• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.348-353
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• 2008

This paper proposes a novel touch interface for recognizing user's touch pattern and understanding emotional information by eliciting natural user interaction. To classify physical touches, we represent the similarity between touches by analyzing touches based on its dictionary meaning and design the algorithm to recognize various touch patterns in real time. Finally we suggest the methodology to estimate user's emotional state based on touch.

• Journal of Power System Engineering
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• v.20 no.4
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• pp.26-31
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• 2016

The implementation of chirplet transform to locate axially aligned defects in pipes has been investigated. The results are obtained from experiments performed on a carbon steel pipe using magnetostrictive sensors. Chirplet transform is applied to the reflected signal to separate the individual modes from dispersive and multimodal waveform. The separated modes are used to calculate reflection coefficients which would be used to characterize defects. It is found that the reflection from a defect consists of the wave pulses with gradually decaying amplitudes. Also the results show that the reflection coefficient initially increases with the crack length but finally reaches an oscillating regime.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.6
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• pp.399-405
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• 2015

In this work we use the mode decomposition technique employing chirplet transform, which is able to separate the individual modes from dispersive and multimodal waveform measured with the magnetostrictive sensor. The mode decomposition technique is also used to estimate the time-frequency centers and individual energies of the reflection, which would be used to locate and characterize axial defects. The arrival times of the separated modes are calculated and the axial defect lengths can be evaluated by using the estimated arrival time. Results from an experiment on a carbon steel pipe are presented and it is shown that the accurate and quantitative defect characterization could become enabled using the proposed technique.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.1049-1050
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• 2015

e-health보드를 이용하여 측정한 생체신호를 실시간으로 블루투스통신을 통한 무선통신을 함으로서 PC와 연결한다. PC에서 송신된 데이터를 텍스트로 저장한 뒤 c#으로 체온, 심전도, 근전도, 피층 전기 반응, 호흡 5가지의 결과 값을 그래프로 보여준다.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.141-146
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• 2008

As the interest about ubiquitous environment is increasing, there are many researches about the services in this environment. These services have important issues in interpreting the users' context, using many kinds of sensors, like PDA, GPS and accelerometers. Low level raw data, which sensors like accelerometers calibrates, are hard to use, and to provide real-time services preprocessing and interpreting the data into context, in real-time, is important. This paper describes a context integrate system which can integrate these sensors and also sensors which has raw data, like accelerometers and physiological sensors, and define the context interpret rule with XML. The proposing system reduces programming operations when adding a sensor to the sensor network or modifying the context interpreting rule by using XML. By using this system, we implemented a real-time data monitoring system which can describe the numeric data into graphs, and assist the user to validate the data and results of the preprocess phase, and also support the external services and applications to use the context of the user.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.510-512
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• 2019

Recently, there have been growing requirements in the public safety sector to ensure safety through detection of hazardous situations or preemptive predictions. It is noteworthy that various sensor data can be analyzed and utilized as a result of mobile device's dissemination, and many advantages can be used in terms of safety and security. An effective modeling technique is needed to combine sensor data generated by smart-phones and wearable devices to analyze users' moving patterns and behavioral patterns, and to ensure public safety by fusing location-based crime risk data provided.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.248-258
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• 2021

24-hour monitoring is required to maintain the city's lifeline function in the underground facility for public utilities. And it is necessary to develop technology to exchange the shortage of human resources. It is difficult to reflect the specificity of underground space management in general management methods. This study proposes underground facility for public utilities digital twin system requirements. The concept of space is divided into physical space and virtual space, and the physical space constitutes the type and layout of the sensor that is the basis for the construction of the multimodal image sensor system, and the virtual space constitutes the system architecture. It also suggested system functions according to the task. It will be effective in preventing disasters and maintaining the lifeline function of the city through the digital twins.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.483-503
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• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.567-575
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• 2017

Location-based services play an important role in many applications such as disaster warning systems and recommendation systems. These applications often require not only location information (e.g., name, latitude, longitude, etc.) but also the impact of events (e.g., earthquake, typhoon, etc.) on locations. Recently, to provide the impact of an event on a location, how to calculate location trustiness by using multimodal information such as earthquake information and disaster sensor data is researched. In the previous approach, the linear decrement of impact value of an event is applied to obtain the location trustiness of a specific location. In this paper, we propose a new approach to enrich location trustiness, that is, the impact of an event on a location, by using social media information additionally. Firstly, we design a collecting system for earthquake information and social media data. Secondly, we present an approach of location trustiness calculation based on earthquake information. Finally, we propose a new approach to enrich location trustiness by augmenting the trustiness in spatially distributed manner based on social media.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.