• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.141-156
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• 2011

In this paper we develop a link cost function for data delivery in sensor network. Usually most conventional methods determine the optimal coefficients in the cost function without considering the surrounding environment of the node such as the wireless propagation environment or the topological environment. Due to this reason, there are limitations to improve the quality of data delivery such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of Partially Connected Neural Network (PCNN) which is modeled according to the input types whether inputs are correlated or uncorrelated. The correlated inputs are connected to the hidden layer of the PCNN in a coupled fashion but the uncoupled inputs are in an uncoupled fashion. We also propose the training technique for finding an optimal weight vector in the link cost function. The link cost function is trained to the direction that the packet transmission success ratio of each node maximizes. In the experimental section, we show that our method outperforms other conventional methods in terms of the quality of data delivery and the energy efficiency.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.139-150
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• 2019

Purpose: LST-II is a special naval ship to carry out landing operations by transporting tanks, armored vehicles and military vehicles. Bad weather, maximum wave height of 4-5 m, caused damage to the LCM cradles while the LST-II No.O ship was moving to Thailand for training in February 2016. Methods: Based on the results of the field check, DTaQ conducted a study on the causes analysis and improvement measures. Results: The improvement plan that was derived was verified through a structural analysis and the improvement plan was applied to the follow-up ships. Conclusion: The improvement of LCM cradle has increased the safety of the crew and landing forces, and improved the operational efficiency.

• The Journal of Churna Manual Medicine for Spine and Nerves
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• v.14 no.1
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• pp.39-47
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• 2019

Objectives : The purpose of this study was to evaluate the scientific literature demonstrating the effectiveness of Chuna manual therapy (CMT) in the treatment of spinal scoliosis. Methods : A literature search was conducted using eight electronic databases to identify all randomized controlled clinical trials (RCTs) that investigated CMT as a treatment for spinal scoliosis. The Cochrane risk of bias tool was used to assess the methodological quality of each RCT. Results : Five RCTs met our inclusion criteria and were included in the analysis. These studies demonstrated positive results of CMT with respect to the reduction of Cobb's angle, vertebral rotation angle score, bending test score, and efficacy rate compared with brace treatment. Positive results were also assured, in terms of the reduction of Cobb's angle, pulmonary function, and efficacy rate when comparing CMT combined with other therapy with brace treatment, gymnastic training, or traction therapy. Conclusions : This review has identified encouraging and limited evidence of CMT for the treatment of spinal scoliosis. However, to obtain stronger evidence, without the disadvantages of study design and quality, we recommend that treatment effectiveness of CMT for spinal scoliosis is investigated further using a well-designed RCT.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1590-1609
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• 2021

Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysis which consist of extracting the log magnitude spectrogram from the laughter database, (2) the generative models training, (3) the synthesis stage which incorporate the involvement of an intermediate mechanism: the vocoder. To improve the synthesis quality, we suggest two hybrid models (LSTM-VAE, GRU-VAE and CNN-VAE) that combine the representation learning capacity of variational autoencoder (VAE) with the temporal modelling ability of a long short-term memory RNN (LSTM) and the CNN ability to learn invariant features. To figure out the performance of our proposed audio laughter generation process, objective evaluation (RMSE) and a perceptual audio quality test (listening test) were conducted. According to these evaluation metrics, we can show that the GRU-VAE outperforms the other VAE models.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.229-237
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• 2021

This study aimed to help build a quality control program to improve cardiac arrest treatment via analysis of medical records in a local tertiary general hospital to evaluate factors that influence clinical outcomes of in-hospital cardiac arrest. At first, the medical records of in-hospital cardiac arrest were analyzed, and targeted surveys about functional and structural factors associated with cardiopulmonary resuscitation (CPR) were conducted amongst the workforce in charge of cardiac arrest treatment. From January 2012 through June 2013, a total of 486 adult cases of in-hospital cardiac arrests, except for those occurring in the emergency room, were enrolled in this study. Among the patients, those of recovery of spontaneous circulation were 57.8%; 13.8% of patients were discharged alive; 8.9% of patients were discharged without significant neurologic sequela. Despite CPR is usually successful when administered as quickly as possible, in this analysis showed that prompt reaction after initial recognition was significantly lower in nurses compared with doctors. Analysis of survey results showed that confidence in performing CPR was significantly associated with the experience of CPR in doctors, while in nurses educational experience showed a correlation. In order to improve quality of in-hospital CPR system maintaining and increasing confidence of CPR performance is the most important factor. Therefore it can be helpful to develop and apply a phased, customized education program using training simulators as well as personalizing them to increase the personnel's confidence in CPR performance.

• Journal of Distribution Science
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• v.20 no.8
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• pp.1-8
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• 2022

Purpose: Research on attracting foreign direct investment (FDI) plays an important role in helping provinces attract more FDI projects. However, with local competition, FDI enterprises also have to consider their investment. This study evaluates the provincial competitiveness to attract FDI in Thai Nguyen province, a province of Vietnam. In which provincial distribution of competitiveness is measured through nine indicators. Research design, data, and methodology: The study collects data (FDI and the provincial competitiveness index) from 2006 to 2020. The study uses Autoregressive Distributed Lag (ARDL) to text the impact of distribution of competitivenes on foreign direct investment. With time-series, the ARDL is suitable for data analysis. Results: The regression results indicate that the competition index of market entry and informal costs negatively impact attracting FDI into the province; The human resource training quality index has a positive effect on FDI. The results show that FDI enterprises pay much attention to business establishment procedures, hidden costs, and quality of human resources in the province. Conclusions: At the same time, in terms of practice, the results of this study, the authors also offer solutions to help improve the ability to attract FDI into Thai Nguyen province. The significant provincial competitiveness indicators should be taken into account for improvement first.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.123-133
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• 2022

This paper explores the key factors influencing the business performance of enterprises, specifically women-owned small and medium enterprises (SMEs) in Vietnam. The extant literature on factors affecting the business performance of women-owned SMEs in Vietnam is still quite scarce. The researchers used a self-administered questionnaire to achieve a sample of 265 female SME owners in Vietnam to find these factors. The obtained data was analyzed by using SPSS 20.0. Cronbach's α test and factor analysis have been carried out to test the reliability of data and validate the hypothesis. The results showed that these enterprises' performances had a significant positive relationship with the business management competencies of the directors of the business, quality of human resources, financial access, socio-cultural factors, and government policy. Also, the study showed no relationship between the enterprise's business network and business performance. The results suggest that female business owners should improve their business management capacity, focusing on training activities to improve the quality of human resources of enterprises. The Government needs policies to support small and medium enterprises in accessing financial and other resources and propagate to reduce gender stereotypes.