• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.6
• /
• pp.659-665
• /
• 2019

Various placards are attached to the surface of a container depending on the risk of the cargo loaded. Containers with dangerous goods should be managed separately from ordinary containers. Therefore, as part of the port automation system, there is a demand for automatic recognition of placards. In this paper, proposed is a system that automatically extracts the placard area based on the shape features of the placard and recognizes the contents in it. Various distortions can be caused by the surface curvature of the container, therefore, attention should be paid to the area extraction and recognition process. The proposed system can automatically extract the region of interest and recognize the placard using the feature that the placard is diamond shaped and the class number is written just above the lower vertex. When the proposed system is applied to real images, the placard can be recognized without error, and the used techniques can be applied to various image analysis systems.

• Journal of the Korea Convergence Society
• /
• v.10 no.6
• /
• pp.33-39
• /
• 2019

As cyber attack methods are becoming more intelligent, incidents such as security breaches and international crimes are increasing. In order to predict and respond to these cyber attacks, the characteristics, methods, and types of attack techniques should be identified. To this end, many security companies are publishing security intelligence reports to quickly identify various attack patterns and prevent further damage. However, the reports that each company distributes are not structured, yet, the number of published intelligence reports are ever-increasing. In this paper, we propose a method to extract structured data from unstructured security intelligence reports. We also propose an automatic intelligence report analysis system that divides a large volume of reports into sub-groups based on their topics, making the report analysis process more effective and efficient.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.1
• /
• pp.225-234
• /
• 2019

By using the Triboelectric nanogenerators, known as TENG, we can take advantages of high conversion efficiency and continuous power output even with small vibrating energy sources. Nonlinear energy extraction techniques for Triboelectric vibration energy harvesting usually requires synchronized active electronic switches in most electronic interface circuits. This study presents a nonlinear energy harvesting with high energy conversion efficiency to harvest and save energies from human active motions. Moreover, the proposed design can harvest and store energy from sway motions around different directions on a horizontal plane efficiently. Finally, we conducted a comparative analysis of a multi-mode energy storage board developed by a silicon-based piezoelectricity and a transparent TENG cell. As a result, the experiment showed power generation of about 49.2mW/count from theses multi-fully harvesting source with provision of stable energy storages.

• The Korean Journal of Applied Statistics
• /
• v.32 no.2
• /
• pp.301-317
• /
• 2019

There has been significant research conducted on how to discover various insights through text data using statistical techniques. In this study we analyzed text data produced by the Korean National Police Agency to identify trends in the work by year and compare work characteristics among local authorities by identifying distinctive keywords in documents produced by each local authority. A preprocessing according to the characteristics of each data was conducted and the frequency of words for each document was calculated in order to draw a meaningful conclusion. The simple term frequency shown in the document is difficult to describe the characteristics of the keywords; therefore, the frequency for each term was newly calculated using the term frequency-inverse document frequency weights. The L2 norm normalization technique was used to compare the frequency of words. The analysis can be used as basic data that can be newly for future police work improvement policies and as a method to improve the efficiency of the police service that also help identify a demand for improvements in indoor work.

• Journal of Broadcast Engineering
• /
• v.24 no.1
• /
• pp.67-76
• /
• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.1
• /
• pp.145-152
• /
• 2019

In this paper, we proposed a shaking image stabilization algorithm using deep learning. The proposed algorithm utilizes deep learning, unlike some 2D, 2.5D and 3D based stabilization techniques. The proposed algorithm is an algorithm that extracts and compares features of shaky images through CNN network structure and LSTM network structure, and transforms images in reverse order of movement size and direction of feature points through the difference of feature point between previous frame and current frame. The algorithm for stabilizing the shake is implemented by using CNN network and LSTM structure using Tensorflow for feature extraction and comparison of each frame. Image stabilization is implemented by using OpenCV open source. Experimental results show that the proposed algorithm can be used to stabilize the camera shake stability in the up, down, left, and right shaking images.

• The Journal of the Korean dental association
• /
• v.57 no.3
• /
• pp.149-160
• /
• 2019

Atrophic alveolar ridge of maxillary anterior area is commonly observed after the extraction of teeth in patients with severely compromised periodontal disease, causing difficulties with implant placement. Successful esthetics and functional implant rehabilitation rely on sufficient bone volume, adequate bone contours, and ideal implant positioning and angulation. The present case report categorized the ridge augmentation techniques using guided bone regeneration (GBR) on the maxillary anterior site by Seibert classification. Case I patient presented for implant placement in the position of tooth #11. The alveolar ridge was considered a Seibert classification I ridge defect. Simultaneous implant placement and GBR were performed. Eight months after implantation, clinical and radiological examinations were performed. Case III patient presented with discomfort due to mobility of the upper maxillary anterior site. Due to severe destruction of alveolar bone, teeth #11 and #12 were extracted. After three months, the alveolar ridge was considered a Seibert classification III ridge defect. A GBR procedure was performed; implantation was performed 6 months later. Approximately 1-year after implantation, clinical and radiological examinations were performed. During the whole treatment period, healing was uneventful without membrane exposure, severe swelling, or infection in all cases. Radiographic and clinical examinations revealed that atrophic hard tissues and buccal bone contour were restored to the acceptable levels for implant placement and esthetic restoration. In conclusion, severely resorbed alveolar ridge of the maxillary anterior area can be reconstructed with ridge augmentation using the GBR procedure so that dental implants could be successfully placed.

• Journal of the Korean Society of Mineral and Energy Resources Engineers
• /
• v.55 no.6
• /
• pp.614-623
• /
• 2018

The recent occurrence of consecutive large earthquakes in the southeastern part of the Korean peninsula has brought significant attention to the prevention of earthquake damage in Korea. This article aims to explore a technology-based approach for earthquake drills using state-of-the-art visual intelligence and virtual reality technologies. The technical process consists of several stages, including acquisition of image information in living spaces using a camera, recognition of objects from the acquired image information, extraction of three dimensional geometric information, simulation of virtual earthquakes using dynamic modelling techniques such as the discrete element method, and realization of the simulated earthquake in a virtual reality environment. This article provides a comprehensive analysis of the individual processes at each stage of the technical process, a survey on the current status of related technologies, and discussion of the technical challenges in its execution.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.8
• /
• pp.311-318
• /
• 2021

Research has been ongoing to detect ships from offshore photographs for a variety of reasons, including maritime security, identifying international trends, and social scientific research. Due to the development of artificial intelligence, R-CNN models for object detection in photographs and images have emerged, and the performance of object detection has risen dramatically. Ship detection in offshore photographs using the R-CNN model has also begun to apply to satellite photography. However, satellite images project large areas, so various objects such as vehicles, landforms, and buildings are sometimes recognized as ships. In this paper, we propose a novel methodology to improve the performance of ship detection in satellite photographs using R-CNN series models. We separate land and sea via marker-based watershed algorithm and perform morphology operations to specify RoI one more time, then detect vessels using R-CNN family models on specific RoI to reduce typology. Using this method, we could reduce the misdetection rate by 80% compared to using only the Fast R-CNN.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2049-2068
• /
• 2021

AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.