• Journal of Korean Academy of Nursing Administration
• /
• v.5 no.2
• /
• pp.297-316
• /
• 1999

The purpose of this study is searching for hemodialysis nursing bahaviors by hemodialysis room nurses and analyzing them. Then, it estimates hemodialysis nursing costs and obtains basic data for development of proper nursing costs. First, it searched for hemodialysis nursing behaviors at a tertiary hospital hemodialysis room in Seoul and classified them. After the content validity was verified by 6 experts, Tool of hemodialysis nursing behaviors was developed. patients who recived hemodialysis were classified by dialysis patient classification tool. The searcher observed hemodialysis nursing behaviors applied to classified patients per 5 minutes. Then hemodialysis nursing hours spent to classified patients were calculated respectively. The direct expenditures and indirect expenditures were estimated. Ultimately, hemodialysis nursing costs were estimated. The results of the study were as follows ; 1. hemodialysis nursing behaviors were grouped by the same knowledge and skills. then, the content validity of them was verified by evaluation tool of nursing intervention classification by expert groups. They consisted of 9 hemodialysis activity domains and 71 hemodialysis nursing behaviors. The predialysis activity domain included 15 nursing behaviors, the activity domain of start-dialysis included 12 nursing behaviors, the activity domain of during- dialysis included 9 nursing behaviors, the activity domain of finish-dialysis included 5 nursing behaviors, the activity domain of after-dialysis included 5 nursing behaviors, the nursing documentation & undertaking and transfering included 5 nursing behaviors, the supply, drug, equipment & environment management activity domain included 7 nursing behaviors, the patient emotional support & education activity domain included 4 nursing behaviors, the emergency activity domain included 9 nursing behaviors. 2. The acute hemodialysis nursing hours were 106.42 minutes per a dialysis and the chroni hemodialysis nursing hours were 72.23 minutes per a dialysis. 3. The direct expenditure was 11.971 won per hour and indirect expenditure was 288won. 4. Finally, the cost of acute hemodialysis was 21,745 won and that of chronic hemodialysis was 14,759 won. By search of hemodialysis nursing behaviors, they will be used as hemodialysis nursing care standard and will be tended toward high qualitative care. Estimation of hemodialysis nursing costs will be used as fundamental data for development of proper nursing costs.

• Journal of Korea Multimedia Society
• /
• v.15 no.8
• /
• pp.1059-1066
• /
• 2012

With the development of Smartphone, Smartphone contains diverse functions including many sensors that can describe users' state. So there has been increased studies rapidly about activity recognition and life pattern recognition with Smartphone sensors. This research suggest modeling of the activity data to classify extracted data in existing activity recognition study. Activity data is divided into two parts: Physical activity and Logical Activity. In this paper, activity data modeling is theoretical analysis. We classified the basic activity(walking, standing, sitting, lying) as physical activity and the other activities including object, target and place as logical activity. After that we suggested a method of visualizing modeling data for users. Our approach will contribute to generalize human's life by modeling activity data. Also it can contribute to visualize user's activity data for existing activity recognition study.

• Journal of the Ergonomics Society of Korea
• /
• v.31 no.2
• /
• pp.271-279
• /
• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.2
• /
• pp.113-118
• /
• 2011

In this paper, we develop a speaker gender classification technique using collaborative sensor fusion for use in a wireless sensor network. The distributed sensor nodes remove the unwanted input data using the BER(Band Energy Ration) based voice activity detection, process only the relevant data, and transmit the hard labeled decisions to the fusion center where a global decision fusion is carried out. This takes advantages of power consumption and network resource management. The Bayesian sensor fusion and the global weighting decision fusion methods are proposed to achieve the gender classification. As the number of the sensor nodes varies, the Bayesian sensor fusion yields the best classification accuracy using the optimal operating points of the ROC(Receiver Operating Characteristic) curves_ For the weights used in the global decision fusion, the BER and MCL(Mutual Confidence Level) are employed to effectively combined at the fusion center. The simulation results show that as the number of the sensor nodes increases, the classification accuracy was even more improved in the low SNR(Signal to Noise Ration) condition.

• Fire Science and Engineering
• /
• v.15 no.3
• /
• pp.49-54
• /
• 2001

Recently, intentional arsons for exploitation and wildland fire caused by abnormal change of weather are increasing as well as the damage scale due to such fire in the world. The number of such big wildland fire is also increasing in Korea these days. Fire prevention activity can be said as more important than fire putting-out activity after a fire occurrence for the most effective way to cope with wildland fire, and the research on wildland fire prevention system is what we need to do urgently, In this study I examined and analyzed the experiments and data about the factors influencing wildland fire and stated the dangerousness of each factor for all of the 6 factors and set up the general dangerousness rating applying each factor's contribution to the dangerousness.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.21-26
• /
• 2009

A sensor system capable of automatically recognize activities would allow many potential Ubiquitous applications. This paper presents a new system for recognizing the activities of daily living(ADL) like walking, running, standing, sitting, lying etc. The system based on the state-dependent motion analysis using Tri-Accelerometer and Zigbee tag. Two accelerometers are used for the classification of body and hand activities. Classification of the environment and instrumental activities is performed based on the hand interaction with an object ID using.

• Journal of the Korean Society of Earth Science Education
• /
• v.3 no.2
• /
• pp.124-131
• /
• 2010

The purpose of this study is examine relationship of attribution styles and attitude toward Science and Science Process Skills of Science-gifted, to understand unique characteristics of the Science-gifted and to give useful information that can be use in develop special programs for the Science-gifted. The result of this study were as follows: First, there was no difference between genders. But there was a significant difference in attribution of luck. Second, there was a correlation between internal tendencies and Scientific attitude. Especially attribution of effort correlated with sub - constituent of Scientific attitude. Internal tendencies correlated with the Cognition in Scientific Professions that sub - constituent of the attitude toward Science. Third, There was a correlation between external tendencies and the interest activity in Science that sub - constituent of the attitude toward Science. There are correlations between sub - constituent of the attitude toward Science and sub - constituent of external tendencies that attribution of luck and interest in Scientific Professions, attribution of task difficulty and Cognition activity in Science. Fourth There was no correlation between Attribution styles and Science Process Skills. But Internal tendencies correlated with classification that sub - constituent of Science Process Skills. And classification correlated with attribution of ability that sub - constituent of external tendencies. Attribution of effort that sub - constituent of internal tendencies correlated with Science Process Skills.

• Journal of Korean Medical classics
• /
• v.32 no.2
• /
• pp.135-146
• /
• 2019

Objectives : This study examines the grounds on which Wulao(五勞) Liuji(六極) Qishang(七傷) which are categories of Xulao(虛勞) are differentiated, along with standards by which each category is further classified. Methods : Based on "Zhubingyuanhoulun(諸病源候論)", the first text to sort the different types and symptoms of Wulao(五勞) Liuji(六極) Qishang(七傷), each classification and its symptoms were analyzed. Texts which were written relatively close in time to "Zhubingyuanhoulun" were referenced in the process. Results & Conclusions : The differentiation of Wulao(五勞) Liuji(六極) Qishang(七傷) is based on the cause of illness. Wulao(五勞) is caused by mental activity which fatigues the Five Zang, Liuji(六極) is caused by exterior pathogens that damage the Five Body Elements, and Qishang(七傷) is caused by emotional factors as well as damaging practices. In close examination, Wulao(五勞) was further classified according to the different layers of mental activity, described in terms of taxation illness of the damaged Zang. Liuji(六極) is damage of the Five Body Elements by exterior pathogens, which was put into the spacial structure of nature and explained in six. Qishang(七傷) refers to the collective of representative symptoms and representative causes of Xulao.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.6
• /
• pp.319-325
• /
• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• Journal of Multimedia Information System
• /
• v.2 no.2
• /
• pp.233-240
• /
• 2015

Mobile devices are becoming increasingly sophisticated and the latest generation of smartphones now incorporates many diverse and powerful sensors. These sensors include acceleration sensor, magnetic field sensor, light sensor, proximity sensor, gyroscope sensor, pressure sensor, rotation vector sensor, gravity sensor and orientation sensor. The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper, we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity that a user is performing. To implement our system, we collected labeled accelerometer data from 10 users as they performed daily activities such as "phone detached", "idle", "walking", "running", and "jumping", and then aggregated this time series data into examples that summarize the user activity 5-minute intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users-just by having them carry cell phones in their pockets.