• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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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.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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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.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.55-61
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• 2009

In order to observe the impact of a type of vehicles for traffic flows and pavement, vehicle classifications is conducted. Korean Ministry of Land, Transport and Maritime Affairs provides 12-type vehicle classifications on National expressways, National highways, and Provincial roads. Current AVC (Automatic Vehicle Classification) devices decide vehicle types comparing measurements of vehicle lengths, wheelbases, overhangs etc. to a reference table including those of all types of models. This study developed an algorithm for macroscopic vehicle classification which is less sensitive to tuning sensors and updating the reference table. For those characteristics, trend lines in vehicle lengths and wheelbases are employed. To assess the algorithm developed, vehicle lengths and wheelbases were collected from an AVC device. In this experiment, this algorithm showed the accuracy of 88.2 % compared to true values obtained from video replaying. Our efforts in this study are expected to contribute to developing devices for macroscopic vehicle classification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.55-67
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• 2022

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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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.

• Journal of ILASS-Korea
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• v.22 no.3
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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.

• Journal of the Society of Disaster Information
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• v.13 no.2
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• pp.199-212
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• 2017

Rapid roadway snow removal is significantly important due to difficult occurrence estimation of heavy snowfall disasters by global warming and climate change. Local governments of S. Korea have snow removal equipments and vehicles based on past experiences without considering snowfall and roadway characteristics. The objective of this research is to develop the demand estimation procedure for snow removal equipments and vehicles based on regional snowfall and roadway characteristics. This research first classifies regional snowfall characteristics using KMO's ten-year snowfall data. Second, roadway snow removal length is computed for local governments. Real possession data is compared with demand estimation of snow removal equipments & vehicles for each local government with roadway snow removal scenarios. Finally, required demands of snow removal equipments & vehicles are predicted by concerning regional snowfall amount and required snow removal hours. Results from this research are used for developing heavy snowfall disaster management policies for optimal demands and snow removal routes of 229 local governments.

• KIEAE Journal
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• v.9 no.1
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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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• v.13 no.5
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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.