waste polyethylene terephthalate (PET) flakes obtained from grinding postconsumer bottles was carried out at 225–250°C and molar ratios of PET/ethylene glycol were taken as 1/1, 1/1.5, 1/5, and 1/10. Reaction product was extracted by hot water for three times and water‐soluble crystallizable fraction and water‐insoluble fraction were obtained. These fractions were characterized by acid and hydroxyl value determinations, differential scanning calorimeter analysis, and ¹H‐NMR analysis. Glycolysis product was used for synthesis of PET‐based epoxy resin. This epoxy resin was used to prepare epoxy ester resins having 40% and 50% oil content. Epoxy ester resin having 40% oil content was modified with urea‐formaldehyde and melamine‐formaldehyde resins for synthesis of epoxy ester–amino resin. Physical and chemical film properties of epoxy ester and modified epoxy ester resins were investigated. All the epoxy ester and modified epoxy ester films were having excellent adhesion, water, and salt water resistance properties. Modification of PET‐based epoxy ester resins with amino resin has significantly improved hardness, impact resistance, and alkaline and acid resistance of resin films. As a result, PET oligomers obtained from glycolysis of postconsumer PET bottles are suitable for manufacturing of amino‐resin‐modified epoxy ester resins that have improved physical and chemical surface coating properties. POLYM. ENG. SCI., 55:2519–2525, 2015. © 2015 Society of Plastics Engineers     

Prediction of the colorimetric parameters and mass loss of heat‐treated bamboo: Comparison of multiple linear regression and artificial neural network method

In this study, the colorimetric parameters (L*, a*, b*) and mass loss of heat‐treated bamboo were investigated, and the obtained results were modeled by using two methods: multiple linear regression (MLR) and artificial neural network (ANN). First, bamboo samples were exposed to heat treatment at different temperatures (110°C, 140°C, 170°C, and 200°C) and durations (15, 30, 45, 60, 75, 90, and 115 minutes) in a laboratory oven. Then, the colorimetric parameters (L*, a*, b*) and mass loss of each sample were measured after each period of heat treatment. All data were modeled by using two methods separately for each parameter and the performances of these proposed methods were compared. It was found that color change and mass loss increased with increasing temperature and duration of heat treatment. Mean absolute percentage error (MAPE) values of all obtained MLR ranged from 0.64% to 10.63%, while the all MAPE values of ANN were found to be lower than 1.5%. Based on these results, it can be said that MLR and ANN could be used to evaluate the changes on the selected properties of heat‐treated bamboo samples. On the other hand, it should be emphasized that the ANN gave more accurate results than the MLR method because of its learning capability.     

A new perspective on data homogeneity in software cost estimation: a study in the embedded systems domain

Cost estimation and effort allocation are the key challenges for successful project planning and management in software development. Therefore, both industry and the research community have been working on various models and techniques to accurately predict the cost of projects. Recently, researchers have started debating whether the prediction performance depends on the structure of data rather than the models used. In this article, we focus on a new aspect of data homogeneity, “cross- versus within-application domain”, and investigate what kind of training data should be used for software cost estimation in the embedded systems domain. In addition, we try to find out the effect of training dataset size on the prediction performance. Based on our empirical results, we conclude that it is better to use cross-domain data for embedded software cost estimation and the optimum training data size depends on the method used.     

A comparative study for estimating software development effort intervals

Software cost/effort estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Until today, software cost estimation has been treated as a regression problem. However, in order to prevent overestimates and underestimates, it is more practical to predict the interval of estimations instead of the exact values. In this paper, we propose an approach that converts cost estimation into a classification problem and that classifies new software projects in one of the effort classes, each of which corresponds to an effort interval. Our approach integrates cluster analysis with classification methods. Cluster analysis is used to determine effort intervals while different classification algorithms are used to find corresponding effort classes. The proposed approach is applied to seven public datasets. Our experimental results show that the hit rate obtained for effort estimation are around 90–100%, which is much higher than that obtained by related studies. Furthermore, in terms of point estimation, our results are comparable to those in the literature although a simple mean/median is used for estimation. Finally, the dynamic generation of effort intervals is the most distinctive part of our study, and it results in time and effort gain for project managers through the removal of human intervention.     

Interpretation of MR images using self-organizing maps and knowledge-based expert systems

A new image segmentation system is presented to automatically segment and label brain magnetic resonance (MR) images to show normal and abnormal brain tissues using self-organizing maps (SOM) and knowledge-based expert systems. Elements of a feature vector are formed by image intensities, first-order features, texture features extracted from gray-level co-occurrence matrix and multiscale features. This feature vector is used as an input to the SOM. SOM is used to over segment images and a knowledge-based expert system is used to join and label the segments. Spatial distributions of segments extracted from the SOM are also considered as well as gray level properties. Segments are labeled as background, skull, white matter, gray matter, cerebrospinal fluid (CSF) and suspicious regions.     

Functional Diversity,Absorptive Capability and Product Success: The Moderating Role of Project Complexity in New Product Development Teams

Absorptive capability appears to be an appealing concept in the technology and innovation management literature. Though absorptive capability attracts researchers from a variety of disciplines, team‐level empirical research on it is scant. In this study, we operationalized team absorptive capability as a multidimensional construct involving knowledge acquisition, assimilation and exploitation. This study also explores the moderating effect of project complexity between team absorptive capability and new product success. In studying the data from 239 new product development projects using partial least squares structural equation modelling, we found that team functional diversity is a significant determinant of team absorptive capability. Moreover, regarding the relationships between team absorptive capability and new product success, we uncovered that (i) new product success is dependent on the ability to understand the acquired knowledge, and (ii) the teams appear to be more cautious in putting the assimilated knowledge into practice to the extent that project complexity increases.     

Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder

Neural Computing and Applications - Recently multimodal neuroimaging which combines signals from different brain modalities has started to be considered as a potential to improve the accuracy of...