• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.291-296
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• 2006

In context-aware computing environment, some context is characterized by a single event, but many other contexts are determined by a sequence of events which happen with some timing constraints. Therefore context inference could be conducted by monitoring the sequence of event occurrence along with checking their conformance with timing constraints. Some context could be described with fuzzy concepts instead of concrete concepts. Multiple entities may interact with a service system in the context-aware environments, and thus the context inference mechanism should be equipped to handle multiple entities in the same situation. This paper proposes a context inference model which is based on the so-called fuzzy colored timed Petri net. The model represents and handles the sequential occurrence of some events along with involving timing constraints, deals with the multiple entities using the colored Petri net model, and employs the concept of fuzzy tokens to manage the fuzzy concepts.

• ETRI Journal
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• v.41 no.6
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• pp.771-781
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• 2019

We propose the VS³-NET model to solve the task of question answering questions with machine-reading comprehension that searches for an appropriate answer in a given context. VS³-NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit-based sentences and self-matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question-type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine-reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS³-NET model has an exact-match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.137-142
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• 2006

상황은 단일 사건에 의해 결정되는 경우도 있지만, 많은 경우 일련의 사건이 특정 시간 제약을 만족하면서 발생할 때 상황이 결정된다. 따라서 상황에 대한 추론은 시간 제약 조건 만족 여부와 함께 사건의 발생을 순서를 확인하는 방법으로 수행될 수 있다. 한편, 어떤 상황은 분명하게 정의되는 것이 아니라 애매한 개념을 사용하여 기술되기 때문에, 퍼지 개념을 이용한 상황 기술과 이에 대한 추론이 필요하다. 한편, 유비쿼터스 환경에서와 같이 여러 대상에 대한 상황을 유추하여 서비스를 제공해야 하는 경우에, 대상 간에 동일한 상황이 발생할 수 있기 때문에 이에 대한 고려가 필요하다. 이러한 상황 추론을 위해서 이 논문에서는 Fuzzy Colored Timed Petri net 모델이라는 상황 추론 모델에 대해서 제안한다. 제안한 모델은 Timed Petri net 성질을 이용하여 일련의 사건 발생을 모델링하고, Colored Petri net의 성질을 이용하여 다수 대상에 대한 상황 추론을 허용하며, fuzzy 토큰 개념을 이용하여 애매한 개념을 사용하여 정의된 상황에 대한 추론을 가능하게 한다.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.79-94
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• 2018

The purpose of this study is to specify a probabilistic tracking mechanism for customer luxury purchase implemented by hidden Markov model, Bayesian inference, customer satisfaction and net promoter score. In this paper, we have designed a probabilistic model based on customer's actual data containing purchase or non-purchase states by tracking the SPC chain : customer satisfaction -> customer referral -> purchase/non-purchase. By applying hidden Markov model and Viterbi algorithm to marketing theory, we have developed the statistical model related to probability theories and have found the best purchase pattern scenario from customer's purchase records.

• Phonetics and Speech Sciences
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• v.13 no.4
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• pp.47-53
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• 2021

Autoregressive Text-to-Speech (TTS) models suffer from inference instability and slow inference speed. Inference instability occurs when a poorly predicted sample at time step t affects all the subsequent predictions. Slow inference speed arises from a model structure that forces the predicted samples from time steps 1 to t-1 to predict the sample at time step t. In this study, an end-to-end non-autoregressive fast text-to-speech model is suggested as a solution to these problems. The results of this study show that this model's Mean Opinion Score (MOS) is close to that of Tacotron 2 - WaveNet, while this model's inference speed and stability are higher than those of Tacotron 2 - WaveNet. Further, this study aims to offer insight into the improvement of non-autoregressive models.

• Journal of KIISE
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• v.42 no.12
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• pp.1522-1534
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• 2015

This work presents a new method that can predict, without the use of training data, whether an input essay is written on a given topic. ConceptNet is a common-sense knowledge base that is generated automatically from sentences that are extracted from a variety of document types. An essay prompt is the topic that an essay should be written about. The method that is proposed in this paper uses ConceptNet and an essay prompt to decide whether or not an input essay is off-topic. We introduce a way to find the shortest path between two nodes on ConceptNet, as well as a way to calculate the semantic similarity between two nodes. Not only an essay prompt but also a student's essay can be represented by concept nodes in ConceptNet. The semantic similarity between the concepts that represent an essay prompt and the other concepts that represent a student's essay can be used for a calculation to rank "on-topicness" ; if a low ranking is derived, an essay is regarded as off-topic. We used eight different essay prompts and a student-essay collection for the performance evaluation, whereby our proposed method shows a performance that is better than those of the previous studies. As ConceptNet enables the conduction of a simple text inference, our new method looks very promising with respect to the design of an essay prompt for which a simple inference is required.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.30-41
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• 1992

As a general model of rule-based systems, we propose a model for a fuzzy production system having chaining rules and an inference engine associated with the model. The concept of so-called 'fuzzy petri nets' is used to model the fuzzy production system and the inference engine is designed to be capable of handling inexact knowledge. The fuzzy logic is adopted to represent vagueness in the rules and the certainty factor is used to express uncertainty of each rules given by a human expert. Parallel, inference schemes are devised by transforming Fuzzy Petri nets to matrix formula. Futher, the inference engine mechanism under the Mamdani's implication method can be desceribed by a simple algebraic formula, which makes real time inference possible.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.543-563
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• 1996

In this paper, we propose a Timed Fuzzy Petri Net(TFPN) model as a new model of real-time fuzzy control. The TFPN model, which is useful for fuzzy inference and fuzzy control is an integrated model of Timed PetriNet and Fuzzy Petri Net. Additionally, a Timed Fuzzy Control Language is defined as a textual specification model of fuzzy control rues, and proposed a TFPN modeling method. The TFPN model is a Petri Net formalism of fuzzy control systems. Execution rule is consisted of marking(i.e,fuzzyfication) and firing(i.e,inference and defuzzyfication) procedures. A simple case work by using TFPN model shows us computing time of inference and defuzzyfication is low and uncertainty and visibility of fuzzy control rule are modeled effectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.405-413
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• 2016

In this paper, we propose a proactive inference method of finding suspicious domains. Our method detects potential malicious domains from the seed domain information extracted from the TLD Zone files and WHOIS information. The inference process follows the three steps: searching the candidate domains, machine learning, and generating a suspicious domain pool. In the first step, we search the TLD Zone files and build a candidate domain set which has the same name server information with the seed domain. The next step clusters the candidate domains by the similarity of the WHOIS information. The final step in the inference process finds the seed domain's cluster, and make the cluster as a suspicious domain set. In experiments, we used .COM and .NET TLD Zone files, and tested 10 seed domains selected by our analysts. The experimental results show that our proposed method finds 55 suspicious domains and 52 true positives. F1 scores 0.91, and precision is 0.95 We hope our proposal will contribute to the further proactive malicious domain blacklisting research.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2704-2710
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• 2024

Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste. We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation.