• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.639-649
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• 2011

The purpose of this study aimed to analyze factors on motives to tree burial and choice conditions to tree burial site in order to suggest policy direction for the desirable settlement of tree burial. For those purpose, this study performed questionnaire, targeting 522 visitors of funeral hall all around Korea. As the result, the factors of motives to tree burial were extracted as follows: funeral ceremony progressed along with trees, simplicity, memorial site's easy insurance, environmental friendliness and consideration toward descendants. The factors on choice conditions to tree burial sites were extracted as follows: beauty of natural scenery, emotional mood as a memorial site, convenience, stability and economic feasibility. Based on the results of factor analysis, this study suggested policies related to motives to tree burial as follows: develop various types of tree burial sites, develop a funeral ceremony suitable for tree burial, come into wide use of tree burial as a social welfare service, develop tree burial methods capable of many burials, and improve professionalism to manage tree burial system. In addition, this study proposed related choice conditions to tree burial sites as follows: establish natural forest scenery, convert existing graveyards into tree burial sites, select easily accessible places for tree burial sites, form tree burial sites as places for both rest and memory, and reduce using fee of tree burial site.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.

• Journal of IKEEE
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• v.26 no.2
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• pp.160-168
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• 2022

In this paper, we propose a 3D mesh reconstruction method from a single image using deep learning and a sphere shape transformation method. The proposed method has the following originality that is different from the existing method. First, the position of the vertex of the sphere is modified to be very similar to the 3D point cloud of an object through a deep learning network, unlike the existing method of building edges or faces by connecting nearby points. Because 3D point cloud is used, less memory is required and faster operation is possible because only addition operation is performed between offset value at the vertices of the sphere. Second, the 3D mesh is reconstructed by covering the surface information of the sphere on the modified vertices. Even when the distance between the points of the 3D point cloud created by correcting the position of the vertices of the sphere is not constant, it already has the face information of the sphere called face information of the sphere, which indicates whether the points are connected or not, thereby preventing simplification or loss of expression. can do. In order to evaluate the objective reliability of the proposed method, the experiment was conducted in the same way as in the comparative papers using the ShapeNet dataset, which is an open standard dataset. As a result, the IoU value of the method proposed in this paper was 0.581, and the chamfer distance value was It was calculated as 0.212. The higher the IoU value and the lower the chamfer distance value, the better the results. Therefore, the efficiency of the 3D mesh reconstruction was demonstrated compared to the methods published in other papers.

• Journal of IKEEE
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• v.26 no.3
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• pp.422-429
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• 2022

A light sensor chip requires a small capacity eFuse (electrical fuse) OTP (One-Time Programmable) memory IP (Intellectual Property) to trim analog circuits or set initial values of digital registers. In this paper, 128-bit eFuse OTP IP is designed using only 3.3V MV (Medium Voltage) devices without using 1.8V LV (Low-Voltage) logic devices. The eFuse OTP IP designed with 3.3V single MOS devices can reduce a total process cost of three masks which are the gate oxide mask of a 1.8V LV device and the LDD implant masks of NMOS and PMOS. And since the 1.8V voltage regulator circuit is not required, the size of the illuminance sensor chip can be reduced. In addition, in order to reduce the number of package pins of the illumination sensor chip, the VPGM voltage, which is a program voltage, is applied through the VPGM pad during wafer test, and the VDD voltage is applied through the PMOS power switching circuit after packaging, so that the number of package pins can be reduced.

• Industry Promotion Research
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• v.6 no.2
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• pp.55-62
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• 2021

The number of single-parent families in South Korea increased since 2000, related to a sharp rise in the divorce rate of 50s and an increase in male mortality rates among those aged 40s-50s. Middle-aged single mothers experience a critical period realizing self-actualization needs, while being in the middle adulthood from the lifespan developmental perspective. In this respect, it is significant to study self-actualization of middle-aged single mothers through guided imagery and music (GIM) in order to provide them with psychological support. This study was conducted from September 2018 to June 2020, and the GIM sessions were conducted at least 10 times. Four participants were selected among the middle-aged single mothers. The imagery experiences of participants in the GIM sessions were classified into four sub-elements: physicalness, emotion, memory, and sense. Within those sub-elements, eight semantic units were categorized into 46 elements. Finally, 152 semantic units were derived. Moreover, the self-actualization which participants experienced through GIM presented three archetypal images: shadow, persona, and the self. In the GIM sessions, experiences of putting their negative emotions associated with family into words and changing passive self-imagery into active one enabled participants to bring the shadow into their consciousness, there by recognizing their positive and bright internal self. Furthermore, participants could map that their current status as people marginalized by siblings and parents, enraged and holding double standards for others, was suppressed by their 'good daughter' and 'religious' personas. This realization lead them to realize and restore their persona. The use of GIM in the study allowed participants to elicit re-experiences of the negative events, while experiencing various imagery and music. This process helped participants achieve self-actualization.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.199-209
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• 2020

"Positive Influence(PI)" of popular star has recently emerged as a social concern, but the lack of prior research has led to confusion over its concept and range of activity. On this point, this study carried out to lay the groundwork for discussions on the systematization of related theories, focused on identifying the current situations by analyzing articles for 15 months from January 2019 to March 2020 when related reports were in full swing. As a result of the analysis of the remarks from the entertainers mentioned in the articles, they were not clearly aware of the concept while doing good deeds under the name of PI in light of the study outcome by Aegean and Singer(2013). The motivations for good deed were classified into six types, including difficulty empathy, fandom reward, participation urge, nidana emphasis, experience subjugation, and memory evocation in the order of frequency of cases. Specific behaviors of PI were followed by donations of money and valuables for 54.4 percent, participation of social agendas for 14.0 percent, volunteering for 13.2 percent, joining campaign for 11.4 percent, other good deeds for 4.0 percent, and philanthropy for 3.0 percent. In occupational analysis, the concentration of donations was also evident. Their activities in the fields of human rights sensitivity, environmental protection and self-management, which are expected to have great effects with their influence, have been extremely poor. The results of the study first require academia to establish a interdisciplinary concept for PI. It also suggests that entertainers and their agencies should take far more strategic approach to evolve the PI event in a way that utilizes the advantages of each job group, such as actors, singers and comedians, and expands the diversity of areas.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.113-122
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• 2021

This study begins on the basis of Jacques Lacan's article 『Logical Time and Assertions of Preemptive Certainty: A New Sophism』 published in the reissue of 『Art Note Les Cahiers d'Art』 in March 1945. In this paper, a guard presents an esoteric problem to three prisoners. If the problem is solved, the prisoner is released. A condition is given to solve a problem. Conversation between prisoners is prohibited, and the disc behind them cannot be seen. In this time and space, prisoners place themselves in logical time through the 'time of understanding' in order to become the chosen ones. We always live in logical time. We will argue the point at which Lacan destroys logical time in psychoanalysis. Time in Lacanian psychoanalysis transcends time divisions of the past, present, and future. Our time is always the past in the present. In Lacanian psychoanalysis, logical time is the time in the Other. The transcendence of the Lacanian psychoanalysis concept of time shows the deviation of logical time. In this text, We try to prove how Lacan contrasts psychoanalysis and the problem of time with time in the other. First, we will examine how logical time and impulse are related in psychoanalysis. Second, the postmortemity of the signifient (signifier) will be discussed. Third, Lacan psychoanalysis will present the transcendence of time. In conclusion, We will present the view that the time of Lacan psychoanalysis is flowing backwards. In Lacanian psychoanalysis, we try to prove that logical time is in the territory of the Other and is infinite time.