• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1225-1230
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• 2014

This paper investigates the group average-consensus and group formation-consensus problems for first-order multi-agent systems. The control protocol for group consensus is designed by considering the positive adjacency elements. Since each intra-group Laplacian matrix cannot be satisfied with the in-degree balance because of the positive adjacency elements between groups, we decompose the Laplacian matrix into an intra-group Laplacian matrix and an inter-group Laplacian matrix. Moreover, average matrices are used in the control protocol to analyze the stability of multi-agent systems with a fixed and undirected communication topology. Using the graph theory and the Lyapunov functional, stability analysis is performed for group average-consensus and group formation-consensus, respectively. Finally, some simulation results are presented to validate the effectiveness of the proposed control protocol for group consensus.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.243-248
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• 2013

In wireless sensor networks, consensus algorithms for dynamic systems may flexibly usable for their data fusion of a sensor network. In this paper, a distributed data fusion filter is implemented using an average consensus based on distributed sensor data, which is composed of some sensor nodes and a sink node to track the mean values of n sensors' data. The consensus filter resolve the problem of data fusion by a distribution Kalman filtering scheme. We showed that the consensus filter has an optimal convergence to decrease of noise propagation and fast tracking ability for input signals. In order to verify for the results of consensus filtering, we showed the output signals of sensor nodes and their filtering results, and then showed the result of the combined signal and the consensus filtering using zeegbee communication.

• Atmosphere
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• v.21 no.2
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• pp.209-220
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• 2011

A new consensus algorithm for the prediction of tropical cyclone track has been developed. Conventional consensus is a simple average of a few fixed models that showed the good performance in track prediction for the past few years. Meanwhile, the consensus in this study is a weighted average of a few models that may change for every individual forecast time. The models are selected as follows. The first step is to find the analogous past tropical cyclone tracks to the current track. The next step is to evaluate the model performances for those past tracks. Finally, we take the weighted average of the selected models. More weight is given to the higher performance model. This new algorithm has been named as DYTRAP (DYnamic data-base Typhoon tRAck Prediction) in the sense that the data base is used to find the analogous past tracks and the effective models for every individual track prediction case. DYTRAP has been applied to all 2009 tropical cyclone track prediction. The results outperforms those of all models as well as all the official forecasts of the typhoon centers. In order to prove the real usefulness of DYTRAP, it is necessary to apply the DYTRAP system to the real time prediction because the forecast in typhoon centers usually uses 6-hour or 12-hour-old model guidances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2299-2318
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• 2019

Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.277-283
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• 2019

In this paper, we propose a distributed fusion estimation for sensor networks using a receding horizon strategy. Communication channels were modelled as Markov jump systems, and a posterior probability distribution for communication channel characteristics was calculated and incorporated into the filter to allow distributed fusion estimation to handle path loss observation situations automatically. To implement distributed fusion estimation, a Kalman-Consensus filter was then used to obtain the average consensus, based on the estimates of sensors randomly distributed across sensor networks. The advantages of the proposed algorithms were then verified using a large-scale sensor network example.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.27-36
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• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.

• ETRI Journal
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• v.46 no.4
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• pp.648-659
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• 2024

Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.

• Korean Journal of Organic Agriculture
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• v.31 no.3
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• pp.167-189
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• 2023

Korea has become the highest nitrogen balance (228 kg/ha) among 34 OECD member countries, and has the stigma of being a 'Nutrient overload country' as of 2019. Accordingly, research on the derivation and utilization of nutrient balance indicators and the 'regional nutrient management system' are being promoted to improve Korea's nutrient balance. It is necessary to support these policies and studies, form a public consensus on improving the nutrient balance, and evaluate the function of the public benefit. This paper aims to estimate the public benefit value of improving the nutrient balance based on an analysis of consumers' willingness to pay and recognition of Korea's nutrient excess for 600 consumers nationwide. As results, 21.2% of the respondents said they were aware of excessive nutrients in Korea, and 76.7% of the respondents said they were aware of the need for nutrient management. The average amount of intention to pay for the improvement of three pollution (soil, water quality, and air) that can occur due to a nutrient overload was ￦2,321.1 for soil pollution improvement, ￦2,391.2 for water pollution improvement, and ￦2,377.9 for air pollution improvement. The average willingness to pay for the three pollution reduction was ￦6,002.3. These results are expected to be used to form a public consensus on the balance of payments and to establish measures to enhance public interest values in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.547-559
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• 2010

As web-based online communities are rapidly growing, the agents in the communities need to know their measurable belief of trust for safe and successful interactions. In this paper, we propose a computational model of trust resulting from available feedbacks in online communities. The notion of trust can be defined as an aggregation of consensus given a set of past interactions. The average trust of an agent further represents the center of gravity of the distribution of its trustworthiness and untrustworthiness. Furthermore, we precisely describe the relationships among reputation, trust and average trust through concrete examples showing their computations. We apply our trust model to online social networks in order to show how trust mechanisms are involved in the rational purchasing decision-making of buyers and sellers, and we summarize our simulation results.

• Asia pacific journal of information systems
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• v.9 no.1
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• pp.143-163
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• 1999

This study investigates the effect of group size on group performance, here the quality of group decision, Four effects are proposed and tested in a field experimental setting : (1) the relationship between the group size and the distribution of individual's problem-solving ability ; (2) the change of the group decision quality as group size increases ; (3) the relationship between the group decision quality and the quality of the best/worst member as group size increases ; (4) the relationship between the group decision quality and the average quality of individuals in the group as group size increases. Data showed that contrary to the exiting results, group decision quality was not improved with the group size. Rather, it showed a little tendency that group decision quality was worsened with the group size. Data also showed that consensus-oriented group decision making process produced the compromised output. Thus, group decision quality was not better than the average group members'. The opinion of the best member was not accepted. The implications of the findings are discussed for the development of a Korean GSS.