• Journal of KIISE:Databases
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• v.27 no.2
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• pp.185-198
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• 2000

Recently the technological advance in the hardware dealing with multimedia data as well as the explosive increase of the volume of multimedia data bring about new interest in the use of multimedia presentations in many application domains. To use multimedia presentations efficiently, the integration of multimedia presentations into DBMS is necessary. This paper presents a multimedia presentatation query language based on contents and query processing techniques. Presently, multimedia presentation authoring tools denote a multimedia presentation using a presentation graph which is a DAG. A Node in the graph is a same type of media stream and edges denote a play-out order and a synchronization way among nodes. The contents of presentations graphs are the information of each stream, the sequential order of the information inside each stream and the play-out order among the streams. GCalculus/S is a calculus-based query language and can deal with the contents of a presentation graph and physical characteristics of multimedia data. It expresses the sequential order of information inside each stream and the play-out order of streams of a presentation graph using temporal operators Next, Connected and Until. O-Algebra, which is object algebra, is extended to process GCalculus/S queries.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of Labour Economics
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• v.27 no.2
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• pp.29-66
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• 2004

In this paper, I investigate job creation and destruction in Korean mining and manufacturing between 1982 and 2000 using the raw data of Annual Mining and Manufacturing Survey. The rate of job creation and destruction of continuing plants averaged 9.75 and 10.33, respectively, which are higher than those of OECD countries, Chile, and Colombia. The created jobs showed weak persistence and the concentration of job reallocation is high, compared with other countries. Job reallocation accounts for major fraction of worker reallocation and the fraction has increased before 1997. Analysis of time series data of job flow revealed a general pattern of pro-cyclic job creation and counter-cyclic job destruction. However job reallocation in Korea is strongly acyclic whereas the rate is known to be counter-cyclical in the U.S.

• Genomics & Informatics
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• v.7 no.2
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• pp.122-130
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• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.2
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• pp.185-188
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• 2003

Data on the use of breeding records of dairy cattle farm of Fenghuang-shan in Chengdu areas during a period of six years is systematically analyzed. The results show that Holstein heifers have their 1st estrus at an average age of $513.6{\pm}46.7$ d which is not related to the calving season. Estrus mostly occurs in the months with mild ambient temperature (March, April, May, November and December). There is a very poor rate of estrus detection; only 30.0% successive estrus is observed within 24 d, 29.3% within 25-48 d, 40.6% over 48 d. The average number of insemination per conception is 1.50 for heifers and 1.74 for cows, but conception rate (CR) is lower in the relatively warm months (July, August and September) ranged from 48.1% to 51.9% compared with 58.1% to 68.5% in other months. High temperature is the most important factor affecting fertilization in warm months, but neither did CR decline immediately with the increased air temperature in June, nor did it increase immediately with the declined air temperature in September. Post partum anestrous period is $119.5{\pm}60.5$ d. The average interval from calving to conception is $159.4{\pm}85.6$ d with only 19.8% of the cows conceived within 85 d of calving. Cows with high milk yield have longer acyclic periods and lower conception rates. Improvement of efficient managements must be a precedent condition in Chengdu areas.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.9_10
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• pp.476-484
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• 2008

Most string problems and their solutions are relevant to diverse applications such as pattern matching, data compression, recently bioinformatics, and so on. However, there have been few works on the relations between string problems and cryptographic problems. In this paper, we consider the following string reconstruction problems and show how these problems can be applied to cryptography. Given a string x of length n over a constant-sized alphabet ${\sum}$ and a set W of strings of lengths at most an integer $k({\leq}n)$, the first problem is to find the sequence of strings in W that reconstruct x by the minimum number of concatenations. We propose an O(kn+L)-time algorithm for this problem, where L is the sum of all lengths of strings in a given set, using suffix trees and a shortest path algorithm for directed acyclic graphs. The other is a dynamic version of the first problem and we propose an $O(k^3n+L)$-time algorithm. Finally, we show that exponentiation problems that arise in cryptography can be successfully reduced to these problems and propose a new solution for exponentiation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2002-2019
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• 2017

RPL routing protocol for low-power and lossy networks is an Internet Engineering Task Force (IETF) recommended IPv6 based protocol for routing over Low power Lossy Networks (LLNs). RPL is proposed for networks with characteristics like small packet size, low bandwidth, low data rate, lossy wireless links and low power. RPL is a proactive routing protocol that creates a Directed Acyclic Graph (DAG) of the network topology. RPL is increasingly used for Internet of Things (IoT) which comprises of heterogeneous networks and applications. RPL proposes a single path routing strategy. The forwarding technique of RPL does not support multiple paths between source and destination. Multipath routing is an important strategy used in both sensor and ad-hoc network for performance enhancement. Multipath routing is also used to achieve multi-fold objectives including higher reliability, increase in throughput, fault tolerance, congestion mitigation and hole avoidance. In this paper, M-RPL (Multi-path extension of RPL) is proposed, which aims to provide temporary multiple paths during congestion over a single routing path. Congestion is primarily detected using buffer size and packet delivery ratio at forwarding nodes. Congestion is mitigated by creating partially disjoint multiple paths and by avoiding forwarding of packets through the congested node. Detailed simulation analysis of M-RPL against RPL in both grid and random topologies shows that M-RPL successfully mitigates congestion and it enhances overall network throughput.

• Journal of IKEEE
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• v.22 no.3
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• pp.590-598
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• 2018

Recently, interest in IoT(Internet of Things) technology, which provides Internet services to objects, is increasing. IoT offers a variety of services in home networks, healthcare, and disaster alerts. IoT with LLN(Low Power & Lossy Networks) feature frequently loses sensor node. RPL, the standard routing protocol of IoT, performs global repair when data loss occurs in a sensor node. However, frequent loss of sensor nodes due to lower sensor nodes causes network performance degradation due to frequent full path reset. In this paper, we propose an additional selection method of the storage mode sensor node to solve the network degradation problem due to the frequent path resetting problem even after selecting the storage mode sensor node, and propose a method of equalizing the total path resetting number of each instance.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.359-368
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• 2013

Mashup is a web application that combines several different sources to create new services using Open APIs(Application Program Interfaces). Although the mashup has become very popular over the last few years, there are several challenging issues when combining a large number of APIs into the mashup, especially when composite APIs are manually integrated by mashup developers. This paper proposes a novel algorithm for automatic Open API composition. The proposed algorithm consists of constructing an operation connecting graph and searching composition candidates. We construct an operation connecting graph which is based on the semantic similarity between the inputs and the outputs of Open APIs. We generate directed acyclic graphs (DAGs) that can produce the output satisfying the desired goal. In order to produce the DAGs efficiently, we rapidly filter out APIs that are not useful for the composition. The algorithm is evaluated using a collection of REST and SOAP APIs extracted from ProgrammableWeb.com.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.1
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• pp.9-18
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• 2018

Enhancing performance of big data analytics in distributed environment has been issued because most of the big data related applications such as machine learning techniques and streaming services generally utilize distributed computing frameworks. Thus, optimizing performance of those applications at Spark has been actively researched. Since optimizing performance of the applications at distributed environment is challenging because it not only needs optimizing the applications themselves but also requires tuning of the distributed system configuration parameters. Although prior researches made a huge effort to improve execution performance, most of them only focused on one of three performance optimization aspect: application design, system tuning, hardware utilization. Thus, they couldn't handle an orchestration of those aspects. In this paper, we deeply analyze and model the application processing procedure of the Spark. Through the analyzed results, we propose performance optimization schemes for each step of the procedure: inner stage and outer stage. We also propose appropriate partitioning mechanism by analyzing relationship between partitioning parallelism and performance of the applications. We applied those three performance optimization schemes to WordCount, Pagerank, and Kmeans which are basic big data analytics and found nearly 50% performance improvement when all of those schemes are applied.