• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.77-84
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• 2023

Given the growing volume of biomedical papers, the ability to efficiently extract keywords has become crucial for accessing and responding to important information in the literature. In this study, we conduct a comprehensive evaluation of different unsupervised learning-based models and BERT-based models for keyword extraction in the biomedical field. Our experimental findings reveal that the BioBERT model, trained on biomedical-specific data, achieves the highest performance. This study offers precise and dependable insights to guide forthcoming research in biomedical keyword extraction. By establishing a well-suited experimental framework and conducting thorough comparisons and analyses of diverse models, we have furnished essential information. Furthermore, we anticipate extending our contributions to other domains by providing comparative experiments and practical guidelines for effective keyword extraction.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1474-1482
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• 2006

Recently, it is advanced about the variable research of IPv6 and the wireless sensor network. However, the existed researches are not enough about the internetworking technology between the wireless sensor network and the Internet based on IPv6 for supporting ubiquitous service. Accordingly, this paper proposes two internetworking models for connecting the wireless sensor network and Internet based on IPv6. The relay router in the first model and the sink node of the wireless sensor network in the second mode] were used as a gateway. Then we analyze and compare with two internetworking models. Additionally we construct the test-bed connected between the wireless sensor network and KOREN(KOrea advanced REsearch Network) based on IPv6 using the first model. And we verified the operation of the test-bed by experiments.

• ETRI Journal
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• v.37 no.5
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• pp.833-844
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• 2015

Recently, new network systems have begun to emerge (for instance, 5G, IoT, and ICN) that require capabilities beyond that provided by existing IP networking. To fulfill the requirements, some new networking technologies are being proposed. The promising approach of the new networking technology is to try to overcome the architectural limitations of IP networking by adopting an identifier (ID)-based networking concept in which communication objects are identified independently from a specific location and mechanism. However, we note that existing ID-based networking proposals only partially meet the requirements of emerging and future networks. This paper proposes a new ID-based networking architecture and mechanisms, named IDNet, to meet all of the requirements of emerging and future networks. IDNet is designed with four major functional blocks-routing, forwarding, mapping system, and application interface. For the proof of concept, we develop numeric models for IDNet and implement a prototype of IDNet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.366-381
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• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.

• Journal of Communications and Networks
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• v.13 no.5
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• pp.499-510
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• 2011

Any server offering a routing service in the Internet would naturally be in competition for clients, and clients may need to utilize service from a specific server in order to achieve a desired result. We study the various properties of this competition, such as the fraction of route requests handled by a routing service provider and the fraction of total revenue obtained. As the routing service providers (i.e., servers or routers in this context) compete, they may alter behavior in order to optimize one of the above properties. For example, a service provider may lower the price charged for its service, in order to increase the number of clients served. Our models are based on servers offering a routing service to clients within representative network topologies based on actual Internet sub-graphs. These models provide, a framework for evaluating competition in the Internet. We monitor key aspects of the service, as several variables are introduced into the models. The first variable is the fraction of client requests that will pay more for a better quality route. The remaining requests are normal client requests that are satisfied by the most economical route. The second variable is the fraction of servers who choose to lower service prices in order to maximize the number of client requests served. As this fraction increases, it is more likely that a server will lower the price. Finally, there are some resource constraints applied to the model, to increase the difficulty in providing a routing solution, i.e., to simulate a realistic scenario. We seek to understand the effect on the overall network, as service providers compete. In simple cases, we show that this competition could have a negative impact on the overall efficiency of a service. We show that the routing variety present in the larger models is unable to mask this tendency and the routing service performance is decreased due to competition.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2544-2552
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• 1997

This paper presents three multiprocess models for parallel protocol implementation, that is, (1)channel communication model, (2)fork-join model, and (3)event polling model. For the specification of parallelism for each model, a parallel programming language, Par. C System, is used. to measure the performance of multiprocess models, we implemented the Internet Protocol Suite(IPS) Internet Protocol (IP) for each model by writing the parallel language on the Transputer. After decomposing the IP functions into two parts, that is, the sending side and the receiving side, the parallelism in both sides is exploited in the form of Multiple Instruction Single Data (MISD). Three models are evaluated and compared on the basis of various run-time overheads, such as an event sending via channels in the parallel channel communication model, process creating in the fork-join model and context switching in the event polling model, at the sending side and the receiving side. The event polling model has lower processing delays as about 77% and 9% in comparison with the channel communication model and the fork-join model at the sending side, respectively. At the receiving side, the fork-join model has lower processing delays as about 55% and 107% in comparison with the channel communication model and the event polling model, respectively.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.105-113
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• 2009

The feature models are widely used to model the commonalities and variabilities among the products in the domain engineering phase of software product line developments. The findings and corrections of the errors or consistencies in the feature models are essential to the successful software product line engineering. The aids of the automated tools are needed to perform the validation of the feature models effectively. This paper describes the approach based on JESS rule-base system to validate the feature models and proposes the feature model validation tool using this approach. The tool of this paper validates the feature models in real-time when modeling the feature models. Then it provides the information on existence of errors and the explanations on causes of those errors, which allows the feature modeler to create the error-free feature models. This attempt to validate the feature model using the rule-based system is supposed to be the first time in this research field.

• Journal of the Korean Home Economics Association
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• v.39 no.12
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• pp.51-63
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• 2001

With the development of information technology, more and more internet sites are available to consumers. Increasing on the interests of diet and health in modem society, there are numerous internet sites dealing with food, nutrition and health. Considering the importance and expected roles of internet sites in information dissemination in the future world, it is important to put more efforts to encourage internet sites with more accurate an useful information. Up to present, not much work has been done on quality analysis and evaluation of the internet information. This study aimed to develop a model to evaluate and rank the internet information according to its quality so that consumers can be guided toward correct information source. Three models were adapted from the literature for pilot study to develop a model suitable for evaluation of contents of sites related to food and nutrition information. From the pilot study, a evaluation model was developed with criteria more relevant to Korean internet site by expert panel. Evaluation criteria of the model is authority, accuracy, objectivity, coverage, and user-friendliness. For the objective and systematic evaluation, scores were assigned totaling maximum 100 point to each evaluation criteria factors. The model developed in this study could be used as one for other internet sites in area other than food and nutrition.