• Journal of Digital Convergence
• /
• v.15 no.10
• /
• pp.429-436
• /
• 2017

A design thinking-based collaborative workshop in which various stakeholders in the milk processing industry circulation ecosystem participated shows a new problem innovation paradigm that encourages the spread of practical prototyping culture. in the expression of empathy and collective intelligence among members on facing issues, the conversion of collaboration and communication methods, the business handling of the organization based on the design work method as 'creativity mechanism'. In this workshop, which was promoted in three stages of 'approach to problems', 're-definition of problems', and 'experience-based future vision design', participants themselves redefine real problems in terms of the accuracy of feed orders between feed suppliers and livestock farmers, ordering of feeds on time, cost reduction of feed supply and present new alternatives and expanded business areas. The results suggested in this workshop suggest the usefulness of design thinking in business innovation in that they presented how to approach the problem and a creative thinking system to find its solution to direct and indirect stakeholders of the industry as well as the improvement of supply and demand rate of livestock feed and quality.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.7
• /
• pp.87-94
• /
• 2019

Recently, the advance of accurate dynamic displacement measurement devices, such as GPS, computer vision, and optic laser sensor, has enhanced the structural monitoring technology. In this study, the dynamic displacement data was used to verify the applicability of the structural physical parameter estimation method through subspace system identification. The subspace system identification theory for estimating state-space model from measured data and physics-based interpretation for deriving the physical parameter of the estimated system are presented. Three-degree-freedom steel structures were fabricated for the experimental verification of the theory in this study. Laser displacement sensor and accelerometer were used to measure the displacement data of each floor and the acceleration data of the shaking table. Discrete state-space model generated from measured data was verified for precision. The discrete state-space model generated from the measured data extracted the floor stiffness of the building after accuracy verification. In addition, based on the story stiffness extracted from the state space model, five column stiffening and damage samples were set up to extract the change rate of story stiffness for each sample. As a result, in case of reinforcement and damage under the same condition, the stiffness change showed a high matching rate.

• International Journal of Highway Engineering
• /
• v.10 no.2
• /
• pp.159-166
• /
• 2008

Video image processing systems (VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on a wide-area detection algorithm provide traffic parameters such as flow and velocity as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. However, unlike human vision, VIPS cameras have difficulty in recognizing vehicle movements over a detection zone longer than 100 meters. Over such a distance, the camera operators need to zoom in to recognize objects. As a result, vehicle tracking with a single camera is limited to detection zones under 100m. This paper develops a methodology capable of monitoring individual vehicle trajectories based on image processing. To improve traffic flow surveillance, a long distance tracking algorithm for use over 200m is developed with multi-closed circuit television (CCTV) cameras. The algorithm is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of incident detection.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.22 no.3
• /
• pp.57-68
• /
• 2017

Continuous monitoring of spatial and temporal changes in key marine environmental parameters such as SST (sea surface temperature) near IORS (Ieodo Ocean Research Station) is demanded to investigate the ocean ecosystem, climate change, and sea-air interaction processes. In this study, we aimed to develop the system for continuously measuring SST using a TIR (thermal infrared) sensor mounted at the IORS. New SST algorithm is developed to provide SST of better quality that includes automatic atmospheric correction and emissivity calculation for different oceanic conditions. Then, the TIR-based SST products were validated against in-situ water temperature measurements during May 17-26, 2015 and July 15-18, 2015 at the IORS, yielding the accuracy of 0.72-0.85 R-square, and $0.37-0.90^{\circ}C$ RMSE. This TIR-based SST observing system can be installed easily at similar Ocean Research Stations such as Sinan Gageocho and Ongjin Socheongcho, which provide a vision to be utilized as calibration site for SST remotely sensed from satellites to be launched in future.

• Journal of Korean Society of Transportation
• /
• v.33 no.3
• /
• pp.284-293
• /
• 2015

The objective of this paper is to determine the conditions of road surface by utilizing the images collected from closed-circuit television (CCTV) cameras installed on roadside. First, a technique was examined to detect wet surfaces at nighttime. From the literature reviews, it was revealed that image processing using polarization is one of the preferred options. However, it is hard to use the polarization characteristics of road surface images at nighttime because of irregular or no light situations. In this study, we proposes a new discriminant for detecting wet and dry road surfaces using CCTV image data at night. To detect the road surface conditions with night vision, we applied the wavelet packet transform for analyzing road surface textures. Additionally, to apply the luminance feature of night CCTV images, we set the intensity histogram based on HSI(Hue Saturation Intensity) color model. With a set of 200 images taken from the field, we constructed a detection criteria hyperplane with SVM (Support Vector Machine). We conducted field tests to verify the detection ability of the wet road surfaces and obtained reliable results. The outcome of this study is also expected to be used for monitoring road surfaces to improve safety.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.18 no.3
• /
• pp.113-127
• /
• 2015

Recently, there has been a growing interest in UAS(Unmanned Aerial System), and it is required to develop techniques to effectively detect water body from the recorded images in order to implement flood monitoring using UAS. This study used a UAS with RGB and NIR+RG bands to achieve images, and applied supervised classification method to evaluate the accuracy of water body detection. Firstly, the result for accuracy in water body image classification by RGB images showed high Kappa coefficients of 0.791 and 0.783 for the artificial neural network and minimum distance method respectively, and the maximum likelihood method showed the lowest, 0.561. Moreover, in the evaluation of accuracy in water body image classification by NIR+RG images, the magalanobis and minimum distance method showed high values of 0.869 and 0.830 respectively, and in the artificial neural network method, it was very low as 0.779. Especially, RGB band revealed errors to classify trees or grasslands of Songsan amusement park as water body, but NIR+RG presented noticeable improvement in this matter. Therefore, it was concluded that images with NIR+RG band, compared those with RGB band, are more effective for detection of water body when the mahalanobis and minimum distance method were applied.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2007.11a
• /
• pp.938-942
• /
• 2007

The importance of process improvement under construction has arisen from recent issue, lower productivity in the construction site. The various 3D modeling program is utilized in the procedure of construction as an alternative solution. However, it's still shortage of the consideration about a specific technical application. The purpose of the study in this paper is helpful to improve the productivity of construction site using 3D realization of constructing place as one of extensive modeling technologies, which leads to not only efficient management of construction site allowing people to check the real time situation in the place but also the revitalization of information flow about building process control and prgress, Therefore, I research into modeling algorithm and extensive construction site realization technology. 3D realization of building place would reduce the safety concerns by providing the real time information about construction site, and it could help to access easily to similar project through collecting and appling the database of sites. Furthermore it can be an opportunity to develop the procedure of production in construction industry and to upgrade the image of this field.

• Journal of Apiculture
• /
• v.34 no.1
• /
• pp.47-55
• /
• 2019

Honey bees are a vital part of the food chain as the most important pollinators for a broad palette of crops and wild plants. The climate change and colony collapse disorder (CCD) phenomenon make it challenging to develop ICT solutions to predict changes in beehive and alert about potential threats. In this paper, we report the test results of the bee-counting system which stands out against the previous analogues due to its comprehensive components including an improved dual infrared sensor to detect honey bees entering and leaving the hive, environmental sensors that measure ambient and interior, a wireless network with the bluetooth low energy (BLE) to transmit the sensing data in real time to the gateway, and a cloud which accumulate and analyze data. To assess the system accuracy, 3 persons manually counted the outgoing and incoming honey bees using the video record of 360-minute length. The difference between automatic and manual measurements for outgoing and incoming scores were 3.98% and 4.43% respectively. These differences are relatively lower than previous analogues, which inspires a vision that the tested system is a good candidate to use in precise apicultural industry, scientific research and education.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.4
• /
• pp.193-198
• /
• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.50 no.6
• /
• pp.42-57
• /
• 2022

This study aims to create a basic model for classifying the activity photos that urban park users shared on social media using Deep Learning through Artificial Intelligence. Regarding the social media data, photos related to urban parks were collected through a Naver search, were collected, and used for the classification model. Based on the indicators of Naturalness, Potential Attraction, and Activity, which can be used to evaluate the characteristics of urban parks, 21 classification categories were created. Urban park photos shared on Naver were collected by category, and annotated datasets were created. A custom CNN model and a transfer learning model utilizing a CNN pre-trained on the collected photo datasets were designed and subsequently analyzed. As a result of the study, the Xception transfer learning model, which demonstrated the best performance, was selected as the urban park user activity image classification model and evaluated through several evaluation indicators. This study is meaningful in that it has built AI as an index that can evaluate the characteristics of urban parks by using user-shared photos on social media. The classification model using Deep Learning mitigates the limitations of manual classification, and it can efficiently classify large amounts of urban park photos. So, it can be said to be a useful method that can be used for the monitoring and management of city parks in the future.