CatBoost-Based Network Intrusion Detection on Imbalanced CIC-IDS-2018 Dataset

Alikhanov Jumabek(알리카노브 주마벡),SeungSam Yang(양승삼),YoungTae Noh(노영태) 한국통신학회 2021 韓國通信學會論文誌 Vol.46 No.12

다중 신경망 레이어에서 특징점을 선택하기 위한 전이 학습 기반의 AdaBoost 기법

주마백 ( Jumabek Alikhanov ),가명현 ( Myeong Hyeon Ga ),고승현 ( Seunghyun Ko ),조근식 ( Geun-sik Jo ) 한국정보처리학회 2016 한국정보처리학회 학술대회논문집 Vol.23 No.1

Convolutional Networks (ConvNets) are powerful models that learn hierarchies of visual features, which could also be used to obtain image representations for transfer learning. The basic pipeline for transfer learning is to first train a ConvNet on a large dataset (source task) and then use feed-forward units activation of the trained ConvNet as image representation for smaller datasets (target task). Our key contribution is to demonstrate superior performance of multiple ConvNet layer features over single ConvNet layer features. Combining multiple ConvNet layer features will result in more complex feature space with some features being repetitive. This requires some form of feature selection. We use AdaBoost with single stumps to implicitly select only distinct features that are useful towards classification from concatenated ConvNet features. Experimental results show that using multiple ConvNet layer activation features instead of single ConvNet layer features consistently will produce superior performance. Improvements becomes significant as we increase the distance between source task and the target task.

Unsupervised feature learning for classification

Mamur Abdullaev,Jumabek Alikhanov,Seunghyun Ko,Geun Sik Jo 한국컴퓨터정보학회 2016 한국컴퓨터정보학회 학술발표논문집 Vol.24 No.2

In computer vision especially in image processing, it has become popular to apply deep convolutional networks for supervised learning. Convolutional networks have shown a state of the art results in classification, object recognition, detection as well as semantic segmentation. However, supervised learning has two major disadvantages. One is it requires huge amount of labeled data to get high accuracy, the second one is to train so much data takes quite a bit long time. On the other hand, unsupervised learning can handle these problems more cheaper way. In this paper we show efficient way to learn features for classification in an unsupervised way. The network trained layer-wise, used backpropagation and our network learns features from unlabeled data. Our approach shows better results on Caltech-256 and STL-10 dataset.

Performance Comparison of Backbone Networks for Multi-Tasking in Self-Driving Operations

Shakhboz Abdigapporov,Shokhrukh Miraliev,Jumabek Alikhanov,Vijay Kakani,Hakil Kim 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11

In the era of big data, increased focus has been on improving neural network based Deep Learning models. This led to various classification networks which can be used as a backbone in multi-task learning. However, depending on the selected backbone, multi-tasking performance differs. While given backbone network shows better performance on a detection task, does not mean such performance generalizes in segmentation task as well. Detailed investigations should be conducted to achieve best inference speed-accuracy trade-off prior to implementing a single neural network, which handles multiple tasks. In this research, the performance comparison among EfficientNet, ResNet101, VGG16, ResNet50 and MobilenetV2 on the Berkeley Driving Dataset (BDD100K) for autonomous driving using multi-tasking architecture are provided. Backbones that offer best time-accuracy trade-off for multi-task learning are evaluated. Implemented architecture contains three most crucial tasks in self-driving operations, object detection, drivable area segmentation and lane detection. EfficientNet based model showed the best mAP on the object detection task, as well as on the segmentation tasks, extracting both the long and wide roads with accurate lane lines. The model with MobilenetV2 backbone however, demonstrates the fastest inference speed with relatively lower performance in all tasks.

Caltech 보행자 감지를위한 Scale-aware Faster R-CNN

바트후 ( Batkhuu Byambajav ),주마벡 ( Jumabek Alikhanov ),조근식 ( Geun-sik Jo ) 한국정보처리학회 2016 한국정보처리학회 학술대회논문집 Vol.23 No.2

We present real-time pedestrian detection that exploit accuracy of Faster R-CNN network. Faster R-CNN has shown to success at PASCAL VOC multi-object detection tasks, and their ability to operate on raw pixel input without the need to design special features is very engaging. Therefore, in this work we apply and adjust Faster R- CNN to single object detection, which is pedestrian detection. The drawback of Faster R-CNN is its failure when object size is small. Previously, small sized object problem was solved by Scale-aware Network. We incorporate Scale-aware Network to Faster R-CNN. This made our method Scale-aware Faster R-CNN (DF R-CNN) that is both fast and very accurate. We separated Faster R-CNN networks into two sub-network,that is one for large-size objects and another one for small-size objects. The resulting approach achieves a 28.3% average miss rate on the Caltech Pedestrian detection benchmark, which is competitive with the other best reported results.

Edge Device Deployment of Multi-Tasking Network self-Driving Operations

Shokhrukh Miraliev,Shakhboz Abdigapporov,Jumabek Alikhanov,Vijay Kakani,Hakil Kim 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10

A safe and robust autonomous driving system relies on accurate perception of the environment for application-oriented scenarios. This paper proposes deployment of the three most crucial tasks (i.e., object detection, drivable area segmentation and lane detection tasks) on embedded system for self-driving operations. To achieve this research objective, multi-tasking network is utilized with a simple encoder-decoder architecture. Comprehensive and extensive comparisons for two models based on different backbone networks are performed. All training experiments are performed on server while Nvidia Jetson Xavier NX is chosen as deployment device.

K-Means 클러스터링을 적용한 향상된 CS-RANSAC 알고리즘

고승현 ( Seunghyun Ko ),윤의녕 ( Ui-nyoung Yoon ),( Jumabek Alikhanov ),조근식 ( Geun-sik Jo ) 한국정보처리학회 2017 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.6 No.6

이미지를 기반으로 하는 증강현실 시스템에서 가상의 객체를 실제 영상에 저작할 때 생기는 이질감을 줄이기 위해서는 실제 영상에 저작된 가상객체의 방향과 위치에 대해 정확하게 추정을 해야 하며, 이때 호모그래피를 사용한다. 호모그래피를 추정하기 위해서는 SURF와 같은 특징점을 추출하고 추출된 특징점들을 통해 호모그래피 행렬을 추정한다. 호모그래피 행렬의 추정을 위해서 RANSAC 알고리즘이 주로 사용되고 있으며, 특히 RANSAC에 제약 조건 만족 문제(Constraint Satisfaction Problem)와 여기에 사용되는 제약조건을 동적으로 적용하여 속도와 정확도를 높인 DCS-RANSAC 알고리즘이 연구되었다. DCS-RANSAC 알고리즘에서 사용된 이미지 그룹 데이터는 수동적인 방법을 통해 직관적으로 분류되어 있지만 특징점 분포 패턴이 다양하지 않고, 이미지들을 정확하게 분류하기가 어려워서 이로 인해 알고리즘의 성능이 저하되는 경우가 있다. 따라서 본 논문에서는 K-means 클러스터링을 적용하여 이미지들을 자동으로 분류하고 각 이미지 그룹마다 각기 다른 제약조건을 적용하는 KCS-RANSAC 알고리즘을 제안한다. 제안하는 알고리즘은 머신러닝 기법인 K-means 클러스터링을 사용하여 전처리 단계에서 이미지를 특징점 분포 패턴에 따라 자동으로 분류하고, 분류된 이미지에 제약조건을 적용하여 알고리즘의 속도와 정확도를 향상시켰다. 실험결과 본 논문에서 제안하는 KCS-RANSAC이 DCS-RANSAC 알고리즘에 비해 수행시간이 약 15% 단축되었고, 오차율은 약 35% 줄어들었으며, 참정보 비율은 약 14% 증가되었다. Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it`s performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.

머신러닝 기법을 적용한 CS-RANSAC 알고리즘

고승현 ( Seunghyun Ko ),윤의녕 ( Ui-nyoung Yoon ),주마백 ( Jumabek Alikhanov ),조근식 ( Geun-sik Jo ) 한국정보처리학회 2016 한국정보처리학회 학술대회논문집 Vol.23 No.2

증강현실에서 영상과 증강된 콘텐츠 간의 이질감을 줄이기 위해서 정확한 호모그래피 행렬을 추정해야 하며, 정확한 호모그래피 행렬을 추정할때 RANSAC 알고리즘이 널리 사용된다. 그러나 RANSAC 알고리즘은 랜덤 샘플링 과정을 반복적으로 거치기 때문에 불필요한 연산 과정이 발생하고 이로 인해 알고리즘의 효율이 저하된다. 이러한 단점을 극복하기 위해 DCS-RANSAC 알고리즘이 제안 되었다. 제안된 DCS-RANSAC 알고리즘은 이미지를 특징점 분포 패턴에 따라 그룹으로 분류하고 각 그룹에 제약조건 문제를 적용하여 불필요한 연산 과정을 줄이고 정확도를 향상시킨 알고리즘이다. 그러나 DCS-RANSAC 알고리즘에서 사용된 이미지 그룹 데이터는 수동적인 방법을 통해 직관적으로 분류되어 있지만 특징점 분포 패턴이 다양하지 않아 분류시 정확도가 저하되는 경우가 있다. 위의 문제점을 해결하기 위해 본 논문에서는 머신러닝 기법을 통해 이미지들을 자동으로 분류하고 각 그룹마다 각기 다른 제약조건을 적용하는 MCS-RANSAC 알고리즘을 제안한다. 제안하는 알고리즘은 머신러닝 기법을 사용하여 전처리 단계에서 이미지를 분류하고 분류된 이미지에 제약조건을 적용시켜 알고리즘의 처리시간을 줄이고 정확도를 향상시켰다. 실험 결과 본 논문에서 제안하는 MCS-RANSAC은 DCS-RANSAC 알고리즘에 비해 수행시간이 약 6% 단축되었고 호모그래피 오차율은 약 15% 줄어들었으며 참정보 비율은 2.8% 증가한 것으로 확인되었다.