• Proceedings of the KSR Conference
• /
• 2008.06a
• /
• pp.1995-2005
• /
• 2008

Recently, the smart sensors and USN (Ubiquitous Sensor Network) technologies are emerging. Smart sensors add the capability of storing local temporary data, processing instant operations, transmitting information outward, to the simple sensing devices. The USN is a wireless network of sensor/smart sensors that can collect data anywhere anytime and exchange the data within the network. In this research, case studies are performed on the smart sensors and USN applications. The cases were grouped in four categories, domestic private, domestic public, foreign private, and foreign public. Based on that survey, promising applications will be proposed and developed to be implemented to the next generation high-speed EMU.

• Journal of Integrative Natural Science
• /
• v.12 no.1
• /
• pp.14-19
• /
• 2019

In this paper, we propose a system for controlling the brightness of street lights by predicting pedestrian paths, identifying the position of pedestrians with motion sensing sensors and obtaining motion vectors based on past walking directions, then predicting pedestrian paths through the route prediction smart street lighting system. In addition, by using motion vector data, the pre-treatment process using linear interpolation method and the fuzzy system and neural network system were designed in parallel structure to increase efficiency and the rough set was used to correct errors. It is expected that the system proposed in this paper will be effective in securing the safety of pedestrians and reducing light pollution and energy by predicting the path of pedestrians in the detection of movement of pedestrians and in conjunction with smart street lightings.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.5
• /
• pp.67-72
• /
• 2019

The self-driving cars identify appropriate navigation paths and obstacles to arrive at their destinations without human control. The autonomous cars are capable of sensing driving environments to improve driver and pedestrian safety by sharing with neighbor traffic infrastructure. In this paper, we have focused on pedestrian protection and have designed an improved localization algorithm to track mobile users on roads by interacting with smart traffic lights in vehicle environments. We developed smart traffic lights with the RSSI sensor and built the proposed method by improving the Kalman filter algorithm to localize mobile users accurately. We successfully evaluated the proposed algorithm to improve the mobile user localization with deployed five smart traffic lights.

• Smart Structures and Systems
• /
• v.20 no.2
• /
• pp.175-180
• /
• 2017

The load-carrying capacity and structural behavior of concrete-filled steel tube (CFST) structures is highly influenced by the grouting compactness in the steel tube. Due to the invisibility of the grout in the steel tube, monitoring of the grouting progress in such a structure is still a challenge. This paper develops an active sensing approach with combined piezoceramic-based smart aggregates (SA) and piezoceramic patches to monitor the grouting compactness of CFST bridge structure. A small-scale steel specimen was designed and fabricated to simulate CFST bridge structure in this research. Before casting, four SAs and two piezoceramic patches were installed in the pre-determined locations of the specimen. In the active sensing approach, selected SAs were utilized as actuators to generate designed stress waves, which were detected by other SAs or piezoceramic patch sensors. Since concrete functions as a wave conduit, the stress wave response can be only detected when the wave path between the actuator and the sensor is filled with concrete. For the sake of monitoring the grouting progress, the steel tube specimen was grouted in four stages, and each stage held three days for cement drying. Experimental results show that the received sensor signals in time domain clearly indicate the change of the signal amplitude before and after the wave path is filled with concrete. Further, a wavelet packet-based energy index matrix (WPEIM) was developed to compute signal energy of the received signals. The computed signal energies of the sensors shown in the WPEIM demonstrate the feasibility of the proposed method in the monitoring of the grouting progress.

• The Research Journal of the Costume Culture
• /
• v.25 no.4
• /
• pp.529-542
• /
• 2017

The purpose of this study is to utilize technology as basic data for smart clothing product research and development. This technology can recognize user's motion according to characteristics types and functions of wearable smart clothing products. In order to analyze the case of motion recognition products, we searched for previous research data and cases referred to as major keywords in leading search engines, Google and Naver. Among the searched cases, information on the characteristics and major functions of the 42 final products selected on the market are examined in detail. Motion recognition for smart clothing products is classified into four body types: head & face, body, arms & hands, and legs & feet. Smart clothing products was developed with various items, such as hats, glasses, bras, shirts, pants, bracelets, rings, socks, shoes, etc., It was divided into four functions health care type for prevention of injuries, health monitor, posture correction, sports type for heartbeat and exercise monitor, exercise coaching, posture correction, convenience for smart controller and security and entertainment type for pleasure. The function of the motion recognition smart clothing product discussed in this study will be a useful reference when designing a motion recognition smart clothing product that is blended with IT technology.

• Smart Structures and Systems
• /
• v.2 no.3
• /
• pp.225-235
• /
• 2006

With the discovery in plants of the proteinaceous forisome crystalloid (Knoblauch, et al. 2003), a novel, non-living, ATP-independent biological material became available to the designer of smart materials for advanced actuating and sensing. The in vitro studies of Knoblauch, et al. show that forisomes (2-4 micron wide and 10-40 micron long) can be repeatedly stimulated to contract and expand anisotropically by shifting either the ambient pH or the ambient calcium ion concentration. Because of their unique abilities to develop and reverse strains greater than 20% in time periods less than one second, forisomes have the potential to outperform current smart materials as advanced, biomimetic, multi-functional, smart sensors or actuators. Probing forisome material properties is an immediate need to lay the foundation for synthesizing forisomebased smart materials for health monitoring of structural integrity in civil infrastructure and for aerospace hardware. Microfluidics is a growing, vibrant technology with increasingly diverse applications. Here, we use microfluidics to study the surface interaction between forisome and substrate and the conformational dynamics of forisomes within a confined geometry to lay the foundation for forisome-based smart materials synthesis in controlled and repeatable environment.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1797-1801
• /
• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.

• Smart Media Journal
• /
• v.4 no.1
• /
• pp.33-38
• /
• 2015

In this paper, we propose the methods not only to eliminate ELF(Extremely Low Frequency) EMI(Electro-Magnetic Interference) noice for extending recognition distance, but also to utilize the the PLN for detecting starting instance of a hand gesture using electric potential sensor. First, we measure strength of electric field generated in the smart devices such as TV and phone, and minimize EMI through efficient arrangement of the sensors. Meanwhile, we utilize the 60 Hz PLN to extract the starting point of hand gesture. Thereafter, we eliminate the PLN generated in the smart device and circuit of sensors. And then, we shield the sensors from an electric noise generated from devices. Finally, through analyzing the frequency components according to the gesture of target, we use the low pass filter and the Kalman filter for elimination of remaining electric noise. We analyze and evaluate the proposed ELF-band EMI eliminating method for non-contact remote sensing of the EPS(Electric Potential Sensor). Combined with a detecting technique of gesture starting point, the recognition distance for gestures has been proven to be extended to more than 3m, which is critical for real application.

• Fashion & Textile Research Journal
• /
• v.21 no.2
• /
• pp.133-140
• /
• 2019

This study developed smart fashion prototypes that provide utilitarian functionality by combining Fashion and Electronics regarding the IT focused convergence tendency in modern industries. A convergence R&D workshop was performed by Fashion design majors and Engineering majors for the study. As a result, 5 functional smart fashion prototypes were developed and the outline of each prototype are as follows. The $1^{st}$ prototype, 'Hidden Camera Detecting Coat' focused on gender-related crimes. The coat uses infrared lighting and LED technologies to provide a function to detect hidden cameras in suspicious public spaces such as toilets. The $2^{nd}$ prototype, 'Heating-massage Suit' targeted patients with musculoskeletal system difficulties. The suit uses heating and vibration technologies to provide a heating massage treatment for patients with ongoing difficulties in their daily lives. The $3^{rd}$ prototype is an air-bag jacket to prevent sexual molestation on public transportation. The jacket extends its volume through pressure sensing, air compressing, motors and 3D-printing technology to secure the wearer's personal preventive space between the user's body and others. The $4^{th}$ prototype is a town wear for people suffering from synesthesia. People with synesthesia inadvertently see colors when exposed to certain sounds. This town wear uses sound sensing, air compressing, motors and 3D-printing technology to provide sound prevention and a comfortable sound playing function. The $5^{th}$ prototype is a set of a vest and a gloves for visually impaired people. The vest and gloves uses DMS, voice playing, vibration technology to provide distance measuring and warning functions.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.113-120
• /
• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.