• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.240-245
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• 2012

A 160 MW drum-type boiler-turbine system is developed in the present research through a multi-multivariable dynamic matrix control (DMC) scheme and a multi-multivariable model approach. A novel intelligence-based decision mechanism (IBDM) is realized to support both model approach and control scheme. In such case, the responsibility of the proposed IBDM is to identify the best multivariable model of the system and the corresponding multivariable DMC scheme to cope with the system at each instant of time in an appropriate manner.

• KNOM Review
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• v.24 no.2
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• pp.13-23
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• 2021

Recently, as networks become more complex due to the activation of IoT devices, research on long-term traffic prediction beyond short-term traffic prediction is being activated to predict and prepare for network congestion in advance. The recursive strategy, which reuses short-term traffic prediction results as an input, has been extended to multi-step traffic prediction, but as the steps progress, errors accumulate and cause deterioration in prediction performance. In this paper, an LSTM-based multi-step traffic prediction method using a multi-output strategy is introduced and its performance is evaluated. As a result of experiments based on actual DNS request traffic, it was confirmed that the proposed LSTM-based multiple output strategy technique can reduce MAPE of traffic prediction performance for non-stationary traffic by 6% than the recursive strategy technique.

• Journal of Information Technology Applications and Management
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• v.22 no.2
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• pp.113-122
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• 2015

Collective intelligence can be an influential factor of decision-making based on collaboration and information exchange between individuals. Our study explores whether collective intelligence can mitigate the loss aversion effect, bias and error in human judgment, and collective intelligence in online communities can reduce the loss aversion effect. Our community settings display both individual-level and group-level loss aversion effect, investigate effective collective intelligence characteristics like investment commitment, participant experience. Using a multi-method approach our research comprises a web-based experiment with 100 participants investing 3 situations from a real-world community, data from a survey measuring loss aversion behavior of participants. The results suggest the loss aversion effect mitigates under the online-circumstance. Overall, our results suggest that, while collective intelligence mitigates the loss aversion effect, participants do not transfer these results to other settings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1437-1459
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• 2020

Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network's life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1050-1053
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• 2005

There is no agreed definition of intelligence. The ability to adapt to the environments is a kind of intelligence. Expert functionally recognize environment using their five senses, and acquire and memorize knowledge necessary for operating machines. Knowledge that they cannot acquire directly is acquired in indirect ways. The purpose of intelligence machines is applying to machines experts' knowledge acquisition process and their skills in operating machine. An agent is an autonomous process that recognizes external environment, exchanges knowledge with external machines and performs an autonomous decision-making function in order to achieve common goals. This paper describes agent application for intelligence machine.

• 대한공업교육학회지
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• v.30 no.1
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• pp.37-45
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• 2005

The purpose of this study is to find out the relationship between the Multiple Intelligence and the technological problem solving and the differences between the two. There were a group of 200 third grade middle school students that were comprised of 100 boys and 100 girls and what the difference is exited between the boys and the girls. To measure the students' Multiple Intelligence, MI(Multiple Intelligent)Test designed by Youngrin, Moon was used. As the testing instrument of the Technological problem Solving, we use the test developed by National Center for Research on Evaluation, Standards, and Students Testing(CRESST). The results were; First, In comparison with the boys and girls' multiple intelligence part, there were individual differences in musical intelligence, bodily-kinesthetic intelligence, logical-mathematical intelligence, and naturalistic intelligence of multiple intelligence. Second, In comparison to the technological problem solving part, there were individual differences in self-regulation and there was a mild difference in understanding of the contents. Third, The multiple intelligence related with the self-regulation is continuous with logical-mathematical intelligence, intra-personal intelligence and linguistic intelligence. Fourth, The multiple intelligence related with the technological problem solving strategy is continuous with logical-mathematical intelligence and musical intelligence. Fifth, The multiple intelligence related with the understanding of the contents is continuous with the logical-mathematical intelligence and naturalistic intelligence. To improve the students' technological problem solving ability, it is required the development of the curriculum which focus on the improvement of logical-mathematical intelligence, musical intelligence, intra-personal intelligence, linguistic intelligence and naturalistic intelligence of the students.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.575-584
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• 2005

The judgement model to warn of possible pollution accident is constructed by multi-perceptron, multi layer neural network, neuro-fuzzy and it is trained stability, notice, and warming situation due to developed standard axis. The water quality forecasting model is linked to the runoff forecasting model, and joined with the judgement model to warn of possible pollution accident, which completes the artificial intelligence warning system. And GUI (Graphic User Interface) has been designed for that system. GUI screens, in order of process, are main page, data edit, discharge forecasting, water quality forecasting, warming system. The application capability of the system was estimated by the pollution accident scenario. Estimation results verify that the artificial intelligence warning system can be a reasonable judgement of the noized water pollution data.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.427-438
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• 2023

Plenty of works have indicated that single image super-resolution (SISR) models relying on synthetic datasets are difficult to be applied to real scene text image super-resolution (STISR) for its more complex degradation. The up-to-date dataset for realistic STISR is called TextZoom, while the current methods trained on this dataset have not considered the effect of multi-scale features of text images. In this paper, a multi-scale and attention fusion model for realistic STISR is proposed. The multi-scale learning mechanism is introduced to acquire sophisticated feature representations of text images; The spatial and channel attentions are introduced to capture the local information and inter-channel interaction information of text images; At last, this paper designs a multi-scale residual attention module by skillfully fusing multi-scale learning and attention mechanisms. The experiments on TextZoom demonstrate that the model proposed increases scene text recognition's (ASTER) average recognition accuracy by 1.2% compared to text super-resolution network.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.