• Journal of IKEEE
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• 제26권4호
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• pp.681-685
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• 2022

Including the COVID-19 crisis, humanity is constantly exposed to viral infections, and efforts are being made to prevent the spread of infection by quickly isolating infected people and tracing contacts. Passive epidemiological investigations that confirm contact with an infected person through contact have limitations in terms of accuracy and speed, so automatic tracking methods using various digital technologies are being proposed. This paper verify contact by utilizing Bluetooth Low Energy (BLE) technology and present an algorithm that identifies close contact through analysis and correction of RSSI (Received Signal Strength Indicator) values. Also, propose a system that can prevent the spread of viruses in a centralized server structure.

• Korean Journal of Artificial Intelligence
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• 제10권1호
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• pp.27-32
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• 2022

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.

• Physical Therapy Korea
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• 제29권2호
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• pp.87-93
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• 2022

To solve the pathological problems of the musculoskeletal system based on evidence, a sophisticated analysis of human motion is required. Traditional optical motion capture systems with high validity and reliability have been utilized in clinical practice for a long time. However, expensive equipment and professional technicians are required to construct optical motion capture systems, hence they are used at a limited capacity in clinical settings despite their advantages. The development of information technology has overcome the existing limit and paved the way for constructing a motion capture system that can be operated at a low cost. Recently, with the development of computer vision-based technology and optical markerless tracking technology, webcam-based 3D human motion analysis has become possible, in which the intuitive interface increases the user-friendliness to non-specialists. In addition, unlike conventional optical motion capture, with this approach, it is possible to analyze motions of multiple people at simultaneously. In a non-optical motion capture system, an inertial measurement unit is typically used, which is not significantly different from a conventional optical motion capture system in terms of its validity and reliability. With the development of markerless technology and advent of non-optical motion capture systems, it is a great advantage that human motion analysis is no longer limited to laboratories.

• Journal of Information Processing Systems
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• 제19권3호
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• pp.385-393
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• 2023

Since the proportion of elderly citizens is increasing every year, the social interest is increasing for the health and the safety of the elderly. The nursing home is continually being created to care for more elderly people. However, the quality of service is not enough due to the lack of elderly caregivers. Elderly care and management services are being studied to replace the shortage of caregivers. Existing research for the implementation of an automatic care system has a high initial system cost. Furthermore, it lacks the ability to store and manage large amounts of data. In this paper, we propose a system that manages a large amount of data continuously generated through CCTV and provides a streaming service with a high level of quality-of-service (QoS) to users with collected video. Through the proposed system, it is possible to record and manage the behavioral information of the elderly occurring in the nursing home together with the video. In addition, according to the user's request, it has built a service that streams the video and behavioral information according to the date and time in real-time.

• Science of Emotion and Sensibility
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• 제22권4호
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• pp.3-14
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• 2019

The café has become an important representative "third place" where people study and rest. Hence, it is worthwhile for researchers to understand the needs of individual users as well as the requirements of people who visit such venues in groups. The identification of strategies that can help achieve larger, wider, higher, or deeper interior spaces in small and compact locations can generate benefits for both users and designers. In this study, where 56 interior design students participated, we used an eye-tracker and images of cafes to explore the relationships between spatial depth and the intention to visit a cafe space. The researchers digitally developed fifteen different conditions of space and measured the eye movements of the participants using an eye-tracker when they examined images that appeared to convey the most depth. Participants were also asked to imagine the proposed space images as cafes and to select one of the 15 images as the location that they would be most likely to visit individually and one that they would frequent in the company of other people. The research results revealed that certain ways of using interior design elements altered the participants' perceptions of spatial depth without any change being effected to the actual volume or the size of the space. The participants tended to perceive a space with a small decorative artwork on a dark toned wall with unconnected furniture as deeper than a space with no or large artwork on a light toned wall with contiguous furniture. Spatial depth was a more important consideration for an individual visit than for a group visit. The results of this exploratory study will help scholarly understanding of the role played by spatial depth in customer intentions to visit a cafe.

• Journal of Intelligence and Information Systems
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• 제24권3호
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of Broadcast Engineering
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• 제20권1호
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• pp.140-152
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• 2015

HCI(Human Computer Interaction) enables the interaction between people and computers by using a human-familiar interface called as Modality. Recently, to provide an optimal interface according to various devices and service environment, an advanced HCI method using multiple modalities is intensively studied. However, the multimodal interface has difficulties that modalities have different data formats and are hard to be cooperated efficiently. To solve this problem, a multimodal communicator is introduced, which is based on EMMA(Extensible Multimodal Annotation Markup language) and MMI(Multimodal Interaction Framework) of W3C(World Wide Web Consortium) standards. This standard based framework consisting of modality component, interaction manager, and presentation component makes multiple modalities interoperable and provides a wide expansion capability for other modalities. Experimental results show that the multimodal communicator is facilitated by using multiple modalities of eye tracking and gesture recognition for a map browsing scenario.

• Journal of IKEEE
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• 제24권2호
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• pp.638-648
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• 2020

Currently, the method of user-follwoing in intelligent cooperative robots usually based in vision system and using Lidar is common and have excellent performance. But in the closed space of Corona 19, which spread worldwide in 2020, robots for cooperation with medical staff were insignificant. This is because Medical staff are all wearing protective clothing to prevent virus infection, which is not easy to apply with existing research techniques. Therefore, in order to solve these problems in this paper, the ultrasonic sensor is separated from the transmitting and receiving parts, and based on this, this paper propose that estimating the user's position and can actively follow and cooperate with people. However, the ultrasonic sensors were partially applied by improving the Median filter in order to reduce the error caused by the short circuit in communication between hard reflection and the number of light reflections, and the operation technology was improved by applying the curvature trajectory for smooth operation in a small area. Median filter reduced the error of degree and distance by 70%, vehicle running stability was verified through the training course such as 'S' and '8' in the result.

• Journal of the Korean Institute of Intelligent Systems
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• 제20권6호
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• pp.884-889
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• 2010

Recently, a music genre classification has been studied. However, experts use different criteria to classify each of these classifications is difficult to derive accurate results. In addition, when the emergence of a new genre of music genre is a newly re-defined. Music as a genre rather than to separate search should be classified as emotional words. In this paper, the feelings of people on the basis of brightness and darkness tries to categorize music. The proposed classification system by applying VCM(Variance Considered Machines) is the contrast of the music. In this paper, we are using three kinds of musical characteristics. Based on surveys made throughout the learning, based on musical attributes(beat, timbre, note) was used to study in the VCM. VCM is classified by the trained compared with the results of the survey were analyzed. Note extraction using the MATLAB, sampled at regular intervals to share music via the FFT frequency analysis by the sector average is defined as representing the element extracted note by quantifying the height of the entire distribution was identified. Cumulative frequency distribution in the entire frequency rage, using the difference in Timbre and were quantified. VCM applied to these three characteristics with the experimental results by comparing the survey results to see the contrast of the music with a probability of 95.4% confirmed that the two separate.

• Journal of the Korean Institute of Intelligent Systems
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• 제24권6호
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• pp.640-645
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• 2014

Many people use social network services as to communicate, to share an information and to build social relationships between others on the Internet. Twitter is such a representative service, where millions of tweets are posted a day and a huge amount of data collection has been being accumulated. Social mining that extracts the meaningful information from the massive data has been intensively studied. Typically, Twitter easily can deliver and retweet the contents using the following-follower relationships. Topic modeling in tweet data is a good tool for issue tracking in social media. To overcome the restrictions of short contents in tweets, we introduce a notion of reply graph which is constructed as a graph structure of which nodes correspond to users and of which edges correspond to existence of reply and retweet messages between the users. The LDA topic model, which is a typical method of topic modeling, is ineffective for short textual data. This paper introduces a topic modeling method that uses reply graph to reduce the number of short documents and to improve the quality of mining results. The proposed model uses the LDA model as the topic modeling framework for tweet issue tracking. Some experimental results of the proposed method are presented for a collection of Twitter data of 7 days.