Analysis of Ionospheric foF2 by Solar Activity over the Korean Peninsula

The F2 layer is the upper sector of the ionospheric F region, and it is ~250 km above sea level. It has a high electron density and thus plays an important role in shortwave communications. The variations of the critical frequency of the F2 layer (foF2) offer clues regarding the events happening within the entire F2 layer, and foF2 analysis is essential for stable shortwave communications. This study analyzes the seasonal and annual variations of the foF2 as well as the reactions of the F2 layer height at two locations in South Korea by employing the mean and standard deviation (SD) used in previous studies. To ensure a more elaborate analysis, the median and quartiles were used for analyzing the ionosphere. We thereby compensate for the limitations of the mean and SD in developing the SD, despite the convenience of the SD for probability analysis. The application of the median and quartiles for the analysis of ionospheric data led to analysis results with greater detail. This was achieved by determining the relative SD and concurrently displaying the outliers and range of variation

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Abstract In this paper, we proposed a novel location estimation algorithm based on the concept of space-time signature matching in a moving target environment. In contrast to previous fingerprint-based approaches that rely on received signal strength (RSS) information only, the proposed algorithm uses angle, delay, and RSS information from the received signal to form a signature, which in turn is utilized for location estimation. We evaluated the performance of the proposed algorithm in terms of the average probability of error and the average error distance as a function of target movement. Simulation results confirmed the effectiveness of the proposed algorithm for location estimation even in moving target environment

Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter

Demand for more accurate particulate matter forecasts is accumulating owing to the increased interest and issues regarding particulate matter. Incredibly low concentration particulate matter, which accounts for most of the overall particulate matter, is often underestimated when a particulate matter prediction model based on machine learning is used. This study proposed a concentration-specific separation prediction model to overcome this shortcoming. Three prediction models based on Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), commonly used for performance evaluation of the proposed prediction model, were used as comparative models. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and accuracy were utilized for performance evaluation. The results showed that the prediction accuracy for all Air Quality Index (AQI) segments was more than 80 percent in the entire concentration spectrum. In addition, the study confirmed that the over-prediction phenomenon of single neural network models concentrated in the ‘normal’ AQI region was alleviated

Comparative analysis of multiple classification models to improve PM10 prediction performance

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model

Development of Automatic Mold Shot Measurement and Management System for Smart Factory

Many small- and medium-sized car-part manufacturers are either still managing their mold manually or rarely managing it, and therefore, experience significant manufacturing cost and loss in time. In such a situation, a module has been developed in the present work which can count the number of mold used. Such a module is extremely important for small and medium-sized enterprises (SMEs) applying which in the production line they will be able to manage the mold life cycle and improve product quality. This is expected to have both direct and indirect effects on their business activities. The developed system uses a photo sensor, distance measurement sensor, Atmega128 MCU, tablet pc and Bluetooth communication module. The actual module developed in this study was set up on a molding equipment for test and data were collected using an existing tablet PC. The test showed that the number of shots increased when the upper mold touched the lower mold. The maximum and minimum value between the upper and lower molds could be adjusted with the automatic mold shot measurement and management system. Therefore, any molding equipment with various upper-lower gaps will be able to apply the newly developed system

Easy Diagnosis of Asthma: Computer-Assisted, Symptom-Based Diagnosis

Diagnosis of asthma is often challenging in primary-care physicians due to lack of tools measuring airway obstruction and variability. Symptom-based diagnosis of asthma utilizing objective diagnostic parameters and appropriate software would be useful in clinical practice. A total of 302 adult patients with respiratory symptoms responded to a questionnaire regarding asthma symptoms and provoking factors. Questions were asked and recorded by physicians into a computer program. A definite diagnosis of asthma was made based on a positive response to methacholine bronchial provocation or bronchodilator response (BDR) testing. Multivariate logistic regression analysis was used to evaluate the significance of questionnaire responses in terms of discriminating asthmatics. Asthmatic patients showed higher total symptom scores than non-asthmatics (mean 5.93 vs. 4.93; p<0.01). Multivariate logistic regression analysis identified that response to questions concerning the following significantly discriminated asthmatics; wheezing with dyspnea, which is aggravated at night, and by exercise, cold air, and upper respiratory infection. Moreover, the presence of these symptoms was found to agree significantly with definite diagnosis of asthma (by kappa statistics). Receiver-operating characteristic curve analysis revealed that the diagnostic accuracy of symptom-based diagnosis was high with an area under the curve of 0.647±0.033. Using a computer-assisted symptom-based diagnosis program, it is possible to increase the accuracy of diagnosing asthma in general practice, when the facilities required to evaluate airway hyperresponsiveness or BDR are unavailable