• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.61-72
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• 2020

The fear of crime, discussed in the early 1960s in the United States, is a psychological response, such as anxiety or concern about crime, the potential victim of a crime. These anxiety factors lead to the burden of the individual in securing the psychological stability and indirect costs of the crime against the society. Fear of crime is not a good thing, and it is a part that needs to be adjusted so that it cannot be exaggerated and distorted by the policy together with the crime coping and resolution. This is because fear of crime has as much harm as damage caused by criminal act. Eric Pawson has argued that the popular impression of violent crime is not formed because of media reports, but by official statistics. Therefore, the police should watch and analyze news related to fear of crime to reduce the social cost of fear of crime and prepare a preemptive response policy before the people have 'fear of crime'. In this paper, we propose a deep - based news classification system that helps police cope with crimes related to crimes reported in the media efficiently and quickly and precisely. The goal is to establish a system that can quickly identify changes in security issues that are rapidly increasing by categorizing news related to crime among news articles. To construct the system, crime data was learned so that news could be classified according to the type of crime. Deep learning was applied by using Google tensor flow. In the future, it is necessary to continue research on the importance of keyword according to early detection of issues that are rapidly increasing by crime type and the power of the press, and it is also necessary to constantly supplement crime related corpus.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.4
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• pp.533-538
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• 2017

In this thesis, the ambulatory aid mechanism was implemented so that blind people could be safer at risk of walking outdoors. Using ultrasonic sensors, the obstacles can be detected when the distance between the obstacle is within 50 cm of the obstacle. If the light sensor becomes less than 25 lux, the LED will automatically turn on and help the safety of the visually impaired and the security of sight of the peripheral walkers. Color recognition sensors increase the rate of recognition of yellow color by the detection distance is 1cm, it vibrated when yellow light was detected. Using GPS with 7.3 m of error range, the guardian was able to check the location of the visually impaired.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.673-681
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• 2005

As it is the most important to make sure security and reliability for nuclear Power Plant, it's considered the most crucial issues to develop a fault detective and diagnostic system in spite of multiple hardware redundancy in itself. To develop an algorithm for a fault diagnosis in the nuclear PWR steam generator, this paper proposes a method based on ART2(adaptive resonance theory 2) neural network that senses and classifies troubles occurred in the system. The fault diagnosis system consists of fault detective part to sense occurred troubles, parameter estimation part to identify changed system parameters and fault classification part to understand types of troubles occurred. The fault classification part Is composed of a fault classifier that uses ART2 neural network. The Performance of the proposed fault diagnosis a18orithm was corroborated by applying in the steam generator.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.74-80
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• 2007

Advances in electronic and computer technologies have paved the way for the proliferation of WSN(wireless sensor networks). Accordingly, necessity of anti-collusion and authentication technology is increasing on the sensor network system. Some of the algorithm developed for the anti-collision sensor network can be easily adopted to wireless sensor network platforms and in the same time they can meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings. To achieve security in wireless sensor networks, it is important to be able to establish safely channel among sensor nodes. In this paper, we proposed the USN(Ubiquitous Sensor Network) channel establishment algorithm for sensor's authentication and anti-collision. Two different data aggregation architectures will be presented, with algorithms which use wavelet filter to establish channels among sensor nodes and BIBD (Balanced Incomplete Block Design) which use anti-collision methods of the sensors. As a result, the proposed algorithm based on BIBD and wavelet filter was made for 98% collision detection rate on the ideal environment.

• Journal of KIISE:Information Networking
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• v.33 no.2
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• pp.131-138
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• 2006

To achieve security in wireless sensor networks(WSN), it is important to be able to encrypt and authenticate messages sent among sensor nodes. Due to resource constraints, many key agreement schemes used in general networks such as Diffie-Hellman and public-key based schemes are not suitable for wireless sensor networks. The current pre-distribution of secret keys uses q-composite random key and it randomly allocates keys. But there exists high probability not to be public-key among sensor nodes and it is not efficient to find public-key because of the problem for time and energy consumption. To remove problems in pre-distribution of secret keys, we propose a new cryptographic key management protocol, which is based on the clustering scheme but does not depend on probabilistic key. The protocol can increase efficiency to manage keys because, before distributing keys in bootstrap, using public-key shared among nodes can remove processes to send or to receive key among sensors. Also, to find outcompromised nodes safely on network, it selves safety problem by applying a function of lightweight attack-detection mechanism.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.6
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• pp.379-386
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• 2014

Recently, middleboxes play a key role in many network settings such as firewalls, VPN gateways, proxies, intrusion detection and prevention systems, and WAN optimizers. However, achieving the performance and security benefits that middleboxes offer is highly complex, and therefore it is essential to manage middleboxes efficiently and dynamically. In this respect, Software-Defined Networking (SDN) offers a promising solution for middlebox policy enforcement by using logically centralized management, decoupling the data and control planes, and providing the ability to programmatically configure forwarding rules. Also, cloud computing and distributed Network Function Virtualization (NFV) can enable to manage middleboxes more easily. We introduce SDN-based middlebox management framework in integrated wired and wireless networks and discuss the further issues.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.221-229
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• 2007

Security of the port TCP/80 has been demanded by reason that the others besides web services have been rapidly increasing use of the port. Existing traffic analysis approaches can't distinguish web services traffic from application services when traffic passes though the port. monitoring method based on protocol and port analysis were weak in analyzing harmful traffic using the web port on account of being unable to distinguish payload. In this paper, we propose a method of detecting harmful traffic by web traffic analysis. To begin, traffic Capture by real time and classify by web traffic. Classed web traffic sorts each application service details and apply weight and detect harmful traffic. Finally, method propose and implement through coding. Therefore have a purpose of these paper to classify existing traffic analysis approaches was difficult web traffic classified normal traffic and harmful traffic and improved performance.

• Toxicological Research
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• v.33 no.2
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• pp.119-124
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• 2017

Hexavalent chromium is a bio accumulative toxic metal in water and fish. It enters aquaculture ponds mainly through anthropogenic sources. Hexavalent chromium concentrations and its effects on the morphology and behavior of Clarias gariepinus were investigated from four aquaculture ponds for 12 weeks. Chromium was measured using diphenyl carbohdrazide method; alkalinity and hardness were measured using colometric method and analyzed with Bench Photometer. Temperature and pH were measured using pH/EC/TDS/Temp combined tester. Temporal and spatial replications of samples were done with triplicates morphological and behavioural effects of the metal on fish were observed visually. Chromium ranged from no detection to 0.05 mg/L, alkalinity 105 to 245 mg/L, hardness 80 to 165 mg/L, pH 6.35 to 8.03 and temperature 29.1 to $35.9^{\circ}C$. Trend in the chromium concentrations in the ponds is natural > earthen > concrete > collapsible. There was a significant difference (P < 0.05) in chromium, alkalinity, water hardness, pH and temperature among the four ponds. Significant positive correlation also existed between alkalinity, water hardness, pH, with chromium. Morphological and behavioural changes observed in the fish include irregular swimming, frequent coming to the surface, dark body colouration, mucous secretion on the body, erosion of gill epithelium, fin disintegration, abdominal distension and lethargy. High chromium concentration in natural pond was due to anthropogenic run-off of materials in to the pond. Acidic pH, low alkalinity, low water hardness also contributed to the high chromium concentration. Morphological and behavioural changes observed were attributed to the high concentrations, toxicity and bio accumulative effect of the metal. Toxicity of chromium to fish in aquaculture could threaten food security. Watershed best management practices and remediation could be adopted to reduce the effects of toxicity of chromium on pond water quality, fish flesh quality and fish welfare.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1904-1926
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• 2016

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.207-237
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• 2009

One of the main problems in Korea's public assistance program, the NBLS (National Basic Livelihood Security), is that the loophole of welfare system is continuously growing. Living wage program is the largest sub-program of the NBLS, and the most important determinant of amount of living wage for each beneficiary is the level of reported income. Therefore, accurate and effective income detection is essential in improving policy effects and furthermore reducing the leakage of wage expenditure as beneficiaries always have an incentive to underreport their income. Since most of them do not pay income tax, the welfare authority should exert an independent effort to effectively detect their income. Considering that living wage is a special kind of income tax of which marginal tax rate is -1, one can apply a classical theory of tax evasion to understand illegal or excessive receipt of living wage caused by income underreporting. Utilizing a classical theory given by Alingham and Sandmo (1972), this paper provides a theoretical analysis of the optimal income reporting of the beneficiary. Then an optimization problem is constructed from the government's viewpoint to derive optimal income detecting device (auditing). This paper proves that cut-off discriminated auditing outperforms random auditing and cut-off auditing which implies if the government assigns a positive audit probability to every reported income less than a certain level and the probability is inversely proportional to the level of reported income, it can minimize underreporting and then gradually reduce the leakage of wage expenditure.