• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.11-20
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• 2018

Applications about systems integration of sensors and virtual environments have been developed increasingly. Accordingly, there is a need for the ability to represent, control, and manage physical sensors directly in a 3D virtual environment. In this research, a method of representing physical sensor devices in a 3D virtual environment has been defined using mixed and augmented reality, including virtual and real worlds, where sensors and virtual objects co-exist. The research is intended to control and manage various physical sensors through data sharing and interchange between heterogeneous computing environments. In order to achieve this, general sensor types have been classified, and a sensor based 3D scene graph for representing the functions of sensors has been defined. In addition, a sensor data model has been defined using the scene graph. Finally, a sensor 3D viewer has been implemented based on the scene graph and the data model so as to simulate the functions of sensors in indoor and outdoor 3D environments.

• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.455-466
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• 2006

In this paper, 3D objects is created from the real scene that is used by an active sensor, which gets depth and RGB information. To get the depth information, this paper uses the $Zcam^{TM}$ camera which has built-in an active sensor module. <중략> Thirdly, calibrate the detailed parameters and create 3D mesh model from the depth information, then connect the neighborhood points for the perfect 3D mesh model. Finally, the value of color image data is applied to the mesh model, then carries out mapping processing to create 3D object. Experimentally, it has shown that creating 3D objects using the data from the camera with active sensors is possible. Also, this method is easier and more useful than the using 3D range scanner.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.525-534
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• 2023

Gas leak detection system is a key to minimize the loss of life due to the explosiveness and toxicity of gas. Most of the leak detection systems detect by gas sensors or thermal imaging cameras. To improve the performance of gas leak detection system using single-modal methods, the paper propose multimodal approach to gas sensor data and thermal camera data in developing a gas type identification model. MultimodalGasData, a multimodal open-dataset, is used to compare the performance of the four models developed through multimodal approach to gas sensors and thermal cameras with existing models. As a result, 1D CNN and GasNet models show the highest performance of 96.3% and 96.4%. The performance of the combined early fusion model of 1D CNN and GasNet reached 99.3%, 3.3% higher than the existing model. We hoped that further damage caused by gas leaks can be minimized through the gas leak detection system proposed in the study.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.42-49
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• 2010

현재 대규모 실내공간이 증가하고 있고, 이에 따른 화재 등의 사고가 발생함에 따라 화재대피시스템과 같은 실시간 실내 응용에 대한 관심이 증대되고 있다. 그러나 학문적 연구 또는 상업용 시스템에서 사용되는 화재모델이내 보행자 모델은 대부분 2D 기반의 CAD와 같은 파일구조를 기반으로 하고 있으며 가상데이터를 이용한 시뮬레이션에 초점을 두고 있다. 따라서 이들을 실내 센서 등을 이용한 실시간 시스템으로 구축하기 위해서는 몇 가지 문제점들이 해결되어야 한다. 우선, 실내공간의 의미 있는 관계정보를 포함하는 위상적인 3D 모델이 필요하다. 또한, 실내 센서들에 의해 감지된 보행자의 이동을 저장하고, 저장된 데이터를 이용하여 실시간 안내를 위해서는 실내데이터 구축에 공간 DBMS를 이용해야 한다. 본 연구에서는 실내보행자 모델에서 두 가지 개선점을 제시하고자 한다. 첫째는, 간단한 3D 실내 모델구축과 공간 DBMS와 연계된 보행자 시뮬레이터를 구현하는 과정을 제시한다. 둘째는, 보행자의 가시성(visibility)에 대한 영향이 반영된, 개선된 floor field 모델을 제안한다. 이와 같은 과정을 캠퍼스 건물에 적용하고 시뮬레이션을 수행하는 과정을 예시하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.1
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• pp.1-9
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• 2007

3D data are in great demand for pedestrian navigation recently. For pedestrian navigation, we needs to reconstruct 3D model in detail from people's eye. In order to present spatial features in detail for pedestrian navigation, it is indispensable to develop 3D model not only in outdoor environment but also in indoor environment such as underground shopping complex. However, it is very difficult to acquire 3D data efficiently by mobile mapping without GPS. In this research, 3D shape was acquired by Laser scanner, and texture by CCD(Charge Coupled Device) sensor. Continuous changes position and attitude of sensors were measured by IMU(Inertial Measurement Unit). Moreover, IMU was corrected by relative orientation of CCD images without GPS(Global Positioning System). In conclusion, Reliable, quick, and handy method for acquiring 3D data for indoor environment is proposed by a combination of a digital camera and a laser scanner with IMU.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1453-1461
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• 2022

The accuracy of peripheral object recognition algorithms using 3D data sensors such as LiDAR in autonomous vehicles has been increasing through many studies, but this requires high performance hardware and complex structures. This object recognition algorithm acts as a large load on the main processor of an autonomous vehicle that requires performing and managing many processors while driving. To reduce this load and simultaneously exploit the advantages of 3D sensor data, we propose 2D data-based recognition using the ROI generated by extracting physical properties from 3D sensor data. In the environment where the brightness value was reduced by 50% in the basic image, it showed 5.3% higher accuracy and 28.57% lower performance time than the existing 2D-based model. Instead of having a 2.46 percent lower accuracy than the 3D-based model in the base image, it has a 6.25 percent reduction in performance time.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.298-300
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• 2023

자율주행 및 robot navigation 시스템에서 물체 인식 성능향상을 위해 대부분 MSF(Multi-Sensor Fusion) 기반 설계를 한다. 따라서 각 센서로부터 들어온 정보를 정합하는 것은 정확한 MSF 알고리즘을 위한 필요조건이다. 다양한 선행 연구에서 2D 데이터에 대한 공격을 진행했다. 자율주행에서는 3D 데이터를 다루어야 하므로 선행 연구에서 하지 않았던 3D 데이터 공격을 진행했다. 본 연구에서는 스케일링 공격 기반 카메라-라이다 센서 간 정합 모델의 정확도를 저하시키는 공격 방법을 제안한다. 제안 방법은 입력 라이다의 포인트 클라우드에 스케일링 공격을 적용하여 다운스케일링 단계에서 공격하고자 한다. 실험 결과, 입력 데이터에 공격하였을 때 공격 전보다 평균제곱 이동오류는 56% 이상, 평균 사원수 각도 오류는 98% 이상 증가했음을 보였다. 다운스케일링 크기 별, 알고리즘별 공격을 적용했을 때, 10×20 크기로 다운스케일링 하고 lanczos4 알고리즘을 적용했을 때 가장 효과적으로 공격할 수 있음을 확인했다.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.329-332
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• 2023

3D Object Detection using deep neural network has been developed a lot for obstacle detection in autonomous vehicles because it can recognize not only the class of target object but also the distance from the object. But in the case of 3D Object Detection models, the detection performance for distant objects is lower than that for nearby objects, which is a critical issue for autonomous vehicles. In this paper, we introduce a technique that increases the performance of 3D object detection models, particularly in recognizing distant objects, by generating virtual 3D vehicle data and adding it to the dataset used for model training. We used a spherical point tracing method that leverages the characteristics of 3D LiDAR sensor data to create virtual vehicles that closely resemble real ones, and we demonstrated the validity of the virtual data by using it to improve recognition performance for objects at all distances in model training.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.157-166
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• 2009

There is a growing interest in ubiquitous-related research and applications. Among them, GPS-based LBS have been developed and used actively. Recently, with the increase of large size buildings and disastrous events, indoor spaces are getting attention and related research activities are being carried out. Core technologies regarding indoor applications may include 3D indoor data modeling and localization sensor techniques that can integrate with indoor data. However, these technologies have not been standardized and established enough to be applied to indoor implementation. Thus, in this paper, we propose a method to build a relatively simple 3D indoor data modeling technique that can be applied to indoor location based applications. The proposed model takes the form of 2D-based multi-layered structure and has capability for 2D and 3D visualization. We tested three prototype applications using the proposed model; CA(cellular automata)-based 3D evacuation simulation, network-based routing, and indoor moving objects tracking using a stereo camera.

• 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.