• Journal of Korean Society of Archives and Records Management
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• v.1 no.1
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• pp.19-35
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• 2001

After almost one century of discontinuity in the archival tradition of Chosun dynasty, Korea entered the new age of records and archival management by legislating and executing the basic laws (The Records and Archives Management of Public Agencies Ad of 1999). Annals of Chosun dynasty recorded major historical facts of the five hundred years of national affairs. The Annals are major accomplishment in human history and rare in the world. It was possible because the Annals were composed of collected, selected and complied records of primary sources written and compiled by generations of historians, As important public records are needed to be preserved in original forms in modern archives, we had to develop and establish a modern archival system to appraise and select important national records for archival preservation. However, the colonialization of Korea deprived us of the opportunity to do the task, and our fine archival tradition was not succeeded. A centralized archival system began to develop since the establishment of GARS under the Ministry of Government Administration in 1969. GARS built a modem repository in Pusan in 1984 succeeding to the tradition of History Archives of Chosun dynasty. In 1998, GARS moved its headquarter to Taejon Government Complex and acquired state-of-the-art audio visual archives preservation facilities. From 1996, GARS introduced an automated archival management system to remedy the manual registration and management system complementing the preservation microfilming. Digitization of the holdings was the key project to provided the digital images of archives to users. To do this, the GARS purchased new computer/server systems and developed application softwares. Parallel to this direction, GARS drastically renovated its manpower composition toward a high level of professionalization by recruiting more archivists with historical and library science backgrounds. Conservators and computer system operators were also recruited. The new archival laws has been in effect from January 1, 2000. The new laws made following new changes in the field of records and archival administration in Korea. First, the laws regulate the records and archives of all public agencies including the Legislature, the Judiciary, the Administration, the constitutional institutions, Army, Navy, Air Force, and National Intelligence Service. A nation-wide unified records and archives management system became available. Second, public archives and records centers are to be established according to the level of the agency; a central archives at national level, special archives for the National Assembly and the Judiciary, local government archives for metropolitan cities and provinces, records center or special records center for administrative agencies. A records manager will be responsible for the records management of each administrative divisions. Third, the records in the public agencies are registered in the computer system as they are produced. Therefore, the records are traceable and will be searched or retrieved easily through internet or computer network. Fourth, qualified records managers and archivists who are professionally trained in the field of records management and archival science will be assigned mandatorily to guarantee the professional management of records and archives. Fifth, the illegal treatment of public records and archives constitutes a punishable crime. In the future, the public records find archival management will develop along with Korean government's 'Electronic Government Project.' Following changes are in prospect. First, public agencies will digitize paper records, audio-visual records, and publications as well as electronic documents, thus promoting administrative efficiency and productivity. Second, the National Assembly already established its Special Archives. The judiciary and the National Intelligence Service will follow it. More archives will be established at city and provincial levels. Third, the more our society develop into a knowledge-based information society, the more the records management function will become one of the important national government functions. As more universities, academic associations, and civil societies participate in promoting archival awareness and in establishing archival science, and more people realize the importance of the records and archives management up to the level of national public campaign, the records and archival management in Korea will develop significantly distinguishable from present practice.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.367-376
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• 2007

In this paper, we introduce an application-specific and adaptive power management technique for portable systems that support dynamic voltage scaling (DVS). We exploit both the idle time of multitasking systems running soft real-time tasks as well as memory- or CPU-bound code regions. Detailed power and execution time profiles guide an adaptive power manager (APM) that is linked to the operating system. A post-pass optimizer marks candidate regions for DVS by inserting calls to the APM. At runtime, the APM monitors the CPU's performance counters to dynamically determine the affinity of the each marked region. for each region, the APM computes the optimal voltage and frequency setting in terms of energy consumption and switches the CPU to that setting during the execution of the region. Idle time is exploited by monitoring system idle time and switching to the energy-wise most economical setting without prolonging execution. We show that our method is most effective for periodic workloads such as video or audio decoding. We have implemented our method in a multitasking operating system (Microsoft Windows CE) running on an Intel XScale-processor. We achieved up to 9% of total system power savings over the standard power management policy that puts the CPU in a low Power mode during idle periods.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.1
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• pp.33-40
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• 2013

Purpose: The purpose of this study was to evaluate the possibility to optimize the gated treatment delivery time and maintenance of stable respiratory by the introduction of breath with the assistance of auditory-visual-tactile sense. Materials and Methods: The experimenter's respiration were measured by ANZAI 4D system. We obtained natural breathing signal, monitor-induced breathing signal, monitor & ventilator-induced breathing signal, and breath-hold signal using real time monitor during 10 minutes beam-on-time. In order to check the stability of respiratory signals distributed in each group were compared with means, standard deviation, variation value, beam_time of the respiratory signal. Results: The stability of each respiratory was measured in consideration of deviation change studied in each respiratory time lapse. As a result of an analysis of respiratory signal, all experimenters has showed that breathing signal used both Real time monitor and Ventilator was the most stable and shortest time. Conclusion: In this study, it was evaluated that respiratory gated radiation therapy with auditory-visual-tactual sense and without auditory-visual-tactual sense feedback. The study showed that respiratory gated radiation therapy delivery time could significantly be improved by the application of video feedback when this is combined with audio-tactual sense assistance. This delivery technique did prove its feasibility to limit the tumor motion during treatment delivery for all patients to a defined value while maintaining the accuracy and proved the applicability of the technique in a conventional clinical schedule.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Technology Innovation
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• v.26 no.3
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• pp.37-68
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• 2018

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.

• Journal of the Korean Society of School Health
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• v.12 no.1
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• pp.149-167
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• 1999

To investigate the actual conditions of smoking in middle and high school students in Chinan County, I used a sturctured questionnaire for 1,579 students attending twelve middle-high schools from December 1, 1998 to December 20, 1998. I collected and data correlated the using an $SPSS-PC^+$ 1. The smoking rate of middle-high schoo1 students in Chinan County was 17.9%, relatively high. This smoking rate was different according to the gender, grade, religion, and economic situation. In mals, high school students, non-religious, students low income family students, the smoking rate was higher. The smoking rate of high school students was almost the same as the smoking rate of adults, generally higher than that of foreign teenagers. Because the smoking rat of studinets in the third grade of middle school and in the first grade of high School was six times higher, increased education should be conducted during this time in an attempt to curb the sudden increase of the smoking rate. The smoking rate of girl students was 5.0%; this has increased mor than three times from ten years ago. Consequently, counter measures should be taken against the smoking of female students as well as juvenile smoking in general. In addition, the smoking rate of middle-high school students showed interesting differences when correnated with enviornmental factors. Students with low grades, who are not satisfied with school life, who don't have both parents, who have uncaring parents who nare too strict or too arbitary, who have smoking parents, or who have experienced smoking commonly smoked. Therefore, to lower the smoking rate we should improve the school environment, improve a student's interest in school life. And parents or siblings should lead by example and quit smoking at home. Schools should educate students more effectively concerning the harmful effects of smoking and create an accurate understanding of its dancers. From the beginning, we should teach students never ever to touch cigaretts. 2. The surve discovered that most students started out of curiosity, or solicitantion from friends or elders at middle school, and had been smoking one to five cigarettes for more than a year. They obtained cigarettes at stores and most of them have friends who smoke. As a result anti-smoking education should be conducted at elementary schools prior to middle school. More than 95% of the teenagers who smoke had friends who smote and smoked out of curiosity or the recommendation of elders. Thus, we must focus on teenagers who smoke in group, rather than individually. Fuyrthermore, the strict application of the regulation of tobacco sales as well as tobacco cooperation from retailers are needed. While students did not show any mood or academic achievement difference after beginning smoking, 58.1% of the students a health situdation that was worse. Juvenile smoking is more harmful to the juvenile than adult smoking is to the adult. This should be focused on in an anti-smoking campaign. 3. Students who smoke hada more positive attitude toward smoking than students who don't smoke. Students who smoke had a tendency to have a nuetral position and are not concerned about smoking compared to non-smoking students. The survey showed that the great number of students had a nuetral position. Because this nuetrality may increase Juvenile smoking, education that provides an exact understanding of smoking should be performed to build the correct attidude toward smoking. 4. Middle school students smoke when angry, gloomy, anxious, a lone and when they have some problems to solve, on when they feel inconveniened in other wores, they smoke to reliver stress. They also smok due to addiction. Because smoking is not a praetical method to relieve stress, a program which helps to acquire positive relief stress should be provided to help reduce smoking. 5. About 65% of students who smoke want to quit smoking because of health problem, 78% of them have tried mor than once to quit but failed due to weak will power and peer pressure from friends who smoke. Juvenile smoking is group, oriented. Thus, the program that advances less smoking will be the one that focuseds on groups. 6. As for advice to students who want to quit smoking, "persuasion" was used most commonly, followed by a "presentation on how to quit smoking". Another method were severe punishment. About 70% of the students wanted the anti-smoking guide at school. 7. Most students (73.5%) had a position that more anti-smoking education at school is needed. Obriously, then, anti-smoking education at middle-high schools should be reinfoced. Although the education which explains the harmful influence of tobacco is known as an efficient way prevent smoking; it does not influence students who already smoke. Therefore, for students who smoke, multi-dimensional approaches must be attempted that include physical training, phychokogical approache, consultation and discussion, medical chek-ups, audio-visual education technigues, and professonal instructors, in addition, because smoking students have more negative on lukewarm attitude to anti-smoking education anti-smoking education should be conducted through a communicative style by dedicated teachers who care about students. In order to increase the effectiveness of this program.

• Fiber Technology and Industry
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• v.2 no.4
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• pp.490-508
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• 1998

One of the latest fields of research in the area of output devices is tactual display devices [13,31]. These tactual or haptic devices allow the user to receive haptic feedback output from a variety of sources. This allows the user to actually feel virtual objects and manipulate them by touch. This is an emerging technology and will be instrumental in enhancing the realism of wearable augmented environments for certain applications. Tactual displays have previously been used for scientific visualization in virtual environments by chemists and engineers to improve perception and understanding of force fields and of world models populated with the impenetrable. In addition to tactual displays, the use of wearable audio displays that allow sound to be spatialized are being developed. With wearable computers, designers will soon be able to pair spatialized sound to virtual representations of objects when appropriate to make the wearable computer experience even more realistic to the user. Furthermore, as the number and complexity of wearable computing applications continues to grow, there will be increasing needs for systems that are faster, lighter, and have higher resolution displays. Better networking technology will also need to be developed to allow all users of wearable computers to have high bandwidth connections for real time information gathering and collaboration. In addition to the technology advances that make users need to wear computers in everyday life, there is also the desire to have users want to wear their computers. In order to do this, wearable computing needs to be unobtrusive and socially acceptable. By making wearables smaller and lighter, or actually embedding them in clothing, users can conceal them easily and wear them comfortably. The military is currently working on the development of the Personal Information Carrier (PIC) or digital dog tag. The PIC is a small electronic storage device containing medical information about the wearer. While old military dog tags contained only 5 lines of information, the digital tags may contain volumes of multi-media information including medical history, X-rays, and cardiograms. Using hand held devices in the field, medics would be able to call this information up in real time for better treatment. A fully functional transmittable device is still years off, but this technology once developed in the military, could be adapted tp civilian users and provide ant information, medical or otherwise, in a portable, not obstructive, and fashionable way. Another future device that could increase safety and well being of its users is the nose on-a-chip developed by the Oak Ridge National Lab in Tennessee. This tiny digital silicon chip about the size of a dime, is capable of 'smelling' natural gas leaks in stoves, heaters, and other appliances. It can also detect dangerous levels of carbon monoxide. This device can also be configured to notify the fire department when a leak is detected. This nose chip should be commercially available within 2 years, and is inexpensive, requires low power, and is very sensitive. Along with gas detection capabilities, this device may someday also be configured to detect smoke and other harmful gases. By embedding this chip into workers uniforms, name tags, etc., this could be a lifesaving computational accessory. In addition to the future safety technology soon to be available as accessories are devices that are for entertainment and security. The LCI computer group is developing a Smartpen, that electronically verifies a user's signature. With the increase in credit card use and the rise in forgeries, is the need for commercial industries to constantly verify signatures. This Smartpen writes like a normal pen but uses sensors to detect the motion of the pen as the user signs their name to authenticate the signature. This computational accessory should be available in 1999, and would bring increased peace of mind to consumers and vendors alike. In the entertainment domain, Panasonic is creating the first portable hand-held DVD player. This device weight less than 3 pounds and has a screen about 6' across. The color LCD has the same 16:9 aspect ratio of a cinema screen and supports a high resolution of 280,000 pixels and stereo sound. The player can play standard DVD movies and has a hour battery life for mobile use. To summarize, in this paper we presented concepts related to the design and use of wearable computers with extensions to smart spaces. For some time, researchers in telerobotics have used computer graphics to enhance remote scenes. Recent advances in augmented reality displays make it possible to enhance the user's local environment with 'information'. As shown in this paper, there are many application areas for this technology such as medicine, manufacturing, training, and recreation. Wearable computers allow a much closer association of information with the user. By embedding sensors in the wearable to allow it to see what the user sees, hear what the user hears, sense the user's physical state, and analyze what the user is typing, an intelligent agent may be able to analyze what the user is doing and try to predict the resources he will need next or in the near future. Using this information, the agent may download files, reserve communications bandwidth, post reminders, or automatically send updates to colleagues to help facilitate the user's daily interactions. This intelligent wearable computer would be able to act as a personal assistant, who is always around, knows the user's personal preferences and tastes, and tries to streamline interactions with the rest of the world.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Korean Academy of Nursing
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• v.2 no.1
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• pp.3-33
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• 1971

Nursing of today has as one of its objectives the solving of problems related to human needs arising from the demands of a rapidly changing society. This nursing objective, I believe, can he attained by the appropriate application of scientific principles in the giving of comprehensive nursing care. Comprehensive nursing care may be defined as nursing care which meets all of the patient's needs. the needs of patients are said to fall into five broad categories: physical needs, psychological needs, environmental needs, socio-economic needs, and teaching needs. Most people who become ill have adjustment problems related to their new situation. Because patient teaching is one of the most important functions of professional nursing, the success of this teaching may be used as a gauge for evaluating comprehensive nursing care. This represents a challenge foe the future. A questionnaire consisting of 67 items was distributed to 200 professional nurses working ill direct patient care at Yonsei University Medical Center in Seoul, Korea. 160 (80,0%) nurses of the total sample returned completed questionnaires 81 (50.6%) nurses were graduates of 3 fear diploma courser 79 (49.4%) nurses were graduates of 4 year collegiate nursing schools in Korea 141 (88,1%) nurses had under 5 years of clinical experience in a medical center, while 19 (11.9%) nurses had more than 5years of clinical experience. Three hypotheses were tested: 1. “Nurses had high levels of concept and knowledge toward patient teaching”-This was demonstrated by the use of a statistical method, the mean average. 2. “Nurses graduating from collegiate programs and diploma school programs of nursing show differences in concepts and knowledge toward patient teaching”-This was demonstrated by a statistical method, the mean average, although the results showed little difference between the two groups. 3. “Nurses having different amounts of clinical experience showed differences in concepts and knowledge toward patient teaching”-This was demonstrated by the use of a statistical method, the mean average. 2. “Nurses graduating from collegiate programs and diploma school programs of nursing show differences in concepts and knowledge toward patient teaching”-This was demonstrated by a statistical method, the mean average, although the results showed little difference between the two groups. 3. “Nurses having different amounts of clinical experience showed differences in concepts and knowledge toward patient teaching”-This was demonstrated by the use of the T-test. Conclusions of this study are as follow: Before attempting the explanation, of the results, the questionnaire will he explained. The questionnaire contained 67 questions divided into 9 sections. These sections were: concept, content, time, prior preparation, method, purpose, condition, evaluation, and recommendations for patient teaching. 1. The nurse's concept of patient teaching: Most of the nurses had high levels of concepts and knowledge toward patient teaching. Though nursing service was task-centered at the turn of the century, the emphasis today is put on patient-centered nursing. But we find some of the nurses (39.4%) still are task-centered. After, patient teaching, only a few of the nurses (14.4%) checked this as “normal teaching.”It seems therefore that patient teaching is often done unconsciously. Accordingly it would he desirable to have correct concepts and knowledge of teaching taught in schools of nursing. 2. Contents of patient teaching: Most nurses (97.5%) had good information about content of patient teaching. They teach their patients during admission about their diseases, tests, treatments, and before discharge give nurses instruction about simple nursing care, personal hygiene, special diets, rest and sleep, elimination etc. 3. Time of patient teaching: Teaching can be accomplished even if there is no time set aside specifically for it. -a large part of the nurse's teaching can be done while she is giving nursing care. If she believes she has to wait for time free from other activities, she may miss many teaching opportunities. But generally proper time for patient teaching is in the midmorning or midafternoon since one and a half or two hours required. Nurses meet their patients in all stages of health: often tile patient is in a condition in which learning is impossible-pain, mental confusion, debilitation, loss of sensory perception, fear and anxiety-any of these conditions may preclude the possibility of successful teaching. 4. Prior preparation for patient teaching: The teaching aids, nurses use are charts (53.1%), periodicals (23.8%), and books (7.0%) Some of the respondents (28.1%) reported that they had had good preparation for the teaching which they were doing, others (27.5%) reported adequate preparation, and others (43.8%) reported that their preparation for teaching was inadequate. If nurses have advance preparation for normal teaching and are aware of their objectives in teaching patients, they can do effective teaching. 5. Method of patient teaching: The methods of individual patient teaching, the nurses in this study used, were conversation (55.6%) and individual discussion (19.2%) . And the methods of group patient teaching they used were demonstration (42.3%) and lecture (26.2%) They should also he prepared to use pamphlet and simple audio-visual aids for their teaching. 6. Purposes of patient teaching: The purposes of patient teaching is to help the patient recover completely, but the majority of the respondents (40.6%) don't know this. So it is necessary for them to understand correctly the purpose of patient teaching and nursing care. 7. Condition of patient teaching: The majority of respondents (75.0%) reported there were some troubles in teaching uncooperative patients. It would seem that the nurse's leaching would be improved if, in her preparation, she was given a better understanding of the patient and communication skills. The majority of respondents in the total group, felt teaching is their responsibility and they should teach their patient's family as well as the patient. The place for teaching is most often at the patient's bedside (95.6%) but the conference room (3.1%) is also used. It is important that privacy be provided in learning situations with involve personal matters. 8. Evaluation of patient teaching: The majority of respondents (76.3%,) felt leaching is a highly systematic and organized function requiring special preparation in a college or university, they have the idea that teaching is a continuous and ever-present activity of all people throughout their lives. The suggestion mentioned the most frequently for improving preparation was a course in patient teaching included in the basic nursing program. 9. Recommendations: 1) It is recommended, that in clinical nursing, patient teaching be emphasized. 2) It is recommended, that insertive education the concepts and purposes of patient teaching he renewed for all nurses. In addition to this new knowledge, methods and materials which can be applied to patient teaching should be given also. 3) It is recommended, in group patient teaching, we try to embark on team teaching.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.