• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.67-76
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• 2018

BLE (Bluetooth Low Energy) has been extensively used for communication between mobile applications and wearable devices in IoT (Internet of Things). In developing Android applications, wearable devices, on which the applications can run, should be available because the existing Android SDK does not support any BLE emulation facility. In this study, we have designed and implemented the first Android BLE emulator. Using this, we are able to develop and test BLE-based Android applications even when without wearable devices. We have also proposed an automatic generation method of Android BLE scenarios based on graph model. We have shown that the method is useful for systematically testing BLE application protocols by running the generated scenarios on the Android BLE emulator.

• Information and Communications Magazine
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• v.32 no.11
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• pp.41-49
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• 2015

현대 사회는 스마트폰 사용량의 급증으로 인한 모바일 트래픽의 폭발적인 증가와 IoT(Internet of Things)를 비롯한 융복합 유무선 서비스의 확대 등으로 국가적으로 한정적 자원인 주파수의 효율적 사용을 인한 대책 마련이 시급한 실정이다. 이에 미국 및 유럽을 중심으로 다양한 용도의 무선기기들이 한정적 주파수 대역을 효율적으로 이용하기 위한 방안으로 주파수 공동사용에 대한 정책도입 및 연구개발이 지속적으로 추진되고 있다. 본 고에서는 수요 대비 공급 부족 현상이 점차 심각해 지고 있는 한정적 주파수 자원의 효율적 이용을 위한 대안으로 급부상하고 있는 주파수 공동사용 기술에 대한 국내외 관련 정책 및 기술추진 현황에 대해 소개하고자 한다. 특히 본 고에서는 2010년 국가광대역계획(NBP, National Broadband Plan) 발표를 시작으로 전 세계적으로 주파수 공동사용 추진에 있어 가장 적극적인 행보를 보여주고 있는 미국에서의 3.5GHz 대역 주파수 공동사용을 위한 전파규칙인 CFR(Code of Federal Regulation) Part 96의 주요 내용을 자세히 소개하고자 한다. 본 고의 II장에서는 국내외에서 진행되고 있는 대표적인 주파수 공동사용 정책추진 현황에 대해 소개하였으며, III장에서는 올해 4월 최종 전파규칙이 발표된 미국의 SAS(Spectrum Access System) 기반 주파수 공동사용 전파규칙에 대해 소개하였다. 또한 IV장에서는 SAS 기반의 주파수 공동사용 기법 및 기존에 개발되고 있는 다양한 동적 스펙트럼 접속 기법들을 미국 내 주요 도심지역에 적용하기 위한 대규모 테스트베드 프로그램인'Model City'프로그램 소개 및 결론을 통하여 주파수 공동사용 도입의 당위성을 설명하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2377-2398
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• 2021

In the recent modernized world, utilization of natural resources (renewable & non-renewable) is increasing drastically due to the sophisticated life style of the people. The over-consumption of non-renewable resources causes pollution which leads to global warming. Consequently, government agencies have been taking several initiatives to control the over-consumption of non-renewable natural resources and encourage the production of renewable energy resources. In this regard, we introduce an IoT powered integrated framework called as green home architecture (GHA) for green score calculation based on the usage of natural resources for household purpose. Green score is a credit point (i.e.,10 pts) of a family which can be calculated once in a month based on the utilization of energy, production of renewable energy and pollution caused. The green score can be improved by reducing the consumption of energy, generation of renewable energy and preventing the pollution. The main objective of GHA is to monitor the day-to-day usage of resources and calculate the green score using the proposed green score algorithm. This algorithm gives positive credits for economic consumption of resources and production of renewable energy and also it gives negative credits for pollution caused. Here, we recommend a green score based tax calculation system which gives tax exemption based on the green score value. This direct beneficiary model will appreciate and encourage the citizens to consume fewer natural resources and prevent pollution. Rather than simply giving subsidy, this proposed system allows monitoring the subsidy scheme periodically and encourages the proper working system with tax exemption rewards. Also, our GHA will be used to monitor all the household appliances, vehicles, wind mills, electricity meter, water re-treatment plant, pollution level to read the consumption/production in appropriate units by using the suitable sensors. These values will be stored in mass storage platform like cloud for the calculation of green score and also employed for billing purpose by the government agencies. This integrated platform can replace the manual billing and directly benefits the government.

• 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.

• Database Research
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• v.34 no.3
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• pp.86-98
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• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.155-162
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• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.121-134
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• 2019

The Fourth Industrial Revolution has differentiated technologies such as artificial intelligence, IoT(Internet of things), big data, and mobile. As the civilization develops more and more, humanity enjoy the cultural activities more than economic activity for the food and shelter. The platform structure based on the advanced information technology of the present will expand the cultural contents area in a variety of ways. Cultural contents respond sensitively to changes in consumer and will be useful experiences of human activities. Therefore, it should be noted again that the contents industry should not be limited to the discussion of the application of the fourth technology, but should be produced with emphasis on useful experiences of human being. In other words, the discussion of human activities around cultural contents should be focused on how to apply beyond the use of fourth industrial technology. Therefore, it is necessary to analyze the basis of the successful storytelling of the planning stage to connect the fourth industrial technology and human useful experience as a method for developing cultural contents, and to build and propose a model as a strategic method. This study analyzes domestic and foreign cases made by using big data among the visual contents which show continuous increase of consumption among culture industry field, and draws success factors and limit points. Next, we extract what is the successful matching factor that influenced consumer 's consciousness, and find out that the structure of culture prototype has been applied in the long history of mankind, and presents it as a storytelling model. Through the above research, this study aims to present a new interpretation and creative activity of cultural contents by presenting a storytelling model as a methodology for connecting creative knowledge, away from the general interpretation of social phenomenon applied with big data.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.3
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• pp.291-300
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• 2016

The emergence of smart devices and IoT leads to the appearance of O2O service to blur the difference between online and offline. As online services' merits were added to the offline market, it caused a change in the dynamics of the offline industry, which means the offline-space's digitization. Unlike these changing aspects of the offline market, exhibition industry grows steadily in the industry, however it is also possible to create a new value added by combining O2O service. We conducted a survey targeting 20 spectators in '2015 Seoul Design Festival' at COEX. The survey was used to analysis of the spatial structure and generate the dataset for machine learning. We identified problems with the analysis study of the existing spatial structure, and based on this investigation we propose a new method for analyzing a spatial structure. Also by processing a machine learning technique based on the generated dataset, we propose a novel evaluation model of exhibition-space cells for O2O service layout.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.289-296
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• 2019

In the era of M-ICT (mobile information communication technology), it is becoming more and more important to establish a city brand image that is "user-centered" and creates a new experience of "interaction between people, people and things, and people and space". The experience of city users on the brand image of a city is the key factor to determine the competitiveness of the city, among which the user's research on the touch point of the city brand is particularly important. The purpose of this study is to enhance the user's brand experience and enhance the city's brand competitiveness. The research methods of this paper are literature review and investigation. Firstly, the background and purpose of the study are expounded, then the characteristics and ways of user experience touch points are defined through the document review, and the multi-latitude composition model of city brand touch points is proposed. By means of user investigation, the structural characteristics of high frequency touch points between digital touch points and physical touch points are obtained. According to the problems found in the investigation, the optimal design strategy of touch point is put forward in combination with the case. The innovation of this study is to study the relationship between the city brand and the touch point of user experience from the perspective of user experience, and propose a multi-latitude model of the touch point of city brand.