• ETRI Journal
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• v.30 no.3
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• pp.394-402
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• 2008

Domain analysis is the process of analyzing related software systems in a domain to find their common and variable parts. In the case of device drivers, they are highly suitable for domain analysis because device drivers of the same domain are implemented similarly for each device and each system that they support. Considering this characteristic, this paper introduces a new approach to the domain analysis of device drivers. Our method uses a code clone detection technique to extract similarity among device drivers of the same domain. To examine the applicability of our method, we investigated whole device drivers of a Linux source. Results showed that many reusable similar codes can be discerned by the code clone detection method. We also investigated if our method is applicable to other kernel sources. However, the results show that the code clone detection method is not useful for the domain analysis of all kernel sources. That is, the applicability of the code clone detection method to domain analysis is a peculiar feature of device drivers.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.17-24
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• 2018

With improvement of speech recognition and natural language processing, dialog systems are recently adapted to various service domains. It became possible to get desirable services by conversation through the dialog system, but it is still necessary to improve separate modules, such as domain detection, intention detection, named entity recognition, and out-of-domain detection, in order to achieve stable service offer. When it misclassifies an in-domain sentence of conversation as out-of-domain, it will result in poor customer satisfaction and finally lost business. As there have been relatively small number of studies related to the out-of-domain detection, in this paper, we introduce a new method using a hierarchical Dirichlet process and demonstrate the effectiveness of it by experimental results on Korean dataset.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.482-489
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• 2007

In this paper, two frequency domain signal processing techniques for pulse shape modulation(PSM) ultra wideband(UWB) systems are presented. Firstly, orthogonal detection of UWB PSM Hermite pulses in frequency domain is addressed. It is important because time domain detection by correlation-based receivers is severely degraded by many sources of distortion. Pulse-shape, the information conveying signal characteristic, is deformed by AWGN and shape-destructive addition of multiple paths from the propagation channel. Additionally, because of the short nature of UWB pulses, timing mismatches and synchronism degrade the performance of PSM UWB communication systems. In this paper, frequency domain orthogonality of the Hermite pulses is exploited to propose an alternative detection method, which makes possible efficient detection of PSM in dense multipath channel environments. Secondly, a ranging method employing the Cepstrum algorithm is proposed. This method is partly processed in the frequency domain and can be implemented without additional hardware complexity in the terminal.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.73-81
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• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.81-89
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• 2018

It is commonplace that high false detection rates interfere with immediate vision-based fire monitoring system. To circumvent this challenge, we propose a fire detection algorithm that can accommodate color variations of RGB in temporal domain, aiming at reducing false detection rates. Despite interrupting images (e.g., background noise and sudden intervention), the proposed method is proved robust in capturing distinguishable features of fire in temporal domain. In numerical studies, we carried out extensive real data experiments related to fire detection using 24 video sequences, implicating that the propose algorithm is found outstanding as an effective decision rule for fire detection (e.g., false detection rate <10%).

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.180-188
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• 2016

In this paper, we propose a fire detection algorithm based on motion information and color information analysis. Conventional fire detection algorithms have as main problem the difficulty to detect fire due to external light, intensity, background image complexity, and little fire diffusion. So we propose a fire detection algorithm that accurate and fast. First, it analyzes the motion information in video data and then set the first candidate. Second, it determines this domain after analyzing the color and the domain. This algorithm assures a fast fire detection and a high accuracy compared with conventional fire detection algorithms. Our algorithm will be useful to real-time fire detection in real world.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.140-145
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• 2009

Significant attention has recently been drawn to human robot interaction system that uses face detection technology. The most conventional face detection methods have applied under pixel domain. These pixel based face detection methods require high computational power. Hence, the conventional methods do not satisfy the robot environment that requires robot to operate in a limited computing process and saving space. Also, compensating the variation of illumination is important and necessary for reliable face detection. In this paper, we propose the illumination invariant face detection that is performed under the compressed domain. The proposed method uses color balancing module to compensate illumination variation. Experiments show that the proposed face detection method can effectively increase the face detection rate under existing illumination.

• The Journal of the Acoustical Society of Korea
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• v.30 no.2
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• pp.73-79
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• 2011

This paper presents a new impulsive noise detection algorithm in speech signal. The proposed method employs the frequency domain characteristic of the impulsive noise to improve the detection accuracy while avoiding the false-alarm problem by the pitch of the speech signal. Furthermore, we proposed time-frequency domain impulsive noise detector that utilizes both the time and frequency domain parameters which minimizes the false-alarm problem by mutually complementing each other. As the result, the proposed time-frequency domain detector shows the best performance with 99.33 % of detection accuracy and 1.49 % of false-alarm rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.323-325
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• 2015

The purpose of the thesis is to suggest the algorithm that can be covered only by software instead of the function of pulse shaper that was used in the time domain. In order to implementing portable radiation detector for digital.

• Journal of Information Technology Applications and Management
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• v.28 no.5
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• pp.41-50
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• 2021

As COVID-19 spread widely, and rapidly, the number of misinformation is also increasing, which WHO has referred to this phenomenon as "Infodemic". The purpose of this research is to develop detection and rectification of COVID-19 misinformation based on Open-domain QA system and text similarity. 9 testing conditions were used in this model. For open-domain QA system, 6 conditions were applied using three different types of dataset types, scientific, social media, and news, both datasets, and two different methods of choosing the answer, choosing the top answer generated from the QA system and voting from the top three answers generated from QA system. The other 3 conditions were the Closed-Domain QA system with different dataset types. The best results from the testing model were 76% using all datasets with voting from the top 3 answers outperforming by 16% from the closed-domain model.