• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.526-529
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• 2015

as the smartphone industry developed, the smartphone's internal hardware devices have become high-end devices and it requires more power consumption than the previous one. therefore a battery of high capacity needed, but there is a limit in order to equip a large battery on account of smartphone minimization. The Linux Kernel provides the DVFS Mechanism to compensate for these limitations by software techniques. DVFS is dynamically adjust the frequency of the CPU to reduce the power consumption of the CPU. ondemand governor, the default policy in DVFS, apply the maximum frequency of the CPU whenever exceeding the up_threshold. so it result in a waste of CPU resources. by paying attention to this point, this paper propose the mechanism that maintain a high CPU utilization in proportion to the current frequency of the cpu to prevent the waste of CPU resources and conserve energy.

• Journal of the Korean Data and Information Science Society
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• 제23권2호
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• pp.247-256
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• 2012

Recently, estimation of greenhouse gas (GHG) emission has continuously emerged as an important global issue. This study compares various statistical methods for estimation of fuel consumption, which is necessary for calculation of GHG emission in road transportation sector. Existing methods have focused on using merely transportation fuel supply or distance traveled for calculation of fuel consumption. Estimates of GHG emission based on fuel supply, however, cannot reflect various vehicle types or model year. This study suggests and compares, from statistical point of view, several methods, which can be applied to estimate fuel consumption of each vehicle, by combining distance traveled and fuel efficiency (mileage), and total fuel consumption of all vehicles. It also suggests practical measures that can reflect vehicle types and model year to suggested methods for future research.

• Convergence Security Journal
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• 제18권5_1호
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• pp.67-73
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• 2018

From a software engineering point of view, tracing is a special form of logging that records program execution information. Tracers using dedicated hardware are often used because of the characteristics of tracers that need to generate and decode huge amounts of data in real time. Intel(R) PT uses proprietary hardware to record all information about software execution on each hardware thread. When the software execution is completed, the PT can process the trace data of the software and reconstruct the correct program flow. The hardware trace program can be integrated into the operating system, but in the case of the window system, the integration is not tight due to problems such as the kernel opening. Also, it is possible to trace only a single process and not provide a way to trace multiple process streams. In this paper, we propose a method to extend existing PT trace program to support multi - process stream traceability in Windows environment in order to overcome these disadvantages.

• Applied Chemistry for Engineering
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• 제22권3호
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• pp.296-300
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• 2011

In this work, various wood biomasses were used to determine the combustion characteristics for the fuel of cogeneration plant. Combustion characteristics of four types, i.e., (i) forest products, (ii) recycled wood, (iii) empty fruit bunch, and (iv) palm kernel shell, were examined via thermal gravimetric analyzer (TGA) in air atmosphere and coal was used as a comparison group. From the TGA results, the combustion of the wood biomass was occurred in the range of 280 to $420^{\circ}C$, which was lower than that of coal. Forest product showed the lowest activation energy (0.4 kJ/mol) compared to that of other wood biomasses (about 6 to 14 kJ/mol) and coal (64 kJ/mol). In addition, the reaction rate constant of the wood biomass was lower than that of coal. These results indicate the higher combustion initiation rate of wood biomass due to the high content of volatile matter, which had a low boiling point.

• Journal of Positioning, Navigation, and Timing
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• 제12권2호
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• pp.91-100
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• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제46권2호
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

• Nuclear Engineering and Technology
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• 제56권9호
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• pp.3571-3584
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• 2024

Reactor core transient calculation is very important for the reactor safety analysis, in which the kernel is neutron kinetics calculation by simulating the variation of neutron density or thermal power over time. Compared with the point kinetics method, the time-space neutron kinetics calculation can provide accurate variation of neutron density in both space and time domain. But it consumes a lot of resources. It is necessary to develop a surrogate model that can quickly obtain the temporal and spatial variation information of neutron density or power with acceptable calculation accuracy. This paper uses the time-varying characteristics of power to construct a time function, parameterizes the time-varying characteristics which contains the information about the spatial change of power. Thereby, the amount of targets to predict in the space domain is compressed. A surrogate method using the machine learning is proposed in this paper. In the construction of a neural network, the input is processed by a convolutional layer, followed by a fully connected layer or a deconvolution layer. For the problem of time sequence disturbance, a structure combining convolutional neural network and recurrent neural network is used. It is verified in the tests of a series of 1D, 2D and 3D reactor models. The predicted values obtained using the constructed neural network models in these tests are in good agreement with the reference values, showing the powerful potential of the surrogate models.

• Journal of the Korean earth science society
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• 제36권1호
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• pp.36-50
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• 2015

The purpose of this study is to assign emission source profiles of volatile organic compounds (VOCs) and particulate matters (PMs) for chemical speciation, and to correct the temporal allocation factor and the chemical speciation of source profiles according to the source classification code within the sparse matrix operator kernel emission system (SMOKE) in the Seoul metropolitan area. The chemical speciation from the source profiles of VOCs such as gasoline, diesel vapor, coating, dry cleaning and LPG include 12 and 34 species for the carbon bond IV (CBIV) chemical mechanism and the statewide air pollution research center 99 (SAPRC99) chemical mechanism, respectively. Also, the chemical speciation of PM2.5 such as soil, road dust, gasoline and diesel vehicles, industrial source, municipal incinerator, coal fired, power plant, biomass burning and marine was allocated to 5 species of fine PM, organic carbon, elementary carbon, $NO_3{^-}$, and $SO_4{^2-}$. In addition, temporal profiles for point and line sources were obtained by using the stack telemetry system (TMS) and hourly traffic flows in the Seoul metropolitan area for 2007. In particular, the temporal allocation factor for the ozone modeling at point sources was estimated based on $NO_X$ emission inventories of the stack TMS data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제1권1호
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• pp.41-51
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• 2002

As Advanced Traveler Information System(ATIS) is the kernel of the Intelligent Transportation System, it is very important how to manage data from traffic information collectors on a road and have at borough grip of the travel time's change quickly and exactly for doing its part. Link travel time can be obtained by two method. One is measured by area detection systems and the other is estimated by point detection systems. Measured travel time by area detection systems has the limitation for real time information because it Is calculated by the probe which has already passed through the link. Estimated travel time by point detection systems is calculated by the data on the same time of each. section, this is, it use the characteristic of the various cars of each section to estimate travel time. For this reason, it has the difference with real travel time. In this study, Artificial Neural Networks is used for estimating link travel time concerned about the relationship with vehicle detector data and link travel time. The method of estimating link travel time are classified according to the kind of input data and the Absolute value of error between the estimated and the real are distributed within 5$\~$15minute over 90 percent with the result of testing the method using the vehicle detector data and AVI data of Namsan Tunnel $\#$1. It also reduces Time lag of the information offered time and draws late delay generation and dissolution.

• Korean Journal of Agricultural Science
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• 제13권1호
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• pp.113-122
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• 1986

In order to promote mechanization of corn harvesting in Korea, this study was conducted to find out the effect of moisture content on compressive properties such as force, deformation, energy and modulus of stiffness to the bioyield and the rupture point for Korean corn kernel. In this study, the loading positions of corn were flat, edge, longitude and the moisture contents were about 13, 17, 21, 25% in wet basis. The compression test was carreied out with flat plate by use of dynamic straingage for three varieties of Korean corn under quasi-static force when the loading rate was 1.125mm/min. The results of this study are summarized as follows; 1. When the moisture content of corn ranged from 12.5 to 24.5 percent, at flat position, the bioyied force was in the range of 13.63-26.73 kg and the maximum compressive strength was in the range of 21.55-47.65kg. Their values were reached minimum at about 17% and maximum at about 21% moisture content. The bioyield force was in the range of 13.58-6.70kg at edge position and the maximum compressive strength which was 16.42 to 7.82kg at edge position was lower than that which was 18.55-9.05kg at longitudinal position. 2. Deformation of corn varied from 0.43 to 1.37 mm at bioyield point and from 0.70 to 2.66mm at rupture point between 12.5 to 24.5% moisture content. As the moisture content increased, deformation was increased. 3. The moduli of resilience and toughness of corn ranged from 2.60 to 8.57kg. mm and from 6.41 to 34.36kg. mm when the moisture content ranged from 12.5 to 24.5 percent, respectively. As the moisture content increased, the modulus of toughness was increased at edge position and decreased at longitudinal position. And their values were equal each other at 22-23% moisture content. 4. The modulus of stiffness was decreased with increase in the moisture content. Its values ranged from 32.07 to 5.86 kg/mm at edge position and from 42.12 to 18.68kg/mm at flat position, respectively. Also, the values of Suweon 19 were higher than those of Buyeo. 5. It was considered that the compressive properties of corn at flat position were more important on the design data for corn harvesting and processing machinery than those of edge or longitudinal position. Also, grinding energy would be minimized when a corn was processed between about 12.5 to 17% moisture content and corn damage would be reduced when a corn was handled between about 19 to 24% moisture content in wet basis.