• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.65-73
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• 2008

In this study, we have looked into the cellular phone consumption behavior of seniors who are 50-65 years old because of the prediction that senior cellular phone market will be expanded. Therefore, we made up a questionnaire which is based on theoretical study about silver consumer and marketing, and we also analyzed main features and differences of senior and active senior after split by lifestyle analysis. Through the analysis result, we found out the practical approaching way for the items that seniors' dissatisfaction was high and inquired whether it's possible turning into interface's direct input system or not. As a result, we discovered the change of a pattern of behavior by the increase of seniors's experience, differences of consumption behavior factors by lifestyle, and the possibility of market value of new interface.

• Economic and Environmental Geology
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• v.41 no.3
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• pp.345-357
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• 2008

Korea has been one of the top ranked countries in the per capita and total consumption of Cu, Zn, Pb, and Ni since economic development based on manufacturing industries. The current instability of mineral demand and supply in Korea is likely to continue or exacerbate in accordance with economic growth in developing countries such as BRICs. Korea needs to increase the self-development portion of strategic mineral resources including Cu, Zn, Pb, and Ni. Our analysis of mineral demand and supply data predicts a long-run instability of supply and demand for main minerals used in the Korean manufacturing industries, and suggests a long range government policy for stable supply of core mineral resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.27-34
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• 2022

Drunk driving defines a driver as unable to drive a vehicle safely due to drinking. To crack down on drunk driving, alcohol concentration evaluates through breathing and crack down on drinking using S-shaped courses. A method for assessing drunk driving without using BAC or BrAC is measurement via biosignal. Depending on the individual specificity of drinking, alcohol evaluation studies through various biosignals need to be conducted. In this study, we measure biosignals that are related to alcohol concentration, predict BrAC through SVM, and verify the effectiveness of the S-shaped course. Participants were 8 men who have a driving license. Subjects conducted a d2 test and a scenario evaluation of driving an S-shaped course when they attained BrAC's certain criteria. We utilized SVR to predict BrAC via biosignals. Statistical analysis used a one-way Anova test. Depending on the amount of drinking, there was a tendency to increase pupil size, HR, normLF, skin conductivity, body temperature, SE, and speed, while normHF tended to decrease. There was no apparent change in the respiratory rate and TN-E. The result of the D2 test tended to increase from 0.03% and decrease from 0.08%. Measured biosignals have enabled BrAC predictions using SVR models to obtain high Figs in primary and secondary cross-validations. In this study, we were able to predict BrAC through changes in biosignals and SVMs depending on alcohol concentration and verified the effectiveness of the S-shaped course drinking control method.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.47-54
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• 2024

The global decrease in traditional energy resources has prompted increasing energy demand, necessitating efforts to replace and optimize energy sources. This study focuses on enhancing energy efficiency in manufacturing plants, known for their high energy consumption. Through simulations and analyses, the study proposes a temperature-based control system for HVAC (Heating, Ventilating, and Air Conditioning) operations, utilizing machine learning algorithms to predict and optimize factory temperatures. The results indicate that this approach, particularly the prediction-based free cooling algorithm, can achieve over 10% energy savings compared to existing systems. This paper presents that implementing an efficient HVAC control system can significantly reduce overall factory energy consumption, with plans to apply it to real factories in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• Korean Journal of Agricultural Science
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• v.43 no.1
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• pp.116-126
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• 2016

The objective of this study is to predict the fuel efficiency of an agricultural tractor. The fuel efficiency of the tractor during rotary tillage was predicted using numerical modeling. A numerical model was developed using Simulation X. Based on tractor power flow, numerical modeling consisted of an engine, transmission, PTO (power take off), and hydraulics. The specifications of major components utilized in the numerical model were the same as those of a 71 kW tractor (field test tractor). The load that was inputted for fuel efficiency prediction into the simulation model was obtained from a field test. Fuel efficiency predictions were conducted by comparing field test results and simulation results. In addition, it was performed by dividing the rotary tillage and steering section. Main results are as follows: first, t-values of engine torque were measured to be 0.31 in the rotary tillage and 0.92 in the steering section. Second, t-values of fuel consumption were measured to be 0.51 and 5.41 in the rotary tillage and the steering section, respectively. Finally, t-values of fuel efficiency were measured to be 1.72 and 40 in the rotary tillage and the steering section, respectively. The results show no significant differences with t-values of less than 5% in the rotary tillage. But, it shows significant differences in the steering section. Therefore, simulation for accurate fuel efficiency prediction requires a suitable algorithm or detailed design of the simulation model in the steering section.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.78-87
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• 2002

A macroblock(MB) in MPEG coded domain can have zero, one, or two motion vectors depending on its frame type and prediction direction (forward-, backward-, or hi-directionally). In this paper, we propose a method that converts these motion vectors on MPEG coded domain as a uniform set, independent of the frame type and the direction of prediction, and directly utilizes these re-analyzed motion vectors for understanding video contents. Also, using this frame-type-independent motion vector, we propose novel methods for detecting and tracking moving objects with frame-based detection accuracy on the compressed domain. These algorithms are performed directly from the MPEG bitstreams after VLC decoding with little time consumption. Experimental results show validity and outstanding performance of our methods.

• Transactions of the KSME C: Technology and Education
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• v.4 no.1
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• pp.27-34
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• 2016

This preliminary study investigated data mining-based methods to assess and predict the performance of geothermal heat pump(GHP) system. Data mining is a key process of the knowledge discovery in database (KDD), which includes five steps: 1) Selection; 2) Pre-processing; 3) Transformation; 4) Analysis(data mining); and 5) Interpretation/Evaluation. We used two analysis models, categorical and numerical decision tree models to ascertain the patterns of performance(COP) and electrical consumption of the GHP system. Prior to applying the decision tree models, we statistically analyzed measurement database to determine the effect of sampling intervals on the system performance. Analysis results showed that 10-min sampling data for the performance analysis had highest accuracy of 97.7% over the actual dataset of the GHP system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.1094-1101
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• 2013

This paper proposes an improved branch predictor that reduces the number execution cycles of applications by selectively accessing a specific element in 4-way associative cache. When a branch instruction is fetched, the proposed branch predictor acquires a branch target address from the selected element in the cache by referring to MRU buffer. Branch prediction rate and application execution speed are considerably improved by increasing the number of BTAC entries in restricted power condition, when compared with that of previous branch predictor which accesses all elements. The effectiveness of the proposed dynamic branch predictor is verified by executing benchmark applications on the core simulator. Experimental results show that number of execution cycles decreases by an average of 10.1%, while power consumption increases an average of 7.4%, when compared to that of a core without a dynamic branch predictor. Execution cycles are reduced by 4.1% in comparison with a core which employs previous dynamic branch predictor.