• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.92-100
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• 2009

In this paper, we analyze the hardware architecture of Low Density Parity Check (LDPC) decoder using Log Likelihood Ration-Belief Propagation (LLR-BP) decoding algorithm. Various design issues that affect the decoding performance and the hardware complexity are discussed and the tradeoffs between the hardware complexity and the performance are analyzed. The message data for passing error probability is quantized to 7 bits and among them the fractional part is 4 bits. To maintain the decoding performance, the integer and fractional parts for the intrinsic information is 2 bits and 4 bits respectively. We discuss the alternate implementation of $\Psi$(x) function using piecewise linear approximation. Also, we improve the hardware complexity and the decoding time by applying overlapped scheduling.

• Journal of IKEEE
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• v.26 no.3
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• pp.396-400
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• 2022

In this study, an ICT-based underwater communication monitoring device for underwater structures is implemented based on lifting fixture that transport human bodies found on the seabed to sea level. The lifting fixture is packaged with a retback, sideback, and cartridge that injects air. Monitoring systems are developed in a mobile manner in a portable structure. The underwater ultrasonic sensor signal is supplied using a USB port, and the O/S consists of Linux. For the underwater communication dong test, a measurement test was conducted in real time from 6m to 40m in depth on the east coast. The ultrasonic sound sensor is converted to 2,400 bps to verify the transmission error according to the duality. The communication speed of sensor to monitoring is 115,200 bps, and the speed of communication from controller to receiver is 2,400 bps. In the commercialization stage of the lifting device, it is easy to develop a low-end type and the compatibility is wide.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.61-67
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• 2023

In order to optimize the pulse electroforming copper process, a double hidden layer BP (Back Propagation) neural network is constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties is accurately established, and the prediction of microhardness and tensile strength of the electroforming layer in the pulse electroforming copper process is realized. The predicted results are verified by electrodeposition copper test in copper pyrophosphate solution system with pulse power supply. The results show that the microhardness and tensile strength of copper layer predicted by "3-4-3-2" structure double hidden layer neural network are very close to the experimental values, and the relative error is less than 2.32%. In the parameter range, the microhardness of copper layer is between 100.3~205.6MPa and the tensile strength is between 112~485MPa.When the microhardness and tensile strength are optimal,the corresponding process conditions are as follows: current density is 2A-dm-2, pulse frequency is 2KHz and pulse duty cycle is 10%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.9
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• pp.822-829
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• 2013

Hypertension is one of the most common clinical diseases, with an increasing prevalence globally. Hypertension triggers various harmful consequences and affects multiple organs. Life-long care may be required in some cases. According to the Korea Center for Disease Control and Prevention, the prevalence of hypertension is gradually increasing. A 2011 survey revealed that 28.9% of Korean adults had hypertension. The prevalence rates were slightly higher among men than women. Accurate measurement of blood pressure(BP) is crucial to classify patients, to identify BP-related risks, and to inform correct treatment. For accurate blood pressure measurement, the use of a cuff bladder size appropriate for the mid-upper arm circumference(MUAC) is essential. Incorrect sized cuff bladder is one of the main causes of equipment error affecting sphygmomanometer accuracy. When commercial sphygmomanometers were examined, the cuff bladders differed from the dimensions specified in the ISO 81060-1:2007 standards. Undercuffing is responsible for a spurious overestimation of BP in patients with large arms leading to overdiagnosis of hypertension, whereas overcuffing (that is, use of relatively large cuffs with small arms), may be responsible for an opposite problem, leading to erroneous underestimation of BP levels. The cuff bladder sizes recommended by the American Heart Association(AHA) are an arm circumference(AC) of 17-25 cm for small-sized adults, AC of 24-32 cm for adults, AC of 32-42 cm for normal-sized adults, and AC of 42-50 cm for obese adults. In contrast, the AC of Korean adults ranges from 23-31 cm, belonging to a single type of adult bladder. Three types of bladders are necessary for Korean adults with an AC of 23-31cm. Hospitals often use one or two differently-sized Western cuffs for adult patients, which can yield inaccurate BP determinations. Cuff bladders with dimensions based on anthropometric reference data obtained from Koreans will aid hospitals to measure BP more accurately.

• Korean Journal of Food Science and Technology
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• v.27 no.4
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• pp.525-531
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• 1995

Three quality factors of bread during baking process were measured to develop neural network models for bread baking process. Firstly, volume and browning changes during bread baking process were measured using image processing technique and temperature changes inside the bread during process were measured by K-type thermocouples. Relationships among them showed nonlinearity. Secondly, multilayer perception structure with error back propagation learning was used to construct neural network models. Three neural network models for volume, browning, and bread temperature were developed respectively. Developed models showed good performance with predictive error of 4.62% for volume and browning changes after 30 seconds, 7.38% for volume and browning changes after 2 minutes, and 1.09% for temperature change inside the bread respectively.

• Structural Engineering and Mechanics
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• v.85 no.5
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• pp.635-644
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• 2023

The design of honeycomb sandwich structures is often challenging because these structures can be tailored from a variety of possible cores and face sheets configurations, therefore, the design of sandwich structures is characterized as a time-consuming and complex task. A data-driven computational approach that integrates the analytical method and Artificial Neural Network (ANN) is developed by the authors to rapidly predict the design of sandwich structures for a targeted maximum structural deflection. The elaborated ANN reverse design approach is applied to obtain the thickness of the sandwich core, the thickness of the laminated face sheets, and safety factors for composite sandwich structure. The required data for building ANN model were obtained using the governing equations of sandwich components in conjunction with the Monte Carlo Method. Then, the functional relationship between the input and output features was created using the neural network Backpropagation (BP) algorithm. The input variables were the dimensions of the sandwich structure, the applied load, the core density, and the maximum deflection, which was the reverse input given by the designer. The outstanding performance of reverse ANN model revealed through a low value of mean square error (MSE) together with the coefficient of determination (R2) close to the unity. Furthermore, the output of the model was in good agreement with the analytical solution with a maximum error 4.7%. The combination of reverse concept and ANN may provide a potentially novel approach in designing of sandwich structures. The main added value of this study is the elaboration of a reverse ANN model, which provides a low computational technique as well as savestime in the design or redesign of sandwich structures compared to analytical and finite element approaches.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.178-186
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• 2007

This study is attempted to correct an error of electronic blood pressure meter with an optical sensor. In general, for a hospitalized patient, ECG, blood pressure, oxygen saturation, and respiration are basically measured to monitor the patient's condition. Opening of a blood vessel after it is occluded by pressurizing the cuff influences the blood flow of peripheral blood vessels as well as oscillation changes in the cuff. Blood vessels are occluded and peripheral blood flow disappears at cuff pressure above the examinee's blood pressure, while blood vessels are opened and peripheral blood flow appears again at cuff pressure under the examinee's blood pressure. Then Disappear-Appear Point Length(DAPL) of peripheral blood flow can be judged with the signal of peripheral blood flow, thus is available as a factor of error correction for electronic blood pressure meter. Also, systolic or diastolic blood pressure can be corrected with Appear-Point-Pressure(APP) of cuff pressure at a point where blood flow occurs and Appear-Maximum Pressure(AMP) of cuff pressure at the maximum amplitude point of peripheral blood flow after peripheral blood flow appears again. For verification, 27 examinees were selected, and their blood value was obtained through experimental procedure of 4 stages including induction of blood pressure change. The examinees were divided into two groups of experimental group and control group, regression analysis was conducted for experimental group, and correction of a blood pressure error was verified with optical signal by applying the regression equation calculated in experimental group to control group. As an experimental result, mean of the whole measurement errors was 5mmHg or more, which did not meet the standard fur blood pressure meter. As a result of correcting blood pressure measurements with data of DAPL, APP, and AMP as drawn out of PPG signal, systolic blood pressure, mean blood pressure, and diastolic blood pressure were $-0.6{\pm}4.4mmHg,\;-1.0{\pm}3.9mmHg$ and $-1.3{\pm}5.4mmHg$, respectively, indicating that mean of the whole measurement errors was greatly improved, and standard deviation was decreased.

• Journal of Biosystems Engineering
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• v.24 no.2
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• pp.153-158
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• 1999

As a basic research for the development of the automatic grading and sorting system for dried oak mushrooms, the device to acquire both cap and gill side images of mushroom has been developed and neural network based side recognition and quality grading has been proposed via inputting both side images. 20 quality grades have been selected considering the requirement of grade classifications imposed by the mushroom company. Developed DC motor driven‘V’type reversing device for the image acquisition of both side images of mushroom showed more than 95% success. Most error was caused by very small size mushrooms with a radius of around 1cm. However, it required a further research to reduce the reversing time. Grading and side recognition were performed via inputting normalized size factors and average gray levels of $8{\times}8$ grids converted from the raw images of both surfaces to the multi-layer back propagation(BP) network. Accuracy of the grading showed about 88.5% and the total grading time including reversing operation was around 2 seconds.

• 보존과학연구
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• s.26
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• pp.189-202
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• 2005

This study carried out to understand thermoluminescence dating of ancient tiles at Jinju Janghungri Kilin site. Also radiocarbon dating by the benzene synthesis method and Liquid scintillation counting method were performed for comparison for the agedetermination of charcoal sample at the obtained same site.1st and 2nd glow curve were obtained according to the typical method thermoluminescence. Plateau tests of revealed the proper temperature range to be $300~440^{\circ}C$ Palaeodose average values were formed to the 2.44Gy.Annual dose of ancient tiles was calculated from soil samples and ancient tiles it self by measuring alpha radiation dose, potassium concentrations and water contents respectively. Annual dose average values were calculated to be 7.012mGy/yr.The radiocarbon age(BP year) was converted to calibrated age(AD/BC year) using high precision curve. Radiocarbon ages were calculated to be AD 15~17 .Comparison of samples with their radiocarbon and thermoluminescence ages for revealed the in range the AD 15~17. This result means that the measured absolute ages are in good agreement with each other within the margin of error.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.111-130
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• 1998

We propose an imperfect repair model which depends on external effects quantified by covariates. The model is based on the Brown-Proschan imperfect repair model wherefrom the probability of perfect repair is represented by a function of covariates. We are motivated by deficiency of the BP model whose stationarity prevents us from predicting dynamically the time to next failure according to external condition. Five types of function for the probability of perfect repair are proposed. This article also presents a procedure for estimating the parameter of the function for the probability of perfect repair, as well as the inherent lifetime distribution of the system, based on consecutive inter-failure times and the covariates. The estimation procedure is based on the expectation-maximization principle which is suitable to incomplete data problems. focusing on the maximization step, we derive some theorems which guarantee the existence of the solution. A Monte Carlo study is also performed to illustrate the prediction power of the model as well as to show reasonable properties of the estimates. The model reduces significantly the mean square error of the in-sample prediction. so it can be utilized in real fields for evaluating and maintaining repairable systems.