• 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.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

• Korean Security Journal
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• no.33
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• pp.51-80
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• 2012

This study is to summarize main contents which was mentioned by Beomseok Kim' doctoral dissertation. The purpose of this study focuses on presenting the outplacement countermeasure and retention level examination analysis about outplacement competency of special security government official through implement of questionnaire method. The questionnaire for retention level examination including four groups of outplacement competency and twenty subcategories was implemented in the object of six hundered persons relevant to outplacement more than forty age and five grade administration official of special security government officials, who have outplacement experiences as outplacement successors, outplacement losers, and outplacement expectants, in order to achieve this research purpose effectively. The questionnaire examination items are four groups of outplacement competency and twenty subcategories which are the group of knowledge competency & four subcategories including expert knowledge, outplacement knowledge, self comprehension, and organization comprehension, the group of skill competency & nine subcategories including job skill competency, job performance skill, problem-solving skill, reforming skill, communication skill, organization management skill, crisis management skill, career development skill, and human network application skill, the group of attitude-emotion competency & seven subcategories including positive attitude, active attitude, responsibility, professionalism, devoting-sacrificing attitude, affinity, and self-controlling ability, and the group of value-ethics competency & two subcategories including ethical consciousness and morality. The respondents highly regard twenty-two outplacement competency and they consider themselves well-qualified for the subcategories valued over 4.0 such as the professional knowledge, active attitude, responsibility, ethics and morality while they mark the other subcategories below average still need to be improved. Thus, the following is suggestions for successful outplacement. First, individual effort is essential to strengthen their capabilities based on accurate self evaluation, for which the awareness and concept need to be redefined to help them face up to the reality by readjusting career goal to a realistic level. Second, active career development plan to improve shortcoming in terms of outplacement competency is required. Third, it is necessary to establish the infrastructure related to outplacement training such as ON-OFF Line training system and facilities for learning to reinforce user-oriented outplacement training as a regular training course before during after the retirement.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.73-85
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• 2004

Recently, as the growth of the wireless Internet, PDA and HPC, the focus of research and development related with GIS(Geographic Information System) has been changed to the Real-Time Mobile GIS to service LBS. To offer LBS efficiently, there must be the Real-Time GIS platform that can deal with dynamic status of moving objects and a location index which can deal with the characteristics of location data. Location data can use the same data type(e.g., point) of GIS, but the management of location data is very different. Therefore, in this paper, we studied the Real-Time Mobile GIS using the HBR-tree to manage mass of location data efficiently. The Real-Time Mobile GIS which is developed in this paper consists of the HBR-tree and the Real-Time GIS Platform HBR-tree. we proposed in this paper, is a combined index type of the R-tree and the spatial hash Although location data are updated frequently, update operations are done within the same hash table in the HBR-tree, so it costs less than other tree-based indexes Since the HBR-tree uses the same search mechanism of the R-tree, it is possible to search location data quickly. The Real-Time GIS platform consists of a Real-Time GIS engine that is extended from a main memory database system. a middleware which can transfer spatial, aspatial data to clients and receive location data from clients, and a mobile client which operates on the mobile devices. Especially, this paper described the performance evaluation conducted with practical tests if the HBR-tree and the Real-Time GIS engine respectively.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.403-410
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• 2015

The purpose of this study was to investigate the problems of a signal to noise ratio measurement using a two region measurement method that is conventionally used when using a multi-channel coil and a parallel imaging technique. As a research method, after calculating the standard SNR using a single channel head coil of which coil satisfies three preconditions when using a two region measurement method, we made comparisons and evaluations after calculating an SNR by using a two region measurement method of which method is problematic because it is used without considering the methods recommended by reputable organizations and the preconditions at the time of using a multi-channel coil and a parallel imaging technique. We found that a two region measurement method using a multi-channel coil and a parallel imaging technique shows the highest relative standard deviation, and thus shows a low degree of precision. In addition, we found out that the difference of SNR according to ROI location was very high, and thus a spatial noise distribution was not uniform. Also, 95% confidence interval through Blend-Altman plot is the widest, and thus the conformity degree with a two region measurement method using the standard single channel head coil is low. By directly comparing an AAPM method, which serves as a standard of a performance evaluation test of a magnetic resonance imaging device under the same image acquisition conditions, an NEMA method which can accurately determine the noise level in a signal region and the methods recommended by manufacturers of a magnetic resonance imaging device, there is a significance in that we quantitatively verified the inaccurate problems of a signal to noise ratio using a two region measurement method when using a multi-channel coil and a parallel imaging technique of which method does not satisfy the preconditions that researchers could overlook.

• Journal of Food Hygiene and Safety
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• v.27 no.3
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• pp.277-284
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• 2012

The objective of this study was to investigate and evaluate microbial contamination levels of leafy greens (perilla leaf and lettuce) and its distributing conditions at different seasons (Feb, May, Aug, and Nov of the year 2011) in order to provide insight into any potential health hazards associated with consumption of these commodities. Leafy greens were collected from a farm located in Geumsan, Chungnam and wholesale markets (WM) and traditional markets (TM) located in Suwon. At the same time, temperature and relative humidity fluctuations experienced by the leafy greens during distribution from the farm to the distribution center were measured by a data logger. The contamination levels of perilla leaf and lettuce were determined by analyzing total plate count. Coliform groups, Bacillus cereus, Escherichia coli, Escherichia coli O157:H7, Salmonella spp., Listeria monocytogenes and Staphylococcus aureus were determined. The contamination levels of total aerobic bacteria, coliform groups and B. cereus in both vegetables sampled during May and August found to be higher than those sampled during February and November. E. coli O157:H7, Salmonella spp., L. monocytogenes were not detected in the vegetables analyzed in this study. There were no significant trends between samples at WM and TM in the contamination levels. Relative humidity of vegetables distributed from the farm to the distribution center showed over 90% during distribution regardless of measured seasons. In the case of background microflora on leafy greens, the density was significantly increased at 20, 30 and $37^{\circ}C$ during storage of 24h. E. coli O157:H7 and B. cereus inoculated on the leaves also showed similar increases in the storage tests. The microbial contamination levels determined in this study may be used as the fundamental data for microbial risk assessment.

• Radiation Oncology Journal
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• v.22 no.1
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• pp.33-39
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• 2004

Purpose : A tumor registry system for the patients treated by radiotherapy at Samsung Medical Center since the opening of a hospital at 1994 was employed. In this study, the tumor registry system was introduced and the validity of the tumor registration was analyzed. Materials and Methods: The tumor registry system was composed of three parts: patient demographic, diagnostic, and treatment Information. All data were input in a screen using a mouse only. Among the 10,000 registered cases in the tumor registry system until Aug, 2002, 199 were randomly selected and their registration data were compared with the patients' medical records. Results : Total input errors were detected on 15 cases (7.5%). There were 8 error items In the part relating to diagnostic Information: tumor site 3, pathology 2, AJCC staging 2 and performance status 1. In the part relating to treatment information there were 9 mistaken items: combination treatment 4, the date of initial treatment 3 and radiation completeness 2. According to the assignment doctor, the error ratio was consequently variable. The doctors who 010 no double-checks showed higher errors than those that 010 (15.6%:3.7%). Conclusion: Our tumor registry had errors within 2% for each Item. Although the overall data qualify was high, further improvement might be achieved through promoting sincerity, continuing training, periodic validity tests and keeping double-checks. Also, some items associated with the hospital Information system will be input automatically In the next step.

• Journal of Korean Academy of Nursing
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• v.3 no.2
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• pp.31-43
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• 1973

The trend in modern nursing is toward the performance of comprehensive nursing care. Psychiatric nursing emphasizes education which enables the nurse to understand the underlying difficulties being expressed through a wide range of emotions and through practice to be more adept in her selection of a manner of approach which best meets the needs of a given situation. Presently, in Korea, there is nothing in the literature regarding evaluation of the effect of psychiatric nursing education on the attitudes of nurses towards mental illness and mentally ill patients. This stud!1 was attempted in order to understand 1) some of the problems in psychiatric nursing education 2) some of tile factors which affect the attitudes of nurses towards mental illness and mental patients. A questionnaire, a Korean translation of the "Opinions about Mental illness Scale" by Cohen and Stranding, 1962, was administered to 188 nonpsychiatric registered nurses employed in Yonsei University Hospital (Y. Hospital) and Seoul National University Hospital (S. Hospital) located in the city of Seoul. All of the nurses were directly involved with adult patient care. They graduated from various nursing schools. The data was collected during the period of October 2 to October 16,1972. The age, educational background , marital status, type of previous psychiatric experience, experience as a graduate nurse and close personal relationship with someone who was a psychiatric patient were compared with the O.M.I. scores. The mean and standard errors for each of the comparison groups were computed and tile relationships calculated by a t-test. The results of the study are summarized as follow: 1. There is no significant difference between the age of the nurses and their attitudes toward mental illness and mental patients. 2. There is no significant difference between the. educational backgrounds of the nurses and their attitudes toward mental illness and mental patients. 3. There is a significant difference in the nurses ′student psychiatric nursing experience and their attitudes towards mental illness and mental patients for the nurses in 5. Hospital only. The nurses who had 3-4 week of student psychiatric nursing experience had a significantly higher mean score for Benevolence (factor B) than nurses whose student psychiatric experience had been less than 1 Ivcek (P<0.05). The nurses who had 1-2 weeks, 3-4 weeks and more than 4 weeks of student psychiatric nursing experience had significantly higher mean scores for Interpersonal Ethology (factor E) than nurses whose student psychiatric had been less than 1 week (p<0.05), 4. There is a significant difference in the nurses′student psychiatric nursing experience by types of institution and their attitudes towards mental illness and mental patients for S. Hospital nurses only. The nurses who had their student psychiatric nursing experience in the government psychiatric hospitals recorded significantly higher mean score for Authoritarianism (factor A) than nurses who had their. experience in private psychiatric hospitals (p<0.05). 5. There is no significant difference in the nurses′psychiatric nursing experience as a graduate nurse and their attitudes toward mental illness and mental patients. 6. There is no significant difference in the nature and variety of the nurses′experience as a graduate nurse and their attitude toward mental illness and mental patients. 7. There is no significant difference in the presence or absence of a close personal relationship with a mentally ill person and the nurses′attitude toward mental illness and mental patients. 8. There is no significant difference in the nurses′ marital status and their attitude toward mental illness and mental patients. 9. There is no significant difference between the nurses who were employed ill S. and Y. hospitals and their attitudes towards mental illness and mental patients. Major suggestion for further study was to have more larger and wider scale research for establishing of the reliability and validity of the Korean translation of the O.H.I. Scale.

• Journal of Korea Port Economic Association
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• v.30 no.4
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• pp.69-89
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• 2014

The business performance of port industry is steadily getting worse due to international environmental regulation. The port industry should be prepared according to ambient condition change. IMO(International Maritime Organization) is tightening up environmental regulation of vessel and maritime industry field. ECA(Emission Control Area), starting with the Baltic, has initialized and has been expanded. Korea must strengthen the control of vessel in accordance with IMO's restriction, if Korea is designated as emission control area. These situations cause the expansion of LNG-fuelled ships. Add to the larger trend of ships, Korean government should be done a preemptive action against LNG bunkering industry. This study proposes the concept of floating offshore LNG bunkering system and is conducted its economic feasibility evaluation based on empirical analysis. We examine the theoretical foundation and basic information via "A Planning Study on the Engineering Development of Floating Offshore LNG Bunkering Terminal" in 2013 and we evaluate the business potential by using the report above mentioned. The results of this study are as follows. The values of B/C analysis are between 0.679 and 2.516 depending on market share and R&D contributiveness. In case of 10.9%(market share), if market share are 50% and 60%, the value of B/C analysis are 0.697 and 0.837 respectively. Except in two cases, all remaining values are over 1.0. Moreover, the research is conducted sensitivity analysis to remove the project uncertainty. In order to maintain economical validity, a project manager have to establish business strategies which are not to cause increase of expense and sustain market share and R&D contributiveness in the scenario with normal levels.