• Journal of Korean Neurosurgical Society
• /
• v.65 no.1
• /
• pp.151-160
• /
• 2022

Continuous monitoring of intracranial pressure is a well established medical procedure. Still, little is known about long-term behavior of intracranial pressure in normal pressure hydrocephalus. The present study is designed to evaluate periodicity of intracranial pressure over long-time scales using intraventricular pressure monitoring in patients with normal pressure hydrocephalus. In addition, the circadian and diurnal patterns of blood pressure and body temperature in those patients are studied. Four patients, selected with "probable" normal pressure hydrocephalus, were monitored for several dozen hours. Intracranial pressure, blood pressure, and body temperature were recorded hourly. Autocorrelation functions were calculated and cross-correlation analysis were carried out to study all the time-series data. Autocorrelation results show that intracranial pressure, blood pressure, and body temperature values follow bimodal (positive and negative) curves over a day. The cross-correlation functions demonstrate causal relationships between intracranial pressure, blood pressure, and body temperature. The results show that long-term fluctuations in intracranial pressure exhibit cyclical patterns with periods of about 24 hours. Continuous intracranial pressure recording in "probable" normal pressure hydrocephalus patients reveals circadian fluctuations not related to the day and night cycle. These fluctuations are causally related to changes in blood pressure and body temperature. The present study reveals the complete loss of the diurnal blood pressure and body temperature rhythmicities in patients with "probable" normal pressure hydrocephalus.

• Proceedings of the KIEE Conference
• /
• 2000.11d
• /
• pp.666-668
• /
• 2000

In this paper, a fuzzy prediction method is proposed for time series data having uncertainty and non-stationary characteristics. Conventional methods, which use past data directly in prediction procedure, cannot properly handle non-stationary data whose long-term mean is floating. To cope with this problem, a data preprocessing technique utilizing the differences of original time series data is suggested. The difference sets are established from data. And the optimal difference set is selected for input of fuzzy predictor. The proposed method based the Takigi-Sugeno-Kang(TSK or TS) fuzzy rule. Computer simulations show improved results for various time series.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2011.05a
• /
• pp.177-178
• /
• 2011

Recently a great deal of people are taking legal action against the housing provider due to the defects of their Apartment house. And most of the housing companies are spending a huge amount of expenses and efforts to keep their brand value. This essay will carry out time series analysis the 20 housing district which are constructed by huge construction companies. This analysis itemised by metropolitan area(Seoul) and others to keep the degree of reliability, and converted future defect maintenance cost into current cost applied by discount rate to figure out suitability of defect maintenance cost. Even though, this essay is not able to represent standard of defect maintenance cost due to the insufficiency of record, while it will be assisted as a referance when long-term record of time series is estabilished.

• Journal of information and communication convergence engineering
• /
• v.9 no.4
• /
• pp.369-374
• /
• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• Fisheries and Aquatic Sciences
• /
• v.2 no.2
• /
• pp.199-209
• /
• 1999

Before investigating the long-term variations in macrobenthic communities sampled in the Chokchon macrotidal flat in Inchon, Korea, from 1989 to 1996, we need to understand how environmental factors in the area vary. As potential governing agents of tidal flat communities, abiotic factors such as mean sea level, seawater, air temperature, and precipitation were considered. Data for these factors were collected at equal intervals from 1976 or 1980 to 1996, and were analyzed using a decomposition method. In this analysis, all the above variables showed strong seasonal nature, and yielded a significant trend and cyclical variation. Positive trends were seen in the seawater and air temperatures, and based upon this relationship, it was found that the biological sampling period of our program has been carried out during warmer periods in succession. This paper puts forth some hypotheses concerning the response of tidal flat macrobenthos communities to the changing environment including mild winters in succession.

• Journal of Korean Neurosurgical Society
• /
• v.67 no.2
• /
• pp.237-248
• /
• 2024

Objective : Postoperative data on Cushing's disease (CD) are equivocal in the literature. These discrepancies may be attributed to different series with different criteria for remission and variable follow-up durations. Additional data from experienced centers may address these discrepancies. In this study, we present the results obtained from 96 endoscopic transsphenoidal surgeries (ETSSs) for CD conducted in a well-experienced center. Methods : Pre- and postoperative data of 96 ETSS in 87 patients with CD were included. All cases were handled by the same neurosurgical team between 2014 and 2022. We obtained data on remission status 3-6 months postoperatively (medium-term) and during the latest follow-up (long-term). Additionally, magnetic resonance imaging (MRI) and pathology results were obtained for each case. Results : The mean follow-up duration was 39.5±3.2 months. Medium and long-term remission rates were 77% and 82%, respectively. When only first-time operations were considered, the medium- and long-term remission rates were 78% and 82%, respectively. The recurrence rate in this series was 2.5%. Patients who showed remission between 3-6 months had higher long-term remission rates than did those without initial remission. Tumors >2 cm and extended tumor invasion of the cavernous sinus (Knosp 4) were associated with lower postoperative remission rates. Conclusion : Adenoma size and the presence/absence of cavernous sinus invasion on preopera-tive MRI may predict long-term postoperative remission. A tumor size of 2 cm may be a supporting criterion for predicting remission in Knosp 4 tumors. Further studies with larger patient populations are necessary to support this finding.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.1-9
• /
• 2020

The study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and in based on artificial intelligence-based HPAI spread analysis and patterning. The model that is actively used in time series and text mining recently is LSTM (Long Short-Term Memory Models) model utilizing deep learning model structure. The LSTM model is a model that emerged to resolve the Long-Term Dependency Problem that occurs during the Backpropagation Through Time (BPTT) process of RNN. LSTM models have resolved the problem of forecasting very well using variable sequence data, and are still widely used.In this paper study, we used the data of the Call Detailed Record (CDR) provided by KT to identify the migration path of people who are expected to be closely related to the virus. Introduce the results of predicting the path of movement by learning the LSTM model using the path of the person concerned. The results of this study could be used to predict the route of HPAI propagation and to select routes or areas to focus on quarantine and to reduce HPAI spread.

• Korean Journal of Metals and Materials
• /
• v.47 no.10
• /
• pp.613-620
• /
• 2009

The Kachanov-Rabotnov (K-R) creep model was proposed to accurately model the long-term creep curves above $10^5$ hours of Alloy 617. To this end, a series of creep data was obtained from creep tests conducted under different stress levels at $950^{\circ}C$. Using these data, the creep constants used in the K-R model and the modified K-R model were determined by a nonlinear least square fitting (NLSF) method, respectively. The K-R model yielded poor correspondence with the experimental curves, but the modified K-R model provided good agreement with the curves. Log-log plots of ${\varepsilon}^{\ast}$-stress and ${\varepsilon}^{\ast}$-time to rupture showed good linear relationships. Constants in the modified K-R model were obtained as ${\lambda}$=2.78, and $k=1.24$, and they showed behavior close to stress independency. Using these constants, long-term creep curves above $10^5$ hours obtained from short-term creep data can be modeled by implementing the modified K-R model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3923-3942
• /
• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Journal of Ocean Engineering and Technology
• /
• v.36 no.5
• /
• pp.295-302
• /
• 2022

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.