• Journal of Information Processing Systems
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• v.12 no.2
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• pp.295-309
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• 2016

Spectrum sensing is an essential function that enables cognitive radio technology to explore spectral holes and resourcefully access them without any harmful interference to the licenses user. Spectrum sensing done by a single node is highly affected by fading and shadowing. Thus, to overcome this, cooperative spectrum sensing was introduced. Currently, the advancements in multiple antennas have given a new dimension to cognitive radio research. In this paper, we propose a multiple energy detector for cooperative spectrum sensing schemes based on the evidence theory. Also, we propose a reporting mechanism for multiple energy detectors. With our proposed system, we show that a multiple energy detector using a cooperative spectrum sensing scheme based on evidence theory increases the reliability of the system, which ultimately increases the spectrum sensing and reduces the reporting time. Also in simulation results, we show the probability of error for the proposed system. Our simulation results show that our proposed system outperforms the conventional energy detector system.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.75-85
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• 2006

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

• Journal of Korean Library and Information Science Society
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• v.45 no.3
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• pp.5-36
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• 2014

The Web is rich with various sources of information that go beyond the contents of documents, such as hyperlinks and manually classified directories of Web documents such as Yahoo. This research extends past fusion IR studies, which have repeatedly shown that combining multiple sources of evidence (i.e. fusion) can improve retrieval performance, by investigating the effects of combining three distinct retrieval approaches for Web IR: the text-based approach that leverages document texts, the link-based approach that leverages hyperlinks, and the classification-based approach that leverages Yahoo categories. Retrieval results of text-, link-, and classification-based methods were combined using variations of the linear combination formula to produce fusion results, which were compared to individual retrieval results using traditional retrieval evaluation metrics. Fusion results were also examined to ascertain the significance of overlap (i.e. the number of systems that retrieve a document) in fusion. The analysis of results suggests that the solution spaces of text-, link-, and classification-based retrieval methods are diverse enough for fusion to be beneficial while revealing important characteristics of the fusion environment, such as effects of system parameters and relationship between overlap, document ranking and relevance.

• Journal of Korean Library and Information Science Society
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• v.46 no.1
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• pp.321-344
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• 2015

This paper presents a fusion approach to sentiment detection that combines multiple sources of evidence to retrieve blogs that contain opinions on a specific topic. Our approach to finding opinionated blogs on topic consists of first applying traditional information retrieval methods to retrieve blogs on a given topic and then boosting the ranks of opinionated blogs based on the opinion scores computed by multiple sentiment detection methods. Our sentiment detection strategy, whose central idea is to rely on a variety of complementary evidences rather than trying to optimize the utilization of a single source of evidence, includes High Frequency module, which identifies opinions based on the frequency of opinion terms (i.e., terms that occur frequently in opinionated documents), Low Frequency module, which makes use of uncommon/rare terms (e.g., "sooo good") that express strong sentiments, IU Module, which leverages n-grams with IU (I and you) anchor terms (e.g., I believe, You will love), Wilson's lexicon module, which uses a collection-independent opinion lexicon constructed from Wilson's subjectivity terms, and Opinion Acronym module, which utilizes a small set of opinion acronyms (e.g., imho). The results of our study show that combining multiple sources of opinion evidence is an effective method for improving opinion detection performance.

• Journal of the Korean Society for information Management
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• v.32 no.1
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• pp.7-22
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• 2015

This paper describes a Web search optimization study that investigates both static and dynamic tuning methods for optimizing system performance. We extended the conventional fusion approach by introducing the "dynamic tuning" process with which to optimize the fusion formula that combines the contributions of diverse sources of evidence on the Web. By engaging in iterative dynamic tuning process, where we successively fine-tuned the fusion parameters based on the cognitive analysis of immediate system feedback, we were able to significantly increase the retrieval performance. Our results show that exploiting the richness of Web search environment by combining multiple sources of evidence is an effective strategy.

• Journal of Korean Neurosurgical Society
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• v.64 no.4
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• pp.486-494
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• 2021

The growth of pulsed electromagnetic field (PEMF) therapy and its progress over the years for use in post-operative bone growth has been revolutionary in its effect on bone tissue proliferation and vascular flow. However, further progress in PEMF therapy has been difficult due to lack of more evidence-based understanding of its mechanism of action. Our objective was to review the current understanding of bone growth physiology, the mechanism of PEMF therapy action along with its application in spinal surgery and associated outcomes. The authors of this review examined multiple controlled, comparative, and cohort studies to compare fusion rates of patients undergoing PEMF stimulation. Examining spinal fusion rates, a rounded comparison of post-fusion outcomes with and without bone stimulator was performed. Results showed that postoperative spinal surgery PEMF stimulation had higher rates of fusion than control groups. Though PEMF therapy was proven more effective, multiple factors contributed to difficulty in patient compliance for use. Extended timeframe of treatment and cost of treatment were the main obstacles to full compliance. This review showed that PEMF therapy presented an increased rate of recovery in patients, supporting the use of these devices as an effective post-surgical aid. Given the recent advances in the development of PEMF devices, affordability and access will be much easier suited to the patient population, allowing for more readily available treatment options.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.419-425
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• 2004

This study has proposed a data fusion method based on the Dempster-Shafer evidence theory The Dempster-Shafer fusion uses mass functions obtained under the assumption of class-independent Gaussian assumption. In the Dempster-Shafer approach, uncertainty is represented by 'belief interval' equal to the difference between the values of 'belief' function and 'plausibility' function which measure imprecision and uncertainty By utilizing the Dempster-Shafer scheme to fuse the data from multiple sensors, the results of classification can be improved. It can make the users consider the regions with mixed classes in a training process. In most practices, it is hard to find the regions with a pure class. In this study, the proposed method has applied to the KOMPSAT-EOC panchromatic image and LANDSAT ETM+ NDVI data acquired over Yongin/Nuengpyung. area of Kyunggi-do. The results show that it has potential of effective data fusion for multiple sensor imagery.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Journal of Korean Neurosurgical Society
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• v.45 no.2
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• pp.81-84
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• 2009

Objective : Many biomechanical and clinical studies on adjacent segment degeneration (ASD) have addressed cranial segment. No study has been conducted on caudal segment degeneration after upper segment multiple lumbar fusions. This is a retrospective investigation of the L5-S1 segment after spinal fusion at and above L4-5, which was undertaken to analyze the rate of caudal ASD at L5-S1 after spinal fusion on and above L4-5 and to determine that factors that might have influenced it. Methods : The authors included 67 patients with L4-5, L3-5, or L2-5 posterior fusions. Among these patients, 28 underwent L4-5 fusion, 23 L3-5, and 16 L2-5 fusions. Pre- and postoperative radiographs were analyzed to assess degenerative changes at L5-S1. Also, clinical results after fusion surgery were analyzed. Results : Among the 67 patients, 3 had pseudoarthrosis, and 35 had no evidence of ASD, cranially and caudally. Thirteen patients (19.4%) showed caudal ASD, 23 (34.3%) cranial ASD, and 4 (6.0%) both cranial and caudal ASD. Correlation analysis for caudal ASD at L5-S1 showed that pre-existing L5-S1 degeneration was most strongly correlated. In addition, numbers of fusion segments and age were also found to be correlated. Clinical outcome was not correlated with caudal ASD at L5-S1. Conclusion : If caudal and cranial ASD are considered, the overall occurrence rate of ASD increases to 50%. The incidence rate of caudal ASD at L5-S1 was significantly lower than that of cranial ASD. Furthermore, the occurrence of caudal ASD was found to be significantly correlated with pre-existing disc degeneration.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.262-269
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• 2010

This paper presents a probabilistic head tracking method, mainly applicable to face recognition and human robot interaction, which can robustly track human head against various variations such as pose/scale change, illumination change, and background clutters. Compared to conventional particle filter based approaches, the proposed method can effectively track a human head by regularizing the sample space and sequentially weighting multiple visual cues, in the prediction and observation stages, respectively. Experimental results show the robustness of the proposed method, and it is worthy to be mentioned that some proposed probabilistic framework could be easily applied to other object tracking problems.