• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.181-199
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• 2021

Recently, financial companies are promoting chatbot services in line with the reduction of branches and the expansion of non-face-to-face services. However, it is difficult to expand the chatbot services at once in the presence of technical limitations and constraints of internal and external environment. Therefore, it is necessary to analyze the various situations of chatbot service to preemptively identify problems that can occur in stages and seek solutions. This study conducted interviews with 12 field practitioners and researchers to examine the intentions and behaviors of financial chatbot service users and interpreted them using TPB. The study revealed the characteristics of 'feelings and attitudes' such as convenience or inconvenience from the chatbot experience, 'subjective norms' such as herd behavior or the yearning for empathy of others, and 'behavioral control' according to the recognition of difficulty or convenience of chatbot use process. This study shows that this characteristic can affect the intention and actual behavior of users to use chatbot service continuously. In the future research, it is necessary to empirically study specific intentions and influence factors for actual users.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.121-127
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• 2022

Smart mirror is an IoT device that attaches a display and an embedded computer to the mirror and provides various information to the useer along with the mirror function. This paper went beyond the form of dealing with smart mirrors only stand alone device the provide information to users, and constructed a network in which smart mirrors are connected, and proposed and implemented a CoMirror system that allows users to talk and share information with other smart mirror users. The CoMirror system has a structure in which several CoMirror clients are connected on one CoMirror server. The CoMirror client consists of Raspberry Pi, a mirror film, a touch pad, a display device, an web camera, etc. The server has functions such as face learning and recognition, user management, a relay role for exchanging messages between clients, and setting up for video call. Users can communicate with other CoMirror users via the server, such as text, image, and audio messages, as well as 1:1 video call.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.3
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• pp.5-30
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• 2022

In this study, a survey of public library librarians was conducted to examine the current librarian's job status by conducting a survey on the library's environment, such as the size of books, number of employees, and operating subjects. As a result of the study, it was found that based on the current job content and recognition, librarians are mainly working in the order of operating reading culture programs, planning reading culture programs, and user information services. In addition, it can be seen that librarians highly value the importance of their respective duties, and in the case of labor intensity, more than half of them recognize that the current labor intensity is high. Among the detailed competency units corresponding to each task in the future, more appropriate competency unit elements should be considered in consideration of the essential competency, additional supplementation, and lower factors through this study. In addition, the higher the number and experience of regular workers in the library, the higher the degree of librarian certification, and the higher the current position, the higher the importance, difficulty, and expertise of the current work, so it is expected that the library should continue to build experience in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.41-43
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• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.581-586
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• 2023

Recently a drama that features a lawyer with autism spectrum disorder has attracted a lot of attention, raising interest in the difficulties faced by people with autism spectrum disorders. If the Autism spectrum gets detected early and proper education and treatment, the prognosis can be improved, so the development of the treatment is urgently needed. Drugs currently used to treat autism spectrum often have side effects, so Digital Therapeutics that have no side effects and can be supplied in large quantities are drawing attention. In this paper, we introduce 'AEmotion', an application and a Digital Therapeutic that provides emotion and facial expression learning for toddlers with an autism spectrum disorder. This system is developed as an application for smartphones to increase interest in training autistic children and to test easily. Using machine learning, this system consists of three main stages: an 'emotion learning' step to learn emotions with facial expression cards, an 'emotion identification' step to check if the user understood emotions and facial expressions properly, and an 'expression training' step to make appropriate facial expressions. Through this system, it is expected that it will help autistic toddlers who have difficulties with social interactions by having problems recognizing facial expressions and emotions.

• Information Systems Review
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• v.25 no.3
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• pp.99-120
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• 2023

Courier services users' experience of violating privacy affects psychology and behavior of protecting personal privacy. Depending on what privacy infringement experience (PIE) of courier services users, learning about perceived privacy infringement incidents is made, recognition is formed, affection is formed, and behavior is appeared. This paradigm of changing in privacy psychologies of courier services users has an important impact on predicting responses of privacy protective action (PPA). In this study, a theoretical research framework are developed to explain the privacy protective action (PPA) of courier services users by applying attitude theory. Based on this framework, the relationships among past privacy infringement experience (PIE), perceived privacy risk (PPR), privacy concerns (i.e., concerns in unlicensed secondary use (CIUSU), concerns in information error (CIE), concerns in improper access (CIA), and concern in information collection (CIC), and privacy protective action (PPA) are analyzed. In this study, the proposed research model was surveyed by people with experience in using courier services and was analyzed for finding relationships among research variables using structured an equation modeling software, SMART-PLS. The empirical results show the causal relationships among PIE, PPR, privacy concerns (CIUSU, CIE, CIA, and CIC), and PPA. The results of this study provide useful theoretical implications for privacy management research in courier services, and practical implications for the development of courier services business model.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.400-405
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• 2024

When cavitation noise occurs in ship propellers, the level of underwater radiated noise abruptly increases, which can be a critical threat factor as it increases the probability of detection, particularly in the case of naval vessels. Therefore, accurately and promptly assessing cavitation signals is crucial for improving the survivability of submarines. Traditionally, techniques for determining cavitation occurrence have mainly relied on assessing acoustic/vibration levels measured by sensors above a certain threshold, or using the Detection of Envelop Modulation On Noise (DEMON) method. However, technologies related to this rely on a physical understanding of cavitation phenomena and subjective criteria based on user experience, involving multiple procedures, thus necessitating the development of techniques for early automatic recognition of cavitation signals. In this paper, we propose an algorithm that automatically detects cavitation occurrence based on simple statistical features reflecting cavitation characteristics extracted from acoustic signals measured by sensors attached to the hull. The performance of the proposed technique is evaluated depending on the number of sensors and model test conditions. It was confirmed that by sufficiently training the characteristics of cavitation reflected in signals measured by a single sensor, the occurrence of cavitation signals can be determined.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Korean Journal of Construction Engineering and Management
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• v.19 no.2
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• pp.15-24
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• 2018

Despite the recognition of the need for productivity information and its importance, the feedback of productivity information is not well-established in the construction industry. Effective use of productivity information is required to improve the reliability of construction planning. However, in many cases, on-site productivity information is hardly management effectively, but rather it relies on the experience and/or intuition of project participants. Based on the literature review and expert interviews, the authors recognized that one of the possible solutions is to develop a systematic approach in dealing with productivity information of the construction job-sites. It is required that the new system should not be burdensome to users, purpose-oriented information management, easy-to follow information structure, real-time information feedback, and productivity-related factor recognition. Based on the preliminary investigations, this study proposed a framework for a novel system that facilitate the effective management of construction productivity information. This system has utilized Sketchup software which has good user accessibility by minimizing additional data input and related workload. The proposed system has been designed to input, process, and output the pertinent information through a four-stage process: preparation, input, processing, and output. The inputted construction information is classified into Task Breakdown Structure (TBS) and Material Breakdown Structure (MBS), which are constructed by referring to the contents of the standard specification of building construction, and converted into productivity information. In addition, the converted information is also graphically visualized on the screen, allowing the users to use the productivity information from the job-site. The productivity information management system proposed in this study has been pilot-tested in terms of practical applicability and information availability in the real construction project. Very positive results have been obtained from the usability and the applicability of the system and benefits are expected from the validity test of the system. If the proposed system is used in the planning stage in the construction, the productivity information and the continuous information is accumulated, the expected effectiveness of this study would be conceivably further enhanced.