• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.9-11
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• 2022

Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the "information cocoon".

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1433-1436
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• 2004

The ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure of centralized administration. Load-Balanced Ad hoc Routing(LBAR) protocol is an on-demand routing protocol intended for delay-sensitive applications where users are most concern with packet transmission delay. Although LBAR mechanism is a novel load balancing routing protocol for ad hoc network, it has own limitation in route path maintenance phase. Therefore, in this paper, we propose Advanced Load-Balanced Ad hoc Routing(A-LBAR) that is delay-sensitive and has an efficient path maintenance scheme. The robust path maintenance scheme is maintained by considering about nodal loads all over network and misbehavior of overloaded or selfish nodes. The proposed scheme provides good performance over DSR and AODV in terms of packet delay and packet loss rate when some misbehaving nodes exist in the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.591-609
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• 2024

In this study, preprocessings with all combinations were examined in terms of the effects on decreasing word number, shortening the duration of the process and the classification success in balanced and imbalanced datasets which were unbalanced in different ratios. The decreases in the word number and the processing time provided by preprocessings were interrelated. It was seen that more successful classifications were made with Turkish datasets and English datasets were affected more from the situation of whether the dataset is balanced or not. It was found out that the incorrect classifications, which are in the classes having few documents in highly imbalanced datasets, were made by assigning to the class close to the related class in terms of topic in Turkish datasets and to the class which have many documents in English datasets. In terms of average scores, the highest classification was obtained in Turkish datasets as follows: with not applying lowercase, applying stemming and removing stop words, and in English datasets as follows: with applying lowercase and stemming, removing stop words. Applying stemming was the most important preprocessing method which increases the success in Turkish datasets, whereas removing stop words in English datasets. The maximum scores revealed that feature selection, feature size and classifier are more effective than preprocessing in classification success. It was concluded that preprocessing is necessary for text classification because it shortens the processing time and can achieve high classification success, a preprocessing method does not have the same effect in all languages, and different preprocessing methods are more successful for different languages.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.200-214
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• 2024

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.83-95
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• 2020

Because sensor nodes have limited resources in wireless sensor networks, data aggregation can efficiently reduce communication overhead and extend the network lifetime. Although many existing methods are particularly useful for data aggregation applications, they incur unbalanced communication cost and waste lots of sensors' energy. In this paper, we propose a privacy-preserving, energy-saving data aggregation scheme (EBPP). Our method can efficiently reduce the communication cost and provide privacy preservation to protect useful information. Meanwhile, the balanced energy of the nodes can extend the network lifetime in our scheme. Through many simulation experiments, we use several performance criteria to evaluate the method. According to the simulation and analysis results, this method can more effectively balance energy dissipation and provide privacy preservation compared to the existing schemes.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.101-103
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• 2011

E-Health is one of the most promising applications of wireless sensor networks. This paper describes a prototype for e-Health systems. Based on the system, we propose the energy-balanced location-aided routing protocol. The location and energy information of the neighbor Coordinators is collected and stored in the neighbor discovery procedure. And then the Coordinator selects the most suitable neighbor to forward the data. The simulation results show that the proposed protocol has better performance than the three other routing protocols.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.510-523
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• 2022

Considering the problems of poor control effect, weak disturbance rejection ability and adaptive ability of two-wheeled self-balanced robot (TWSBR) systems on undulating roads, this paper proposes a fuzzy neural network active disturbance rejection controller (FNNADRC), that is based on fuzzy neural network (FNN) for online correction of active disturbance rejection controller (ADRC)'s nonlinear control rate. Firstly, the dynamic model of the TWSBR is established and decoupled, the extended state observer (ESO) is used to compensate dynamically and linearize the upright and displacement subsystems. Then, the nonlinear PD control rate and FNN are designed, and the FNN is used to modify the control parameters of the nonlinear PD control rate in real time. Finally, the proposed control strategy is simulated and compared with the traditional ADRC and fuzzy active disturbance rejection controller (FADRC). The simulation results show that the control effect of the proposed control strategy is slightly better than ADRC and FADRC.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.6
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• pp.55-65
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• 1998

In industrial fields, RTP(rapid thermal processing) system is widely used for improving the oxidation and the annealing in semiconductor manufacturing process. The main control factors are temperature control of wafer and uniformity in the wafer. In this paper, we propose an $H^{\infty}$ controller design of RTP system satisfying robust stability and performance using weighted mixed sensitivity miniimization and loop shaping technique. And we need reduction technique because of the difficulty of implementation with the obtained high order controller for original model and reduced models, namely, Hankel, square-root balanced, and Schur balanced methods. An example is proposed to show the validity of the proposed method.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.123-133
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• 2021

We study confirmed the factors influencing the organizational culture(collective culture, development culture, rational culture, hierarchical culture) perceived by members of the organization and the manager's authentic leadership(self-awareness, balanced information processing, relational transparency, internalized moral perspective) on job crafting. In addition, the relationship between organizational culture and authentic leadership was empirically analyzed. In order to verify the hypothesis of the research model, the survey results of 269 parts were verified as follows using the statistical program of SPSS 24.0. First, the organizational cultures perceived by members of the organization, development culture and rational culture, showed positive (+) influence on job crafting. In other words, Hypothesis 1 established by the research model was partially adopted. Second, the group culture, development culture, and rational culture of organizational culture were statistically significant in the relationship between the hypothesis 2 organizational culture and authentic leadership. In other words, Hypothesis 2 was partially adopted. Third, the three hypotheses of authentic leadership (self-awareness, balanced information processing, relational transparency, and an internalized moral perspective) all showed positive (+) effects on job crafting. As a result of this study, it was possible to confirm the importance of the organizational culture that improves the job-crafting of the members of the organization and the strategic activation plan for authentic leadership. Therefore, the necessity of strategic human resource development for the development and application of programs to revitalize organizational culture and improve the manager's authentic leadership has emerged.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.314-333
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• 2014

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.