• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.8
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• pp.102-116
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• 2009

There have been lots of researches for H.264/AVC performance enhancement on a multi-core processor. The enhancement has been performed through parallelization methods. Parallelization methods can be classified into a task-level parallelization method and a data level parallelization method. A task-level parallelization method for H.264/AVC decoder is implemented by dividing H.264/AVC decoder algorithms into pipeline stages. However, it is not suitable for complex and large bitstreams due to poor load-balancing. Considering load-balancing and performance scalability, we propose a horizontal data level parallelization method for H.264/AVC decoder in such a way that threads are assigned to macroblock lines. We develop a mathematical performance expectation model for the proposed parallelization methods. For evaluation of the mathematical performance expectation, we measured the performance with JM 13.2 reference software on ARM11 MPCore Evaluation Board. The cycle-accurate measurement with SoCDesigner Co-verification Environment showed that expected performance and performance scalability of the proposed parallelization method was accurate in relatively high level

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.801-812
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• 2002

On-line reorganization in a shared nothing database cluster is crucial to the performance of the database system in a dynamic environment like WWW where the number of users grows rapidly and changing access patterns may exhibit high skew. In the existing method of on-line reorganization have a drawback that needs excessive data migrations in case more than two nodes within a cluster have overload at the same time. In this paper, we propose an advanced B$^{+}$ tree based on-line reorganization method that solves data skew on multi-nodes. Our method facilitates fast and efficient data migration by including spare nodes that are added to cluster through on-line scaling. Also we apply CSB$^{+}$ tree (Cache Sensitive B$^{+}$ tree) to our method instead of B$^{+}$ tree for fast select and update queries. We conducted performance study and implemented the method on Ultra Fault-Tolerant Database Cluster developed for high scalability and availability. Empirical results demonstrate that our proposed method is indeed effective and fast than the existing method. method.

• The KIPS Transactions:PartA
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• v.18A no.2
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• pp.45-52
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• 2011

In the past, a patient went to the room where an ultrasound image diagnosis device was set, and then he or she was examined by a doctor. However, currently a doctor can go and examine the patient with a handheld ultrasound device who stays in a room. However, it was implemented with only fundamental functions, and can not meet the high performance required by the focusing algorithm of ultrasound beam which determines the quality of ultrasound image. In addition, low energy consumption was satisfied for the mobile ultrasound device. To satisfy these requirements, this paper proposes a high-performance and low-power single instruction, multiple data (SIMD) based multi-core processor that supports a representative beamforming algorithm out of several focusing methods of mobile ultrasound image signals. The proposed SIMD multi-core processor, which consists of 16 processing elements (PEs), satisfies the high-performance required by the beamforming algorithm by exploiting considerable data-level parallelism inherent in the echo image data of ultrasound. Experimental results showed that the proposed multi-core processor outperforms a commercial high-performance processor, TI DSP C6416, in terms of execution time (15.8 times better), energy efficiency (6.9 times better), and area efficiency (10 times better).

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.6 s.324
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• pp.59-68
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• 2004

Thanks to the development of microelectromechanical systems(MEMS), wireless communication technology and microsensor technology, it was Possible to manufacture a very small and low costdata acquisition node with sensing function, processing function, wireless communication function and battery. Thus sensor networks begin to be prevailed. The sensor network is a spontaneous system which sets up automatically routing paths and transmits asignificant data to the destination. Sensor nodes requires low-power operation because most of them use a battery as operating power. Sensor nodes transmit a sensing data to the destination. Moreover, they play a router. In fact, because the later consumes more energy than the former, the low-power routing is very important. Sensor networks don't have a routing standard unlike general wireless Ad-hoc networks. So This paper proposes a low-power routing method for anting to sensor networks. It is based on AODV and adapts a method to drop probably RREQ depending on remaining power. We examined it through simulations. From simulation results, we could confirm to reduce power consumption about $10-20\%$ and distribute equally power consumption among nodes.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.3-12
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• 2010

In previous studies, the digital measurement systems and analysis algorithms were developed by using the related techniques, such as the aerial photograph detection and high resolution satellite image process. However, these studies were limited in 2-dimensional geo-processing. Therefore, it is necessary to apply the 3-dimensional spatial information and coordinate system for higher accuracy in recognizing and locating of geo-features. The objective of this study was to develop a stochastic algorithm for the optimal sensor placement using the 3-dimensional spatial analysis method. The 3-dimensional information of the LiDAR was applied in the sensor field algorithm based on 2- and/or 3-dimensional gridded points. This study was conducted with three case studies using the optimal sensor placement algorithms; the first case was based on 2-dimensional space without obstacles(2D-non obstacles), the second case was based on 2-dimensional space with obstacles(2D-obstacles), and lastly, the third case was based on 3-dimensional space with obstacles(3D-obstacles). Finally, this study suggested the methodology for the optimal sensor placement - especially, for ground-settled sensors - using the LiDAR data, and it showed the possibility of algorithm application in the information collection using sensors.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1096-1103
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• 2000

The default server strategy is commonly used for location and state managements of mobile host in mobile computing. With this strategy, we can find the cell of destination mobile host to send data by querying the default server. In SDN(single Default Notification) strategy which is a kind of default server strategy, the call is established after the location and state of the callee is acquired to the query server by querying the default server. But the communication cost overhead from the default server is increased if there are large number of cells and query requests, and if it is too far from the default server to a base station. Still more it will be unable to establish any calls to a mobile host when there is a fault in the default server of this host. In this paper, we suggest add evaluate a default server replication strategy to reduce the communication cost overhead and to make the service available.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.21-31
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• 2014

Singular value decomposition (SVD) has been widely used to identify unique features from a data set in various fields. However, a complex matrix calculation of SVD requires tremendous computation time. This paper improves the performance of a representative one-sided block Jacoby algorithm using a two-dimensional (2D) multi-core system. In addition, this paper explores an optimal multi-core system by varying the number of processing elements in the 2D multi-core system with the same 400MHz clock frequency and TSMC 28nm technology for each matrix-based one-sided block Jacoby algorithm ($128{\times}128$, $64{\times}64$, $32{\times}32$, $16{\times}16$). Moreover, this paper demonstrates the potential of the 2D multi-core system for the one-sided block Jacoby algorithm by comparing the performance of the multi-core system with a commercial high-performance graphics processing unit (GPU).

• Spatial Information Research
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• v.18 no.5
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• pp.119-131
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• 2010

Researchers require repetitive works such as data format analysis, reformatting and map reprojection in order to use geospatial data. To solve above problems, they are building web-based simulation systems with web developers. But the web-based systems are not efficiently developed because there is not the appropriate simulation framework for a web-based system using geospatial data. In this study, the geospatial simulation framework that can be effectively applied to the web-based system was designed and proposed. Also, the framework was composed of 7 modules; web mapping service, GIS mapping, statistics, model, processing,graphics, and geospatial datasets. In order to evaluate the effectiveness of the framework, a case study of urban growth has been verified. Experts who are not specialized in geospatial information disciplines expect to build easily a web-based system using geospatial data.