• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.499-510
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• 2022

This paper introduces a video editing system for senior creator who is not familiar to video editing. Based on video analysis techniques, it provide various information and delete unwanted shot. The system detects shot boundaries based on RNN(Recurrent Neural Network), and it determines the deletion of video shots. The shots can be deleted using shot-level significance, which is computed by detecting focused area. It is possible to delete unfocused shots or motion-blurred shots using the significance. The system detects object and face, and extract the information of emotion, age, and gender from face image. Users can create video contents using the information. Decorating tools are also prepared, and in the tools, the preferred design, which is determined from user history, places in the front of the design element list. With the video editing system, senior creators can make their own video contents easily and quickly.

• Journal of Mechanical Science and Technology
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• v.16 no.10
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• pp.1222-1228
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• 2002

This research is the development of a flexible tactile sensor array for service robots using PVDF (polyvinylidene fluoride) film for the detection of a contact state in real time. The prototype of the tactile sensor which has 8${\times}$8 array using PVDF film was fabricated. In the fabrication procedure, the electrode patterns and the common electrode of the thin conductive tape were attached to both sides of the 281$\mu\textrm{m}$ thickness PVDF film using conductive adhesive. The sensor was covered with polyester film for insulation and attached to the rubber base for a stable structure. The proposed fabrication method is simple and easy to make the sensor. The sensor has the advantages in the implementing for practical applications because its structure is flexible and the shape of the each tactile element can be designed arbitrarily. The signals of a contact force to the tactile sensor were sensed and processed in the DSP system in which the signals are digitized and filtered. Finally, the signals were integrated for taking the force profile. The processed signals of the output of the sensor were visualized in a personal computer, and the shape and force distribution of the contact object were obtained. The reasonable performance for the detection of the contact state was verified through the sensing examples.

• Journal of Biosystems Engineering
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• v.36 no.5
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• pp.377-383
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• 2011

This study proposed the new method of discerning the assembled parts and presuming the position of central point in a Cowl Cross Bar (CCB) using a Charge-Couple Device (CCD) camera attached to a robot in the auto assembly line. Three control points of an ellipse were decided by three reference points, which were equally distanced. The radii of these reference points were determined by the size of the object, and the repeated presumption secured the precise determination. The comparison of the central point of ellipse presumed by Randomized Hough Transform (RHT) with the part information stored in a database was used for determining the faulty part in an assembly. The method proposed in this study was applied for the real-time inspection of elliptical parts, such as bolt, nut hole and so on, connected to a CCB using a CCD camera. The findings of this study showed that the precise decision on whether the parts are inferior or not can be made irrespective of the lighting condition of industrial site and the noises of the surface of the part. In addition, the defect decision on the individual elliptic parts assembled in a CCB showed more than 98% accuracy within a 500-millisecond period at most.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.5
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• pp.58-64
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• 2001

Recently many informations are transmitted ,md stored as video data, and they are on the rapid increase because of popularization of high performance computer and internet. In this paper, to retrieve video data, shots are found through analysis of video stream and the method of detection of key frame is studied. Finally users can retrieve the video efficiently. This Paper suggests a new feature that is robust to object movement in a shot and is not sensitive to change of color in boundary detection of shots, and proposes the characterizing value that reflects the characteristic of kind of video (movie, drama, news, music video etc,). The key frames are pulled out from many frames by using the local minima and maxima of differential of the value. After original frame(not de image) are reconstructed for key frame, indexing process is performed through computing parameters. Key frames that arc similar to user's query image arc retrieved through computing parameters. It is proved that the proposed methods are better than conventional method from experiments. The retrieval accuracy rate is so high in experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.15-21
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• 2017

In the active SONAR system, various studies have been carried out to enhance the resolution of a received signal. In order to obtain higher resolution for detecting a bottoming cylindrical object, the design of a planar array for SONAR is investigated in this paper. It is necessary to employ planar structures for SONAR array to obtain narrower beam pattern which gives high resolution. In this study, the transmit frequency of each acoustic transducer, which consists of an array is 13 kHz. For efficient detection of a target of an asymmetric size, the concept of areal angle is applied, which considers resolution according to both azimuth and elevation angles in array design. In the design, the areal angle is first investigated to satisfy the resolution requirements, and then based on the value of areal angles, the azimuth angle and the elevation angle are calculated respectively to design an array.

• Journal of Radiation Protection and Research
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• v.43 no.3
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• pp.85-96
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• 2018

Background: A variety of inorganic scintillators have been developed and improved for use in radiation detection and measurement, and in situ gamma-ray spectrometry in the environment remains an important area in nuclear safety. In order to verify the feasibility of promising scintillators in an actual environment, a performance test is necessary to identify gamma-ray peaks and calculate the radioactivity from their net count rates in peaks. Materials and Methods: Among commercially available scintillators, $LaBr_3(Ce)$ scintillators have so far shown the highest energy resolution when detecting and identifying gamma-rays. However, the intrinsic background of this scintillator type affects efficient application to the environment with a relatively low count rate. An algorithm to subtract the intrinsic background was consequently developed, and the in situ calibration factor at 1 m above ground level was calculated from Monte Carlo simulation in order to determine the radioactivity from the measured net count rate. Results and Discussion: The radioactivity of six natural radionuclides in the environment was evaluated from in situ gamma-ray spectrometry using an $LaBr_3(Ce)$ detector. The results were then compared with those of a portable high purity Ge (HPGe) detector with in situ object counting system (ISOCS) software at the same sites. In addition, the radioactive cesium in the ground of Jeju Island, South Korea, was determined with the same assumption of the source distribution between measurements using two detectors. Conclusion: Good agreement between both detectors was achieved in the in situ gamma-ray spectrometry of natural as well as artificial radionuclides in the ground. This means that an $LaBr_3(Ce)$ detector can produce reliable and stable results of radioactivity in the ground from the measured energy spectrum of incident gamma-rays at 1 m above the ground.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1950-1956
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• 2017

In food-printing field, precise positioning technique for a printing object is very important. In this paper, we propose cup positioning method for a latte-art printer through image processing. A camera sensor is installed on the upper side of the printer, and the image obtained from this is projected and converted into a top-view image. Then, the edge lines of the image is detected first, and then the coordinate of the center and the radius of the cup are detected through a Circular Hough transformation. The performance evaluation results show that the image processing time is 0.1 ~ 0.125 sec and the cup detection rate is 92.26%. This means that a cup is detected almost perfectly without affecting the whole latte-art printing time. The center point coordinates and radius values of cups detected by the proposed method show very small errors less than an average of 1.5 mm. Therefore, it seems that the problem of the printing position error is solved.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.55-62
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• 2013

This paper aims to detect Speed Bump by using Omni-directional Camera and to suggest Real-time update scheme of Speed Bump through Vision Based Approach. In order to detect Speed Bump from sequence of camera images, noise should be removed as well as spot estimated as shape and pattern for speed bump should be detected first. Now that speed bump has a regular form of white and yellow area, we extracted speed bump on the road by applying erosion and dilation morphological operations and by using the HSV color model. By collecting huge panoramic images from the camera, we are able to detect the target object and to calculate the distance through GPS log data. Last but not least, we evaluated accuracy of obtained result and detection algorithm by implementing SLAMS (Simultaneous Localization and Mapping system).

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Fisheries and Aquatic Sciences
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• v.25 no.3
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• pp.151-157
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• 2022

Various approaches have been applied to transform aquaculture from a manual, labour-intensive industry to one dependent on automation technologies in the era of the fourth industrial revolution. Technologies associated with the monitoring of physical condition have successfully been applied in most aquafarm facilities; however, real-time biological monitoring systems that can observe fish condition and behaviour are still required. In this study, we used a video recorder placed on top of a fish tank to observe the swimming patterns of rock bream (Oplegnathus fasciatus), first one fish alone and then a group of five fish. Rock bream in the video samples were successfully identified using the you-only-look-once v3 algorithm, which is based on the Darknet-53 convolutional neural network. In addition to recordings of swimming behaviour under normal conditions, the swimming patterns of fish under abnormal conditions were recorded on adding an anaesthetic or lowering the salinity. The abnormal conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s) involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon 2-phenoxyethanol treatment) before the fish stopped moving, as well as changing from swimming upright to dying lying on their sides. Machine learning was applied to datasets consisting of normal or abnormal behaviour patterns, to evaluate the fish behaviour. The proposed algorithm showed a high accuracy (98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude that artificial intelligence-based detection of abnormal behaviour can be applied to develop an automatic bio-management system for use in the aquaculture industry.