• Journal of Fashion Business
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• v.27 no.4
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• pp.125-140
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• 2023

This study aimed to develop a smart wearable device for assessing the risk angle associated with turtle neck syndrome in patients with Video Display Terminal (VDT) syndrome. Turtle neck syndrome, characterized by forward head posture resulting from upper cross syndrome, leads to thoracic kyphosis. In this research, a stretch sensor was used to monitor the progression of turtle neck syndrome, and the sensor data was analyzed using a Universal Testing Machine (UTM) and the Gauge Factor (GF) calculation method. The scapula and cervical spine angles were measured at five stages, with 15-degree increments from 0° to 60°. During the experimental process, the stretch sensor was attached to the thoracic spine in three different lengths: 30mm, 50mm, and 100mm. Among these, the attachment method yielding the most reliable data was determined by measuring with three techniques (General Trim Adhesive, PU film, and Heat Transfer Machine), and clothing using the heat transfer machine was selected. The experimental results confirmed that the most significant change in thoracic kyphosis occurred at approximately 30° of forward head posture. Prolonged deformity can lead to various issues, highlighting the need for textile sensor solutions. The developed wearable device aims to provide users with real-time feedback on their turtle neck posture and incorporate features that can help prevent or improve the condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2214-2229
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• 2024

Wireless sensor network (WSN) consists of large number of sensor nodes that are deployed in geographical locations to collect sensed information, process data and communicate it to the control station for further processing. Due the unfriendly environment where the sensors are deployed, there exist many possibilities of malicious nodes which performs malicious activities in the network. Therefore, the security threats affect performance and life time of sensor networks, whereas various security aspects are there to address security issues in WSN namely Cryptography, Trust Management, Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS). However, IDS detect the malicious activities and produce an alarm. These malicious activities exploit vulnerabilities in the network layer and affect all layers in the network. Existing feature selection methods such as filter-based methods are not considering the redundancy of the selected features and wrapper method has high risk of overfitting the classification of intrusion. Due to overfitting, the classification algorithm fails to detect the intrusion in better manner. The main objective of this paper is to provide the efficient feature selection algorithm which was suitable for any type classification algorithm to detect the intrusion in an effective manner. This paper, the security of the network is addressed by proposing Feature Selection Algorithm using Chi Squared with Ensemble Method (FSChE). The proposed scheme employs the combination of decision tree along with the random forest classification algorithm to form ensemble classifier. The experimental results justify the feasibility of the proposed scheme in terms of attack detection, packet delivery ratio and time analysis by employing NSL KDD cup data Set. The obtained results shows that the proposed ensemble method increases the overall performance by 10% to 25% with respect to mentioned parameters.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.635-640
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• 2016

It is important factors for a production system to get a profitable result in quality and reliability process. For this reason, there's are various type of research papers in a certain type of data acquisition and application to reliability and quality of the level of M2M devices. In general, a classification problem of slightly different signal such as sensing data is difficult to do with classical statistical methods. There's required real-time and instantaneous calculation properties in machine process. Especially a type of injection molding machine which has a property to be decided in accordance with short-term cycle process needs a solution that can be done a certain type of decision like as good or bad quality immediately. This paper presents a simple application of Kalman Filtering by single sensing data to injection molding process in order to get a correct answer from the real time sensing data.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.6
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• pp.31-38
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• 2009

To improve the image quality and lower the power consumption of the mobile applications, it is the one of the best candidate to control the backlight unit of the LCD module with ambient light. Ambient light sensor and readout circuit were integrated in LCD panel for the mobile applications, and we designed them with LTPS TFT. We proposed noble start-up correction in order to correct the variation of the photo sensors in each panel. We used time-to-digital method for converting photo current to digital data. To effectively merge time-to-digital method with start-up correction, we proposed noble dual slope correction method. The entire readout circuit was designed and estimated with LTPS TFT process. The readout circuit has very simple and stable structure and timing, so it is suitable for LTPS TFT process. The readout circuit can correct the variation of the photo sensors without an additional equipment, and it outputs the 4-levels digital data per decade for input luminance that has a dynamic range of 60dB. The readout rate is 100 times/sec, and the linearity error for digital conversion is less than 18%.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.21-27
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• 2019

Recently, embedded systems have been introduced in various fields such as smart factories, industrial drones, and medical robots. Since sensor data collection and IoT functions for machine learning and big data processing are essential in embedded systems, it is essential to port the operating system that is suitable for the function requirements. However, in embedded systems, it is necessary to separate the hard real-time system, which must process within a fixed time according to service characteristics, and the flexible real-time system, which is more flexible in processing time. It is difficult to port the operating system to a low-performance embedded device such as 8BIT MCU to perform simultaneous real-time. When porting a real-time OS (RTOS) to a low-specification MCU and performing a number of tasks, the performance of the real-time and general processing greatly deteriorates, causing a problem of re-designing the hardware and software if a hard real-time system is required for an operating system ported to a low-performance MCU such as an 8BIT MCU. Research on the technology that can process real-time processing system requirements on RTOS (ported in low-performance MCU) is needed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.446-450
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• 2006

Recently, the advances on sensor technology increases the study on data processing oriented middleware. Usually, most of middleware uses the naive and delta method for queue management to process data effectively. But such a queue management method it is difficult to support guaranteeing of the requested quality of services because it simply discards the data in a queue when the overflow is occurred. To handle this problem, some methods for minimizing data volumes in a wireless sensor network have been studied, but most of them cause another problem that needs the huge processing time additionally with higher complexity of the proposed algorithm. In this paper, we propose a new method of delta-average queue management by applying the mean value to the exiting delta queue management method using the data differences to handle the problem of queue overflow. The proposed method has lower complexity than the others and increases the QoS of a WSN application by using mean value instead of using data discarding policy. In addition, it is designed to manage a queue effectively by controlling the range of data differences adaptively to the target sensor network environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.2-78.2
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• 2019

The temperature control of a scientific device is essential because extreme temperature conditions can cause hazard issues for the operation. We developed a software which can interact with the temperature sensor using the GMT SDK(Giant Magellan Telescope Software Development Kit) version 1.6.0. The temperature sensor interacts with the EtherCAT(Ethernet for Control Automation Technology) slave via the hardware adapter, sending and receiving data by a packet. The PDO(Process Data Object) and SDO(Service Data Object), which are the packet interacts with each EtherCAT slave, are defined on the TwinCAT program that enables the real-time control of the devices. The user can receive data from the device via grs(GMT Runtime System) tools and log service. Besides, we programmed the software to print an alert message on the log when the temperature condition changes to certain conditions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.2
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• pp.1-9
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• 2013

In recent years, numerous mega-size and complex civil infrastructures have been constructed worldwide. For the more precise construction and maintenance process management of these civil infrastructures, the application of a variety of smart sensor-based structural health monitoring (SHM) systems is required. The efficient management of both sensors and collected databases is also very important. Recently, several kinds of database access technologies using Quick Response (QR) code and Augmented Reality (AR) applications have been developed. These technologies provide software tools incorporated with mobile devices, such as smart phone, tablet PC and smart pad systems, so that databases can be accessed very quickly and easily. In this paper, an AR-based structural health monitoring technique is suggested for sensor management and the efficient access of databases collected from sensor networks that are distributed at target structures. The global positioning system (GPS) in mobile devices simultaneously recognizes the user location and sensor location, and calculates the distance between the two locations. In addition, the processed health monitoring results are sent from a main server to the user's mobile device, via the RSS (really simple syndication) feed format. It can be confirmed that the AR-based structural health monitoring technique is very useful for the real-time construction process management of numerous mega-size and complex civil infrastructures.

• Science of Emotion and Sensibility
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• v.27 no.1
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• pp.59-68
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• 2024

This study aimed to develop a soft fabric-based elbow-bending angle sensor that can replace conventional hard-type inertial sensors and a system for estimating bending angles using it. To enhance comfort during exercise, this study treated four fabrics (Bergamo, E-band, span cushion, and polyester) by single-walled carbon nanotube dip coating to create conductive textiles. Subsequently, one fabric was selected based on performance evaluations, and an elbow flexion angle sensor was fabricated. Gauge factor, hysteresis, and sensing range were employed as performance evaluation metrics. The data obtained using the fabricated sensor showed different trends in sensor values for the changes in the angle during bending and extending movements. Because of this divergence, the two movements were separated, and this constituted the one-step process. In the two-step process, multilayer perceptron (MLP) was employed to handle the complex nonlinear relationships and achieve high data accuracy. Based on the results of this study, we anticipate effective utilization in various smart wearable and healthcare domains. Consequently, a soft- fabric bending angle sensor was developed, and using MLP, nonlinear relationships can be addressed, enabling angle estimation. Based on the results of this study, we anticipate the effective utilization of the developed system in smart wearables and healthcare.