• Preventive Nutrition and Food Science
• /
• v.13 no.3
• /
• pp.157-165
• /
• 2008

Cancer-preventive effects of ethanol extract of elm tree root (EEE) were investigated. In the in vitro cytotoxicity assay, colon cancer cells were incubated with a chloroform fraction of EEE (CF-EEE). CF-EEE significantly inhibited the proliferation of cells and induced apoptotic cell death in a dose-dependent manner. For the assessment of chemopreventive efficacy in vivo, male F344 rats were fed with EEE (0.5 or 1%) in diet for 8 weeks, and were subcutaneously injected with 1,2-dimethylhydrazine (DMH) to induce colonic aberrant crypt foci (ACF). EEE (0.5 and 1%) significantly decreased both the numbers of AC (1191.1/colon) and ACF (529.3/colon) induced by DMH. In addition, in the Western blot analysis on the colonic mucosa, administration of EEE triggered expression of caspase-3, a key factor of an apoptotic cascade. These results suggest that extract of elm tree root may have potential chemopreventive principles that lead to apoptosis of cancer cells, and thereby suppress colorectal carcinogenesis during the initiation stage.

• The Journal of Natural Sciences
• /
• v.11 no.1
• /
• pp.143-150
• /
• 1999

A natural dyeing makes fabrics look unique and elegant which cannot be obtain by synthetic dyestuffs. The natural dyestuffs are harmless to human, and it is easy to get them. Also, the natural dyestuffs are environmentally frendly, which is the anther merit for natural dyeing,We investigated dyeability with several fabrics (cotton, wool, ramie, silk and nylon) using an elm bark. We mordants(natural and synthetic). Also, colorfastness in dyed fabrics was estimated by laundering and light. The optimum condition of dyeability in elm bark was 60 min as time, $80^{\circ}C$ as temperature and 1: 40 as bath ratio. The dyeability in silk and nylon was the best among the sample. The treatment of mordants improved the dyeability and colorfastness in silk, wool and nylon. We obtain various color by the mordants.

• Steel and Composite Structures
• /
• v.41 no.6
• /
• pp.831-850
• /
• 2021

Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winklersoil model, analytical equations for the moment-rotation response ofsoil during mining induced ground movements are developed. To define the full static moment-rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment-rotation curve. The maximal moment-rotation response of the footing is defined by X = 0:6. Despite the use of Winkler model, the computed moment-rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.8
• /
• pp.2529-2551
• /
• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Geomechanics and Engineering
• /
• v.31 no.2
• /
• pp.129-147
• /
• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Proceedings of the Plant Resources Society of Korea Conference
• /
• 2020.08a
• /
• pp.77-77
• /
• 2020

Purple loosestrife-Lythrum anceps (Koehne) Makino is a herbaceous perennial plant belonging to the Lythraceae family. It has been used for centuries in Korea and other Asian traditional medicine. It has been showed pharmacological effects, including anti-oxidant and anti-microbial effects. However, the mechanisms underlying its anti-cancer mechanisms are not yet understood. In this study, we investigated the mechanism of apoptosis signaling pathways by ethanol extract of Lythrum anceps (Koehne) Makino (ELM) in human leukemia U937 cells. Treatment with ELM significantly inhibited cell growth in a dose-dependent manner by inducing apoptosis, as evidenced by the formation of apoptotic bodies (ApoBDs), DNA fragmentation and increased populations of sub-G1 ratio. Induction of apoptosis by ELM was connected with up-regulation of death receptor (DR) 4 and DR5, pro-apoptotic Bax protein expression and down-regulation of anti-apoptotic Bcl-2 protein, and inhibitor of apoptosis protein (IAP) family proteins (XIAP, cIAP-1, survivin), depending on dosage. This induction was associated with Bid truncation, mitochondrial dysfunction, proteolytic activation of caspases (-3, -8 and -9) and cleavage of poly(ADP-ribose) polymerase protein. Therefore, our data indicate that ELM suppresses U937 cell growth by activating the intrinsic and extrinsic apoptosis pathways, and thus may have applications as a potential source for an anti-leukemic chemotherapeutic agent.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1527-1534
• /
• 2016

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.7
• /
• pp.1705-1720
• /
• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.65 no.2
• /
• pp.108-113
• /
• 2016

The real-world problems usually show nonlinear and multi-variate characteristics, so it is difficult to establish concrete mathematical models for them. Thus, it is common to practice data-driven modeling techniques in these cases. Among them, most widely adopted techniques are regression model and intelligent model such as neural networks. Regression model has drawback showing lower performance when much non-linearity exists between input and output data. Intelligent model has been shown its superiority to the linear model due to ability capable of effectively estimate desired output in cases of both linear and nonlinear problem. This paper proposes modeling method of daily photovoltaic power systems using ELM(Extreme Learning Machine) based modular networks. The proposed method uses sub-model by fuzzy clustering rather than using a single model. Each sub-model is implemented by ELM. To show the effectiveness of the proposed method, we performed various experiments by dataset acquired during 2014 in real-plant.

• Bulletin of the Korean Chemical Society
• /
• v.33 no.7
• /
• pp.2320-2324
• /
• 2012

A novel approach for metabolite extraction and fingerprinting was introduced based upon the nano-baskets and emulsion liquid membrane-nuclear magnetic resonance (ELM-NMR) technique. The objective of this method is optimizing the fingerprints, minimizing the metabolic variation from analysis, increasing the likelihood differences, and obtaining the maximum extraction yield. Low molecular weight metabolites in rat serum were recovered by ELMs using 12 nano-baskets of calixarene, as both emulsifier and carrier. The yields of ELMs were optimized by the method of one-at-a-time. According to NMR data, the maximum metabolic variation was achieved using scaffold 4 (4 wt %), n-decane membrane, stirring rate of 300 rpm, treat and phase ratios of 0.3 and 0.8, respectively. The results revealed that some calixarenes tend to extract non-specific macromolecules; and repeatability of fingerprints for 7-mediated ELM was maximum and for 3-mediated ELM was minimum. The yield of extractions was obtained to be higher for n-decane and lower for carbon tetrachloride. Among different membranes, the fingerprints by chlorinated liquid membranes were more repeatable than using toluene or n-decane.