• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1259-1264
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• 2019

Nowadays, cloud computing and big data analytics are at the center of many industries' concerns to take advantage of the potential benefits of building future smart cities. The integration of cloud computing and big data analytics is the main reason for massive adoption in many organizations, avoiding the potential complexities of on-premise big data systems. With these two technologies, the manufacturing industry, healthcare system, education, academe, etc. are developing rapidly, and they will offer various benefits to expand their domains. In this issue, we present a summary of 18 high-quality accepted articles following a rigorous review process in the field of cloud computing and big data analytics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.323-343
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• 2020

With the rapid development of computer and communication techniques, copyright protection of vector geographic data has attracted considerable research attention because of the high cost of such data. A novel adaptive watermark detection algorithm is proposed for vector geographic data that can be used to qualitatively analyze the robustness of watermarks against data addition attacks. First, a watermark was embedded into the vertex coordinates based on coordinate mapping and quantization. Second, the adaptive watermark detection model, which is capable of calculating the detection threshold, false positive error (FPE) and false negative error (FNE), was established, and the characteristics of the adaptive watermark detection algorithm were analyzed. Finally, experiments were conducted on several real-world vector maps to show the usability and robustness of the proposed algorithm.

• Journal of electromagnetic engineering and science
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• v.1 no.1
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• pp.105-111
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• 2001

This paper presents a new and robust technique for a coarse frequency offset estimation in OFDM systems. As an evaluation of the proposed algorithm, we apply it to Eureka 147 DAB system. The proposed coarse frequency offset estimation algorithm is based on the differential detection technique between adjacent subcarriers to eliminate the phase shift effects of symbol timing offset and fractional frequency offset. A coarse frequency offset is determined from the correlation output between a received interarrier differential phase reference symbol and several locally generated but frequency-shifted intercarrier differential phase reference symbols. The performance of our estimation algorithm is evaluated by means of computer simulation and is compared with those of previous proposed algorithms for DAB transmission modes I, II, III, and IV. Simulation results show that the proposed algorithm generates extremely accurate estimates with low complexity irrespective of the symbol timing offset.

• Smart Structures and Systems
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• v.11 no.1
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• pp.123-134
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• 2013

Identifying structural modal parameters, especially those modes within high frequency range, from ambient data is still a challenging problem due to various kinds of uncertainty involved in vibration measurements. A procedure applying an ensemble empirical mode decomposition (EEMD) method is proposed for accurate and robust structural modal identification. In the proposed method, the EEMD process is first implemented to decompose the original ambient data to a set of intrinsic mode functions (IMFs), which are zero-mean time series with energy in narrow frequency bands. Subsequently, a Sub-PolyMAX method is performed in narrow frequency bands by using IMFs as primary data for structural modal identification. The merit of the proposed method is that it performs structural identification in narrow frequency bands (take IMFs as primary data), unlike the traditional method in the whole frequency space (take original measurements as primary data), thus it produces more accurate identification results. A numerical example and a multiple-span continuous steel bridge have been investigated to verify the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.78-81
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• 2003

This paper proposes a fuzzy classifier system (FCS) using hyper-cone membership functions (HCMFs) and rule reduction techniques. The FCS can generate excellent rules which have the best number of rules and the best location and shape of membership functions. The HCMF is expressed by a kind of radial basis function, and its fuzzy rule can be flexibly located in input and output spaces. The rule reduction technique adopts a decreasing method by merging the two appropriate rules. We applay the FCS to a tubby rule generation for the inverted pendulum control.

• Journal of Computing Science and Engineering
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• v.10 no.1
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• pp.9-20
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• 2016

Several task clustering heuristics are proposed for allocating tasks in heterogeneous systems to achieve a good response time in data intensive jobs. However, one of the challenging problems is the process in task scheduling after task allocation by task clustering. We propose a task scheduling method after task clustering, leveraging worst schedule length (WSL) as an upper bound of the schedule length. In our proposed method, a task in a WSL sequence is scheduled preferentially to make the WSL smaller. Experimental results by simulation show that the response time is improved in several task clustering heuristics. In particular, our proposed scheduling method with the task clustering outperforms conventional list-based task scheduling methods.

• Korean Journal of Food Science and Technology
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• v.18 no.5
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• pp.389-392
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• 1986

To evaluate a pigment of the flower of cockscomb, Cclosia critata. as a natural food colorant, jelly-po, candy and sherbet were chosen as model foods and colorized to red with the pigment of the flower. Color changes were evaluated by analyses with Hunter color difference colorimeter. Lovibond tintometer and UV-visible spectrophotometer. Also sensory evaluation was carried out. The results obtained indicated that the red pigment of the flower had a good potential as food colorant, when it is utilized under the certain limited conditions: low water activity such as candy or low temperature. Data obtained indicated good correlation between instrumental analyses and sensory evaluation as well.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.30-43
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• 2015

The purpose of this study is to figure out current situation of safety education to improve safety awareness and practice in Korean school students. In order to do this, it is necessary to identify the current level of safety awareness and practice with the actual status of accident occurrence. Furthermore it is try to figure out the most influencing factors on the safety education for Korean middle and high school students. The 412 students were taken from a random sample. The samples were one class of 2nd grade students in five different middle schools and four different high schools in Seoul, Korea. The survey was conducted from 29 September 2010 through 15 October 2010. An additional samples for the questionnaires posted in web were collected. The 305 respondents from school students and 80 respondents from web survey were used to analyze for this study out of 800 respondents. SPSS was used to analyze the questionnaires. The overall safety-awareness score was relatively high at 4.56/5 for fire safety and 4.32/5 for traffic safety. Safety awareness was higher for girls than boys and also for high school students than middle school students. Safety education by parents at home gives a good impact on high safety practices. Safety awareness was improved by feeling of necessity for safety training. The safety prevention training provided during the class by teacher and home training by parents improved safety practice. The correct direction of safety education for younger students can be easier in future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3126-3145
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• 2014

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users' job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users' budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.

• Journal of Engineering Education Research
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• v.21 no.3
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• pp.56-65
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• 2018

With rapid advance of technologies including information and communication technologies, jobs are evolving faster than ever. Architectural engineering is no exception in this regard, and the green architectural engineering is emerging fast as a promising new field. In this study, a Delphi study of expert architectural engineers are conducted to find out (1) near future prospects of the field, (2) near future emerging jobs, (3) competencies needed for these jobs, and (4) educational content necessary to build these competencies with regards to the green architectural engineering. Initial Delphi survey consisting of open-ended questions in the above four areas were conducted and came out with 65 items after duplicate removal and semantic refinements. Further refinements via second and third wave of Delphi results into 40 items that the 13 architectural engineering experts may largely agree upon as future prospects with regards to the green architectural engineering. Findings indicate that it is expected that the demand for green architectural engineering and needs for automatic energy control system increase. Also, collaborations with other fields is becoming more and more important in green architectural engineering. The professional work management skills such as knowledge convergence, problem solving, collaboration skills, and creativity linking components from various related areas seem to also be on the increasing need. Near future ready critical skills are found to be the building environment control techniques (thermal, light, sound, and air), the data processing techniques like data mining, energy monitoring, and the control and utilization of environmental analysis software. Experts also agree on new curriculum for green building architecture to be developed with more of converging subjects across disciplines for future ready professional skills and experiences. Major topics to be covered in the near future includes building environment studies, building energy management, energy reduction systems, indoor air quality, global environment and natural phenomena, and machinery and electrical facility. Architectural engineering community should be concerned with building up the competencies identified in this Delphi preparing for fast advancing future.