• Journal of KIISE
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• v.41 no.11
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• pp.982-991
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• 2014

The integrity assurance of recorded video by car black boxes are necessary as the car black box is becoming more popular. In this paper, we propose a video data integrity assurance scheme reflecting the features of car black box. The proposed method can detect any kind of deletion, insertion, modification of frames by cyclic chaining using inter block relation. And, it provides the integrity assurance function consistently even in cases of file overwriting because of no more free space in storage, partial file data lost. And non-repudiation is supported. Experimental results with a car black box embedded system with A8 application processor show that our method has a feasible computational overhead to process full HD resolution video at 30 frames per second in a real time.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.2
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• pp.164-172
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• 2007

Surface cracks in concrete are common defects that can cause significant deterioration and failure of concrete structures. Therefore, the early detection, assessment, and repair of the cracks in concrete are very important for the structural health. Among studies for crack depth assessment, self-calibrating surface wave transmission method seems to be a promising nondestructive technique, though it is still difficult in determination of the crack depth due to the variation of the experimentally obtained transmission functions. In this paper, the spectral energy transmission method is proposed for the crack depth estimation in concrete structures. To verify this method, an experimental study was carried out on a concrete slab with various surface-opening crack depths. Finally, effectiveness of the proposed method is validated by comparing the conventional time-of-flight and cutting frequency based methods. The results show an excellent potential as a practical and reliable in-situ nondestructive method for the crack depth estimation in concrete structures.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.9-18
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• 2012

Many audio watermarking researches which have been adapted to authenticate contents can not recover the original media after watermark removal. Therefore, reversible watermarking can be regarded as an effective method to ensure the integrity of audio data in the applications requiring high-confidential audio contents. Reversible watermarking inserts watermark into digital media in such a way that perceptual transparency is preserved, which enables the restoration of the original media from the watermarked one without any loss of media quality. This paper presents a new interval-based audio integrity authentication algorithm which can detect malicious tampering. To provide complete reversibility, we used differential histogram-based reversible watermarking. To authenticate audio in parts, not the entire audio at once, the proposed algorithm processes audio by dividing into intervals and the confirmation of the authentication is carried out in each interval. Through experiments using multiple kinds of test data, we prove that the presented algorithm provides over 99% authenticating rate, complete reversibility, and higher perceptual quality, while maintaining the induced-distortion low.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1973-1980
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• 2017

In the case of a small UUV operating an active synthetic aperture sonar, various velocity disturbances may occur on the path due to the influence of external underwater environment, and this causes phase errors in coherent synthetic aperture processing, which has a large influence on the detected image. In this paper, when a periodic sinusoidal velocity disturbance is generated in the traveling direction, the phase generated by the round trip slope range at each position is estimated the cross correlation coefficient for multiple received signals and compensated the position variation in the overlapped DPC by the average value within the maximum allowable width. Through simulations, it has been confirmed that the images degraded by the velocity disturbance amplitude and fluctuating frequency of the UUV are removed from the false targets and the performance of azimuth resolution is improved by the proposed phase compensation method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.69-74
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• 2018

Fiber-Reinforced Cementitious Composites (FRCCs) have electrical conductivity by inserting reinforced conductive fibers into a cementitious matrix. Such characteristic allows us to utilize FRCCs for crack monitoring of a structure by measuring electrical responses without sensor installation. However, the electrical responses are often sensitively altered by temperature variation as well as crack initiation. The temperature variation may disturb crack detection on the measured electrical responses. Moreover, as sensing probes for measuring electrical reponses increase, undesired contact noises are often augmented. In this paper, a self-sensing impedance circuit is specially designed for reducing the number of sensing probes. The crack initiation and temperature variation effects on the self-sensing impedance responses of FRCCs are experimentally investigated using the self-sensing impedance circuit. The experiment results reveal that the electrical impedance response are more sensitively changed due to temperature variation than crack initiation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.1
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• pp.105-113
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• 2002

In this study, the steady state performance analysis program was developed for a turboprop engine, and its performance was analyzed at uninstalled and installed conditions. For the purpose of evaluation the developed program was compared with the performance data provided by the engine manufacturer and analysis results of GASTURB8.0 program. It was confirmed that the developed program was reliable because the results by the developed program were well agreed with those by GASTURB8.0 within %%. The linear and non-linear GPA(Gas Path Analysis) programs for performance diagnotics were developed, and selection of optimal measurement variables was studied. Furthermore, in order to investigate effects of the number and the kind of measurement variables, the linear and non-linear GPAs were analyzed with various measurement set. If the measurement parameters were properly selected, the reliable and economic faults detection might be possible even thought the small number of measuring parameters were used.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.283-294
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• 2022

The feature map used in the network for deep learning generally has larger data than the image and a higher compression rate than the image compression rate is required to transmit the feature map. This paper proposes a method for transmitting a pyramid feature map with high compression rate, which is used in a network with an FPN structure that has robustness to object size in deep learning-based image processing. In order to efficiently compress the pyramid feature map, this paper proposes a structure that predicts a pyramid feature map of a level that is not transmitted with pyramid feature map of some levels that transmitted through the proposed prediction network to efficiently compress the pyramid feature map and restores compression damage through the proposed reconstruction network. Suggested mAP, the performance of object detection for the COCO data set 2017 Train images of the proposed method, showed a performance improvement of 31.25% in BD-rate compared to the result of compressing the feature map through VTM12.0 in the rate-precision graph, and compared to the method of performing compression through PCA and DeepCABAC, the BD-rate improved by 57.79%.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.197-207
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• 2023

Concrete is a widely used material due to its excellent compressive strength and durability. However, depending on the surrounding environment and the characteristics of the materials used in the construction, various defects may occur, such as cracks on the surface and subsidence of the structure. The detects on the surface of the concrete structure occur after completion or over time. Neglecting these cracks may lead to severe structural damage, necessitating regular safety inspections. Traditional visual inspections of concrete walls are labor-intensive and expensive. This research presents a deep learning-based semantic segmentation model designed to detect cracks in concrete walls. The model addresses surface defects that arise from aging, and an image augmentation technique is employed to enhance feature extraction and generalization performance. A dataset for semantic segmentation was created by combining publicly available and self-generated datasets, and notable semantic segmentation models were evaluated and tested. The model, specifically trained for concrete wall fracture detection, achieved an extraction performance of 81.4%. Moreover, a 3% performance improvement was observed when applying the developed augmentation technique.