• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.10
• /
• pp.465-472
• /
• 2017

In the past, researchers mainly used the supervised learning technique of machine learning to analyze power data and investigated the identification of patterns through the data mining technique. Data analysis research, however, faces its limitations with the old data classification and analysis techniques today when the size of electric power data has increased with the possible real-time provision of data. This study thus set out to propose a clustering architecture to analyze large-sized electric power data. The clustering process proposed in the study supplements the K-means algorithm, an unsupervised learning technique, for its problems and is capable of automating the entire process from the collection of electric power data to their analysis. In the present study, power data were categorized and analyzed in total three levels, which include the row data level, clustering level, and user interface level. In addition, the investigator identified K, the ideal number of clusters, based on principal component analysis and normal distribution and proposed an altered K-means algorithm to reduce data that would be categorized as ideal points in order to increase the efficiency of clustering.

• Journal of Yeungnam Medical Science
• /
• v.18 no.1
• /
• pp.39-50
• /
• 2001

Background: Power spectrum analysis method is a powerful noninvasive tool for quantifying autonomic nervous system activity. In this paper, we developed a data acquistion system for estimating the activity of the autonomic nervous system by the analysis of heart rate and respiratory rate variability using power spectrum analysis. Materials and methods: For the detection of QRS peak and measurement of respiratory rate from patient's ECG, we used low-pass filter and impedence method respectively. This system adopt an isolated power for patient's safety. In this system, two output signals can be obtained: R-R interval heart rate) and respiration rate time series. Experimental ranges are 30-240 BPM for ECG and 15-80 BPM for respiration. Results: The system can acquire two signals accurately both in the experimental test using simulator and in real clinical setting. Conclusion: The system developed in this paper is efficient for the acquisition of heart rate and respiration signals. This system will play a role in research area for improving our understanding of the pathophysiologic involvement of the autonomic nervous system in various disease states.

• Convergence Security Journal
• /
• v.18 no.5_1
• /
• pp.113-120
• /
• 2018

Today, the development of information and communication technology is rapidly increasing the number of users of network-based service, and enables real-time information sharing among users on the Internet. There are various methods in the information sharing process, and information sharing based on portal service is generally used. However, the process of information sharing serves as a cause of illegal activities in order to amplify the social interest of the relevant stakeholders. Public opinion attack using macro function can distort normal public opinion, so security measures are urgent. Therefore, security measures are urgently needed. Macro attacks are generally defined as attacks in which illegal users acquire multiple IP or ID to manipulate public opinion on the content of a particular web page. In this paper, we analyze network path information based on traceback for macro attack of a specific user, and then detect multiple access of the user. This is a macro attack when the access path information for a specific web page and the user information are matched more than once. In addition, when multiple ID is accessed for a specific web page in the same region, it is not possible to distort the overall public opinion on a specific web page by analyzing the threshold count value.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.59-68
• /
• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.7
• /
• pp.279-286
• /
• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• The Journal of the Korea Contents Association
• /
• v.21 no.7
• /
• pp.127-133
• /
• 2021

In industrial IoT environments, sensors generate data for their detection targets and deliver the data to IoT gateways. Therefore, managing large amounts of real-time sensor data is an essential feature for IoT gateways, and key-value storage engines are widely used to manage these sensor data. However, key-value storage engines used in IoT gateways do not take into account the characteristics of sensor data generated in industrial IoT environments, and this limits the performance of key-value storage engines. In this paper, we optimize the key-value storage engine by utilizing the features of sensor data in industrial IoT environments. The proposed optimization technique is to analyze the key, which is the input of a key-value storage engine, for further indexing. This reduces excessive write amplification and improves performance. We implement our optimization scheme in LevelDB and use the workload of the TPCx-IoT benchmark to evaluate our proposed scheme. From experimental results we show that our proposed technique achieves up to 21 times better than the existing scheme, and this shows that the proposed technique can perform high-speed data ingestion in industrial IoT environments.

• Journal of Internet of Things and Convergence
• /
• v.6 no.4
• /
• pp.27-32
• /
• 2020

Along with the aging of the population, the number of dementia patients is increasing, and the social and economic burden is also increasing. Currently, the effective way to manage dementia patients is to identify patients with dementia early. However, in rural and island areas where medical staff are scarce, there is a problem that it is difficult to visit a hospital and get an early examination. Therefore, we propose a remote early detection system for dementia to solve the problems. The remote dementia early diagnosis system is a system that allows a patient to receive examination and treatment from a remote dementia expert using remote medical technology based on real-time image communication. The remote early diagnosis system for dementia consists of a local client system used by medical staff at health centers in the island, an image server that transmits, stores and manages images, and an expert client used by remote dementia experts. The local client subsystem satisfies the current medical law's remote collaboration by allowing the patient to use it with the health center's medical staff. In addition, expert clients are used by dementia experts, and can store/manage patient information, analyze patient history information, and predict the degree of dementia progression in the future.

• Clean Technology
• /
• v.26 no.4
• /
• pp.293-303
• /
• 2020

According to the World Health Organization (WHO), air pollution is a typical health hazard, resulting in about 7 million premature deaths each year. Sulfur dioxide (SO2) is one of the major air pollutants, and the combustion process with sulfur-containing fuels generates it. Measuring SO2 generation in large combustion environments in real time and optimizing reduction facilities based on measured values are necessary to reduce the compound's presence. This paper describes the concentration measurement for SO2, a particulate matter precursor, using a wavelength modulation spectroscopy (WMS) of tunable diode laser absorption spectroscopy (TDLAS). This study employed a quantum cascade laser operating at 7.6 ㎛ as a light source. It demonstrated concentration measurement possibility using 64 multi-absorption lines between 7623.7 and 7626.0 nm. The experiments were conducted in a multi-pass cell with a total path length of 28 and 76 m at 1 atm, 296 K. The SO2 concentration was tested in two types: high concentration (1000 to 5000 ppm) and low concentration (10 ppm or less). Additionally, the effect of H2O interference in the atmosphere on the measurement of SO2 was confirmed by N2 purging the laser's path. The detection limit for SO2 was 3 ppm, and results were compared with the electronic chemical sensor and nondispersive infrared (NDIR) sensor.

• Korean Journal of Clinical Laboratory Science
• /
• v.53 no.1
• /
• pp.41-48
• /
• 2021

Essential thrombocythemia (ET) is a clonal bone marrow stem cell disorder, primarily involving the megakaryocytic lineage. The WHO 2016 guidelines include the molecular detection of JAK2, MPL, and CALR mutations as a major diagnostic criterion for ET. This study aimed to determine the frequency of JAK2 V617F, MPL W515L, and CALR mutations in Iraqi Kurdish patients afflicted with ET, and to analyze their clinical and hematological features. A total of 73 Iraqi Kurdish patients with ET were enrolled as subjects, and analysis was achieved utilizing real-time PCR. The frequency of JAK2 V617F, CALR, and MPL W515L mutations was determined to be 50.7%, 22%, and 16.4%, respectively. No statistically significant difference was obtained when considering the age and gender among different genotypes. The JAK2 V617F mutated patients had significantly higher white blood cell counts and hemoglobin levels than the CALR-positive patients (P-value=0.000, 0.007, respectively), MPL W515L-positive patients (P-value=0.000, 0.000, respectively), and triple negative patients (P-value=0.000, 0.000, respectively). Also, the JAK2 V617F mutated patients showed higher platelet count as compared to the MPL W515L-positive patients (P-value=0.02) and triple negative patients (P-value=0.04). Furthermore, significantly lower white blood cell count and hemoglobin levels were associated with CALR positivity (P-value=0.000, 0.01, respectively), MPL W515L-positivity (P-value=0.001, 0.000, respectively), and triple negativity (P-value=0.000, 0.000, respectively), as compared to patients with combined mutations. In conclusion, apart from a relatively high frequency of MPL W515L mutation, our data is comparable to earlier reports, and highlights the importance of genotyping the JAK2 V617F, MPL W515L, and CALR mutations for accurate diagnosis of patients with ET.

• Korean Chemical Engineering Research
• /
• v.60 no.1
• /
• pp.1-6
• /
• 2022

As the demand for wearable devices increases, many studies have been studied on the development of flexible electrode materials recently. In particular, the development of high-performance flexible electrode materials is very important for wearable sensors for healthcare because it is necessary to continuously monitor and accurately detect body information such as body temperature, heart rate, blood glucose, and oxygen concentration in real time. In this study, we fabricated the nonenzymatic glucose sensor based on polyaniline/carbon nanotube fiber (PANI/CNT fiber) electrode. PANI layer was synthesized on the flexible CNT fiber electrode through electrochemical polymerization process in order to improve the performance of a flexible CNT fiber based electrode material. Surface morphology of the PANI/CNT fiber electrode was observed by scanning electron microscopy. And its electrochemical characteristics were investigated by chronoamperometry, cyclic voltammetry, electrochemical impedance spectroscopy. Compared to bare CNT fiber electrode, this PANI/CNT fiber electrode exhibited small electron transfer resistance, low peak separation potential and large surface area, resulting in enhanced sensing properties for glucose such as wide linear range (0.024~0.39 and 1.56~50 mM), high sensitivity (52.91 and 2.24 ㎂/mM·cm²), low detection limit (2 μM) and good selectivity. Therefore, it is expected that it will be possible to develop high performance CNT fiber based flexible electrode materials using various nanomaterials.