• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2171-2185
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• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권12호
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• pp.4489-4501
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• 2014

We propose a novel lane detection method based on classification in image sequences. Both structural and statistical features of the extracted bright shape are applied to the neural network for finding correct lane marks. The features used in this paper are shown to have strong discriminating power to locate correct traffic lanes. The traffic lanes detected in the current frame is also used to estimate the traffic lane if the lane detection fails in the next frame. The proposed method is fast enough to apply for real-time systems; the average processing time is less than 2msec. Also the scheme of the local illumination compensation allows robust lane detection at nighttime. Therefore, this method can be widely used in intelligence transportation systems such as driver assistance, lane change assistance, lane departure warning and autonomous vehicles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3243-3257
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• 2021

Deep neural networks provide excellent performance in pattern recognition, audio classification, and image recognition. It is important that they accurately recognize input data, particularly when they are used in autonomous vehicles or for medical services. In this study, we propose a data correction method for increasing the accuracy of an unknown classifier by modifying the input data without changing the classifier. This method modifies the input data slightly so that the unknown classifier will correctly recognize the input data. It is an ensemble method that has the characteristic of transferability to an unknown classifier by generating corrected data that are correctly recognized by several classifiers that are known in advance. We tested our method using MNIST and CIFAR-10 as experimental data. The experimental results exhibit that the accuracy of the unknown classifier is a 100% correct recognition rate owing to the data correction generated by the proposed method, which minimizes data distortion to maintain the data's recognizability by humans.

• 한국정보통신학회논문지
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• 제26권2호
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• pp.313-316
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• 2022

In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an analysis model is created that judges the data received from the charger as normal and abnormal. In addition, a model is created to determine the cause of the abnormality using the existing charging data based on the analysis of the type of charger abnormality. Finally, it is solved using unsupervised learning method to find new patterns of abnormal data.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 춘계학술발표대회
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• pp.737-740
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• 2004

본 논문에서는 차종 식별을 위해 차량 영상의 질감 특징을 사용하였다. 차량의 질감 특징 정보를 얻기 위한 관심영역으로 라디에이터 그릴 부분을 선택하였다. 추출된 관심영역으로부터 GLCM(Gray Level Co-occurrence Matrix)을 사용하여 질감 특징 값을 추출하였고, 그 특징 값들을 입력으로 취하는 3층의 신경회로망을 구성한 후 역전파 학습 알고리즘을 사용하여 학습을 시켜서 차종 식별을 시도하였다.

• 한국분무공학회지
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• 제22권3호
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• pp.122-128
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• 2017

Currently, the proportion of diesel vehicles in all automobile has grown significantly over the past few years. Air pollutant also grew up and became a social problem. In particular, the issue of NOx emissions caused by NOx high emission in real driving has become a global issue. Despite the fact that the regulatory and reduction project of the new vehicle is actively carried out, there are no existence regulations of In-use diesel vehicle's NOx emission. Therefore, the emission characteristics of the in-use diesel vehicles were investigated to seek ways to reduce NOx emissions in this study. The test targets were used in 237 close inspection of exhaust gases and model year varied from 1996 to 2011. However, the classification of emissions by emission standards differed considerably from NOx emissions. This means that the selection method for early retirement targets should be converted from model year to amount of emissions. If the current early retirement program was applied to the existing system, pre-Euro 3 was 22.530 g/km and Euro 4 was 21.810 g/km to NOx reduction. However, when the vehicle was changed to high emission target vehicle, NOx reduction increase maximum 84.705 kg/yr. According to the study results, an effective reduction in NOx emissions can be achieved if an earlier target in expanded to Euro 4 vehicles.

• 한국재난정보학회 논문집
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• 제13권2호
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• pp.199-212
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• 2017

기후변화로 인한 지구 온난화 현상으로 대설재난의 발생을 예측하기가 더욱 어려워지면서 신속한 도로제설이 중요해지고 있다. 우리나라의 지자체는 관할 행정구역의 강설 및 도로특성을 고려하지 않고 과거 경험을 참고하여 제설장비와 차량을 보유하고 있다. 본 연구는 우리나라 지자체의 강설과 도로 특성을 고려하여 제설장비와 차량 수요를 추정하는 절차를 개발하는 데 목적이 있다. 본 연구는 첫 단계로 기상청의 과거 10년 강설자료를 이용하여 지역의 강설 특성을 유형화한 후 관리기관별 도로제설 연장을 산정하였다. 다음으로 도로제설 시나리오를 설정하여 지자체의 제설장비와 차량 수요를 추정하고 실제 보유량과 비교하였다. 마지막으로 지역별 강설량과 제설시간을 고려하여 지자체별 필요 제설장비와 차량수요를 추정하였다. 본 연구의 결과는 229개 지자체별로 적정 제설차량과 장비를 보유하고, 제설노선을 결정하는 대설재난 관리정책을 수립하는데 활용할 수 있다.

• KIEAE Journal
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• 제9권1호
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• pp.31-38
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• 2009

The purpose of this study is to present direction in outdoors space planning and design after direction through user characteristic analysis through spectacle component establishment of culture art center outdoors space through on-the-site analysis and literature investigation to culture art center of Seoul city and capital region 17 places in this research. The data was collected from classification and bisection kind, subdivision kind, and great classification composed to 17 items. User satisfaction side and Variable that is looked below satisfaction than average appeared to bench, pergola, sculpture facilities, pavement facilities, border facilities. And these facilities were analyzed dissatisfaction. When see satisfaction model, when make up culture art center or similar facilities in local government hereafter because parking facilities and rest area cause big effect in satisfaction, is judged that is item to consider most preferentially. In most case, parking lot security from outdoors space, resting place security, security of field performance facilities etc. taking a serious view because tendency that users see performance or use most vehicles except neighborhood walking area for a rest, a walk etc.. is trend. But, is judged that physical side so that can feel satisfaction as space security of quantitative side is important but users utilize substantially and side that is the program are more important in hereafter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2509-2528
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• 2019

There are various styles of license plates for different countries and use cases that require style-specific methods. In this paper, we propose and illustrate a multi-style license plate recognition system. The proposed system performs a series of processes for license plate candidates detection, structure classification, character segmentation and character recognition, respectively. Specifically, we introduce a license plate structure classification process to identify its style that precedes character segmentation and recognition processes. We use a K-Nearest Neighbors algorithm with pre-training steps to recognize numbers and characters on multi-style license plates. To show feasibility of our multi-style license plate recognition system, we evaluate our system for multi-style license plates covering single line, double line, different backgrounds and character colors on Korean and the U.S. license plates. For the evaluation of Korean license plate recognition, we used a 50 minutes long input video that contains 138 vehicles of 6 different license plate styles, where each frame of the video is processed through a series of license plate recognition processes. From two experiments results, we show that various LP styles can be recognized under 50 ms processing time and with over 99% accuracy, and can be extended through additional learning and training steps.