• Journal of the Korean Physical Society
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• v.80
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• pp.247-256
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• 2022

Our objective was to investigate radiomics signatures and prediction models defined by four segmentation methods in using 2-[¹⁸F]fluoro-2-deoxy-d-glucose positron emission tomography (¹⁸F-FDG PET) imaging of lung metastases of soft-tissue sarcomas (STSs). For this purpose, three fixed threshold methods using the standardized uptake value (SUV) and gradient-based edge detection (ED) were used for tumor delineation on the PET images of STSs. The Dice coefficients (DCs) of the segmentation methods were compared. The least absolute shrinkage and selection operator (LASSO) regression and Spearman's rank, and Friedman's ANOVA test were used for selection and validation of radiomics features. The developed radiomics models were assessed using ROC (receiver operating characteristics) curve and confusion matrices. According to the results, the DC values showed the biggest difference between SUV40% and other segmentation methods (DC: 0.55 and 0.59). Grey-level run-length matrix_run-length nonuniformity (GLRLM_RLNU) was a common radiomics signature extracted by all segmentation methods. The multivariable logistic regression of ED showed the highest area under the ROC (receiver operating characteristic) curve (AUC), sensitivity, specificity, and accuracy (AUC: 0.88, sensitivity: 0.85, specificity: 0.74, accuracy: 0.81). In our research, the ED method was able to derive a significant model of radiomics. GLRLM_RLNU which was selected from all segmented methods as a meaningful feature was considered the obvious radiomics feature associated with the heterogeneity and the aggressiveness. Our results have apparently showed that radiomics signatures have the potential to uncover tumor characteristics.

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.73-80
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• 2007

In conventional on-demand mobile ad hoc routing algorithms, an alternate path is sought only after an active path is broken. It incurs a significant cost in terms of money and time in detecting the disconnection and establishing a new route. In this thesis, we propose proactive route selection and maintenance to conventional mobile ad hoc on-demand routing algorithms. The key idea for this research is to only consider a path break to be likely when the signal power of a received packet drops below an optimal threshold value and to generate a forewarning packet. In other words, if a path is lost with high probability, the neighboring node that may easily be cut off notifies the source node by sending a forewarning packet. Then the source node can initiate route discovery early and switched to a reliable path potentially avoiding the disconnection altogether. For the simulational study, network simulator(NS2) is used. The result of simulation shows that the algorithm significantly improves the performance of networks comparing to conventional on-demand routing protocols based on DSR and AODV in terms of packet delivery ratio, packet latency and routing overhead.

• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.133-140
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• 2008

The cable TV network has delivered downward broadcasting signals from distribution centers to subscribers. Since the traditional coaxial cable has been upgraded by the Hybrid Fiber Coaxial(HFC) cable, the upward channels has expanded broadband services such as Internet. This upward channel is vulnerable to ingress noises. When the noises from the children nodes accumulated in an amplifier exceeds a certain level, that node has to be cut off to prevent the noise propagation. The node selection problem(NSP) is defined to select nodes so that the noise in each node does not exceed the given threshold value and the sum of Profits of selected nodes can be maximized. The NSP has shown to be NP-hard. In this paper, we have proposed heuristics to find the near-optimal solution for NSP. The experimental results show that interval partitioning is better than greedy approach. Our heuristics can be used by the HFC network management system to provide privileged services to the premium subscribers on HFC networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.165-172
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• 2010

In clinical data minig, choosing the optimal subset of features is such important, not only to reduce the computational complexity but also to improve the usefulness of the model constructed from the given data. Moreover the threshold values (i.e., cut-off points) of selected features are used in a clinical decision criteria of experts for differential diagnosis of diseases. In this paper, we propose a fuzzy discretization approach, which is evaluated by measuring the degree of separation of redundant attribute values in overlapping region, based on spatial distribution of data with continuous attributes. The weighted average of the redundant attribute values is then used to determine the threshold value for each feature and rough set theory is utilized to select a subset of relevant features from the overall features. To verify the validity of the proposed method, we compared experimental results, which applied to classification problem using 668 patients with a chief complaint of dyspnea, based on three discretization methods (i.e., equal-width, equal-frequency, and entropy-based) and proposed discretization method. From the experimental results, we confirm that the discretization methods with fuzzy partition give better results in two evaluation measures, average classification accuracy and G-mean, than those with hard partition.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.558-564
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• 2013

Wireless sensor networks (WSNs) contain limited energy resources and are left in open environments. Since these sensor nodes are self-operated, attacks such as sinkhole attacks are possible as they can be compromised by an adversary. The sinkhole attack may cause to change initially constructed routing paths, and capture of significant information at the compromised node. A localized encryption and authentication protocol (LEAP) has been proposed to authenticate packets and node states by using four types of keys against the sinkhole attack. Even though this novel approach can securely transmits the packets to a base station, the packets are forwarded along the constructed paths without checking the next hop node states. In this paper, we propose the next hop node selection method to cater this problem. Our proposed method evaluates the next hop node considering three factors (i.e., remaining energy level, number of shared keys, and number of filtered false packets). When the suitability criterion for next hop node selection is satisfied against a fix threshold value, the packet is forwarded to the next hop node. We aim to enhance energy efficiency and a detour of attacked areas to be effectively selected Experimental results demonstrate validity of the proposed method with up to 6% energy saving against the sinkhole attack as compared to the LEAP.

• Journal of the Korean Society of Radiology
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• v.8 no.1
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• pp.19-26
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• 2014

This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, we choose new approximation coefficients using a non-linear approximation of luminance term. This boosts the fidelity. Our experiment employing the above methods shows enhancement in the coding time more than two times over traditional coding methods and shows improvement in PSNR value by about 1-3dB at the same compression rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.571-573
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• 2018

Wireless sensor networks have unbalanced energy consumption due to the convergence structure in which data is concentrated to sink nodes. To solve this problem, in the previous research, the relay node was placed between the source node and the sink node to merge the data before being concentrated to the sink node. However, selecting a relay node that does not consider the link quality causes packet loss according to the link quality of the reconfigured routing path. Therefore, in this paper, we propose a link cost calculation method for data reliability in routing path reconfiguration for relay node selection. We propose a link cost estimation formula considering the number of hops and RSSI as the routing metric value and select the RSSI threshold value through the packet transmission experiment between the sensor modules.

• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.183-205
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• 2007

An investigation was undertaken of the optimal discriminant model for predicting the likelihood of insolvency in advance for medium-sized firms based on the technology evaluation. The explanatory variables included in the discriminant model were selected by both factor analysis and discriminant analysis using stepwise selection method. Five explanatory variables were selected in factor analysis in terms of explanatory ratio and communality. Six explanatory variables were selected in stepwise discriminant analysis. The effectiveness of linear discriminant model and logistic discriminant model were assessed by the criteria of the critical probability and correct classification rate. Result showed that both model had similar correct classification rate and the linear discriminant model was preferred to the logistic discriminant model in terms of criteria of the critical probability In case of the linear discriminant model with critical probability of 0.5, the total-group correct classification rate was 70.4% and correct classification rates of insolvent and solvent groups were 73.4% and 69.5% respectively. Correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify the present sample. However, the actual correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify a future observation. Unfortunately, the correct classification rate underestimates the actual correct classification rate because the data set used to estimate the discriminant function is also used to evaluate them. The cross-validation method were used to estimate the bias of the correct classification rate. According to the results the estimated bias were 2.9% and the predicted actual correct classification rate was 67.5%. And a threshold value is set to establish an in-doubt category. Results of linear discriminant model can be applied for the technology financing banks to evaluate the possibility of insolvency and give the ranking of the firms applied.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1A
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• pp.19-30
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• 2012

Recently, vision-based dynamic deflection measurement techniques have significant interests and are getting more popular owing to development of the high-quality and low-price camcorder and also image processing algorithm. However, there are still several research issues to be improved including the self-vibration of vision device, i.e. camcorder, and the image processing algorithm in device aspect, and also the application area should be extended to measure three dimensional movement of floating structures in application aspect. In this study, vision-based dynamic motion measurement technique using multiple targets is proposed to measure three dimensional dynamic motion of floating structures. And also a new scheme to select threshold value to discriminate the background from the raw image containing targets. The proposed method is applied to measure the dynamic motion of large concrete floating quay in open sea area under several wave conditions, and the results are compared with the measurement results from conventional RTK-GPS(Real Time Kinematics-Global Positioning System) and MRU(Motion Reference Unit).

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.149-156
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• 2011

This paper proposes the effective edge selection algorithm for the rapid processing time and low memory usage of efficient graph-based image segmentation on mobile device. The graph-based image segmentation algorithm is to extract objects from a single image. The objects are consisting of graph edges, which are created by information of each image's pixel. The edge of graph is created by the difference of color intensity between the pixel and neighborhood pixels. The object regions are found by connecting the edges, based on color intensity and threshold value. Therefore, the number of edges decides on the processing time and amount of memory usage of graph-based image segmentation. Comparing to personal computer, the mobile device has many limitations such as processor speed and amount of memory. Additionally, the response time of application is an issue of mobile device programming. The image processing on mobile device should offer the reasonable response time, so that, the image segmentation processing on mobile should provide with the rapid processing time and low memory usage. In this paper, we demonstrate the performance of the effective edge selection algorithm, which effectively controls the edges of graph for the rapid processing time and low memory usage of graph-based image segmentation on mobile device.