• Journal of Environmental Impact Assessment
• /
• v.28 no.6
• /
• pp.592-603
• /
• 2019

This study is about the implementation of low energy sewage management technology through effective control of blower which consumes the most energy in sewage treatment. In calculating the amount of oxygen required for microorganisms, unlike the existing method using the operating index in the bioreactor or TMS data in the discharge port, the CODcr and NH₄⁺-N concentration changes in sewage flowing into the sewage treatment plant were detected in advance before entering the bioreactor and the amount of air was controlled based on this. The pre-sensing was found to have a high correlation compared with conventional products. As a result of blower control, it was possible to save about 9.9% energy more than the manual control. Consequently, this study suggested the possibility of blower's real-time control combined with pre-sensing technology. Also, it is expected that the low energy sewage treatment can be applied to sewage treatment facilities dependent on operation by manpower, and it will contribute to the reduction of greenhouse gas emissions.

• Journal of the Korean Society for information Management
• /
• v.27 no.4
• /
• pp.33-47
• /
• 2010

The growth of IT technology and the popularity of network based information sharing increase the number of digital contents in military area. Thus, there arise issues of finding suitable public information with the growing number of long-term preservation of digital public information. According to the source of raw data and the time of compilation may be variable and there can be existed in many correlations about digital contents. The business service ontology makes knowledge explicit and allows for knowledge sharing among information provider and information consumer for public digital archive engaged in improving the searching ability of digital public information. The business service ontology is at the interface as a bridge between information provider and information consumer. However, according to the difficulty of semantic knowledge extraction for the business process analysis, it is hard to realize the automation of constructing business service ontology for mapping from unformed activities to a unit of business service. To solve the problem, we propose a new business service auto-acquisition method for the first step of constructing a business service ontology based on Enterprise Architecture.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1053-1065
• /
• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Journal of Korean Society of Transportation
• /
• v.27 no.5
• /
• pp.97-111
• /
• 2009

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a signal intersection and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, we intend to present a technology capable of overcoming problems in which advanced existing technologies exhibited limitations in handling real-time due to large data capacity such as object separation of vehicles and tracking, which pose difficulties due to environmental diversities and changes at a signal intersection with complex traffic situations, as pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian complex model analytical method which has been considered the best among well-known environmental obstacle reduction methods. To prove that the technology developed by this research has performance advantage over existing automatic traffic accident recording systems, a test was performed by entering image data from an actually operating crossroad online in real-time. The test results were compared with the performance of other existing technologies.

• Journal of agriculture & life science
• /
• v.51 no.6
• /
• pp.137-144
• /
• 2017

A diverse of recommendation has been made for the structure and management of dairy cows, despite demanding research, the relationship between lactation number and various factors is yet to be established. The present study was aimed to investigate the covariance among lactation number, growth performance, calving interval, and milk production was considered to increase an efficiency of selection schemes and to manage more efficiently Holstein dairy cows that have been raised on small-scale family farms in Republic of Korea. For that purpose, the data were observed from 850 Holstein dairy cows, which a total of 3929 milking, since April 2016 - January 2017. We measured the body weight, height, age, calving interval, and milk production of the each dairy cow. Also, information about the date of lactation, calving interval, and milk production was recorded using an automatic milking system(AMS) with identification numbers. Milk production was calculated per udder quarter in the AMS. Our study results showed the increased average body weight(p>0.05) in 1, 2, 3, and $4^{th}$ lactating dairy cows and afterwards, we noticed the tendency on the average body weight(p<0.05) per lactation progressed. There was no significant difference noticed on height measurement of dairy cows. From the processing data of 850 Holstein dairy cows, the lactation number 1 and 7 had a greater calving interval with significantly lowered milk production, and the lactation number 2, 3, 4, 5, and 6 had significantly lowered the calving interval(p<0.05) with a greater milk production. From our study results, we evidenced that there is a significant relationship between the lactation number, growth performance, calving interval, and milk yield, and the maximum production of milk occurring in the $3^{rd}$ and $4^{th}$ lactation dairy cows. The achieved results from this study can be used by the small-scale farmers to encourage the structure and management of growth performance, calving interval, and milk yield in Holstein dairy cows in Korea.

• The Journal of Society for e-Business Studies
• /
• v.26 no.1
• /
• pp.93-106
• /
• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.

• Journal of the Korean Society for information Management
• /
• v.38 no.2
• /
• pp.153-172
• /
• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.541-551
• /
• 2021

Among the numerous duties of a librarian in a library, the work of arranging books is a job that the librarian has to do one by one. Thus, the cost of labor and time is large. In order to solve this problem, the interest in book-arranging robots based on artificial intelligence has recently increased. In this paper, we propose the K-ACO algorithm, which is the shortest path algorithm for multi-stops that can be applied to the library book arrangement robots. The proposed K-ACO algorithm assumes multiple robots rather than one robot. In addition, the K-ACO improves the ANT algorithm to create K clusters and provides the shortest path for each cluster. In this paper, the performance analysis of the proposed algorithm was carried out from the perspective of book arrangement time. The proposed algorithm, the K-ACO algorithm, was applied to a university library and compared with the current book arrangement algorithm. Through the simulation, we found that the proposed algorithm can allocate fairly, without biasing the work of arranging books, and ultimately significantly reduce the time to complete the entire work. Through the results of this study, we expect to improve quality services in the library by reducing the labor and time costs required for arranging books.

• Journal of Korean Society of Archives and Records Management
• /
• v.22 no.1
• /
• pp.43-59
• /
• 2022

Electronic public records are classified simultaneously as production, a preservation period is granted, and after a certain period, they are transferred to an archive and preserved. This study intends to find a way to improve the efficiency in classifying transferred records and maintain consistent standards. To this end, the current record classification work process carried out by the National Archives of Korea was analyzed, and problems were identified. As a way to minimize the manual work of record classification by converging the required improvement, the process of identifying outlier candidates based on a list consisting of classified information of the transferred records was proposed and systemized. Furthermore, the proposed outlier discrimination process was applied to the actual records transferred to the National Archives of Korea. The results were standardized and constructed as a training data format that can be used for machine learning in the future.

• Korean Journal of Construction Engineering and Management
• /
• v.24 no.5
• /
• pp.52-62
• /
• 2023

The objective of this study is to propose an automated algorithm for precise cutting length of slab rebar complying with regulations such as anchorage length, standard hooks, and lapping length. This algorithm aims to improve the traditional manual quantity take-off process typically outsourced by external contractors. By providing accurate rebar quantity data at BBS(Bar Bending Schedule) level from the bidding phase, uncertainty in quantity take-off can be eliminated and reliance on out-sourcing reduced. In addition, the algorithm allows for early determination of precise quantities, enabling construction firms to preapre competitive and optimized bids, leading to increased profit margins during contract negotiations. The proposed algorithm not only streamlines redundant tasks across various processes, including estimating, budgeting, and BBS generation but also offers flexibility in handling post-contract structural drawing changes. In particular, the proposed algorithm, when combined with BIM, can solve the technical problems of using BIM in the early phases of construction, and the algorithm's formulas and shape codes that built as REVIT-based family files, can help saving time and manpower.