• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Journal of Korean Clinical Nursing Research
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• v.25 no.3
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• pp.251-264
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• 2019

Purpose: This study was a systematic review and meta-analysis to explore the factors related to quality of life in heart transplant recipients. Methods: To identify studies that suggested the factors related to the quality of life in heart transplant recipients, we searched the articles published from 1974 to November 2018 using Six databases, PubMed, CINAHL, EMBASE, Cochrane, KMBASE and RISS. A total of 22 studies were selected out of 5,234 for the systematic review and meta-analysis on the basis of the PRISMA flow. The quality of study was assessed by assessment tool form the NIH and meta-analysis was performed using the 'R 3.5.2' version to analyze the correlated effect sizes. Results: Factors related to quality of life in heart transplant recipients were categorized into six domains based on the health-related quality of life model introduced by Ferrans: individual, environmental, biological function, symptoms, functional status, and general health perception. In the meta-analysis, 34 factors were used and 17 factors having significant effect sizes were as follows: self-efficacy, demoralization, perceived control, current occupational status, age, marital status, health promotion life style in the individual characteristics; stress in environmental characteristics; physical function status, creatinine level, left ventricular ejection fraction (LVEF) in biological function; anxiety, depression, symptom frequency and distress in symptoms domain; coping, self-care compliance in functional status. Conclusion: The findings indicate that the multi-dimensional factors influencing the quality of life in heart transplant recipients and provide the evidence for developing effective interventions for improving the quality of life of recipients.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1522-1545
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• 2012

Call admission control (CAC) plays an important role in mobile cellular network to guarantee the quality of service (QoS). In this paper, a dynamic hybrid CAC scheme with integrated cutoff priority and handoff queue for mobile cellular network is proposed and some performance metrics are derived. The unique characteristic of the proposed CAC scheme is that it can support any number of service types and that the cutoff thresholds for handoff calls are dynamically adjusted according to the number of service types and service priority index. Moreover, timeouts of handoff calls in queues are also considered in our scheme. By modeling the proposed CAC scheme with a one-dimensional Markov chain (1DMC), some performance metrics are derived, which include new call blocking probability ($P_{nb}$), forced termination probability (PF), average queue length, average waiting time in queue, offered traffic utilization, wireless channel utilization and system performance which is defined as the ratio of channel utilization to Grade of Service (GoS) cost function. In order to validate the correctness of the derived analytical performance metrics, simulation is performed. It is shown that simulation results match closely with the derived analytic results in terms of $P_{nb}$ and PF. And then, to show the advantage of 1DMC modeling for the performance analysis of our proposed CAC scheme, the computing complexity of multi-dimensional Markov chain (MDMC) modeling in performance analysis is analyzed in detail. It is indicated that state-space cardinality, which reflects the computing complexity of MDMC, increases exponentially with the number of service types and total channels in a cell. However, the state-space cardinality of our 1DMC model for performance analysis is unrelated to the number of service types and is determined by total number of channels and queue capacity of the highest priority service in a cell. At last, the performance comparison between our CAC scheme and Mahmoud ASH's scheme is carried out. The results show that our CAC scheme performs well to some extend.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• Journal of Distribution Science
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• v.12 no.10
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• pp.11-17
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• 2014

Purpose - The purpose of this study is to investigate the relationship that is found to exist between the technology-based self-service convenience orientation factor and the factors of convenience and quality. Research design, data, and methodology - The questionnaire was developed by using the modified and supplementary questions that were examined in previous studies. We used the SPSS/PC 18.0 and lisrel 8.3 statistical packages to analyze the results of the research. For validating the research hypothesis and structural relationship of the research model, path analysis was used in this study. Results - The ease of use exerted a significant influence on the four dimensions. Information had a significant influence on transaction, benefit, and post-benefit convenience. Control had a significant influence on five dimensions. Conclusions - This study suggests that technology-based self-service convenience is classified into five multi-dimensional levels. Further, the study reveals that control, ease of use, and information are important variables in order to increase convenience. Therefore, for improving technology-based self-service convenience, it is important to improve the control, ease of use, and information variables.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.4
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• pp.100-106
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• 2006

We analyze a voice/data integrated traffic model of the cellular mobile communication system with hybrid guard channels for voice and handoff calls. In a multi-service integrated wireless environment, quality of service guarantee is crucial for smooth transportation of real time information. Real time voice traffic requires a guaranteed upper bounded on both delay and packet error rate, whereas data traffic does not. Voice traffic has high transmission priority over data packets. Thus one of the important problems is the design of admission control schemes which can efficiently accommodate the differential quality of service requirements. In this paper, a hybrid guard channel scheme is considered in which arriving calls are assigned channels as long as the number of busy channels in the cell is below a predetermined first threshold. When the number of busy channels reaches the first threshold, new originating data calls are queued in the infinite data buffer. Then reaches second threshold, only handoff calls are assigned the remaining channels and new originating voice calls are blocked. We evaluate the system by a two-dimensional Markov chain approach and generating function method and obtain performance measures included blocking probability and forced termination probability.

• 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.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• Journal of Korean Public Health Nursing
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• v.36 no.3
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• pp.361-374
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• 2022

Purpose: This study applies an ecological model to investigate individual and organizational levels to identify factors influencing the HRQOL of industrial employees. Methods: Totally, 133 industrial workers of a vehicle company were enrolled, who understood the purpose and consented to participate in the study. The collected data were analyzed by frequency, percentage, mean, standard deviation, independent t-test, one-way ANOVA, Scheffe Test and hierarchical regression analysis using the SPSS 20.0 program. Results: Hierarchical regression analysis showed that job Stress(β=-.44, p<.001), and hobbies(β=-.21, p=.013) were the major influencing factors of the Physical Component Summary of HRQOL, which had an additional explanatory power of 11.5%. The influencing factors for the Mental Component Summary of HRQOL were job stress(β=-.43, p<.001), and coronary artery disease(β=.17, p=.034) with an additional explanatory power of 13.5%. Conclusion: Results of this study, reveal that a multidimensional approach based on an ecological model is suitable as a health promotion intervention strategy to improve the HRQOL. We further propose developing a multi-dimensional health promotion program that consider the individual and organizational factors such as job stress, activation of in-house clubs, and assessing and managing of the risk of cerebral and cardiovascular diseases.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.125-133
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• 2008

In this paper, we propose active queue management mechanism (Active-WRED) to guarantee quality of the high priority service class in multi-class traffic service environment. In congestion situation, this mechanism increases drop probability of low priority traffic and reduces the drop probability of the high priority traffic, therefore it can improve the quality of the high priority service. In order to analyze the performance of our mechanism we introduce the stochastic analysis of a discrete-time queueing systems for the performance evaluation of the Active Queue Management (AQM) based congestion control mechanism called Weighted Random Early Detection (WRED) using a two-state Markov-Modulated Bernoulli arrival process (MMBP-2) as the traffic source. A two-dimensional discrete-time Harkov chain is introduced to model the Active-WRED mechanism for two traffic classes (Guaranteed Service and Best Effort Service) where each dimension corresponds to a traffic class with its own parameters.