• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3712-3729
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• 2020

Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.

• The KIPS Transactions:PartC
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• v.13C no.1 s.104
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• pp.45-54
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• 2006

Nowadays, a lot of techniques have been applied for the detection of malicious behavior. However, the current techniques taken into practice are facing with the challenge of much variations of the original malicious behavior, and it is impossible to respond the new forms of behavior appropriately and timely. There are also some limitations can not be solved, such as the error affirmation (positive false) and mistaken obliquity (negative false). With the questions above, we suggest a new method here to improve the current situation. To detect the malicious code, we put forward dealing with the basic source code units through the conceptual graph. Basically, we use conceptual graph to define malicious behavior, and then we are able to compare the similarity relations of the malicious behavior by testing the formalized values which generated by the predefined graphs in the code. In this paper, we show how to make a conceptual graph and propose an efficient method for similarity measure to discern the malicious behavior. As a result of our experiment, we can get more efficient detection rate.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.891-899
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• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.37-41
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• 2014

Recently, a lot of research efforts has been directed toward spectrum sensing techniques exploiting the some characteristics of power spectrum. Among them, a sensing technique employing the maximum of power spectrum as a test statistic has appeared in the literature and its false alarm probability was also derived under the assumption that the test statistic follows the Gaussian distribution. This paper provides an exact form of the false alarm probability without using the assumption and compares it with the previous work.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.203-205
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• 2003

A false signal problem of the bleed air leak detection system in a widely used modem airplane is studied in this paper. It is considered to be a problem of stability of the controller unit. An equivalent circuit is extracted from the study. The circuit is considered to be an alternative bridge. A mathematical model is derived to describe the stability of the circuit. The conclusion is drawn that the sending of the false signal is relevant to not only the sensors, but also the detector. If a parameter in the detector is readjusted, then the problem may be avoided.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.61-65
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• 2013

In this paper, we propose system model to solve conventional threshold problem of using fixed false alarm for energy spectrum sensing. Spectrum sensing reliability is ensured when Secondary user have high SNR. Thus, it is not reasonable using fixed optional false alarm without considering CR user's SNR. So, we propose adaptive threshold method. adaptive threshold is decided by controling FA according to CR user's SNR.

• ETRI Journal
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• v.16 no.1
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• pp.17-34
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• 1994

In this paper, we present an architecture of the constant false alarm rate (CFAR) detector called the generalized order statistics (GOS) CFAR detector, which covers various order statistics (OS) and cell-averaging (CA) CFAR detectors as special cases. For the proposed GOS CFAR detector, we obtain unified formulas for the false alarm and detection probabilities. By properly choosing coefficients of the GOS CFAR detector, one can utilize any combination of ordered samples to estimate the background noise level. Thus, if we use a reference window of size N, we can realize $(2^N-1)$ kinds of CFAR processors and obtain their performances from the unified formulas. Some examples are the CA, the OS, the censored mean level, and the trimmed mean CFAR detectors. As an application of the GOS CFAR detector to multiple target detection, we propose an algorithm called the adaptive mean level detector, which censors adaptively the interfering target returns in a reference window.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.50-57
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• 1992

This paper proposes and analyzes a new CFAR(Constant False Alarm Rate) detector called the EXGO(Excision Greatest Of)-CFAR. This is the combination of the EXCA(Excision Cell Averaging)-CFAR that shows a good performance under the influence of interferences and the GO(Greatest Of)-CFAR that fights well with clutter edges. For the performance analysis, the formulas for the detection probability and the false alarm probability are derived and computed, and the results are compared with other existing CFAR detectors. Our analysis shows that the proposed EXGO-CFAR considerably improves the false-alarm-rate performance of the EXCA-CFAR at clutter edges while maintaining the high detection probability performance of the EXCA-CFAR in the homogeneous and/or interference noise environment.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.141-145
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• 2008

Wireless sensor network (WSN) is expected to be used in many applications. However, sensor nodes still have some secure problems to use them in the real applications. They are typically deployed on open, wide, and unattended environments. An adversary using these features can easily compromise the deployed sensor nodes and use compromised sensor nodes to inject fabricated data to the sensor network (false data injection attack). The injected fabricated data drains much energy of them and causes a false alarm. To detect and drop the injected fabricated data, a filtering-based security method and adaptive methods are proposed. The number of different partitions is important to make event report since they can make a correctness event report if the representative node does not receive message authentication codes made by the different partition keys. The proposed methods cannot guarantee the detection power since they do not consider the filtering scheme. We proposed clustering method for filtering-based secure methods. Our proposed method uses fuzzy system to enhance the detection power of a cluster.