• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.27-37
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• 2020

Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.89-97
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• 2017

Recent Internet of Things(IoT), and in addition to wearable PC, such as software development methodologies based on a variety of object-oriented design and design patterns of GoF(Gang of Four) with OOP(Object-Oriented Programming) intended for portable devices. However, incorrect application design specification is that the higher the importance of the optimization of the program on the device because it can cause problems such as decreased operating speed, increase the memory occupancy and battery usage. In this paper, we propose an optimized design pattern based on the method of application, such as Android (Android) OS Strategy Pattern, State Pattern, Observer pattern. Test results show that the proposed scheme selection patterns can be selected to optimize the design pattern in the device that specification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.139-144
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of IoT technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatial-temporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• Korean Journal of Child Studies
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• v.4
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• pp.37-51
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• 1983

The purpose of this study was the investigation of theoretical and empirical relationships between Piagetian and psychometric assessments of intelligence. Specifically, the factor structure of Piagetian-type scales, the relationship between Piagetian scales and psychometric intelligence tests, and differences in the factor structure of Piagetian and psychometric assessments of intelligence were studied. The subjects of this stuby were 70 children (35 boys and 35 girls) in the 1st grade of an elementary school in Seoul The Piagetian-type scales and the K-WISC were administered individually, and the General Intelligence Test was administered to groups of children. Statistical analysis of the obtained data consisted of the SPSS Computer program including factor analysis and Pearson's product moment correlation coefficient. The Piagetian-type scales were found to consist of three factors, which accounted for 55 percent of the total common-factor variance. Factor-I was a factor indicating "conservation". Factor-II was a factor indicating "moral judgements". Factor-III was a factor indicating "classification and identity". Correlations between subtests of psychometric tests and Piagetian scales were relatively low or moderate. Relations between IQs assessed by the psychometric tests and Piagetian scales were also relativeyly low or moderate. Eight factors were extracted from the joint factor analysis of psychometric intelligence tests and Piagetian scales, and they accounted for 67 percent of the total common-factor variance. Factors-I, II, III, and V consisted of subtests of psychometric assessments, and Factors-IV, VI, VII and VIII were composed of Piagetian scales. Factor-I was a factor for "reasoning ability based upon language". Factor-II was a factor for "performance ability". Factor-III was a factor for "grouping ability". Factor-IV was a factor for "conservation". Factor-V was a factor indicating "symbol and language usage ability". Factor- VI was a factor indicating "moral judgments". Factor-VII was a factor indicating "length consevation". Factor-VIII was a factor indicating "classification and identity".

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.397-404
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• 2018

As the usage of internet is increasing, a lot of user behavior are written in a log file and the researches and industries using the log files are getting activated recently. This paper uses the Hadoop based on open source distributed computing platform and proposes a customized tourism system by analyzing user behaviors in the log files. The proposed system uses Google Analytics to get user's log files from the website that users visit, and stores search terms extracted by MapReduce to HDFS. Also it gathers features about the sight-seeing places or cities which travelers want to tour from travel guide websites by Octopus application. It suggests the customized cities by matching the search terms and city features. NBP(next bit permutation) algorithm to rearrange the search terms and city features is used to increase the probability of matching. Some customized cities are suggested by analyzing log files for 39 users to show the performance of the proposed system.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.569-575
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• 2020

The serial arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet, and statistical features have been used, additional steps such as transformation and feature extraction are required. On the contrary, deep learning models directly use the raw data without any feature extraction processes. Therefore, the usage of time-domain data is preferred, but the performance is not satisfactory. To solve this problem, subsequent 1-D signals are transformed into 2-D data that can feed into a convolutional neural network (CNN). Experiments validated that CNN model outperforms deep neural network (DNN) by the classification accuracy of 8.6%. In addition, data augmentation is utilized, resulting in the accuracy improvement by 14%.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.12
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• pp.568-575
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• 2008

Server consolidation by sever virtualization can make one physical machine(PM) to run several virtual machines simultaneously. Although It is attractive in cost, it has complex workload behaviors. For that reason, efficient resource management method is required. Dynamic relocation of virtual machine(VM)[3,4] by live migration[1,2] is one of resource management methods. We proposed SCOA(Server Consolidation Optimizing Algorithm) : a fine-grained load balancing mechanism worked on this dynamic relocation mechanism. We could obtain accurate resource distribution information through pointed physical machines on multi dimensional resource usage coordination, so we could maintain more balanced resource state. In this paper, we show the effectiveness of our algorithm by comparison of experimental results between SCOA and sandpiper[3] by software simulation.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.6
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• pp.567-582
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• 2000

We propose a new way of managing flash memory space for flash memory-specific file system based on log-structured file system. Flash memory has attractive features such as non-volatility, and fast I/O speed, but it also suffers from inability to update in place and limited usage cycles. These drawbacks require many changes to conventional storage (file) management techniques. Our focus is on lowering cleaning cost and evenly utilizing flash memory cells while maintaining a balance between the two often-conflicting goals. The proposed cleaning method performs well especially when storage utilization and the degree of locality are high. The cleaning efficiency is enhanced by dynamically separating cold data and non-cold data. The second goal, cycle-leveling is achieved to the degree where the maximum difference between erase cycles is below the error range of the hardware. Simulation results show that the proposed method has significant benefit over naxve methods: maximum of 35% reduction in cleaning cost with even spreading writes across segments.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.4
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• pp.401-407
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• 2000

Modern microprocessors exploit instruction-level parallel processing to increase the performance. Especially VLIW processors supported by the parallelizing compiler are used more and more in specific applications such as high-end DSP and graphic processing. Bus-based VLIW architecture was proposed for these specific applications and it was designed to reduce the overhead of forwarding unit and the instruction width. In this paper, a optimizing scheduling compiler developed for the proposed bus-based VLIW processor is introduced. First, the method to model interconnections between buses and resource usage patterns is described. Then, on the basis of the modeling, machine-dependent optimization techniques such as bus-to-register promotion, copy coalescing and operand substitution were implemented. Optimization techniques for general-purpose VLIW microprocessors such as selective scheduling and enhanced pipelining scheduling(EPS) were also implemented. The experiment result shows about 20% performance gain for multimedia application benchmarks.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.12-21
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• 2008

As the usage of computer systems is increasing in our lives, the reliability and safely of these systems need to be thoroughly checked through the verification techniques. As a basic formalism for several modeling methods, the finite state machine (FSM) is widely used in specification and verification of system models. And there is a technique for ing internal events of FSM in order to effectively analyze the system. However, this technique does not handle the state explosion problem since it can be applied after completely generating all the state space of the system. In this research, we provide a new approach for efficiently representing concurrent properties of FSM, the slice model and provide an efficient transition reduction method based on the slice model. Our approach is effective in time and space perspective since it is peformed by partially generating the needed system states while the existing abstraction technique can be applied to all the system states.