• International journal of advanced smart convergence
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• v.10 no.1
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• pp.151-158
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• 2021

The main cause of ulcer is pressure, which starts to develop when the critical body pressure (32mmHg) is exceeded, and when the critical time elapses, ulcer occurs. In this study, the keyboard mechanism of the medical bed with 4 bar links was adopted, and each key can be controlled vertically. A key has one servo drive and one sensor controller which hasseveral body pressure sensors. The sensor controllers and the servo drives are connected to the main controller by two CAN (Car Are Network) in series, respectively. By reading the maximum body pressure value of each keyboard sensor, and by calculating the error value based on the critical body pressure, the fuzzy controller moves each key so that the total error becomes zero. If the fuzzy controller fails, then it prevents ulcer by lifting and lowering the keys of the bed alternatively within a short time. Thus, the controller operates in two-stage. The validity and effectiveness of the proposed approach have been verified through experiments.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.7
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• pp.253-260
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• 1996

In this paper, a simple scheme of two-stage free-space photonic swiching system for nonblocking optical interconnections has been investigated using holgorams. The multistage system requires a smaller number of interconnections for a given number of inpuut-output nonblocking ports than the single stage system does. Here hologram elements were used to change interconnection beam paths. In order to increase the idffraction efficiency of the hologram elements in photographic plates, a bleaching technique was used, which converts the amplitude hologram to the phase hologram. To show sthe feasibility of our optical interconnection system, it was implemented using the bleached hologram arrays and an LCTV spatial light modulator, and the sytem as a photonic switching system was demonstrated.

• Earthquakes and Structures
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• v.8 no.4
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• pp.821-841
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• 2015

This paper proposes a two-stage imaging approach for quantitative inspection of damages in metallic plates using the fundamental anti-symmetric mode of ($A_0$) Lamb wave. The proposed approach employs a number of transducers to transmit and receive $A_0$ Lamb wave pulses, and hence, to sequentially scan the plate structures before and after the presence of damage. The approach is applied to image the corrosion damages, which are simplified as a reduction of plate thickness in this study. In stage-one of the proposed approach a damage location image is reconstructed by analyzing the cross-correlation of the wavelet coefficient calculated from the excitation pulse and scattered wave signals for each transducer pairs to determine the damage location. In stage-two the Lamb wave diffraction tomography is then used to reconstruct a thickness reduction image for evaluating the size and depth of the damage. Finite element simulations are carried out to provide a comprehensive verification of the proposed imaging approach. A number of numerical case studies considering a circular transducer network with eight transducers are used to identify the damages with different locations, sizes and thicknesses. The results show that the proposed methodology is able to accurately identify the damage locations with inaccuracy of the order of few millimeters of a circular inspection area of $100mm^2$ and provide a reasonable estimation of the size and depth of the damages.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.8
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• pp.215-222
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• 2021

In order to alleviate the limited resource problem and interference problem in cellular networks, the dual connectivity technology has been introduced with the cooperation of small cell base stations. In this paper, we design a new efficient and fair resource allocation scheme for the dual connectivity technology. Based on two different bargaining solutions - Generalizing Tempered Aspiration bargaining solution and Gupta and Livne bargaining solution, we develop a two-stage radio resource allocation method. At the first stage, radio resource is divided into two groups, such as real-time and non-real-time data services, by using the Generalizing Tempered Aspiration bargaining solution. At the second stage, the minimum request processing speeds for users in both groups are guaranteed by using the Gupta and Livne bargaining solution. These two-step approach can allocate the 5G radio resource sequentially while maximizing the network system performance. Finally, the performance evaluation confirms that the proposed scheme can get a better performance than other existing protocols in terms of overall system throughput, fairness, and communication failure rate according to an increase in service requests.

• Journal of Microbiology and Biotechnology
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• v.27 no.10
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• pp.1808-1819
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• 2017

Knowledge on the functional characteristics and temporal variation of anaerobic bacterial populations is important for better understanding of the microbial process of two-stage anaerobic reactors. However, owing to the high diversity of anaerobic bacteria, close attention should be prioritized to the frequently abundant bacteria that were defined as core bacteria and putatively functionally important. In this study, using MiSeq sequencing technology, the core bacterial community of 98 operational taxonomic units (OTUs) was determined in a two-stage upflow blanket filter reactor treating pharmaceutical wastewater. The core bacterial community accounted for 61.66% of the total sequences and accurately predicted the sample location in the principal coordinates analysis scatter plot as the total bacterial OTUs did. The core bacterial community in the first-stage (FS) and second-stage (SS) reactors were generally distinct, in that the FS core bacterial community was indicated to be more related to a higher-level fermentation process, and the SS core bacterial community contained more microbes in syntrophic cooperation with methanogens. Moreover, the different responses of the FS and SS core bacterial communities to the temperature shock and influent disturbance caused by solid contamination were fully investigated. Co-occurring analysis at the Order level implied that Bacteroidales, Selenomonadales, Anaerolineales, Syneristales, and Thermotogales might play key roles in anaerobic digestion due to their high abundance and tight correlation with other microbes. These findings advance our knowledge about the core bacterial community and its temporal variability for future comparative research and improvement of the two-stage anaerobic system operation.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.881-895
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• 2023

Modern image inpainting techniques based on deep learning have achieved remarkable performance, and more and more people are working on repairing more complex and larger missing areas, although this is still challenging, especially for facial image inpainting. For a face image with a huge missing area, there are very few valid pixels available; however, people have an ability to imagine the complete picture in their mind according to their subjective will. It is important to simulate this capability while maintaining the identity features of the face as much as possible. To achieve this goal, we propose a three-stage network model, which we refer to as the identity and structure feature refinement network (ISFRNet). ISFRNet is based on 1) a pre-trained pSp-styleGAN model that generates an extremely realistic face image with rich structural features; 2) a shallow structured network with a small receptive field; and 3) a modified U-net with two encoders and a decoder, which has a large receptive field. We choose structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), L1 Loss and learned perceptual image patch similarity (LPIPS) to evaluate our model. When the missing region is 20%-40%, the above four metric scores of our model are 28.12, 0.942, 0.015 and 0.090, respectively. When the lost area is between 40% and 60%, the metric scores are 23.31, 0.840, 0.053 and 0.177, respectively. Our inpainting network not only guarantees excellent face identity feature recovery but also exhibits state-of-the-art performance compared to other multi-stage refinement models.

• IE interfaces
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• v.10 no.3
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• pp.179-187
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• 1997

The efficiency of material flow systems in terms of optimal network flow and minimum cost flow has always been an important design and operational goal in material handling and distribution system. In this research, an attempt was made to develop a new algorithm and the model to solve a stochastic material flow network with bidirectional and uncertain flows. A stochastic material flow network with bidirectional flows can be considered from a finite set with unknown demand probabilities of each node. This problem can be formulated as a special case of a two-stage linear programming problem which can be converted into an equivalent linear program. To find the optimal solution of proposed stochastic material flow network, some terminologies and algorithms together with theories are developed based on the partitioning and subgradient techniques. A computer program applying the proposed method was developed and was applied to various problems.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.89-98
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• 2004

Conversational agent is a system that provides users with proper information and maintains the context of dialogue on the natural language. Analyzing and modeling process of user's query is essential to make it more realistic, for which Bayesian network is a promising technique. When experts design the network for a domain, the network is usually very complicated and is hard to be understood. The separation of variables in the domain reduces the size of networks and makes it easy to design the conversational agent. Composing Bayesian network as two stages, we aim to design conversational agent easily and analyze user's query in detail. Also, previous information of dialogue makes it possible to maintain the context of conversation. Actually implementing it for a guide of web pages, we can confirm the usefulness of the proposed architecture for conversational agent.