• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.967-974
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• 2017

In this paper, we propose an inter-symbol interference (ISI) estimation scheme based on the maximum a posteriori (MAP) algorithm for faster-than-Nyquist (FTN) systems. Unfortunately, the ISI estimator based on the MAP algorithm requires relatively high computational complexity. To reduce the complexity of the MAP based ISI estimator, we propose a hybrid ISI estimation scheme based on the MAP and successive interference cancellation (SIC) algorithms. The proposed scheme not only offers good ISI estimation performances but also requires reasonably low complexity.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.451-462
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• 2009

Recently, there are many active studies to enhance low I/O performance of hard disk device. The studies on the hardware make good progress whereas those of the system software to enhance I/O performance may not support the hardware performance due to its poor progress. In this paper, we propose a new method of prefetching n-blocks into the flash memory. The proposed method consists of three steps: (1)analyzing the pattern of read requests in block units; (2)determining the number of blocks prefetched to flash memory; (3)replacing blocks according to block replacement policy. The proposed method can reduce the latency time of hard disk and optimize the power consumption of the computer system. Experimental results show that the proposed dynamic n-block method provides better average response time than that of the existing AMP(Adaptive multi stream prefetching) method by 9.05% and reduces the average power consumption than that of the existing AMP method by 11.11%.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1176-1194
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• 2018

The object classification using the images' contents is a big challenge in computer vision. The superpixels' information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects' locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.64-69
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• 2014

Ultrasound system is one of the complex wireless signal processing systems that are widely used in the fields of modern industry such as medical diagnostics, underwater communications, and sensor-networks. Miniaturization of ultrasound system has been raging recently. In this paper, a hybrid LNA that is suitable for miniaturization and mobile diagnostic ultrasound system has been developed. The proposed LNA has low noise figure of less than 5dB, and the feedback resistor is designed to be electrically adjusted in order to attain the impedance-matching for various ultrasound transducers. It supports the whole ultrasound frequencies from 10KHz to 150MHz frequency band and also provides sleep modes. A gain from -18.8 to -29.5 dB is achieved by adjusting each transducer to fit the system character. Power consumption can be reduced up to 90% in similar performance as compared to the existing LNA.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.179-185
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• 2014

Average temperature and temperature uniformity in a battery cell are the important criteria of the thermal management of the battery pack for hybrid electric vehicles and electric vehicles (HEVs and EVs) because high power with large size cell is used for the battery pack. Thus, liquid cooling system is generally applied for the HEV/EV battery pack. The liquid cooling system is made of multiple cooling plates with coolant flow paths. The cooling plates are inserted between the battery cells to reject the heat from batteries to coolant. In this study, the cooling plate with U-shaped coolant flow paths is considered to evaluate the effects of coolant flow condition on the cooling performance of the system. The counter flow and parallel flow set up is compared and the effect of flow rate is evaluated using CFD tool (FLUENT). The number of counter-flows and flow rate are changed and the effect on the cooling performance including average temperature, differential temperature, and standard deviation of temperature are investigated. The results show that the parallel flow has better cooling performance compared with counter flow and it is also found that the coolant flow rate should be chosen with the consideration of trade-off between the cooling performance and pressure drop.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1316-1332
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• 2012

Formation flying is an important technology that enables high cost-effective organization of outer space aircrafts. The ad-hoc wireless network based on direct-sequence ultra-wideband (DS-UWB) techniques is seen as an effective means of establishing wireless communication links between aircrafts. In this paper, based on the theory of matched filter and error bits correction, a hybrid detection algorithm is proposed for realizing multiuser detection (MUD) when the DS-UWB technique is used in the ad-hoc wireless network. The matched filter is used to generate a candidate code set which may contain several error bits. The error bits are then recognized and corrected by an novel error-bit corrector, which consists of two steps: code mapping and clustering. In the former step, based on the modified optimum MUD decision function, a novel mapping function is presented that maps the output candidate codes into a feature space for differentiating the right and wrong codes. In the latter step, the codes are clustered into the right and wrong sets by using the K-means clustering approach. Additionally, in order to prevent some right codes being wrongly classified, a sign judgment method is proposed that reduces the bit error rate (BER) of the system. Compared with the traditional detection approaches, e.g., matched filter, minimum mean square error (MMSE) and decorrelation receiver (DEC), the proposed algorithm can considerably improve the BER performance of the system because of its high probability of recognizing wrong codes. Simulation results show that the proposed algorithm can almost achieve the BER performance of the optimum MUD (OMD). Furthermore, compared with OMD, the proposed algorithm has lower computational complexity, and its BER performance is less sensitive to the number of users.

• Smart Structures and Systems
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• v.26 no.4
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• pp.451-468
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• 2020

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

• Smart Structures and Systems
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• v.26 no.4
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• pp.403-418
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• 2020

In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Restorative Dentistry and Endodontics
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• v.28 no.3
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• pp.222-231
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• 2003

The purpose of this study was to compare shear bond strength and interfacial pattern of composite bond-ed to dentin using self-etching adhesive systems. Sixty extracted human molars with exposed occlusal dentin were divided into four groups and bonded with four adhesives and composites. Single Bond/Filtek Z 350(SB), Tyrian SPE-One-Step Plus/Aelitefil(TY), Prompt L-Pop/Filtek Z 250(LP), and One-Up Bond F/palfique Toughwell(OU). The results of this study were as follows; 1 Shear bond strength for OU was significantly lower than that of other groups(p＜0.05). No significant difference was founded among SB, TY, and LP. 2. Failure modes to dentin showed adhesive and mixed for SB TY and LP, but them for OU showed adhesive in all spceimens. 3. Dentin-resin interface showed close adaptation for SB, TY, and LP, but it showed gap for OU. 4. The hybrid layers for TY, LP OU were thinner than that of SB. Adhesive layers were observed between composite and hybrid layer, which were 5 $\mu\textrm{m}$ thick for TY and 10 $\mu\textrm{m}$ thick for OU.