Charge transport and light emission in bilayer organic field-effect transistors

Recently there has been some major interest in the charge transport and light emission properties of organic field-effect transistors (OFETs). Different device structures have been proposed and they can be divided into two broad categories consisting of either a single layer or a bilayer. In the case of the single-layer OFETs, efficient light emission has not been observed while the performance of the bilayer OFETs appear to be more promising (for instance: recent work on a bilayer OFET has shown distinct ambipolar characteristics as well as limited light emission). In this work, we examined the electroluminescence intensities of bilayer OFETs reported in the open literature and attempted to identify the transport and recombination mechanisms. As observed, light emission in these devices appeared to be linked to a narrow region at the interface acting as a light-emitting source. To understand the recombination mechanisms, we computed the spatial charge distributions under various biasing conditions and correlated the results to the reported electroluminescence intensity data. Our overall results re-affirmed the significance of the light-emitting interface layer and the fact that device operation critically depended on the alignment of the energy levels at the respective interface.     

Extension of the Optimised Virtual Fields Method to Estimate Viscoelastic Material Parameters from 3D Dynamic Displacement Fields

In vivo measurement of the mechanical properties of soft tissues is essential to provide necessary data in biomechanics and medicine (early cancer diagnosis, study of traumatic brain injuries, etc.). Imaging techniques such as magnetic resonance elastography can provide 3D displacement maps in the bulk and in vivo, from which, using inverse methods, it is then possible to identify some mechanical parameters of the tissues (stiffness, damping, etc.). The main difficulties in these inverse identification procedures consist in dealing with the pressure waves contained in the data and with the experimental noise perturbing the spatial derivatives required during the processing. The optimised virtual fields method (OVFM) ( 26 ), designed to be robust to noise, presents natural and rigorous solution to deal with these problems. The OVFM has been adapted to identify material parameter maps from magnetic resonance elastography data consisting of 3D displacement fields in harmonically loaded soft materials. In this work, the method has been developed to identify elastic and viscoelastic models. The OVFM sensitivity to spatial resolution and to noise has been studied by analysing 3D analytically simulated displacement data. This study evaluates and describes the OVFM identification performances: Different biases on the identified parameters are induced by the spatial resolution and experimental noise. The well‐known identification problems in the case of quasi‐incompressible materials also find a natural solution in the OVFM. Moreover, an a posteriori criterion to estimate the local identification quality is proposed. The identification results obtained on actual experiments are briefly presented.     

A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model

In bibliometric research, keyword analysis of publications provides an effective way not only to investigate the knowledge structure of research domains, but also to explore the developing trends within domains. To identify the most representative keywords, many approaches have been proposed. Most of them focus on using statistical regularities, syntax, grammar, or network-based characteristics to select representative keywords for the domain analysis. In this paper, we argue that the domain knowledge is reflected by the semantic meanings behind keywords rather than the keywords themselves. We apply the Google Word2Vec model, a model of a word distribution using deep learning, to represent the semantic meanings of the keywords. Based on this work, we propose a new domain knowledge approach, the Semantic Frequency-Semantic Active Index, similar to Term Frequency-Inverse Document Frequency, to link domain and background information and identify infrequent but important keywords. We adopt a semantic similarity measuring process before statistical computation to compute the frequencies of “semantic units” rather than keyword frequencies. Semantic units are generated by word vector clustering, while the Inverse Document Frequency is extended to include the semantic inverse document frequency; thus only words in the inverse documents with a certain similarity will be counted. Taking geographical natural hazards as the domain and natural hazards as the background discipline, we identify the domain-specific knowledge that distinguishes geographical natural hazards from other types of natural hazards. We compare and discuss the advantages and disadvantages of the proposed method in relation to existing methods, finding that by introducing the semantic meaning of the keywords, our method supports more effective domain knowledge analysis.     

Uncovering secrets behind low-resistance planing craft hull forms through optimization

There has always been significant interest within the naval architectural research community to identify ship hull forms with low resistance. While numerous design optimization frameworks have been proposed over the years to support the activity, very little attention has been paid towards the process of gaining an understanding of ‘what makes a good ship design superior?’. Furthermore, there have been limited attempts to identify computationally cheap indicators that can be used to distinguish between good and poor designs. A recent technique named discovery of innovative design principles, which is aimed at understanding the relationship between the design variables, is incorporated in this work. In this article, optimal high-speed planing craft hull forms with minimum calm-water resistance are identified through the use of three state-of-the-art optimization algorithms. Collections of such designs are then used to uncover insights into the underlying relationships between the variables. The importance of such relationships is further analysed to identify computationally cheap performance indicators that can be used in lieu of detailed calm-water resistance calculations. Such indicators are useful at the concept and preliminary design stages, where one needs to sieve efficiently through a number of candidate designs to identify the better ones for further analysis.     

Instrumentation effects on the detection of resistance transientsduring accelerated testing of VLSI interconnects

Some researchers have proposed using noise as an interconnect reliability tool. Often, however, noise spectral density has been used without concurrently examining the fluctuations in the time-domain which generate the noise spectra. Recently, the presence of abrupt changes in resistance (ACR's) has been documented in VLSI interconnects at accelerated stressing conditions. These ACR's are investigated here to identify their impact on typical spectra produced in noise measurements. Three results are reported in this paper, First, the presence of ACR's usually coincides with generation of 1/ℱ² noise. Second, the location of the ACR in the Fast Fourier Transform window is significant. Third, cross-correlation techniques can be used to identify ACR's in the time-domain signal     

DOI errors and possible solutions for Web of Science

As unique and permanent alphanumeric strings to identify objects, digital object identifier (DOI) has been increasingly used to identify academic publications. Previous studies have reported the incorrect assignment of a single DOI name to multiple papers in the Scopus database, yet it remains unknown if this also holds in other datasets. In this paper we found incorrect DOI names are also problematic in the Web of Science but with different errors of duplicate DOI names. Tentative solutions are proposed in the end.

     

Automatic identification of spatial defect patterns for semiconductor manufacturing

This research proposes an on-line diagnosis system based on denoising and clustering techniques to identify spatial defect patterns for semiconductor manufacturing. Today, even with highly automated and precisely monitored facilities used in a near dust-free clean room and operated with well-trained process engineers, the occurrence of spatial signatures on the wafer still cannot be avoided. Typical defect patterns shown on the wafer, including edge ring, linear scratch, zone type and mixed type, usually contain important information for quality engineers to remove their root causes of failures. In this paper, a spatial filter is simultaneously used to judge whether the input data contains any systematic cluster and to extract it from the noisy input. Then, an integrated clustering scheme combining fuzzy C means (FCM) with hierarchical linkage is adopted to separate various types of defect patterns. Furthermore, a decision tree based on two cluster features (convexity and eigenvalue ratio) is applied to a separated pattern to provide decision support for quality engineers. Experimental results show that both real dataset and synthetic dataset have been successfully extracted and classified. More importantly, the proposed method has potential to be further applied to other industries, such as liquid crystal display (LCD) and plasma display panel (PDP).     

A HEVC Video Steganalysis Algorithm Based on PU Partition Modes

Steganalysis is a technique used for detecting the existence of secret information embedded into cover media such as images and videos. Currently, with the higher speed of the Internet, videos have become a kind of main methods for transferring information. The latest video coding standard High Efficiency Video Coding (HEVC) shows better coding performance compared with the H.264/AVC standard published in the previous time. Therefore, since the HEVC was published, HEVC videos have been widely used as carriers of hidden information.
In this paper, a steganalysis algorithm is proposed to detect the latest HEVC video steganography method which is based on the modification of Prediction Units (PU) partition modes. To detect the embedded data, All the PU partition modes are extracted from P pictures, and the probability of each PU partition mode in cover videos and stego videos is adopted as the classification feature. Furthermore, feature optimization is applied, that the 25-dimensional steganalysis feature has been reduced to the 3-dimensional feature. Then the Support Vector Machine (SVM) is used to identify stego videos. It is demonstrated in experimental results that the proposed steganalysis algorithm can effectively detect the stego videos, and much higher classification accuracy has been achieved compared with state-of-the-art work.     

LNG decision making approaches compared

Hazard zones associated with LNG handling activities have been a major point of contention in recent terminal development applications. Debate has reflected primarily worst case scenarios and discussion of these. This paper presents results from a maximum credible event approach. A comparison of results from several models either run by the authors or reported in the literature is presented. While larger scale experimental trials will be necessary to reduce the uncertainty, in the interim a set of base cases are suggested covering both existing trials and credible and worst case events is proposed. This can assist users to assess the degree of conservatism present in quoted modeling approaches and model selections.     

改进模态应变能法在混凝土组合箱梁桥损伤诊断中的应用

为提高结构损伤识别方法的准确性和对噪声的鲁棒性,将多源信息融合技术引入到结构损伤识别方法中。针对模态应变能法诊断结构损伤一般需要完备模态的特点,采用信息融合技术对各阶模态应变能进行融合,建立了基于信息融合的改进的模态应变能法。通过对一座预应力混凝土组合箱梁桥损伤识别数值算例的分析,可以看出该方法具有良好的损伤敏感性,仅需要较少的低阶模态就可识别结构的早期损伤;同时,该方法具有噪声鲁棒性,当加入噪声等于或小于10%时可以准确识别结构的早期损伤。因此该文提出的基于信息融合的改进的模态应变能法具有较好的工程实用性。