Plasmon-induced trap filling at grain boundaries in perovskite solar cells

The deep-level traps induced by charged defects at the grain boundaries (GBs) of polycrystalline organic–inorganic halide perovskite (OIHP) films serve as major...     

Platform‐independent profiling in a virtual execution environment

Virtual execution environments, such as the Java virtual machine, promote platform‐independent software development. However, when it comes to analyzing algorithm complexity and performance bottlenecks, available tools focus on platform‐specific metrics, such as the CPU time consumption on a particular system. Other drawbacks of many prevailing profiling tools are high overhead, significant measurement perturbation, as well as reduced portability of profiling tools, which are often implemented in platform‐dependent native code. This article presents a novel profiling approach, which is entirely based on program transformation techniques, in order to build a profiling data structure that provides calling‐context‐sensitive program execution statistics. We explore the use of platform‐independent profiling metrics in order to make the instrumentation entirely portable and to generate reproducible profiles. We implemented these ideas within a Java‐based profiling tool called JP. A significant novelty is that this tool achieves complete bytecode coverage by statically instrumenting the core runtime libraries and dynamically instrumenting the rest of the code. JP provides a small and flexible API to write customized profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that, despite the presence of dynamic instrumentation, JP causes significantly less overhead than a prevailing tool for the profiling of Java code. Copyright © 2008 John Wiley & Sons, Ltd.     

Pointwise and pairwise clothing annotation: combining features from social media

In this paper, we present effective algorithms to automatically annotate clothes from social media data, such as Facebook and Instagram. Clothing annotation can be informally stated as recognizing, as accurately as possible, the garment items appearing in the query photo. This task brings huge opportunities for recommender and e-commerce systems, such as capturing new fashion trends based on which clothes have been used more recently. It also poses interesting challenges for existing vision and recognition algorithms, such as distinguishing between similar but different types of clothes or identifying a pattern of a cloth even if it has different colors and shapes. We formulate the annotation task as a multi-label and multi-modal classification problem: (i) both image and textual content (i.e., tags about the image) are available for learning classifiers, (ii) the classifiers must recognize a set of labels (i.e., a set of garment items), and (iii) the decision on which labels to assign to the query photo comes from a set of instances that is used to build a function, which separates labels that should be assigned to the query photo, from those that should not be assigned. Using this configuration, we propose two approaches: (i) the pointwise one, called MMCA, which receives a single image as input, and (ii) a multi-instance classification, called M3CA, also known as pairwise approach, which uses pair of images to create the classifiers. We conducted a systematic evaluation of the proposed algorithms using everyday photos collected from two major fashion-related social media, namely pose.com and chictopia.com. Our results show that the proposed approaches provide improvements when compared to popular first choice multi-label, multi-modal, multi-instance algorithms that range from 20 % to 30 % in terms of accuracy.     

An adaptive generation method for electrophoretic display driving waveform design

Electrophoretic display (EPD) technology is attractive when used for reading devices such as e‐paper because of its paper‐like appearance. EPD driving is more complex than other display technologies such as liquid crystal display or organic light emission diode because the driving result of a pixel strongly depends on the initial display state. The particle size and distribution in the EPD film may vary, even if the initial state is the same. Therefore, the display devices vary between different manufacturing batches. Furthermore, different display modes such as videos, pictures or documents need different driving waveforms to achieve an optimal result. EPD manufacturers need to build a customized driving waveform for every manufacturing batch. This is very inconvenient if new applications on EPD are to be developed. And the workload is huge. In this work, an adaptive method is described for automatically creating EPD driving waveforms to fit different conditions. The central idea of this method is generally adjusting the driving time and the voltage state after getting the feedback from a measurement model. In this method, a new driving waveform is used to reduce refresh time and visual flicker. The experimental results show that the proposed approach can automatically and adaptively generate an EPD driving waveform with reasonable quality.     

Numerical Analysis of Second Order,Fully Discrete Energy Stable Schemes for Phase Field Models of Two-Phase Incompressible Flows

In this paper, we propose several second order in time, fully discrete, linear and nonlinear numerical schemes for solving the phase field model of two-phase incompressible flows, in the framework of finite element method. The schemes are based on the second order Crank–Nicolson method for time discretization, projection method for Navier–Stokes equations, as well as several implicit–explicit treatments for phase field equations. The energy stability and unique solvability of the proposed schemes are proved. Ample numerical experiments are performed to validate the accuracy and efficiency of the proposed schemes.     

Geographical origin classification of olive oils by PTR-MS

The volatile compositions of 192 olive oil samples from five different European countries were investigated by PTR-MS sample headspace analysis. The mass spectra of all samples showed many masses with high abundances, indicating the complex VOC composition of olive oil. Three different PLS-DA models were fitted to the data to classify samples into ‘country’, ‘region’ and ‘district’ of origin, respectively. Correct classification rates were assessed by cross-validation. The first fitted model produced an 86% success rate in classifying the samples into their country of origin. The second model, which was fitted to the Italian oils only, also demonstrated satisfactory results, with 74% of samples successfully classified into region of origin. The third model, classifying the Italian samples into district of origin, yielded a success rate of only 52%. This lower success rate might be due to either the small class set, or to genuine similarities between olive oil VOC compositions on this tight scale.     

Evidential reasoning using extended fuzzy Dempster–Shafer theory for handling various facets of information deficiency

This work investigates the problem of combining deficient evidence for the purpose of quality assessment. The main focus of the work is modeling vagueness, ambiguity, and local nonspecificity in information within a unified approach. We introduce an extended fuzzy Dempster–Shafer scheme based on the simultaneous use of fuzzy interval‐grade and interval‐valued belief degree (IGIB). The latter facilitates modeling of uncertainties in terms of local ignorance associated with expert knowledge, whereas the former allows for handling the lack of information on belief degree assignments. Also, generalized fuzzy sets can be readily transformed into the proposed fuzzy IGIB structure. The reasoning for quality assessment is performed by solving nonlinear optimization problems on fuzzy Dempster–Shafer paradigm for the fuzzy IGIB structure. The application of the proposed inference method is investigated by designing a reasoning scheme for water quality monitoring and validated through the experimental data available for different sampling points in a water distribution network. © 2011 Wiley Periodicals, Inc.     

Invariant ellipsoid method for minimization of unmatched disturbances effects in sliding mode control

A new sliding mode control design algorithm for a linear and a class of nonlinear quasi-Lipschitz disturbed system is presented. It is based on the appropriate selection of the sliding surface via invariant ellipsoid method. The designed control guarantees minimization of unmatched disturbances effects to system motions in a sliding mode. The obtained theoretical results are approved by numerical simulations.