• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1187-1208
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• 2022

One of the main goals of wireless sensor networks (WSNs) is to utilize the energy of sensor nodes effectively and maximize the network lifetime. Thus, this paper proposed a routing protocol for WSNs based on virtual force disturbing mobile Sink node (VFMSR). According to the number of sensor nodes in the cluster, the average energy and the centroid factor of the cluster, a new cluster head (CH) election fitness function was designed. At the same time, a hexagonal fixed-point moving trajectory model with the best radius was constructed, and the virtual force was introduced to interfere with it, so as to avoid the frequent propagation of sink node position information, and reduce the energy consumption of CH. Combined with the improved ant colony algorithm (ACA), the shortest transmission path to Sink node was constructed to reduce the energy consumption of long-distance data transmission of CHs. The simulation results showed that, compared with LEACH, EIP-LEACH, ANT-LEACH and MECA protocols, VFMSR protocol was superior to the existing routing protocols in terms of network energy consumption and network lifetime, and compared with LEACH protocol, the network lifetime was increased by more than three times.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1180-1197
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• 2013

Network coding is one of the most promising techniques to increase the reliability and reduce the energy consumption for wireless sensor networks (WSNs). However, most of the previous works mainly focus on the network coding for multicast or unicast in WSNs, in spite of the fact that the converge-cast is the most common communication style in WSNs. In this paper, we investigate, for the first time as far as we know, the feasibility of acquiring network coding benefits in converge-cast, and we present that with the ubiquitous convergent structures self-organized during converge-casting in the network, the reliability benefits can be obtained by applying linear network coding. We theoretically derive the network coding benefits obtained in a general convergent structure, and simulations are conducted to validate our theoretical analysis. The results reveal that the network coding can improve the network reliability considerably, and hence reduce number of retransmissions and improve energy-efficiency.

• Journal of Biosystems Engineering
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• 제37권4호
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• pp.271-278
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• 2012

Worldwide trends in animal welfare have resulted in an increased interest in individual management of sows housed in groups within hog barns. Estrus detection has been shown to be one of the greatest determinants of sow productivity. Purpose: We conducted this study to develop a method that can automatically detect the estrus state of a sow by selecting optimal texture parameters from images of a sow's pudendum and by optimizing the number of neurons in the hidden layer of an artificial neural network. Methods: Texture parameters were analyzed according to changes in a sow's pudendum in estrus such as mucus secretion and expansion. Of the texture parameters, eight gray level co-occurrence matrix (GLCM) parameters were used for image analysis. The image states were classified into ten grades for each GLCM parameter, and an artificial neural network was formed using the values for each grade as inputs to discriminate the estrus state of sows. The number of hidden layer neurons in the artificial neural network is an important parameter in neural network design. Therefore, we determined the optimal number of hidden layer units using a trial and error method while increasing the number of neurons. Results: Fifteen hidden layers were determined to be optimal for use in the artificial neural network designed in this study. Thirty images of 10 sows were used for learning, and then 30 different images of 10 sows were used for verification. Conclusions: For learning, the back propagation neural network (BPN) algorithm was used to successful estimate six texture parameters (homogeneity, angular second moment, energy, maximum probability, entropy, and GLCM correlation). Based on the verification results, homogeneity was determined to be the most important texture parameter, and resulted in an estrus detection rate of 70%.

• 전자공학회논문지SC
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• 제38권5호
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• pp.7-13
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• 2001

단일 신경망에 기반한 웨이블릿 이론과 모듈라 개념을 결합하여 기존의 웨이블릿 신경망이나 모듈라 네트워크의 일종인 모듈라 웨이블릿 신경망이 제안되었다. 본 논문에서는 유전 알고리즘을 사용하여 모듈라 웨이블릿 신경망의 최적구조를 효과적으로 설계하는 방법을 제시하였다. 각 모듈을 구성하는 웨이블릿 신경망의 웨이블릿 기저함수의 팽창과 이동계수를 결장하기 위해 유전 알고리즘을 사용하였다. 제안한 최적 구조 설계 알고리즘을 근사화 문제에 적용하여 우수성을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권9호
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• pp.2134-2153
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• 2013

A network-coding-based scheme is proposed to improve the energy efficiency of distributed storage systems in WSNs (Wireless Sensor Networks). We mainly focus on two problems: firstly, consideration is given to effective distributed storage technology; secondly, we address how to effectively repair the data in failed storage nodes. For the first problem, we propose a method to obtain a sparse generator matrix to construct network codes, and this sparse generator matrix is proven to be the sparsest. Benefiting from this matrix, the energy consumption required to implement distributed storage is reduced. For the second problem, we designed a network-coding-based iterative repair method, which adequately utilizes the idea of re-encoding at intermediate nodes from network coding theory. Benefiting from the re-encoding, the energy consumption required by data repair is significantly reduced. Moreover, we provide an explicit lower bound of field size required by this scheme, which implies that it can work over a small field and the required computation overhead is very low. The simulation result verifies that the proposed scheme not only reduces the total energy consumption required to implement distributed storage system in WSNs, but also balances energy consumption of the networks.

• Journal of Mechanical Science and Technology
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• 제20권10호
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• pp.1534-1540
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• 2006

Concurrent Engineering (CE) is a popular method employed in product development. It treats the whole product design process by the consideration of product quality, cost, rate of progress, and demands of customers. The development of computer and network technologies provides a strong support to the realization of CE in practice. Aiming at the characteristics of CE and network collaborative design, this paper built network collaborative design system frame. Through the analysis of the network collaborative design modes based on CE, this paper provided a novel network collaborative design integration model. This model can integrate the product design information, design process, and knowledge. Intelligent collaboration was considered in the proposed model. The study showed that the proposed model considered main factors such as information, knowledge, and design process in collaborative design. It has potential application in CE fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.158-161
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• 2003

A cascade structured neural network called Sigma-Pi$_{t}$ Cascaded Hybrid Neural Network ($\sigma$$\pi$$_{t}$-CHNN) is Proposed. It is an extended version of the Sigma-Pi Cascaded extended Hybrid Neural Network ($\sigma$$\pi$-CHNN), where the classical multiplicative neuron ($\pi$-neuron) is replaced by the translated multiplicative ($\pi$$_{t}$-neuron) model. The learning algorithm of $\sigma$$\pi$$_{t}$-CHNN is composed of an evolutionary programming method, responsible for determining the network architecture, and of a Levenberg-Marquadt algorithm, responsible for tuning the weights of the network. The $\sigma$$\pi$$_{t}$-CHNN is evaluated in 2 pattern classification problems: the 2 spirals and the sonar problems. In the 2 spirals problem, $\sigma$$\pi$$_{t}$-CHNN can generate neural networks with 10％ less hidden neurons than that in previous neural models. In the sonar problem, $\sigma$$\pi$$_{t}$-CHNN can find the optimal solution for the problem i.e., a network with no hidden neurons. These results confirm the expanded information processing capabilities of $\sigma$$\pi$$_{t}$-CHNN, when compared to previous neural network models. network models.

• Journal of Ginseng Research
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• 제48권4호
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• pp.373-383
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• 2024

Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.620-623
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• 2003

The TAM (Topographic Attentive Mapping) network is a biologically-motivated neural network. Fuzzy rules are acquired from the TAM network by the pruning algorithm. In this paper we formulate a new input layer using Gabor function for TAU network to realize receptive field of human visual cortex.