• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.20-20
• /
• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.8B
• /
• pp.712-721
• /
• 2012

In this paper, we propose an Enhanced cross-layer Geographic Forwarding (EGF) protocol for wireless sensor networks (WSNs). EGF uses an optimal back-off time to make the packet forwarding decisions using only source and destination's location information and energy cost without information about neighbor nodes' location or the number of one hop neighbor nodes. EGF is also a cross-layer protocol by combining efficient asynchronous MAC and geographic routing protocol. The proposed protocol can find optimal next hop location quickly without broadcasting node's location update and with minimizing overhead. In our performance evaluation, EGF has better performance in terms of packet success ratio, energy efficiency and end-to-end delay in wireless sensor networks.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.44 no.2 s.314
• /
• pp.11-18
• /
• 2007

Back propagation algorithm based on a gradient-decent method has been widely used to the training of a neural network. However, this algorithm have some problems such as dropping the minimum value in a local area according to an initial value and setting the number of units in a hidden layer when training the neural network. Accordingly, to solve the above-mentioned problems, this paper proposes a genetic algorithm using the training method of the neural network. Thus, the improved genetic algorithm using a new crossover and mutation method is proposed to discriminate 3 bit parity. Experiments confirm that the proposed system is effective for training speed after demonstrating for generation gap, the number of units in the hidden layer, and the number of individuals.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.2 no.1
• /
• pp.17-32
• /
• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Journal of Advanced Marine Engineering and Technology
• /
• v.28 no.1
• /
• pp.90-97
• /
• 2004

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as speed detectors. but they increase cost and size of the motor and restrict the industrial drive applications. So in these days. many Papers have reported on the sensorless operation or DC motor(3)-(5). This paper Presents a new sensorless strategy using neural networks(6)-(8). Neural network structure has three layers which are input layer. hidden layer and output layer. The optimal neural network structure was tracked down by trial and error and it was found that 4-16-1 neural network has given suitable results for the instantaneous rotor speed. Also. learning method is very important in neural network. Supervised learning methods(8) are typically used to train the neural network for learning the input/output pattern presented. The back-propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2009.11a
• /
• pp.35-35
• /
• 2009

Capacitorless one transistor dynamic random access memory (1T-DRAM) cells were fabricated on the fully depleted strained-silicon-on-insulator (FD sSOI) and the effects of silicon back interface state on buried oxide (BOX) layer on the memory properties were evaluated. As a result, the fabricated 1T-DRAM cells showed superior electrical characteristics and a large sensing current margin (${\Delta}I_s$) between "1" state and "0" state. The back interface of SOI based capacitorless 1T-DRAM memory cell plays an important role on the memory performance. As the back interface properties were degraded by increase rapid thermal annealing (RTA) process, the performance of 1T-DRAM was also degraded. On the other hand, the properties of back interface and the performance of 1T-DRAM were considerably improved by post RTA annealing process at $450^{\circ}C$ for 30 min in a 2% $H_2/N_2$ ambient.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.4
• /
• pp.191-200
• /
• 1996

This paper presetns a new fuzzy neural network for fuzzy modeling.The fuzzy neural network is composed of 4 layers and then odes of each layer represent the each step of the if-then fuzzy inference. A heuristic based on the back-propagation algorithm is proposed to ajdust the parameters of the fuzzy nerual network. We prove the feasibility of the network using the experiments on modeling a nonlinear mathematical system and the comparison with previous research.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.896-899
• /
• 1991

This paper presents the neural network method to predict the Korea composition stock price index. The error back-propagation method is used to train the multi-layer perceptron network. Ten of the various economic indices of the past 7 Nears are used as train data and the monthly transition of the composition stock price index is represented by five output neurons. Test results of this method using the data of the last 18 months are very encouraging.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.40 no.2
• /
• pp.224-233
• /
• 1991

The multilayer expanson of single layer NN (Neural Network) was needed to solve the linear seperability problem as shown by the classic example using the XOR function. The EBP (Error Back Propagation ) learning rule is often used in multilayer Neural Networks, but it is not without its faults: 1)D.Rimmelhart expanded the Delta Rule but there is a problem in obtaining Ca from the linear combination of the Weight matrix N between the hidden layer and the output layer and H, wich is the result of another linear combination between the input pattern and the Weight matrix M between the input layer and the hidden layer. 2) Even if using the difference between Ca and Da to adjust the values of the Weight matrix N between the hidden layer and the output layer may be valid is correct, but using the same value to adjust the Weight matrixd M between the input layer and the hidden layer is wrong. Recognitron III was proposed to solve these faults. According to simulation results, since Recognitron III does not learn the three layer NN itself, but divides it into several single layer NNs and learns these with learning patterns, the learning time is 32.5 to 72.2 time faster than EBP NN one. The number of patterns learned in a EBP NN with n input and output cells and n+1 hidden cells are 2**n, but n in Recognitron III of the same size. [5] In the case of pattern generalization, however, EBP NN is less than Recognitron III.

• Asian-Australasian Journal of Animal Sciences
• /
• v.29 no.6
• /
• pp.865-871
• /
• 2016

The objective of this study was to investigate the effects of canola and flaxseed oils on the physicochemical properties and sensory quality of emulsion-type sausage made from spent layer meat. Three types of sausage were manufactured with different fat sources: 20% pork back fat (CON), 20% canola oil (CA) and 20% flaxseed oil (FL). The pH value of the CA was significantly higher than the others (p<0.05). The highest water holding capacity was also presented for CA; in other words, CA demonstrated a significantly lower water loss value among the treatments (p<0.05). CA had the highest lightness value (p<0.05). However, FL showed the highest yellowness value (p<0.05) because of its own high-density yellow color. The texture profile of the treatments manufactured with vegetable oils showed higher values than for the CON (p<0.05); furthermore, CA had the highest texture profile values (p<0.05) among the treatments. The replacement of pork back fat with canola and flaxseed oils in sausages significantly increased the omega-3 fatty acid content (p<0.05) over 15 to 86 times, respectively. All emulsion sausages containing vegetable oil exhibited significantly lower values for saturated fatty acid content and the omega-6 to omega-3 ratios compared to CON (p<0.05). The results show that using canola or flaxseed oils as a pork fat replacer has a high potential to produce healthier products, and notably, the use of canola oil produced characteristics of great emulsion stability and sensory quality.