• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.815-829
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• 2021

The purpose of this study is to construct a flatfish outlook model that is consistent with the "Fisheries outlook" monthly publication of the fisheries outlook center of the Korea Maritime Institute (KMI). In particular, it was designed as a partial equilibrium model limited to flatfish items, but a model was constructed with a dynamic ecological equation model (DEEM) system, considering biological breeding and shipping times. Due to limited amounts of monthly data, the market equilibrium price was calculated using a recursive model method as the inverse demand. The main research results and implications are as follows. As a result of estimating young fish inventory levels, the coefficient of the young fish inventory in the previous period was estimated to be 0.03, which was not statistically significant. Because there is distinct seasonality, when estimating the breeding outcomes, the elasticity of breeding in the previous period was found to exceed 0.7, and it increased more as the weight of the fish increased, in addition, the shipment coefficient gradually increased as the weight increased, which means that as the fish weight increased, the shipment compared to the breeding volume increased. When estimating shipments, the elasticity of breeding in previous period was estimated to respond elastically as the weight increases. The price flexibility coefficient of the total supply was inelastically estimated to be -0.19. Finally, according to a model predictive power test, the Theil U1 was estimated to be very low for all of the predictors, indicating excellent predictive power.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.567-574
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• 2006

VG-AnyLAN is a local area network standard developed by the IEEE 802.12 project. While preserving the frame format of IEEE 802.3, VG-AnyLAN adopts a new medium access control called Demand Priority where transmission requests of stations are arbitrated by a control hub in a round-robin manner. Unlike CSMA/CD which is the medium access control of IEEE 802.3, the Demand Priority, while providing the maximum bound on the packet delay, does not put the limit on the network segment size. In this paper, we analyze the delay and the channel utilization performances of the medium access control of IEEE 802.12 VG-AnyLAN. We develope an analytic model of the system under assumptions that each station generates traffic of the equal priority and that the packets are of fixed length. Using the analytic model, we obtain the recursive expression of the average channel utilization and the average access delay The numerical results obtained via analysis are compared to the simulation results of the system for a partial validation of our analysis.

• IE interfaces
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• v.13 no.3
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• pp.378-387
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• 2000

As the globalization of manufacturing companies continues, the scope of dependence between these companies and distributors, and other suppliers are growing very rapidly since no one company manufactures or distributes the whole product by themselves. And, the need to increase the efficiency of the whole supply chain is increasing. This paper deals with a multi-plant lot-sizing problem(MPLSP) which happens in a decentralized manufacturing system of a supply chain. In this study, we assume that the whole supply chain is driven by a single source of independent demand and many levels of dependent demands among manufacturing systems in the supply chain. We consider setup cost, transportation cost and time, and inventory holding cost as a decision factor in the MPLSP. The MPLSP is decomposed into two sub-problems: a planning problem of the whole supply chain and a lot-sizing problem of each manufacturing system. The supply chain planning problem becomes a pure linear programming problem and a Generalized Goal Decomposition method is used to solve the problem. Its result is used as a goal of the lot-sizing problem. The lot-sizing problem is solved using the CPLEX package, and then the coefficients of the planning problem are updated reflecting the lot-sizing solution. This procedure is repeated until termination criteria are met. The whole solution process is similar to Lagrangian relaxation method in the sense that the solutions are approaching the optimum in a recursive manner. Through experiments, the proposed closed-loop hierarchical planning and traditional hierarchical planning are compared to optimal solution, and it is shown that the proposed method is a very viable alternative for solving production planning problems of decentralized manufacturing systems and in other areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.321-340
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• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• Computers and Concrete
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• v.15 no.5
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• pp.771-794
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• 2015

This paper proposes an innovative method for selection of measurement sets in static parameter identification of concrete or steel bridges. This method is proved as a systematic tool to address the first steps of Structural System Identification procedures by observability techniques: the selection of adequate measurement sets. The observability trees show graphically how the unknown estimates are successively calculated throughout the recursive process of the observability analysis. The observability trees can be proved as an intuitive and powerful tool for measurement selection in beam bridges that can also be applied in complex structures, such as cable-stayed bridges. Nevertheless, in these structures, the strong link among structural parameters advises to assume a set of simplifications to increase the tree intuitiveness. In addition, a set of guidelines are provided to facilitate the representation of the observability trees in this kind of structures. These guidelines are applied in bridges of growing complexity to explain how the characteristics of the geometry of the structure (e.g. deck inclination, type of pylon-deck connection, or the existence of stay cables) affect the observability trees. The importance of the observability trees is justified by a statistical analysis of measurement sets randomly selected. This study shows that, in the analyzed structure, the probability of selecting an adequate measurement set with a minimum number of measurements at random is practically negligible. Furthermore, even bigger measurement sets might not provide adequate SSI of the unknown parameters. Finally, to show the potential of the observability trees, a large-scale concrete cable-stayed bridge is also analyzed. The comparison with the number of measurements required in the literature shows again the advantages of using the proposed method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.3
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• pp.43-56
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• 1982

A recursive algorithms for prediction of streamflows by Kalman filtering theory and Self-tuning predictor based on the state space description of the dynamic systems have been studied and the applicabilities of the algorithms to the rainfall-runoff processes have been investigated. For the representation of the dynamics of the processes, a low-order ARMA process has been taken as the linear discrete time system with white Gaussian disturbances. The state vector in the prediction model formulated by a random walk process. The model structures have been determined by a statistical analysis for residuals of the observed and predicted streamflows. For the verification of the prediction algorithms developed here, the observed historical data of the hourly rainfall and streamflows were used. The numerical studies shows that Kalman filtering theory has better performance than the Self-tuning predictor for system identification and prediction in rainfall-runoff processes.

• 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.

• Journal of KIISE
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• v.43 no.8
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• pp.910-918
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• 2016

A tensor is a multi-dimensional array that represents many data such as (user, user, time) in the social network system. A tensor generator is an important tool for multi-dimensional data mining research with various applications including simulation, multi-dimensional data modeling/understanding, and sampling/extrapolation. However, existing tensor generators cannot generate sparse tensors like real-world tensors that obey power law. In addition, they have limitations such as tensor sizes that can be processed and additional time required to upload generated tensor to distributed systems for further analysis. In this study, we propose TeT, a distributed tera-scale tensor generator to solve these problems. TeT generates sparse random tensor as well as sparse R-MAT and Kronecker tensor without any limitation on tensor sizes. In addition, a TeT-generated tensor is immediately ready for further tensor analysis on the same distributed system. The careful design of TeT facilitates nearly linear scalability on the number of machines.

• Smart Structures and Systems
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• v.32 no.1
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• pp.9-21
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• 2023

The parameters of civil engineering structures have time-variant characteristics during their service. When extremely large external excitations, such as earthquake excitation to buildings or overweight vehicles to bridges, apply to structures, sudden or gradual damage may be caused. It is crucially necessary to detect the occurrence time and severity of the damage. The unscented Kalman filter (UKF), as one efficient estimator, is usually used to conduct the recursive identification of parameters. However, the conventional UKF algorithm has a weak tracking ability for time-variant structural parameters. To improve the identification ability of time-variant parameters, an adaptive UKF with forgetting factor (AUKF-FF) algorithm, in which the state covariance, innovation covariance and cross covariance are updated simultaneously with the help of the forgetting factor, is proposed. To verify the effectiveness of the method, this paper conducted two case studies as follows: the identification of time-variant parameters of a simply supported bridge when the vehicle passing, and the model updating of a six-story concrete frame structure with field test during the Yangbi earthquake excitation in Yunnan Province, China. The comparison results of the numerical studies show that the proposed method is superior to the conventional UKF algorithm for the time-variant parameter identification in convergence speed, accuracy and adaptability to the sampling frequency. The field test studies demonstrate that the proposed method can provide suggestions for solving practical problems.