• Journal of KIISE:Computing Practices and Letters
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• v.11 no.6
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• pp.503-513
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• 2005

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity($77\%$) and specificity($95\%$) are achieved for test protein pairs containing common domains with teaming sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.

• The KIPS Transactions:PartA
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• v.12A no.4 s.94
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• pp.305-312
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• 2005

In this paper, we present algorithm-level techniques for energy-efficient design at the algorithm level using FPGAs. We then use these techniques to create energy-efficient designs for two signal processing kernel applications: fast Fourier transform(FFT) and matrix multiplication. We evaluate the performance, in terms of both latency and energy efficiency, of FPGAs in performing these tasks. Using a Xilinx Virtex-II as the target FPGA, we compare the performance of our designs to those from the Xilinx library as well as to conventional algorithms run on the PowerPC core embedded in the Virtex-II Pro and the Texas Instruments TMS320C6415. Our evaluations are done both through estimation based on energy and latency equations on high-level and through low-level simulation. For FFT, our designs dissipated an average of $50\%$ less energy than the design from the Xilinx library and $56\%$ less than the DSP. Our designs showed an EAT factor of 10 times improvement over the embedded processor. These results provide a concrete evidence to substantiate the idea that FPGAs can outperform DSPs and embedded processors in signal processing. Further, they show that PFGAs can achieve this performance while still dissipating less energy than the other two types of devices.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.256-270
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• 2023

In this study, a sliding mode (SM) controller for dynamic positioning (DP) was specifically designed for a turret connection operation of a ship or an offshore structure in which an arbitrary point on the structure could be controlled as the motion center instead of the center of mass. The SM controller allows control of the arbitrary point and provides capability to manage uncertainties in the dynamics of ships and offshore structures, external forces caused by unknown changing marine environments, and transient performance of DP systems. The Jacobian matrix included in kinematic equations of the controlled object was modified to design the SM controller to control based on an arbitrary point of ships or offshore structures. To ensure robustness of the controller, the Lyapunov stability theory was applied in the design of the SM controller. In general, for robustness in DP control, gain scheduling based on a proportional-derivative (PD) control algorithm is employed. However, finding appropriate gains for gain scheduling complicates the application of DP systems. Therefore, in this study, the SM control algorithm was considered to mitigate the complexity of the DP controller for ships and offshore structures. To validate the proposed SM control algorithm, time-domain simulations were conducted and utilized to evaluate the performance of the control algorithm. The effectiveness of the proposed SM controller was assessed by comparing simulation results with results of a conventional PD control algorithm applied in DP control.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.1
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• pp.22-30
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• 2011

In this paper, we propose a memory efficient multi-rate Low Density Parity Check (LDPC) decoder for China Mobile Multimedia Broadcasting (CMMB). We find the best trade-off between the performance and the circuit area by designing a partially parallel decoder which is capable of passing multiple messages in parallel. By designing an efficient address generation unit (AGU) with an index matrix, we could reduce both the amount of memory requirement and the complexity of computation. The proposed regular LDPC decoder was designed in Verilog HDL and was synthesized by Synopsys' Design Compiler using Chartered $0.18{\mu}m$ CMOS cell library. The synthesized design has the gate size of 455K (in NAND2). For the two code rates supported by CMMB, the rate-1/2 decoder has a throughput of 14.32 Mbps, and the rate-3/4 decoder has a throughput of 26.97 Mbps. Compared with a conventional LDPC for CMMB, our proposed design requires only 0.39% of the memory.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.167-171
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• 2007

Application areas of Knowledge Discovery in Database (KDD) have been expanded into many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has recently fully utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not cheap, korean language process not available, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information (KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. Knowledge Matrix main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. KnowledgeMatrix show better performances and offer more various functions than extant systems.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.68-74
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• 2008

Application areas of Knowledge Discovery in Database(KDD) have been expanded to many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not moderate, korean language processing is impossible, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information(KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. KnowledgeMatrix's main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. Matrix generation unit help extract information items which will be analyzed, and calculate occurrence, co-occurrence, proximity of the items. Cluster analysis unit enable matrix data to be clustered by hierarchical or non-hierarchical clustering methods and present tree-type structure of clustered data. Visualization unit offer various methods such as chart, FDP, strategic diagram and PFNet. Data pre-processing unit consists of data import editor, string editor, thesaurus editor, grouping method, field-refining methods and sub-dataset generation methods. KnowledgeMatrix show better performances and offer more various functions than extant systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.164-173
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• 2014

In underwater robotics, vision would be a key element for recognition in underwater environments. However, due to turbidity an underwater optical camera is rarely available. An underwater imaging sonar, as an alternative, delivers low quality sonar images which are not stable and accurate enough to find out natural objects by image processing. For this, artificial landmarks based on the characteristics of ultrasonic waves and their recognition method by a shape matrix transformation were proposed and were proven in Part 1. But, this is not working properly in undulating and dynamically noisy sea-bottom. To solve this, we propose a framework providing a selection phase of likelihood candidates, a selection phase for final candidates, recognition phase and tracking phase in sequence images, where a particle filter based selection mechanism to eliminate fake candidates and a mean shift based tracking algorithm are also proposed. All 4 steps are running in parallel and real-time processing. The proposed framework is flexible to add and to modify internal algorithms. A pool test and sea trial are carried out to prove the performance, and detail analysis of experimental results are done. Information is obtained from tracking phase such as relative distance, bearing will be expected to be used for control and navigation of underwater robots.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.63-72
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• 2013

Business Process Intelligence (BPI) is one of the emerging technologies in the knowledge discovery and analysis area. BPI deals with a series of techniques from discovering knowledge to analyzing the discovered knowledge in BPM-supported organizations. By means of the BPI technology, we are able to provide the full functionality of control, monitoring, prediction, and optimization of process-supported organizational knowledge. Particularly, we focus on the focal organizational knowledge, which is so-called the BPM activity-performer affiliation networking knowledge that represents the affiliated relationships between performers and activities in enacting a specific business process model. That is, in this paper we devise a statistical analysis method to be applied to the BPM activity-performer affiliation networking knowledge, and dubbed it the activity-performer correspondence analysis method. The devised method consists of a series of pipelined phases from the generation of a bipartite matrix to the visualization of the analysis result, and through the method we are eventually able to analyze the degree of correspondences between a group of performers and a group of activities involved in a business process model or a package of business process models. Conclusively, we strongly expect the effectiveness and efficiency of the human resources allotments, and the improvement of the correlational degree between business activities and performers, in planning and designing business process models and packages for the BPM-supported organization, through the activity-performer correspondence analysis method.