[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The aeroengine control system is a piece of complex thermal machinery which works under high-speed, high-load, and high-temperature environmental conditions over lengthy periods of time; it must be designed for the utmost reliability and safety to function effectively. The consequences of sensor faults are often extremely serious. The inherent complexity of the engine structure creates difficulty in establishing accurate mathematical models for the model-based sensor fault diagnosis. This paper proposes an intelligent fault diagnosis method for aeroengine sensors combining a deep learning algorithm with time-frequency analysis wherein the signal recognition problem is transformed into an image recognition problem. The continuous wavelet transform (CWT) is first applied to seven common health condition signals in an engine control system sensor in order to generate scalograms that capture the characteristics of the signal. A convolutional neural network (CNN) model trained with preprocessed and labeled datasets is then used to extract the features of a time-frequency graph based on which faults can be identified and isolated. This method does not require modeling and design thresholds, so it has strong robustness and accuracy rate of over 97%. The trained model effectively reveals faults in sensor signals and allows for accurate identification of fault types.

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Aeroengine Control System Sensor Fault Diagnosis Based on CWT and CNN

Extractive multi-document text summarization based on graph independent sets

Increases in the amount of text resources available via the Internet has amplified the need for automated document summarizing tools. However, further efforts are needed in order to improve the quality of the existing summarization tools currently available. The current study proposes Karci Summarization, a novel methodology for extractive, generic summarization of text documents. Karci Entropy was used for the first time in a document summarization method within a unique approach. An important feature of the proposed system is that it does not require any kind of information source or training data. At the stage of presenting the input text, a tool for text processing was introduced; known as KUSH (named after its authors; Karci, Uckan, Seyyarer, and Hark), and is used to protect semantic consistency between sentences. The Karci Entropy-based solution chooses the most effective, generic and most informational sentences within a paragraph or unit of text. Experimentation with the Karci Summarization approach was tested using open-access document text (Document Understanding Conference; DUC-2002, DUC-2004) datasets. Performance achievement of the Karci Summarization approach was calculated using metrics known as Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The experimental results showed that the proposed summarizer outperformed all current state-of-the-art methods in terms of 200-word summaries in the metrics of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-W-1.2. In addition, the proposed summarizer outperformed the nearest competitive summarizers by a factor of 6.4% for ROUGE-1 Recall on the DUC-2002 dataset. These results demonstrate that Karci Summarization is a promising technique and it is therefore expected to attract interest from researchers in the field. Our approach was shown to have a high potential for adoptability. Moreover, the method was assessed as quite insensitive to disorderly and missing texts due to its KUSH text processing module.

Karcı summarization: A simple and effective approach for automatic text summarization using Karcı entropy

Bug reports facilitate software development teams in improving the quality of software. These reports include significant information related to problems encountered within a software, possible enhancement suggestions, and other potential issues. Bug reports are typically complex and are too detailed; hence a lot of resources are required to analyze and process them manually. Moreover, it leads to delays in the resolution of high priority bugs. Accurate and timely processing of bug reports based on their category and priority plays a significant role in improving the quality of software maintenance. Therefore, an automated process of categorization and prioritization of bug reports is needed to address the aforementioned issues. Automated categorization and prioritization of bug reports have been explored recently by many researchers; however, limited progress has been made in this regard. In this research, we present a novel framework, titled CaPBug, for automated categorization and prioritization of bug reports. The framework is implemented using Natural Language Processing (NLP) and supervised machine learning algorithms. A baseline corpus is built with six categories and five prioritization levels by analyzing more than 2000 bug reports of Mozilla and Eclipse repository. Four classification algorithms i.e., Naive Bayes, Random Forest, Decision Tree, and Logistic Regression have been used to categorize and prioritize bug reports. We demonstrate that the CaPBug framework achieved an accuracy of 88.78% by using a Random Forest classifier with a textual feature for predicting the category. Similarly, using the CaPBug framework, an accuracy of 90.43% was achieved in predicting the priority of bug reports. Synthetic Minority Over-Sampling Technique (SMOTE) has been applied to address the class imbalance issue in priority classes.

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CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

Nowadays, it is clear that the old mathematical models are incomplete because of the large size of image data set. For this reason, the Deep Learning models introduced in the field of image processing meet this need in the software field. In this study, Convolutional Neural Network (CNN) model from the Deep Learning Algorithms and the Optimization Algorithms used in Deep Learning have been applied to international image data sets. Optimization algorithms were applied to both datasets respectively, the results were analyzed and compared. The success rate was approximately 96.21% in the Caltech 101 data set, while it was observed to be approximately 10% in the Cifar-100 data set.

Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks

There is growing interest in automatic summarizing systems. This study focuses on a subtractive, general and unsupervised summarization system. It is provided to represent the texts to be summarized with graphs and then graph entropy is used to interpret the structural stability and structural information content on the graphs representing the text files. The performance of the proposed text summarizing approach for the purpose of summarizing the text on the data set of Document Understanding Conference (DUC-2002) including open access texts and summaries of these texts was calculated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluation metrics. Experimental processes were repeated for 200 and 400 word abstracts. Experimental results reveale that the proposed text summarizing system performs competitively with competitive methods for different ROUGE metrics.

Extractive Text Summarization via Graph Entropy Çizge Entropi ile Çıkarıcı Metin Özetleme

Gunumuzde oldukca fazla sayida metasezgisel algoritma yontemi onerilmis ve bircok genel probleme uygulanarak kullanilmaktadirlar. Bu metasezgisel yontemlerden bazilari ise belirli ve ozel problemlere uygulanmislardir. Genetik Algoritma, Yapay Sinir Aglari, Parcacik Suru Optimizasyonu vb. 150’ye yakin metasezgisel yaklasim mevcuttur ve her gecen gun bir baska problemin cozumunde kullanilmaktadirlar. Fidan Gelisim Algoritmasi (FGA) ve Lig Sampiyonluk Algoritmasi (LCA) da son yillarda populer olan metasezgisel yaklasimlardir. Enerji surekliliginin oldukca onemli oldugu guc sistemlerinde, surekliligin saglanmasi noktasinda cesitli yontemler soz konusudur. Bu yontemlerden birisi de iyi bir asiri-akim role koordinasyonudur. Bu calismada, elektrik enerjisi nakil hatlarinda surekli bir guc isletmeciliginin saglanmasi,  yerinde koruma koordinasyonun kurulmasi ve dagitim sistemlerinde meydana gelebilecek olasi arizalarin onune gecilebilmesi adina metasezgisel algoritma yontemlerinden FGA ve LCA kullanilmistir. Gerekli uyarlama ve kodlamalar .NET platformu kullanilarak gerceklestirilmis ve asiri-akim role koordinasyon degerlerinde oldukca iyi sayilabilecek sonuclara ulasilmistir.

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Fidan Gelişim ve Lig Şampiyonluk Algoritmaları Kullanarak Aşırı-Akım Rölelerin Optimum Koordinasyonu

Unstructured document and archive stacks that are formed in the past years are growing in size faster these days and they need to be clarified with various methods. This increases the interest in natural language processing discipline day by day and makes it more popular. In this study, we've tried to calculate the similarities between document stacks, that no information is presented onbehalf of their similarities and are completely independent of each other. Text mining approaches have been utilized in order to calculate the similarities of non-structural documents given. For this purpose, the R programming language labels have been used to structure only text based documents stacks in order to make them proccessable and to determine their similiraties afterwards.

Taner Uçkan

Papers

Extractive multi-document text summarization based on graph independent sets

Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks

Extractive Text Summarization via Graph Entropy Çizge Entropi ile Çıkarıcı Metin Özetleme

Fidan Gelişim ve Lig Şampiyonluk Algoritmaları Kullanarak Aşırı-Akım Rölelerin Optimum Koordinasyonu

Doğal dil İşleme yaklaşimlari ile yapisal olmayan dökümanlarin benzerliği