In this paper, a motion-based ROI extraction algorithm from a high resolution fisheye image is proposed for multi-view monitoring systems. Lately fisheye cameras are widely used because of the wide angle-of-view and they basically provide a lens correction functionality as well as various viewing modes. However, since the distortion-free angle of conventional algorithms is quite narrow due to the severe distortion ratio, there are lots of unintentional dead areas and they require much computation time in finding undistorted coordinates. Thus, the proposed algorithm adopts an image decimation and a motion detection methods, that can extract the undistorted ROI image with a standard angle-of-view for the fast and intelligent surveillance system. In addition, a mesh-type ROI is presented to reduce the lens correction time, so that this independent ROI scheme can parallelize and maximize the processor's utilization.

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

This paper proposes a performance-improving face recognition system based on a super resolution method for low-resolution images. The conventional face recognition algorithm has a rapidly decreased accuracy rate due to small image resolution by a distance. To solve the previously mentioned problem, this paper generates a super resolution images based o deep learning method. The proposed method improved feature information from low-resolution images using a super resolution method and also applied face recognition using a feature extraction and an classifier. In experiments, the proposed method improves the face recognition rate when compared to conventional methods.

Recently, with the development of IoT technology and the increase of energy consumption, interest in energy saving and energy efficiency improvement is rapidly increasing. In particular, in the case of a device connected to a power plug with the power shutoff, a problem with standby power has been raised. Thus technology development through a low power method such as Zigbee is in progress. However, Zigbee, which is generally used, has a problem that a separate gateway is required because it is not an IP-based technology, and there is a problem that it is not suitable for a traditional computer network to which a variety of devices are connected. Therefore, in this paper, we designed and implemented a standby power saving system using MQTT, an IoT standard protocol, in Z-Wave environment. In order to evaluate the implemented standby power saving system, the same environment as the existing Zigbee-based standby power saving system was compared and evaluated.

The government of the Republic of Korea has introduced SNI blocking method since February 2019 to block illegal sites. Currently, this policy has been considered as a controversial policy in South Korea therefore, about 270,000 South Koreans have been calmed down by and the presidential office petition because people believe that blocking https by SNI would invasion of privacy and freedom of use of the Internet. As soon as the SNI blocking method was used, the bypass method was opened to the Internet, causing side effects. In this study, identifies the development of Internet blocking technology and the development of new technology that bypasses the technology. It also examines Internet censorship in other countries and identifies problems in blocking technology. Through this study would present policy suggestions and technical methodologies for sound Internet use.

The application field of smart terminals is increasing and its application is also spreading in the defense field. The use of smart terminal based map application is very important in battle fields. The problem is that the communication infrastructure is easy to collapse and the use of GPS is usually disturbed. In this paper, we studied the maps stored in the QR tag at the battle field. The problem is to abstract the map information so that it can be stored in the small QR tag. We have abstracted path information on a vector basis and require only a small amount of data compared to imaged path information. We analyzed the amount of data generated by the abstraction and mathematically analyzed the boundary where the amount does not exceed the capacity limit of the QR tag. Our research can be applied not only to battlefields, but also to disaster / disaster scenes, or in environments with difficult Internet communications, such as mountainous areas.

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.

The medical service, accompanied by various disease and disease of modern people, must collect various information about patients through various tests, and demand continued to increase. As a result, interest in waiting time for medical services is rising, and various studies are underway to reduce waiting time. However, medical procedure is complex and diverse in the procedure of the services need to be conducted for each procedure. In this study, we are going to propose ways to improve the waiting time of the test procedures that are basically experienced by outpatients through the simulation experimental method. Based on the actual measurement data, the Arena Simulation program was used to implement the hospital's ophthalmic examination model and alternative model. Through this analysis, the waiting time of the examination procedure, the size of the queue, and density of the waiting room were analyzed.

In this paper, we proposed a three-dimensional visualization system for medical images in augmented reality based on deep learning. In the proposed system, the artificial neural network model performed fully automatic segmentation of the region of lung and pulmonary nodule from chest CT images. After applying the three-dimensional volume rendering method to the segmented images, it was visualized in augmented reality devices. As a result of the experiment, when nodules were present in the region of lung, it could be easily distinguished with the naked eye. Also, the location and shape of the lesions were intuitively confirmed. The evaluation was accomplished by comparing automated segmentation results of the test dataset to the manual segmented image. Through the evaluation of the segmentation model, we obtained the region of lung DSC (Dice Similarity Coefficient) of 98.77%, precision of 98.45%, recall of 99.10%. And the region of pulmonary nodule DSC of 91.88%, precision of 93.05%, recall of 90.94%. If this proposed system will be applied in medical fields such as medical practice and medical education, it is expected that it can contribute to custom organ modeling, lesion analysis, and surgical education and training of patients.

In this paper, we propose a block-based modularity architecture design method for distributed machine learning. The proposed architecture is a block-type module structure with various machine learning algorithms. It allows free expansion between block-type modules and allows multiple machine learning algorithms to be organically interlocked according to the situation. The architecture enables open data communication using the metadata query protocol. Also, the architecture makes it easy to implement an application service combining various edge computing devices by designing a communication method suitable for surrounding applications. To confirm the interlocking between the proposed block-type modules, we implemented a hardware-based modularity application system.

Advancements in mobile phone hardware and network connectivity made communication becoming more and more convenient. Compared to pictures or texts, people prefer to share videos to convey information. For intentions clearer, the way to annotating comments directly on the video are quite important issues. Recently there have been many attempts to make annotations on video. These previous works have many limitations that do not support user-defined handwritten annotations or annotating on local video. In this sense, we propose an augmented reality based real-time video annotation system which allowed users to make any annotations directly on the video freely. The contribution of this work is the development of a real-time video annotation system based on recent augmented reality platforms that not only enables annotating drawing geometry shape on video in real-time but also drastically reduces the production costs. For practical use, we proposed a real-time collaboration system based on the proposed annotation method. Experimental results show that the proposed annotation method meets the requirements of real-time, accuracy and robustness of the collaboration system.

This study researched the elements about seafood culture among the archetypes of 『Jasan Urbo』. We built a hypothesis on 100 species of fish presented in 『Jasan Urbo』 to extract archetypes from them. To prove this hypothesis, we analyzed the properties of archetypes according to recipes. Next, we grasped the food preferences of past ages by inspecting 'taste' words of the literature. Finally, we examined the relations between dominant features of cuisine and fish preferences. We have found four attributes of maritime cultural archetypes. Our research has limitations since we could not had accounted the whole fish species of 『Jasan Urbo』. However, we achieved the huge outcome by our research, in which we applied various extraction methods of archetypes and acquired the dominance of maritime cultural archetypes and preferences.

This paper is one of the types of 'sharing economy', a social economic model that borrows and shares things, spaces, and services from the development of the 4th Industrial Revolution. Through sharing of empty space and time in the city, I would like to suggest a way to reduce the closure of small business owners in order to create jobs, which is one of urban problems in the community. We also build a platform that utilizes the free time and space of buildings through space sharing, one of the types of sharing economy, and provides education programs for start-up education, promotion, marketing, and consulting by matching small business owners with building owners. Therefore, in this paper, by sharing the space and time, the landlord and the small business can share the profits of the small business by reducing the business owner's closure and the job creation plan. Coaching urban regeneration was proposed.

For the success of mobile messenger emoticons, it is important to grab the attentions of users or consumers and identify the influence factors that can satisfy empathy and emotional satisfaction. In this study, first, subjective evaluation of the mobile messenger emoticons of the subjects was examined through a preliminary survey, and then Eye-tracking experiments were conducted to identify the influence factors that can attention of the subject's eyes in the emoticons. The study revealed that emoticons such as Ompangi and Onaeui yeosin highlighting their characters mainly focus on characters(face). Secondly, Gyuiyomjueui and Handprinting emoticons focused on Text. Contrary to earlier studies, such results showed that people are presumed to focus on characteristic elements such as size, form, color and location of visually exposed elements rather than primarily having a keen interest in characters.