• Journal of Adhesion and Interface
• /
• v.4 no.4
• /
• pp.22-27
• /
• 2003

Chemical modification of carbon nanotube (CNT) was carried out using $HNO_3$ and $H_2SO_4$ and characterized by analyzing the CNT before and after the modification using FT-IR and titration. Aggregation behaviors were investigated using a real-time video microscope after the chemically modified CNT(mCNT) had been dispersed in organic solvents such as toluene, dimethylformamide (DMF) and N-methylpyrrolidone (NMP) by ultrasonication. The mCNT showed better dispersion in polar sovents of DMF and NMP than the rCNT. CNT/ poly(methylmethacrylate) (PMMA) films were prepared from solution DMF/PMMA solutions. The films containing mCNT also revealed the improved dispersion.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.6
• /
• pp.111-120
• /
• 2018

This study focuses on utilizing drones for performance evaluation of ITS detectors and analyzing economic feasibility when performance evaluation is conducted by the traffic management center's own personnel using drones. The study sites were selected from DSRC, video detector, and radar detector locations and drone filming was conducted to obtain travel speed, queue length, and delay time and compare with the detector data. It was shown that drones can be very effectively used to evaluate performance of major ITS detectors such as DSRC and video detectors. In addition, it was analyzed that a drone operated by the traffic management center's own personnel provides very economic solution for ITS detector performance evaluation when compared to consignment by external agencies.

• Communications of Mathematical Education
• /
• v.19 no.4 s.24
• /
• pp.733-767
• /
• 2005

During the past decades, there has been a fundamental change in the objectives and nature of mathematics education, as well as a shift in research paradigms. The changes in mathematics education emphasize learning mathematics from realistic situations, students' invention or construction solution procedures, and interaction with other students of the teacher. This shifted perspective has many similarities with the theoretical . perspective of Realistic Mathematics Education (RME) developed by Freudental. The RME theory focused the guide reinvention through mathematizing and takes into account students' informal solution strategies and interpretation through experientially real context problems. The heart of this reinvention process involves mathematizing activities in problem situations that are experientially real to students. It is important to note that reinvention in a collective, as well as individual activity, in which whole-class discussions centering on conjecture, explanation, and justification play a crucial role. The overall purpose of this study is to examine the developmental research efforts to adpat the instructional design perspective of RME to the teaching and learning of differential equation is collegiate mathematics education. Informed by the instructional design theory of RME and capitalizes on the potential technology to incorporate qualitative and numerical approaches, this study offers as approach for conceptualizing the learning and teaching of differential equation that is different from the traditional approach. Data were collected through participatory observation in a differential equations course at a university through a fall semester in 2003. All class sessions were video recorded and transcribed for later detailed analysis. Interviews were conducted systematically to probe the students' conceptual understanding and problem solving of differential equations. All the interviews were video recorded. In addition, students' works such as exams, journals and worksheets were collected for supplement the analysis of data from class observation and interview. Informed by the instructional design theory of RME, theoretical perspectives on emerging analyses of student thinking, this paper outlines an approach for conceptualizing inquiry-oriented differential equations that is different from traditional approaches and current reform efforts. One way of the wars in which thus approach complements current reform-oriented approaches 10 differential equations centers on a particular principled approach to mathematization. The findings of this research will provide insights into the role of the mathematics teacher, instructional materials, and technology, which will provide mathematics educators and instructional designers with new ways of thinking about their educational practice and new ways to foster students' mathematical justifications and ultimately improvement of educational practice in mathematics classes.

• Journal of Veterinary Science
• /
• v.22 no.3
• /
• pp.44.1-44.15
• /
• 2021

Background: Intraoperative fluids are still poorly studied in veterinary medicine. In humans the dosage is associated with significant differences in postoperative outcomes. Objectives: The aim of this study is to verify the influence of three different fluid therapy rates in dogs undergoing video-assisted ovariohysterectomy. Methods: Twenty-four female dogs were distributed into three groups: G5, G10, and G20. Each group was given 5, 10, and 20 mL·kg^-1·h^-1 of Lactate Ringer, respectively. This study evaluated the following parameters: central venous pressure, arterial blood pressure, heart rate, respiratory rate, temperature, acid-base balance, and serum lactate levels. Additionally, this study evaluated the following urinary variables: urea, creatinine, protein to creatinine ratio, urine output, and urine specific gravity. The dogs were evaluated up to 26 h after the procedure. Results: All animals presented respiratory acidosis during the intraoperative period. The G5 group evidenced intraoperative oliguria (0.80 ± 0.38 mL·kg^-1·h^-1), differing from the G20 group (2.17 ± 0.52 mL·kg^-1·h^-1) (p = 0.001). Serum lactate was different between groups during extubation (p = 0.036), with higher values being recorded in the G5 group (2.19 ± 1.65 mmol/L). Animals from the G20 group presented more severe hypothermia at the end of the procedure (35.93 ± 0.61℃) (p = 0.032). Only the members of the G20 group presented mean potassium values below the reference for the species. Anion gap values were lower in the G20 group when compared to the G5 and G10 groups (p = 0.017). Conclusions: The use of lactated Ringer's solution at the rate of 10 mL·kg^-1·h^-1 seems to be beneficial in the elective laparoscopic procedures over the 5 or 20 mL·kg^-1·h^-1 rates of infusion.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.53-65
• /
• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Journal of the Korea Computer Graphics Society
• /
• v.13 no.4
• /
• pp.7-12
• /
• 2007

With the advent of digital cinema, more and more movies are digitally produced, distributed via digital medium such as hard drives and network, and finally projected using a digital projector. However, digital cameras capable of shotting at 2K or higher resolution for digital cinema are still very expensive and bulky, which impedes rapid transition to digital production. As a low-cost solution for acquiring high resolution digital videos, we propose a hybrid camera consisting of a low-resolution CCD for capturing videos and a high-resolution CCD for capturing still images at regular intervals. From the output of the hybrid camera, we can synthesize high-resolution videos by software as follows: for each frame, 1. find pixel correspondences from the current frame to the previous and subsequent keyframes associated with high resolution still images, 2. synthesize a high-resolution image for the current frame by copying the image blocks associated with the corresponding pixels from the high-resolution keyframe images, and 3. complete the synthesis by filling holes in the synthesized image. This framework can be extended to making NPR video effects and capturing HDR videos.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.2 s.302
• /
• pp.27-36
• /
• 2005

This thesis proposed an effective encryption method for the DCT-based image/video contents and made it possible to operate in a high speed by implementing it as an optimized hardware. By considering the increase in the amount of the calculation in the image/video compression, reconstruction and encryption, an partial encryption was performed, in which only the important information (DC and DPCM coefficients) were selected as the data to be encrypted. As the result, the encryption cost decreased when all the original image was encrypted. As the encryption algorithm one of the multi-mode AES, DES, or SEED can be used. The proposed encryption method was implemented in software to be experimented with TM-5 for about 1,000 test images. From the result, it was verified that to induce the original image from the encrypted one is not possible. At that situation, the decrease in compression ratio was only $1.6\%$. The hardware encryption system implemented in Verilog-HDL was synthesized to find the gate-level circuit in the SynopsysTM design compiler with the Hynix $0.25{\mu}m$ CMOS Phantom-cell library. Timing simulation was performed by Verilog-XL from CadenceTM, which resulted in the stable operation in the frequency above 100MHz. Accordingly, the proposed encryption method and the implemented hardware are expected to be effectively used as a good solution for the end-to-end security which is considered as one of the important problems.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.48 no.9
• /
• pp.22-30
• /
• 2011

Recently, the rising demand for intelligent video surveillance system leads to high-performance face recognition systems. The solution for low-resolution images acquired by a long-distance camera is required to overcome the distance limits of the existing face recognition systems. For that reason, this paper proposes a hardware design of an image resolution enhancement algorithm for real-time intelligent video surveillance systems. The algorithm is synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images, called training set. When we checked the performance of the algorithm at 32bit RISC micro-processor, the entire operation took about 25 sec, which is inappropriate for real-time target applications. Based on the result, we implemented the hardware module and verified it using Xilinx Virtex-4 and ARM9-based embedded processor(S3C2440A). The designed hardware can complete the whole operation within 33 msec, so it can deal with 30 frames per second. We expect that the proposed hardware could be one of the solutions not only for real-time processing at the embedded environment, but also for an easy integration with existing face recognition system.

• The KIPS Transactions:PartB
• /
• v.15B no.6
• /
• pp.575-584
• /
• 2008

According to the network condition, the communication network overload could be occurred when media transmitting. Many researches are being carried out to lessen the network overload, such as the filtering, load distributing, frame dropping and many other methods. Among these methods, one of effective method is frame dropping that reduces specified video frames for bandwidth diminution. B frames are dropped and then I, P frames are dropped according to dependency among the frames in frame dropping. This paper proposes a scheme for protecting copyrights by encryption, when we apply frame dropping to reduce bandwidth of media following MPEG-4 file format. We designed two kinds of frame dropping: first one stores and then sends the dropped files and the other drops frames in real-time when transmitting. We designed three kinds of encryption methods in which DES algorithm is used to encrypt MPEG-4 data: macro block encryption in I-VOP, macro block and motion vector encryption in P-VOP, and macro block and motion vector encryption in I, P-VOP. Based on these three methods, we implemented a digital right management solution for MPEG-4 data streaming. We compared the results of dropping, encryption, decryption and quality of video sequences to select an optimal method, and there is no noticeable difference between the video sequences recovered after frame dropping and the ones recovered without frame dropping. The best performance in encryption and decryption of frames was obtained when we apply the macro block and motion vector encryption in I, P-VOP.

• Journal of Broadcast Engineering
• /
• v.27 no.3
• /
• pp.318-331
• /
• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.