• Translational and Clinical Pharmacology
• /
• v.26 no.3
• /
• pp.134-140
• /
• 2018

This study aimed to develop a UPLC-MS/MS method for determining plasma levels of L-aspartic acid and L-asparagine and the activity of L-asparaginase. L-aspartic acid, L-asparagine, and L-aspartic acid-2,3,3-$d_3$ were extracted from human plasma by protein precipitation with sulfosalicylic acid (30%, v/v). The plasma samples were analyzed using an Imtakt Intrada amino acid analysis column with 25 mM ammonium formate and 0.5% formic acid in acetonitrile as the mobile phase with step gradient method at a flow rate of 0.5 mL/min. The injection volume was $5{\mu}L$, and the total run time was 15 min. Inter- and intra-batch accuracies (%) ranged from 96.62-106.0% for L-aspartic acid and 89.85-104.8%, for L-asparagine, and the coefficient of variation (CV%) did not exceed 7%. The validation results for L-aspartic acid and L-asparagine satisfied the specified criterion, however, the results for L-asparaginase activity assay showed a borderline validity. This study could be a foundation for further development of therapeutic drug monitoring systems using UPLC-MS/MS.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.12
• /
• pp.6000-6017
• /
• 2018

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.1
• /
• pp.147-165
• /
• 2021

In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

• Journal of ILASS-Korea
• /
• v.26 no.1
• /
• pp.1-8
• /
• 2021

Droplet evaporation has been known as a common phenomenon in daily life, and it has been widely used for many applications. In particular, the influence of the different heated substrates on evaporation flux and flow characteristics is essential in understanding heat and mass transfer of evaporating droplets. This study aims to simulate the droplet evaporation process by considering variation of thermal property depending on the substrates and the surface temperature. The commercial program of ANSYS Fluent (V.17.2) is used for simulating the conjugated heat transfer in the solid-liquid-vapor domains. Moreover, we adopt the diffusion-limited model to predict the evaporation flux on the different heated substrates. It is found that the evaporation rate significantly changes with the increase in substrate temperature. The evaporation rate substantially varies with different substrates because of variation of thermal property. Also, the droplet evaporates more rapidly as the surface temperature increases owing to an increase in saturation vapor pressure as well as the free convection effect caused by the density gradient.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.6
• /
• pp.1877-1891
• /
• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• Journal of the Korea Safety Management & Science
• /
• v.23 no.4
• /
• pp.93-104
• /
• 2021

In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.

• Genomics & Informatics
• /
• v.19 no.1
• /
• pp.2.1-2.10
• /
• 2021

BRAF inhibitors (e.g., vemurafenib) are widely used to treat metastatic melanoma with the BRAF V600E mutation. The initial response is often dramatic, but treatment resistance leads to disease progression in the majority of cases. Although secondary mutations in the mitogen-activated protein kinase signaling pathway are known to be responsible for this phenomenon, the molecular mechanisms governing acquired resistance are not known in more than half of patients. Here we report a genome- and transcriptome-wide study investigating the molecular mechanisms of acquired resistance to BRAF inhibitors. A microfluidic chip with a concentration gradient of vemurafenib was utilized to rapidly obtain therapy-resistant clones from two melanoma cell lines with the BRAF V600E mutation (A375 and SK-MEL-28). Exome and transcriptome data were produced from 13 resistant clones and analyzed to identify secondary mutations and gene expression changes. Various mechanisms, including phenotype switching and metabolic reprogramming, have been determined to contribute to resistance development differently for each clone. The roles of microphthalmia-associated transcription factor, the master transcription factor in melanocyte differentiation/dedifferentiation, were highlighted in terms of phenotype switching. Our study provides an omics-based comprehensive overview of the molecular mechanisms governing acquired resistance to BRAF inhibitor therapy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3991-4007
• /
• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.1
• /
• pp.97-115
• /
• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.263-266
• /
• 1994

Recently, complicated and dexterous tasks with two or more arms are needed in ninny robot manipulator applications which can not be accomplished with one manipulator. In general, when two arms manipulate an object, tile dynamics of the arms and the object should be considered simultaneously. In order to control the force of tile arms, we can use various control schemes based upon dynamic modeling. But, there are difficulties in solving inverse dynamics equations, and the environment where a manipulator performs various tasks is usually unknown, and we can not describe a model precisely, for instances, the effect of the joint flexibility, and the friction between the arm and the object. Therefore, in this paper, we suggest a new force control method employing fuzzy inference without solving dynamic equations. Fuzzy inference rules and parameters are designed and adjusted with the automatic fuzzy modeling method using the Hough transform and gradient descent method.