• Journal of Information Processing Systems
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• v.15 no.3
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• pp.604-615
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• 2019

Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase. When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED) is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR with low complexity. The local sensing node transmits the perceived results through the control channel to the fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is effectively saved. Simulation results show that the proposed scheme can effectively improve the system detection probability, shorten the average sensing time, and has better robustness without largely increasing the costs of sensing system.

• Nutrition Research and Practice
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• v.17 no.6
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• pp.1255-1266
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• 2023

BACKGROUND/OBJECTIVES: This study aimed to predict the association between nutritional intake and diabetes mellitus (DM) by developing an artificial neural network (ANN) model for older adults. SUBJECTS/METHODS: Participants aged over 65 years from the 7th (2016-2018) Korea National Health and Nutrition Examination Survey were included. The diagnostic criteria of DM were set as output variables, while various nutritional intakes were set as input variables. An ANN model comprising one input layer with 16 nodes, one hidden layer with 12 nodes, and one output layer with one node was implemented in the MATLAB^® programming language. A sensitivity analysis was conducted to determine the relative importance of the input variables in predicting the output. RESULTS: Our DM-predicting neural network model exhibited relatively high accuracy (81.3%) with 11 nutrient inputs, namely, thiamin, carbohydrates, potassium, energy, cholesterol, sugar, vitamin A, riboflavin, protein, vitamin C, and fat. CONCLUSIONS: In this study, the neural network sensitivity analysis method based on nutrient intake demonstrated a relatively accurate classification and prediction of DM in the older population.

• Journal of KIISE:Information Networking
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• v.35 no.3
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• pp.192-206
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• 2008

Multihop is the main communication style for wireless sensor networks composed of tiny sensor nodes. Until now, most applications have treated the periodic small sized sensing data. Recently, the burst traffic with the transient and continuous nature is increasingly introduced due to the advent of wireless multimedia sensor networks. Therefore, the efficient communication protocol to support this trend is required. In this paper, we propose a novel PIGAB(Packet Interval Gap based on Adaptive Backoff) protocol to efficiently transmit the burst data in multihop wireless sensor networks. The contention-based PIGAB protocol consists of the PIG(Packet Interval Gap) control algorithm in the source node and the MF(MAC-level Forwarding) algorithm in the relay node. The PIGAB is on basis of the newly proposed AB(Adaptive Backoff), CAB(Collision Avoidance Backoff), and UB(Uniform Backoff). These innovative algorithms and schemes can achieve the performance of network by adjusting the gap of every packet interval, recognizing the packet transmission of the hidden node. Through the simulations and experiments, we identify that the proposed PIGAB protocol considerably has the stable throughput and low latency in transmitting the burst data in multihop wireless sensor networks.

• Journal of the Korean Geotechnical Society
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• v.27 no.1
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• pp.17-24
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• 2011

The characteristics of flow and strength of CLSM depend on the combination ratio including the fly ash, pond ash, cement, water quantity and etc. However, it is very difficult to draw the mechanism about the flow, strength and the mixing ratio of each components. Therefore, the method of calculation drawing the flow about the component ratio of CLSM and compression strength value is needed for the valid practical use of CLSM. To verify the efficiency of artificial neural network, new data which were not used for establishing the model were predicted and compared with the results of laboratory tests. In this research, it was used to evaluate the learning efficiency of the artificial neural network model and the prediction ability by changing the node number of hidden layer, learning rate, momentum, target system error and hidden layer. By using the results, the optimized artificial neural network model which is suitable for a flow and compressive strength estimate of CLSM was determined.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.374-382
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• 2020

The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.

• Advances in nano research
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• v.9 no.1
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• pp.1-13
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• 2020

In this work, the electrical potential (EP) technique with an artificial neural networks (ANNs) for monitoring of nanostructures are used for the first time. This study employs an expert system to identify size and localize hidden nano-delamination (N.Del) inside layers of nano-pipe (N.P) manufactured from Basalt Fiber Reinforced Polymer (BFRP) laminate composite by using low-cost monitoring method of electrical potential (EP) technique with an artificial neural networks (ANNs), which are combined to decrease detection effort to discern N.Del location/size inside the N.P layers, with high accuracy, simple and low-cost. The dielectric properties of the N.P material are measured before and after N.Del introduced using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to N.Del, a finite element (FE) simulation model for N.Del location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic, therefore, FE analyses are employed to make sets of data for the learning of the ANNs. The method is applied for the N.Del monitoring, to minimize the number of FE analysis in order to keep the cost and save the time of the assessment to a minimum. The FE results are in excellent agreement with an ANN and the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.8
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• pp.1894-1912
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• 2012

This paper proposes a system called Wireless Link based on Global Communication Channel (WLGCC) to enhance the spectrum sharing between digital broadcasting (DB) services and the cognitive radio (CR) system in the licensed band of 470-790 MHz. The WLGCC aims to enhance the spectrum sensing and geolocation database (GLD) spectrum sharing methods in the CR system. Spectrum sensing can be enhanced by receiving the status of the used frequencies from the WLGCC, thereby eliminating the need for a low detection threshold (i.e., avoiding the hidden node problem). In addition, the GLD can be enhanced by providing a reliable communication link between the database and the CR device in the form of an unused TV white space that is reserved as the proposed Global Communication Channel (GCC). This paper analyzes the coexistence of the new WLGCC system and the DB service in terms of avoiding additional interference. Specifically, we mathematically determine the WLGCC parameters, such as the in-band and out-of-band power levels, and operation coverage, and verify them using Monte Carlo simulation. The results show that WLGCC does not degrade the existing DB service and reliably transmits information of the vacant (or used) frequency bands to the CR.

• Journal of KIISE:Information Networking
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• v.37 no.4
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• pp.301-306
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• 2010

We propose the control channel access scheme for multi-interface multi-hop cognitive radio (CR) environment having a cluster structure. Due to the difficulty of obtaining common channels across the entire CR network, most multi-interface multi-hop CR networks put the control channel outside the CR bandwidth and dedicate one network interface to it in order to exchange the control information such as the activation of licensed users. However, this will be the waste of the network interface. Our focus is how to alternate between the control and the data channel without multichannel hidden node problem under the cluster structure where CR nodes connect with neighbors through multiple data channels. By using simulation, we evaluate the performance of the proposed scheme. The results show that the proposed scheme achieves higher network throughput than the dedicated scheme where one network interface card should dedicate to the control channel and cannot be used for data transmission.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.1
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• pp.37-53
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• 2012

There is considerable uncertainty in ground properties used in tunnel designs. In this study, a back analysis was performed to find optimal ground properties based on the artificial neural network facility of MATLAB program of using tunnel monitoring data. Total 81 data were constructed by changing elastic modulus and coefficient of lateral pressure which have great influence on tunnel convergence. A sensitivity analysis was conducted to establish an optimal training model by varying the number of hidden layers, the number of nodes, learning rate, and momentum. Meanwhile, the optimal training model was selected by comparing MSE (Mean Squared Error) and coefficient of determination ($R^2$) and was used to find the correct elastic moduli of layers and the coefficient of lateral pressure. In future, it is expected that the suggested method of this study can be applied to determine the optimum tunnel support pattern under given ground conditions.

• Advances in aircraft and spacecraft science
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• v.4 no.5
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• pp.515-528
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• 2017

Since composite structures are widely used in structural engineering, delamination in such structures is an important issue of research. Delamination is one of a principal cause of failure in composites. In This study the electrical potential (EP) technique is applied to detect and locate delamination in basalt fiber reinforced polymer (FRP) laminate composite pipe by using electrical capacitance sensor (ECS). The proposed EP method is able to identify and localize hidden delamination inside composite layers without overlapping with other method data accumulated to achieve an overall identification of the delamination location/size in a composite, with high accuracy, easy and low-cost. Twelve electrodes are mounted on the outer surface of the pipe. Afterwards, the delamination is introduced into between the three layers (0º/90º/0º)s laminates pipe, split into twelve scenarios. The dielectric properties change in basalt FRP pipe is measured before and after delamination occurred using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to delamination, a finite element simulation model for delamination location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Response surfaces method (RSM) are adopted as a tool for solving inverse problems to estimate delamination location/size from the measured electrical potential changes of all segments between electrodes. The results show good convergence between the finite element model (FEM) and estimated results. Also the results indicate that the proposed method successfully assesses the delamination location/size for basalt FRP laminate composite pipes. The illustrated results are in excellent agreement with the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.