• Journal of KIISE:Information Networking
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• v.33 no.4
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• pp.289-298
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• 2006

Algorithms for application-level multicast often use trees to deliver data from the source to the multiple receivers. With the tree structure, the throughput experienced by the descendant nodes will be determined by the performance of the slowest ancestor node. Furthermore, the failure of an ancestor node results in the suspension of the session of all the descendant nodes. This paper focuses on the transmission of data using multiple virtual forwarders, and suggests a scheme to overcome the drawbacks of the plain tree-based application layer multicast schemes. The proposed scheme elects multiple forwarders other than the parent node of the delivery tree. A receiver receives data from the multiple forwarders as well as the parent node and it can increase the amount of receiving data per time unit. The multiple forwarder helps a receiver to reduce the impact of the failure of an ancestor node. The proposed scheme suggests the forwarder selection algorithm to avoid the receipt of duplicate packets. We implemented the proposed scheme using MACEDON which provides a development environment for application layer multicast. We compared the proposed scheme with Bullet by applying the implementation in PlanetLab which is a global overlay network. The evaluation results show that the proposed scheme enhanced the throughput by 20 % and reduced the control overhead over 90 % compared with Bullet.

• Journal of KIISE:Information Networking
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• v.35 no.5
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• pp.402-408
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• 2008

This paper considers the problem of minimizing network-wide broadcast latency in multi-rate wireless mesh networks where a node can dynamically adjust its link layer transmission rates to its neighbors. We propose a broadcast algorithm that complements existing broadcast construct algorithm which chooses a multicast node randomly when each candidate node has same metric. We consider the currently accumulated broadcast latency from source node to the each candidate node so far to choose the next broadcast node. The proposed broadcast algorithm for minimizing latency in a multi-rate mesh networks which exploit wireless advantage and the multi-rate nature of the network. Simulation based on current 802.11 parameters shows that proposed MinLink_WCDS algorithm improves overall latency than the previous existing broadcast algorithm.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.388-402
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• 2003

In this paper, we carried out the word, 4 continuous digits. continuous, and task-independent word recognition experiments to verify the effectiveness of the re-defined phoneme-likely units (PLUs) for the phonetic decision tree based HM-Net (Hidden Markov Network) context-dependent (CD) acoustic modeling in Korean appropriately. In case of the 48 PLUs, the phonemes /ㅂ/, /ㄷ/, /ㄱ/ are separated by initial sound, medial vowel, final consonant, and the consonants /ㄹ/, /ㅈ/, /ㅎ/ are also separated by initial sound, final consonant according to the position of syllable, word, and sentence, respectively. In this paper. therefore, we re-define the 39 PLUs by unifying the one phoneme in the separated initial sound, medial vowel, and final consonant of the 48 PLUs to construct the CD acoustic models effectively. Through the experimental results using the re-defined 39 PLUs, in word recognition experiments with the context-independent (CI) acoustic models, the 48 PLUs has an average of 7.06%, higher recognition accuracy than the 39 PLUs used. But in the speaker-independent word recognition experiments with the CD acoustic models, the 39 PLUs has an average of 0.61% better recognition accuracy than the 48 PLUs used. In the 4 continuous digits recognition experiments with the liaison phenomena. the 39 PLUs has also an average of 6.55% higher recognition accuracy. And then, in continuous speech recognition experiments, the 39 PLUs has an average of 15.08% better recognition accuracy than the 48 PLUs used too. Finally, though the 48, 39 PLUs have the lower recognition accuracy, the 39 PLUs has an average of 1.17% higher recognition characteristic than the 48 PLUs used in the task-independent word recognition experiments according to the unknown contextual factor. Through the above experiments, we verified the effectiveness of the re-defined 39 PLUs compared to the 48PLUs to construct the CD acoustic models in this paper.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.159-168
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• 2021

Building Information Modeling (BIM) data is a digitized construction design by worldwide construction design stands rules. Some research are being conducted to utilize blockchain for safe sharing and trade of BIM data, but there is no way to store BIM data directly in the blockchain due to the size of BIM data and technical limitation of the blockchain. In this paper, we propose a method of storing BIM data by combining a distributed file system and a blockchain. We propose two network overlays for storing BIM data, and we also propose generating the Level of Detail (LOD)-based merkle tree for efficient verification of BIM data. In addition, this paper proposes a system design for distributed storage of BIM data by using blockchain besu client and IPFS client. Our system design has a result that the processing speed stably increased despite the increase in data size.

• Fisheries and Aquatic Sciences
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• v.26 no.9
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• pp.569-581
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• 2023

The morphology and genetic identification of Rasbora lateristriata and Rasbora argyrotaenia between cultivated and wild populations has never been reported. This study compares morphology and cytochrome c oxidase (COI) genes between farmed and wild stock Rasbora spp. in Java and Sumatra island, Indonesia. We analyzed the truss network measurement (TNM) characters of 80 fish using discriminant function analysis statistical tests. DNA was extracted from muscle tissue of 24 fish specimens, which was then followed by polymerase chain reaction, sequencing, phylogenetic analysis, fixation index analysis, and statistical analysis of haplotype networks. Basic Local Alignment Search Tool analysis validated the following species: R. lateristriata and R. argyrotaenia from farming (Jogjakarta); Rasbora agryotaenia (Purworejo), R. lateristriata (Purworejo and Malang), Rasbora dusonensis (Palembang), and Rasbora einthovenii (Riau) from natural resources. Based on TNM characters, Rasbora spp. were divided into four groups, referring to four distinct characters in the middle of the body. The phylogenetic tree is divided into five clades. The genetic distance between R. argyrotaenia (Jogjakarta) and R. lateristriata (Malang) populations (0.66) was significantly different (p < 0.05). R. lateristriata (Purworejo) has the highest nucleotide diversity (0.43). R. argyrotaenia from Jogjakarta and Purworejo shared the same haplotype. The pattern of gene flow among them results from the two populations' close geographic proximity and environmental effects. R. argyrotaenia had low genetic diversity, therefore, increasing heterozygosity in cultivated populations is necessary to avoid inbreeding. Otherwise, R. lateristriata (Purworejo) had a greater gene variety that could be used to develop breeding. In conclusion, the middle body parts are a distinguishing morphometric character of Rasbora spp., and the COI gene is more heterozygous in the wild population than in farmed fish, therefore, enrichment of genetic variation is required for sustainable Rasbora fish farming.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• The Journal of the Acoustical Society of Korea
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• v.24 no.8
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• pp.498-504
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• 2005

We introduce a Korean speech recognition platform (ECHOS) developed for education and research Purposes. ECHOS lowers the entry barrier to speech recognition research and can be used as a reference engine by providing elementary speech recognition modules. It has an easy simple object-oriented architecture, implemented in the C++ language with the standard template library. The input of the ECHOS is digital speech data sampled at 8 or 16 kHz. Its output is the 1-best recognition result. N-best recognition results, and a word graph. The recognition engine is composed of MFCC/PLP feature extraction, HMM-based acoustic modeling, n-gram language modeling, finite state network (FSN)- and lexical tree-based search algorithms. It can handle various tasks from isolated word recognition to large vocabulary continuous speech recognition. We compare the performance of ECHOS and hidden Markov model toolkit (HTK) for validation. In an FSN-based task. ECHOS shows similar word accuracy while the recognition time is doubled because of object-oriented implementation. For a 8000-word continuous speech recognition task, using the lexical tree search algorithm different from the algorithm used in HTK, it increases the word error rate by $40\%$ relatively but reduces the recognition time to half.

• Journal of the Korean Wood Science and Technology
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• v.47 no.2
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• pp.229-238
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• 2019

Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.6
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• pp.235-242
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• 2021

In this work, we develop a Korean dependency parser based on a stack-pointer network that consists of a pointer network and an internal stack. The parser has an encoder and decoder and builds a dependency tree for an input sentence in a depth-first manner. The encoder of the parser encodes an input sentence, and the decoder selects a child for the word at the top of the stack at each step. Since the parser has the internal stack where a search path is stored, the parser can utilize information of previously derived subtrees when selecting a child node. Previous studies used only a grandparent and the most recently visited sibling without considering a subtree structure. In this paper, we introduce graph attention networks that can represent a previously derived subtree. Then we modify our parser based on the stack-pointer network to utilize subtree information produced by the graph attention networks. After training the dependency parser using Sejong and Everyone's corpus, we evaluate the parser's performance. Experimental results show that the proposed parser achieves better performance than the previous approaches at sentence-level accuracies when adopting 2-depth graph attention networks.