• Journal of Digital Contents Society
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• v.16 no.5
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• pp.805-813
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• 2015

Many researchers expect the compressive sensing and sparse recovery problem can overcome the limitation of conventional digital techniques. However, these new approaches require to solve the l1 norm optimization problems when it comes to signal reconstruction. In the signal reconstruction process, the transform computation by multiplication of a random matrix and a vector consumes considerable computing power. To address this issue, parallel processing is applied to the optimization problems. In particular, due to huge size of original signal, it is hard to store the random matrix directly in memory, which makes one need to design a procedural approach in handling the random matrix. This paper presents a new parallel algorithm to calculate random partial Haar wavelet transform based on Single Instruction Multiple Threads (SIMT) platform.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4C
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• pp.321-329
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• 2008

H.264/AVC video coding standard enables a considerably higher improvement in coding efficiency compared with previous standards such as MPEG-2, H.263 and MPEG-4. To achieve this, for each macro-block in H.264/AVC, Rate-Distortion Optimization (RDO) technique is employed to select the best motion vector, reference frame, and macro-block mode. As a result, computational complexity is increased significantly whereas RDO achieve higher improvement. This paper presents fast intra mode selection algorithm based on constraints of frequency characteristics which are derived from intra coding modes of H.264/AVC. First of all, we observe the features of each intra mode through the frequency analysis of image. And then proposed Frequency Error Costs (FECs) are calculated to select the best mode which has minimum cost. Computational complexity is considerably reduced because rate-distortion costs only calculate the candidate modes which are set of best mode and its neighbouring two modes. Experimental results show that proposed algorithm reduces the complexity dramatically maintaining the rate-distortion performance compared with H.264/AVC reference software.

• Journal of Aerospace System Engineering
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• v.15 no.5
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• pp.72-80
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• 2021

Albatross uses dynamic soaring technique to obtain energy from horizontal winds and fly long distances without flapping. These dynamic soaring technique can be applied to manned/unmanned aircraft to reduce the components required for the aircraft and achieve light weight and small volume to effectively perform a given task. In this paper, to simulate the dynamic soaring technique of Albatross, we defined the optimization problem and set each boundary condition to derive the optimal flight trajectory and carry out simulations to follow it. In particular, to model dynamic soaring simulations more closely with reality, we proposed a horizontal wind model that changes every moment. This identifies and analyzes the effect of the time-variable horizontal wind model on the dynamic soaring mission of unmanned aircraft.

• Journal of Microbiology and Biotechnology
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• v.31 no.4
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• pp.570-583
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• 2021

Pyrococcus furiosus α-amylase can hydrolyze α-1,4 linkages in starch and related carbohydrates under hyperthermophilic condition (~ 100℃), showing great potential in a wide range of industrial applications, while its relatively low productivity from heterologous hosts has limited the industrial applications. Bacillus subtilis, a gram-positive bacterium, has been widely used in industrial production for its non-pathogenic and powerful secretory characteristics. This study was conducted to increase production of P. furiosus α-amylase in B. subtilis through three strategies. Initial experiments showed that co-expression of P. furiosus molecular chaperone peptidyl-prolyl cis-trans isomerase through genomic integration mode, using a CRISPR/Cas9 system, increased soluble amylase production. Therefore, considering that native P. furiosus α-amylase is produced within a hyperthermophilic environment and is highly thermostable, heat treatment of intact culture at 90℃ for 15 min was performed, thereby greatly increasing soluble amylase production. After optimization of the culture conditions (nitrogen source, carbon source, metal ion, temperature and pH), experiments in a 3-L fermenter yielded a soluble activity of 3,806.7 U/ml, which was 3.3- and 28.2-fold those of a control without heat treatment (1,155.1 U/ml) and an empty expression vector control (135.1 U/ml), respectively. This represents the highest P. furiosus α-amylase production reported to date and should promote innovation in the starch liquefaction process and related industrial productions. Meanwhile, heat treatment, which may promote folding of aggregated P. furiosus α-amylase into a soluble, active form through the transfer of kinetic energy, may be of general benefit when producing proteins from thermophilic archaea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.520-541
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• 2023

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.

• Steel and Composite Structures
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• v.51 no.5
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

• Wind and Structures
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• v.38 no.2
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• pp.109-118
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• 2024

A solar concentrator is a reflective surface in the shape of a parabola that collects solar rays in a focal area. This concentrator follows the path of the sun during the day with the help of a tracking system. One of the most important issues in the design and construction of these reflectors is the force exerted by the wind. This force can sometimes disrupt the stability of the concentrator and overturn the entire system. One of the ways to estimate the force is to use the numerical solution of the air flow in three dimensions around the dish. Ansys Fluent simulation software has been used for modeling several angles of attack between 0 and 180 with respect to the horizon. From the comparison of the velocity vector lines on the dish at angles of 90 to - 90 degrees, it was found that the flow lines are more concentrated inside the dish and there is a tendency for the flow to escape around in the radial direction, which indicates the presence of more pressure distribution inside the dish. It was observed that the pressure on the concave surface was higher than the convex one. Then, the effect of adding a hole with various diameter of 200, 300, 400, 500, and 600 mm on the dish was investigated. By increasing the diameter up to the optimized size of 400 mm, a decrease in the maximum pressure value in the pressure distribution was shown inside the dish. This pressure drop decreased the drag coefficient. The effect of the hole on the dish was also investigated for the 30-degree angled dish, and it was found that the results of the 90-degree case should be considered as the basis of the design.

• Journal of Korean Neurosurgical Society
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• v.63 no.5
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• pp.579-589
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• 2020

Objective : No optimum genetic rat Huntington model both neuropathological using an adeno-associated virus (AAV-2) vector vector has been reported to date. We investigated whether direct infection of an AAV2 encoding a fragment of mutant huntingtin (AV2-82Q) into the rat striatum was useful for optimizing the Huntington rat model. Methods : We prepared ten unilateral models by injecting AAV2-82Q into the right striatum, as well as ten bilateral models. In each group, five rats were assigned to either the 2×10¹² genome copies (GC)/mL of AAV2-82Q (×1, low dose) or 2×10¹³ GC/mL of AAV2-82Q (×10, high dose) injection model. Ten unilateral and ten bilateral models injected with AAV-empty were also prepared as control groups. We performed cylinder and stepping tests 2, 4, 6, and 8 weeks after injection, tested EM48 positive mutant huntingtin aggregates. Results : The high dose of unilateral and bilateral AAV2-82Q model showed a greater decrease in performance on the stepping and cylinder tests. We also observed more prominent EM48-positive mutant huntingtin aggregates in the medium spiny neurons of the high dose of AAV2-82Q injected group. Conclusion : Based on the results from the present study, high dose of AAV2-82Q is the optimum titer for establishing a Huntington rat model. Delivery of high dose of human AAV2-82Q resulted in the manifestation of Huntington behaviors and optimum expression of the huntingtin protein in vivo.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.191-199
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• 2021

A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.