• Korean Institute of Interior Design Journal
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• v.18 no.6
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• pp.133-141
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• 2009

Since Adolf Loos' Ornament and Crime was released, ornament had been regarded as a vice in Modern architecture. In Post Modern architecture, the ornamental vocabularies extracted from the classical architecture have been symbolically expressed. As a result, the significance of existence of and the use value of architecture have increased with improved intimacy to the public. However, many studies that have analyzed architecture in terms of semantics and semiotics focusing on communication between architecture and the public have admitted instability in architectural meaning because of the fundamental difference between architecture and language. At the same time, the architectural representation has encountered a lot of limitations along with the deconstruction and non-presentation theory in Post-Structuralism. Even under these circumstances, the ornamental representation can be a critical factor which makes communication possible while expressing spatial identity in this pluralistic contemporary society. Hence, this paper has established logical grounds for research based on theoretical investigation on the concept of representation, semiotics, and ornamental representation, with regard to spatial communication through ornamental representation in commercial space. Based on Peirce's Semiotics which is the typical theoretical science in terms of representation, this paper has analyzed the three kinds of signs (Icon, Index and Symbol) that he proposed. Peirce's classification of signs becomes ideal when the factors are well balanced, with room for combination instead of strict classification. It appears that his theory of signs would be useful in understanding and analyzing ornamental representation. This study on representational ornament could offer a plan for ideal communication and abundant spatial experience in the interior design of commercial space in which the expression of creative space identity is required.

• Korean Security Journal
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• no.61
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• pp.377-400
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• 2019

Photography is the most commonly used method of documenting the crime and incident scene as it helps maintaining chain of custody (COC) and prove integrity of the physical evidence. It can also capture phenomena as they are. However, digital images can be manipulated and lose their authenticity as admissible evidence. Thus only limited techniques can be used to enhance images, and one of them is Fourier transform. Fourier transform refers to transformation of images into frequency signals. Fast Fourier transform (FFT) is used in this study. In this experiment, we overlapped fingerprints with graph paper or other fingerprints and separated the fingerprints. Then we evaluated and compared quality of the separated fingerprints to the original fingerprints, and examined whether the two fingerprints can be identified as same fingerprints. In the case of the fingerprints on graph paper and general pattern-overlapping fingerprints, fingerprint ridges are enhanced. On the other hand, in case of separating complicated fingerprints such as core-to-core overlapping and delta-to-delta overlapping fingerprints, quality of fingerprints can be deteriorated. Quality of fingerprints is known to possibly bring negative effects on the credibility of examiners. The result of this study may be applicable to other areas using digital imaging enhancement technology.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.59-69
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• 2021

The Moon Jae-in administration insisted on reforming the prosecution for the reform of power institutions, and also tried to control the power of the prosecution through the establishment of a high-ranking public officials criminal investigation office. Then, what is the direction of the legal prosecution reform? Above all, it is a logic to drastically reduce the power of the prosecution. How to shrink it? First, it is necessary to control or check the power of prosecution and investigation that the prosecution has. Second, the key to the reform of the prosecution is perhaps to separate personnel rights from power. In order to exclude external pressure from the prosecution's personnel rights, there must be no instruction or intervention from the attorney general. To do this, the prosecutor general must go through various personnel evaluations, such as multi-faceted evaluation. Third, the essence of the High-ranking Officials Criminal Investigations Act is to deal with crimes of prosecutors, judges, police, and members of the National Assembly. It is also the core of the institutional mechanism to cover the corruption of one's family members while holding all the right to investigate, prosecution, and warrant claims, and to check the omnipresent prosecutors who have abused their right to investigate for political purposes. Then, what is necessary for the success of the high-ranking officials criminal investigation office? Above all, We think political neutrality is very necessary. The Ministry of Airborne has a very high authority of the Director. This paper intends to examine the functions and roles of the prosecution and high-ranking officials criminal investigation offices to prevent corruption. We analyzed the contents of the prosecution and the public officials criminal investigation office, and examined the limitations and problems, and the measures to control corruption in public offices.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Korean Security Journal
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• no.37
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• pp.137-163
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• 2013

This research will incorporate cases from U.S public schools to analyze the factors which influences the security within the school and efficient security patterns to suggest an adequate suggestion to elevate domestic school security system. This study is divided into two following models: a serious criminal offense model, which considers crimes occurred on campus as subordinate variables, and a school violence model, which considers as subordinate variables after limiting an act of delinquency and an a group action that can harm the safety of students, although they are not included in the categories of crimes. First, from analyzing the factors which influences security within school, the explanation power of serious crime offense safety model and school violence safety model is measured 12% and 11.3%. In serious crime offense safety model, the safety education for students, among the safety programs provided by schools(t=2.548, p=0.011), parent participation to school management(t=10.694, p=0.000), Security activities on campus(t=3.643, p=0.000), and CPTED activity(t=6.467, p=0.000) are statistically significant, as affecting factors on the safety from serious crimes. Similarly in school violence model, the safety education for students, among the safety programs provided by schools(t=3.228, p=0.001), parent participation to school management(t=12.034, p=0.000), security activities on campus(t=2.663, p=0.000), and CPTED activity(t=3.928, p=0.000) are statistically significant, as affecting factors on the safety from school violence. Second, according to the analytic results on figuring out the optimal pattern to heighten the security activities, the serious offence model's explanatory power was 4.4% and school violence safety model rated 3.9%. With the serious offense safety model, the activity factors which showed statistically significant in influencing safety from serious offenses were cooperation with local police force (t=2112, p=0.035), school policy management (t=3.309, p=0.001), security patrolling activity (t=2.548, p=0.011). In the school violence model, security activities initiated by the school which showed statistically significant from serious offenses were cooperation with local police force (t=2.364, p=0.018) and policy management (t=4.142, p=0.000). In accordance with the result of this study, education for students rather than education for teachers is more positive in terms of the safety on campus, and parent participation, like education, is consistently needed for the safety on campus. In case of CPTED activity, reinforcing plans should be prepared by intactly accepting examples in the USA. In case of security activity, plans that can increase visibility and reinforce cooperation with local police in a smooth way will provide a positive effect to the safety on campus.

• Journal of Korea Entertainment Industry Association
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• v.13 no.2
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• pp.101-110
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• 2019

Filmic spaces are not only a core expressive factors that can embody both internal and external meanings of films, but also play an important role for initiating dramatic context in terms of narratives. They establish visible environment in which provocative incidents occur and characters' behaviors are induced, include characters' psychology or emotional meanings of narratives and provoke tensions through symbolic meanings and the implicit function of surface background. This paper, therefore, analyzes the film, , by focusing on the dramatic function of narrative space. This film provides insights and thoughts through deep philosophical reflection, by escaping from the convention of such a genre, through deep philosophical reflection, by escaping from the convention of such a genre, though it belongs seemingly to the crime thriller genre using Mexico's drug cartel as it main material. In the film, narrative spaces are responsible for emotions invoking dramatic tensions beyond provocative incidents and the elaborately planned and controlled mise-en-scene absorbed audiences' attention by organizing ultimate suspense. In conclusion, flow and dramatic lingering imagery of this film might be achieved by power of scenes rather than plot factors. This study thus explained the correlation between narrative spaces and dramatic immersion, by analyzing spaces appearing in and visualized construction. It is hoped that this will further extends the pattern of researches on dramatic immersion, which have been primarily focused on plots and characters.

• The Journal of Natural Sciences
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• v.14 no.1
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• pp.63-81
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• 2004

It is the purpose of this study to arrange in ornamental trees and shrubs the planting that leads to an appropriate type of templescape. The study was designed primarily as an examples for each Buddhist temple which desires further decoration with several ornamental plants, doubles the effectiveness of the Sen-Buddhist meditation, and boosts tourists' attraction. To investigate the planting status and type of trees and shrubs in the precincts of Buddhism temples, We close three temples : They are Nagsansa, Boolgoogsa and Booseugsa, which are intermonatane area temples all together. The results investigated were summarized as follows :1. Planting status and pattern in temples Open spaces of the Daewoogjeon in all temples, a main Sanctuaries in temple buildings, where Buddha is enshrined in, we could not find any kind of trees of shrubs to be planted.Muryangsujeon, a symbol of "Future", which can be also found at Boosugsa temple, is living in Western Elysium world and takes mercy on mankind of this life. Taxus cuspidata was planted at this Muryangsujeon, known as an immeasurable bliss building, where an Amitabha is enshrined in.Total 25 species of trees and shrubs were planted around Birozani building of Buddhist temples, Birozani is enshrined at the Birojion of Boollgoogsa temple.The buddhist Goddess of Mercy which is a buddhist saint for pursuit of fortune and blessing to relieve the mankind is enshrined at Wonchonjeon, Daebijeon and Kwaneumjeon which are able to observe at both Boolgoogsa and Nagsnsa temples, where Euonymus japonicus trees including other 26 species could be found in common at both temples.2. Correlation between trees/shrubs and temple buildingsTrees and flowers symbolizing Buddha are often planted as material sources of gardening to decorate : They are Logerstroemia indica as Buddha's flower, Viburmum opulus var. calvescens resembling Buddha's head, Tilia mandshurica producing the beads of rosary, Gardenia jaminoides Ellis with white flower blade and flower of bliss, not flower to this day and Lotus flowering clearly in the pond filling with dirty water which is able to clarifies the world full of crime, infidelity and injustification. Among these Buddhist' plants, however, Logerstroemia indica could be found in all three temples, and Viburmum opulus var. calvescens at both Nagsansa and Boosugsa. Also, Lager stroemia indica was planted at all three temples and Viburmum opulus var. calvescens at both temples of Nagsansa and Boosugsa. Tilia mandshurica and Gardenia jasminoides Ellis were not found in any temples which might become the subject of investigation.In relation of the buildings of each temples as a sanctified space, the planting of trees and shrubs was not considered for the arrangement, templescape architecture or species. And, also, we could not find in the study any special relationship of trees/ shrubs with the characteristics of temples.With the results obtained through precise studies we presented here in this paper newly designed model of templescape in intermane buddhist temple which can be applied for planting and arrangement of trees or/and shrubs. Basic principles of model in mind are:To consider the correlation of the dominant between plants and temple buildings.To plant trees/shrubs for special functions as well as conditions of temple location.To make tree arrangement correlating to Buddhism spirit.To induce environment friendly plants to be planted, suitable to regional conditions.This redecorated model of templescape might be used as a canon of the tree planting and arrangement in the precincts of Buddhism temples.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.