• Journal of Positioning, Navigation, and Timing
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• v.7 no.2
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• pp.53-59
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• 2018

This paper proposes a pseudo-random beamforming technique for time-synchronized mobile base stations (BSs) for multi-cell downlink networks which have mobility. The base stations equipped with multi-antennas and mobile stations (MSs) are time-synchronized based on global positioning system (GPS) signals and generate a number of transmit beamforming matrix candidates according to the predetermined pseudo-random pattern. In addition, MSs generate receive beamforming vectors that correspond to the beam index number based on the minimum mean square error (MMSE) using transmit beamforming vectors that make up a number of transmit beamforming matrices and wireless channel matrices from BSs estimated via the reference signals (RS). Afterward, values of received signal-to-interference-plus-noise ratio (SINR) with regard to all transmit beamforming vectors are calculated, and the resulting values are then feedbacked to the BS of the same cells along with the beam index number. Each of the BSs calculates each of the sum-rates of the transmit beamforming matrix candidates based on the feedback information and then transmits the calculated results to the BS coordinator. After this, optimum transmit beamforming matrices, which can maximize a sum-rate of the entire cells, are selected at the BS coordinator and informed to the BSs. Finally, data signals are transmitted using them. The simulation results verified that a sum-rate of the entire cells was improved as the number of transmit beamforming matrix candidates increased. It was also found that if the received SINR values and beam index numbers are feedbacked opportunistically from each of the MSs to the BSs, not only nearly the same performance in sum-rate with that of applying existing feedback techniques could be achieved but also an amount of feedback was significantly reduced.

• 대한생식의학회:학술대회논문집
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• 2000.06a
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• pp.13-28
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• 2000

Human spermatozoa exhibit a capacity to generate ROS and initiate peroxidation of the unsaturated fatty acids in the sperm plasma membrane, which plays a key role in the etiology of male infertility. The short half-life and limited diffusion of these molecules is consistent with their physiologic role in key biological events such as acrosome reaction and hyperactivation. The intrinsic reactivity of these metabolites in peroxidative damage induced by ROS, particularly $H_2O_2$ and the superoxide anion, has been proposed as a major cause of defective sperm function in cases of male infertility. The number of antioxidants known to attack different stages of peroxidative damage is growing, and it will be of interest to compare alpha-tocopherol and ascorbic acid with these for their therapeutic potential in vitro and in vivo. Both spermatozoa and leukocytes generate ROS, although leukocytes produce much higher levels. The clinical significance of leukocyte presence in semen is controversial. Seminal plasma confers some protection against ROS damage because it contains enzymes that scavenge ROS, such as catalase and superoxide dismutase. A variety of defense mechanisms comprising a number of antioxidants can be employed to reduce or overcome oxidative stress caused by excessive ROS. Determination of male infertility etiology is important, as it will help us develop effective therapies to overcome excessive ROS generation. ROS can have both beneficial and detrimental effects on the spermatozoa and the balancing between the amounts of ROS produced and the amounts scavenged at any moment will determine whether a given sperm function will be promoted or jeopardized. Accurate assessment of ROS levels and, subsequently, OS is Vital, as this will help clinicians both elucidate the fertility status and identify the subgroups of patients that respond or do not respond to these therapeutic strategies. The overt commercial claims of antioxidant benefits and supplements for fertility purposes must be cautiously looked into, until proper multicentered clinical trials are studied. From the current data it appears that no Single adjuvant will be able to enhance the fertilizing capacity of sperm in infertile men, and a combination of the possible strategies that are not toxic at the dosage used would be a feasible approach.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.1
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• pp.7-16
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• 2004

The tool path needs to be determined in an efficient manner to generate the final NC (numerical control) code for efficient machining. This is particularly important in machining free form pockets with an arbitrary wall geometry on a three-axis CNC machine. Many CAD/CAM systems use linear interpolation to generate NC tool paths for curved surfaces. However, this needs to be modified to improve the smoothness of the machined bottom surface, reduce machining time and CL (cutter location) file size. Curved machining can be a solution to reduce these problems. The unified rough and finish cut algerian and the tool motion is graphically simulated. In this paper, a grid based 3D navigation algorithm for generating NC tool path data for both linear interpolation and a combination of linear and circular interpolation for three-axis CNC milling of general pockets with sculptured bottom surfaces is developed.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.409-420
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• 2012

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting down the number of policy decisions by agents. Macro-Actions were originally defined as combinations of the same primitive actions. Based on studies that showed the generation of Macro-Actions by learning, Macro-Actions are now thought to consist of diverse kinds of primitive actions. However an enormous amount of learning time and state space are required to generate Macro-Actions. To resolve these issues, we can apply insights from studies on the learning of tasks through Programming by Demonstration (PbD) to generate Macro-Actions that reduce the learning time and state space. In this paper, we propose a method to define and execute Macro-Actions. Macro-Actions are learned from a human subject via PbD and a policy is learned by reinforcement learning. In an experiment, the proposed method was applied to a car simulation to verify the scalability of the proposed method. Data was collected from the driving control of a human subject, and then the Macro-Actions that are required for running a car were generated. Furthermore, the policy that is necessary for driving on a track was learned. The acquisition of Macro-Actions by PbD reduced the driving time by about 16% compared to the case in which Macro-Actions were directly defined by a human subject. In addition, the learning time was also reduced by a faster convergence of the optimum policies.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.121-128
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• 2018

A Korean morphological analyzer adopts sequence-to-sequence (seq2seq) model, which can generate an output sequence of different length from an input. In general, a seq2seq based Korean morphological analyzer takes a syllable-unit based sequence as an input, and output a syllable-unit based sequence. Syllable-based morphological analysis has the advantage that unknown words can be easily handled, but has the disadvantages that morpheme-based information is ignored. In this paper, we propose a reranking model as a post-processor of seq2seq model that can improve the accuracy of morphological analysis. The seq2seq based morphological analyzer can generate K results by using a beam-search method. The reranking model exploits morpheme-unit embedding information as well as n-gram of morphemes in order to reorder K results. The experimental results show that the reranking model can improve 1.17% F1 score comparing with the original seq2seq model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.115-119
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• 2006

The channel is one of the precious and limited resources in wireless networks. There are many researches on the channel management. Recently, the optimization problem of guard channels has been an important issue. In this paper, we propose an intelligent channel management technique based on the neural networks. An SRN channel alteration model is developed to generate the learning data for the neural networks and the performance analysis of system. In the proposed technique, the neural network is trained to generate optimal guard channel number g, using backpropagation supervised learning algorithm. The optimal g is computed using the neural network and compared to the g computed by the SRN model. The numerical results show that the difference between the value of g by backpropagation and that value by SRN model is ignorable.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.9-20
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• 2010

The LIDAR data can be efficiently utilized for automatic reconstruction of 3D models of objects on the terrain and the terrain itself. In this paper, we attempted to generate simulated waveforms of FW (Full-Waveform) LIDAR (LIght Detection And Ranging). We performed the geometric modeling of the sensor and objects, and the radiometric modeling of the waveform intensity. First, we compute the origins and directions of the sub-beams by considering the divergence effects of a laser beam. We then searched for the locations at which the sub-beams intersected with the objects, such as ground, buildings and trees. Finally, we generate the individual waveforms of the reflected sub-beams and the waveform of the entire beam by summing the individual ones. With the experimental results, we confirmed the waveforms were reasonably generated, showing the characteristics of the surfaces the beam interacted with.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.1001-1008
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• 2019

A high-quality pseudorandom sequence generation is an important part of many cryptographic applications, including encryption protocols. Therefore, a pseudorandom number generator (PRNG) is an essential element for generating key sequences in a cryptosystem. A PRNG must effectively generate a large, high-quality random data stream. It is well known that the bitstreams output by the CA-based PRNG are more random than the bitstreams output by the LFSR-based PRNG. In this paper, we prove that the complemented CA derived from 90/150 maximum length cellular automata(MLCA) is a MLCA to design a PRNG that can generate more secure bitstreams and extend the key space in a secret key cryptosystem. Also we give a method for calculating the cell positions outputting a nonlinear sequence with maximum period in complemented MLCA derived from a 90/150 MLCA and a complement vector.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.623-629
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• 2018

In this paper, we propose a real-time obstacle detection and avoidance path generation algorithm for UAV. 2-D Lidar is used to detect obstacles, and the detected obstacle data is used to generate real-time histogram for local avoidance path and a 2-D SLAM map used for global avoidance path generation to the target point. The VFH algorithm for local avoidance path generation generates a real-time histogram of how much the obstacles are distributed in the vector direction and distance, and this histogram is used to generate the local avoidance path when detecting near fixed or dynamic obstacles. We propose an algorithm, called modified $RRT^*-Smart$, to overcome existing limitations. That generates global avoidance path to the target point by creating lower costs because nodes are checked whether or not straight path to a target point, and given arbitrary lengths and directionality to the target points when nodes are created. In this paper, we prove the efficient avoidance maneuvering through various simulation experiment environment by creating efficient avoidance paths.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.13-21
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• 2019

The content based on image retrieval makes use of features of information within image such as color, texture and share for Retrieval data. we present a novel approach for improving retrieval accuracy based on DCT Filter-Bank. First, we perform DCT on a given image, and generate a Filter-Bank using the DCT coefficients for each color channel. In this step, DC and the limited number of AC coefficients are used. Next, a feature vector is obtained from the histogram of the quantized DC coefficients. Then, AC coefficients in the Filter-Bank are separated into three main groups indicating horizontal, vertical, and diagonal edge directions, respectively, according to their spatial-frequency properties. Each directional group creates its histogram after employing Otsu binarization technique. Finally, we project each histogram on the horizontal and vertical axes, and generate a feature vector for each group. The computed DC and AC feature vectors bins are concatenated, and it is used in the similarity checking procedure. We experimented using 1,000 databases, and as a result, this approach outperformed the old retrieval method which used color information.