• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.10 s.340
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• pp.25-30
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• 2005

This paper generalizes the concept of the signal space detection to construct a fixed delay tree search (FDTS) detector which estimates a block of n channel symbols at a time. This technique is applicable to high speed implementation. Two approaches are discussed both of which are based on efficient signal space partitioning. In the first approach, symbol detection is performed based on a multi-class partitioning of the signal space. This approach is a generalization of binary symbol detection based on a two-class pattern classification. In the second approach, binary signal detection is combined with a look-ahead technique, resulting in a highly parallel detector architecture.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.1907-1914
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• 1998

This paper proposes the adaptive connection admission cotrol using the variale MRR(measurement reflection ratio) and the distribution of the number of cells arriving during the fixed interval. This distribution is estimated from the measured number of cells arriving at the output buffer during the fixed interval and traffic parameters specified by user. MRR is varied by the difference of estimated distribution and measurement distribution. As MRR is adaptively varied by estimated distribution error of accepted connections, it quickly reduces estimation error. Also, the scheduling scheme is proposed for multiplexed traffic with various traffic characteristics. For each traffic class, this scheme estimates adaptively equivalent bandwidth and schedules according to equivalent bandwidth ratio of each traffic class, so it improves cell loss rate and link utilization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7A
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• pp.717-724
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• 2006

In order to maximize the throughput and provide the fairness between users in MIMO-OFDM system, FATM(fairness-aware throughput maximization) scheduling algorithm was proposed. In this paper, a QoS-aware scheduling algorithms for MINO-OFDM system are proposed, each of which is based on FATM. These scheduling algorithms aim to satisfy the different service requirements of various service classes. Three proposed QoS scheduling algorithms called SPQ (Strict Priority Queueing), DCBQ (Delay Constraint Based Queuing), HDCBQ (Hybrid Delay Constraint Based Queuing) are compared through computer simulations. It is shown that HDCBQ algorithm outperforms other algorithms in satisfying different requirements of various service classes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2233-2248
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• 1999

The TMN that appears to operate the various communication networks generally and efficiently is developed under the different platform environment such as the different hardware and the different operating system. One of the main problems is that all the agents of the TMN system must be duplicated and maintain the software and the data blocks that perform the identical function. Therefore, the standard of the Q3 interface development cannot be defined and the multi-platform cannot be supported in the development of the TMN agent. In order to overcome these problems, the Farming methodology that is based on the Farmer model has been suggested. With the Farming methodology, the software and the data components which are duplicated and stored in each distributed object are saved in the platform independent class repository(PICR) by converting into the format of the independent componentware in the platform, so that the componentwares that are essential for the execution can be loaded and used statically or dynamically from PICR as described in the framework of each distributed object. The distributed TMN agent of the personal communication network is designed and developed by using the Farmer model.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.1-11
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• 2004

In this paper. we propose a storage system for XML documents using relational databases. Additional processing is required to store XML documents in the relational databases due to the discrepancy between XML structures and relational schema. This study aims to store XML documents with DTD in the relational databases. We propose the association inlining that exploits shred inlining and hybrid inlining and avoids relation fragments and excessive joins. Experiments show some improvements in the performance with the proposed method. The information of the storage structures is extracted from the simplified DTD. Existing map classes are extended in order to map various structures of XML to relational schema. Map classes are defined for various structures such as elements with multiple values, elements with multiple super elements, and elements with recursive structures through analyzing XML documents. Map files that are XML structures and used in generating SQL statements are created by using the extracted information of storage structures and map classes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1A
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• pp.132-141
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• 2000

In this paper, we proposed the MAC protocol of APON supporting multi-class traffic such as CBBUVBR, ABR, UBR, to guarantee the required QoS of each service. For this, we analyze the performance of variousrequest mechanisms and employee the different request mechanism for each traffic classes. Upstream anddownstream frame structures to minimize transmission overhead are proposed based on our request mechanism.The proposed MAC protocol applies the different priority to permit distribution process. CBBWBR traffic, withthe stringent requirements on CDV or delay, is allocated prior to any other class. ABR traffic, which hasnon-strict CDV or delay criteria, uses flexibly the available bandwidth but ensures a minimum cell rate (MCR).UBR traffic is allocated with lowest priority for the remaining capacity. The performance of proposed protocol isevaluated in terms of transfer delay and 1-point CDV with various offered load. The result of simulation showsthat the proposed protocol guarantees the required QoS of the corresponding category, while making use of theavailable resources in both an efficient and dynamical way.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.77-84
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• 2022

In this paper, we propose a Label Reassignment Strategy to improve the performance of an object detection algorithm. Our approach involves two stages: an inference stage and an assignment stage. In the inference stage, we perform multi-scale inference with predefined scale sizes on a trained model and re-infer masked images to obtain robust classification results. In the assignment stage, we calculate the IoU between bounding boxes to remove duplicates. We also check box and class occurrence between the detection result and annotation label to re-assign the dominant class type. We trained the YOLOX-L model with the re-annotated dataset to validate our strategy. The model achieved a 3.9% improvement in mAP and 3x better performance on AP_S compared to the model trained with the original dataset. Our results demonstrate that the proposed Label Reassignment Strategy can effectively improve the performance of an object detection model.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.3
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• pp.554-561
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• 2017

IoT (Internet of Things) devices are often located in environments where indoor or underground, signals are difficult to reach. In addition, the transmission power is low, the base station should be designed to be able to receive signals even at low reception sensitivity. For this reason, a device having a poor channel condition can be transmitted at a low data rate using a low coding rate or repetition. When the coverage class is divided according to the channel condition and the data rate, the packet length may vary from one coverage class to another, and the performance of the slotted aloha random access may be degraded. We will focus on two methods of using shared-resource and seperate resources among multiple slotted aloha methods. In particular, when devices with different coverage classes use shared resources, performance of a device with a bad channel condition may deteriorate. Conversely, when using separate resources for each coverage class, there is a problem that congestion may occur which increases the number of devices that perform random access to one resource area. In this paper, we propose some methods to overcome this problem. This study is mainly focused on MTC devices, and is considered to be a high possibility of future development.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.121-128
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• 2016

Many studies are focused on image data and classifier for comparison or improvement of classification accuracy. Therefore studies are needed aspect of the training samples on supervised classification which depend on reference data or skill of analyst. This study tries to assess usability of field spectra as training samples on supervised classification. Classification accuracies of hyperspectral and multispectral images were assessed using training samples from image itself and field spectra, respectively. The results shown about 90% accuracy with training sample collected from image. Using field spectra as training sample, accuracy was decreased 10%p for hyperspectral image, and 20%p for multispectral image. Especially, some classes shown very low accuracies due to similar spectral characteristics on multispectral image. Therefore, field spectra might be used as training samples on classification of hyperspectral image, although it has limitation for multispectral image.