• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2351-2357
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• 2008

Noninvasive radial artery pulse wave has been widely used not only for the pulse wave analysis(PWA) itself but also for assessment of arterial stiffness with estimated aortic pulse wave from peripheral pulse wave. However, it has been found that the deformation of pulse shape can be caused readily by changing measuring position, indentation pressure, and so on. So, in this study, we have developed a system which can measure radial pulse wave and skin displacement simultaneously while the indentation body goes down to occlude subject's radial artery. This system can be divided into a measuring apparatus part, an indentation control hardware part, a data acquisition part and a control and computation part. And, the measuring apparatus consists of an arm-rest, a step motor, an indentation body, a laser displacement sensor(LK-G30, Keyence Co.) and pulse wave sensor. Under load-free condition and radial artery loaded condition, the evaluation of developed system has been performed. From these results, we can conclude: 1) The developed system can control the indentation body quantitatively and the adopted laser displacement sensor shows linear output characteristic even with skin as a reflector. 2) This system can measure the pulse wave and the displacement of indentation body, that is, skin displacement simultaneously at each specific level of indentation body. 3) This system can provide the number of motor steps used to get down the indentation body, the measured skin displacement, the calculated indentation pressure, the calculated pulse pressure and the pulse waveform as well as the information generated by combining these with each others. 4) This system can reveal the relationship between the morphological changes of pulse wave and the estimated displacement of radial artery wall by indentation. Consequently, the developed system can furnish more abundant information on radial artery than previous diagnosis systems based on tonometric measurement. In further study, we expect to setup the standard measuring process and to concrete the algorithm for the estimation of radial artery's diameter and of displacement of radial artery's wall. Furthermore, with well designed clinical studies, we hope to turn out the usefulness of developed system in the field of cardiovascular system evaluation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1001-1006
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• 2023

Globally, rising energy costs and increased energy demand are important issues for the food processing and manufacturing industries, which consume significant amounts of energy throughout the supply chain. Accordingly, there is a need for the development of a real-time energy monitoring and analysis system that can optimize energy use. In this study, a food factory energy monitoring system was proposed based on IoT installed in a food factory, including monitoring of each facility, energy supply and usage monitoring for the heat treatment process, and search functions. The system is based on the IoT sensor of the food processing plant and consists of PLC, database server, OPC-UA server, UI server, API server, and CIMON's HMI. The proposed system builds big data for food factories and provides facility-specific monitoring through collection functions, as well as energy supply and usage monitoring and search service functions for the heat treatment process. This data collection-based energy monitoring system will serve as a guide for the development of a small and medium-sized factory energy monitoring and management system for energy savings. In the future, this system can be used to identify and analyze energy usage to create quantitative energy saving measures that optimize process work.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.507-516
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• 2019

Costs invested in road maintenance and road development are on the rise. However, due to accidents such as portholes and ground subsidence, the risks to the drivers' safety and the material damage caused by accidents are also increasing. Following this trend, we have developed a system that determines road damage, according to the magnitude of vibration generated without directly intervening the driver when driving. In this paper, we implemented the system using a remote control car (RC car) simulator due to the limitation of the environment in which the actual vehicle is not available in the process of developing the system. In addition, we attached a vibration sensor and GPS sensor to the body of the RC car simulator to measure the vibration value and location information generated by the movement of the vehicle in real-time while driving, and transmitting the corresponding data to the server. In this way, we implemented a system that allows external users to check the damage of roads and the maintenance of the repaired roads based on data more easily than the existing systems. By using this system, we can perform early prediction of road breakage and pattern prediction based on the data. Further, for the RC car simulator, commercialization will be possible by combining it with business in other fields that require flatness.

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.85-92
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• 2008

National Oceanographic Research Institute is carrying out an oceanographic survey for the entire sea areas around Korean Peninsula annually starting with the East Sea from 1996 by establishing a national oceanographic basic map survey plan for the sea areas under the jurisdiction of Korea, so this paper used the oceanographic geomagnetism data measured at the southern area of the Yellow Sea using 'Hae Yang 2000' in 1999, aiming at clarifying the cause of geomagnetic abnormality zone during the course of treating and analyzing the geomagnetic data. For treatment of magnetic data, we obtained electromagnetic force values and geomagnetic abnormality values around the investigated sea area through a process of searching and removal of bad data, correction of sensor positions, correction of magnetic field effects around the hull, correction of diurnal variation, normal correction, correction of cross point errors, etc. The electromagnetic force distribution around the investigated sea area was $49000\;{\sim}\;51600\;nT$, which is judged to be within the normal electromagnetic force intensity distribution range around the Yellow Sea. The isodynamic lines are distributed in Northeast-Southwest direction, and electromagnetic force values are increasing toward the northwest. The result of comparing the magnetic abnormality around the sea area among $124^{\circ}$ 49' 48" E, $35^{\circ}$ 10' 48" N $\sim$ $125^{\circ}$ 7' 48" E, and $35^{\circ}$ 33' 00" N sections with the elastic wave cross section and the result of modeling coincide well with the underground geological structure clarified from the existing elastic wave survey cross section. Therefore, it is judged that the distribution of magnetic force abnormality generally shows the effect pursuant to the distribution of the sedimentary basins in the Tertiary period and the bedrocks in the Cretaceous period which are well developed in the bottom of the sea.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.3-12
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• 2016

Bluetooth Low Energy (BLE) protocol has attracted attention as a promising technology for low data throughput and low energy wireless sensor networks. Fast device discovery is very important in a BLE based wireless network. It is necessary to configure the network to work with minimized energy consumption because the BLE network nodes are expected to operate a long time typically on a coin cell battery. However, since it is difficult to obtain low energy and low latency at the same time, the BLE standard introduces wide range setting of parameters related to device discovery process and let the network operators to set up parameter values for the application. Therefore, it is necessary to analyze the performance of device discovery according to the related parameter values prior to BLE network operation. In this paper we analyze the expected value and the cumulative distribution function of the number of advertisements for device discovery in the BLE network. In addition, we propose a scheme for controlling the interval between advertising events that can improve the performance of device discovery without increasing energy consumption.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.5
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• pp.34-41
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• 2011

This paper presents a 24 channel capacitive touch sensing ASIC. This ASIC consists of analog circuit part and digital circuit part. Analog circuits convert user screen touch into electrical signal and digital circuits represent this signal change as digital data. Digital circuit also has an I2C interface for operation parameter reconfiguration from host machine. This interface guarantees the stable operation of the ASIC even against wide operation condition change. This chip is implemented with 0.18 um CMOS process. Its area is about 3 $mm^2$ and power consumption is 5.3mW. A number of EDA tools from Cadence and Synopsys are used for chip design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2435-2441
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• 2010

A global healthcare system based on M2M technology is proposed to support a good mobility, flexibility and scalability to the patients in 6LoWPAN. Sensor nodes integrated with wearable sensors are linked to gateway with IEEE 802.15.4 protocol and 6LoWPAN protocol for data acquisition and transmission purpose via external network. In the server, heart rate variability signals are obtained by signal processing and used for time and frequency domain performance analysis to evaluate the patient's health status. Our approach for global healthcare system with non-invasive and continuous IP-based communication is managed to process large amount of biomedical signals in the large scale of service range accurately.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.141-150
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• 2011

Precise image-to-image registration is required to process multi-sensor data together. The purpose of this paper is to develop an algorithm that register between high-resolution optical and SAR images using linear features. As a pre-processing step, initial alignment was fulfilled using manually selected tie points to remove any dislocations caused by scale difference, rotation, and translation of images. Canny edge operator was applied to both images to extract linear features. These features were used to design a cost function that finds matching points based on their similarity. Outliers having larger geometric differences than general matching points were eliminated. The remaining points were used to construct a new transformation model, which was combined the piecewise linear function with the global affine transformation, and applied to increase the accuracy of geometric correction.