• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2669-2688
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• 2016

An efficient and effective unsupervised single moving object detection framework is presented in this paper. Given the sparsely labelled trajectory points, we adopt a coarse-to-fine strategy to detect and segment the foreground from the background. The superpixel level coarse segmentation reduces the complexity of subsequent processing, and the pixel level refinement improves the segmentation accuracy. A distance measurement is devised in the coarse segmentation stage to measure the similarities between generated superpixels, which can then be used for clustering. Moreover, a Quadmap is introduced to facilitate the refinement in the fine segmentation stage. According to the experiments, our algorithm is effective and efficient, and favorable results can be achieved compared with state-of-the-art methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1377-1382
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• 2012

In this paper, we propose a novel recognition method that extract source data from encoded signal that are displayed on FND mounted on home appliances. First of all, it find a candidate FND region from sequential difference images taken by smartphone and extract segment image using clustering RGB value. After that, it normalize segment images to correct a slant error and recognize each segments using a relative distance. Experiments show the robustness of the recognition algorithm on smartphone.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2550-2556
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• 2012

We proposed the energy-efficient routing algorithm ALPS (Ad hoc network Localized Positioning System) algorithm that is range-free based on the distance information. The routing coordinate method of ALPS algorithm consists of hierarchical cluster routing that provides immediately relative coordinate location using RSSI(Received Signal Strength Indication) information. Existing conventional DV-hop algorithm also to manage based on normalized the range free method, the proposed hierarchical cluster routing algorithm simulation results show more optimized energy consumption sustainable path routing technique to improve the network management.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.232-238
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• 2020

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.609-616
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• 2023

In a wireless network environment, since sensors are not always connected to power, the life of a battery, which is an energy source supplied to sensors, is limited. Therefore, various studies have been conducted to extend the network life, and a layer-based routing protocol, LEACH(: Low-energy Adaptive Clustering Hierarchy), has emerged for efficient energy use. However, the LEACH protocol, which transmits fused data directly to the sink node, has a limitation in that it consumes as much energy as the square of the transmission distance when transmitting data. To improve these limitations, this paper proposes an algorithm that can minimize the transmission distance with multi-hop transmission where cluster heads are chained between cluster heads through relative distance calculation from sink nodes in every round.

• Smart Media Journal
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• v.9 no.3
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• pp.122-129
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• 2020

The possibility that an asymptotic coronavirus-19 infected person around the world is not aware of his infection and can spread it to people around him is still a very important issue in that the public is not free from anxiety and fear over the spread of the epidemic. In this paper, the K-means clustering algorithm and deep learning-based crowd aggregation were proposed to determine the possibility of contact with confirmed cases of infectious diseases. As a result of 300 iterations of all input learning images, the PSNR value was 21.51, and the final MAE value for the entire data set was 67.984. This means the average absolute error between observations and the average absolute error of fewer than 4,000 people in each CCTV scene, including the calculation of the distance and infection rate from the confirmed patient and the surrounding persons, the net group of potential patient movements, and the prediction of the infection rate.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.319-330
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• 2017

Recently, various universities in Korea have started to work on strengthening their liberal arts and character education through the residential college (RC) system, carrying out various community programs for this purpose. However, because most programs are based on student-to-student relationships, problems can often arise within the community living environments. This paper proposes the roommate assignment algorithm in the context of a residential college, as to effectively achieve character education goals. The clustering algorithm we propose is based on the similarity hypothesis. As a result of the assignment, the degree of similarity (euclidean distance) between roommates was significantly higher than that assigned randomly. The algorithm developed in this study was applied to the data of the students living in the international campus of H University.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1273-1287
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• 2009

PEGASIS, a chain-based protocol, forms chains from sensor nodes so that each node transmits and receives from a neighbor. In this way, only one node (known as a HEAD) is selected from that chain to transmit to the sink. Although PEGASIS is able to balance the workload among all of the nodes by selecting the HEAD node in turn, a considerable amount of energy may be wasted when nodes which are far away from sink node act as the HEAD. In this study, DERP (Distance-based Energy-efficient Routing Protocol) is proposed to address this problem. DERP is a chain-based protocol that improves the greedy-algorithm in PEGASIS by taking into account the distance from the HEAD to the sink node. The main idea of DERP is to adopt a pre-HEAD (P-HD) to distribute the energy load evenly among sensor nodes. In addition, to scale DERP to a large network, it can be extended to a multi-hop clustering protocol by selecting a "relay node" according to the distance between the P-HD and SINK. Analysis and simulation studies of DERP show that it consumes up to 80% less energy, and has less of a transmission delay compared to PEGASIS.