• Journal of KIISE:Information Networking
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• v.31 no.1
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• pp.20-26
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• 2004

A congestion control algorithm to prevent buffer overflow in MSR(Mobility Support Router) for I-TCP is proposed. Due to high bit error rate and frequent hand-offs over wireless environment, the current congestion control scheme in TCP Reno over mixed(wired and wireless) network exhibits lower throughput than the throughput achieved over wired only network. I-TCP has been proposed to address this by splitting a TCP connection into two TCP connections over wired section and wireless section, respectively. However, buffer overflow in MSR may occur whenever there are excessive bit errors or frequent hand-offs. This may lead to the loss of packets acked by MSR(resident in buffer) to the sender, but not received by the receiver, breaking TCP end-to-end semantics. In this Paper, a new scheme is proposed to prevent the MSR buffer from overflow by introducing “flow control” between the sender and the MSR. Advertised window for the TCP connection between the sender and the MSR is tied to the remaining MSR buffer space, controlling the flow of packets to the MSR buffer before overflow occurs.

• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.43-60
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• 2016

Recently, Unsupervised Word Sense Disambiguation research has focused on Graph based disambiguation. Graph-based disambiguation has built a semantic graph based on words collocated in context or sentence. However, building such a graph over all ambiguous word lead to unnecessary addition of edges and nodes (and hence increasing the error). In contrast, our work uses Word2Vec to consider the most similar words to an ambiguous word in the context or sentences, to rebuild a graph of the matched words. As a result, we show a higher F1-Measure value than the previous methods by using Word2Vec.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.658-668
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• 2015

The purpose of present study tested the effects of context, transparency, familiarity and related variables on comprehension of 32 idioms in 87 Chinese learners of Korean who were attending the S university in Jeonnam providence. In the first assessment, idiomatic phrases were presented out of context. In another assessment, idiomatic phrases were embedded in supportive story contexts. To examine the difference based on task types, paired t-test or one-way ANOVA was used to test differences on related variables such as TOPIK, years of residence in Korea, major and etc. on idiom comprehension. The results of this study are summarized as follows. First, task type, familiarity and transparency were found to have no significant effect on idiom comprehension for Chinese learners of Korean. Second, the related variables such as TOPIK, and major had a significant effect on idiom comprehension. Third, percentage of context related interpretation error in context task was the highest. Literal interpretation errors were followed by it. It means they have a tendency to use contextual cues and semantic analysis of the phrase to comprehend Korean idioms. The results of study will be used to make a plan for teaching Chinese learners of Korean.

• Phonetics and Speech Sciences
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• v.8 no.2
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• pp.31-39
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• 2016

In emergency dispatching at 119 Command & Dispatch Center, some inconsistencies between the 'standard emergency aid system' and 'dispatch protocol,' which are both mandatory to follow, cause inefficiency in the dispatcher's performance. If an emergency dispatch system uses automatic speech recognition (ASR) to process the dispatcher's protocol speech during the case registration, it instantly extracts and provides the required information specified in the 'standard emergency aid system,' making the rescue command more efficient. For this purpose, we have developed a Korean large vocabulary continuous speech recognition system for 400,000 words to be used for the emergency dispatch system. The 400,000 words include vocabulary from news, SNS, blogs and emergency rescue domains. Acoustic model is constructed by using 1,300 hours of telephone call (8 kHz) speech, whereas language model is constructed by using 13 GB text corpus. From the transcribed corpus of 6,600 real telephone calls, call logs with emergency rescue command class and identified major symptom are extracted in connection with the rescue activity log and National Emergency Department Information System (NEDIS). ASR is applied to emergency dispatcher's repetition utterances about the patient information. Based on the Levenshtein distance between the ASR result and the template information, the emergency patient information is extracted. Experimental results show that 9.15% Word Error Rate of the speech recognition performance and 95.8% of emergency response detection performance are obtained for the emergency dispatch system.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.241-248
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• 2011

Homographs can have multiple senses. In order to understand the meaning of a sentence, it is necessary to identify which sense isused for each word in the sentence. Previous researches on this problem heavily relied on the word co-occurrence information. However, we noticed that in case of verbs, information about subordinating cases of verbs can be utilized to further improve the performance of word sense disambiguation. Different senses require different sets of subordinating cases. In this paper, we propose the verb sense disambiguation using subordinating case information. The case information acquire postposition features in Standard Korean Dictionary. Our experiment on 12 high-frequency verb homographs shows that adding case information can improve the performance of word sense disambiguation by 1.34%, from 97.3% to 98.7%. The amount of improvement may seem marginal, we think it is meaningful because the error ratio reduced to less than a half, from 2.7% to 1.3%.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.173-180
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• 2009

The purpose of developing an automated scoring system for English composition is to score English writing tests and to give diagnostic feedback to the test-takers without human's efforts. The system developed through our research detects grammatical errors of a single sentence on morphological, syntactic and semantic stages, respectively, and those errors are calculated into the final score. The error detecting stages are independent from one another, which causes duplicating the identical errors with different labels at different stages. These duplicated errors become a hindering factor to calculating an accurate score. This paper presents a solution to detecting the duplicated errors and improving an accuracy in calculating the final score by eliminating one of the errors.

• 재활복지
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• v.20 no.4
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• pp.229-246
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• 2016

The aim of this study is to conduct superordinate word selection task to compare their performance and reaction time, and superordinate word writing task to compare the differences in their performance and error pattern in 40 elderly adults and 43 young adults. As a result, first, in both tasks, elderly adults had a smaller number of correct responses. Second, elderly adults showed slower reaction time than young adults. Third, in superordinate word writing task, elderly adults showed more relevant errors than irrelevant errors. The reason elderly adults had a smaller number of correct responses in both tasks was that the links among the pieces of information in the semantic lexicon weakened or deteriorated due to normal aging. Slower reaction time was based on neurophysiological changes of the brain and cognitive processing speed. In addition, the relevant errors showed that they could access the lexicon for target words and produce explanation the relevant characteristics, even though they could not retrieve the target words.

• Korean Journal of Biological Psychiatry
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• v.28 no.2
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• pp.70-77
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• 2021

Objectives Our study aimed to present the distinctive correlates of formal thought disorder in patients with schizophrenia, using the Clinical Language Disorder Rating Scale (CLANG). Methods We compared clinical characteristics between schizophrenia patients with (n = 84) and without (n = 82) formal thought disorder. Psychometric scales including the CLANG, the Brief Psychiatric Rating Scale (BPRS), the Young Mania Rating Scale (YMRS), the Calgery Depression Scale for Schizophrenia (CDSS) and the Word Fluency Test (WFT) were used. Results After adjusting the effects of age, sex and total scores on the BPRS, YMRS and WFT, the subjects with disorganized speech presented significantly higher score on the abnormal syntax (p = 0.009), lack of semantic association (p = 0.005), discourse failure (p < 0.0001), pragmatics disorder (p = 0.001), dysarthria (p < 0.0001), and paraphasic error (p = 0.005) items than those without formal thought disorder. With defining the mentioned item scores as covariates, binary logistic regression model predicted that discourse failure (adjusted odds ratio [aOR] = 5.88, p < 0.0001) and pragmatics disorder (aOR = 2.17, p = 0.04) were distinctive correlates of formal thought disorder in patients with schizophrenia. Conclusions This study conducted Clinician Rated Dimensions of Psychosis Symptom Severity (CRDPSS) and CLANG scales on 166 hospitalized schizophrenia patients to explore the sub-items of the CLANG scale independently related to formal thought disorders in schizophrenia patients. Discourse failure and pragmatics disorder might be used as the distinctive indexes for formal thought disorder in patients with schizophrenia.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3991-4010
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• 2021

The Question Similarity Measurement of Chinese Crop Diseases and Insect Pests (QSM-CCD&IP) aims to judge the user's tendency to ask questions regarding input problems. The measurement is the basis of the Agricultural Knowledge Question and Answering (Q & A) system, information retrieval, and other tasks. However, the corpus and measurement methods available in this field have some deficiencies. In addition, error propagation may occur when the word boundary features and local context information are ignored when the general method embeds sentences. Hence, these factors make the task challenging. To solve the above problems and tackle the Question Similarity Measurement task in this work, a corpus on Chinese crop diseases and insect pests(CCDIP), which contains 13 categories, was established. Then, taking the CCDIP as the research object, this study proposes a Chinese agricultural text similarity matching model, namely, the AgrCQS. This model is based on mixed information extraction. Specifically, the hybrid embedding layer can enrich character information and improve the recognition ability of the model on the word boundary. The multi-scale local information can be extracted by multi-core convolutional neural network based on multi-weight (MM-CNN). The self-attention mechanism can enhance the fusion ability of the model on global information. In this research, the performance of the AgrCQS on the CCDIP is verified, and three benchmark datasets, namely, AFQMC, LCQMC, and BQ, are used. The accuracy rates are 93.92%, 74.42%, 86.35%, and 83.05%, respectively, which are higher than that of baseline systems without using any external knowledge. Additionally, the proposed method module can be extracted separately and applied to other models, thus providing reference for related research.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.