• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제8권5호
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• pp.520-529
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• 2002

기존의 MPEG-4 저작 도구들은 대부분 BUS 전문가용 시스템으로 저작 도구 사용에 앞서 MPEG-4 컨텐트 표현 구조인 BIFS에 대한 선지식을 요구하며, 저작자들은 이를 습득하기 위해 많은 시간과 노력을 소비해야 한다. 본 논문에서는 MPEG-4 컨텐트 개발에 소요되는 이러한 노력을 절감하기 위해 사용자에게는 기존 멀티미디어 저작도구의 편리한 저작 환경을 제공하고, 이를 저작 도구 내부에서 BIFS로 변환하는 방법을 제시한다. 저작 환경은 BIFS 생성을 위해 요구되는 최소한의 저작 정보를 생성시키고, BIFS 생성 모듈은 이를 미리 정의해둔 BIFS 구성 규칙을 이용하여 BIFS로 재구성한다. 생성된 BIFS는 장면 트리, 객체 기술자, 연결경로(Route)로 구성되며, 이들은 MPEG-4 파일 포맷에 따라 기본 스트림과 먹싱되어 MP4 파일로 만들어진다.

• 한국정보과학회논문지:데이타베이스
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• 제36권2호
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• pp.150-167
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• 2009

XML은 인터넷과 유비쿼터스 환경의 데이타에 대한 저장과 교환, 출판의 목적으로 널리 사용되고 있다. XML의 광범위한 사용에 따라 XML 데이타를 효율적으로 저장하고 활용하기 위한 방법으로 레이블링 방법이 연구되고 있다. 레이블링 방법에 대한 최근 연구들은 동적으로 업데이트 가능한 XML 문서에 대한 효과적인 레이블링 방법에 중점을 두고 있다. 그러나 레이블 재작성 비용, 레이블 저장을 위한 큰 저장공간 할당 등의 문제점이 있다. 이러한 문제점은 새로운 데이타가 지속적으로 삽입될 경우 더욱 심화된다. 본 논문에서는 XML 문서를 원으로 나타냄으로써 회전수, 부모/자식원의 개념을 적용하여 전체 레이블 저장공간의 효율을 얻는 방법을 제시한다. 그리고 반지름 개념을 적용하여 동일 위치에 지속적인 새로운 데이타 삽입 시에도 레이블의 길이가 증가하지 않으면서 기존 레이블의 변경을 초래하지 않는 방법을 제시한다. 또한 실험을 통해 제안하는 원형 레이블링 방법의 우수성을 보인다. 본 논문은 XML 문서를 원으로 이해하는 새로운 시도를 한 점과 XML 문서의 크기 증가 시 레이블 저장공간의 효율을 얻을 수 있는 점과 동적 XML 환경에서 새로운 데이타의 업데이트 시에 기존 노드들에 대해 레이블 재작성을 피할 수 있는 점에 의미가 있다.

• Current Research on Agriculture and Life Sciences
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• 제20권
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• pp.77-82
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• 2002

This study was carried out in order to produce useful material for the forest multiple use and forest protection by physico-chemical soil analysis of studied area in Sokwang-ri Forest Genetic Resource Protection Forest which was divided into in standard plots include Tricholoma matsutake and Sarcodon aspratus production forest. The result of physico-chemical soil analysis represented as following. The soil type of T. matsutake production forest was Dry brown forest soil(B1), while on the other hand the soil type of S. aspratus production forest was Moderately moist brown forest soil(B3). Between T. matsutake and S. aspratus production forest did not result in significant changes in soil pH(5.22-5.60) and soil depth(47cm), but available phosphorus, carbon, and nitrogen contents were different results. CN ratio of the fairy ring of T. matsutake was quite lower than that in S. aspratus production forests, which indicated that T. matsutake production forest was built up in the relatively immature soils which contain little organic matter. Generally, it was predicted that Pinus densiflora for. erecta forest succeeded to deciduous tree forest in stable soil environments. To conserve these T. matsutake and S. aspratus production forest, the contents of available phosphorous and exchangeable cation should be increased by continuous soil environment management and it should be established the secondary growth forests of old aged Pinus densiflora for. erecta trees as soon as possible.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.959-974
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• 2020

High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.

• 상하수도학회지
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• 제35권1호
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• pp.83-91
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• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• 창의정보문화연구
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• 제7권3호
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• pp.157-165
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• 2021

웹, 모바일, IoT 등의 기술을 활용한 다양한 어플리케이션 서비스의 활용과 이에 따른 대용량 데이터 관리의 필요성이 확대됨에 따라 효율적인 데이터 표현 및 교환 방법과 데이터에 대한 질의 처리의 중요성이 증가하고 있다. 간결함을 특징으로 갖는 JSON은 웹 상의 표준 데이터 표현 및 교환 언어인 XML를 대신하여 데이터 교환 및 대용량 데이터 저장의 포맷으로 다양한 영역에서 활용되고 있다. 이는 JSON으로 표현된 대량의 데이터를 효과적으로 접근 및 검색하기 위한 인덱싱 및 질의 처리 기법의 개발이 중요함을 의미한다. 이에 본 논문에서는 계층적 구조를 특징으로 가지는 JSON 데이터를 트리 형태로 모델링 하고 경로 개념을 이용한 인덱싱 및 질의 처리 방안을 제안한다. 특히, 텍스트 검색에서 널리 사용되는 접미사 배열을 활용한 인덱스 구조를 설계하였으며 이를 활용하여 단순 및 복합 경로 기반의 JSON 데이터 질의 처리 방안들을 소개하였다.

• 한국전문물리치료학회지
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• 제28권2호
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• pp.123-131
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• 2021

Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.

• ETRI Journal
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• 제44권4호
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• pp.573-587
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• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• 식물병연구
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• 제28권2호
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• pp.98-107
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• 2022

The incidence of Tomato brown rugose fruit virus (ToBRFV) and biological and molecular characterization of the Palestinian isolates of ToBRFV are described in this study. Symptomatic leaf samples obtained from Solanum lycopersicum L. (tomatoes) and Nicotiana tabacum L. (cultivated tobacco) plants were tested for tobamoviruses infection by reverse transcription polymerase chain reaction. Tomato leaf samples collected from Tulkarm and Qalqilia are infected with ToBRFV-PAL with an infection rate of 76% and 72.5%, respectively. Leaf samples collected from Jenin and Nablus were found to be mixed infected with ToBRFV-PAL and Tobacco mosaic virus (TMV) (100%). Sequence analysis of the ToBRFV-PAL genome showed that the net average nucleotide divergence between ToBRFV/F48-PAL strain and the Israeli and Turkish strains was 0.0026398±0.0006638 (±standard error of mean), while it was 0.0033066±0.0007433 between ToBRFV/F42-PAL and these two isolates. In the phylogenetic tree constructed with the complete genomic sequence, all the ToBRFV isolates were clustered together and formed a sister branch with the TMV. The sequenced Palestinian isolates of ToBRFV-PAL shared the highest nucleotide identity with the Israeli ToBRFV isolate suggesting that the virus was introduced to Palestine from Israel. The findings of this study enhance our understanding of the biological and molecular characteristics of ToBRFV which would help in the management of the disease.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.549-561
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• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.