• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.4
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• pp.69-79
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• 2017

The purpose of this study is to reconsider the rating method for the floor impact sound insulation performance in current criterion. Although there are some arguments about proper standard heavy impact source with reproducibility of actual impact source in residence building, bang machine is adopted as the only standard heavy impact source in domestic criterion. To inspect the rating methods of evaluation criteria, this study conducted vibration test for both of standard heavy impact sources and actual impact sources. Using the test results, the floor impact sound insulation performance levels were assessed by each of several criteria. In addition, low frequency noise beyond current criteria was evaluated. Consequently, the floor impact sound levels have different performance levels according to adopted criteria, and measured floor impact sounds are bound to annoy the neighbors in the low frequency range. Current criteria does not consider the spectrum characteristics of floor impact sound according to impact sources and low frequency noise. This may cause the difference between the floor impact sound insulation performance level and human perception. Thus current criterion needs to be complemented to reflect the spectrum characteristics of floor impact sound levels according to impact sources and sound pressure levels in low frequency range.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.75-85
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• 2020

This study examines machine learning cross-validation to utilized create ROI for classification of land cover. The study area located in Sejong and one KOMPSAT-3A image was used in this analysis: procedure on October 28, 2019. We used four bands(Red, Green, Blue, Near infra-red) for learning cross validation process. In this study, we used K-fold method in cross validation and used SVM kernel type with cross validation result. In addition, we used 4 kernels of SVM(Linear, Polynomial, RBF, Sigmoid) for supervised classification land cover map using extracted ROI. During the cross validation process, 1,813 data extracted from 3,500 data, and the most of the building, road and grass class data were removed about 60% during cross validation process. Based on this, the supervised SVM linear technique showed the highest classification accuracy of 91.77% compared to other kernel methods. The grass' producer accuracy showed 79.43% and identified a large mis-classification in forests. Depending on the results of the study, extraction ROI using cross validation may be effective in forest, water and agriculture areas, but it is deemed necessary to improve the distinction of built-up, grass and bare-soil area.

• 재활복지
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• v.21 no.3
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• pp.129-146
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• 2017

In an event of an emergency, such as fire in a building, visually impaired and blind people are prone to exposed to a level of danger that is greater than that of normal people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable because it usually uses chemical sensor based technology to detect fire particles. But by using vision sensor instead, fire can be proven to be detected much faster as we show in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't work very well because these techniques require hand-crafted features that do not generalize well to various scenarios. But with the help of recent advancement in the field of deep learning, this research can be conducted to help solve this problem by using deep learning-based object detector that can detect fire using images from security camera. Deep learning based approach can learn features automatically so they can usually generalize well to various scenes. In order to ensure maximum capacity, we applied the latest technologies in the field of computer vision such as YOLO detector in order to solve this task. Considering the trade-off between recall vs. complexity, we introduced two convolutional neural networks with slightly different model's complexity to detect fire at different recall rate. Both models can detect fire at 99% average precision, but one model has 76% recall at 30 FPS while another has 61% recall at 50 FPS. We also compare our model memory consumption with each other and show our models robustness by testing on various real-world scenarios.

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

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.325-330
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• 2021

This study set out to investigate the forms, attributes, and escape methods of post-subjects projected on the investigator's works in connection with rhizomatic thinking proposed as a way of social transformation by Deleuze and Guattari and examine their social connotations. Post-subjects projected on the investigator's works are not completed wholes of some sort, but like materials whose constant premise is change and creation. In the investigator's works, post-subjects have conscious and unconscious desire. It is the desire of creation with positive attributes including Deleuze's and Guattari's pursuit of changes in a contradicting society. When desire is deployed in post-subjects, they will carry out an escape. This way of escape is rhizomatic proposed by Deleuze and Guattari. It deconstructs contradicting things and repeats connection, contact, and severance with the outside world, building a new order. Rhizomatic post-subjects appearing in the investigator's works depict the escape process and method in abstract ways through the variable installation of objets combined with a color field of repeating brushes. In this work, the goal of post-subjects is to make a safe landing in a space where beings are recognized for their values and free and creative lives. These post-subjects are nomads creating a new landscape continuously, wandering around vast plains, and also artists and literary figures resisting a contradicting society. That is, they are connected to the concept of a war machine proposed by Deleuze and Guattari as a concept of social transformation and to the concept of Nietzsche's Agon to devise and create new values and politics based on street passion. They seek after a space where they can co-exist with otherness recognized rather than the complete deconstruction of the old order.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.429-437
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• 2022

Recently, the demand for a QA (Question Answering) system for human-machine communication has increased. Among the QA systems, a closed domain QA system that can handle spatial-related questions is called GeoQA. In this study, a new type of graph database, LPG (Labeled Property Graph) was used to overcome the limitations of the RDF (Resource Description Framework) based database, which was mainly used in the GeoQA field. In addition, GraphQL (Graph Query Language), an API-type query language, is introduced to address the fact that the LPG query language is not standardized and the GeoQA system may depend on specific products. In this study, database was built so that answers could be retrieved when spatial-related questions were entered. Each data was obtained from the national spatial information portal and local data open service. The spatial relationships between each spatial objects were calculated in advance and stored in edge form. The user's questions were first converted to GraphQL through FOL (First Order Logic) format and delivered to the database through the GraphQL server. The LPG used in the experiment is Neo4j, the graph database that currently has the highest market share, and some of the built-in functions and QGIS were used for spatial calculations. As a result of building the system, it was confirmed that the user's question could be transformed, processed through the Apollo GraphQL server, and an appropriate answer could be obtained from the database.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.869-875
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• 2009

Safety is a big issue in the construction sites. For safe and secure management, tracking locations of construction resources such as labors, materials, machineries, vehicles and so on is important. The materials, machineries and vehicles could be controlled by computer, whereas the movement of labors does not have fixed pattern. So, the location and movement of labors need to be monitored continuously for safety. In general, Global Positioning System(GPS) is an opt solution to obtain the location information in outside environments. But it cannot be used for indoor locations as it requires a clear Line-Of-Sight(LOS) to satellites Therefore, indoor location monitoring system could be a convenient alternative for environments such as tunnel and indoor building construction sites. This paper presents a case study to investigate feasibility of Wi-Fi based indoor location monitoring system in construction site. The system is developed by using fingerprint map of gathering Received Signal Strength Indication(RSSI) from each Access Point(AP). The signal information is gathered by Radio Frequency Identification (RFID) tags, which are attached on a helmet of labors to track their locations, and is sent to server computer. Experiments were conducted in a shield tunnel construction site at Guangzhou, China. This study consists of three phases as follows: First, we have a tracking test in entrance area of tunnel construction site. This experiment was performed to find the effective geometry of APs installation. The geometry of APs installation was changed for finding effective locations, and the experiment was performed using one and more tags. Second, APs were separated into two groups, and they were connected with LAN cable in tunnel construction site. The purpose of this experiment was to check the validity of group separating strategy. One group was installed around the entrance and the other one was installed inside the tunnel. Finally, we installed the system inner area of tunnel, boring machine area, and checked the performance with varying conditions (the presence of obstacles such as train, worker, and so on). Accuracy of this study was calculated from the data, which was collected at some known points. Experimental results showed that WiFi-based indoor location system has a level of accuracy of a few meters in tunnel construction site. From the results, it is inferred that the location tracking system can track the approximate location of labors in the construction site. It is able to alert the labors when they are closer to dangerous zones like poisonous region or cave-in..

• Magazine of the Korean Society of Agricultural Engineers
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• v.21 no.2
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• pp.21-27
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• 1979

Canal construction is not the only area in which mechanization has advanced with great strides. All phases of the construction industry, including earthmoving, land clearing and levelling, road construction, and drainage and water control projects, have benefited from today's technological advancements. Lasers, an excellant example of advanced technology, have been refined for use as guidance systems for construction machinery, increasing accuracy and the speed of operation. The use of explosives by contractors is becoming more commonplace. One of the most valuable modern tools available today is the two-way radio. On today's sophisticated projects a single machine being down can frequently stop the progress of the entire project, delaying hundreds of men and machines from completing their assigned work for the day. The use of two-way radios in all the pickups and cars being used on a project facilitates communication so that emergency repairs can be effected immediately, and costly down time on any project can be reduced to a minimum. Not every construction project is suitable to mechanization. However, on the majority of projects mechanization has a great deal to offer the Korean contractor, and all contractors, in savings of time and money. Each and every project being considered by a contractor, should be closely examined for the most effective and efficient machinery application available. The International Commission on Irrigation and Drainage (ICID) has formed a committee on construction techniques being used in canal construction today. Two publications are now available describing the advances made in recent years. Standards for construction have been established for mechanized systems and this information is being distributed worldwide.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.75-82
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• 2023

Automatic speech recognition (ASR) has been revolutionized with deep learning-based approaches, among which self-supervised learning methods have proven to be particularly effective. In this study, we aim to enhance the performance of OpenAI's Whisper model, a multilingual ASR system on the Korean language. Whisper was pretrained on a large corpus (around 680,000 hours) of web speech data and has demonstrated strong recognition performance for major languages. However, it faces challenges in recognizing languages such as Korean, which is not major language while training. We address this issue by fine-tuning the Whisper model with an additional dataset comprising about 1,000 hours of Korean speech. We also compare its performance against a Transformer model that was trained from scratch using the same dataset. Our results indicate that fine-tuning the Whisper model significantly improved its Korean speech recognition capabilities in terms of character error rate (CER). Specifically, the performance improved with increasing model size. However, the Whisper model's performance on English deteriorated post fine-tuning, emphasizing the need for further research to develop robust multilingual models. Our study demonstrates the potential of utilizing a fine-tuned Whisper model for Korean ASR applications. Future work will focus on multilingual recognition and optimization for real-time inference.