• Fashion & Textile Research Journal
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• v.23 no.5
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• pp.655-666
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• 2021

This study aims to classify wearable devices for infants and children according to their function, and to analyze the types and attachment methods of the devices by function, operating system, characteristics of materials, and types of batteries, and to identify the points for improvement. Forty-eight types of devices investigated through previous studies and keyword research online were analyzed. Wearable devices for infants and children were classified according to their functions into wearable monitors, wearable thermometers, GPS trackers, and smart watches. Devices had different shapes and attachment methods according to their functions, and were mainly clothes or accessory types. The accessory type devices were attached to the body using velcro, clips, bands, or adhesives. Wearable monitors and thermometers mainly used Bluetooth to transmit data wirelessly, and location trackers used various combinations of 4G(LTE), 5G networks, GPS, Wi-Fi, and Bluetooth. Smartwatches had different functions depending on whether smart phones were linked to them or not. Wearable monitors and thermometers mainly used by infants provided material information, but other devices did not. These devices used rechargeable, replaceable, non-rechargeable or non-replaceable batteries. Wearable devices need to be improved to reduce the discomfort experienced by infants and children due to the attachment position, malfunction, skin trouble caused by materials, short time of use of batteries, version conflict and complexity with the device when linking with a smart phone, and non-operation when using Bluetooth.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.109-115
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• 2019

Currently, LPWA(Low Power Wide Area) communication technology is widely used due to the development of IoT(Internet of Things) technology. Among the LPWA technologies, LoRaWAN(Long Range Wide Area Network) is widely used in many fields due to its wide coverage, stable communication speed, and low-cost modem module prices. In particular, LoRa(Long Range) can easily construct LoRaWAN with a dedicated gateway. So many organizations are building their own LoRaWAN-based networks. The LoRaWAN Gateway receives the LoRa packet transmitted from an End-device installed in the adjacent location, converts it into the Internet protocol, and sends the packet to the final destination server. Current LoRa Gateway uses a single-hop method, and each gateway must include a communication network capable of the Internet. If it is the mobile communication(i.e., WCDMA, LTE, etc.) network, it is required to pay the internet usage fee which is installed in each gateway. If the LoRa communication is frequent, the user has to spend a lot of money. We propose an idea on how to design a multi-hop protocol which enables packet routing between gateways by analyzing the LoRaWAN communication method implemented in its existing single-hop way in this paper. For this purpose, this paper provides an analysis of the standard specification of LoRaWAN and explains what was considered when such protocol was designed. In this paper, two gateways have been placed based on the functional role so as to make the multi-hop protocol realized: (i) hopping gateway which receives packets from the end-device and forwards them to another gateway; and (ii) main gateway which finally transmits packets forwarded from the hopping gateway to the server via internet. Moreover, taking into account that LoRaWAN is wireless mobile communication, a level-based routing method is also included. If the protocol proposed by this paper is applied to the LoRaWAN network, the monthly internet fee incurred for the gateway will be reduced and the reliability of data transmission will be increased.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.87-109
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• 2020

Currently, thanks to the major stride made in developing wired and wireless communication technology, a variety of IT services are available on land. This trend is leading to an increasing demand for IT services to vessels on the water as well. And it is expected that the request for various IT services such as two-way digital data transmission, Web, APP, etc. is on the rise to the extent that they are available on land. However, while a high-speed information communication network is easily accessible on land because it is based upon a fixed infrastructure like an AP and a base station, it is not the case on the water. As a result, a radio communication network-based voice communication service is usually used at sea. To solve this problem, an additional frequency for digital data exchange was allocated, and a ship ad-hoc network (SANET) was proposed that can be utilized by using this frequency. Instead of satellite communication that costs a lot in installation and usage, SANET was developed to provide various IT services to ships based on IP in the sea. Connectivity between land base stations and ships is important in the SANET. To have this connection, a ship must be a member of the network with its IP address assigned. This paper proposes a SANET-CC protocol that allows ships to be assigned their own IP address. SANET-CC propagates several non-overlapping IP addresses through the entire network from land base stations to ships in the form of the tree. Ships allocate their own IP addresses through the exchange of simple requests and response messages with land base stations or M-ships that can allocate IP addresses. Therefore, SANET-CC can eliminate the IP collision prevention (Duplicate Address Detection) process and the process of network separation or integration caused by the movement of the ship. Various simulations were performed to verify the applicability of this protocol to SANET. The outcome of such simulations shows us the following. First, using SANET-CC, about 91% of the ships in the network were able to receive IP addresses under any circumstances. It is 6% higher than the existing studies. And it suggests that if variables are adjusted to each port's environment, it may show further improved results. Second, this work shows us that it takes all vessels an average of 10 seconds to receive IP addresses regardless of conditions. It represents a 50% decrease in time compared to the average of 20 seconds in the previous study. Also Besides, taking it into account that when existing studies were on 50 to 200 vessels, this study on 100 to 400 vessels, the efficiency can be much higher. Third, existing studies have not been able to derive optimal values according to variables. This is because it does not have a consistent pattern depending on the variable. This means that optimal variables values cannot be set for each port under diverse environments. This paper, however, shows us that the result values from the variables exhibit a consistent pattern. This is significant in that it can be applied to each port by adjusting the variable values. It was also confirmed that regardless of the number of ships, the IP allocation ratio was the most efficient at about 96 percent if the waiting time after the IP request was 75ms, and that the tree structure could maintain a stable network configuration when the number of IPs was over 30000. Fourth, this study can be used to design a network for supporting intelligent maritime control systems and services offshore, instead of satellite communication. And if LTE-M is set up, it is possible to use it for various intelligent services.