• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2355-2362
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• 2016

Seg fog removal is an important issue concerned by both computer vision and image processing. Sea fog or haze removal is widely used in lots of fields, such as automatic control system, CCTV, and image recognition. Color image dehazing techniques have been extensively studied, and expecially the dark channel prior(DCP) technique has been widely used. This paper propose a fast and efficient image prior - dark channel prior to remove seg-fog from a single digital image based on the GPU. We implement the basic parallel program and then optimize it to obtain performance acceleration with more than 250 times. While paralleling and the optimizing the algorithm, we improve some parts of the original serial program or basic parallel program according to the characteristics of several steps. The proposed GPU programming algorithm and implementation results may be used with advantages as pre-processing in many systems, such as safe navigation for ship, topographical survey, intelligent vehicles, etc.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.133-141
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• 2008

As interactive multimedia have come into wide use, user interfaces such as remote controllers or classical computer mice have several limitations that cause inconvenience. We propose a vision-based pointing device to resolve this problem. We analyzed the moving image from the camera which is embedded in the pointing device and estimate the movement of the device. The pose of the cursor can be determined from this result. To process in the real time, we used the low resolution of $288{\times}208$ pixel camera and comer points of the screen were tracked using local optical flow method. The distance from screen and device was calculated from the size of screen in the image. The proposed device has simple configurations, low cost, easy use, and intuitive handhold operation like traditional mice. Moreover it shows reliable performance even in the dark condition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.355-358
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• 2009

Due to growth of electronics and control devices, automation and situational awareness systems have been applied by automobile. Vision systems with the introduction of unmanned system being actively developed, but are still high price and visual information is passed through the cable, because of cars are difficult to install. In this paper, can be installed inside the car at low-cost, simple image processing device through a wireless communication know the obstacles and the alarm system based on Zigbee wireless communication, infrared and ultrasonic sensors to monitor the situation through with easy parking cars outside the system design was implemented.

• Journal of Auto-vehicle Safety Association
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• v.13 no.2
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• pp.35-41
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• 2021

In this study, improved path tracking control algorithm based on pure pursuit algorithm is newly proposed by using improved lane detection algorithm through real time post-processing with interpolation methodology. Since the original pure pursuit works well only at speeds below 20 km/h, the look-ahead distance is implemented as a sigmoid function to work well at an average speed of 45 km/h to improve tracking performance. In addition, a smoothing filter was added to reduce the steering angle vibration of the original algorithm, and the stability of the steering angle was improved. The post-processing algorithm presented has implemented more robust lane recognition system using real-time pre/post processing method with deep learning and estimated interpolation. Real time processing is more cost-effective than the method using lots of computing resources and building abundant datasets for improving the performance of deep learning networks. Therefore, this paper also presents improved lane detection performance by using the final results with naive computer vision codes and pre/post processing. Firstly, the pre-processing was newly designed for real-time processing and robust recognition performance of augmentation. Secondly, the post-processing was designed to detect lanes by receiving the segmentation results based on the estimated interpolation in consideration of the properties of the continuous lanes. Consequently, experimental results by utilizing driving guidance line information from processing parts show that the improved lane detection algorithm is effective to minimize the lateral offset error in the diverse maneuvering roads.

• English & American cultural studies
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• v.18 no.4
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• pp.33-64
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• 2018

Through the experimental narrative construction by authorial divinity, Muriel Spark's novels and films based on her fiction show the difficulty of living like a human being under various inhumane and manipulative circumstances of the modern capitalistic society. By adopting flash-forward, self-reflection, and deceptive omnipotent viewpoints, her work has surprisingly predicted the post-modern trend in which humans are increasingly attracted and interpellated to the digitalized media. Muriel Spark called the recent anesthetic situation by stimulation "a driver's seat" because it is a symbol of how humans should act to maintain the critical subject. Emphasizing the value of self-reflection, religion and morality in the mechanized society, Muriel Spark stressed literature should play the role of helmsman who sails safely in the rough sea. In Muriel Spark's works, God is often synonymous with writers. As a Jewish immigrant she experienced alienation in Scotland, marital violence, prejudices of the London-based publishing world, Nazism, and Watergate. For her, the harsh reality of the modern society needs to be guided and complemented by something beyond human control. But rather than relying entirely on traditional Catholic doctrines such as Graham Greene and Evelyn Waugh, Muriel Spark has taken a personal, religious view of literature and insists that the genuine writer must play God's play. Seeking for the speculative vision for the future of human life in God's plan, she tries to understand the complex twisted motives of human beings which are often far from the ideal form. Simply put, her search of self-reflection, religion and ethics is modeled on the God's plan for the ideal human being who is supposed as the writer with the transfigurative imagination of the trinity.

• Journal of Animal Science and Technology
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• v.62 no.6
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• pp.765-776
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• 2020

The retinal degenerative disease, progressive retinal atrophy (PRA) is a major reason of vision impairment in canine population. Canine PRA signifies an inherently dissimilar category of retinal dystrophies which has solid resemblances to human retinis pigmentosa. Even though much is known about the biology of PRA, the knowledge about the intricate connection among genetic loci, genes and pathways associated to this disease in dogs are still remain unknown. Therefore, we have performed a genome wide association study (GWAS) to identify susceptibility single nucleotide polymorphisms (SNPs) of PRA. The GWAS was performed using a case-control based association analysis method on PRA dataset of 129 dogs and 135,553 markers. Further, the gene-set and pathway analysis were conducted in this study. A total of 1,114 markers associations with PRA trait at p < 0.01 were extracted and mapped to 640 unique genes, and then selected significant (p < 0.05) enriched 35 gene ontology (GO) terms and 5 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways contain these genes. In particular, apoptosis process, homophilic cell adhesion, calcium ion binding, and endoplasmic reticulum GO terms as well as pathways related to focal adhesion, cyclic guanosine monophosphate)-protein kinase G signaling, and axon guidance were more likely associated to the PRA disease in dogs. These data could provide new insight for further research on identification of potential genes and causative pathways for PRA in dogs.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.315-327
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• 2017

This study was carried out to examine the converging factors related to academic burnout in students from health-related majors who are involved in studying for the Test of English for International Communication (TOEIC). Research subjects included 291 randomly selected students from the J-region, who were enrolled in TOEIC classes. Data collection took place from April 3, 2017 to April 14, 2017, using anonymous self-administered questionnaires. The results of a multiple regression analysis in female students showed higher academic burnout pertaining to studying for the TOEIC when sleeping hours, self-worth, self-efficacy, school adaptation resilience, and study immersion were low, and job-seeking stress and test anxiety were high. The explanatory power of this model was 65%. Based on these results, to reduce academic burnout pertaining to studying for the TOEIC in health majors, it is first necessary to increase sleeping hours, self-worth, self-efficacy, school adaptation resilience, and study immersion, and to make efforts to manage self-competence, job-seeking stress, and test anxiety. The results of this study may be used to decrease the academic burnout caused by studying for the TOEIC in health major students and to increase their aptitude for studying English, to cultivate globalized capabilities. Future studies need to conduct an effect analysis on the control and mediation effect that these factors have on academic burnout.

• Journal of The Korean Association For Science Education
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• v.37 no.4
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• pp.553-564
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• 2017

This study investigated the change of pre-service elementary teachers' professional visions through video-based reflection on science teaching with focus on their attention and pedagogical reasoning about student learning. Specifically, we compared two reflection cycles before and after pre-service elementary teachers went through the collaborative video-based reflection process in a professional learning community. The primary data were collected from eight pre-service elementary teachers and included their science lesson plans, videotaped lessons, video-reflection papers, and transcripts from the interviews. Pre-service elementary teachers' attention was categorized in five aspects: classroom management & control, teacher's instruction, students' thinking & learning, subject knowledge, and assessment. The level of their pedagogical reasoning about student thinking and learning was determined with six levels based on the number of evidence, evidence area, and evidence type. The findings revealed that 1) individual reflection is not enough - collaborative reflection is essential to change their attention toward students learning and thinking 2) pedagogical reasoning levels increase gradually throughout the individual and collaborative video-based reflection processes. The participants not only attributed student learning solely to the characteristics of students but also connected it with their own instruction or science content knowledge and used different types of evidences as they went through two reflection cycles. Implications for using video in the teacher education program were discussed.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.