• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09b
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• pp.704-705
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• 2006

Discrete element analysis is used to map various log-normal particle size distributions into measures of the in-sphere pore size distribution. Combinations evaluated range from monosized spheres to include bimodal mixtures and various log-normal distributions. The latter proves most useful in providing a mapping of one distribution into the other (knowing the particle size distribution we want to predict the pore size distribution). Such metrics show predictions where the presence of large pores is anticipated that need to be avoided to ensure high sintered properties.

• Kyungpook Mathematical Journal
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• v.48 no.1
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• pp.109-121
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• 2008

We determine maximal space-like hypersurfaces which are the images of subbundles of the normal bundle of m-dimensional totally geodesic space-like submanifolds of an (m + 2)-dimensional Lorentzian space form $\tilde{M}_1^{m+2}$(c) under the normal exponential map. Then we construct examples of maximal space-like hypersurfaces of $\tilde{M}_1^{m+2}$(c).

• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.1-5
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• 2006

This paper introduces the surface detailed painterly rendering using heightfield map. To do this, we implement painterly rendering using normal mapping and displacement mapping method by heightfield map. The suggested method can apply to the 3D visualization program and game engine for representing the surface detailed realtime rendering using GPU Programming.

• Journal of Magnetics
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• v.17 no.3
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• pp.219-224
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• 2012

In the present study, changes in signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) of the diffusion-weighted images in the normal livers were investigated using changes in b values in 1.5 T MR (magnetic resonance) instruments. Respective diffusion-weighted images and ADC map images were obtained from 20 healthy individuals by increasing b values from 50 to 400 and 800 $s/mm^2$ using 1.5T MR scanner between January 2011 and November 2011. At each ADC map image obtained at each b value, ADCs in the right hepatic lobe, spleen and kidney were measured. As a result, ADCs of the right hepatic lobe, spleen and kidney have gradually decreased in the diffusion-weighted images in accordance with the reduced b value. This outcome may be used as preliminary data for applications to various abdominal diseases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.1064-1079
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• 2019

Information security has become increasingly important with the rapid development of mobile devices and internet. An efficient encryption system is a key to this end. In this paper, we propose an image encryption method based on the cross diffusion of two chaotic maps. We use two chaotic sequences, namely the Logistic map and the Chebyshev map, for key generation which has larger security key space than single one. Moreover, we use these two sequences for further image encryption diffusion which decreases the correlation of neighboring pixels significantly. We conduct extensive experiments on several well-known images like Lena, Baboon, Koala, etc. Experimental results show that our algorithm has the characteristics of large key space, fast, robust to statistic attack, etc.

• Bulletin of the Korean Mathematical Society
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• v.58 no.4
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• pp.973-981
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• 2021

Let R be a commutative ring with identity 1, n ≥ 3, and let 𝒯_n(R) be the linear Lie algebra of all upper triangular n × n matrices over R. A linear map 𝜑 on 𝒯_n(R) is called to be strong commutativity preserving if [𝜑(x), 𝜑(y)] = [x, y] for any x, y ∈ 𝒯_n(R). We show that an invertible linear map 𝜑 preserves strong commutativity on 𝒯_n(R) if and only if it is a composition of an idempotent scalar multiplication, an extremal inner automorphism and a linear map induced by a linear function on 𝒯_n(R).

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.290-296
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• 2022

Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).

• Journal of Institute of Convergence Technology
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• v.12 no.1
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• pp.1-5
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• 2022

This paper describes an algorithm for generating a terrain using a noise function and a height map as one of the procedural terrain generation methods. The polygon mesh data structure to represent the generated terrain concisely and render it is also described. The Perlin noise function is used as the noise technique for terrain mesh, and the height data of the terrain is generated by combining the four noise waves. In addition, the terrain height information can be also obtained from actual image data taken from the satellite. The algorithm presented in this paper generates the geometry part of the polygon topography from the height data obtained, and generated a material for texture mapping with two textures, that is, a diffuse texture and a normal texture. The validity of the terrain method proposed in this paper is verified through application examples, and its possibility can be confirmed through performance verification.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.587-598
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• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.