• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.347-350
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• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.95-104
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• 2007

Mobile multimedia services such as TV-call or video streaming are gradually becoming popular in the 3rd or more generation mobile network (IMT-2000). Multimedia traffic is expected to continue increasing into the coming years, and IP technology is considered to be the efficient way of transporting such huge volumes of multimedia traffic. IP-based IMT network platform represents an evolution from IMT-2000. The structure of IP-based IMT network as ubiquitous platform is three-layered model : Middleware including Network Control PlatForm (NCPF) and Service Support PlatForm (SSPF), IP-BackBone (IP-BB), access network including sensor network. Mobility Management (MM) architecture in NCPF is proposed for IP-based IMT network in order to manage routing information and location information separately. The generous existing method of multicast control in IP-based IMT network is Remote Subscription. But Remote Subscription has problem that should be reconstructed whole multicast tree when sender in multicast tree moves to another area. To solve this problem, we propose the way to put Multicast-manager in NCPF.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1266-1271
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• 2022

Cats have strong wildness so they have a characteristic of hiding diseases well. The disease may have already worsened when the guardian finds out that the cat has a disease. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia, polyuria, and frequent urination more quickly. In this paper, 1) Efficient version of DeepLabCut for pose estimation, 2) YOLO v4 for object detection, 3) LSTM is used for behavior prediction, and 4) BoT-SORT is used for object tracking running on an artificial intelligence device. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the server system.

• Design Convergence Study
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• v.16 no.6
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• pp.123-135
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• 2017

In recent mobile health care service, health management using number of steps is becoming popular. In addition, a variety of activity trackers have made it possible to measure the number of steps more accurately and easily. Nevertheless, the activity tracker is not popularized, and it is a trend to use the pedometer sensor of the smartphone as an alternative. In this study, we tried to find out how much the number of steps collected by the smartphone versus the actual number of steps in actual situations, and what factors make the difference. We conducted an experiment to collect number of steps data of 21 people using the smartphone and wearable device simultaneously for 7 days. As a result, we found that the average number of steps of the smartphone is 62% compared to the actual number of steps, and that there is a large variation among users. We derived a regression model in which the accuracy of smartphone increases with the degree of awareness of smartphone. We expect that this can be used as a factor to correct the difference from the actual number of steps in the smartphone alone healthcare service.

• Journal of Industrial Convergence
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• v.22 no.7
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• pp.15-21
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• 2024

A hiking stick is generally one of the walking aids that allow hikers to walk while relying on their own bodies when walking. A rechargeable battery must be built into the hiking stick, which is an auxiliary device, in order to perform various functions. A separate power supply is required to charge the rechargeable battery. This study is about a self-generated power supply and develops a power generation device using a screw with higher power generation efficiency than the existing method. It is differentiated from the method suggested in this study by comparing and analyzing it with the existing power generation method, and identifying problems therewith. The screw-type power generation device generates power when the climbing stick comes into contact with the ground and when it is separated from the ground. The built-in power generation device does not require a separate power supply, and it can be used by attaching the role of a mobile phone auxiliary battery and a lighting lamp, and it has the effect of being able to find it through location tracking by embedding a GPS sensor, etc., and using lighting to keep the user safe in emergency situations such as distress. The existing generator with built-in mountain climbing stick is difficult to charge due to very weak current and low practicality, but the generator developed in this research could achieve high efficiency to obtain a sufficient current, so it is possible to charge a battery and practicality.