• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1201-1206
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• 2003

A measurement device for gas flow rate using hot-wire module is developed for the utilization in low-accuracy industrial applications. The module has three wires of measuring and heating, and a bridge circuit is installed to detect electric current through the wire in the module. An amplification of the signal and conversion to digital output are conducted for the online measurement with a personal computer. In addition, temperature distribution in the module is numerically analyzed to examine the measured outcome from the module experiment. The flow rate of air and carbon dioxide gas is separately measured for the performance examination of the device. The experimental relation of measurement and flow agrees with the prediction from the numerical analysis. The outcome of the performance test indicates that the accuracy and reproducibility of the module is satisfactory for the purpose of industrial applications.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.87-95
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• 2005

value prediction in high performance micro processors is a technique that exploits Instruction Level Parallelism(ILP) by predicting the outcome of an instruction and by breaking and executing true data dependences. In this paper, the mean Performance improvements by predictor according to a point of time for update of each table as well as prediction accuracy and Prediction rate are measured and assessed by comparison and analysis of value predictor that issues in parallel and run by predicting value, which is for Performance improvements of ILP in micro Processor. For the verification of its validity the SPECint95 benchmark through the simulation is compared by making use of execution driven system.

• KEPCO Journal on Electric Power and Energy
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• v.3 no.2
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• pp.133-140
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• 2017

The performance prediction software developed in this paper is a process analysis tool that enables one to foretell the behavior of processes when certain conditions of operation are altered. The immediate objective of this research is to predict the process characteristics of combined heat and power plant under varying operating conditions. A cogeneration virtual power plant that mimics the mechanical performance of the actual plant was constructed and the performance of the power plant was predicted in the following varying atmospheric conditions: temperature, pressure and humidity. This resulted in a positive outcome where the performance of the power plant under changing conditions were correctly predicted as well as the calorific value of low calorific gas fuel such as shale gas and PNG. The performance prediction tool can detect the operation characteristics of the power plant through the performance index analysis and thus propose the operation method taking into consideration the changes in environmental conditions.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.11
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• pp.601-612
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• 2001

In superscalar processors, value prediction is a technique that breaks true data dependences by predicting the outcome of an instruction in order to exploit instruction level parallelism(ILP). A value predictor looks up the prediction table for the prediction value of an instruction in the instruction fetch stage, and updates with the prediction result and the resolved value after the execution of the instruction for the next prediction. However, as the instruction fetch and issue rates are increased, the same instruction is likely to fetch again before is has been updated in the predictor. Hence, the predictor looks up the stale value in the table and this mostly will cause incorrect value predictions. In this paper, a stride value predictor with the capability of speculative updates, which can update the prediction table speculatively without waiting until the instruction has been completed, is proposed. Also, the performance of the scheme is examined using Simplescalar simulator for SPECint95 benchmarks in which our value predictor is added.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.113-114
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• 2013

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging is a powerful technique for producing chemical images of small biomolecules (ex. metabolites, lipids, peptides) "as received" because of its high molecular specificity, high surface sensitivity, and submicron spatial resolution. In addition, matrix-assisted laser desorption and ionization time-of-flight (MALDI-TOF) imaging is an essential technique for producing chemical images of large biomolecules (ex. genes and proteins). For this talk, we will show that label-free mass imaging technique can be a platform technology for biomedical studies such as early detection/diagnostics, accurate histologic diagnosis, prediction of clinical outcome, stem cell therapy, biosensors, nanomedicine and drug screening [1-7].

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.853-865
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• 2013

Inflatable panels made of modern and new textile materials can be inflated at high pressure to have a high mechanical strength. This paper is based on the finite element method as a general solution to determine the characteristics of deformed inflatable panels at high pressure in various end and loading conditions. Proposed method is based on the construction of weak form of formulation and application of Reduced Integration Element method (RIE) to solve the numerical problem of shear locking. The numerical results are validated as an outcome of comparison with other published results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.672-675
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• 1995

Because of a sudden growth of the research of fatigue failure, recent machines or structures have been designed by damage tolerance design in many fields. Consequently, it is the most primary factor to clarity the specific character of fatique failure in the design of machines or structures considering reliability. A statistical analysis is required to analyze the outcome of an experiment or a life estimate by reason of that fatigue failure contains lots of random elements. Reliability analysis which has tukenn the place of the existing analyses in the consideration of the uncertainty of a material, is a very efficient way. Even reliability analysis, however, is not a perfect way to analyses the uncertainties of all the materials. This thesis would refer to a newly conceived data analysis that the coefficient of a system could cause the ambiguity of the relationship of an input and output.

• The korean journal of orthodontics
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• v.44 no.6
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• pp.342-349
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• 2014

Because of the potential morbidity and complications associated with surgical procedures, limiting the extent of orthognathic surgery is a desire for many orthodontic patients. An eighteen-year-old woman had a severe Class III malocclusion and required bi-maxillary surgery. By changing the patient's maxillary occlusal plane using orthodontic mini-implants, she was able to avoid the maxillary surgery; requiring only a mandibular setback surgery. To accurately predict the post-surgery outcome, we applied a new soft tissue prediction method. We were able to follow and report the long-term result of her combined orthodontic and orthognathic treatment. The changes to her occlusal plane continue to appear stable over 6 years later.