• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1136-1141
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• 2016

IoT technology has been used as a core technology of convergence service that needs intelligent information processing, and the importance is largely emerging now. And internal network construction thru IoT interaction device can connect with IoT device effectively, provide diverse services by connection with open platform. Especially, beacon that is based on low electric power bluetooth device is receiving attention as one of core technology of IoT. Beacon technology is utilized widly in various fields of industry, and there are lot of demands in the specific environment and conditions beyond the basic function. On this thesis, the authors are proposing the beacon device that utilized acceleration sensor and hole sensor. this beacon device can control the target on specific situation thru sensing of moving target. For the more, we will expect to apply to the various type of factory environments like detachable installation, optimized management using sensor.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.110-116
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• 2006

In this paper, we implement a speech recognition system to support ubiquitous sensor network application services such as switch control, authentication, etc. using wireless audio sensors. The proposed system is consist of the wireless audio sensor, the speech recognition algorithm using psychoacoustic model and LDPC(low density parity check) for correcting errors. The proposed speech recognition system is inserted in a HOST PC to use the sensor energy effectively mil to improve the accuracy of speech recognition, a FEC(Forward Error Correction) system is used. Also, we optimized the simulation coefficient and test environment to effectively remove the wireless channel noises and correcting wireless channel errors. As a result, when the distance between sensor and the source of voice is less then 1.0m FAR and FRR are 0.126% and 7.5% respectively.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.934-941
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• 2014

In this paper, we present a low profile 2-layered meander built-in monopole antenna for portable RFID reader using FDTD(Finite Difference Time Domain) method. The input impedance, return loss, and VSWR in the frequency domain are calculated by Fourier transforming the time domain results. The double meander 2-layer structure is used to enhance the impedance matching and increase the antenna gain. The measured bandwidth of the antenna is 0.895 GHz ~ 0.930 GHz for a S11 of less than -10dB. The measured peak gain of proposed low profile RFID built-in antenna is 2.3 dBi. And the proposed built-in antenna for portable RFID reader can offers relatively wide-bandwidth and high-gain characteristics, in respectively. Experimental data for the return loss and the gain of the antenna are also presented, and they are relatively in good agreement with the FDTD results. This antenna can be also applied to mobile communication field, energy fields, RFID, and home-network operations, broadcasting, and other low profile mobile systems.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.107-116
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• 2011

Recently, femtocell has been concerned as one of effective solutions to relieve shadow region and provide high quality services to users in indoor environments. Even though femtocell offers various benefits to cellular operators and users, many technical issues, such as interference coordination, network synchronization, self-configuration, self-optimization, and so on, should be solved to deploy the femtocell in current network. In this paper, we develop a simulator for evaluating performance of long term evolution (LTE) femtocell systems under various interference scenarios. The simulator consists of a main-module and five sub-modules. The main-module connects and manages five sub-modules which have the functionality managing user mobility, packet scheduling, call admission control, traffic generation, and modulation and coding scheme (MCS). To provide user convenience, the simulator adopts graphical user interface (GUI) which can observes simulation results in real time. We expect that this simulator can contribute to developing effective femtocell systems by supporting a tool for analyzing the effect of interference between macrocell and femtocell.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.505-507
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• 2021

In a Wireless Sensor Network (WSN), the wireless data transmission environment plays an important role in system performance. In the proposed mobility model moving distance of sensor nodes has a great influences on communication performance. Transmission/receiving distance (d), path loss (L), sensitivity, Bit Error Rate (BER), Signal-to-Noise Ratio (SNR) are considerations when designing a wireless communication system. MANET is a form of network in which only wireless terminals communicate with each other independently and move without any assistance of an existing infrastructure network. This paper is research on the optimized power usage method which is study on the effect of the moving distance of mobile nodes on the overall energy efficiency of the system in WSN. The purpose of this study is to extend the life of the entire network by proposing the mobile distance of sensor nodes within the communication available range.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.141-148
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• 2021

In some intersections or busy traffic roads, there are more pedestrians in a specific period of time, and there are many traffic accidents caused by road congestion. Especially at the intersection where there are schools nearby, it is particularly important to protect the traffic safety of students in busy hours. In the past, when designing traffic lights, the safety of pedestrians was seldom taken into account, and the identification of motor vehicles and traffic optimization were mostly studied. How to keep the road smooth as far as possible under the premise of ensuring the safety of pedestrians, especially students, will be the key research direction of this paper. This paper will focus on person, motorcycle, bicycle, car and bus recognition research. Through investigation and comparison, this paper proposes to use YOLO v4 network to identify the location and quantity of objects. YOLO v4 has the characteristics of strong ability of small target recognition, high precision and fast processing speed, and sets the data acquisition object to train and test the image set. Using the statistics of the accuracy rate, error rate and omission rate of the target in the video, the network trained in this paper can accurately and effectively identify persons, motorcycles, bicycles, cars and buses in the moving images.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.6
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• pp.171-178
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• 2010

Pipeline structure is one of core underground infrastructure which transports primary sources. Since the almost pipeline structures are placed underground and connected each other complexly, it is difficult to monitor their structural health condition continuously. In order to overcome this limitation of recent monitoring technique, recently, a Ubiquitous Sensor Network (USN) system based on on-line and real-time monitoring system is being developed by the authors' research group. In this study, real-time pipeline health monitoring (PHM) methodology is presented based on electromechanical impedance methods using USN. Two types of damages including loosened bolts and notches are artificially inflicted on the pipeline structures, PZT and MFC sensors that have piezoelectric characteristics are employed to detect these damages. For objective evaluation of pipeline conditions, Damage metric such as Root Mean Square Deviation (RMSD) value was computed from the impedance signals to quantify the level of the damage. Optimal threshold levels for decision making are estimated by generalized extreme value(GEV) based statistical method. Throughout a series of experimental studies, it was reviewed the effectiveness and robustness of proposed PHM system.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.1
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• pp.78-85
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• 2019

Membrane proteins are involved in many common diseases, including heart disease and cancer. In various disease states, such as cancer, abnormal signaling pathways that are related to the membrane proteins cause the cells to divide out of control and the expression of membrane proteins can be altered. Membrane proteins have the hydrophobic environment of a lipid bilayer, which makes an analysis of the membrane proteins notoriously difficult. Therefore, this study evaluated the efficacy of two different methods for optimal membrane protein extraction. High-speed centrifuge and reagent-based method with a -/+ filter aided sample preparation (FASP) were compared. As a result, the high-speed centrifuge method is quite effective in analyzing the mitochondrial inner membranes, while the reagent-based method is useful for endoplasmic reticulum membrane analysis. In addition, the function of the membrane proteins extracted from the two methods were analyzed using GeneGo software. GO processes showed that the endoplasmic reticulum-related responses had higher significance in the reagent-based method. An analysis of the process networks showed that one cluster in the high-speed centrifuge method and four clusters in the reagent-based method were visualized. In conclusion, the two methods are useful for the analysis of different subcellular membrane proteins, and are expected to assist in selecting the membrane protein extraction method by considering the target subcellular membrane proteins for study.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.