• KIEAE Journal
• /
• v.16 no.3
• /
• pp.89-94
• /
• 2016

Purpose: This study aimed at developing an artificial neural network (ANN) model to predict the optimal start moment of the setback temperature during the normal occupied period of a building. Method: For achieving this objective, three major steps were conducted: the development of an initial ANN model, optimization of the initial model, and performance tests of the optimized model. The development and performance testing of the ANN model were conducted through numerical simulation methods using transient systems simulation (TRNSYS) and matrix laboratory (MATLAB) software. Result: The results analysis in the development and test processes revealed that the indoor temperature, outdoor temperature, and temperature difference from the setback temperature presented strong relationship with the optimal start moment of the setback temperature; thus, these variables were used as input neurons in the ANN model. The optimal values for the number of hidden layers, number of hidden neurons, learning rate, and moment were found to be 4, 9, 0.6, and 0.9, respectively, and these values were applied to the optimized ANN model. The optimized model proved its prediction accuracy with the very storing statistical correlation between the predicted values from the ANN model and the simulated values in the TRNSYS model. Thus, the optimized model showed its potential to be applied in the control algorithm.

• Journal of radiological science and technology
• /
• v.5 no.1
• /
• pp.55-62
• /
• 1982

Measurement of triiodothyronine($T_3$) concentration is useful for the diagnosic and treatment of thyroid gland diseases. Fundermental studies of measurement of $T_3$ concentration by radioimmunoassay were performed and values determined by commercially available kit, Coat-A-Count, Diagnostic Products Corporation. The optimal utilization of the radioimmunoassay in measuring $T_3$ concentrations is dependant not only on high quality performance of the assay, but also on their appropriate application to the clinical situation. These are several aspects that must be considered in every individual case. These include factors such as accurate pippeting in reagent preparation in the assay and through decanting to remove all visible moisture after incubation steps and so forth. In attempt to assess quality control of the radioimmunoassay of serum $T_3$, serum pools with high, medium, low $T_3$ concentrations were assayed for each of 5 samples. The results obtained with this study were as follow: 1. The coefficient of variation(C. V.) for the standard curve ranged between $0.2{\sim}3.5%$. 2. It was necessary that both incubation time and temperature should correctly be maintained all the in the assay performance. 3. The precison with the $T_3$ RIA procedure was good. 4. The measured values of serially diluted serum $T_3$ concentration with Ong/dl standard solution was proportional to the predicted values. However dilution curve of distilled water was not strait. 5. Calculated $T_3$ values of patient serum in normal group was $107.8{\pm}25.90ng/dl$ in male patient and $127.29{\pm}24.08ng/dl$ in female patient.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.1
• /
• pp.125-132
• /
• 2011

Fire monitoring system detects a fire based on the values of various sensors, such as smoke, CO, temperature, or change of temperature. It detects a fire by comparing sensed values with predefined threshold values for each sensor. However, to prevent a fire it is required to predict a situation which has a possibility of fire occurrence. In this work, we propose a fire recognition system using a fuzzy inference method. The rule base is constructed as a combination of fuzzy variables derived from various sensed values. In addition, in order to solve generalization and formalization problems of rule base construction from expert knowledge, we analyze features of fire patterns. The constructed rule base results in an improvement of the recognition accuracy. A fire possibility is predicted as one of 3 levels(normal, caution, danger). The training data of each level is converted to fuzzy rules by FCM(fuzzy C-means clustering) and those rules are used in the inference engine. The performance of the proposed approach is evaluated by using forest fire data from the UCI repository.

• The Korean Journal of Nuclear Medicine
• /
• v.34 no.1
• /
• pp.39-54
• /
• 2000

Purpose: Dipyridamole stress myocardial perfusion SPECT could predict prognosis, however, long-term follow-up showed change of hazard ratio in patients with suspected coronary artery disease. We investigated how long normal SPECT could predict the benign prognosis on the long-term follow-up. Materials and Methods: We followed up 1169 patients and divided these patients into groups in whom coronary angiography were performed and were not. Total cardiac event rate and hard event rate were predicted using clinical, angiographic and SPECT findings. Predictive values of normal and abnormal SPECT were examined using survival analysis with Mantel-Haenszel method, multivariate Cox proportional hazard model analysis and newly developed statistical method to test time-invariance of hazard rate and changing point of this rate. Results: Reversible perfusion decrease on myocardial perfusion SPECT predicted higher total cardiac event rate independently and further to angiographic findings. However, myocardial SPECT showed independent but not incremental prognostic values for hard event rate. Hazard ratio of normal perfusion SPECT was changed significantly (p<0.001) and the changing point of hazard rate was 4.4 years of follows up. However, the ratio of abnormal SPECT was not. Conclusion: Dipyridamole stress myocardial perfusion SPECT provided independent prognostic information in patients with known and suspected coronary artery disease. Normal perfusion SPECT predicted least event rate for 4.4 years.

• Journal of the Korean earth science society
• /
• v.23 no.7
• /
• pp.587-596
• /
• 2002

A super ensemble model was developed for the seasonal prediction of regional precipitation in Korea using the lag correlated large scale predictors, based on the empirical orthogonal function (EOF) analysis and multiple linear regression model. The predictability of this model was also evaluated by cross-validation. Correlation between the predicted and the observed value obtained from the super ensemble model showed 0.73 in spring, 0.61 in summer, 0.69 in autumn and 0.75 in winter. The predictability of categorical forecasting was also evaluated based on the three classes such as above normal, near normal and below normal that are clearly defined in terms of a priori specified by threshold values. Categorical forecasting by the super ensemble model has a hit rate with a range from 0.42 to 0.74 in seasonal precipitation.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.2
• /
• pp.115-123
• /
• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Journal of Korean Society of Disaster and Security
• /
• v.14 no.4
• /
• pp.17-27
• /
• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Nuclear Engineering and Technology
• /
• v.55 no.10
• /
• pp.3543-3548
• /
• 2023

Shutdown chemistry evolution is performed in nuclear power plants at each refueling outage (RFO) to establish safe conditions to open system and minimize inventory of corrosion products in the reactor coolant system (RCS). After hydrogen peroxide is added to RCS during shutdown chemistry evolution, corrosion products are released and are removed by filters and ion exchange resins in the chemical volume control system (CVCS). Shutdown chemistry evolution including RCS clean-up time to remove released corrosion products impacts the critical path schedule during RFOs. The estimation of clean-up time prior to RFO can provide more reliable actions for RCS clean-up operations and transients to operators during shutdown chemistry. Electric Power Research Institute (EPRI) shutdown calculator (SDC) enables to provide clean-up time by Co-58 peak activity through operational data from nuclear power plants (NPPs). In this study, we have investigated the results of EPRI SDC by shutdown chemistry data of Co-58 activity using NPP data from previous cycles and modeled the estimated clean-up time by EPRI SDC using average Co-58 activity of the NPP. We selected two RFO data from the NPP to evaluate EPRI SDC results using the purification time to reach to 1.3 mCi/cc of Co-58 after hydrogen peroxide addition. Comparing two RFO data, the similar purification time between actual and computed data by EPRI SDC, 0.92 and 1.74 h respectively, was observed with the deviation of 3.7-7.2%. As the modeling the estimated clean-up time, we calculated average Co-58 peak concentration for normal cycles after cycle 10 and applied two-sigma (2σ, 95.4%) for predicted Co-58 peak concentration as upper and lower values compared to the average data. For the verification of modeling, shutdown chemistry data for RFO 17 was used. Predicted RCS clean-up time with lower and upper values was between 21.05 and 27.58 h, and clean-up time for RFO 17 was 24.75 h, within the predicted time band. Therefore, our calculated modeling band was validated. This approach can be identified that the advantage of the modeling for clean-up time with SDC is that the primary prediction of shutdown chemistry plans can be performed more reliably during shutdown chemistry. This research can contribute to improving the efficiency and safety of shutdown chemistry evolution in nuclear power plants.

• Geophysics and Geophysical Exploration
• /
• v.14 no.1
• /
• pp.50-57
• /
• 2011

For effective and accurate prediction of overpressure in the Efomeh field, located in the Niger delta basin of Nigeria, integrated seismic and borehole analyses were undertaken. Normal and abnormal pore pressure zones were delineated based on the principle of normal and deviation from normal velocity trends. The transition between the two trends signifies the top of overpressure. The overpressure tops were picked at regular intervals from seismic data using interval velocities obtained by applying Dix's approximation. The accuracy of the predicted overpressure zone was confirmed from the sonic velocity data of the Efomeh 01 well. The variation to the depth of overpressure between the predicted and observed values was less than 10mat the Efomeh 01 well location, with confidence of over 99 per cent. The depth map generated shows that the depth distribution to the top of the overpressure zone of the Efomeh field falls within the sub-sea depth range of 2655${\pm}$2m (2550 ms) to 3720${\pm}$2m (2900 ms). This depth conforms to thick marine shales using the Efomeh 01 composite log. The lower part of the Agbada Formation within the Efomeh field is overpressured and the depth of the top of the overpressure does not follow any time-stratigraphic boundary across the field. Prediction of the top of the overpressure zone within the Efomeh field for potential wells that will total depth beyond 2440m sub-sea is very important for safer drilling practice as well as the prevention of lost circulation.

• Wind and Structures
• /
• v.4 no.3
• /
• pp.177-196
• /
• 2001

At present the most popular turbulence models used for engineering solutions to flow problems are the $k-{\varepsilon}$ and Reynolds stress models. The shortcoming of these models based on the isotropic eddy viscosity concept and Reynolds averaging in flow fields of the type found in the field of Wind Engineering are well documented. In view of these shortcomings this paper presents the implementation of a non-linear model and its evaluation for flow around a building. Tests were undertaken using the classical bluff body shape, a surface mounted cube, with orientations both normal and skewed at $45^{\circ}$ to the incident wind. Full-scale investigations have been undertaken at the Silsoe Research Institute with a 6 m surface mounted cube and a fetch of roughness height equal to 0.01 m. All tests were originally undertaken for a number of turbulence models including the standard, RNG and MMK $k-{\varepsilon}$ models and the differential stress model. The sensitivity of the CFD results to a number of solver parameters was tested. The accuracy of the turbulence model used was deduced by comparison to the full-scale predicted roof and wake recirculation zone lengths. Mean values of the predicted pressure coefficients were used to further validate the turbulence models. Preliminary comparisons have also been made with available published experimental and large eddy simulation data. Initial investigations suggested that a suitable turbulence model should be able to model the anisotropy of turbulent flow such as the Reynolds stress model whilst maintaining the ease of use and computational stability of the two equations models. Therefore development work concentrated on non-linear quadratic and cubic expansions of the Boussinesq eddy viscosity assumption. Comparisons of these with models based on an isotropic assumption are presented along with comparisons with measured data.