• Animal Bioscience
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• v.36 no.5
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• pp.810-817
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• 2023

Objective: The aim was to compare the behavioral response to full separation of cows and calves maintained together for 100 days or 24 h. Methods: Twelve Holstein-Friesian cow-calf pairs were enrolled into either treatment or industry groups (n = 6 cow-calf pairs/group). The treatment cows and calves were maintained on pasture together for 106±8.6 d and temporarily separated twice a day for milking. The Industry cows and their calves, were separated within 24 h postpartum. Triaxial accelerometer neck-mounted sensors were fitted to cows 3 weeks before separation to measure hourly rumination and activity. Before separation, cow and calf behavior was observed by scan sampling for 15 min. During the separation process, frequency of vocalizations and turn arounds were recorded. At separation, cows were moved to an observation pen where behavior was recorded for 3 d. A CCTV camera was used to record video footage of cows within the observation pens and behavior was documented from the videos in 15 min intervals across the 3 d. Results: Before separation, industry calves were more likely to be near their mother than Treatment calves. During the separation process, vocalization and turn around behavior was similar between groups. After full separation, treatment cows vocalized three times more than industry cows. However, the frequency of time spent close to barrier, standing, lying, walking, and eating were similar between industry and treatment cows. Treatment cows had greater rumination duration, and were more active, than industry cows. Conclusion: These findings suggest a similar behavioral response to full calf separation and greater occurrence of vocalizations, from cows maintained in a long-term, pasture-based, cow-calf rearing system when ompared to cows separated within 24 h. However, further work is required to assess the impact of full separation on calf behavior.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.149-162
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• 2005

This paper suggest a way to detect a specific wanted figure in public places such as subway stations and banks by comparing color face images extracted from the real time CCTV with the face images of designated specific figures. Assuming that the characteristic of the surveillance camera allows the face information in screens to change arbitrarily and to contain information on numerous faces, the accurate detection of the face area was focused. To solve this problem, the normalization work using subsampling with $20{\times}20$ pixels on arbitrary face images, which is based on the Perceptron Neural Network model suggested by R. Rosenblatt, created the effect of recogning the whole face. The optimal linear filter and the histogram shaper technique were employed to minimize the outside interference such as lightings and light. The addition operation of the egg-shaped masks was added to the pre-treatment process to minimize unnecessary work. The images finished with the pre-treatment process were divided into three reception fields and the information on the specific location of eyes, nose, and mouths was determined through the neural network. Furthermore, the precision of results was improved by constructing the three single-set network system with different initial values in a row.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.7-13
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• 2019

Unlike a typical video wall system, video wall systems used for integrated monitoring in smart city environments should be able to display various videos, images, and texts simultaneously. In this paper, we propose an Internet Protocol (IP)-based video wall system that has no limit on the number of videos that can be monitored simultaneously, and that can arrange the monitor screen layout without restrictions. The proposed system is composed of multiple display servers, a wall controller, and video source providers, and they communicate with each other through an IP network. Since the display server receives and decodes the video stream directly from the video source devices, and displays it on the attached monitor screens, more videos can be simultaneously displayed on the entire video wall. When one video is displayed over several screens attached to multiple display servers, only one display server receives the video stream and transmits it to the other display servers by using IP multicast communications, thereby reducing the network load and synchronizing the video frames. Experiments show that as the number of videos increases, a system consisting of more display servers shows better decoding and rendering performance, and there is no performance degradation, even if the display server continues to be expanded.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.9-21
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• 2017

The purpose of this study is to identify the current situation and actual condition of the video security industry, The research method was based on interviews with twelve specialists, and examined the market trends, the problems of the video security industry, the improvement plan and the government promotion strategy. The problem with the domestic video security industry is that there are the decline in overseas exports and the slowdown in exports to China, insufficient measures to overcome certification barriers due to the strengthening of national certification system, domestic demand growth slowed, expansion of domestic market share of Chinese products, lack of high-tech development of domestic products, lack of expertise in technology development and operation and inadequate legislation for revitalizing the video security industry. The improvement plan is as follows. Need to implement export expansion support policy, need to build tailored response system for each country, need improvement of security related demand creation system, take measures such as domestic industrial protection policy, certification barriers and tariff barriers, induce future core technology to create high added value. The government also needs to actively support human resources development, and induce stabilization of relevant laws and institutions. This study will contribute to the development of related industries by suggesting the development direction of the video security industry.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.333-338
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• 2012

Human resource management is bringing the various changes with the IT technology. In particular, if HRM is non-scientific method such as group management, physical plant, working hours constraints, personal contacts, etc, the current enterprise human resources management(e-HRM) appeared in the individual dimension management, virtual workspace (for example: smart work center, home work, etc.), working time flexibility and elasticity, computer-based statistical data and the scientific method of analysis and management has been a big difference in the sense. Therefore, depending on changes in the environment, companies have introduced a variety of techniques as RFID card, fingerprint time & attendance systems in order to build more efficient and strategic human resource management system. In this paper, time and attendance, access control management system was developed using multi camera for 2D and 3D face recognition technology-based for efficient enterprise human resource management. We had an issue with existing 2D-style face-recognition technology for lighting and the attitude, and got more than 90% recognition rate against the poor readability. In addition, 3D face recognition has computational complexities, so we could improve hybrid video recognition and the speed using 3D and 2D in parallel.

• Progress in Medical Physics
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• v.15 no.1
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• pp.30-38
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• 2004

Purpose: To develop a whole body frame for the purpose of reducing patient motion and minimizing setup error for extra-cranial stereotactic radiotherapy, and to evaluate the repositioning setup error of a patient in the frame. Materials and Methods: The developed whole body frame is composed of a base plate, immobilizer, vacuum cushion, ruler and belts. The dimension of the base plate is 130 cm in length, 50 cm in width and 1 cm in thickness. The material used in the base plate of the frame was bakelite and the immobilizer was made of acetal. In addition, Radiopaque angio-catheter wires were engraved on the base plate for a coordinate system to determine the target localization. The measurement for radiation transmission and target localization is peformed in order to test the utilization of the frame. Also, a Matlab program analyzed the patients setup error by using the patient's setup images obtained from a CCTV camera and digital record recorder (DVR). Results: A frame that is useful for CT simulation and radiation treatment was fabricated. The frame structure was designed to minimize collisions from the changes in the rotation angle of the gantry and to maximize the transmission rate of the Incident radiation at the lateral or posterior oblique direction. The lightening belts may be used for the further reduction of the patient motion, and the belts can be adjusted so that they are not in the way of beam direction. The radiation transmission rates of this frame were measured as 95% and 96% at 10 and 21 MV, respectively. The position of a test target on the skin of a volunteer is accurately determined by CT simulation using the coordinate system in the frame. The estimated setup errors by Matlab program are shown 3.69$\pm$1.60, 2.14$\pm$0.78 mm at the lateral and central chest, and 7.11 $\pm$2.10, 6.54$\pm$2.22 mm at lateral and central abdomen, respectively. The setup error due to the lateral motion of breast is shown as 6.33$\pm$ 1.55 mm. Conclusion: The development and test of a whole body frame has proven very useful and practical in the radiosurgery for extra-cranial cancers. It may be used in determining target localization, and it can be used as a patient immobilization tool. More experimental data should be obtained in order to improve and confirm the results of the patient setup error.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.