• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.6
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• pp.912-918
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• 2001

This paper presents a performance analysis of experimental observations of a uplink smart antenna system operating in a wide-band CDMA environment designed for a WLL (Wireless Local Loop) system. The numerical results shown in this paper have been obtained from practical experiments using commercially developed mobile terminals. The beam pattern provided by the weight vector and the resultant BER (Bit Error Rate) are observed in both indoor and outdoor signal environments. From the experimental observations, it is concluded that the proposed smart antenna system provides significant improvements in receiving both indoor and outdoor signal environments.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1363-1370
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• 2018

In visible light (VL) positioning based on angle of arrival (AOA) estimation for smart indoor service, the AOA parameters obtained at the receiver has sometimes a random and distributed angle form instead of a point angle form due to the multipath transfer of the actual visible light and short positioning distance. The AOA estimation of a VL signal with a random and parametric distributed angle form may give incorrect AOA parameter estimates, which may result in poor VL positioning performance. In this paper, we classify the AOA parameters of the received VL signal into three forms according to the actual positioning channel environment and consider the short-range VL positioning method. We propose a subspace-based AOA parameter estimation technique and a data fusion method, and analyzed the proposed method by simulation and the measurement of the real VL channel characteristics.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.480-485
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• 2010

In this paper, we developed mobile robot platform called Ecobot for the application system of friendly environment smart home appliance. Ecobot fulfills the purposes of monitoring of the healthy environment and guidance in the application system of friendly smart environment home appliance, home network formed by Zigbee network. For the healthy environment, the system contains monitoring sensor. Moreover, it continuously keeps the healthy environment by controlling the smart home appliances linkng with Zigbee network. And also using the URG-04LX laser distance sensor, it monitors indoor environment through autonomous moving and collision avoidance.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.271-278
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• 2021

This paper is a study on the implementation of smart filter system that recognizes the external environment and automatically removes pollutants according to pollution level. Recently, the occurrence of various pollutants in indoor and outdoor space has adversely affected the human body. Especially, various fine dust generated in the atmosphere becomes worse in closed residential space or office space. Although air pollution can be temporary lowered through ventilation, it is difficult to respond to fine dust changes in real time, and such problems become serious in the space where many people reside, such as at home or industry. Therefore, it is necessary to measure the pollution level of fine dust inside the residential space in real time and to reduce the pollution of indoor ventilation through automatic ventilation with the outside. To improve these problems, this paper proposes the implementation of smart filter system for external environment recognition. The structure of smart filter system that automatically measures air quality inside and outside, removes pollutants, implements the function, and confirms the operability by manufacturing prototypes. Finally, the effectiveness of the smart filter system for solving fine dust problems was examined.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.278-286
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• 2008

Reliable functionalities for autonomous navigation and object recognition/handling are key technologies to service robots for executing useful services in human environments. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. With the new paradigms on robot technologies, we attempted to apply smart environments technologies-such as RFID, sensor network and wireless network- to robot functionalities for executing reliable services. In this paper, we introduce concepts of proposed smart environments based robot navigation and object recognition/handling method and present results on robot services. Even though our methods are different from existing robot technologies, successful implementation result on real applications shows the effectiveness of our approaches.

• Journal of People, Plants, and Environment
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• v.24 no.3
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• pp.257-266
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• 2021

Background and objective: Vertical smart farm (VSF) is an alternative that contributes to solving various problems such as climate change and food shortage. This study focused on the types and awareness of VSF to introduce and diffuse VSF. We aimed to investigate the types of VSF and citizens' awareness on VSF. We analyzed 1) where the smart farm technology could be implemented on a building; 2) what citizens think about VSF; and 3) suggested what is most necessary for the introduction and diffusion of VSF in the future based on citizens' perception. Methods: VSF types were investigated through case studies on VSF in Korea and overseas. Citizens' perception on VSF was investigated through a questionnaire survey. A statistical analysis was conducted with the survey results for implications of the introduction and diffusion of VSF. Results: Four types of VSF were derived: rooftop farms, facade farms, indoor farms, and farms using the whole building. The survey showed that 29.2%, 27.8%, and 22.2% of respondents knew well about urban agriculture, smart farms, and vertical smart farms, respectively. Respondents answered that improving awareness is the most important factor to introduce VSF. According to the statistical analysis, it was determined that education and promotion of the necessity of VSF would be important to diffuse the VSF. Conclusion: VSF can be a solution to a variety of problems we face. The results of this study suggest a direction for the introduction and diffusion of VSF. In order to introduce VSF in the future, additional studies must be conducted on the legal system.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.473-480
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• 2019

The smart home is a technology that can monitor and control by connecting everything to a communication network in various fields such as home appliances, energy consumers, and security devices. The Smart home is developing not only automatic control but also learning situation and user's taste and providing the result accordingly. This paper proposes a model that can provide a comfortable indoor environment control service for the user's characteristics by detecting the user's behavior as well as the automatic remote control service. The whole system consists of ESP 8266 with sensor and Wi-Fi, Firebase as a real-time database, and a smartphone application. This model is divided into functions such as learning mode when the home appliance is operated, learning control through learning results, and automatic ventilation using indoor and outdoor sensor values. The study used moving averages for temperature and humidity in the control of home appliances such as air conditioners, humidifiers and air purifiers. This system can provide higher quality service by analyzing and predicting user's characteristics through various machine learning and deep learning.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• Journal of IKEEE
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• v.19 no.4
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• pp.603-609
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• 2015

Thermal comfort is one of the fundamental aspects of indoor environmental quality and it is strongly related to occupant satisfaction and energy used in building. In this paper, we proposes smart comfort algorithm that save energy and provide a pleasant and comfortable environment for workers by the indoor comfort conditions(Predictive Mean Vote) detection and controlling the temperature and humidity, air flow. Simulation results, heating and cooling control of the thermal comfort control can be compared with the existing general air conditioners reduces the power of 0.5kW and indoor comfort can be maintained. Also, It showed a 49.2% improvement in the light by lighting control algorithm.