• Journal of Internet of Things and Convergence
• /
• v.6 no.4
• /
• pp.21-26
• /
• 2020

An autonomous driving system is based on the deep learning system built by big data which are obtained by various IoT sensors. The miniaturization and high performance of the IoT sensors are needed for diverse devices including the autonomous driving system. Specially, the miniaturization of the sensors leads to compel the miniaturization of the fixer structures. In the viewpoint of the miniaturization, metallic structure is a best solution to attach the small IoT sensors to the main body. However, it is hard to manufacture the small metallic structure with a conventional machining process or manufacturing cost greatly increases. As one of solutions for the problems, in this work, metallic FDM (Fused depositon modeling) based on metallic filament was proposed and the FDM process was investigated to fabricate the small metallic structure. Final part was obtained by the post-process that consists of debinding and sintering. In this work, the relationship between infill rate and the density of the part after the post-process was investigated. The investigation of the relationship is based on the fact that the infill rate and the density obtained from the post-processing is not same. It can be said that this work is a fundamental research to obtain the higher density of the printed part.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.3
• /
• pp.294-301
• /
• 2020

Man-In-Simulant Test(MIST) provides a test method to evaluate chemical protective equipments such as protective garments, gloves, footwear and gas mask. The MIST chamber is built to control concentration of chemical vapor that has a activity space for two persons. Non-toxic methyl-salicylate(MeS) is used to simulate chemical agent vapor. We carried out to measure inward leakage MeS vapors by using passive adsorbent dosimeter(PAD) which are placed on the skin at specific locations of the body while man is activity according to the standard procedure in MIST chamber. But more time is required for PADs and there is concern of contamination in PADs by recovering after experiment. Therefore detector for measuring in real time is necessary. In order to analyze in real time the contamination of the personal protective equipment inside the chemical environment, we have developed a wireless real-time gas detector. The detector consists of 8 gas-sensors and 1 control-board. The control-board includes a CPU for processing a signal, a power supply unit for biasing the sensor and Bluetooth-chipset for transmission of signals to external PC. All signals from gas-sensors are converted into digital signals simultaneously in the control-board. These digital signals are stored in external PC via Bluetooth wireless communication. The experiment is performed by using protective equipment worn on manikin. The detector is mounted inside protective equipment which is capable of providing a real-time monitoring inward leakage MeS vapor. Developed detector is demonstrated the feasibility as real-time detector for MIST.

• Journal of the Korean Society for Precision Engineering
• /
• v.33 no.1
• /
• pp.23-29
• /
• 2016

In this paper, a real-time condition diagnosis system for the lens injection molding process is developed through the use of LabVIEW. The built-in-sensor (BIS) mold, which has pressure and temperature sensors in their cavities, is used to capture real-time signals. The measured pressure and temperature signals are processed to obtain features such as maximum cavity pressure, holding pressure and maximum temperature by the feature extraction algorithm. Using those features, an injection molding condition diagnosis model is established based on a response surface methodology (RSM). In the real-time system using LabVIEW, the front panels of the data loading and setting, feature extraction and condition diagnosis are realized. The developed system is applied in a real industrial site, and a series of injection molding experiments are conducted. Experimental results show that the average real-time condition diagnosis rate is 96%, and applicability and validity of the developed real-time system are verified.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.3
• /
• pp.119-125
• /
• 2018

Various techniques for indoor positioning using a smart phone have been studied. Among them, the positioning technology using the acceleration sensor and the gyro sensor built in the smartphone is widely used in conjunction with the WiFi fingerprint technology. The location tracking technology using sensors has been used for a long time, but the performance environment of the smartphone is poor and the user is moving with the smartphone in a certain posture. Therefore, in order to improve the accuracy of location tracking in a smartphone environment, it is necessary to study and develop appropriate algorithms in a mobile environment. In this paper, we analyze the performances of frequency analysis method, maximum sum of minimum acceleration method and adaptive threshold method, which are the user's moving step count detection algorithms, and determine the most accurate method.

• Journal of Korea Society of Industrial Information Systems
• /
• v.25 no.3
• /
• pp.53-59
• /
• 2020

This paper proposes color segmentation for detecting missing persons in a smart lighting system using radar and camera sensors. Recently, smart lighting systems built-in radar and cameras have been efficient in saving energy and searching for missing persons, simultaneously. In smart lighting systems, radar detects moving objects and then the lights turn on and camera records. The video recorded is useful to find out missing persons. The color of their clothes worn in missing persons is one of critical hints to look for missing persons. Therefore, color segmentation is an effective means for detecting the color of their clothes. In this paper, during the color segmentation step, the ROI(Region of interest) setting based on the size of an object is applied and the background is reduced. According to experimental results, the color segmentation has good accuracy of more than 97%.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.6
• /
• pp.1233-1242
• /
• 2017

Various attempts have been made to prevent crime risk. One of the cases where outdoor pedestrians are attacked by criminals is the abnormal health condition. When a mental or mental condition that can not sustain normal walking due to drunkenness is exposed, the case of being a crime is revealed through crime case analysis. In this study, we propose a method for estimating the state of an individual that can be detected in outdoor activities. In order to avoid the inconvenience of installing a separate terminal for event information transmission of sensors and sensors, it is possible to estimate an abnormal state by using a 3-axis acceleration sensor built in a smart phone. The state of the user can be estimated by analyzing the momentum of the user and analyzing it with the passage of time. It is possible to distinguish the flow of time at regular intervals, to recognize the activity patterns in each time band, and to distinguish between normal and abnormal. In this study, we have evaluated the total amount of kinetic energy and kinetic energy in each direction of the acceleration sensor and the Fourier transformed value of the total energy amount to distinguish the abnormal state.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.4
• /
• pp.709-718
• /
• 2021

In this paper, it can be seen that the number of abandoned dogs increases every year through the statistics on the occurrence of abandoned animals. With the goal of reducing the number of stray dogs, a smart pet dog shoulder strap is implemented based on a real-time location tracking system using the ESP32 module and GPS sensor. It is an ESP32 module with a built-in Bluetooth module. It is input to the MCU using various sensors, and finally outputs to a smart-phone application, and communicates through the built-in blue-tooth module. In addition, it uses Neopixels to compensate the weaknesses at night through LED light emission, and automatically sets the warning distance to design a music playback system through the LED flashing effect and MP3 module. In addition, a smart pet dog shoulder strap was designed to help pet dog health care by measuring the moving distance according to the amount of activity through the gyro sensor.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.35 no.12
• /
• pp.1351-1358
• /
• 2011

To approximate the threshold of the fault detection and diagnosis (FDD) system, validation of the measurements is mandatory. Naturally, the system shows uncertainties due to measuring sensors - mostly thermocouples or RTDs - and due to repeatability. The uncertainty of a thermocouple comes from natural variation or a drift of the thermocouple measurement. Considering the natural variation behaves like zero-mean white noise, its natural variation can be characterized closely by the steady-state standard deviation. However, residuals between measurements and no-fault references in FDD systems show a statistical distribution with various uncertainties. In this paper, steady-state variations of measurement residuals were investigated by utilizing built-in temperature sensors in a heat pump for the model development and the final application.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.12
• /
• pp.1687-1692
• /
• 2019

In the era of the fourth industrial revolution, advanced technologies such as the Internet of Things(IoT) and big data are emerging. However, the level of application of IoT to indoor environment is very weak. Therefore, it is necessary to develop a system for analyzing air pollutants or indoor air quality through real-time monitoring using the IoT. This paper implements a system that measures indoor environmental values using Arduino and various sensors, and stores the information obtained from various sensors into a database of server. The information stored in the server was built as a database and utilized in the ventilation system or air cleaner installed in the home or company's room. In the proposed system, it is possible to check the immediate indoor environmental condition through the LED status display of the monitoring sensor module while reducing the cost of the sensor used to implement IoT technology.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.4
• /
• pp.113-118
• /
• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.