• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.1-8
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• 2008

Recently, there is developing various ubiquitous application services using sensor networks based on TinyOS represented the operating system of sensor node. These sensor networks perform the collection and the transmission of sensing data from sensor node to get the context information. In this paper, we proposes the sensor node control algorithm which converts a sensor node to sleep, active, power off mode according to monitoring result of the voltage state of sensor node. Also, we designs and implement the sensor control module on server, sink, sensor node of sensor networks using this algorithm. It designs a sensor voltage control module of sensor node, data receive and display module of USN server using a java language and TinyOS. And, it checks the voltage state of sensor node, and it changes one of the sleep or power off modes in case of high voltage loss. Accordingly, we effectively use the power of sensor nodes as changing control modes of sensor nodes.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.3
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• pp.246-252
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• 2004

This paper presents a position sensorless control system of Switched Reluctance Motor (SRM) using neural network. The control of SRM depends on the commutation of the stator phases in synchronism with the rotor position. The position sensing requirement increases the overall cost and complexity. In this paper, the current-flux-rotor position lookup table based position sensorless operation of SRM is presented. Neural network is used to construct the current-flux-rotor position lookup table, and is trained by sufficient experimental data. Experimental results for a 1-hp SRM is presented for the verification of the proposed sensorless algorithm.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.275-285
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• 2015

To improve the efficiency of carrier sense multiple access (CSMA)-based medium access control (MAC) protocols, CSMA with collision resolution (CSMA/CR) has been proposed. In the CSMA/CR, a transmitting station can detect a collision by employing additional sensing after the start of a data transmission and then resolve the next collision that might occur by broadcasting a jam signal during a collision detection (CD) period. In this paper, we analyze the delay of a CSMA/CR based on a generic p- persistent CSMA model and obtain the minimum achievable delay of the CSMA/CR by finding the optimal length of the CD period according to the number of contending stations. Through this delay analysis, we also investigate the throughput-delay characteristics of the CSMA/CR protocol according to various parameters. Analysis and simulation results show that the CSMA/CR has a considerably lower delay and its throughput-delay characteristic is significantly improved than the conventional CSMA/CA and wireless CSMA/CD protocols.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.502-512
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• 2016

The wireless carrier sense multiple access with collision detection (WCSMA/CD) and carrier sense multiple access with collision resolution (CSMA/CR) protocols are considered representative distributed collision detection protocols for fully connected dense wireless local area networks. These protocols identify collisions through additional short-sensing within a collision detection (CD) period after the start of data transmission. In this study, we analyze their throughput numerically and show that the throughput has a trade-off that accords with the length of the CD period. Consequently, we obtain the optimal length of the CD period that maximizes the throughput as a closed-form solution. Analysis and simulation results show that the throughput of distributed collision detection protocols is considerably improved when the optimal CD period is allocated according to the number of stations and the length of the transmitted packet.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.12
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• pp.1-7
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• 2010

In this paper, we suggested a sensing method of PoRAM with the multilevel cell When a specific voltage is applied between top and bottom electrodes of PoRAM unit cell, we can distinguish cell states by changing resistance values of the cell. Especially, we can use the PoRAM as the multilevel cell due to have four stable resistance values per cell. Therefore, we proposed an address decoding method, sense amplifier and control signal for sensing of a multilevel cell. The sense amplifier is designed based on a current comparator that compared a cell current the cell with a reference current, and have a low input impedance for a amplification of the current. The proposed circuit was designed in a $0.13{\mu}m$ CMOS technology, we verified to sense each data "00", "01", "10", "10" by four states of a cell current.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.109-114
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• 2010

In this paper, the implementation of a human signal sensing module that has capabilities to check and restore the weak signals from the human body is presented. A module presented in this paper consists of processing and sensing elements related to human pulse and body temperature and a controller implemented with SoC design method. PPG data is detected by a noise filtering process toward the amplified signal which is from the operating frequency between 0.1Hz - 10Hz. A digital temperature sensor is used to check the body temperature. A sensor outputs the corresponding value of the electric voltage according to the body temperature. Moreover, this paper discusses the implementation of an enhanced microprocessor which is synthesized with VHDL as a part of the SoC development and used to control the entire module. The SoC processor is implemented on a Xilinx Spartan 3 XC3S1000 device and has the achieved operating frequency of 10MHz. The implemented SoC processor core is successfully tested with macro memories in FPGA and the experimental results are hereby shown.

• Journal of Korean Society on Water Environment
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• v.32 no.6
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• pp.520-527
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• 2016

Phycocyanin (PC) is one of the water-soluble accessory pigments of cyanobacteria species, and its concentration is used to estimate the presence and relative abundance of cyanobacteria. In laboratory experiments, PC content of field data were determined using Sarada's freeze-thaw method in algal bloom season. The effectiveness of three selected extraction methods (repeated freeze-thaw method, homogenization, power control) for PC were determined. The extraction efficiency of phycocyanin was the highest (of the methods compared) when a single freezing-thawing cycle was followed by pre-sonication. Applying this optimized method to surface water of Korean inland waters, the average concentration distribution was estimated at $2.9{\sim}51.9mg/m^3$. It has been shown that the optimized pre-sonication method is suitable to measure cyanobacteria PC content for the characterization of inland waters. The approach and results of this study indicates the potential of effective methods for remote monitoring and management of water quality in turbid inland waters using hyperspectral remote sensing.

• Korean Journal of Remote Sensing
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• v.18 no.6
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• pp.359-367
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• 2002

In this paper, a new embedded wavelet packet image coder algorithm is proposed for an effective image coder using correlation between partitioned coefficients. This new algorithm presents parent-child relationship for reducing image reconstruction error using relations between individual frequency sub-bands. By parent-child relationship, every coefficient is partitioned and encoded for the zerotree data structure. It is shown that the proposed wavelet packet image coder algorithm achieves low bit rates and rate-distortion. It also demonstrates higher PSNR under the same bit rate and an improvement in image compression time. The perfect rate control is compared with the conventional method. These results show that the encoding and decoding processes of the proposed coder are simpler and more accurate than the conventional ones for texture images that include many mid and high-frequency elements such as aerial and satellite photograph images. The experimental results imply the possibility that the proposed method can be applied to real-time vision system, on-line image processing and image fusion which require smaller file size and better resolution.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.9-18
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• 2024

Small streams, despite their rich ecosystems, face challenges in vegetation assessment due to the limitations of traditional, time-consuming methods. This study presents a groundbreaking approach, combining unmanned aerial vehicles(UAVs), convolutional neural networks(CNNs), and the vegetation differential vegetation index (VDVI), to revolutionize both assessment and management of stream vegetation. Focusing on Idong Stream in South Korea (2.7 km long, 2.34 km² basin area)with eight diverse revetment methods, we leveraged high-resolution RGB images captured by UAVs across five dates (July-December). These images trained a ResNeXt101 CNN model, achieving an impressive 89% accuracy in classifying vegetation cover(soil,water, and vegetation). This enabled detailed spatial and temporal analysis of vegetation distribution. Further, VDVI calculations on classified vegetation areas allowed assessment of vegetation vitality. Our key findings showcase the power of this approach:(a) TheCNN model generated highly accurate cover maps, facilitating precise monitoring of vegetation changes overtime and space. (b) August displayed the highest average VDVI(0.24), indicating peak vegetation growth crucial for stabilizing streambanks and resisting flow. (c) Different revetment methods impacted vegetation vitality. Fieldstone sections exhibited initial high vitality followed by decline due to leaf browning. Block-type sections and the control group showed a gradual decline after peak growth. Interestingly, the "H environment block" exhibited minimal change, suggesting potential benefits for specific ecological functions.(d) Despite initial differences, all sections converged in vegetation distribution trends after 15 years due to the influence of surrounding vegetation. This study demonstrates the immense potential of UAV-based remote sensing and CNNs for revolutionizing small-stream vegetation assessment and management. By providing high-resolution, temporally detailed data, this approach offers distinct advantages over traditional methods, ultimately benefiting both the environment and surrounding communities through informed decision-making for improved stream health and ecological conservation.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1763-1770
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• 2023

The availability of high-resolution satellite image time series data has led to an increase in change detection research. Various methods are being studied, such as satellite image pixel and object-level change detection algorithms, as well as algorithms that apply deep learning technology. In this paper, we propose a QGIS plugin-based system to enhance the utilization of these useful results and present an actual implementation case. The proposed system is a system for intensive change detection and monitoring of areas of interest, and we propose a convenient system expansion method for algorithms to be developed in the future. Furthermore, it is expected to contribute to the construction of satellite image utilization systems by presenting the basic structure of commercialization of change detection research.