• 디지털산업정보학회논문지
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• 제5권4호
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• pp.87-97
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• 2009

Internet network routing system is used to prevent spread and distribution of malicious data traffic. This study is based on analysis of diagnostic weakness structure in the network security domain. We propose an improved integrated multi-level protection domain for in the internal route of groupware. This paper's protection domain is designed to handle the malicious data traffic in the groupware and finally leads to lighten the load of data traffic and improve network security in the groupware. Infrastructure of protection domain is transformed into five-stage blocking domain from two or three-stage blocking. Filtering and protections are executed for the entire server at the gateway level and internet traffic route ensures differentiated protection by dividing into five-stage. Five-stage multi-level network security domain's malicious data traffic protection performance is better than former one. In this paper, we use a trust evaluation metric for measuring the security domain's performance and suggested algorithm.

• Journal of Information Processing Systems
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• 제17권5호
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• Journal of Mechanical Science and Technology
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• 제15권5호
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• pp.555-561
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• 2001

A new approach to analyze the multi-domain acoustic system divided and enclosed by flexible structures is presented in this paper. The boundary element formulation of the Helmholtz integral equation is used for the internal fields and the finite element formulation for the structures surrounding the fields. We developed a numerical analysis program for the structural-acoustic coupling problems of the multi-domain system, in which boundary conditions such as the continuity of normal particle velocity and sound pressure in the structural interfaces between Field 1 and Field 2 are not needed. The validity of the numerical analysis program is verified by comparing the numerical results with the experimental ones. Example problems are included to investigate the characteristics of the coupled multi-domain system.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.489-493
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• 2007

Mmulti-domain noise analysis method using Power Flow Boundary Element Method(PFBEM) has been developed successfully. Some applications are introduced. several examples. PFBEM is a numerical analysis method formulated by applying Boundary Element Method(BEM) to Power Flow Analysis(PFA). PFBEM is very powerful in predicting noise level in medium-to-high frequency ranges. However there are restrictions in analyzing the coupled structures and multi-media. In this paper, an analysis method for multi-domain acoustic problems in the diverse acoustic fields is suggested. And the developed method is applied to the car interior and exterior multi-domain noise analysis.

• 한국해양공학회지
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• 제20권5호
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• pp.36-41
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• 2006

The main object of this study is to develop an accurate and convenient method for the response analysis of offshore structures in real sea states. A numerical procedure is described for predicting the motion responses and tension variations of the ISSC TLP in multi-directional irregular waves. The developed numerical approach in the frequency domain is based on acombination of the three dimensional source distribution method, the dynamic response analysis method, and the spectral analysis method. Frequency domain analysis in the multi-directional irregular waves is expanded to a time domain analysis by using a convolution integral after obtaining the impulse response by Fourier transformation. The results of the comparison between responses in the frequency and time domain confirmed the validity of the proposed approach.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.819-832
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• 2022

Cross-domain recommender system transfers knowledge across different domains to improve the recommendation performance in a target domain that has a relatively sparse model. However, they suffer from the "negative transfer" in which transferred knowledge operates as noise. This paper proposes a novel Multiple Fusion-based Deep Cross-Domain Recommendation named MFDCR. We exploit Doc2Vec, one of the famous word embedding techniques, to fuse data user-wise and transfer knowledge across multi-domains. It alleviates the "negative transfer" problem. Additionally, we introduce a simple multi-layer perception to learn the user-item interactions and predict the possibility of preferring items by users. Extensive experiments with three domain datasets from one of the most famous services Amazon demonstrate that MFDCR outperforms recent single and cross-domain recommendation algorithms. Furthermore, experimental results show that MFDCR can address the problem of "negative transfer" and improve recommendation performance for multiple domains simultaneously. In addition, we show that our approach is efficient in extending toward more domains.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1992년도 봄 학술발표회 논문집
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• pp.58-64
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• 1992

It is usual that underground structures are constructed within multi-layered medium. In this paper, an efficient numerical model ling of multi-layered structural systems is studied using coupled analysis of finite elements and boundary elements. The finite elements are applied to the area in which the material nonlinearity is dominated, and the boundary elements are applied to the far field area where the nonlinearity is relatively weak. In the boundary element model 1 ins of the multi-layered medium, fundamental solutions are restricted. Thus, methods which can utilize existing Kelvin and Melan solution are sought for the interior multi-layered domain problem and semi infinite domain problem. Interior domain problem which has piecewise homogeneous layers is analyzed using boundary elements with Kelvin solution; by discretizing each homogeneous subregion and applying compatibility and equilibrium conditions between interfaces. Semi-infinite domain problem is analyzed using boundary elements with Melan solution, by superposing unit stiffness matrices which are obtained for each layer by enemy method. Each methodology is verified by comparing its results which the results from the finite element analysis and it is concluded that coupled analysis using boundary elements and finite elements can be reasonable and efficient if the superposition technique is applied for the multi-layered semi-infinite domain problems.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.250-253
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• 2005

A Programmable Automation Controller (PAC) has been developed by Turbotek Co., Ltd. The developed system has multi-domain functionality-including sequence control, motion control and HMI- on a single platform. The PAC also has a common development platform for the design and integration of multi-domain automated systems. Since hardware of the developed system has modular architectures, performance and specification of the controller are determined by combination of specific modules. The developed system employs de facto standards such as OPC interface that allow users to easily exchange data as part of networked multi-vendor systems.

• ETRI Journal
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• 제32권4호
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• pp.622-625
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• 2010

In this letter, we assess the scalability of a path computation flooding (PCF) approach to compute optimal end-to-end inter-domain paths in a path computation element-based multi-domain network. PCF yields a drastically reduced network blocking probability compared to a blind per-domain path computation but introduces significant network control overhead and path computation complexity. In view of this, we introduce and compare an alternative low overhead PCF (LoPCF) solution. From the obtained results, LoPCF leads to similar blocking probabilities to PCF while exhibiting around 50% path computation complexity and network control overhead reduction.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.308-310
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• 2022

With the rise of the Al wave, task-oriented dialogue systems have become one of the popular research directions in academia and industry. Currently, task-oriented dialogue systems mainly adopt pipelined form, which mainly includes natural language understanding, dialogue state decision making, dialogue state tracking and natural language generation. However, pipelining is prone to error propagation, so many task-oriented dialogue systems in the market are only for single-round dialogues. Usually single- domain dialogues have relatively accurate semantic understanding, while they tend to perform poorly on multi-domain, multi-round dialogue datasets. To solve these issues, we developed a paddle-based multi-domain task-oriented Chinese dialogue system. It is based on NEZHA-base pre-training model and CrossWOZ dataset, and uses intention recognition module, dichotomous slot recognition module and NER recognition module to do DST and generate replies based on rules. Experiments show that the dialogue system not only makes good use of the context, but also effectively addresses long-term dependencies. In our approach, the DST of dialogue tracking state is improved, and our DST can identify multiple slotted key-value pairs involved in the discourse, which eliminates the need for manual tagging and thus greatly saves manpower.