• 한국정밀공학회지
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• 제14권7호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2298-2315
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• 2024

This paper introduces a method aimed at diagnosing the presence or absence of lesions by detecting anomalies in Chest X-ray images. The proposed approach is based on the PatchCore anomaly detection method, which extracts a feature vector containing location information of an image patch from normal image data and calculates the anomaly distance from the normal vector. However, applying PatchCore directly to medical image processing presents challenges due to the possibility of diseases occurring only in specific organs and the presence of image noise unrelated to lesions. In this study, we present an image alignment method that utilizes affine transformation parameter prediction to standardize already captured X-ray images into a specific composition. Additionally, we introduce a region-specific abnormality detection method that requires affine-transformed chest X-ray images. Furthermore, we propose a method to enhance application efficiency and performance through feature map hard masking. The experimental results demonstrate that our proposed approach achieved a maximum AUROC (Area Under the Receiver Operating Characteristic) of 0.774. Compared to a previous study conducted on the same dataset, our method shows a 6.9% higher performance and improved accuracy.

• Journal of Cardiovascular Imaging
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• 제31권1호
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• pp.41-48
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• 2023

BACKGROUND: The function of left atrium (LA) is difficult to assess because of its ventricle-dependent, dynamic movement. The aim of this study was to assess LA function using velocity vector imaging (VVI) and compare LA function in patients with hypertrophic cardiomyopathy (HCMP) and left ventricular hypertrophy (LVH) with normal controls. METHODS: Fourteen patients with HCMP (72% male, mean age of 52.6 ± 9.8), 15 hypertensive patients with LVH (88% male, mean age of 54.0 ± 15.3), and 10 age-matched controls (83% male, mean age of 50.0 ± 4.6) were prospectively studied. Echocardiographic images of the LA were analyzed with VVI, and strain rate (SR) was compared among the 3 groups. RESULTS: The e' velocity (7.7 ± 1.1; 5.1 ± 0.8; 4.5 ± 1.3 cm/sec, p = 0.013), E/e' (6.8 ± 1.6; 12.4 ± 3.3; 14.7 ± 4.2, p = 0.035), and late diastolic SR at mid LA (-1.65 ± 0.51; -0.97 ± 0.55; -0.82 ± 0.32, p = 0.002) were significantly different among the groups (normal; LVH; HCMP, respectively). The e' velocity, E/e', and late diastolic SR at mid LA were significantly different between normal and LVH (p = 0.001; 0.022; 0.018), whereas LA size was similar between normal and LVH (p = 0.592). The mean late diastolic peak SR of mid LA was significantly correlated with indices of diastolic function (E/e', e', and LA size). CONCLUSIONS: The SR is a useful tool for detailed evaluation of LA function, especially early dysfunction of LA in groups with normal LA size.

• International Journal of Computer Science & Network Security
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• 제21권6호
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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 Power Electronics
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• 제18권3호
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• pp.702-713
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• 2018

Power loss reduction and total harmonic distortion(THD) minimization are two important goals of improving three-level inverters. In this paper, an optimized pulse width modulation (PWM) strategy that can reduce switching losses and balance the neutral point with an optional THD of three-level neutral-point-clamped inverters is proposed. An analysis of the two-level discontinuous PWM (DPWM) strategy indicates that the optimal goal of the proposed PWM strategy is to reduce switching losses to a minimum without increasing the THD compared to that of traditional SVPWMs. Thus, the analysis of the two-level DPWM strategy is introduced. Through the rational allocation of the zero vector, only two-phase switching devices are active in each sector, and their switching losses can be reduced by one-third compared with those of traditional PWM strategies. A detailed analysis of the impact of small vectors, which correspond to different zero vectors, on the neutral-point potential is conducted, and a hysteresis control method is proposed to balance the neutral point. This method is simple, does not judge the direction of midpoint currents, and can adjust the switching times of devices and the fluctuation of the neutral-point potential by changing the hysteresis loop width. Simulation and experimental results prove the effectiveness and feasibility of the proposed strategy.

• 로봇학회논문지
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• 제9권1호
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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.

• 한국멀티미디어학회논문지
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• 제5권5호
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• pp.552-561
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• 2002

본 논문은 쾌속조형(rapid prototyping) 시스템에서 사용되며 3D 기하학적 형상을 가지는 STL 파일에 워터마크를 삽입하고 추출하는 방법에 관한 연구이다. 제안된 알고리즘은 3D 형상의 왜곡이 없도록 하기위해, 패싯의 법선 영역과 꼭지점 영역에 워터마크를 삽입한다. 워터마크 비트들은 법선의 위치와 꼭지점의 순서 정보를 이용하여 삽입된다 제안된 알고리즘은 패싯의 저장 순서에 대한 종속성이 없으며, 워터마크의 비가시성 (invisibility)을 충족한다. 제안된 알고리즘으로 3D 기하학적 형상에 워터마크를 삽입하고 추출하는 실험 결과들은 STL로 표현된 3D원형상에 영향을 주지 않고 워터마크의 삽입과 추출이 가능함을 보여준다.

• Journal of information and communication convergence engineering
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• 제20권3호
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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%.

• 한국정밀공학회지
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• 제23권7호
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• pp.187-193
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• 2006

This paper introduces and illustrates the results of a new method for offsetting the triangular mesh generated from multiple surfaces. The meshes generated from each surface are separated each other and normal directions are different. The face normal vectors are flipped to upward and the lower faces covered by upper faces are deleted. The virtual normal vectors are introduced and used to of feet boundary. It was shown that new method is better than previous methods in offsetting the triangular meshes generated from multiple surfaces. The introduced offset method was applied for 3-axis tool path generation system and tested by NC machining.

• 한국연초학회지
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• 제23권2호
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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.