• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.103-111
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• 2020

Performance indicators have often investigated and developed in order to identify foundational elements and factors for an enhancement of performance in sports. In order to identify the valid performance indicators it is important that the indicators used within a performance analysis system discriminate between the winning and losing performances within a match (Hughes and Bartlett, 2002). However, the performance indicators proposed in research studies on basketball performance have not been used for real-time analysis and feedback within a coaching context. Such real-time support for the coach and players has been described within research on other sports (Choi et al., 2004; O'Donoghue, 2001; Palmer et al., 1997). Within the process of real-time feedback, the identification of relevant performance indicators that distinguish winning and losing performances should be the first stage of the development of a real-time analysis system. Therefore, this study investigated the differences between winning and losing teams in terms of a set of performance indicators gathered during the analysis of 10 English National Basketball League matches. Winning and losing teams were compared using whole match data (N=10) as well as individual quarters (N=40). A series of Wilcoxon Signed Ranks tests was used to identify the relevant performance indicators that discriminate between winning and losing performers within whole matches and individual quarters. The tests found that 3 point shots made (p<0.05) and Assists (p<0.05) were significantly different between winning and losing teams within matches. However, 2 point shots made (p<0.05), 2 point shots attempted (P<0.05), percentages of 2 point shots scored (p<0.05), 3 point shots made (p<0.05), Defensive Rebounds (p<0.05) and Assists (p<0.05) were significantly different between winning and losing performance within quarters. The analysis task should be based on relevant performance indicators which explain the current performances to performance analysts and coaches. Within a real-time analysis and feedback scenario, this will have the additional benefit of supporting a decision based on immediate performance within the most recent quarter. Consequently, the real-time analysis system would use performance indicators which have the property of construct validity to support the decisions of the coach.

• Korean Security Journal
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• no.40
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• pp.57-86
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• 2014

As the revision of Building Code including applying crime prevention design to buildings passed recently and target hardening ought to be evidence-based, we studied the Modus Operandi (MO) and intrusion tools of domestic burglary to earn basic data for improvement of crime prevention hardware in the future. To be specific, we reviewed related academic literature and police official statistics of domestic burglary critically and interviewed detectives in charge of burglary to specify and categorize MO and tools. We can derive some implications from research findings, including improvement of the statistical system for the MO of burglary, active sharing of the MO of burglary among the criminal justice agencies and related industries and experts. Also, crime prevention advice and education for the local residents focused on MO of burglary can be recommended. Based on this research, to enhance the level of community safety significantly, performance tests of crime prevention hardware such as security doors and windows etc. and the study on related certification system should be vitalized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.382-389
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• 2018

Recently cyber attacks can cause serious damage on various information systems. Log data analysis would be able to resolve this problem. Security log analysis system allows to cope with security risk properly by collecting, storing, and analyzing log data information. In this paper, a security log analysis system is designed and implemented in order to analyze security log data using the Logstash in the Elasticsearch, a distributed search engine which enables to collect and process various types of log data. The Kibana, an open source data visualization plugin for Elasticsearch, is used to generate log statistics and search report, and visualize the results. The performance of Elasticsearch-based security log analysis system is compared to the existing log analysis system which uses the Flume log collector, Flume HDFS sink and HBase. The experimental results show that the proposed system tremendously reduces both database query processing time and log data analysis time compared to the existing Hadoop-based log analysis system.

• Journal of KIISE
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• v.44 no.5
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• pp.496-502
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• 2017

Pointer Networks is a deep-learning model for the attention-mechanism outputting of a list of elements that corresponds to the input sequence and is based on a recurrent neural network (RNN). The coreference resolution for pronouns is the natural language processing (NLP) task that defines a single entity to find the antecedents that correspond to the pronouns in a document. In this paper, a pronoun coreference-resolution method that finds the relation between the antecedents and the pronouns using the Pointer Networks is proposed; furthermore, the input methods of the Pointer Networks-that is, the chaining order between the words in an entity-are proposed. From among the methods that are proposed in this paper, the chaining order Coref2 showed the best performance with an F1 of MUC 81.40 %. The method showed performances that are 31.00 % and 19.28 % better than the rule-based (50.40 %) and statistics-based (62.12 %) coreference resolution systems, respectively, for the Korean pronouns.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1385-1398
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• 2016

A pressurizer is a major equipment system in a nuclear power plant (NPP) and controls the reactor cooling system pressure within the allowable range. Faults in the pressurizer can be critical to the NPP; therefore, early fault detection in the pressurizer is significant for NPP safety. This study applies Mahalanobis Taguchi system (MTS), which is one of the promising pattern classification methods, based on the Mahalanobis distance concept and Taguchi quality engineering theory to the early fault detection problem of the pressurizer pressure control system. We conducted experiments using data from full scope NPP simulator based on a pressurizer pressure transmitter faults scenario to validate the faults detection performance of MTS. As a result, MTS can rapidly detect the faults compared to conventional faults detection based on single sensor monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.1
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• pp.125-133
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• 2010

A versatile micro-robotic platform for micro/nano scale assembly has been demanded in a variety of application areas such as micro-biology and nanotechnology. In the near future, a flexible and compact platform could be effectively used in a scanning electron microscope chamber. We are developing a platform that consists of miniature mobile robots and a compact positioning stage with multi degree-of-freedom. This paper presents the design and the implementation of a low-cost and compact multi degree of freedom position sensor that is capable of measuring absolute translational and rotational displacement. The proposed sensor is implemented by using a CMOS type image sensor and a target with specific hole patterns. Experimental design based on statistics was applied to finding optimal design of the target. Efficient algorithms for image processing and absolute position decoding are discussed. Simple calibration to eliminate the influence of inaccuracy of the fabricated target on the measuring performance also presented. The developed sensor was characterized by using a laser interferometer. It can be concluded that the sensor system has submicron resolution and accuracy of ${\pm}4{\mu}m$ over full travel range. The proposed vision-based sensor is cost-effective and used as a compact feedback device for implementation of a micro robotic platform.

• Journal of KIISE
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• v.41 no.12
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• pp.1081-1089
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• 2014

Context-sensitive spelling-error correction methods are largely classified into rule-based methods and statistical data-based methods, the latter of which is often preferred in research. Statistical error correction methods consider context-sensitive spelling error problems as word-sense disambiguation problems. The method divides a vocabulary pair, for correction, which consists of a correction target vocabulary and a replacement candidate vocabulary, according to the context. The present paper proposes a method that integrates a word-phrase n-gram model into a conventional model in order to improve the performance of the probability model by using a correction vocabulary pair, which was a result of a previous study performed by this research team. The integrated model suggested in this paper includes a method used to interpolate the probability of a sentence calculated through each model and a method used to apply the models, when both methods are sequentially applied. Both aforementioned types of integrated models exhibit relatively high accuracy and reproducibility when compared to conventional models or to a model that uses only an n-gram.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.189-196
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• 2009

We propose a hybrid broadcast scheduling based on a combination of periodic and on-demand data scheduling methods for mobile data broadcasting in convergence networks from communication and broadcasting. We consider an environment in which the forward channel is for data broadcasting and the reverse channel is for sending data requests via cellular phones, WLAN, WiBro, etc. Collecting statistics of requests from clients, the server partitions the data items into hot-item and cold-item sets. Hot items are sent based on a push-based scheduling. An on-demand scheduling method is applied to cold items. Performance evaluation from simulations shows that our proposed scheduling algorithm yields small response time with high successful response ratio.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.23-32
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• 2001

Electrical impedance tomography (EIT) is a relatively new imaging modality in which the resistivity (conductivity) distribution of the unknown object is estimated based on the known sets of injected currents and measured voltages on the surface of the object. In this paper, we propose a dynamical EIT reconstruction algorithm based on the regularized extended Kalman filter(EKF). The EIT inverse problem is formulated as dynamic equation which consists of the slate equation and the observation equation, and the unknown state(resistivity) is estimated recursively with the aid of the EKF. In doing so, the generalized Tikhonov regularization technique is employed in the cost functional to mitigate the ill-posedness characteristics of the inverse problem. Computer simulations for the 16-channel synthetic data are provided to illustrate the reconstruction performance of the proposed algorithm.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.49-55
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• 2020

Information theoretic learning (ITL) methods based on random symbols (RS) use a set of random symbols generated according to a target distribution and are designed nonparametrically to minimize the cost function of the Euclidian distance between the target distribution and the input distribution. One drawback of the learning method is that it can not utilize the input power statistics by employing a constant stepsize for updating the algorithm. In this paper, it is revealed that firstly, information potential input (IPI) plays a role of input in the cost function-derivative related with information potential output (IPO) and secondly, input itself does in the derivative related with information potential error (IPE). Based on these observations, it is proposed to normalize the step-size with the statistically varying power of the two different inputs, IPI and input itself. The proposed algorithm in an communication environment of impulsive noise and multipath fading shows that the performance of mean squared error (MSE) is lower by 4dB, and convergence speed is 2 times faster than the conventional methods without step-size normalization.