• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.455-460
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• 2016

Recently, with more severe types felonies such as robbery and sexual violence, the importance of crime prediction and prevention is emphasized. For accurate and prompt crime prediction and prevention, both a classification model of crime with high accuracy based on past criminal records and well-designed system interface are required. However previous studies on the analysis of crime factors have limitations in terms of accuracy due to the difficulty of data preprocessing. In addition, existing crime monitoring systems merely offer a vast amount of crime analysis results, thereby they fail to provide users with functions for more effective monitoring. In this paper, we propose a classification model for types of crime based on random-forest algorithms and system design factors for real-time crime prediction. From our experiments, we proved that our proposed classification model is superior to others that only use criminal records in terms of accuracy. Through the analysis of existing crime monitoring systems, we also designed and developed a system for real-time crime monitoring.

• Smart Media Journal
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• v.13 no.4
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• pp.48-56
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• 2024

In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detection. The first method is an outlier detection using Mean Squared Error (MSE), and the second is an outlier detection using a Dynamic Threshold, which analyzes variability against the average value of previous data to identify outliers. The MSE-based method demonstrated a 94.98% accuracy rate, while the Dynamic Threshold method, which uses standard deviation, showed superior performance with 99.66% accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.

• The Journal of Bigdata
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• v.7 no.2
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• pp.1-9
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• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.595-604
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• 2004

By the help of expansion of computer network and rapid growth of Internet, the information infrastructure is now able to provide a wide range of services. Especially open architecture - the inherent nature of Internet - has not only got in the way of offering QoS service, managing networks, but also made the users vulnerable to both the threat of backing and the issue of information leak. Thus, people recognized the importance of both taking active, prompt and real-time action against intrusion threat, and at the same time, analyzing the similar patterns of in-trusion already known. There are now many researches underway on Intrusion Detection System(IDS). The paper carries research on the in-trusion detection system which hired supervised learning algorithm and Fuzzy membership function especially with Neuro-Fuzzy model in order to improve its performance. It modifies tansigmoid transfer function of Neural Networks into fuzzy membership function, so that it can reduce the uncertainty of anomaly intrusion detection. Finally, the fuzzy logic suggested here has been applied to a network-based anomaly intrusion detection system, tested against intrusion data offered by DARPA 2000 Intrusion Data Sets, and proven that it overcomes the shortcomings that Anomaly Intrusion Detection usually has.

• Korean Journal of Community Nutrition
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• v.8 no.5
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• pp.744-754
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• 2003

At present, health care industries throughout the world are struggling with the challenges to set up financial structures as cost-effective ways and means of satisfying customer needs for health care services. Many hospitals consign foodservice management to foodservice companies for the purpose of efficiency. The companies taking charge of hospital foodservice are also striving to gain an advantage over keen competitions. This study applied Quality Function Deployment(QFD) to one hospital (which will be shown as $\ulcorner$A hospital$\lrcorner$ below) managed by a contract foodservice company for the purpose of strategy planning to provide sustainable competitive advantage. First of all, this study scanned internal and external environment of $\ulcorner$A hospital$\lrcorner$ by means of a Quality Measurement Tool and a fieldwork study. With the result of environment scanning, this study elicited 20 strategies through SWOT analysis, which were categorized by 4 perspectives such as financial, customer, internal process, learning and growth perspectives. Finally, the priorities of 20 strategies were extracted from QFD methodology. According to the results obtained by applying QFD to $\ulcorner$A hospital$\lrcorner$'s foodservice, the strategies which $\ulcorner$A hospital$\lrcorner$ foodservice was obliged to introduce and implement were : the specialization of Children's hospital foodservice, scientific foodservice management through the standardization of foodservice operations, the maintenance of sanitary quality through sanitary system, the remodeling of facilities, the introduction of new equipment, the prompt and accurate response to customer needs, the development of appropriate patient menus, the provision of competitively priced meals for patient selection, the development of a demand forecast model by considering the characteristics of a children's hospital, improvement of productivity and the reduction of labor costs through the employment of experienced employees based on their seniority.

• Journal of The Korean Association For Science Education
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• v.34 no.3
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• pp.273-283
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• 2014

The purpose of this study was to examine the potential of the use of the Classroom Response System (CRS), a kind of new ICT medium, in a quiz problem-solving oriented high school life science class. To find the usefulness of CRS as a teaching and learning strategy, the CRS group (n=34) sent prompt individual answers to the teachers' questions using the CRS terminal (Clicker), and the teacher then asked additional reasons of the individuals and gave personalized feedback. In the control group (n=35), the CRS was not used while the teacher asked overall questions and gave feedback in an undifferentiated way. As a result, the CRS increased students' interest and concentration during class, but there were no significant differences in study achievement between the two groups. However, there were significant differences between the medium-level groups when the two groups were divided into smaller ones based on their pre-scores. We suggest that, for effective use of the CRS for diagnostic and formative assessment, teachers should develop a teaching and learning strategy that can produce appropriate questions of various levels in advance, investigate the exact reasons for students' answers, and give customized feedback by individual as much as possible.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.160-165
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• 2023

In this study, we propose an SNS sentence writing assistance system that utilizes YOLO and GPT to assist users in writing texts with images, such as SNS. We utilize the YOLO model to extract objects from images inserted during writing, and also extract meta-information such as GPS information and creation time information, and use them as prompt values for GPT. To use the YOLO model, we trained it on form image data, and the mAP score of the model is about 0.25 on average. GPT was trained on 1,000 blog text data with the topic of 'restaurant reviews', and the model trained in this study was used to generate sentences with two types of keywords extracted from the images. A survey was conducted to evaluate the practicality of the generated sentences, and a closed-ended survey was conducted to clearly analyze the survey results. There were three evaluation items for the questionnaire by providing the inserted image and keyword sentences. The results showed that the keywords in the images generated meaningful sentences. Through this study, we found that the accuracy of image-based sentence generation depends on the relationship between image keywords and GPT learning contents.

• Journal of The Korean Association For Science Education
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• v.31 no.5
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• pp.694-708
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• 2011

In this study, we developed tasks including cognitive scaffolding for students to explain scientific phenomena using valid evidences in science classroom and sought to investigate how tasks influence the development of small group scientific argumentation. Heterogeneous small groups in gender and achievement were organized in one classroom and the tasks were applied to the class. Students were asked to write down their own ideas, share individual ideas, and then choose the most plausible opinion in a group. One group was chosen for investigating the effect of tasks on the development of small group argumentation through the analysis of discourse transcripts of the group in 10 lessons, students' semi-structured interview, field note, and students' pre- and post argument tests. The discrepant argument examples were included in the tasks for students to refute an argument presenting evidences. Moreover, comparing opinion within the group and persuading others were included in the tasks to prompt small group argumentation. As a result, students' post-argument test grades were increased than pre-test grades, and they argued involving evidences and reasoning. The high level of arguments has appeared with high ratio of advanced utterances and lengthening of reasoning chain as lessons went on. Students had elaborate claims involving valid evidences and reasoning by reflective and critical thinking while discussing about the tasks. In addition, tasks which could have various warrants based on the data led to students' spontaneous participation. Therefore, this study has significance in understanding the context of developing small group argumentation, providing information about teaching and learning context prompting students to construct arguments in science inquiry lessons in middle school.