• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.39-60
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• 2017

In the Cloud, for the scheduling of parallel jobs, there are many tasks in a job and those tasks are executed concurrently on different VMs (Visual machines), where each task of the job will be executed synchronously. The goal of scheduling is to reduce the execution time and to keep the fairness between jobs to prevent some jobs from waiting more time than others. We propose a Cloud model which has multiple Clouds, and under this model, jobs are in different lists according to the waiting time of the jobs and every job has different parallelism. At the same time, a new method-ZOMT (the scheduling parallel tasks based on ZERO-ONE scheduling with multiple targets) is proposed to solve the problem of scheduling parallel jobs in the Cloud. Simulations of ZOMT, AFCFS (Adapted First Come First Served), LJFS (Largest Job First Served) and Fair are executed to test the performance of those methods. Metrics about the waiting time, and response time are used to test the performance of ZOMT. The simulation results have shown that ZOMT not only reduces waiting time and response time, but also provides fairness to jobs.

• Journal of the Korean Solar Energy Society
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• v.40 no.1
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• pp.25-33
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• 2020

It has been reported about the energy saving performance of UFAD(under floor air distribution) system and NPC(night purge cooling) system respectively which are applied for commercial buildings. However, when two systems are used at the same time, the effect of heat transfer from floor plenum to slab may vary depending on the operating conditions of NPC. In this study, cooling energy demands were analyzed for building models with UFAD and NPC by using TRNSYS 17 program. UFAD was applied as a cooling system of the base building model, and the cooling energy demands were compared for 64 cases in which the operating time, supply airflow rate, and outdoor air temperature(T_o) of NPC. As a result, it was confirmed that the cooling energy demands were reduced to 30 ~ 80% level compared to UFAD alone, and in particular, the energy demand was reduced in proportion to the supply airflow rate or the operating time while T_o was 16 ~ 20℃. However, when T_o was 22℃, the increase in the supply airflow rate or the operating time results in a disadvantage in terms of cooling energy demands. In addition, the cooling energy demands for UFAD+NPC model were analyzed by applying weather data from three regions with different average outdoor air temperatures. As a result, the cooling energy demand of operating NPC only when T_o was below 20℃ was reduced by 27% compared to that of operating NPC continuously for 8 hours.

• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.198-205
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• 2015

The aim of this study was to provide the basic design parameters for developing logging residue compression machines by investigating compressive deformation characteristics of different types of logging residues. To achieve these objectives, Pinus rigida, Pinus koraensis and Quercus mongolica were selected as specimens, and compression-deformation tests by UTM(universial testing machine) were conducted. The experimental dataset were used to set up the model based on the compression-deformation ratio in the form of exponential function. The results showed that stress coefficient in terms of mechanical properties of logging residues was decreased, whereas strain coefficient tended to be increased as the number of compression increased at target density of $350kg/m^3$ and $400kg/m^3$. The model presented that the required stress was decreased as the number of compression increased, and the stress growth rate was swelled compared to the change of the deformation rate. Therefore, it showed that proper initial compression force was a significant variable in order to achieve the target density of logging residue.

• Aerospace Engineering and Technology
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• v.1 no.1
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• pp.177-187
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• 2002

Some kinds of precision optical systems for spaceborne high resolution cameras were designed at preliminary design level and an optical design for a hyperspectral imager was performed for its development model. A Cassegrain-based catadioptric system and an unobscured reflective triplet system are illustrated in detail for spaceborne high resolution electro optical cameras which have performance of 5m resolution at an altitude of 685km and the design are evaluated in its spot-diagram and MTF to prove they have good performance enough to implement the requirements for realistic satellite payload taking the fabrication conditions and the on-orbit operation into consideration. For the development of hyperspectral imager as a next-generation payload, an optical system has been designed and elaborated. It can be divided into two parts, a catoptric telescope forming an off-axis 2 mirror type and a dispersive spectrometer which comprises collimator, grating and reimaging lens cell. From its optical design to the system characteristics are shown with the MTF performance reaching 25% approximately.

• Tribology and Lubricants
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• v.31 no.6
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• pp.294-301
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• 2015

In this paper, we predict the rotordynamic force coefficients of tilting pad journal bearings (TPJBs) with rocker-back pivots, and we compare the predictions to recently published predictions and test data. The present TPJB model considers the rocker-back pivot stiffness calculated based on the Hertzian contact-stress theory, which is nonlinear with the application of a force . For the five-pad TPJB in load-between-pad and load-on-pad configurations, the predictions show the pressure- and film-thickness distributions, the deflection and stiffness of the individual pivots, and bearing stiffness and damping coefficients. The minimum film thickness and peak pressure occur at the bottom pad on which the applied load is directed. Because of the preload, the pres- sure is positive even at the upper pad in the opposite direction to the applied load. The pivot deflection and stiff- ness are maximum at the bottom pad that receives the heaviest pressure load. The predicted stiffness coefficients increase as the static load and rotor speed increase, while the damping coefficients decrease as the rotor speed increases, but increase as the static load increases. In general, the predicted stiffness coefficients agree well with the test data. The predicted damping coefficients overestimate the test data, particularly for large static loads. In general, the current predictive model considering the pivot stiffness improves the accuracy of the rotordynamic performance compared to previously reported models.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.55-64
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• 2020

Since the European Union-South Korea Free Trade Agreement entered into force in 2011, the Korean automobile market has grown rapidly, resulting in intensifying competition among companies in the market. European automakers gained price competitiveness, which intensified competition with Korean automakers. In such a situation, various studies on the Korean automobile market have been conducted, but studies such as market influencing factor analysis and consumer analysis have mainly been conducted, and there is no research on the analysis of competitive dynamics in the market. In this study, the competitive dynamics between Hyundai Motors, Kia Motors, Mercedes-Benz, and BMW, which are major automakers in the Korean automobile market, are analyzed. The competitive relationship between major automakers are modeled using the Lotka-Volterra (LV) model and the competitive dynamics over time are analyzed by applying the Moving Window. In order to explain the competitive dynamics effectively, we analyze it by subdividing it based on various influencing factors.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.199-208
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• 2022

Korean is an agglutinative language, and one or more morphemes are combined to form a single word. Part-of-speech tagging method separates each morpheme from a word and attaches a part-of-speech tag. In this study, we propose a new Korean part-of-speech tagging method based on the Head-Tail tokenization technique that divides a word into a lexical morpheme part and a grammatical morpheme part without decomposing compound words. In this method, the Head-Tail is divided by the syllable boundary without restoring irregular deformation or abbreviated syllables. Korean part-of-speech tagger was implemented using the Head-Tail tokenization and deep learning technique. In order to solve the problem that a large number of complex tags are generated due to the segmented tags and the tagging accuracy is low, we reduced the number of tags to a complex tag composed of large classification tags, and as a result, we improved the tagging accuracy. The performance of the Head-Tail part-of-speech tagger was experimented by using BERT, syllable bigram, and subword bigram embedding, and both syllable bigram and subword bigram embedding showed improvement in performance compared to general BERT. Part-of-speech tagging was performed by integrating the Head-Tail tokenization model and the simplified part-of-speech tagging model, achieving 98.99% word unit accuracy and 99.08% token unit accuracy. As a result of the experiment, it was found that the performance of part-of-speech tagging improved when the maximum token length was limited to twice the number of words.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.729-740
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• 2022

Netflix, Amazon Prime, and YouTube are the most popular and fastest-growing streaming services globally. It is a matter of great interest for the streaming service providers to preview their service infrastructure and streaming strategy in order to provide new streaming services. Hence, the first part of the paper presents a detailed survey of the Content Distribution Network (CDN) and cloud infrastructure of these service providers. To understand the streaming strategy of these service providers, the second part of the paper deduces a common quality-of-service (QoS) model based on rebuffering time, bitrate, progressive download ratio, and standard deviation of the On-Off cycle. This model is then used to analyze and compare the streaming behaviors of these services. This study concluded that the streaming behaviors of all these services are similar as they all use Dynamic Adaptive Streaming over HTTP (DASH) on top of TCP. However, the amount of data that they download in the buffering state and steady-state vary, resulting in different progressive download ratios, rebuffering levels, and bitrates. The characteristics of their On-Off cycle are also different resulting in different QoS. Hence a thorough adaptive bit rate (ABR) analysis is presented in this paper. The streaming behaviors of these services are tested on different access network bandwidths, ranging from 75 kbps to 30 Mbps. The survey results indicate that Netflix QoS and streaming behavior are significantly consistent followed by Amazon Prime and YouTube. Our approach can be used to compare and contrast the streaming services' strategies and finetune their ABR and flow control mechanisms.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.649-657
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• 2023

ICAO adopted the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) at the 39th General Assembly in 2016, and 115 countries, including South Korea, expressed their intention to participate in CORSIA as of January 1, 2023. Since carbon generated in the aviation industry is mainly caused by greenhouse gases emitted from aircraft engines, fuel consumption must be reduced to reduce carbon emissions. Prior research, such as simulation, is essential to predict the effectiveness of each plan and to make decisions about its implementation. High-quality data is needed to derive accurate results, but it has been difficult to secure actual fuel consumption data, as they are considered to be classified airline data. Therefore, in this paper, after establishing a model that estimates fuel consumption based on actual fuel consumption data, the model is to be advanced to improve its accuracy.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.