• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.40-50
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• 2021

Science museum are dealing with exhibits on field of changing science and technology, and previous research suggested that exhibits replacement should carried out at least every 5 years. In order to efficiently replace exhibits within a limited budget, various studies analyzed visitors' preferences to exhibits. Recently, studies use various technologies to collect the data on visitors' preferences automatically, but almost of studies had a high dependency on their visitors such as visitors needed to carry specific sub-devices in the museums for gathering data. As complementing the limitations of previous research, this study introduces the improved method which is able to automatically collect and quantify visitors' preferences to exhibits using TensorFlow, a deep learning technology. By the proposed analysis method, it was possible to collect 2,520 data of visitors' experience on exhibits in totality. Based on collected data, attraction power and holding power indicating the preference of visitors on exhibits were able to be calculated. The result also confirmed antecedent research conclusion that the attraction power and holding power of the exhibit which consists of 3 dimensional structures work are higher than other exhibits. As a conclusion, the proposed method will provide more convenient data collection method for detecting visitors' preference.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• 한국지반신소재학회논문집
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• 제21권1호
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• pp.33-48
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• 2022

This paper presents illustrative examples of the application of advanced digital image correlation (DIC) technology in the geotechnical laboratory tests, such as shallow footing test, trapdoor test, retaining wall test, and wide width tensile test on geogrid. The theoretical background of the DIC technique is first introduced together with fundamental equations. Relevant reduced-scale model tests were then performed using standard sand while applying the DIC technique to capture the movement of target materials during tests. A number of different approaches were tried to obtain optimized images that allow efficient tracking of material speckles based on the DIC technique. In order to increase the trackability of soil particles, a mix of dyed and regular sand was used during the model tests while specially devised painted speckles were applied to the geogrid. A series of images taken during tests were automatically processed and analyzed using software named VIC-2D that automatically generates displacements and strains. The soil deformation field and associated failure patterns obtained from the DIC technique for each test were found to compare fairly well with the theoretical ones. Also shown is that the DIC technique can also general strains appropriate to the wide width tensile test on geogrid, It is demonstrated in this study that the advanced DIC technique can be effectively used in monitoring the deformation and strain field during a reduced-scale geotechnical model laboratory test.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.1-11
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• 2022

Online video telephony systems have been increasingly used in several industrial areas because of coronavirus disease 2019 (COVID-19) spread. The existing session initiation protocol (SIP)-based video call system is being usefully utilized, however, there is a limitation that it is very inconvenient for users to transmit additional information during conversation to the other party in real time. To overcome this problem, an enhanced scheme is presented based on augmented real-time reality (AR). In this scheme, augmented information is automatically searched from the Internet and displayed on the user's device during video telephony. The proposed approach was qualitatively evaluated by comparing it with other conferencing systems. Furthermore, to evaluate the feasibility of the approach, we implemented a simple network application that can generate SIP call requests and answer with AR object pre-fetching. Using this application, the call setup time was measured and compared between the original SIP and pre-fetching schemes. The advantage of this approach is that it can increase the convenience of a user's mobile phone by providing a way to automatically deliver the required text or images to the receiving side.

• Journal of Information Processing Systems
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• 제17권6호
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• pp.1179-1190
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• 2021

A data acquisition and monitoring system was developed for an automated system of a smart fish farm. The fish farm is located in Jang Hang, South Korea, and was designed as circulation filtration system. Information of every aquaculture pool was automatically measured by pH sensors, dissolved oxygen sensors, and water temperature sensors and the data were stored in the database in a remoted server. Modbus protocol was used for gathering the data which were further used to optimize the pool water quality to predict the rate of growth and death of fish, and to deliver food automatically as planned by the fish farmer. By using JSON protocol, the collected data was delivered to the user's PC and mobile phone for analysis and easy monitoring. The developed monitoring system allowed the fish farmers to improve fish productivity and maximize profits.

• 한국컴퓨터정보학회논문지
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• 제27권3호
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• pp.99-104
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• 2022

본 논문에서는 기후변화에 능동적으로 대처하여 빨래 감을 자동으로 제어하는 베란다용 스마트 빨래 건조 시스템을 처음으로 개발하고 소개하고자 한다. 개발된 스마트 빨래 건조 시스템은 앱을 통해 빨랫감 위치 정보를 받은 후, 위치 정보에 따른 온도, 습도 등의 기상청 데이터를 통해 기후 변화를 실시간으로 감지하여 비가 오는 상황이 발생할 경우 건조대 위에 빨래 감을 자동으로 제어한다. 아두이노 습도 센서와 기상청 Open-API 를 통해 기상 정보를 취득하고 이는 라즈베리파이가 스위치 봇을 제어하는데 이용된다. 사용자 인터페이스는 Blynk를 사용하였으며, 스위치 봇은 빨랫감을 제어한다. 제안 시스템은 기상 악화를 감지하고 원격지에 있는 빨래감을 자동으로 제어하여 비 피해를 예방해줄 수 있다.

• Genomics & Informatics
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• 제19권3호
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• pp.21.1-21.5
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• 2021

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present. However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don't generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.

• 로봇학회논문지
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• 제17권2호
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• pp.221-229
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• 2022

Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.

• 문화기술의 융합
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• 제7권4호
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• pp.629-634
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• 2021

최근 일회용품의 사용 증가로 인한 환경 파괴를 방지하지 위해, 일회용품의 사용 축소와 더불어 재활용 비율을 최대한 높이는 노력이 절실하게 필요하다. 본 논문에서는 재활용 관련 정보 제공 및 올바른 분리배출을 지원하는 스마트폰용 애플리케이션 를 소개한다. 본 시스템은 효과적인 사물 인식 기법을 적용하여, 카메라를 배출할 물품에 비추면 즉시 해당 물품의 종류를 자동 인식 및 자동 분류하여 그 물품에 알맞은 분리배출 방법을 현장에서 즉시 제공한다는 점에서 기존의 분리배출 정보 애플리케이션에 비해 효과적이고 편리하다. 이 시스템이 널리 활용된다면, 일상 생활 속 재활용 비율 확대를 통한 환경보호에 기여할 수 있을 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.814-836
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• 2021

Wildlife ecologists and biologists recapture deer to collect tracking data from deer collars or wait for a drop-off of a deer collar construction that is automatically detached and disconnected. The research teams need to manage a base camp with medical trailers, helicopters, and airplanes to capture deer or wait for several months until the deer collar drops off of the deer's neck. We propose an intelligent robust base-station research with a low-cost and time saving method to obtain recording sensor data from their collars to a listener node, and readings are obtained without opening the weatherproof deer collar. We successfully designed the and implemented a robust base station system for automatically collecting data of the collars and listener motes in harsh wilderness environments. Intelligent solutions were also analyzed for improved data collections and pattern predictions with drone-based detection and tracking algorithms.