• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.125-130
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• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.39-44
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• 2018

Communication in real-time distributed system should have high reliability. To develop group communication Protocol with high reliability, potential fault should be known and when fault occurs, it should be detected and a necessary action should be taken. Existing detection method by Ack and Time-out is not proper for real time system due to load to Ack which is not received. Therefore, group communication messages from real-time distributed processing systems should be communicated to all receiving processors or ignored by the message itself. This paper can make be sure of transmission of reliable message and deadline by suggesting and experimenting fault detection technique applicable in the real time distributed system based on ring, and analyzing its results. The experiment showed that the shorter the cycle of the heartbeat signal, the shorter the time to propagate the fault detection, which is the time for other nodes to detect the failure of the node.

• Journal of Food Hygiene and Safety
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• v.34 no.3
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• pp.290-295
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• 2019

Salmonella spp. are frequently associated with food and are among the most important foodborne pathogens. The recent Salmonella out breaks in Korea was associated with chocolate mousse cakes served with school meals during September 2018. The objective of this research was to compare the 3M Molecular Detection Assay 2 - Salmonella and the Korean Standard Method of Salmonella in artificially inoculated mousse (chocolate and cheese) and tiramisu cakes. Mousse (chocolate and cheese) and tiramisu cakes were artificially inoculated with S. Typhimurium. Twenty five gram of sample was enriched with 225 mL buffered peptone water for incubation at $37^{\circ}C$ for 24 h. After enrichment, the cultures were analyzed by using the 3M Molecular Detection Assay 2 - Salmonella and the Korean Standard Method. Most of the inoculated samples showed similar results except the chocolate mousse cakes, in which real-time PCR was unable to detect S. Typhimurium even after $10^4CFU/25g$ of inoculation. However, S. Typhimurium inoculated at a concentration of $10^0CFU/25g$ was detected by using 3M Molecular Detection Assay 2 - Salmonella. In chocolate mousse, detection of S. Typhimurium using real-time PCR was partially successful when dark chocolate was added at less than 15%. Negative results in real-time PCR and 3M Molecular Detection Assay 2 - Salmonella were confirmed by gel electrophoresis. The data indicated that dark chocolate could inhibit amplification of the target gene in the PCR reactions. In conclusion, the 3M Molecular Detection Assay 2 - Salmonella was better than the Korean Standard Method (real-time PCR) for the detection of S. Typhimurium in chocolate mousse cakes and chocolate mousse.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3001-3010
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• 1999

The most of intrusion detection methods do not detect intrusion on real-time because it takes a long time to analyze an auditing data for intrusions. To solve the problem, we are studying a real-time intrusion detection. Therefore, this paper proposes an agent model using multi warning level for real-time intrusion detection. It applies to distributed environment using an extensibility and communication mechanism among agents, supports a portability, an extensibility and a confidentiality of IDS.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.159-166
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• 2021

COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.9-13
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• 2005

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.3
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• pp.137-143
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• 2013

This paper describes an implementation of face detection technology for a real-time application on the Android platform. Java class of Face-Detection for detection of human face is provided by the Android API. However, this function is not suitable to apply for the real-time applications due to inadequate detection speed and accuracy. In this paper, the AdaBoost based classification method which utilizes Local Binary Pattern (LBP) histogram is employed for face detection. The face detection module has been developed by C/C++ language for high-speed image processing, and this module is included to the Android platform using the Java Native Interface (JNI). The experiments were carried out in the Java-based environment and JNI-based environment. The experimental results have shown that the performance of JNI-based is faster than Java-based method and our system is well enough to apply for real-time applications.

• Biomedical Science Letters
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• v.16 no.2
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• pp.127-131
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• 2010

Mycobacterium leprae detection is difficult even with molecular biological techniques due to the low sensitivity of current methodologies. In this report, real-time PCR targeting the M. leprae-specific repetitive element (RLEP) sequence was developed as a new diagnostic tool and evaluated using clinical specimens. For this, M. leprae DNAs were extracted from skin biopsy specimens from 80 patients and analyzed by real-time PCR using TaqMan probe. Then, the detection efficiency of the real-time PCR was compared with that of standard PCR. In brief, the rate of positive detection by the standard PCR and real-time PCR was 32.50% and 66.25%, respectively. The results seemed to clearly show that the TaqMan real-time PCR developed in this study may be a useful tool for sensitive detection of M. leprae from clinical specimens.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.644-660
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• 2016

The real-time detection of malware remains an open issue, since most of the existing approaches for malware categorization focus on improving the accuracy rather than the detection time. Therefore, finding a proper balance between these two characteristics is very important, especially for such sensitive systems. In this paper, we present a fast portable executable (PE) malware detection system, which is based on the analysis of the set of Application Programming Interfaces (APIs) called by a program and some technical PE features (TPFs). We used an efficient feature selection method, which first selects the most relevant APIs and TPFs using the chi-square ($KHI^2$) measure, and then the Phi (${\varphi}$) coefficient was used to classify the features in different subsets, based on their relevance. We evaluated our method using different classifiers trained on different combinations of feature subsets. We obtained very satisfying results with more than 98% accuracy. Our system is adequate for real-time detection since it is able to categorize a file (Malware or Benign) in 0.09 seconds.