• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.135-140
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• 2017

The demand for aquatic products in the domestic and overseas is increased, so that the aquaculture industry can achieve high performance by controlling and standardizing the production even with a relatively small amount of resources compared with existing fisheries. However, traditional method has problems of low productivity such as natural disasters and ecosystem pollution, and it is necessary to develop a new culture system that can move to the optimal culture site. In order to find the optimal location, you need to collect and analyze the necessary data such as temperature and DO in real time. Data analysis was performed by using K-means clustering method based on machine learning, so that it was possible to decision when and where to move the farm by repeated unsupervised learning. The proposed research could solve the problems of low productivity such as natural disasters and ecosystem pollution if applied to regressive fish farmers.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.71-75
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• 2019

Sleep apnea is a disease that causes various complications, and the polysomnography is expensive and difficult to measure. The purpose of this study is to develop an unrestricted wearable monitoring system so that patients can be examined in a familiar environment. We used a method to detect sleep apnea events and to determine sleep satisfaction by non-constrained method using SpO2 measurement sensor and 3-axis acceleration sensor. Heart rate and SpO2 were measured at the finger using max30100. After acquiring the SpO2 data of the user in real time, the apnea measurement algorithm was used to transmit the number of apnea events of the user to the mobile phone using Bluetooth (HC-06) on the wrist. Using the three-axis acceleration sensor (mpu6050) attached to the upper body, the number of times of tossing and turning during sleep was measured. Based on this data, this algorithm evaluates the patient's tossing and turning during sleep and transmits the data to the mobile phone via Bluetooth. The power source used 9 volts battery to operate Arduino UNO and sensors for portability and stability, and the data received from each sensor can be used to check the various degree between sleep apnea and sleep tossing and turning on the mobile phone. Through thisstudy, we have developed a wearable sleep apnea measurement system that can be easily used at home for the problem of low sleep efficiency of sleep apnea patients.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.93-99
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• 2019

In this paper, we propose a smart meter that uses GRU model, which is one of artificial neural networks, for the efficient energy management. We collected power consumption data that train GRU model through the proposed smart meter. The implemented smart meter has automatic power measurement and real-time observation function and load control function through power consumption prediction. We determined a reference value to control the load by using Root Mean Squared Error (RMS), which is one of performance evaluation indexes, with 20% margin. We confirmed that the smart meter with automatic load control increases the efficiency of energy management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.63-71
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• 2019

Recently, there has been a lot of research on the construction of smart factory in the 4th Industrial Revolution era. Among the various technologies involved in the deployment of smart factory, one of the key technologies is the IoT protocol sector that handles the transmission and reception of data. In this regard, the MQTT protocol is generally used most commonly, but the existing MQTT technology lacks the concept of priorities of messages, so it is somewhat insufficient to be applied to an industrial field requiring real-time property. Priority handling of urgent messages is critical, especially in emergency situations, such as the emergency shutdown of the entire relevant facility following the failure of a particular facility. To improve this, research on priority-based MQTT is being conducted somewhat, but these studies have problems with actual field use because they are a variant of the MQTT standard. Therefore, this study conducts and verifies studies related to MQTT, which can prioritize messages while adhering to existing MQTT standards.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.19-24
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• 2019

The development of ICT technology has a huge impact on the existing closed CCTV security equipment market. With the importance of video data particularly highlighted in areas such as self-driving cars, unmanned vehicles and smart cities, various technologies using video are emerging. In this paper, we proposed a method to transmit videos and metadata as a part of smart city integration, and to solve the traffic, environment and security problems caused in urban life by utilizing the metadata instead of using CCTV videos for simple recording purposes, and reverse tunneling technique was designed and implemented as a method for accessing CCTV videos for vehicles from remote locations. Integrated management of CCTV videos and metadata for vehicles that have been used only for limited purposes in closed environments will enable efficient operation of integrated centers in real time required by smart cities, such as vehicle status check, road conditions and facility management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.135-140
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• 2021

In this paper, we propose an on-line mechanism that simultaneously detects cracks and profiling pavement blocks to detect the displacement of ground surface adjacent to the excavation in the urban area. The proposed method utilizes a 2D laser to profile the information about pavement blocks including the depth and distance among them. In particular, it is designed to enable the detection of cracks and portholes at runtime. For the experiment, real data was collected through Gocator, and trainng was carried out using Faster R-CNN. The performance evaluation shows that our detection precision and recall are more than 90% and the pavement blocks are profiled at the same time. Our proposed mechanism can be used for monitoring management to quantitatively detect the level of excavation risk before a large-scale ground collapse occurs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.105-109
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• 2021

In this paper, we evaluate Boosted 3-D PCA as a Dataset and evaluate its performance. After that, we will analyze the network features and performance. In this paper, the learning was performed using the 300W-LP data set using the same learning method as Boosted 3-D PCA, and the evaluation was evaluated using the AFLW2000 data set. The results show that the performance is similar to that of the Boosted 3-D PCA paper. This performance result can be learned using the data set of face images freely than the existing Landmark-to-Pose method, so that the poses can be accurately predicted in real-world situations. Since the optimization of the set of key points is not independent, we confirmed the manual that can reduce the computation time. This analysis is expected to be a very important resource for improving the performance of network boosted 3-D PCA or applying it to various application domains.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.155-161
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• 2021

Gesture recognition analytics through a camera in real time have been widely studied in recent years. Since a small number of features from human joints are extracted, low accuracy of classifying models is get in conventional gesture recognition studies. In this paper, CBAM (Convolutional Block Attention Module) with high accuracy for classifying images is proposed as a classification model and algorithm calculating the angle of joints depending on actions is presented to solve the issues. Employing five exercise gestures images from the fitness posture images provided by AI Hub, the images are applied to the classification model. Important 8-joint angles information for classifying the exercise gestures is extracted from the images by using MediaPipe, a graph-based framework provided by Google. Setting the features as input of the classification model, the classification model is learned. From the simulation results, it is confirmed that the exercise gestures are classified with high accuracy in the proposed model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.161-167
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• 2019

Recently, demand forecasting techniques have been actively studied due to interest in stable power supply with surging power demand, and increase in spread of smart meters that enable real-time power measurement. In this study, we proceeded the deep learning prediction model experiments which learns actual measured power usage data of home and outputs the forecasting result. And we proceeded pre-processing with moving average method. The predicted value made by the model is evaluated with the actual measured data. Through this forecasting, it is possible to lower the power supply reserve ratio and reduce the waste of the unused power. In this paper, we conducted experiments on three types of networks: Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) and we evaluate the results of each scheme. Evaluation is conducted with following method: MSE(Mean Squared Error) method and MAE(Mean Absolute Error).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.197-202
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• 2019

A small millimeter-wave tracking radar power supply must provide stable power with minimal ripple noise and the switching frequency noise of the DC-DC converter must have a real-time self-test capability through on-the-fly monitoring without causing false alarms and ghost In this study, we developed a multi-output switching power supply with output power of more than 80% (@ 100% load) and 10 output power by adopting + 28VDC input for application to small millimeter wave tracking radar, DC-DC converter is applied for large power output and multi-output flyback method is applied for the remaining small power output. The test results show that 85% efficiency efficiency is achieved under 100% load condition.