• Food Science of Animal Resources
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• v.44 no.2
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• pp.284-298
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• 2024

Restructured meat (RM) products are gaining importance as an essential component of the meat industry due to consumers' interest in health benefits. RM products imply the binding or holding of meat, meat by-products, and vegetable proteins together to form a meat product with meat's sensory and textural properties. RM products provide consumers with diversified preferences like the intake of low salt, low fat, antioxidants, and high dietary fiber in meat products. From the point of environmental sustainability, RM may aid in combining underutilized products and low-valued meat by adequately utilizing them instead of dumping them as waste material. RM processing technique might also help develop diversified and new hybrid meat products. It is crucial to have more knowledge on the quality issues, selection of binding agents, their optimum proportion, and finally, the ideal processing techniques. It is observed in this study that the most crucial feature of RM could be its healthy products with reduced fat content, which aligns with the preferences of health-conscious consumers who seek low-fat, low-salt, high-fiber options with minimal synthetic additives. This review briefly overviews RM and the factors affecting the quality and shelf life. Moreover, it discusses the recent studies on binding agents in processing RM products. Nonetheless, the recent advancements in processing and market scenarios have been summarized to better understand future research needs. The purpose of this review was to bring light to the ways of sustainable and economical food production.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.558-568
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• 2019

Objective: To evaluate whether computed tomography (CT) reconstruction algorithms affect the CT texture features of the liver parenchyma. Materials and Methods: This retrospective study comprised 58 patients (normal liver, n = 34; chronic liver disease [CLD], n = 24) who underwent liver CT scans using a single CT scanner. All CT images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR) (iDOSE⁴), and model-based IR (IMR). On arterial phase (AP) and portal venous phase (PVP) CT imaging, quantitative texture analysis of the liver parenchyma using a single-slice region of interest was performed at the level of the hepatic hilum using a filtration-histogram statistic-based method with different filter values. Texture features were compared among the three reconstruction methods and between normal livers and those from CLD patients. Additionally, we evaluated the inter- and intra-observer reliability of the CT texture analysis by calculating intraclass correlation coefficients (ICCs). Results: IR techniques affect various CT texture features of the liver parenchyma. In particular, model-based IR frequently showed significant differences compared to FBP or hybrid IR on both AP and PVP CT imaging. Significant variation in entropy was observed between the three reconstruction algorithms on PVP imaging (p < 0.05). Comparison between normal livers and those from CLD patients revealed that AP images depend more strongly on the reconstruction method used than PVP images. For both inter- and intra-observer reliability, ICCs were acceptable (> 0.75) for CT imaging without filtration. Conclusion: CT texture features of the liver parenchyma evaluated using the filtration-histogram method were significantly affected by the CT reconstruction algorithm used.

• Journal of Cerebrovascular and Endovascular Neurosurgery
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• v.25 no.1
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• pp.1-12
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• 2023

Objective: Although chronic carotid artery occlusion seems to be associated with significant risk of ischemic stroke, revascularization techniques are neither well established nor widespread. In contrast, extracranial-intracranial bypass is common despite the lack of evidence regarding neurological improvement or prevention of ischemic events. The aim of current review is to evaluate the effectiveness of various methods of recanalization of chronic carotid artery occlusion. Methods: Comprehensive literature search through PubMed, Scopus, Cochrane and Web of Science databases performed. Various parameters were assessed among patients underwent surgical, endovascular and hybrid recanalization for chronic carotid artery occlusion. Results: 40 publications from 2005 to 2021 with total of more than 1300 cases of revascularization of chronic carotid artery occlusion have been reviewed. Further parameters were assessed among patients underwent surgical, endovascular and hybrid recanalization for chronic carotid artery occlusion: mean age, male to female ratio, mean duration of occlusion before treatment, rate of successful recanalization, frequency of restenosis and reocclusion, prevalence of ischemic stroke postoperatively, neurological or other symptoms improvement and complications. Based on proposed through reviewed literature indications for revascularization and predictive factors of various recanalizing procedures, an algorithm for clinical decision making have been formulated. Conclusions: Although treatment of chronic carotid artery occlusion remains challenging, current literature suggests revascularization as single option for verified neurological improvement and prevention of ischemic events. Surgical and endovascular procedures should be taken into account when treating patients with symptomatic chronic carotid artery occlusion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.95-112
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• 2007

Spatial Multiplexing techniques, which is a kind of Multiple antenna techniques, provide high data transmission rate by transmitting independent data at different transmit antenna with the same spectral resource. OFDM (Orthogonal Frequency Division Multiplexing) is applied to MIMO (Multiple-Input Multiple-Output) system to combat ISI (Inter-Symbol Interference) and frequency selective fading channel, which degrade MIMO system performance. But, orthogonality between subcarriers of OFDM can't be guaranteed under high-mobility condition. As a result, severe performance degradation due to ICI is induced. In this paper, both ICI and CAI (Co-Antenna Interference) which occurs due to correlation between multiple antennas, and performance degradation due to both ICI and CAI are analyzed. In addition to the proposed CIR (Channel Impulse Response) estimation method for avoiding loss in data transmission rate, HIC (Hybrid Interference Cancellation) approach for guaranteeing QoS of MIMO-OFDM receiver is proposed. We observe the results on analytical performance degradation due to both ICI & CAI are coincide with the simulation results and performance improvement due to HIC are also verified by simulation under SCM-E Sub-urban Macro MIMO channel.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Journal of Wind Energy
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• v.13 no.4
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• pp.26-41
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• 2022

Three similarities (i.e., geometrical similarity, kinematic similarity and dynamic similarity) between a prototype and model must be satisfied to perform an experiment for a floating offshore wind turbine (FOWT). For dynamic similarity, most of the model tests in ocean engineering basins are performed based on the Froude number, so the scale effect for the wind turbine of an FOWT occurs by different Reynolds numbers between the prototype and model. In this study, various model test techniques for overcoming the scale effect of the wind turbine part of the FOWT are investigated. Firstly, model test techniques using simple approaches are reviewed, and the advantages and disadvantages of the simple approaches are summarized. Secondly, the model test techniques in recent projects that apply improved approaches are introduced including advantages and disadvantages. Finally, new approaches applying digitalization are reviewed, and the characteristics of the new approaches are introduced.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.

• Journal of Chest Surgery
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• v.32 no.11
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• pp.1009-1016
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• 1999

Background: Minimally invasive direct coronary artery bypass surgery(MIDCAB) has been increasing in interest along with the new techniques in myocardial immobilization for easier and safer procedures. Until the opening of the era of new techniques, adequate accuracy and good patency of grafts were debatable. Our experiences of MIDCAB were studied according to the stages of technical developments. Material and Methods: Since March 1996, 55 patients have undergone MIDCAB procedures. The patients of off-pump CABG(no cardiopulmonary bypass under full sternotomy) were excluded from the study. In the early experience(Stage I), a left anterior small thoracotomy through the left parasternal incision was performed(n=6); then an approach through the lower partial sternotomy was used(Stage II, n=33); and recently, a chest wall elevator for harvesting the internal thoracic artery and the foot plate for myocardial immobilization have been used(USSC, Norwalk, CT)(Stage III, n=16). Result: The surgical procedures of four patients in the Stage II group have been converted to conventional bypass because of the deeply seated left anterior descending coronary artery in two patients, fracture of the calcific lesion in the right coronary artery in one patient, and a cardiogenic shock during hypothermia in the other patient with ventricular dysfunction. Two patients in stage II experienced symptomatic recurrences after surgery and restenosis was verified on angiocardiography. They were managed by interventional procedures. All the other patients were doing well without symptoms, except one patients in Stage II who underwent PTCA procedure for a lesion in the circumflex artery during the follow up period. Conclusion: The new and specialized devices are essential to the development of MIDCAB surgery. MIDCAB and the hybrid procedures in multi-vessel disease are on the way to further development. So far, our experience is limited only to a single device among the many new devices for the purpose.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.