• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.367-371
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• 2006

Multilayer percepteon has the advantage of learning their optimal parameters and efficiency. However, MLP shows some drawbacks when dealing with high dimensional data within the input space. Also, it Is very difficult to find the optimal parameters when the input data are highly correlated such as large scale face dataset. In this paper, we propose a novel technique for face recognition based on LDA and local MLP. To resolve the main drawback of MLP, we calculate the reduced features by LDA in advance. And then, we construct a local MLP per group consisting of subset of facedatabase to find its optimal learning parameters rather than using whole faces. Finally, we designed the face recognition system combined with the local MLPs. From various experiments, we obtained better classification performance in comparison with the results produced by conventional methods such as PCA and LDA.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.6
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• pp.872-879
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• 2016

This study evaluated the quality characteristics of muffins prepared with different amounts (0%, 1%, 3%, 5%, and 7%) of moringa (Moringa oleifera Lam.) leaf powder (MLP). The weight of muffins increased as the amount of MLP increased. The height and pH of muffins significantly decreased as the amount of MLP increased (P<0.05). The moisture content was higher in groups containing 3% MLP. The hardness was higher at 0% MLP. Cohesiveness decreased as the amount of MLP increased (P<0.05), whereas springiness was not significantly different among all samples. Chewiness and brittleness decreased with increasing MLP concentration. Substitution of wheat flour with MLP yielded muffins with a higher 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity and total polyphenol content (P<0.05). Alcohol dehydrogenase and acetaldehyde dehydrogenase activity significantly increased upon addition of MLP (P<0.05). In the sensory evaluation, appearance scores of muffins were higher in groups containing 7% MLP, whereas taste, flavor, texture, and overall acceptability scores were lowest in muffins with 7% MLP. Therefore, up to 3% MLP can be incorporated into muffins to satisfy the sensory quality and functional needs of the consumer. Furthermore, this study proposes the possibility of development of various products using MLP.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.212-216
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• 2006

MLP는 뛰어난 학습능력으로 인하여 많은 분야에 성공적으로 적용되고 있다. 그러나, 학습 방법으로서 최급경사법에 근거한 오차역전파 알고리즘을 적용하기 때문에 학습시간이 오래 걸리는 단점이 있다. 또한 입력차원의 크기가 크거나 클래스간 학습데이터의 유사성이 클 경우 최적의 파라미터를 구하는데는 한계가 있다. 이러한 문제점을 해결하기 위해 본 논문에서는 LDA와 local MLP을 이용한 새로운 얼굴인식시스템을 제안하고자 한다. 제안된 방법은 우선 LDA 기법에 의해 차원이 축소된 얼굴의 특징벡터를 계산한다. 다음 단계로서 전체 학습영상을 사용하기 보다는 그룹별로 분할된 얼굴영상에 대해 MLP를 수행하므로서 그룹별로 최적인 파라미터를 결정한다. 마지막 단계로 그룹별로 수행된 local MLP를 결합함으로써 전체 얼굴인식 시스템을 구성한다. 제안된 방법의 타당성을 보이기 위해 ORL 얼굴영상을 대상으로 실험한 결과 기존 방법인 PCA나 LDA에 비해 향상된 결과를 보임을 확인할 수 있었다.

• The Korean Journal of Food And Nutrition
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• v.20 no.4
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• pp.406-413
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• 2007

LOHAS(Lifestyles Of Health And Sustainability) drinks were prepared by the addition of mulberry leaf powder(MLP) at various levels(0%, 5%, 10% and 20%). Their characteristics of the drinks were examined according to antioxidant activity and sensory evaluation. In the sensory evaluation, there was no significant difference between the control group(0% MLP group) and 5% MLP group for overall quality. For the proximate compositions of the LOHAS drinks, moisture, crude ash, crude protein and crude fat were all increased as the ratio of MLP increased. For the mineral contents, the amounts of calcium and magnesium were increased, but sodium, according to adding levels of MLP.

• Journal of Sericultural and Entomological Science
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• v.42 no.2
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• pp.78-82
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• 2000

In this study, a comparative removal of chlorine from aqueous solutions of mulberry leaf powder(MLP) and activated carbon(AC) was investigated. The chlorine removal capacities of MLP and AC were shown as a function of contact time, pH and initial chlorine concentration. Optimum contact time and removal pH value of MLP were determined as 2 hr and pH 10, respectively. Chlorine removal increased with increasing initial chlorine concentration up to 1.3g/L. Both Langmuir and Freundlich adsorption models were suitable for describing the short-term removal of chlorine by MLP and AC. According to Freundlich adsorption isotherms, the maximum removal capacity of MLP(0.264 mg Cl$_2$/mg) was nearly two times greater than that of AC(0.56 mg Cl$_2$/mg). These results suggested that MLP might potentially be used as an alternative to traditional water treatment materials for removal of residual chlorine in drinking water or process wastewater.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.88-98
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• 2002

The multilayer perceptron (MLP) has several advantages against other pattern recognition methods, and is expected to be used as the learning and recognizing speakers of speaker verification system. But because of the low learning speed of the error backpropagation (EBP) algorithm that is used for the MLP learning, the MLP learning requires considerable time. Because the speaker verification system must provide verification services just after a speaker's enrollment, it is required to solve the problem. So, this paper tries to make short of time required to enroll speakers with the MLP based speaker verification system, using the method of improving the EBP learning speed and the method of reducing background speakers which adopts the cohort speakers method from the existing speaker verification.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.429-438
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• 2006

Neural Networks(NNs) are applied for solving a wide variety of nonlinear problems in several areas, such as image processing, pattern recognition etc. Although NN can be simulated by using software, many potential NN applications required real-time processing. Thus they need to be implemented as hardware. The hardware implementation of multi-layer perceptrons(MLPs) in several kind of NNs usually uses a fixed-point arithmetic due to a simple logic operation and a shorter processing time compared to the floating-point arithmetic. However, the fixed-point arithmetic-based MLP has a drawback which is not able to apply the MLP software that use floating-point arithmetic. We propose a design method for MLPs which has the floating-point arithmetic-based fully-pipelining architecture. It has a processing speed that is proportional to the number of the hidden nodes. The number of input and output nodes of MLPs are generally constrained by given problems, but the number of hidden nodes can be optimized by user experiences. Thus our design method is using optimized number of hidden nodes in order to improve the processing speed, especially in field of a repeated processing such as image processing, pattern recognition, etc.

• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• Korean journal of food and cookery science
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• v.25 no.3
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• pp.275-282
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• 2009

This study investigated the quality characteristics of Pongnip Injeulmi samples according to different mulberry leaf powder(MLP) contents. The moisture levels of the samples ranged from 47.43 to 49.16%, with the 0% MLP sample presenting the highest moisture level. The amounts of crude protein, crude fat, and crude ash in samples were in ranges of 3.82${\sim}$5.01%, 0.05${\sim}$0.2%, and 0.65${\sim}$1.62%, respectively, and the values increased with increasing MLP content. Color L and b values decreased, while the a-value increased, with increasing MLP content. The 0% sample showed the highest gelatinization temperature of 63.4$^{\circ}$C, and the viscosity decreased with increasing MLP content. Texture and hardness also decreased with increasing MLP content, however, over 3 days storage, they increased in all samples. Finally, in the sensory tests, the 6% MLP sample received the highest scores for color, flavor, sweetness, texture and overall acceptability.

• Smart Structures and Systems
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• v.26 no.2
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• pp.241-251
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• 2020

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.