• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.1
• /
• pp.1-6
• /
• 2020

To make an automated system for a salt field, it is necessary to measure the salinity and water level of the evaporation site. In this paper, a method to simultaneously measure the salinity and water level by measuring the buoyancy forces of two fixed buoyancy bodies is proposed. The proposed measurement method measures the buoyancy of the main part and reference part when the measuring device is immersed in the salty water, and simultaneously measures the salinity and water level through the sum and difference of the two buoyancy forces. Since there is no mechanical movement in the measurement of buoyancy, measurement errors and maintenance needs can be reduced in the mudy environment of salt field. By applying the proposed method, we developed a system that can simultaneously measure salinity and water level remotely at the evaporation site of a salt field. Through a measurement experiment using a reference salty water having various levels of salinity, the results of a salinity error of 0% and a water level error of 2mm were obtained, and the effectiveness of the proposed salinity and water level measuring device was verified. When an automated system is constructed using the developed salinity and water level measuring device, labor reduction, work environment improvement, and productivity improvement are expected.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.446-452
• /
• 2021

As a higher standard for food consumption is required, the consumption of chicken meat that can satisfy the subdivided food preferences is increasing. In March 2003, the quality criteria for chicken carcasses notified by the Livestock Quality Assessment Service suggested quality grades according to fecal contamination and the size and weight of blood and bruises. On the other hand, it is too difficult for human inspection to qualify mass products, which is key to maintaining consistency for grading thousands of chicken carcasses. This paper proposed the computer vision algorithm as a non-destructive inspection, which can identify chicken carcass parts according to the detailed standards. To inspect the chicken carcasses conveyed at high speed, the image calibration was involved in providing robustness to the side effect of external lighting interference. The separation between chicken and background was achieved by a series of image processing, such as binarization based on Expectation Maximization, Erosion, and Labeling. In terms of shape analysis of chicken carcasses, the features are presented to reveal geometric information. After applying the algorithm to 78 chicken carcass samples, the algorithm was effective in segmenting chicken carcass against a background and analyzing its geometric features.

• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.1
• /
• pp.28-36
• /
• 2022

Currently, the automation and intelligence of the shipbuilding industry have improved its work production capacity and cost competitiveness, but the reduction rate of safety accidents among industrial site workers is still low and the damage caused by safety accidents is very serious, so there is a need for improvement according to the workplace. This research aims to demonstrate the connectivity between smart safety helmets in the demonstration area to verify the effectiveness along with the development of smart helmets for worker protection and environmental safety in shipyards. For efficient communication between workers, impact noise of over 95dB was confirmed in the workplace, and noise reduction was required. To solve this problem, the filtering performance was compared and analyzed using the Butterworth, Chebyshev, and elliptic algorithms. The connectivity test and noise reduction method between smart helmets proposed in this study will increase the usability and safety of the field through the development of advanced smart helmets tailored to the shipbuilding workplace in the future.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.30-31
• /
• 2009

Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.3
• /
• pp.66-73
• /
• 2024

Stay-cable is one of the most important load carrying members in cable-stayed bridges. Monitoring structural integrity of stay-cables is crucial for evaluating the structural condition of the cable-stayed bridge. For stay-cables, tension and damping ratio are estimated based on modal properties as a measure of structural integrity. Since the monitoring system continuously measures the vibration for the long-term period, data acquisition systems should be stable and power-efficiency as the hardware system. In addition, massive signals from the data acquisition systems are continuously generated, so that automated analysis system should be indispensable. In order to fulfill these purpose simultaneously, this study presents an autonomous cable monitoring system based on domain-knowledge using IoT for continuous cable monitoring systems of cable-stayed bridges. An IoT system was developed to provide effective and power-efficient data acquisition and on-board processing capability for Edge-computing. Automated peak-picking algorithm using domain knowledge was embedded to the IoT system in order to analyze massive data from continuous monitoring automatically and reliably. To evaluate its operational performance in real fields, the developed autonomous monitoring system has been installed on a cable-stayed bridge in Korea. The operational performance are confirmed and validated by comparing with the existing system in terms of data transmission rates, accuracy and efficiency of tension estimation.

• Journal of Broadcast Engineering
• /
• v.17 no.2
• /
• pp.411-421
• /
• 2012

The stagnant growth in 3D market due to 3D movie contents shortage is encouraging development of techniques for production cost reduction. Elimination of vertical disparity generated during image acquisition requires heaviest time and effort in the whole stereoscopic film-making process. This matter is directly related to competitiveness in the market and is being dealt with as a very important task. The removal of vertical disparity, i.e. image rectification has been treated for a long time in the photogrammetry field. While computer vision methods are focused on fast processing and automation, photogrammetry methods on accuracy and precision. However, photogrammetric approaches have not been tried for the 3D film-making. In this paper, proposed is a photogrammetry-based rectification algorithm that enable to eliminate the vertical disparity precisely by reconstruction of geometric relationship at the time of shooting. Evaluation of proposed algorithm was carried out by comparing the performance with two existing computer vision algorithms. The epipolar constraint satisfaction, epipolar line accuracy and vertical disparity of result images were tested. As a result, the proposed algorithm showed excellent performance than the other algorithms in term of accuracy and precision, and also revealed robustness about position error of tie-points.

• The KIPS Transactions:PartD
• /
• v.14D no.6
• /
• pp.675-688
• /
• 2007

Memory access errors are frequently occurred in C programs. A number of tools and research works have been trying to detect the errors automatically. However, they have one or more of the following problems: inability to detect all memory errors, changing the memory allocation mechanism, incompatibility with libraries, and excessive performance overhead. In this paper, we suggest a new method to solve these problems, and then present a result of comparison to the previous research works through the experiments. Our approach consists of two phases. First is to transform source code at compile time through inserting instrumentation into the source code. And second is to detect memory errors at run time with a bitmap that maintains information about memory allocation. Our approach has improved the error detection abilities against the binary code analysis based ones by using the source code analysis technique, and enhanced performance in terms of both space and time, too. In addition, our approach has no problem with respect to compatibility with shared libraries as well as does not need to modify memory allocation mechanism.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.12
• /
• pp.491-498
• /
• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Geophysics and Geophysical Exploration
• /
• v.10 no.1
• /
• pp.50-59
• /
• 2007

The advent of towed geo-electrical array surveying on water and land has resulted in datasets of magnitude approaching that of airborne electromagnetic surveying and most suited to 1D inversion. Robustness and complete automation is essential if processing and reliable interpretation of such data is to be viable. Sharp boundaries such as river beds and the top of saline aquifers must be resolved so use of smoothness constraints must be minimised. Suitable inversion algorithms must intelligently handle low signal-to-noise ratio data if conductive basement, that attenuates signal, is not to be misrepresented. A noise-level aware inversion algorithm that operates with one elastic thickness layer per electrode configuration has been coded. The noise-level aware inversion identifies if conductive basement has attenuated signal levels so that they are below noise level, and models conductive basement where appropriate. Layers in the initial models are distributed to span the effective depths of each of the geo-electric array quadrupoles. The algorithm works optimally on data collected using geo-electric arrays with an approximately exponential distribution of quadrupole effective depths. Inversion of data from arrays with linear electrodes, used to reduce contact resistance, and capacitive-line antennae is plausible. This paper demonstrates the effectiveness of the algorithm using theoretical examples and an example from a salt interception scheme on the Murray River, Australia.

• Journal of Digital Contents Society
• /
• v.9 no.4
• /
• pp.661-667
• /
• 2008

The dynamics of business cycles has been changed by the macroscopic economic forces because of the introduction of new technical know-how each year. These the dynamics of business has a significant influence on the investment of enterprise in the information communication field. Today, the most important goal of the IT investment is simply not to lower the production cost any more, but to improve the usefulness for the customers and partners in order to obtain the optimized mass products. Therefore, the enterprises have been concentrating their all abilities on the automation, integration, and optimization of business process using BPM. In addition, they are concentrating their efforts on the business expansion by approaching the technical aspect using RFID application system. However, in order to accomplish a successful enterprise ability, the technical view, business process view, and organization view must be considered together. We suggested the method considering organization view, via the technical element, i.e., RFID system for approaching the business process. Furthermore, we tried the optimization of assignment using Context Analysis methodology and proposed the method to reduce the element with respect to the time, human, and expense by applying the Case Study method that minimizes the iteration times through the transmitted processing procedure and type. The proposed method gave us the expectation that it will bring out the innovative improvement with respect to the time, expense, quality, and customer's satisfaction in the process from the analysis of business process to the analysis and design of system.