• Convergence Security Journal
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• v.14 no.1
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• pp.3-10
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• 2014

Private security organizations are complementary to the national safety of life and property of individuals as a social role to play in maintaining peace and order. Pursuit of profit is to the public practice according to the logic of capitalist markets and customers seeking to protect the lives and property. However, the legal and institutional constraints of private security is being requirements inhibited by the development. Crime prevention as a private security role that the private companies, which will pursue the public interest. After all, the expansion of the private security crime is results in an increase in unit. The current level of private security in the 1970s remain, and the constraints is being under goodwill and expertise outside of the training system on the market. Variety of crimes, including cyber crime increases and considering the reality of the constraints on private security requirements are able to improve or supplement shall be realistic. In particular, the legal, regulatory and institutional factors must be improved, with goodwill, and for the creation of new industrial policy as a complement to the public interest should be also provided. The private security law interests through integration of private security guards should be guaranteed, and the term of the theorem, sales activities, ensuring the training of professional staff with professional qualifications system is to be settled. As a private security guard industry growth and development can be based on this composition.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

• Journal of the Korean Geographical Society
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• v.38
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• pp.15-36
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• 1988

In this paper, I consider the development of methods in contemporary human geography in terms of a dialectical relation of action and structure, and try to draw a new horizon of method toward which geographical research and spatial theory would develop. The positivist geography which was dominent during 1960s has been faced both with serious internal reflections and strong external criticisms in the 1970s. The internal reflections that pointed out its ignorance of spatial behavior of decision-makers and its simplication of complex spatial relations have developed behavioural geography and systems-theoretical approach. Yet this kinds of alternatives have still standed on the positivist, geography, even though they have seemed to be more real and complicate than the previous one, The external criticisms that have argued against the positivist method as phenomenalism and instrumentalism suggest some alternatives: humanistic geography which emphasizes intention and action of human subject and meaning-understanding, and structuralist geography which stresses on social structure as a totality which would produce spatial phenomena, and a theoretical formulation. Human geography today can be characterized by a strain and conflict between these methods, and hence rezuires a synthetic integration between them. Philosophy and social theory in general are in the same in which theories of action and structural analysis have been complementary or conflict with each other. Human geography has fallen into a further problematic with the introduction of a method based on so-called political ecnomy. This method has been suggested not merely as analternative to the positivist geography, but also as a theoretical foundation for critical analysis of space. The political economy of space with has analyzed the capitalist space and tried to theorize its transformation may be seen either as following humanistic(or Hegelian) Marxism, such as represented in Lefebvre's work, or as following structuralist Marxism, such as developed in Castelles's or Harvey's work. The spatial theory following humanistic Marxism has argued for a dialectic relation between 'the spatial' and 'the social', and given more attention to practicing human agents than to explaining social structures. on the contray, that based on structuralist Marxism has argued for social structures producing spatial phenomena, and focused on theorising the totality of structures, Even though these two perspectives tend more recently to be convergent in a way that structuralist-Marxist. geographers relate the domain of economic and political structures with that of action in their studies of urban culture and experience under capitalism, the political ecnomy of space needs an integrated method with which one can overcome difficulties of orthhodox Marxism. Some novel works in philosophy and social theory have been developed since the end of 1970s which have oriented towards an integrated method relating a series of concepts of action and structure, and reconstructing historical materialism. They include Giddens's theory of structuration, foucault's geneological analysis of power-knowledge, and Habermas's theory of communicative action. Ther are, of course, some fundamental differences between these works. Giddens develops a theory which relates explicitly the domain of action and that of structure in terms of what he calls the 'duality of structure', and wants to bring time-space relations into the core of social theory. Foucault writes a history in which strategically intentional but nonsubjective power relations have emerged and operated by virtue of multiple forms of constrainst wihthin specific spaces, while refusing to elaborate any theory which would underlie a political rationalization. Habermas analyzes how the Western rationalization of ecnomic and political systems has colonized the lifeworld in which we communicate each other, and wants to formulate a new normative foundation for critical theory of society which highlights communicatie reason (without any consideration of spatial concepts). On the basis of the above consideration, this paper draws a new norizon of method in human geography and spatial theory, some essential ideas of which can be summarized as follows: (1) the concept of space especially in terms of its relation to sociery. Space is not an ontological entity whch is independent of society and has its own laws of constitution and transformation, but it can be produced and reproduced only by virtue of its relation to society. Yet space is not merlely a material product of society, but also a place and medium in and through which socety can be maintained or transformed.(2) the constitution of space in terms of the relation between action and structure. Spatial actors who are always knowledgeable under conditions of socio-spatial structure produce and reproduce their context of action, that is, structure; and spatial structures as results of human action enable as well as constrain it. Spatial actions can be distinguished between instrumental-strategicaction oriented to success and communicative action oriented to understanding, which (re)produce respectively two different spheres of spatial structure in different ways: the material structure of economic and political systems-space in an unknowledged and unitended way, and the symbolic structure of social and cultural life-space in an acknowledged and intended way. (3) the capitalist space in terms of its rationalization. The ideal development of space would balance the rationalizations of system space and life-space in a way that system space providers material conditions for the maintainance of the life-space, and the life-space for its further development. But the development of capitalist space in reality is paradoxical and hence crisis-ridden. The economic and poltical system-space, propelled with the steering media like money, and power, has outstriped the significance of communicative action, and colonized the life-space. That is, we no longer live in a space mediated communicative action, but one created for and by money and power. But no matter how seriously our everyday life-space has been monetalrized and bureaucratised, here lies nevertheless the practical potential which would rehabilitate the meaning of space, the meaning of our life on the Earth.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Korean Journal of Soil Science and Fertilizer
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• v.9 no.1
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• pp.1-8
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• 1976

1. Under an asumption that Ficks diffusion equation could be applicable in soil systems, the diffusivities of NaCl in several flooded soil systems were measured to range from $0.4{\times}10^{-5}cm^2sec^{-1}$ to $0.83{\times}10^{-5}cm^2sec^{-1}$ 2. It was discussed that, when a polder soil with a uniform initial salt content through the profile is desalinated only by diffusion to flooding water, the salt content in profile is a function of soil depth, diffusion time, and diffusivity as following $$C=C^{\circ}erf\frac{x}{\sqrt[2]{Dt}}$$ 3. On the basis of Kirkham, et al's integration of complementary erra function, the speed of desalting was discussed to be inversely proportional to the square root of time as following $$dq/dt=C^{\circ}{\sqrt{D/{\pi}t}}$$ 4. It was estimated enough to exchange the flooding water once or twice, even when desalination of polder soil is carried out only by diffusion, if the desalination begins in June, the used flooding water is fresh water, and flooding depth is 10cm. 5. Desalination of polder soil by diffusion requires 2 month for good standing of planted rice.

• 지역과문화
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• v.7 no.2
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• pp.173-202
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• 2020

This study aims to enhance the family-friendliness of the region by examining the relationship between family relations and the community environment, and objectively comparing the local environment surrounding the family. To this end, we reviewed the areas of socio-cultural and economic sectors that affect the family relationship satisfaction, and in particular, analyzed the trend of changes in regional index by utilizing the Korean Family Happiness Composite Index (KFHCI) developed as a community indicators. This index utilizes community indicators published in the National Statistical Portal's "e-Region indicators," and these variables are related to family relationship satisfaction. Therefore, this study compared the seven areas of the Family Happiness Composite Index (Population Family, Health Culture, Education, Income Consumption, Employment Labor, Housing Transportation, Environment and Social Integration) by region, and examined the trends for 10 years. According to the study, the average score of KFHCI's entire region was rising from 2008 to 2018. Overall, the community environment that affects family relationship satisfaction is also improving. The regions belonging to the upper level were Jeonnam, Gangwon, Chungnam, Jeonbuk, and Gyeongbuk. Areas belonging to the lower level are Seoul, Busan, Daegu, Incheon, and Gwangju. In almost sectors, the lower-level regions did not have sufficient physical infrastructure compared to population density and over-density, and improved little by little, but not enough to reflect the needs of local people and improve the quality of life. In the future, we should develop more regular and complementary indicators to develop customized policies for each region that can improve the quality of family relationships. It will also be necessary to study the impact of each index field when a socioeconomic crisis occurs due to social disasters, and try to change indicators

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.