• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• Journal of the Korea Society for Simulation
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• v.16 no.2
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• pp.75-81
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• 2007

Previous queue inference has been studied with some limits. Larson's inference engine, which is the basis for this paper, also processed with basic assumption that arrival process is poisson process. Our inference method, which relaxes the poisson process assumption, must be a useful tool for looking into unobservable inside of queueing systems, as well as calculating accurate system performance. This paper employs these inference methods and proves the validity. Then we apply this method to system analysis for more complicated models. At first, we suggest methods to system with known number of servers, then expand to unknown number of servers. For validating our inference approach, we run some simulation models and compare true values with our results.

• Journal of KIISE
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• v.42 no.12
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• pp.1590-1599
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• 2015

As a variety of personal medical devices appear, it is possible to acquire a large number of diverse medical contexts from the devices. There have been efforts to analyze the medical contexts via software applications. In this paper, we propose a generic model of medical analytics schemes that are used by medical experts, identify inference methods for realizing each medical analytics scheme, and present guidelines for applying the inference methods to the medical analytics schemes. Additionally, we develop a PoC inference system and analyze real medical contexts to diagnose relevant diseases so that we can validate the feasibility and effectiveness of the proposed medical analytics schemes and guidelines of applying inference methods.

• ETRI Journal
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• v.42 no.5
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• pp.658-668
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• 2020

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.

• Journal of Information Technology Services
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• v.9 no.3
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• pp.123-144
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• 2010

As knowledge becomes a critical success factor of companies nowadays, lots of rule-based systems have been and are being developed to support their activities. Large number of rule-based systems serve as Web sites to advise, or recommend their customers. They usually use a backward chaining inference algorithm based on backtrack to implement those interactive Web-enabled rule-based systems. However, when the users like customers are using these systems interactively, it happens frequently where the users do not know some of the answers for the questions from the rule-based systems. We are going to design a backward chaining inference methodology considering unknown facts based on backtrack technique. Firstly, we review exact and inexact reasoning. After that, we develop a backward chaining inference algorithm for exact reasoning based on backtrack, and then, extend the algorithm so that it can consider unknown facts and reduce its search space. The algorithm speeded-up inference and decreased interaction time with users by eliminating unnecessary questions and answers. We expect that the Web-enabled rule-based systems implemented by our methodology would improve users' satisfaction and make companies' competitiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1591-1598
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• 2008

Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. In this paper, we propose Z. Cao's fuzzy inference method with learning ability which is used a gradient descent method in order to improve the performances. It is hard to determine the relation matrix elements by trial and error method. Because this method is needed many hours and effort. Simulation results are applied nonlinear systems show that the proposed inference method using a gradient descent method has good performances.

• Journal of Distribution Science
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• v.20 no.1
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• pp.87-98
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• 2022

Purpose: This research investigates whether the existence of preannouncing price discount before purchase has an effect on after regret about purchasing and price fairness perception. Moreover, this paper examines whether the preannouncing effects on regret (or price fairness perception) are moderated by motive inference type (or brand trust). Research design, data and methodology: This experimental design consisted of total 8 between-subjects full factorial, which is completed by 2 (preannouncing price discount before purchase) × 2 (motive inference type) × 2 (consumer's brand trust level). Results: First, regret (or price fairness) differs depending on the presence/absence of preannouncing price discount before purchase and price discount motive inference type. Second, interaction effect of preannouncing price discount presence/absence before purchase and price discount motive inference type on regret (or price fairness) after purchase differs depending on motive inference type (or brand trust). Conclusions: Preannouncing external cue could decrease the possibility of consumers to regret and prevent consumers perceiving price change as unfair. Thus, corporations should sufficiently explain to consumers about preannouncing and specific reason of price fall in order to decrease regret caused by price fall and to increase price fairness perception from preannouncing effect.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1111-1123
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• 2023

In recent years, as generative models have developed, research that threatens them has also been actively conducted. We propose a new membership inference attack against text-to-image model. Existing membership inference attacks on Text-to-Image models produced a single image as captions of query images. On the other hand, this paper uses personalized embedding in query images through Textual Inversion. And we propose a membership inference attack that effectively generates multiple images as a method of generating Adversarial Prompt. In addition, the membership inference attack is tested for the first time on the Stable Diffusion model, which is attracting attention among the Text-to-Image models, and achieve an accuracy of up to 1.00.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.67-81
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• 2006

While the existing Web focuses on the interface with human users based on HTML, the next generation Web will focus on the interaction among software agents by using XML and XML-based standards and technologies. The inference engine, which will serve as brains of software agents in the next generation Web, should thoroughly understand the Semantic Web, the standard language of the next generation Web. As abasis for the service, the W3C (World Wide Web Consortium) has recommended SWRL (Semantic Web Rule Language) which had been made by compounding OWL (Web Ontology Language) and RuleML (Rule Markup Language). In this research, we develop a backward chaining inference engine SMART-B (SeMantic web Agent Reasoning Tools -Backward chaining inference engine), which uses SWRL and OWL to represent rules and facts respectively. We analyze the requirements for the SWRL-based backward chaining inference and design analgorithm for the backward chaining inference which reflects the traditional backward chaining inference algorithm and the requirements of the next generation Semantic Web. We also implement the backward chaining inference engine and the administrative tools for fact and rule bases into Java components to insure the independence and portability among different platforms under the environment of Ubiquitous Computing.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.167-176
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• 2020

The automated fish feed system currently used in Korea supplies a certain amounts of feed to water tanks at a certain time. This automated system can reduce the labor cost of managing aqua farms, but it is very difficult to control intelligently and appropriately the amount of expensive feed that is critical to aqua farm productivity. In this paper, we propose the FIIFF Inference Model( Fuzzy Inference-based Intelligent Fish Feeding Model) that can solves the problems of these existing automatic fish feeding devices and maximizes the efficiency of feed supply while properly maintaining the growth rate of fish in aqua farms. The proposed FIIFF inference model has the advantage of being able to control feed amounts appropriately since it computes the amount of feed using the current water environments and fish activity state of the aqua farms. The result of the feed amount yield experiment with the proposed FIIFF Inference Model represents the effect of saving 14.8% over the eight months of actual feed amount in the aqua farm.