• Journal of Korean Public Health Nursing
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• v.10 no.1
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• pp.118-138
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• 1996

Because of accelerated urbanization public body visiting nursing project that started according as matter of health on urban class in the lower brackets of income was concentrated on Social interests has a unsatisfied points to propel project efficiently from the lack of rating materials. Therefore centering around written contents in documentary literature of citizen health by household in five years from starting year of project to now. visiting frequency by medical manpower was evaluated quantitatively and qualitatively in aspect of management hereupon. for the sake of giving a basic materials for public health project of this field. This research presents documentary literature of citizen health which become materials is that as one person's charged region of nurse in duty scale. district is Kang-Buck Gu. the object is resident in the lower brackets of income grounded livelihood protection law and who is admitted by the head of organ~chief of health care). and the number of material centering around the head of a household is 415 copy. The result of research is summarized. as follow. 1. Average visiting frequency examinated by medical manpower show difference according to valuables of supervision characteristics namely average visiting. Frequency of nurse has long term residence in case registration season is early and supervision season is the first year and is high incase a kind of house is unlicdnsed mountain town. Average visiting frequency with doctor is high incase supervision season is the first year and the medical insurance system is admitted by chief of health care. That shows that a man of discomfort behavior left alone are yet many in local society. The meaning of this result shows that the continuity of official relation about class in the lowest brackets of income of long term residence goes well between househole who is a user of visiting nursing service of the object according to midway income under management influences a given duty of nurse s and so causes quantitative decrease. 2. In case behavier and condition of health that nurse diagnoses are bad. as the type matter is a lack of health and the number of patient is large. the average visiting frequency of nurse is high. because average visiting frequency with doctor is high as the condition of health is bad and the number of patient is large. That is similar with that of nurse. CD Average visiting frequency of nurse s seen by matter of disease is very high only in apoplexy by 39.50 and is confined within limits from 7.63 to 11.36 in other disease. But average visiting frequency with doctor is double as many as that of nurse but defined in apoplexy hypertension and articulate. (1) Average visiting frequency of nurse by existence in inoculation of hepatitis is low by 6.73 in unidentified group and very high by 26.89 in group of non-inoculation and the case of the antigenic positive man of B type hepatitis or epileptic who can't be inoculated shows 13.00 and that even family nursing service is needed to them. That result shows that though one person nurse of local charge has a large scale of duty. as visting nursing service is given a class who has a large demand preferentially by respectively accurate nursing diagnosis. the number of diagnosis service is similar with it. 3. During five years. average visiting frequency of nurse is 10.84 and average visiting frequency with doctor is 76.50 seeing from the official scale of nurse. visiting by household is performed two more per year to the average. Seeing this by type of service. average visiting frequency of nurse is higher in indirectly nursing than in directly nursing and that suggests that at the time of visiting household nurse performs education of protection lively save patient but at the time of contrastedly visiting with doctor. directly nursing is more contents of service show no difference by man power and medication dressing by demand is 14.3 and 18.6 the aid of hardship term of doctor and nurse is high by 18.7 and 17.00 in the request of hospitalization when seeing by demands. 4. Action by turns exemplified 1994 is well in sequence of 2/4 turn. 3/4 turn. 1/4 turn. 4/4 turn. When seen by average visiting frequency of nurse but gradually is even. Without difference by turns. average visiting frequency of doctor is much higher in 1/4 turn than other turns. Type of service by turns is all even but directly nursing is inactive in 4/4 and indirectly nursing. Very increases in 4/4 and so. Nurse's quantity of duty is plentiful that shows that by evaluation of last turn and plan of project. Contents of service follows that medication and dressing is the highest by' 5.57 in 1/4turn. goes down gradually by turn. becomes 3.57 in 3/4 turn. and increases again by 4.83 in 4/4 turn. the rest service is higher in 2/4 turn than other turns. 5. Total visiting frequency of nurse is explained to total $37.5\%$ by six valuables of visiting frequency of doctor. nursing demand. demand of diagnosis. condition of behavior. year. Special terms and magnitude of influential power is the same as sequence of enumerated valuables. Namely. the higher the visiting frequency of doctor. the bigger nursing and demand of diagnosis is. the worse the condition of behavior is. the older the object is and the more the household of special terms is. the high total visiting frequency of nurse is.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.133-149
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• 2014

One of the main purposes of universities is to create new intellectual resources that will increase social values. These intellectual resources include academic research papers, lecture notes, patents, and creative ideas produced by both professors and students. However, intellectual resources in universities are often not distributed to the actual users or companies; and moreover, they are not even systematically being managed inside of the universities. Therefore, it is almost impossible for companies to access the knowledge created by university students and professors to utilize them. Thus, the current level of knowledge sharing between universities and industries are very low. This causes a great extravagant with high-quality intellectual and human resources, and it leads to quite an amount of social loss in the modern society. In the 21st century, the creative ideas are the key growth powers for many industries. Many of the globally leading companies such as Fedex, Dell, and Facebook have established their business models based on the innovative ideas created by university students in undergraduate courses. This indicates that the unconventional ideas from young generations can create new growth power for companies and immensely increase social values. Therefore, this paper suggests of a new platform for intellectual properties distribution with university-industry cooperation. The suggested platform distributes intellectual resources of universities to industries. This platform has following characteristics. First, it distributes not only the intellectual resources, but also the human resources associated with the knowledge. Second, it diversifies the types of compensation for utilizing the intellectual properties, which are beneficial for both the university students and companies. For example, it extends the conventional monetary rewards to non-monetary rewards such as influencing on the participating internship programs or job interviews. Third, it suggests of a new knowledge map based on the relationships between key words, so that the various types of intellectual properties can be searched efficiently. In order to design the system platform, we surveyed 120 potential users to obtain the system requirements. First, 50 university students and 30 professors in humanities and social sciences departments were surveyed. We sent queries on what types of intellectual resources they produce per year, how many intellectual resources they produce, if they are willing to distribute their intellectual properties to the industries, and what types of compensations they expect in returns. Secondly, 40 entrepreneurs were surveyed, who are potential consumers of the intellectual properties of universities. We sent queries on what types of intellectual resources they want, what types of compensations they are willing to provide in returns, and what are the main factors they considered to be important when searching for the intellectual properties. The implications of this survey are as follows. First, entrepreneurs are willing to utilize intellectual properties created by both professors and students. They are more interested in creative ideas in universities rather than the academic papers or educational class materials. Second, non-monetary rewards, such as participating internship program or job interview, can be the appropriate types of compensations to replace monetary rewards. The results of the survey showed that majority of the university students were willing to provide their intellectual properties without any monetary rewards to earn the industrial networks with companies. Also, the entrepreneurs were willing to provide non-monetary compensation and hoped to have networks with university students for recruiting. Thus, the non-monetary rewards are mutually beneficial for both sides. Thirdly, classifying intellectual resources of universities based on the academic areas are inappropriate for efficient searching. Also, the various types of intellectual resources cannot be categorized into one standard. This paper suggests of a new platform for the distribution of intellectual materials and human resources, with university-industry cooperation based on these survey results. The suggested platform contains the four major components such as knowledge schema, knowledge map, system interface, and GUI (Graphic User Interface), and it presents the overall system architecture.

• Korean Journal of Heritage: History & Science
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• v.54 no.1
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• pp.206-219
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• 2021

The warming of the climate is increasing the damage caused by termites to wooden buildings, cultural properties and houses. A group removal system can be installed around the building to detect and remove termite damage; however, if the site is not visited regularly, every one to two months, you cannot observe whether termites have spread within, and it is difficult to take prompt effective action. In addition, since the system is installed and operated in an exposed state for a long period of time, it may be ineffective or damaged, resulting in a loss of function. Furthermore if the system is installed near a cultural site, it may affect the aesthetic environment of the site. In this study, we created a detection system that uses wood, cellulose, magnets, and magnetic sensors to determine whether termites have entered the area. The data was then transferred to a low power LoRa Network which displayed the results without the necessity of visiting the site. The wood was made in the shape of a pile, and holes were made from the top to the bottom to make it easier for termites to enter and produce a cellulose sample. The cellulose sample was made in a cylindrical shape with a magnet wrapped in cellulose and inserted into the top of a hole in the wood. Then, the upper part of the wood pile was covered with a stopper to prevent foreign matter from entering. It also served to block external factors such as light and rainfall, and to create an environment where termites could add cellulose samples. When the cellulose was added by the termites, a space was created around the magnet, causing the magnet to either fall or tilt. The magnetic sensor inside the stopper was fixed on the top of the cellulose sample and measured the change in the distance between the magnet and the sensor according to the movement of the magnet. In outdoor experiments, 11 cellulose samples were inserted into the wood detection system and the termite inflow was confirmed through the movement of the magnet without visiting the site within 5 to 17 days. When making further improvements to the function and operation of the system it in the future, it is possible to confirm that termites have invaded without visiting the site. Then it is also possible to reduce damage and fruiting due to product exposure, and which would improve the condition and appearance of cultural properties.

• Journal of the Korean Vacuum Society
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• v.17 no.1
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• pp.16-22
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• 2008

We studied plasma etching of polycarbonate in $O_2/SF_6$, $O_2/N_2$ and $O_2/CH_4$. A capacitively coupled plasma system was employed for the research. For patterning, we used a photolithography method with UV exposure after coating a photoresist on the polycarbonate. Main variables in the experiment were the mixing ratio of $O_2$ and other gases, and RF chuck power. Especially, we used only a mechanical pump for in order to operate the system. The chamber pressure was fixed at 100 mTorr. All of surface profilometry, atomic force microscopy and scanning electron microscopy were used for characterization of the etched polycarbonate samples. According to the results, $O_2/SF_6$ plasmas gave the higher etch rate of the polycarbonate than pure $O_2$ and $SF_6$ plasmas. For example, with maintaining 100W RF chuck power and 100 mTorr chamber pressure, 20 sccm $O_2$ plasma provided about $0.4{\mu}m$/min of polycarbonate etch rate and 20 sccm $SF_6$ produced only $0.2{\mu}m$/min. However, the mixed plasma of 60 % $O_2$ and 40 % $SF_6$ gas flow rate generated about $0.56{\mu}m$ with even low -DC bias induced compared to that of $O_2$. More addition of $SF_6$ to the mixture reduced etch of polycarbonate. The surface roughness of etched polycarbonate was roughed about 3 times worse measured by atomic force microscopy. However examination with scanning electron microscopy indicated that the surface was comparable to that of photoresist. Increase of RF chuck power raised -DC bias on the chuck and etch rate of polycarbonate almost linearly. The etch selectivity of polycarbonate to photoresist was about 1:1. The meaning of these results was that the simple capacitively coupled plasma system can be used to make a microstructure on polymer with $O_2/SF_6$ plasmas. This result can be applied to plasma processing of other polymers.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Sleep Medicine and Psychophysiology
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• v.7 no.2
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• pp.96-101
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• 2000

Objectives: Through comparing sleep variables and autonomic activities among three shifts in shift workers, the authors intended to clarify which shift is most tolerable and to identify the characteristics of their psychological and physical problems. This study is also expected to help shift workers to adapt themselves to their work more effectively. Methods: Fifty one shift workers took part in this study. They were working in a rapidly rotating system in which they worked for 3 days in one shift with one day off between each shift. Based on a sleep diary, sleep latency (SL), sleep period time (SPT), and number of wake after sleep onset (NWASO) were estimated and compared among the three shifts. In assessing sleepiness, Epworth sleepiness scale (ESS) and visual analogue scale (VAS) were used. To evaluate mood states among the three shifts, profile of mood states (POMS) was administered. Heart rate variability (HRV), and the level of adrenaline and noradrenaline were measured to assess autonomic activities. HRV included low frequency power (LF), high frequency power (HF), and LF/HF. Results: SPT was significantly lengthened during the evening shift and SL was shortened during the night shift. The workers showed a drop in alertness at wake-up during morning shift and a drop in alertness at work during night shift. During night shift the subjects complained of physical fatigue and cognitive decline. Comparison of HRV showed that parasympathetic activity was most prominent during the evening shift. Secretion of adrenaline and noradrenaline decreased during the evening shift, though statistically not significant. Conclusion: We found that the evening shift was most tolerable among the three shifts. It is recommended that morning light exposure be done during the morning shift and nocturnal light exposure during the night shift.

• Management & Information Systems Review
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• v.39 no.1
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• pp.35-57
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• 2020

Recently, research F&E(Facilities and Equipment) have become very important as tools and means to lead the development of science and technology. The government has been continuously expanding investment budgets for R&D and research F&E, and the need for efficient operation and systematic management of research F&E built up nationwide has increased. In December 2010, The government developed and completed a standardized classification system for national research F&E. However, accuracy and trust of information classification are suspected because information is collected by a method in which a user(researcher) directly selects and registers a classification code in NTIS. Therefore, in the study, we analyzed linearly using linear discriminant analysis(LDA) and analysis of variance(ANOVA), to measure the classification accuracy for the standardized classification system(8 major-classes, 54 sub-classes, 410 small-classes) of the national research facilities and equipment established in 2010, and revised in 2015. For the analysis, we collected and used the information data(50,271 cases) cumulatively registered in NTIS(National Science and Technology Service) for the past 10 years. This is the first case of scientifically verifying the standardized classification system of the national research facilities and equipment, which is based on information of similar classification systems and a few expert reviews in the in-outside of the country. As a result of this study, the discriminant accuracy of major-classes organized hierarchically by sub-classes and small-classes was 92.2 %, which was very high. However, in post hoc verification through analysis of variance, the discrimination power of two classes out of eight major-classes was rather low. It is expected that the standardized classification system of the national research facilities and equipment will be improved through this study.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.12
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• pp.913-921
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• 2011

This study examined the overall feasibility of low carbon green village formed in rural area. The check method is analyzing its environmental and economic feasibility and energy self-reliance. The biomass of the villages was set as 28 ton/day of livestock feces and 2 ton/day of cut fruit tree branches which make up the total of 30 ton/day. The facility consisted of a bio gasfication facility using wet (livestock feces) biomass and combined heat power generator, composting facility and wood boiler using dry (cut fruit tree branches) biomass. When operating the system, 540,540 kWh/yr of electricity and 1,762 Gcal/yr of heat energy was produced. The region's electricity energy and heat energy self-reliance rate will be 100%. The economic feasibility was found as a loss of 140 million won where the facility installation cost is 5.04 billion won, operation cost is 485.09 million won and profit is 337.12 million won. There will be a loss of about 2.2 billion won in 15 years but in the environmental analysis, it was found that crude replacement effect is about 178 million won, greenhouse gas reduction effect is about 92 million won making up the total environmental benefit of 270 million won. This means, there will be a yearly profit of about 130 million won. In terms of its environmental and economic feasibility and energy self-reliance, this project seemed to be a feasible project in overall even if it manages to get help from the government or local government.