Cell segmentation and counting represent one of the most important tasks required in order to provide an exhaustive understanding of biological images. Conventional features suffer the lack of spatial consistency by causing the joining of the cells and, thus, complicating the cell counting task. We propose, in this work, a cascade of networks that take as inputs different versions of the original image. After constructing a Gaussian pyramid representation of the microscopy data, the inputs of different size and spatial resolution are given to a cascade of deep convolutional autoencoders whose task is to reconstruct the segmentation mask. The coarse masks obtained from the different networks are summed up in order to provide the final mask. The principal and main contribution of this work is to propose a novel method for the cell counting. Unlike the majority of the methods that use the obtained segmentation mask as the prior information for counting, we propose to utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor. While the segmentation part of our method performs as good as the conventional deep learning methods, the proposed cell counting approach outperforms the state-of-the-art methods.

When a prescription change occurs in the hospital depending on a patient's improvement status, pharmacists directly classify manually returned pills which are not taken by a patient. There are hundreds of kinds of pills to classify. Because it is manual, mistakes can occur and which can lead to medical accidents. In this study, we have compared YOLO, Faster R-CNN and RetinaNet to classify and detect pills. The data consisted of 10 classes and used 100 images per class. To evaluate the performance of each model, we used cross-validation. As a result, the YOLO Model had sensitivity of 91.05%, FPs/image of 0.0507. The Faster R-CNN's sensitivity was 99.6% and FPs/image was 0.0089. The RetinaNet showed sensitivity of 98.31% and FPs/image of 0.0119. Faster RCNN showed the best performance among these three models tested. Thus, the most appropriate model for classifying pills among the three models is the Faster R-CNN with the most accurate detection and classification results and a low FP/image.

In this paper, we suggest the tracking method of moving multi-objects in maritime environments. The image acquisition is conducted using IR(InfraRed) camera sensors on an airborne platform. Under the circumstance of maritime, the qualities of IR images can be significantly degraded due to the clutter influence, which directly gives rise to a tracking loss problem. In order to reduce the effects from the clutters, we introduce a technical approach under Man-In-The-Loop(MITL) system for enhancing the tracking performance. To demonstrate the robustness of the proposed approach based on VTG(Valid Tracking Gate), the simulations are conducted utilizing the airborne IR video sequences: Then, the tracking performances are compared with the existing Kalman Filter tracking techniques.

The development of monitoring and analysis systems to increase productivity while saving energy is needed as a method to reduce huge amount of energy consumed in the process of producing large forged products. In this paper, we propose a system to monitor and analyze energy usage in real-time collected from gas-meter, wattmeter, and thermometer based on IoT installed in forging factories. The system consists of a data collection server for collecting and processing data from IoT- based platform and existing SCADA equipment and ERP/MES system in forging factories, and an application server for providing services to users. To develop the system, the overall system structure is logically diagrammed, and the databases configuration and implementation modules to efficiently store and manage data are presented. In the future, the system will be utilized to reduce energy consumption by analyzing energy usage pattern and optimizing process works with real-time energy usage and production process data for each facility.

A Web Crawler is a program, which is commonly used by search engines to find the new brainchild on the internet. The use of crawlers has made the web easier for users. In this paper, we have used unstructured data by structuralization to collect data from the web pages. Our system is able to choose the word near our keyword in more than one document using unstructured way. Neighbor data were collected on the keyword through word2vec. The system goal is filtered at the data acquisition level and for a large taxonomy. The main problem in text taxonomy is how to improve the classification accuracy. In order to improve the accuracy, we propose a new weighting method of TF-IDF. In this paper, we modified TF-algorithm to calculate the accuracy of unstructured data. Finally, our system proposes a competent web pages search crawling algorithm, which is derived from TF-IDF and RL Web search algorithm to enhance the searching efficiency of the relevant information. In this paper, an attempt has been made to research and examine the work nature of crawlers and crawling algorithms in search engines for efficient information retrieval.

This research will describe algorithm for class diagram extraction from problem statements. Class diagram notation consist of class name, attributes, and operations. Class diagram can be extracted from the problem statement automatically by using Natural Language Processing (NLP). The extraction results heavily depends on the algorithm and preprocessing stage. The algorithm obtained from various sources with additional rules that are obtained in the implementation phase. The evaluation features using five problem statement with different domains. The application will capture the problem statement and draw the class diagram automatically by using Windows Presentation Foundation(WPF). The classification accuracy of 100% was achieved. The final algorithm achieved 92 % of average precision score.

Star characters play a key role in the background of contents that secure global competitiveness. In this study, we study the growth factors and transitional forms of Nintendo Super Mario Bros. and suggest the direction of nurturing Korean star characters. As a result of the analysis of Super Mario Bros., it was possible to derive the growth factors of Originality, repeatability, Continuity, Pertain, and Star Fame and the transition result of step development method. Robot Taekwon V was presented as a reference model while Korean game characters with global competitiveness were excavated. Robot Taekwon V, which combines the features of friendly, unique, symbolism, and narrative, could be used to present a development model of the type of jump development. In the jump development model, character development and Upbringing, diversification of genre, pioneering of new markets combined with new technology, and fostering of characters through linkage with other industries were presented.

The purpose of this paper was to analyze multicultural elements and propose measures to strengthen them in the 2009and the 2015 school curriculum. Based on Kim Cheong-ha (2015), the multi-cultural guidance elements and Bennett (2003:32-35) the target system and Choi Moo-sung and Kim Soon-ja (2009) were reconstructed and analyzed. Multicultural elements shown in the 2015 revised curriculum were analyzed for cultural diversity, recognition and tolerance, community awareness, and self-identity. The analysis targets analyzed multicultural elements of 'seeking human beings', 'core competences' and 'school organization and operation' during the elementary education curriculum in 2009. As a result of analyzing the multicultural elements of the former primary education process, 'cultural diversity' is 1,Recognition and tolerance'0, community spirit'3, and 'self-reliant'2 total6. In the new elementary school curriculum, 18 were analyzed as 'cultural diversity'1, 'Recognition and tolerance'1, 'community spirit'8, and 'self-identity'8. The first analysis of multi-cultural factors appearing in the new elementary school curriculum should include the addition of multicultural elements 'cultural diversity' and 'cultural diversity area' and 'education and tolerance' to the following: Second, to grow into a balanced multicultural society member, it proposes an enhanced multicultural curriculum in four areas: cultural diversity, recognition and tolerance, ccommunity spirit, and self-identity.