A Convolution Neural Network-Based Representative Spatio-Temporal Documents Classification for Big Text Data

With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on extracting spatiotemporal information from online text data and utilizing it for event analysis is being actively conducted. However, if spatiotemporal information that does not describe the core subject of a document is extracted, it is rather difficult to guarantee the accuracy of core event analysis. Therefore, it is important to extract spatiotemporal information that describes the core topic of a document. In this study, spatio-temporal information describing the core topic of a document is defined as ‘representative spatio-temporal information’, and documents containing representative spatiotemporal information are defined as ‘representative spatio-temporal documents’. We proposed a character-level Convolution Neuron Network (CNN)-based document classifier to classify representative spatio-temporal documents. To train the proposed CNN model, 7400 training data were constructed for representative spatio-temporal documents. The experimental results show that the proposed CNN model outperforms traditional machine learning classifiers and existing CNN-based classifiers

Machine Learning Based Representative Spatio-Temporal Event Documents Classification

As the scale of online news and social media expands, attempts to analyze the latest social issues and consumer trends are increasing. Research on detecting spatio-temporal event sentences in text data is being actively conducted. However, a document contains important spatio-temporal events necessary for event analysis, as well as non-critical events for event analysis. It is important to increase the accuracy of event analysis by extracting only the key events necessary for event analysis from among a large number of events. In this study, we define important 'representative spatio-temporal event documents' for the core subject of documents and propose a BiLSTM-based document classification model to classify representative spatio-temporal event documents. We build 10,000 gold-standard training datasets to train the proposed BiLSTM model. The experimental results show that our BiLSTM model improves the F1 score by 2.6% and the accuracy by 4.5% compared to the baseline CNN model

FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data

Synthesis of a halogenated low bandgap polymeric donor for semi-transparent and near-infrared organic solar cells

Semi-transparent organic solar cells (ST-OSCs) have garnered significant interest because of their potential in aesthetic and space-saving solar energy systems such as multi-colored semitransparent building???integrated photovoltaic or grow light transparent agrivoltaic systems. As visibly semitransparent photoactive materials, the low bandgap (LBG) donor polymer and acceptor present new opportunities for the realization of ST-OSCs because they can facilitate photovoltaic generation of electricity from near-infrared (NIR) light without significant absorption of visible light. However, while various LBG non-fullerene acceptors have been recently developed to realize highly efficient ST-OSCs, there are only a few reports on LBG donor polymers that achieve efficient photo-induced charge generation from NIR light as well as allow the propagation of visible light. In this study, LBG donor polymers consisting of BD-F and BD-Cl as the halogenated derivatives of poly{2,6???-4,8-di(5-ethylhexylthienyl)benzo[1,2-b; 3,4-b]dithiophene-alt-5-dibutyloctyl-3,6-bis(5-bromothiophen-2-yl)pyrrolo[3,4-c]pyrrole-1,4-dione} (BD-H) were synthesized. The BD-F:Y6 and BD-Cl:Y6 OSCs showed higher open-circuit voltages and fill factors than BD-H:Y6 due to their downshifted energy level and efficient charge extraction characteristics. Consequently, the BD-Cl:Y6 OSCs achieved a power conversion efficiency (PCE) of 5.62%. Furthermore, with the introduction of a metal oxide/metal/metal oxide transparent electrode, the BD-Cl:Y6 ST-OSC demonstrated a high average visible transmittance of 35.1% and PCE of 3.69%. This approach contributes to enhancing the potential of ST-OSCs

Direct Observation of Confinement Effects of Semiconducting Polymers in Polymer Blend Electronic Systems

Abstract The advent of special types of polymeric semiconductors, known as “polymer blends,” presents new opportunities for the development of next‐generation electronics based on these semiconductors' versatile functionalities in device applications. Although these polymer blends contain semiconducting polymers (SPs) mixed with a considerably high content of insulating polymers, few of these blends unexpectedly yield much higher charge carrier mobilities than those of pure SPs. However, the origin of such an enhancement has remained unclear owing to a lack of cases exhibiting definite improvements in charge carrier mobility, and the limited knowledge concerning the underlying mechanism thereof. In this study, the morphological changes and internal nanostructures of polymer blends based on various SP types with different intermolecular interactions in an insulating polystyrene matrix are investigated. Through this investigation, the physical confinement of donor–acceptor type SP chains in a continuous nanoscale network structure surrounded by polystyrenes is shown to induce structural ordering with more straight edge‐on stacked SP chains. Hereby, high‐performance and transparent organic field‐effect transistors with a hole mobility of ≈5.4 cm2 V–1 s–1 and an average transmittance exceeding 72% in the visible range are achieved