• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5564-5569
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• 2011

In this paper we introduced Augmented Reality (AR) on Android platform for ubiquitous learning (u-learning). Android is breaking new ground for mobile computing and open technologies. Android is versatile as it is not limited only to mobile phones, but it can be installed on various devices. Android gives developers the opportunity to leverage their development skills, while also building an exciting and active community. Augmented Reality (AR) is going to be the future of most apps; all it takes is a decent processor, a camera, a compass and a GPS, all of which are becoming increasingly common on smart phones. Through AR we can have educational tools that provide individuals with total flexibility to receive, send, and review training and detailed product information through an increasingly ubiquitous web-enabled communication device. In this paper, we proposed Augmented Reality for Species Identification using Android Smartphone with augmented reality in species determination. This study is appropriate in the field of Biology. This is useful in outdoor experimental activities of the students. Like for example while they are visiting the zoo, botanical garden and etc.

• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.291-302
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• 2013

Signage can be defined as the media device that provides specific information to many unspecified users in public places. Recently applied context-aware technology, signage provides personal on-demand information services in a way that can continue to evolve. The purpose of this study is to analyze characteristics of types which users identify signage on the perceptions and attitudes about consideration of the perspective of the experts in the fields. The research is carried out by applying Q methodology with in-depth interview. First, interviews are conducted to determine the perceptions and attitudes of experts and practitioners on signage. Thereafter users' perceptions and attitudes toward signage are classified by each types using Q methodology. The first type is named as 'signage as smart media', the second type is named as 'signage as passive media', and the third type is named as 'signage as interactive media' is named. The results of this study will be useful guidelines for conducting further academic researches and R&D.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.67-74
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• 2013

In this paper, we propose the pointing and correction algorithm for optimized performance based on Bluetooth communication. The error from the accelerometer sensor's output must be carefully managed as the accelerometer sensor is more sensitive to data change compared to that of the gyroscope sensor. Thus, we minimize the noise by applying the Kalman filter to data for each axis from the accelerometer. In addition, we can also obtain effect compensating the hand tremor by applying the Kalman filter to the data variation for x and y. In this study, we extract data through the Quaternion mapping process on data from the accelerometer and gyroscope. In turn, we can obtain a tilt compensation by applying a compensation algorithm with acceleration of the gravity of the extracted data. Moreover, in order to correct the inaccuracy on smart sensor due to the rapid movement of a device, we propose a adaptive pointing and correction algorithm using the genetic approach to generate the initial population depending on the user.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.33-41
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• 2013

Intelligent automobile technology and IT convergence, the development of new imaging technology media applications based on open source Android installed on tracked, wheeled smart phone application technology and the development of intelligent vehicles as a new paradigm a lot of research and development being made. Android-based intelligent automotive applications, technology, and evolved into the center of a set of various multimedia technologies move beyond the limits of the means of each of multimedia platforms, services and applications that have been developed in such a distributed environment, has been developed according to a variety of services through technology mobile terminal device technology is an absolute requirement. In this paper, SVC Codec, real-time video and graphics processing and SoC design intelligent vehicles middleware applications with monolithic system specification through Android-based design of intelligent vehicles dedicated middleware research experiments on open platforms, and provides various terminal services functions SoC based on a newly designed and standardized interface analysis techniques in this study were verified through experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1144-1151
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• 2021

The pressing process is a compression process in which a product is made by applying force to a heated or unheated material to transform it into the desired shape. Due to the characteristics of press equipment that produces products through continuous compression for a short time, product defects occur continuously, and systems for solving these problems are being developed using various technologies. This paper proposes a real-time defect detection system based on an artificial intelligence algorithm that detects defects. By attaching various sensors to the press device, the relationship between equipment status and defects is defined and collected based on a big data platform. By developing an artificial intelligence algorithm based on the collected data and implementing the developed algorithm using an embedded board, we will show the practicality of the system by applying it to the actual field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

• Smart Structures and Systems
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• v.33 no.1
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of the Microelectronics and Packaging Society
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• v.21 no.4
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• pp.105-110
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• 2014

It is required to improve the efficiency and the reliability of the polymer solar cells (PSCs) as the energy saving optical device for the future application of the smart farm facilities. In this study, we fabricated the bulk hetero junction PSCs with organic passivation film layer for the reliability improvement of the devices. The effects of the passivation layer on the electrical properties of the PSCs were studied. The materials of passivation layer are composed of poly vinyl alcohol (PVA) and ammonium dichromate, and the passivation films were fabricated by the spin coating method on the P3HT:$PC_{61}BM$/LiF/Al substrate. The prepared structure of the device is the glass/ITO/PEDOT:PSS/P3HT:$PC_{61}BM$/LiF/Al/passivation layer. The performances of the PSCs with the organic passivation film showed better electrical properties compared with the PSCs without passivation layers. The power conversion efficiency (PCE) values of passivated PSCs decreased from 3.0 to 1.3% after air exposure for 140 hrs. In contrast, the PCE values for the devices without passivation decreased sharply from 3.5 to 0.1% under the same exposure condition.