• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Convergence Security Journal
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• v.21 no.2
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• pp.57-66
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• 2021

Deep neural networks (DNNs) provide excellent performance for image, speech, and pattern recognition. However, DNNs sometimes misrecognize certain adversarial examples. An adversarial example is a sample that adds optimized noise to the original data, which makes the DNN erroneously misclassified, although there is nothing wrong with the human eye. Therefore studies on defense against adversarial example attacks are required. In this paper, we have experimentally analyzed the success rate of detection for adversarial examples by adjusting various parameters. The performance of the ensemble defense method was analyzed using fast gradient sign method, DeepFool method, Carlini & Wanger method, which are adversarial example attack methods. Moreover, we used MNIST as experimental data and Tensorflow as a machine learning library. As an experimental method, we carried out performance analysis based on three adversarial example attack methods, threshold, number of models, and random noise. As a result, when there were 7 models and a threshold of 1, the detection rate for adversarial example is 98.3%, and the accuracy of 99.2% of the original sample is maintained.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.59-68
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• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.293-312
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• 2019

The purpose of this study is to find ways to improve the evaluation system of school library operation whose participation rate is getting lower. We conducted analyzing the current status of operating evaluation of school libraries through literature search. In doing so, questionnaire method was conducted to inquire the recognition of the 205 staff members of school libraries. The outcome of the survey shows that the participation rate of operating evaluation of school libraries decreased from 22.2% in 2009 to 7.2% in 2017. The validity of the quantitative evaluation method was significantly low at 2.84 and the validity of the qualitative evaluation method was 2.97. The average score of the validity index for 'Establishing the annual operation plan' was the highest at 3.90, and that of 'Community service' was the lowest at 2.27. The biggest reason for not participating in the evaluation was due to the staff's high workload. There is a need to seek ways to improve the evaluation index and ways to participate since the result shows very low awareness of the school library evaluation. Through this study, it is expected that the school library evaluation will become the foundation for effective revitalization of school library operation.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.71-80
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• 2022

In the past, fish diseases were bacterial in aqua farms, but in recent years, the frequency of fish diseases has increased as they have become viral and mixed. Viral diseases in an enclosed space called a aqua farm have a high spread rate, so it is very likely to lead to mass death. Fast identification of fish diseases is important to prevent group death. However, diagnosis of fish diseases requires a high level of expertise and it is difficult to visually check the condition of fish every time. In order to prevent the spread of the disease, an automatic identification system of diseases or fish is needed. In this paper, in order to improve the performance of the disease identification system of Paralichthys olivaceus based on deep learning, the existing pre-processing method is compared and tested. Target diseases were selected from three most frequent diseases such as Scutica, Vibrio, and Lymphocystis in Paralichthys olivaceus. The RGB, HLS, HSV, LAB, LUV, XYZ, and YCRCV were used as image pre-processing methods. As a result of the experiment, HLS was able to get the best results than using general RGB. It is expected that the fish disease identification system can be advanced by improving the recognition rate of diseases in a simple way.

• Journal of Korea Entertainment Industry Association
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• v.13 no.1
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• pp.121-138
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• 2019

A term 'Wolabal' which is an abbreviation of the words, in South Korea means the balance between work and life. The term reflects on people's thoughts to seek for their happiness these days. In fact, they think that the quality of the life is more valuable than any other things on their lives, reflecting on the issues caused by modern societies. 'Wolabal' has emerged as an alternatives to solve the social issues like economic recession, high unemployment rate, aging society, low birth rate and etc. However, in order to establish 'Wolabal' as a culture the comprehensive agreement between an individual and society should be considered first. In society, the system or policies to forster cultural business should be settled while in individual, it is necessary to change the way they think about their work and the qualities of their lives. From this view we have to focus on the relationship between their work and leisure. On the relationship between their work and leisure we should understand that the relationship is not conflicted but co-exists and understanding the real meaning of the relationship is critical in balancing between work and life. The recognition to the labor which has been from the past would give not only the meaning of individual survival but the one of their whole lives. Despite this, modern society has faded away the real meaning of labor because it has focused on the mass manufacturing and sometimes the long-termed economic sluggish has emerged. This trend has made people think about their lives and seek for their lives' real value.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.95-104
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• 2009

This paper proposes the noise robust algorithm to detect the starting point of music. Detection of starting point of music is necessary to solve computational-waste problem and retrieval-comparison problem with inconsistent input data in music content based retrieval system. In particular, such detection is even more necessary in time sequential retrieval method that compares data in the sequential order of time in contents based music retrieval system. Whereas it has the long point that the retrieval is fast since it executes simple comparison in the order of time, time sequential retrieval method has the short point that data starting time to be compared should be the same. However, digitalized music cannot guarantee the equity of starting time by bit rate conversion. Therefore, this paper ensured that recognition rate shall not decrease even while executing high speed retrieval by applying time sequential retrieval method through detection of music starting point in the pre-processing stage of retrieval. Starting point detection used minimum wave model that can detect effective sound, and for strength against noise, the noises existing in mute sound were swapped. The proposed algorithm was confirmed to produce about 38% more excellent performance than the results to which starting point detection was not applied, and was verified for the strength against noise.

• Korean Journal of Clinical Pharmacy
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• v.32 no.1
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• pp.20-26
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• 2022

Background and objective: The Seoul National University Bundang Hospital (SNUBH) implemented ward-based clinical pharmacy system with designated pharmacists in 10 general wards. Designated pharmacists conduct inpatient medication review, medication intervention, and medication consultation, and provide drug information for health care providers. This study aimed to evaluate the clinical pharmacy services and to examine the perception and expectations of health care providers on the services provided by the designated pharmacists in general wards. Methods: A survey was constructed to include questions on the health care providers' recognition, satisfaction, and perceived needs of designated pharmacists. We determined the frequency and type of interventions of ward pharmacist and their acceptance rate through a retrospective observational study using electronic medical records. Results: A total of 59 health care providers responded the questionnaire and 79.7% of the respondents reported moderate to high levels of satisfaction. Satisfaction with the services was positively associated with clinical interventions and nutrition support team (81.4%). Of 59 respondents, 88.1% agreed that preventing drug-related problems by designated pharmacists' activities were effective. The most common interventions included inadequate dosage (27.4%), omission and additional prescription (14.6%) and inadequate drug form (9.6%). The acceptance rate of intervention was 91.5%, and 151 potentially serious risks and 523 significant risks were prevented by the intervention. Conclusion: Positive results were confirmed in the awareness, satisfaction, and perceived needs of the health care providers for designated pharmacists. Expansion of the ward-based clinical pharmacy system with designated pharmacists to other wards may be considered.

• Convergence Security Journal
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• v.22 no.3
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• pp.25-32
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• 2022

Recently, the image processing industry has been activated as deep learning-based technology is introduced in the image recognition and detection field. With the development of deep learning technology, learning model vulnerabilities for adversarial attacks continue to be reported. However, studies on countermeasures against poisoning attacks that inject malicious data during learning are insufficient. The conventional countermeasure against poisoning attacks has a limitation in that it is necessary to perform a separate detection and removal operation by examining the training data each time. Therefore, in this paper, we propose a technique for reducing the attack success rate by applying modifications to the training data and inference data without a separate detection and removal process for the poison data. The One-shot kill poison attack, a clean label poison attack proposed in previous studies, was used as an attack model. The attack performance was confirmed by dividing it into a general attacker and an intelligent attacker according to the attacker's attack strategy. According to the experimental results, when the proposed defense mechanism is applied, the attack success rate can be reduced by up to 65% compared to the conventional method.