• KIISE Transactions on Computing Practices
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• v.21 no.4
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• pp.283-289
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• 2015

We propose a low cost attendance checking system using NFC (Near Field Communication) and show a case study of an actual operation of the system in a higher education institute. The system offers a direct attendance check service when a student touches NFC tag on a classroom desk with his/her own smartphone. Our service was first developed and operated in 2012, and then additional functions like massive real time processing were reinforced. In the fall semester, 58 courses use the service and 96% of the class attendance was checked with mobile devices. The only hardware requirement of the system was NFC tag on the classroom desk, which reduced hardware cost dramatically. However, it also minimized attendance checking time into 1 minute regardless of enrolled student number.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1845-1865
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• 2019

Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithm based on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of the WRE-CVNE are demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.15 no.1
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• pp.71-76
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• 2019

Actual stress distributions in the nozzle of a pressure vessel may not be in plane strain condition, implying that the crack-tip constraint condition may be relaxed in the nozzle. In this paper, a methodology for evaluating the fracture toughness of the ASME Code is presented considering the relaxation of the constraint effect in the nozzle of the reactor pressure vessel. The crack-tip constraint effect is quantified by the T-stress. The equation, which represent the relation between the fracture toughness in the lower constraint condition and the plane strain fracture toughness, is derived using the T-stress. This equation is similar to the method for evaluating the fracture toughness of the Master Curve for low constraint conditions. As a result of evaluating the fracture toughness considering the constraint effect in the reactor inlet, outlet and direct injection nozzles using the proposed equation, it was confirmed that the fracture toughness in the nozzles is higher than the plane strain fracture toughness. Applying the proposed evaluation methodology, it is possible to reflect the relaxation of the constraint effect in the nozzles of the reactor pressure vessel, therefore, the safe operation area on the pressure-temperature limit curve can be prevented from being excessively limited.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.179-186
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• 2019

Information system audit, which provides effective diagnosis and inspection of IT governance, is applied to all aspects from planning to development and operation. However, there is a difficulty in carrying out the audit because the system for the specialized university IT project is not developed. Therefore, it is necessary to set the internal system as the audit-based application framework in order to apply it to university IT governance. In this paper, we propose a audit-based application framework of university information system developed for university. The framework has a difference from the existing audit system. By using this framework, it is possible to present a standard for the university IT project and easily approach and use it in the field. And it can be used for direct audit through this framework in the level of the auditor as well as the HQ admin. The framework categorizes the audit into three major dimensions and suggests a method that can be applied to the university information system audit through the UAFP(University Audit Framework Process) and quality assurance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3313-3332
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• 2022

This study is an investigation into the factors affecting patient dissatisfaction among Saudi hospitals. The selected factors considered for analysis are security of information sharing, operational practices, disruptive technologies, and the ease of use of EHR patient information management systems. From the literature review section, it was clear that hardly any other studies have embraced these concepts in one as was intended by this study. The theories that the study heavily draws from are the service dominant logic and the feature integration theory. The study surveyed 350 respondents from three large major hospitals in three different metropolitan cities in the Kingdom of Saudi Arabia. This sample came from members of the three hospitals that were willing to participate in the study. The number 350 represents those that successfully completed the online questionnaire or the limited physical questionnaires in time. The study employed the structural equation modelling technique to analyze the associations. Findings suggested that security of information sharing had a significant direct effect on patient satisfaction. Operational practice positively mediated the effect of security of information sharing on patient dissatisfaction. However, ease of use failed to significant impact this association. The study concluded that to improve patient satisfaction, Saudi hospitals must work on their systems to reinforce them against the active threats on the privacy of patients' data by leveraging disruptive technology. They should also improve their operational practices by embracing quality management techniques relevant to the healthcare sector.

• Transactions of Materials Processing
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• v.31 no.2
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• pp.51-56
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• 2022

In this study, finite element analyses were performed by applying a stress ring and split die design to relieve the tensile stress acting on the die due to high surface pressure during warm-closed forging. The applied material was a yield-ratio-control-steel (YRCS). It was used without quenching or tempering after forging. In the case of stress rings design, the number of stress rings and the tolerance for shrink fit were different. Vertical and horizontal splits were applied for insert die split design. Case 5 die with three stress rings, 0.2 % shrink fit tolerance, and vertical split was selected as an effective die design for tensile stress reduction. Based on die stress reduction analyses, Case 5 die for warm-closed forging was produced and smooth forgeability was secured, making it possible to manufacture forging product of yoke with the required geometry. In addition, controlled cooling using warm forging heat was applied to secure mechanical properties of yokes. When oil cooling was used for direct controlled cooling after warm-closed forging, a relatively uniform Rockwell hardness distribution and high mechanical properties could be obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2140-2156
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• 2023

The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.

• Journal of Hydrogen and New Energy
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• v.33 no.6
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• pp.643-659
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• 2022

For the economic analysis of fuel cells, levelized cost of electricity was calculated according to the type, capacity, and annual production of the fuel cells. The cost of every component was calculated through the system component breakdown. The direct cost of the system included stack cost, component cost, assembly, test, and conditioning cost, and profit markup cost were added. The effect of capacity and annual production was analyzed by fuel cell type. Sensitivity analysis was performed according to stack life, capital cost, project period, and fuel cost. As a result, it was derived how much the economic efficiency of the fuel cell improves as the capacity increases and the annual production increases.