• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.659-666
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• 2006

Servo-motors are used as key components of automated system by performing precise motion control as accurate positioning and accurate speed regulation in response to the commands from computers and sensors. Especially, the linear brushless servo-motors have numerous advantages over the rotary servo motors which have connection with the friction induced transfer mechanism such as ball screws, timing belts, rack/pinion. This paper proposes an estimation method of unknown motor system parameters using the informations from the sinusoidal driving type linear brushless DC motor dynamics and outputs. The estimated parameters can be used to tune the controller gain and a disturbance observer. In order to meet this purpose high performance Digital Signal Processor, TMS320F240, designed originally for implementation of a Field Oriented Control(FOC) technology is adopted as a controller of the liner BLDC servo motor. Having A/D converters, PWM generators, rich I/O port internally, this servo motor application specific DSP play an important role in servo motor controller. This linear BLDC servo motor system also contains IPM(Intelligent Power Module) driver and hail sensor type current sensor module, photocoupler module for isolation of gate signals and fault signals.

• Journal of Sensor Science and Technology
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• v.23 no.3
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• pp.185-191
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• 2014

The width of depletion region in a varactor diode can be modulated by varying a reverse bias voltage. Thus, the preferred characteristics of depletion capacitance can obtained by the change in the width of depletion region so that it can select only the desirable frequencies. In this paper, the TV tuner varactor diode fabricated by hyper-abrupt profile control technique is presented. This diode can be operated within 3.3 V of driving voltage with capability of UHF band tuning. To form the hyperabrupt profile, firstly, p+ high concentration shallow junction with $0.2{\mu}m$ of junction depth and $1E+20ions/cm^3$ of surface concentration was formed using $BF_2$ implantation source. Simulation results optimized important factors such as epitaxial thickness and dose quality, diffusion time of n+ layer. To form steep hyper-abrupt profile, Formed n+ profile implanted the $PH_3$ source at Si(100) n-type epitaxial layer that has resistivity of $1.4{\Omega}cm$ and thickness of $2.4{\mu}m$ using p+ high concentration Shallow junction. Aluminum containing to 1% of Si was used as a electrode metal. Area of electrode was $30,200{\mu}m^2$. The C-V and Q-V electric characteristics were investigated by using impedance Analyzer (HP4291B). By controlling of concentration profile by n+ dosage at p+ high concentration shallow junction, the device with maximum $L_F$ at -1.5 V and 21.5~3.47 pF at 0.3~3.3 V was fabricated. We got the appropriate device in driving voltage 3.3 V having hyper-abrupt junction that profile order (m factor) is about -3/2. The deviation of capacitance by hyper-abrupt junction with C0.3 V of initial capacitance is due to the deviation of thermal process, ion implantation and diffusion. The deviation of initial capacitance at 0.3 V can be reduced by control of thermal process tolerance using RTP on wafer.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1307-1307
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• 2024

Recent research on heavy equipment has been conducted for the purposes of enhanced safety, productivity improvement, and carbon neutrality at construction sites. A sensor-based approach is being explored to monitor the location and movements of heavy equipment in real time. However, it poses significant challenges in terms of time and cost as multiple sensors should be installed on numerous heavy equipment at construction sites. In addition, there is a limitation in identifying the collaboration or interference between two or more heavy equipment. In light of this, a vision-based deep learning approach is being actively conducted to effectively respond to various working conditions and dynamic environments. To enhance the performance of a vision-based activity recognition model, it is essential to secure a sufficient amount of training datasets (i.e., video datasets collected from actual construction sites). However, due to safety and security issues at construction sites, there are limitations in adequately collecting training dataset under various situations and environmental conditions. In addition, the videos feature a sequence of multiple activities of heavy equipment, making it challenging to clearly distinguish the boundaries between preceding and subsequent activities. To address these challenges, this study proposed a domain adaptation in vision-based transfer learning for automated excavator activity recognition utilizing 3D ResNet (residual deep neural network). Particularly, this study aimed to identify the optimal depth of 3D ResNet (i.e., the number of layers of the feature extractor) suitable for domain adaptation via fine-tuning process. To achieve this, this study sought to evaluate the activity recognition performance of five 3D ResNet models with 18, 34, 50, 101, and 152 layers, which used two consecutive videos with multiple activities (5 mins, 33 secs and 10 mins, 6 secs) collected from actual construction sites. First, pretrained weights from large-scale datasets (i.e., Kinetic-700 and Moment in Time (MiT)) in other domains (e.g., humans, animals, natural phenomena) were utilized. Second, five 3D ResNet models were fine-tuned using a customized dataset (14,185 clips, 60,606 secs). As an evaluation index for activity recognition model, the F1 score showed 0.881, 0.689, 0.74, 0.684, and 0.569 for the five 3D ResNet models, with the 18-layer model performing the best. This result indicated that the activity recognition models with fewer layers could be advantageous in deriving the optimal weights for the target domain (i.e., excavator activities) when fine-tuning with a limited dataset. Consequently, this study identified the optimal depth of 3D ResNet that can maintain a reliable performance in dynamic and complex construction sites, even with a limited dataset. The proposed approach is expected to contribute to the development of decision-support systems capable of systematically managing enhanced safety, productivity improvement, and carbon neutrality in the construction industry.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.4
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• pp.32-38
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• 2011

This paper describes implementation of the 5.0GHz RF frequency synthesizer with 0.18${\mu}m$ silicon CMOS technology being used as an application of the IEEE802.15.4 USN sensor node transceiver modules. To get good performance of speed and noise, design of the each module like VCO, prescaler, 1/N divider, fractional divider with ${\Sigma}-{\Delta}$ modulator, and common circuits of the PLL has been optimized. Especially to get excellent performance of high speed and wide tuning range, N-P MOS core structure and 12 step cap banks have been used in design of the VCO. The chip area including pads for testing is $1.1{\times}0.7mm^2$, and the chip area only core for IP in SoC is $1.0{\times}0.4mm^2$. Through analysing of the fabricated frequency synthesizer, we can see that it has wide operation range and excellent frequency characteristics.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.5
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• pp.49-54
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• 2009

This paper describes implementation of the 1.9GHz RF frequency synthesizer with $0.18{\mu}m$ silicon CMOS technology being used as an application of the USN sensor node transceiver modules. To get good performance of speed and noise, design of the each module like VCO, prescaler, 1/N divider, fractional divider with ${\Sigma }-{\Delta}$ modulator, and common circuits of the PLL has been optimized. Especially to get good performance of speed, power consumption, and wide tuning range, N-P MOS core structure has been used in design of the VCO. The chip area including pads for testing is $1.2{\times}0.7mm^2$, and the chip area only core for IP in SoC is $1.1{\times}0.4mm^2$. The test results show that there is no special spurs except -63.06dB of the 6MHz reference spurs in the PLL circuitry. There is good phase noise performance like -116.17dBc/Hz in 1MHz offset frequency.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.87-93
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• 2008

This paper describes implementation of the 5.0GHz RF frequency synthesizer with $0.18{\mu}m$ silicon CMOS technology being used as an application of the IEEE802.15.4 USN sensor node transceiver modules. To get good performance of speed and noise, design of the each module like VCO, prescaler, 1/N divider, fractional divider with ${\Sigma}-{\Delta}$ modulator, and common circuits of the PLL has been optimized. Especially to get good performance of speed, power consumption, and wide tuning range, N-P MOS core structure has been used in design of the VCO. The chip area including pads for testing is $1.1*0.7mm^2$, and the chip area only core for IP in SoC is $1.0*0.4mm^2$. Through comparing and analysing of the designed two kind of the frequency synthesizer, we can conclude that if we improve a litter characteristics there is no problem to use their as IPs.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.563-572
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• 2002

Wireless mobile networks tend to drop a large portion of packets due to propagation errors rather than congestion. To Improve reliability over noisy wireless channels, wireless networks can employ forward error correction (FEC) techniques. Static FEC algorithms, however, can degrade the performance by poorly matching their overhead to the degree of the underlying channel error, especially when the channel path loss rate fluctuates widely. This paper investigates the benefits of an adaptable FEC mechanism for wireless networks with severe packet loss by analytical analysis or measurements over a real wireless network called sensor network. We show that our adaptive FEC named FECA (FEC-level Adaptation) technique improves the performance by dynamically tuning FEC strength to the current amount of wireless channel loss. We quantify these benefits through a hybrid simulation integrating packet-level simulation with bit-level details and validate that FECA keeps selecting the appropriate FEC-level for a constantly changing wireless channel.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.197-197
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• 2016

Quantum dot nanocrystals(QDs) have been emerged as next generation materials in the field of energy harvesting, sensor, and light emitting because of their compatibility with solution process and controllable energy band gap. Especially, characteristics of color tuning and color purity make it possible for QDs to be used photoluminescence materials. Photoluminescence devices with QDs have been researched for a long time. Photoluminescence quantum yield(PL QY) is important factor that defines the performance of Photoluminescence devices. One of the ways to achieve better PL QY is ligand modification. If ligands are changed to proper electron donating group, electrons can be confined in the core which results in enhancement of PL QY. Because of the reason, short ligands are preferred for enhancing PL QY. Thiophenol-based ligands are shorter than typical alkyl chain ligands. In this study, the effect of thiophenol-based ligands with different functional groups are investigated. Four different types of thiophenol-based organic materials are used as organic capping ligand. QDs with bare thiophenol and fluorothiophenol show better quantum yield compared to oleic acid.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.1
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• pp.38-46
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• 2000

This study have established proper mathematical equivalent model of Brushless DC (BLDC) motor and estimated the motor parameter by means of the back-emf measurement as being the step input to the controlled target BLDC motor. And the validity of proposed estimation method is confirmed by the test result of step response. As well, we have designed the reasonable digital controller as a consequence of the root locus method which is obtained from the open-loop transfer function of BLDC motor with hall sensor, and the determination of control gain for variable speed control. Here, revised Ziegler-Nichols tuning method is applied for the proper digital gain establishment, and the system stability is verified by the frequency domain analysis with Bode-plot and experimentation.