• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.22-25
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• 2005

The 10 Year Implementation Plan for a Global Earth Observation System of Systems (GEOSS), which was endorsed at the Third Earth Observation Summit in Brussels in February, 2005, emphasizes the importance of data management facilities for diverse and large-volume Earth Observation data from inhomogeneous information sources. A three year research plan for addressing this key target of GEOSS has just approved as the first step by the Japanese government. The goals of this research are, (1) to develop a data management core system consisting of data integration and information fusion functions and interoperability and information service functions; (2) to establish data and information flows between data providers and users; (3) to promote application studies of data integration and information fusion, especially in the fields of weather forecasting, flood forecasting, agricultural management, and climate variability and changes. The research group involves leading scientists on information science and technology, who have been developing giant data archive servers, storage area networks, metadata models, ontology for the earth observations. They are closely cooperating with scientists on earth sciences, water resources management, and agriculture, and establishing an effective collaborative research framework.

• Journal of Life Science
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• v.7 no.4
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• pp.282-288
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• 1997

To obtain a basic information on the development of Genus Viola, ultrastructure and electrofusion process between the two protoplasts from wild Viola callus cells and pansy mesophyll cells were observed with a scanning electron microscopy(SEM) and transmission electron microscopy(TEM). In the ultrastructural observation of wild viola callus protoplasts and pansy mesophyll protoplasts using SEM, their cell walls were removed completely. A knob-like formation was observed on the enlarge surface of viola callus protoplasts. On the surface of pansy mesophyll protoplasts net-like chloroplasts were observed. In SEM observation of pansy mesophyll protoplasts, chloroplasts devoid of membrane were observed on the surface the protoplasts. Pearl chain was formed by applying AC field of 200 V/cm at 1.0 MHz for 43 sec. The lysis of plasma membranes and fusion process occurred by applying a 1,600 V/cm DC pulse twice for 1 sec. After 1-2 hours of a DC pulse application, it was observed that the two protoplasts were fused completely into one cell. In TEM observation of the fused cell, many small vacuoles were located in the fusion area of the two protoplasts. Indeed, two distinct regions were observed during fusing process; in one region, a nucleus was found, while in the other region, both nucleus and nucleous were found.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.277-283
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• 2019

In this paper, we propose a distributed fusion estimation for sensor networks using a receding horizon strategy. Communication channels were modelled as Markov jump systems, and a posterior probability distribution for communication channel characteristics was calculated and incorporated into the filter to allow distributed fusion estimation to handle path loss observation situations automatically. To implement distributed fusion estimation, a Kalman-Consensus filter was then used to obtain the average consensus, based on the estimates of sensors randomly distributed across sensor networks. The advantages of the proposed algorithms were then verified using a large-scale sensor network example.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.773-777
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• 2016

In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9A
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• pp.859-867
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• 2010

In this paper, we consider a novel spectrum sensing system in which firstly, the fusion center (FC) senses and makes the own decision then if its sensing result is not useful for achieving the final decision, the local observations from the cognitive users (CUs) will be required. Moreover, in case that FC needs the results from CUs, we will choose only CU having the highest collected energy to send its local decision to FC. Based on this selecting method, the number of sensing bits can be reduced; hence, we can save the power and the bandwidth for reporting stage in the cognitive radio network (CRN). The mathematical analysis of the key metrics of the sensing schemes (probability of detection, false alarm, e.g.) will be investigated and confirmed by the Monte-Carlo simulation results to show the performance enhancement of the proposed schemes.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.656-658
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• 2022

The purpose of infrared and visible image fusion is to integrate images of different modes with different details into a result image with rich information, which is convenient for high-level computer vision task. Considering many deep networks only work in a single scale, this paper proposes a novel image fusion based on three-scale dense network to preserve the content and key target features from the input images in the fused image. It comprises an encoder, a three-scale block, a fused strategy and a decoder, which can capture incredibly rich background details and prominent target details. The encoder is used to extract three-scale dense features from the source images for the initial image fusion. Then, a fusion strategy called l₁-norm to fuse features of different scales. Finally, the fused image is reconstructed by decoding network. Compared with the existing methods, the proposed method can achieve state-of-the-art fusion performance in subjective observation.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.47-56
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• 2015

In the multi-sensor system, sensor registration errors such as a sensor bias must be corrected so that the individual sensor data are expressed in a common reference frame. If registration process is not properly executed, large tracking errors or formation of multiple track on the same target can be occured. Especially for launch vehicle tracking system, each multiple observation lies on the same reference frame and then fused trajectory can be the best track for slaving data. Hence, this paper describes an on-line bias estimation/correction and asynchronous sensor fusion for launch vehicle tracking. The bias estimation architecture is designed based on pseudo bias measurement which derived from error observation between GPS and radar measurements. Then, asynchronous sensor fusion is adapted to enhance tracking performance.

• KSBB Journal
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• v.5 no.3
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• pp.255-261
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• 1990

Cell fusion between complementary mutants isolated from astaxanthin-producing yeast, Phaffia rhodozyma, was carried out to obtain astaxanthin-overproducing strains by protoplast fusion technique. The frequency of protoplast fusion was ranged from 2.3$\times$10-5 to 6.0$\times$10-5, and nuclear fusion in the cells of hybrids was demonstrated by several techniques such as isolation of recombinants after mitotic segregation of parental genetic markers, estimation of DNA content, direct observation of nuclei with nuclear staining, and comparison of survival rate to UV exposure. One of several hybrids, Fl, showed approximately 3-fold increase in astaxanthin content when compared with wild parent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1633-1649
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• 2017

In remote sensing image processing, the traditional fusion algorithm is based on the Intensity-Hue-Saturation (IHS) transformation. This method does not take into account the texture or spectrum information, spatial resolution and statistical information of the photos adequately, which leads to spectrum distortion of the image. Although traditional solutions in such application combine manifold methods, the fusion procedure is rather complicated and not suitable for practical operation. In this paper, an improved IHS transformation fusion algorithm based on the local variance weighting scheme is proposed for remote sensing images. In our proposal, firstly, the local variance of the SPOT (which comes from French "Systeme Probatoire d'Observation dela Tarre" and means "earth observing system") image is calculated by using different sliding windows. The optimal window size is then selected with the images being normalized with the optimal window local variance. Secondly, the power exponent is chosen as the mapping function, and the local variance is used to obtain the weight of the I component and match SPOT images. Then we obtain the I' component with the weight, the I component and the matched SPOT images. Finally, the final fusion image is obtained by the inverse Intensity-Hue-Saturation transformation of the I', H and S components. The proposed algorithm has been tested and compared with some other image fusion methods well known in the literature. Simulation result indicates that the proposed algorithm could obtain a superior fused image based on quantitative fusion evaluation indices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1761-1777
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• 2021

An image with infrared features and visible details is obtained by processing infrared and visible images. In this paper, a fusion method based on Laplacian pyramid and generative adversarial network is proposed to obtain high quality fusion images, termed as Laplacian-GAN. Firstly, the base and detail layers are obtained by decomposing the source images. Secondly, we utilize the Laplacian pyramid-based method to fuse these base layers to obtain more information of the base layer. Thirdly, the detail part is fused by a generative adversarial network. In addition, generative adversarial network avoids the manual design complicated fusion rules. Finally, the fused base layer and fused detail layer are reconstructed to obtain the fused image. Experimental results demonstrate that the proposed method can obtain state-of-the-art fusion performance in both visual quality and objective assessment. In terms of visual observation, the fusion image obtained by Laplacian-GAN algorithm in this paper is clearer in detail. At the same time, in the six metrics of MI, AG, EI, MS_SSIM, Q_abf and SCD, the algorithm presented in this paper has improved by 0.62%, 7.10%, 14.53%, 12.18%, 34.33% and 12.23%, respectively, compared with the best of the other three algorithms.