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Web-based SNMP and CORBA-based SNMP have been studied as a standard of network management technology in distributed environment. Jini can be used to build robust and reliable networks without modification to any platforms as network plug & play protocol. In this paper, we design and implement Jini-based SNMP system. This system has manager, agent, and lookup service, and communicates each other via Java RMI. The manager supervises networks by user interface component, the agent takes a various task of network management, and the lookup service links manager to agents dynamically. Security and portability are enhanced using JAVA, and communication overheads can be decreased by excluding pollings of servers.
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The aim of this paper is to classify vowel types attested in the TIMIT corpus of American English using a two-layer neural net with the input feature of dynamic formant trajectories. Unlike previous studies which used a single (near the middle) or two static points (near the beginning and ending points within the duration of a vowel), the current study tried to incorporate feature values related to formant trajectories by sampling F1 and F2 values at every 10% of the duration across vowels produced in a connected speech corpus. Experimental results showed that the 20 vowel categories annotated in the TIMIT corpus could be accurately predicted at a rate of about 60.1% with a model of a two-layer neural net. The results confirmed that trajectory-based features were a better predictor then formant values obtained from one or two points. Even though the accuracy reported in the paper is comparable to or better than the accuracy rates reported in previous literature, further research needs to be done which can incorporate contextual information.
Such Distributed object technology as CORBA, DCOM, and Java RMI do not improve directly the reliability of distributed application. it require replication group management of object unit and fault detection and recovery mechanism that distributed object technology has fault tolerance. In this paper, we design group manager and remote interface, and define a fault-tolerant class to develop the fault-tolerant Java RMI object. In order that a fault-tolerant class participate transparantly in a group, we should expend the naming class and RMIRegistry. Because of inheritance from a fault-tolerant class, a application developer can make easily fault-tolerant application object without any other service assistance.
Many methods have been proposed to provide asynchronous communication in distributed object system. But these methods are not compatible with Java RMI. we design and implement asynchronous event delivery system on the basis of Java RMI. It delivers events from Supplier to Consumer directly through Event Channel without consumer's request. Event channel includes proxies, and they make Consumer and Supplier object implemented independently. This system filters events by supplier identification, and eliminates supplier blocking by filtering module which implemented with Java thread and Thread Pool. We also define event information as class, and Java Serialization technique is used to deliver events efficeiently. The proposed system is compatible with Java RMI.
The IEEE 1149.5 Standard MTM (Module Test and Maintenance) Bus standardized a serial, backplane bus for use in test, diagnosis, and maintenance of electronic subsystems and, modules. The salve module interface circuits for MTM bus protocol are implemented to test modules that have IEEE 1149.1 boundary-scan architecture. this interface has a boundary-scan interface and a demultiplexer for selecting boundary-scan chain. In this paper, the testable module is tested in chip functions and interconnects using the slave module interface. It contains boundary-scan path added 4 bits ALU and 8 bits register. A test procedure on it is proposed and simulated. The interface and the test procedure are verified by simulated result.
Web meta search engine has user interface consistents, but it has serious problem to concentrate network load on meta search engine server. In this paper, we have suggested client based web meta search engine architecture and implemented it to solve the problem and enhance performance of meta search engine. It substitute client based web meta search engine for the function of meta search engine. Thus, client based search engine solves network connection problem and reduces transfer time of query forms and search results.
The paper aims at investigating the cross-linguistic distribution of modal auxiliaries by analyzing the written corpus of TOEFL writing samples contributed by 121,000 test takers. The writing samples were POS-tagged and only modal auxiliaries were extracted from the collection of tagged words. The results showed that the learner corpus had a higher percentage of modal auxiliaries than L1 corpora. While English speakers are reported to use around 1.45% of modal auxiliaries in their writing, the L2 learners of English in the TOELF11 corpus make use of modal auxiliaries by 0.5 to 1% more. Simple correspondence analysis conducted on the TOEFL11 corpus also revealed that Romance languages were closer with each other than other groups of languages, and East Asian languages such as Korean and Japanese were measured to be closer to each other than other languages with regard to the distribution of modal auxiliaries. Turkish posed a problem in that even if Turkish belongs to the Altaic language family along with Japanese and Korean, it did not show close relationship to these East Asian languages, but to Chinese and German. Even though more research were to be done, the simple correspondence analysis adopted in this paper turned out to be a robust tool that could capture the closeness of languages regarding the distribution of modal auxiliaries.
This paper aims at modeling topics from TOEFL essay samples in the TOEFL11 corpus. The TOEFL11 corpus is a collection of 12,100 TOEFL writing samples submitted by test-takers from 11 different countries. The paper applied an unsupervised method (i.e. Latent Dirichlet Allocation or LDA) of clustering texts to written samples, with the aim of automatic modeling of topics. For each of the 11 non-native TOEFL test takers, 1,100 TOEFL essays were transformed to a document-term matrix, and then were fed into the LDA function in the R software. The number of potential topics was set to be 8, which was the same number of prompts the test takers had been given when they took the test. The overall accuracy ranged from 83% to 99% depending on the native language of the test takers. Further analysis needs to be conducted to see how reliably the unsupervised LDA method can be used in automatically classifying written samples to potential topics. Nevertheless, the paper provides an empirical foundation that automatic topic modeling can be applied in an unsupervised way even to the writing sample of English learners.