Seok-Cheon Park

Design and Implementation of Support System for Personalized Medical Service Based on Mobile

Abstract In this paper proposes the latest network-assisted online telemedicine service to coincide with the point being discussed for health care providers to match patients, patients with personalized medical service support system. In order to design the system, to understand the requirements of the patient personalized medical support service system, the data were normalized and were designed architecture client server structure. Further, in order to implement the system that was designed to define the structure of server and client, ontology repository, we implement the system. In this paper, as a result of the test by creating a scenario and prerequisites for testing patient personalized medical service support system that is design and implementation, selecting a patients condition, department of symptoms by the selected but it was confirmed that the inference is, inference medical institutions that fits department inferred one following upon the items medical patient has the required

Design and Implementation of Mobile CRM Utilizing Big Data Analysis Techniques

In the recent enterprises and are utilizing the CRM using data mining techniques and new marketing plan. However, data mining techniques are necessary expertise, general public access is difficult, it will be subject to constraints of time and space. in this paper, in order to solve this problem, we have proposed a Mobile CRM applying the data mining method. Thus, to analyze the structure of an existing CRM system, and defines the data flow and format. Also, define the process of the system, was designed sales trend analysis algorithm and customer sales recommendation algorithm using data mining techniques. Evaluation of the proposed system, through the test scenario to ensure proper operation, it was carried out the comparison and verification with the existing system. Results of the test, the value of existing programs and data matches to verify the reliability and use queries the proposed statistical tables to reduce the analysis time of data, it was verified rapidity.

Design and implementation of the SARIMA–SVM time series analysis algorithm for the improvement of atmospheric environment forecast accuracy

With the recent increased interest in atmospheric pollutants in South Korea, studies on the analysis and forecast of atmospheric pollution using Internet-of-Things technology have been actively conducted. To forecast atmospheric pollution, a multiple regression analysis technique based on statistical techniques, data mining, and an analysis technique combining time series models have typically been used. In terms of accuracy, however, multiple regression analysis is insufficient for analyzing atmospheric environment data in South Korea. In addition, although the time series analysis technique is appropriate for analyzing linear data, it is inappropriate for analyzing atmospheric environment data in South Korea, where linear and nonlinear data are mixed. Therefore, this study proposes a seasonal auto regressive integrated moving average–support vector machine (SARIMA–SVM) time series analysis algorithm, combining time series analysis and nonlinear analysis, for data analysis of atmospheric environment information and improvement of pollution forecast accuracy. The proposed algorithm analyzes the seasonality in environmental contamination by using the SARIMA model, and succeeds in improving accuracy in the contamination forecast through an analysis of linear and nonlinear characteristics by applying an SVM nonlinear regression model. A comparative assessment with the existing atmospheric contamination forecast algorithm was conducted as well. The assessment results show that the forecast accuracy of the proposed algorithm improved by 20.81% for fine dust, and by 43.77% for ozone, compared to the performance of the existing models.

Design of Zigbee-BLE Gateway Direct Communication System for Smart Home Environment

With the recent introduction of network technology to home appliances, there is a growing interest in smart homes that provide convenience in life using home gateways. However, as smart devices are not equipped with Zigbee modules, a server is required to access home appliances through their Zigbee modules. Therefore, in this study, a Zigbee-Bluetooth low energy (BLE) gateway direct communication system was designed, which is capable of direct access between smart devices and Zigbee-based home appliances through communication between Zigbee and BLE without a server.

Design of Effective Indexing Technique in Hadoop-Based Database

The recent rapid increase in the amount of data to be processed has led to the increased use of dispersed parallel processing of large-scale data analysis using open-source Hadoop’s MapReduce framework. The large-data processing method proposed by Google and Hadoop which implemented this are representative dispersed parallel processing methods, and the data are dispersedly saved on the HDFS(Hadoop Distributed File System). Such HDFS uses its own indexing technique when it comes to searching specific values from the saved files. Techniques that use conventional index, however, leads to problems like reduced search performance by not considering update and saving index in the disc. Therefore, the paper proposes effective DB indexing technique on Hadoop-based database.

Design of TDD-Based Automation System for Android Application Test Automation

The Android platform does not support interoperability among different platforms, and this causes difficulty for developers because they must test applications on different platforms and devices. To solve this problem, this study proposes a test automation system applying test driven development (TDD) that allows automatic performance of repetitive tests. To design the proposed system, test case generation was automated using Annotation.

Design of ECG Data Compression Algorithm for Efficient M2M-Based Mass Biometric Data Transmission

Thanks to the design of different portable subminiature sensors and wired and wireless communication technology, the U-Healthcare service is getting vitalized. A mass amount of raw data is processed in real time when this U-Healthcare service is provided, and efficient processing and storage technologies are required accordingly. Therefore, this paper proposed an ECG data compression algorithm that is improved to efficiently transmit M2M-based mass biometric data.

Design of Automatic Source Code Generation Based on User Pattern Definition

Software engineering has been established with various development methodologies and software development process models, which have enabled increases in software production efficiency and improvements on product quality. However, the time taken to develop software and input human resources are the main causes of cost increases. Thus, the project will be delayed as the time taken to complete iterative tasks increases. In this regard, this paper designs an automatic source code generation system using meta data based on user patter definition in order to resolve the problem.

Design of Clustering Algorithm for Efficient Energy Management in Wireless Sensor Network Environments

Recently, there has been an active research effort on Wireless Sensor Network (WSN) where the sensor nodes consume energy efficiently by communicating between the nodes directly without a network infrastructure. However, previously proposed protocols require regular re-establishment of clusters, which leads to unnecessary energy consumption. Moreover, there is a large energy consumption because a cluster head that is placed far apart from a sink node directly transmits data to the sink nodes. Therefore, in this paper, we analysis the problems of the previous clustering techniques and protocols, and designed a clustering algorithm for efficient energy consumption through the use of an energy threshold during cluster re-establishment and data transmission route selection.

Design of TensorFlow-Based Proactive Smart Home Managers

In recent years, with IoT(Internet of Things) technology as the main focus, device operation and control technology in smart homes has been attracting considerable attention, and home IoT device management services are being provided by various companies, including communication companies. The smart home manager system manages smart devices used in homes, and it provides only the status value information and control function of the currently registered devices. Thus, unnecessary access procedures occur due to the characteristic of the smart home, which uses a smart device repeatedly for the same purpose. To resolve such shortcomings, in this paper, the Proactive Smart Home Manager has been designed, which can predict and suggest users the next steps to take by user usage pattern analysis and inference via machine learning.