• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.435-446
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• 2022

Identifying ship types is an important process to prevent illegal activities on territorial waters and assess marine traffic of Vessel Traffic Services Officer (VTSO). However, the Terrestrial Automatic Identification System (T-AIS) collected at the ground station has over 50% of vessels that do not contain the ship type information. Therefore, this study proposes a method of identifying ship types through the Random Forest Classifier (RFC) from dynamic and static data of AIS and V-Pass for one year and the Ulsan waters. With the hypothesis that six features, the speed, course, length, breadth, time, and location, enable to estimate of the ship type, four classification models were generated depending on length or breadth information since 81.9% of ships fully contain the two information. The accuracy were average 96.4% and 77.4% in the presence and absence of size information. The result shows that the proposed method is adaptable to identifying ship types.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1301-1308
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• 2016

Internet traffic identification is an essential preliminary step for stable service provision and efficient network management. The payload signature-based-classification is considered as a reliable method for Internet traffic identification. But its performance is highly dependent on the number and the structure of signatures. If the numbers and structural complexity of signatures are not proper, the performance of payload signature-based-classification easily deteriorates. Therefore, in order to improve the performance of the identification system, it is necessary to regulate the numbers of the signature. In this paper, we propose a novel signature quality evaluation method to decide which signature is highly efficient for Internet traffic identification. We newly define the signature quality evaluation criteria and find the highly efficient signature through the method. Quality evaluation is performed in three different perspectives and the weight of each signature is computed through those perspectives values. And we construct the signature map(S-MAP) to find the highly efficient signature. The proposed method achieved an approximately fourfold increased efficiency in application traffic identification.

• Journal of Soil and Groundwater Environment
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• v.18 no.2
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• pp.19-26
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• 2013

This study developed the classification of National Soil Quality Monitoring Network (NSQM) and its site selection criteria to meet the recently established purposes of the NSQM. The NSQM were suggested by this study to classify into the six-purposes site groups from the current classification of land uses. The six purposes site groups were 1) intensive observation sites, 2) contaminant loading sites, 3) human activity sites, 4) background sites, 5) river soil sites, and 6) sites near the groundwater quality monitoring wells. Furthermore, this study developed the site selection criteria of NSQM utilizing the accumulated NSQM data, road traffic data, chemical emission data, census, soil information, and the literature related to soil quality variation due to contaminant loads. For selecting suitable sites for NSQM, this study used road traffic, chemical emission, the distance from the contaminant sources, and population information as specific criteria. The suggested site classification and criteria were appled for the current 100 NSQM sites for evaluation. Forty sites were met to the criteria suggested by this study, but sixty sites were not met to the criteria. However, some of the sixty sites also included the obscure sites that their addresses were not apparent to find them.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.3
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• pp.257-263
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• 2013

The classification of risk factors and the identification of risk acceptance criteria are core works to assess risk levels with high enough confidential level in the field of marine traffic environment. In the previous study work, the twenty kinds of risk factors and its assessment criteria for the domestic marine traffic environment were proposed. In this paper, with these previous studying results, the relative importance of the risk factors were analyzed by questionnaire survey of marine traffic experts using the analytic hierarchy process. The analysis results showed that the relative importance of the visibility restriction is the highest among the twenty kinds of risk factors, and the relative importance of the traffic condition is the highest among the five kinds of risk categories. As results from analysis, it is expected that the approaching method on the relative importance is to be one of basic techniques for the development of risk assessment models in the domestic marine traffic environment.

• Electronics and Telecommunications Trends
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• v.37 no.4
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• pp.28-35
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• 2022

To solve the challenge of waste plastics, this study investigated the related technologies and company trends along the plastic life cycle, and primarily describes ICT technologies to improve efficiency in the process of sorting and sorting waste plastics. Waste plastic discharge caused by the explosive increase in parcel traffic because of COVID-19 is also growing exponentially. Hence, waste treatment is emerging as a social challenge. Most of the domestic waste classification depends on the manual process according to the waste pollution level. The plastic material classification approach using the spectroscopy approach reveals a high error in the contaminated waste plastic classification, but if the Artificial Intelligence-based image classification technology is employed together, the classification precision can be enhanced because of the type of waste plastic product and the contaminated part can be differentiated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.57-68
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• 2005

Vehicle classification system in Korea is operated by two different types depending on operating purpose and place. 8-category classification system operates in Expressway and Provincial road, and 11-category classification system operates in National highway. These different operations decrease the efficiency of practical use of gathering data. Therefore, this study proposes new-modified vehicle classification system for solving this problem. For classification, this study not only focuses on mechanic survey system which is based on vehicle specs, it's also focuses on the applicability of roadside survey. This proposed classification system considers the tendency to vary of vehicle types, and the compatibility with the other classification systems. This system might be the most suitable system for our present situation.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.946-949
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• 2008

네트워크를 사용하는 응용프로그램의 종류가 다양해지면서 네트워크 트래픽의 응용별 분류는 효율적인 네트워크 관리에 있어 그 중요성이 커지고 있지만, 오늘날 응용프로그램의 특징인 유동적인 포트번호 사용 및 패킷의 암호화 등은 트래픽의 응용별 분류를 더욱 어렵게 하고 있다. Well-known 포트기반의 응용별 분류방법의 단점을 극복하기 위하여 머신러닝 알고리즘과 Signature 기반 분석 방법들이 연구되고는 있지만 주장하는 높은 분석률에 비하여 실제 네트워크 트래픽에 적용하기에는 신뢰성이 부족하다. 본 논문에서는 일부 종단 호스트에 설치된 TMA(Traffic Measurement Agent)로 부터 수집한 응용프로그램의 트래픽 사용 정보를 기초로 하여 전체 네트워크 트래픽의 응용프로그램을 판별하는 응용 트래픽 분류 방법론을 제안한다. 제안된 방법론은 트래픽 플로우들의 상관관계를 이용하여 TMA 호스트 트래픽으로부터 TMA가 설치되지 않은 호스트에서 발생한 트래픽들의 응용을 판단하며, 분류 된 결과에 대하여 높은 신뢰성을 보장한다. 제안된 방법론은 학내 네트워크에 적용하여 그 타당성을 검증하였다.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.469-472
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• 2022

With the recent development of intelligent transportation systems, various technologies applying deep learning technology are being used. To crackdown on illegal vehicles and criminal vehicles driving on the road, a vehicle type classification system capable of accurately determining the type of vehicle is required. This study proposes a vehicle type classification system optimized for mobile traffic control systems using YOLO(You Only Look Once). The system uses a one-stage object detection algorithm YOLOv5 to detect vehicles into six classes: passenger cars, subcompact, compact, and midsize vans, full-size vans, trucks, motorcycles, special vehicles, and construction machinery. About 5,000 pieces of domestic vehicle image data built by the Korea Institute of Science and Technology for the development of artificial intelligence technology were used as learning data. It proposes a lane designation control system that applies a vehicle type classification algorithm capable of recognizing both front and side angles with one camera.