• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1538-1543
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• 2018

For n pairs of points (a,b) on a circle, the line segment to connect two points is called a chord. These chords define a new graph G. Each chord corresponds to a vertex of G, and if two chords intersect, the two vertices corresponding to them are connected by an edge. This makes a graph, called by a circle graph. In this paper, we deal with the problem to find the connected components of a circle graph. The connected component of a graph G is a maximal subgraph H such that any two vertices in H can be connected by a path. When the adjacent matrix of G is given, the problem to find them can be solved by either the depth-first search or the breadth-first search. But when only the information for the chords is given as an input, it takes ${\Omega}(n^2)$ time to obtain the adjacent matrix. In this paper, we do not make the adjacent matrix and develop an $O(n{\log}^2n)$ algorithm for the problem.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.13-20
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• 2019

Recently, object detection technology based on CNN has been actively studied. Object detection technology is used as an important technology in autonomous vehicles, intelligent image analysis, and so on. In this paper, we propose a rotation change robust object detection system by applying MI-FL (Moment Invariant-Feature Layer) to SSD (Single Shot Multibox Detector) which is one of CNN-based object detectors. First, the features of the input image are extracted based on the VGG network. Then, a total of six feature layers are applied to generate bounding boxes by predicting the location and type of object. We then use the NMS algorithm to get the bounding box that is the most likely object. Once an object bounding box has been determined, the invariant moment feature of the corresponding region is extracted using MI-FL, and stored and learned in advance. In the detection process, it is possible to detect the rotated image more robust than the conventional method by using the previously stored moment invariant feature information. The performance improvement of about 4 ~ 5% was confirmed by comparing SSD with existing SSD and MI-FL.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.37-53
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• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.895-904
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• 2007

This paper proposes a robust sub-optimal smart antenna in Code Division Multiple Access (CDMA) basestation. It makes use of the property of the Least Mean Square (LMS) algorithm and the Conjugate Gradient Method (CGM) algorithm for beamforming processes. The weight update takes place at symbol level which follows the PN correlators of receiver module under the assumption that the post correlation desired signal power is far larger than the power of each of the interfering signals. The proposed algorithm is simple and has as low computational load as five times of the number of antenna elements(O(5N)) as a whole per each snapshot. The output Signal to Interference plus Noise Ratio (SINR) of the proposed smart antenna system when the weight vector reaches the steady state has been examined. It has been observed in computer simulations that proposed beamforming algorithm improves the SINR significantly compared to the single antenna case. The convergence property of the weight vector has also been investigated to show that the proposed hybrid algorithm performs better than CGM and LMS during the initial stage of the weight update iteration. The Bit Error Rate (BER) characteristics of the proposed array has also been shown as the processor input Signal to Noise Ratio (SNR) varies.

• Journal of KIISE:Information Networking
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• v.35 no.3
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• pp.235-242
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• 2008

To improve video quality and coding efficiency, H.264/AVC adopted an adaptive rate control. But this method has a problem as it cannot predict an accurate quantization parameter(QP) for the first frame. The first QP is decided among four constant values by using encoder input parameters. It does not consider encoding bits, results in significant fluctuation of the image quality and decreases the average quality of the whole coded sequence. In this paper, we propose a new algorithm for the first frame QP decision in the H.264/AVC encoder. The QP is decided by the existing algorithm and the first frame is encoded. According to the encoded bits, the new initial QP is decided. We can predict optimal value because there is a linear relationship between encoded bits and the new initial QP. Next, we re-encode the first frame using the new initial QP. Experimental results show that the proposed algorithm not only achieves better quality than the state of the art algorithm, but also adopts a rate control forthe sequence that was impossible with the existing algorithm. By reducing fluctuation, subjective quality also improved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2291-2297
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• 2015

This paper presents a vehicle detection system using the video as a input image what has moving of vehicles.. Input image has constraints. it has to get fixed view and downward view obliquely from top of the road. Road detection is required to use only the road area in the input image. In introduction, we suggest the experiment result and the critical point of motion history image extraction method, SIFT(Scale_Invariant Feature Transform) algorithm and histogram analysis to detect vehicles. To solve these problem, we propose using applied Gaussian Mixture Model(GMM) that is the Vehicle Detection GMM(VDGMM). In addition, we optimize VDGMM to detect vehicles more and named VDGMM 2.0. In result of experiment, each precision, recall and F1 rate is 9%, 53%, 15% for GMM without road detection and 85%, 77%, 80% for VDGMM2.0 with road detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6B
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• pp.638-644
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• 2002

In this paper, we implemented a three dimensional image display system using stereogram and holographic optical memory techniques which can store many images and reconstruct them automatically. In this system, to store and reconstruct stereo images, incident angle of reference beam must be controlled in real time, so we used BPH(binary phase hologram) and LCD(liquid crystal display) for controlling reference beam. The reference beams are acquired by Fourier transform of BPHs which designed with SA(simulated annealing)algorithm, and the BPHs are represented on the LCD with the 0.05 seconds time interval using application software for reconstructing the stereo images. And input images are represented on the LCD without polarizer/analyzer for maintaining uniform beam intensities regardless of the brightness of input images. The input images and BPHs are edited using application software(Photoshop) with having the same recording scheduled time interval in storing. The reconstructed stereo images are acquired by capturing the output images with CCD camera at the behind of the analyzer which transforms phase information into brightness information of images. In output plane, we used a LCD shutter that is synchronized to a monitor that display alternate left and right eye images for depth perception. We demonstrated optical experiment which store and reconstruct four stereo images in BaTiO$_3$ repeatedly using the proposed holographic optical memory techniques.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1656-1666
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• 1999

The more complex and large digital circuits become, the more important delay test becomes which guarantees that circuits operate in time. In this paper, the proposed algorithm is developed, which enable the fast indirect implication for efficient test pattern generation in sequential circuits of standard scan environment. Static learning algorithm enables application of a new implication value using contrapositive proposition. The static learning procedure found structurally, analyzes the gate structure in the preprocessing phase and store the information of learning occurrence so that it can be used in the test pattern generation procedure if it satisfies the implication condition. If there exists a signal line which include all paths from some particular primary inputs, it is a partitioning point. If paths passing that point have the same partial path from primary input to the signal or from the signal to primary output, they will need the same primary input values which separated by the partitioning point. In this paper test pattern generation can be more effective by using this partitioning technique. Finally, an efficient delay fault test pattern generator using indirect implication is developed and the effectiveness of these algorithms is demonstrated by experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.601-606
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• 1998

In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.