• Journal of Korea Game Society
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• v.9 no.2
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• pp.135-142
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• 2009

Mesh denoising is a method to remove noise applying various filters. However, those methods usually spend much time since filtering is performed on CPU. Because GPU is specialized for floating point operations and faster than CPU, real-time processing for complex operations is possible. Especially mesh denoising is adequate for GPU parallel processing since it repeats the same operations for vertices or triangles. In this paper, we propose mesh denoising algorithm based on bilateral filtering using GPU parallel processing to reduce processing time. It finds neighbor triangles of each vertex for applying bilateral filter, and computes its normal vector. Then it performs bilateral filtering to estimate new vertex position and to update its normal vector.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.7
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• pp.144-153
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• 1997

The inverse kinematics problem of a reclaimer which excavates and transports raw materials in a raw yard is investigated. Because of the geometric feature of the equipment in which scooping buckets are attached around the rotating disk, kinematic redundancy occurs in determining joint variable. Link coordinates are introduced following the Denavit-Hartenbery representation. For a given excavation point the forward kinematics yields 3 equations, however the number of involved joint variables in the equations is four. It is shown that the rotating disk at the end of the boom provides an extra passive degree of freedom. Two approaches are investigated in obtaining inverse kinematics solutions. The first method pre-assigns the height of excavation point which can be determined through path planning. A closed form solution is obtained for the first approach. The second method exploits the orthogonality between the normal vector at the excavation point and the z axis of the end-effector coordinate system. The geometry near the reclaiming point has been approximated as a plane, and the plane equation has been obtained by the least square method considering 8 adjacent points near the point. A closed form solution is not found for the second approach, however a linear approximate solution is provided.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.552-561
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• 2002

In this paper, a new watermarking algorithm for STL files which contains 3D geometric information as triangular facets is proposed. STL files are widely used in rapid prototyping industry as a standard interchange format. The proposed algorithm inserts multi-bit watermark information into the surface normal vector and vertex description area of STL file without distorting the original 3D geometric information. According to the watermark bits, the position of normal vector and the direction of vertex sequence are modulated. The proposed algorithm is robust to the attack of changing the order of the triangular meshes. In addition, the invisibility requirement is also satisfied. Experiment results show that the proposed algorithm can encode and decode watermark bits into the various STL files without any distortion of 3D shape.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.157-164
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• 2003

Volume graphics have received a lot of attention as a medical image analysis tool nowadays. In the visualization based on volume graphics, there is a process called voxelization which transforms the geometrically defined objects into the volumetric objects. It enables us to volume render the geometrically defined data with sampling data. This paper suggests a voxeliration method using the cutting surfaces of cubes, implements the method on a PC, and evaluates it with simple geometric modeling data to explore propriety of the method. This method features the ability of calculating the exact normal vector from a voxel, having no hole among voxels, having multi-resolution representation.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.1
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• pp.35-45
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• 2004

In explosively increasing internet environments, information security is one of the most important consideration. Nowadays, various security solutions are used as such problems countermeasure; IDS, Firewall and VPN. However, basically internet has much vulnerability of protocol itself. Specially, it is possible to establish a covert channel using TCP/IP header fields such as identification, sequence number, acknowledge number, timestamp and so on. In this Paper, we focus cm the covert channels using identification field of IP header and the sequence number field of TCP header. To detect such covert channels, we used Support Vector Machine which has excellent performance in pattern classification problems. Our experiments showed that proposed method could discern the abnormal cases(including covert channels) from normal TCP/IP traffic using Support Vector Machine.

• Journal of the Korean Society of Tobacco Science
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• v.23 no.2
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• pp.109-114
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• 2001

A CAB cDNA vector(pKGCAB), encoding the light harvesting chlorophyll a/b binding protein in Korean ginseng (Panax ginseng C. A. Meyer), was constructed with the CaMV35S promoter of plant expression vector. The chimeric vector was transformed into tobacco(Nicotiana tabacum cv. NC 82) using Agrobacterium tumefaciens LBA 4404 strain, and the transgenic tobacco plant CAB-TP2 was selected. Photosynthetic rates of the CAB-TP2 plant at before-flowering stage were increased about 20% under low irradiance conditions of quantum 100 and 500 $\mu$mol.m$^{-2}$ s$^{-1}$ , however, the rates were similar to those of NC 82 under quantum 1000 and 2000 $\mu$mol.m$^{-2}$ s$^{-1}$ conditions. The plants were germinating under low- or normal irradiance condition and the quantum yield of photosystem III were measured. The differences of the Fv/Em values between conditions were 0.07 and 0.01 in NC 82 and CAB-TP2, respectively. The mature leaves in the position 8-10 of the CAB-TP2 at before-flowering stage revealed l0% higher Fv/Fm values in range of 0.759 to 0.781 and 40% more chlorophyll contents of 70-93mg/$m\ell$ than those of normal NC 82. These data suggest the possibility that the increase in photosynthetic activity of leaves under low light intensity in the canopy of CAB-TP2 transgenic tobacco might lead to increase the quality of lower tobacco leaves.

• Bulletin of the Korean Chemical Society
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• v.31 no.10
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• pp.2841-2848
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• 2010

Quasiclassical trajectory (QCT) method has been used to investigate stereodynamics information of the reaction $O(^1D)+H_2{\rightarrow}\;OH$+H on the DK (Dobbyn and Knowles) potential energy surface (PES) at a collision energy of 23.06 kcal/mol, with the initial quantum state of reactant $H_2$ being set for v = 0 (vibration quantum number) and j = 0-5 (rotation quantum number). The PDDCSs (polarization dependent differential cross sections) and the distributions of P($\theta_r$), P($\phi_r$), P($\theta_r$, $\phi_r$) have been presented in this work. The results demonstrate that the products are both forward and backward scattered. As j increases, the backward scattering becomes weaker while the forward scattering becomes slightly stronger. The distribution of P($\theta_r$) indicates that the product rotational angular momentum j' tends to align along the direction perpendicular to the reagent relative velocity vector k, but this kind of product alignment is found to be rather insensitive to j. Furthermore, the distribution of P($\phi_r$) indicates that the rotational angular momentum vector of the OH product is preferentially oriented along the positive direction of y-axis, and such product orientation becomes stronger with increasing j.