• Journal of Fisheries and Marine Sciences Education
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• v.18 no.1
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• pp.19-30
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• 2006

For fishery stock assessment and optimum sustainable yield of anchovy in Korea, surplus production(SP) models and a maximum entropy(ME) model are employed in this paper. For determining appropriate models, five traditional SP models-Schaefer model, Schnute model, Walters and Hilborn model, Fox model, and Clarke, Yoshimoto and Pooley (CYP) model- are tested for effort and catch data of anchovy that occupies 7% in the total fisheries landings of Korea. Only CYP model of five SP models fits statistically significant at the 10% level. Estimated intrinsic growth rates are similar in both CYP and ME models, while environmental carrying capacity of the ME model is quite greater than that of the CYP model. In addition, the estimated maximum sustainable yield(MSY), 213,287 tons in the ME model is slightly higher than that of CYP model (198,364 tons). Biomass for MSY in the ME model, however, is calculated 651,000 tons which is considerably greater than that of the CYP model (322,881 tons). It is meaningful in that two models are compared for noting some implications about any significant difference of stock assessment and their potential strength and weakness.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.1
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• pp.18-28
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• 2013

It was compared the estimated parameters by the surplus production from three different models, i.e., three types (Schaefer, Gulland, and Schnute) of the traditional surplus production models, a stock production model incorporating covariates (ASPIC) model and a maximum entropy (ME) model. We also evaluated the performance of models in the estimation of their parameters. The maximum sustainable yield (MSY) of small yellow croaker (Pseudosciaena polyactis) in Korean waters ranged from 35,061 metric tons (mt) by Gulland model to 44,844mt by ME model, and fishing effort at MSY ($f_{MSY}$) ranged from 262,188hauls by Schnute model to 355,200hauls by ME model. The lowest root mean square error (RMSE) for small yellow croaker was obtained from the Gulland surplus production model, while the highest RMSE was from Schnute model. However, the highest coefficient of determination ($R^2$) was from the ME model, but the ASPIC model yielded the lowest coefficient. On the other hand, the MSY of Kapenta (Limnothrissa miodon) ranged from 16,880 mt by ASPIC model to 25,373mt by ME model, and $f_{MSY}$, from 94,580hauls by ASPIC model to 225,490hauls by Schnute model. In this case, both the lowest root mean square error (RMSE) and the highest coefficient of determination ($R^2$) were obtained from the ME model, which showed relatively better fits of data to the model, indicating that the ME model is statistically more stable and robust than other models. Moreover, the ME model could provide additional ecologically useful parameters such as, biomass at MSY ($B_{MSY}$), carrying capacity of the population (K), catchability coefficient (q) and the intrinsic rate of population growth (r).

• Environmental and Resource Economics Review
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• v.26 no.4
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• pp.613-639
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• 2017

A Maximum Entropy (ME) Model and an Analytical Model are analyzed in assessing Kapenta stock in Lake Kariba. The ME model estimates a Maximum Sustainable Yield (MSY) of 25,372 tons and a corresponding effort of 109,731 fishing nights suggesting overcapacity in the lake at current effort level. The model estimates a declining stock from 1988 to 2009. The Analytical Model estimates an Acceptable Biological Catch (ABC) annually and a corresponding fishing mortality (F) of 1.210/year which is higher than the prevailing fishing mortality of 0.927/year. The ME and Analytical Models estimate a similar biomass in the reference year 1982 confirming that both models are applicable to the stock. The ME model estimates annual biomass which has been gradually declining until less than one third of maximum biomass (156,047 tons) in 1988. It implies that the stock has been overexploited due to yieldings over the level of ABC compared to variations in annual catch, even if the recent prevailing catch levels were not up to the level of MSY. In comparison, the Analytical Model provides a more conservative value of ABC compared to the MSY value estimated by the ME model. Conservative management policies should be taken to reduce the aggregate amount of annual catch employing the total allowable catch system and effort reduction program.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1240-1245
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• 2006

한국어 문장을 구조 분석할 때에 모호성을 발생시키는 유형 중의 하나가 나열 및 병렬형이다. 문장 구조 복잡도를 증가시키는 나열 및 병렬형을 구조 분석 전에 미리 하나의 단위로 묶어서 처리하는 것이 문장 구조 분석의 정확도를 높이는데 중요하다. 본 연구에서는 형태소 태그를 이용한 기본 규칙으로 문장을 청크 단위로 분할하고 분할된 청크 중에서 나열형을 인식하여 해당되는 청크들을 하나의 나열 청크로 통합하여 청크의 개수를 줄인다. 병렬형에 대해서는 반복되는 병렬 청크의 범위와 생략된 용언을 복원한다. 이러한 인식은 첫 단계로 기호(symbol)를 중심으로 구축된 간단한 규칙으로 인식을 하고 이러한 규칙에 해당되지 않는 형태의 나열 및 병렬형은 Maximum Entropy 모델을 이용하여 적용한다. ME모델은 어휘자질, 형태소 품사 자질, 거리 자질, 의미자질, 구 단위 태그 자질(NP:명사구, VP:동사구, AP:형용사구), BIO 태그(Begin, Inside, Outside) 자질에 대한 ME(Maximum Entropy) 모델을 이용하여 구축되었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.985-987
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• 2010

In this paper, we propose a maximum entropy-based emotion recognition model using individual average difference. In order to accurately recognize an user' s emotion, the proposed model utilizes the difference between the average of the given input physiological signals and the average of each emotion state' signals rather than only the input signal. For the purpose of alleviating data sparse -ness, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of physiological signals based on a second rather than the longer total emotion response time. With the aim of easily constructing the model, it utilizes a simple average difference calculation technique and a maximum entropy model, one of well-known machine learning techniques.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.521-534
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• 2006

This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.

• MALSORI
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• no.53
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• pp.75-92
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• 2005

Prosody Generation Based on C-ToBI Representation for Text-to-SpeechSeungwon Kim, Yu Zheng, Gary Geunbae Lee, Byeongchang KimProsody modeling is critical in developing text-to-speech (TTS) systems where speech synthesis is used to automatically generate natural speech. In this paper, we present a prosody generation architecture based on Chinese Tone and Break Index (C-ToBI) representation. ToBI is a multi-tier representation system based on linguistic knowledge to transcribe events in an utterance. The TTS system which adopts ToBI as an intermediate representation is known to exhibit higher flexibility, modularity and domain/task portability compared with the direct prosody generation TTS systems. However, the cost of corpus preparation is very expensive for practical-level performance because the ToBI labeled corpus has been manually constructed by many prosody experts and normally requires a large amount of data for accurate statistical prosody modeling. This paper proposes a new method which transcribes the C-ToBI labels automatically in Chinese speech. We model Chinese prosody generation as a classification problem and apply conditional Maximum Entropy (ME) classification to this problem. We empirically verify the usefulness of various natural language and phonology features to make well-integrated features for ME framework.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.501-506
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• 2008

Statistical POS tagging is prone to error, because of the inherent limitations of statistical data, especially single source of data. Therefore it is widely agreed that the possibility of further enhancement lies in exploiting various knowledge sources. However these data sources are bound to be inconsistent to each other. This paper shows the possibility of using maximum entropy model to Korean language POS tagging. We use as the knowledge sources n-gram data and trigger pair data. We show how perplexity measure varies when two knowledge sources are combined using maximum entropy method. The experiment used a trigram model which produced 94.9% accuracy using Hidden Markov Model, and showed increase to 95.6% when combined with trigger pair data using Maximum Entropy method. This clearly shows possibility of further enhancement when various knowledge sources are developed and combined using ME method.

• Annual Conference on Human and Language Technology
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• 2004.10d
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• pp.73-81
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• 2004

한국어에서의 품사 결정 문제는 형태론적 중의성 문제도 있지만, 영어에는 발생하지 않는 동품사 중의성 문제로 더 까다롭다. 이러한 문제들은 어휘 문맥을 고려하지 않고서는 해결하기 어렵다. 통계 자료 부족 문제에 쉽게 대처하는 모델이 필요하며 문맥에 따른 품사를 결정하고자 할 때 서로 다른 형태의 여러 가지 어휘 문맥 정보를 반영할 수 있는 모델이 필요하다. 본 논문에서는 이런 점에 가장 적합한 최대 엔트로피(maximum entropy : ME) 모델을 품사태깅 작업에 이용하는 문제에 대해 다룬다. 어휘 문맥 정보를 이용하기 위한 자질함수가 매우 많아지는 문제에 대처하기 위해 필요에 따라 어휘 문맥 정보를 사전화 한다. 본 시스템의 특징으로는 어절 단위 품사 태깅을 위한 처리 기법. 어절의 형태소 분석열에 대한 어절 내부 확률 계산. ME 모델의 정규화 과정 생략에 의한 성능 향상, 디코딩 경로의 확장과 같은 점들이 있다. 실험을 통하여 본 연구의 기법이 높은 성능의 시스템을 달성할 수 있음을 알게 되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.2
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• pp.63-81
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• 2008

WiMAX has been introduced as a competitive alternative for metropolitan broadband wireless access technologies. It is connection oriented and it can provide very high data rates, large service coverage, and flexible quality of services (QoS). Due to the large number of connections and flexible QoS supported by WiMAX, the uplink access in WiMAX networks is very challenging since the medium access control (MAC) protocol must efficiently manage the bandwidth and related channel allocations. In this paper, we propose and investigate a cost-effective WiMAX bandwidth management scheme, named the WiMAX partial sharing scheme (WPSS), in order to provide good QoS while achieving better bandwidth utilization and network throughput. The proposed bandwidth management scheme is compared with a simple but inefficient scheme, named the WiMAX complete sharing scheme (WCPS). A maximum entropy (ME) based analytical model (MEAM) is proposed for the performance evaluation of the two bandwidth management schemes. The reason for using MEAM for the performance evaluation is that MEAM can efficiently model a large-scale system in which the number of stations or connections is generally very high, while the traditional simulation and analytical (e.g., Markov models) approaches cannot perform well due to the high computation complexity. We model the bandwidth management scheme as a queuing network model (QNM) that consists of interacting multiclass queues for different service classes. Closed form expressions for the state and blocking probability distributions are derived for those schemes. Simulation results verify the MEAM numerical results and show that WPSS can significantly improve the network’s performance compared to WCPS.