• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.5
• /
• pp.417-423
• /
• 2017

This paper intend to implement monitoring systems for individual customized diagnostics to maintain ongoing disease management to promote human health. Analyze the threshold of a measured biological signal using a number of measuring sensors. Performance assessment revealed that the SVM algorithm for bio-signal analysis showed an average error rate of 2 %. The accuracy of the classification is 97.2%, and reduced the maximum of 19.2% of the storage space when you split the window into 5,000 pieces. Out of the total 5,000 bio-signals, 84 results showed that results from the system were differently the results of the expert's diagnosis and showed about 98 % accuracy. However, the results of the monitoring system did not occur when the results of the monitoring system were lower than that of experts. And About 98% accuracy was shown.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.1
• /
• pp.220-226
• /
• 2011

Device of the ECG and pulse signal was made to measure PTT signal using non-invasive method and possible to wearable. PTT alterations were observed according to position change using implemented system.It was needed to ECG and pulse to detect the PTT, used the photoplethymorgraphy appeared to change the blood volume. And also wireless sensor node which was able to Zigbee compatibility was used to transfer the detected ECG and pulse signal to PC. Noise was removed from transit data and algorithm was applied to calculate the PTT. After the evaluation of both the conventional measuring systems and the proposed photoplethymography measuring system, a highly effective and efficient formation and distribution sequences were found within the proposed photoplethymography measuring system.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.45 no.5
• /
• pp.1-11
• /
• 2008

Wireless Sensor Networks is applied to improvement of life convenience or service like U-City as well as environment pollution, tunnel and structural health monitoring, storm, and earthquake diagnostic system. To increase the usability of sensor data and applicability, mobile devices and their facilities allow the applications of sensor networks to give mobile users and actuators the results of event detection at anytime and anywhere. In this paper, we present MUSNEMO(Multi-sensor centric Ubiquitous Smart sensor NEtwork using Mobile devices) developed system for providing more efficient and valuable information services with a variety of mobile devices and network camera integrated to WSN. Our system is performed based on IEEE 802.15.4 protocol stack. To validate system usability, we built sensor network environments where were equipped with five application sensors such magnetic, photodiode, microphone, motion and vibration. We also built and tested proposed MUSNEMO to provide a novel model for event detection systems with mobile framework.

• Journal of Korean Society for Atmospheric Environment
• /
• v.26 no.4
• /
• pp.367-379
• /
• 2010

Future climate changes over the Seoul metropolitan area (SMA) were predicted by the Weather Research and Forecasting (WRF) model using future land-use data from the urban growth model (SLEUTH) and forecast fields from ECHAM5/MPI-OM1 GCM (IPCC scenario A1B). Simulations from the SLEUTH model with GIS information (slope, urban, hill-shade, etc.) derived from the water management information system (WAMIS) and the intelligent transportation systems-standard nodes link (ITS-SNL) showed that considerable increase by 17.1% in the fraction of urban areas (FUA) was found within the SMA in 2020. To identify the effects of the urban growth on the temperature and wind variations in the future, WRF simulations by considering urban growth were performed for two seasons (summer and winter) in 2020s (2018~2022) and they were compared with those in the present (2003~2007). Comparisons of model results showed that significant changes in surface temperature (2-meter) were found in an area with high urban growth. On average in model domain, positive increases of $0.31^{\circ}C$ and $0.10^{\circ}C$ were predicted during summer and winter, respectively. These were higher than contributions forced by climate changes. The changes in surface temperature, however, were very small expect for some areas. This results suggested that surface temperature in metropolitan areas like the SMA can be significantly increased only by the urban growth during several decades.

• Journal of Digital Convergence
• /
• v.12 no.11
• /
• pp.617-623
• /
• 2014

This study aims to organize the pharmacological component of native chicken(NC) by analyzing the bill presented in traditional medicine NC relevant literature. And the analyze the heterogeneity and the common prescription of chicken that a variety of traditional technologies to existing treatment books private data to organize information about the scientific verification. After the analysis by building a DB and want a prescription to traditional book, herbal medicine usability throughout the assessment of the special treatment provided the basis for further product and a variety of functional food development. In addition, the continued expansion of the country and future growth engine industry related businesses through added value of cultural knowledge resources. NC of the relevant search and information system for prescription knowledge. so standardized, conceptualization, formalization is to build a knowledge of traditional medicine NC recipe DB assess the usefulness of medical literature through interdisciplinary research systems to suggest practical ways of alternative medicine and functional food development.

• Smart Structures and Systems
• /
• v.17 no.6
• /
• pp.1087-1105
• /
• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Journal of Korean Academy of Nursing
• /
• v.25 no.3
• /
• pp.419-430
• /
• 1995

There is a need to investigate folk remedies used by patients with breast cancer because there is little information about the subject, even though many Korean women with breast cancer have used folk remedies during and after their treatment. The purpose of this study was to investigate and describe the phenomena and the meaning of folk remedies in order to better understand patients with breast cancer and to suggest directions for comprehensive nursing care. The Questions for the study were as follows What kinds of folk remedies do patients with breast cancer use\ulcorner What are the routes of knowing about folk remedies in patients with breast cancer\ulcorner What are the patterns of the usage of the folk remedies\ulcorner Why do patients with breast cancer use folk remedies\ulcorner What are the meanings of folk remedies to patients with breast cancer\ulcorner To answer these questions, a qualitative research method was used. Thirty-nine patients were recruited from university teaching hospitals from March, 1993 to November 1994. Many of them underwent either modified radical mastectomy or received various adjuvant therapy including chemotherapy, radiation therapy, and hormonal therapy. Data were collected by in-depth interviews, observations, medical records, and analyzed step-by-step using qualitative analysis. The results were as follows : 1. Patients with breast cancer have used many different kinds of folk remedies. 2. Patients with breast cancer did not know the exact effects of the folk remedies. Also the effects could not be exactly proven by the patients. 3. Patients with breast cancer received information about many kinds of folk remedies through various communication systems, such as other patients, their families and relatives, friends, and many types of mass media. 4. To use the folk remedies was one kind of illness behavior that was used by these patients. 5. Folk remedies were used to deal with not only anxiety by the patients themselves but also as the expression of affection and concern by families and relatives. 6. The use of folk remedies was one of the adaptation behaviors in patients with breast cancer whose disease was in the terminal stage. Based on the above findings, one suggestion was made : To continue further studies on folk remedies used by other patients with cancer in order to further explain health and illness behavior of Korean people.

• Korean Journal of Clinical Pharmacy
• /
• v.31 no.4
• /
• pp.311-323
• /
• 2021

Objective: The disabled are in a blind spot for obtaining information on drugs, and the pharmacies' counseling on drug use is centered on non-disabled people. Few studies have investigated the current statuses of drug use by type of disability. The purpose of this study is to understand the drug use by type of disability and by life cycle of visually impaired and hearing impaired in Korea. Methods: The study participants consisted of 16 people with visually impairments, 12 people with hearing impairments. One in-depth interview was conducted per participant, and each interview was recorded and documented. Results: Common barriers against safe medication and medical service uses across disability types are 'lack of consideration and service for the disabled, limited access to medical facilities due to disability, limited access to information regarding medication use, psychological anxiety about drug use and side effects, and inconvenience regarding COVID-19 epidemic. The specific factors were 'difficulties in identifying proper medicines and following prescribed dosages' in the case of visually impaired, and 'problems with sign language interpretation system' for the hearing impaired. Conclusion: Disabled people are hindered from using medicines properly due to various factors. Based on the content derived from this study, it is necessary to eliminate the inhibition factors and devise specific measures for the safety of each type of disorder such as developing a method for medication counseling considering disabilities and establishing communication support systems.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.351-363
• /
• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.177-189
• /
• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.