• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6C
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• pp.480-486
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• 2008

The high peak-to-average power ratio (PAPR) of the transmitted signals is one of major drawbacks of the orthogonal frequency division multiplexing (OFDM). A partial transmit sequences (PTS) technique can improve the PAPR statistics of OFDM signals. However, in a PTS technique, the search complexity to select phase weighting factors increases exponentially with the number of sub-blocks. In this paper, a PTS technique with low computational complexity is presented, which adopts micro-genetic algorithm(${\mu}$-GA) as a search algorithm. A search on the phase weighting factors starts with a population of five randomly generated individuals. An elite having the largest fitness value and the other four individuals selected through the tournament selection strategy are determined, and then the next generation members are generated through the crossover operations among those. If the new generation converges, all the four individuals except the elite are randomly generated again. The search terminates when there has been no improvements on the PAPR during the predefined number of generations, or the maximum number of generations has been reached. To evaluate the performance of the proposed PTS technique, the complementary cumulative distribution functions (CCDF) of the PAPR are compared with those of the conventional PTS techniques.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.95-101
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• 2011

In this paper, we propose the gaussian mixture model based pattern classification algorithm of forearm electromyogram. We define the motion of 1-degree of freedom as holding and unfolding hand considering a daily life for patient with prosthetic hand. For the extraction of precise features from the EMG signals, we use the difference absolute mean value(DAMV) and the mean absolute value(MAV) to consider amplitude characteristic of EMG signals. We also propose the D_DAMV and D_MAV in order to classify the amplitude characteristic of EMG signals more precisely. In this paper, we implemented a test targeting four adult male and identified the accuracy of EMG pattern classification of two motions which are holding and unfolding hand.

• Journal of the Korean Solar Energy Society
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• v.37 no.2
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• pp.23-33
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• 2017

Accurate prediction of stochastic behavior of occupants is a well known problem for improving prediction performance of building energy use. Many researchers have been tried various sensors that have information on the status of occupant such as $CO_2$ sensor, infrared motion detector, RFID etc. to predict occupants, while others have been developed some algorithm to find occupancy probability with those sensors or some indirect monitoring data such as energy consumption in spaces. In this research, various sensor data and energy consumption data are utilized for decision tree algorithms (C4.5 & CART) for estimation of sub-hourly occupancy status. Although the experiment is limited by space (private room) and period (cooling season), the prediction result shows good agreement of above 95% accuracy when energy consumption data are used instead of measured $CO_2$ value. This result indicates potential of IoT data for awareness of indoor environmental status.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.3
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• pp.191-201
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• 1997

Accurate positioning of a throttle valve is required to implement the traction control system(TCS) which improves acceleration performance in slippery roads. In this research, position control system is developed for the main throttle actuator(MTA) system which uses one throttle actuation for small volume and DC servo motor for fast response. In order to drive DC motor, PWM signal generator and PWM amplifier were built and interfaced to the motor and controller. Digital PID control law is used as basic control algorithm. In order to prevent overshoot and improve accuracy, velocity profiles are generated and implemented whenever the targer throttle angle is given from the TCS controller. Thanks to velocity profiles, the control performance was very good and only one set of PID gains was used to cover the entire operating range. Also, the resolution of position is about 0.4$^{\circ}C$, which is better than that of stepping motor also used as throttle actuator in some products. The response time of the developed system is also fast enough to implement the engine control based TCS algorithm.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.682-693
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• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.89-95
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• 2004

Intensity-modulated radiation therapy (IMRT) often uses small beam segments. The heterogeneity effect is well known for relatively large field sizes used in the conventional radiation treatments. However, this effect is not known in small fields such as the beamlets used in IMRT. There are many factors that can cause errors in the small field i.e. electronic disequilibrium and multiple electron scattering. This study prepared geometrically regular heterogeneous phantoms, and compared the measurements with the calculations using the Convolution/Superposition algorithm and Monte Carlo method for small beams. This study used the BEAM00/EGS4 code to simulate the head of a Varian 2300C/D. The commissioning of a 6MV photon beam were performed from two points of view, the beam profiles and depth doses. The calculated voxel size was 1${\times}$1${\times}$2$\textrm{cm}^2$ with field sizes of 1${\times}$1$\textrm{cm}^2$, 2${\times}$2$\textrm{cm}^2$, and 5${\times}$5$\textrm{cm}^2$. The XiOTM TPS (Treatment Planning System) was used for the calculation using the Convolution/Superposition algorithm. The 6MV photon beam was irradiated to homogeneous (water equivalent) and heterogeneous phantoms (water equivalent ＋ air cavity, water equivalent ＋ bone equivalent). The beam profiles were well matched within :t1 mm and the depth doses were within ${\pm}$2%. In conclusion, the dose calculations of the Convolution/Superposition and Monte Carlo simulations showed good agreement with the film measurements in the small field.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.81-87
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• 2019

The automatic tool changer of a machining center consists of a tool magazine and a cam box, and the core components of the cam box are a roller gear cam and a turret. Recently, the roller gear cam of a cam box has been replaced by a ball gear cam. In this study, the design and machining method of ball gear cam for an automatic tool changer was studied. Additionally, an algorithm for a 4-axis post processing method was established from an instrumental formula by designing a ball gear cam, thus preventing machining at the bottom of ball end mill and enabling the ball on the turret to be driven at the entrance and exit of a curve without collision due to machining errors. In conclusion, machining using only the 4-axis method including the C-axis on a BC -Type 5-axis machine produced the desired ball gear cam.

• Archives of Craniofacial Surgery
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• v.18 no.1
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• pp.37-43
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• 2017

Background: Skin cancer is the most common type of cancer. Of the 4 million skin lesions excised annually worldwide, approximately 2 million are considered cancerous. In this study, we aimed to describe a regional experience with skin cancers treated by a single senior surgeon and to provide a treatment algorithm. Methods: The medical records of 176 patients with head and neck non-melanocytic skin cancer (NMSC) who were treated by a single surgeon at our institution between January 2010 and May 2016 were retrospectively reviewed, and their data (age, sex, pathological type, tumor location/size, treatment modality) were analyzed. Patients with cutaneous squamous cell carcinoma (cSCC) who were classified as a high-risk group for nodal metastasis underwent sentinel node mapping according to the National Comprehensive Cancer Network guidelines. Results: Among the patients with NMSC who were treated during this period, basal cell carcinoma (BCC; n=102, 57.9%) was the most common pathological type, followed by cSCC (n=66, 37.5%). Most lesions were treated by complete excision, with tumor-free surgical margins determined via frozen section pathology. Thirty-one patients with high-metastasis-risk cSCC underwent sentinel node mapping, and 17 (54.8%) exhibited radiologically positive sentinel nodes. Although these nodes were pathologically negative for metastasis, 2 patients (6.5%) later developed lymph node metastases. Conclusion: In our experience, BCC treatment should comprise wide excision with tumor-free surgical margins and proper reconstruction. In contrast, patients with cSCC should undergo lymphoscintigraphy, as nodal metastases are a possibility. Proper diagnosis and treatment could reduce the undesirably high morbidity and mortality rates.

• Journal of the Korean Society of Marine Environment & Safety
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• v.17 no.3
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• pp.197-202
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• 2011

This study introduces a technique to merge three different sea surface temperature(SST) data obtained from multi-satellite sensors. NGSST algorithm, the most popular method of related society, estimates a center pixel of target SST using temporal and spatial correlations, excluding SST accuracies according to sensing methods or properties of satellites. We suggest a merging method of SST to consider the accuracy by satellite or sensor with a comparison with NGSST method. The data used for a merged daily SST with spatial resolution of 5 km was applied from three different satellite sensors such as MODIS, AVHRR and AMSR-E from April 2 to 4, 2011 around the southern coast of Korea. Results of the comparisons showed that the new method is higher than the NGSST method and its STDEV represents a comparatively low value. In future we are planning to compare and analyze the datasets during the daytime as well as nighttime over total cycle of the day.