• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.174-179
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• 2023

Bulk trailers, used for the transportation of powdered materials, such as cement and fly ash, are crucial in the construction industry. The speedy exhaustion of powdered materials stored in the tank of bulk trailers is relevant to improving transportation efficiency and reducing transportation costs. The exhaust time can be reduced by developing an automatic control system to replace the manual exhaust operation. The instantaneous or accumulated exhausts of powdered materials must be measured for automatic control of the bulk trailer exhaust system. Accordingly, we previously proposed a recurrent neural network (RNN) model that estimated the instantaneous exhaust based on low-cost pressure sensor signals without an expensive flowmeter for powders. Although our previous study utilized only an RNN model, models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are also widely utilized for time-series estimation. This study compares the performance of three neural network models (MLP, CNN, and RNN) in estimating instantaneous and accumulated exhausts. In terms of the instantaneous exhaust estimation, the difference in the performance of neural network models was insignificant (that is, 8.64, 8.62, and 8.56% for the MLP, CNN, and RNN, respectively, in terms of the normalized root mean squared error). However, in the case of the accumulated exhaust, the performance was excellent in the order of CNN (1.67%), MLP (2.03%), and RNN (2.20%).

• Computers and Concrete
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• v.32 no.2
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• The Journal of Bigdata
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• v.8 no.2
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• pp.201-222
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• 2023

As the e-Commerce gets increased based on the platform, a lot of small and medium sized sellers have tried to develop the more effective strategies to maximize the profit. In order to increase the profitability, it is quite important to make the strategic decisions based on the range of promotion, discount rate and categories of products. This research aims to develop the business intelligence application which can help sellers of e-Commerce platform make better decisions. To decide whether or not to promote, it is needed to predict the level of increase in sales after promotion. I n this research, we have applied the various machine learning algorithm such as MLP(Multi Layer Perceptron), Gradient Boosting Regression, Random Forest, and Linear Regression. Because of the complexity of data structure and distinctive characteristics of product categories, Random Forest and MLP showed the best performance. It seems possible to apply the proposed approach in this research in support the small and medium sized sellers to react on the market changes and to make the reasonable decisions based on the data, not their own experience.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.15-24
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• 2007

The recognition of postal address images is indispensable for the automatic sorting of postal envelopes. The process of the address image recognition is composed of three steps-address image preprocessing, character recognition, address interpretation. The extracted character images from the preprocessing step are forwarded to the character recognition step, in which multiple candidate characters with reliability scores are obtained for each character image extracted. aracters with reliability scores are obtained for each character image extracted. Utilizing those character candidates with scores, we obtain the final valid address for the input envelope image through the address interpretation step. The envelope sorting rate depends on the performance of all three steps, among which character recognition step could be said to be very important. The good character recognizer would be the one which could produce valid candidates with very reliable scores to help the address interpretation step go easy. In this paper, we propose the method of generating character candidates with reliable recognition scores. We utilize the existing MLP(multilayered perceptrons) neural network of the address recognition system in the current automatic postal envelope sorters, as the classifier for the each image from the preprocessing step. The MLP is well known to be one of the best classifiers in terms of processing speed and recognition rate. The false alarm problem, however, might be occurred in recognition results, which made the address interpretation hard. To make address interpretation easy and improve the envelope sorting rate, we propose promising methods to reestimate the recognition score (confidence) of the existing MLP classifier: the generation method of the statistical recognition properties of the classifier and the method of the combination of the MLP and the subspace classifier which roles as a reestimator of the confidence. To confirm the superiority of the proposed method, we have used the character images of the real postal envelopes from the sorters in the post office. The experimental results show that the proposed method produces high reliability in terms of error and rejection for individual characters and non-characters.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.147-152
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• 2020

The bidding program uses statistical analysis method of the collected bidding information and the accumulated bidding results from the public/private sector; however, it is not easy to predict the accurate bidding price by winning the bid method through multiple lottery. Therefore, this paper analyzes the accuracy of the current state data of the electric construction bid from January 2015 to August 2019 acquired from the electric net, which is an electronic bidding site, We use MLP and RNN method, and proposes a technique to predict the bidding amount necessary for the winning bid by predicting the amount between the first and the lowest bidder.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4E
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• pp.112-119
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• 2004

This paper presents a method for the automatic recognition of pitch accents with no prior knowledge about the phonetic content of the signal (no knowledge of word or phoneme boundaries or of phoneme labels). The recognition algorithm used in this paper is a time-delay recurrent neural network (TDRNN). A TDRNN is a neural network classier with two different representations of dynamic context: delayed input nodes allow the representation of an explicit trajectory F0(t), while recurrent nodes provide long-term context information that can be used to normalize the input F0 trajectory. Performance of the TDRNN is compared to the performance of a MLP (multi-layer perceptron) and an HMM (Hidden Markov Model) on the same task. The TDRNN shows the correct recognition of $91.9{\%}\;of\;pitch\;events\;and\;91.0{\%}$ of pitch non-events, for an average accuracy of $91.5{\%}$ over both pitch events and non-events. The MLP with contextual input exhibits $85.8{\%},\;85.5{\%},\;and\;85.6{\%}$ recognition accuracy respectively, while the HMM shows the correct recognition of $36.8{\%}\;of\;pitch\;events\;and\;87.3{\%}$ of pitch non-events, for an average accuracy of $62.2{\%}$ over both pitch events and non-events. These results suggest that the TDRNN architecture is useful for the automatic recognition of pitch accents.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.279-286
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• 2005

The objective of this study is to suggest a credit scoring model of a capital company's customers by integration of multiple classifiers using genetic algorithm. For this purpose , an integrated model is derived in two phases. In first phase, three types of classifiers MLP (Multi-Layered Perceptron), RBF (Radial Basis Function) and linear models - are trained, in which each type has three ones respectively so htat we have nine classifiers totally. In second phase, genetic algorithm is applied twice for integration of classifiers. That is, after htree models are derived from each group, a final one is from these three, In result, our suggested model shows a superior accuracy to any single ones.

• International Journal of Industrial Entomology and Biomaterials
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• v.9 no.1
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• pp.149-153
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• 2004

Here we report the molecular cloning of a LIM protein cDNA of the CRP (cysteine-rich protein) family from the mulberry longicorn beetle, Apriona, geramri. The A. germari LIM protein cDNA contains an open reading frame of 276 bp encoding 92 amino acid residues with a calculated molecular weight of approximately 10 kDa. The A. germari LIM protein contains the cysteine-rich consensus sequence of LIM domain and the glycine-rich consensus sequence observed in cysteine-rich protein family 1 (CRP1). The potential nuclear targeting signal is retained. The deduced amino acid sequence of the A. germari LIM protein cDNA showed 81 % identity to both Bombyx mori muscle LIM protein (Mlp) and Drosophila melanogaster Mlp60A and 77% to Epiblema scudderiana Mlp. Northern blot analysis showed that A. germari LIM protein is highly expressed in epidermis and muscle, and less strongly in midgut, but not in the fat body.

• The Journal of the Acoustical Society of Korea
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• v.17 no.7
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• pp.50-56
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• 1998

본 논문은 자동 음소 분할기의 음소 경계 오류를 보상하기 위한 후처리 (Postprocessing)에 관한 연구이다. 이는 현재 음성 합성을 위한 음성/언어학적 연구, 운율 모델링, 합성단위 자동 생성 연구 등에 대량의 음소 단위 분절과 음소 레이블링된 데이터의 필요성에 따른 연구의 일환이다. 특히 수작업에 의한 분절 및 레이블링은 일관성의 유지가 어렵고 긴 시간이 소요되므로 자동 분절 기술이 더욱 중요시 되고 있다. 따라서, 본 논문은 자동 분절 경계의 오류 범위를 줄일 수 있는 후처리기를 제안하여 자동 분절 결과를 직접 합성 단위로 사용할 수 있고 대량의 합성용 운율 데이터 베이스 구축에 유용함을 기술한다. 제안된 후처리기는 수작업으로 조정된 데이터의 특징 벡터를 다층 신경회로망 (MLP:Multi-layer perceptron)을 통해 학습을 한 후, ETRI(Electronics and Telecommunication Research Institute)에서 개발된 음성 언어 번역 시스템을 이용한 자동 분절 결과와 후처리기인 MLP를 이용하여 새로운 음소 경계를 추출한다. 고립단어로 발성된 합성 데이터베이스에서 후처리기로 보정된 분절 결과는 음성 언어 번역 시스템의 분할율보 다 약 25%의 향상된 성능을 보였으며, 절대 오류(｜Hand label position-Auto label position ｜)는 약 39%가 향상되었다. 이는 MLP를 이용한 후처리기로 자동 분절 오류의 범위를 줄 일 수 있고, 대량의 합성용 운율 데이터 베이스 구축 및 합성 단위의 자동생성에 이용될 수 있음을 보이는 것이다.

• Journal of the Korea Computer Industry Society
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• v.3 no.5
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• pp.547-556
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• 2002

This paper was proposed a wave path loss prediction algorithm using multilayer perceptron (MLP) model and wave propagation characteristic parameters for Wireless LAN in indoor radio environments. Receiving power was predicted by calculating indoor path loss in a Wireless LAN that has transmission power of 100mW and frequency of 2.4GHz, and was compared with measured. In the result of measurement shows that there is a difference between predicted and measured receiving power which can be reduced by an accurate analysis of the various path loss factors. In order to fix the access point(AP) positions was used the proposed a wave path loss prediction algorithm, and designed the optimum cell for Wireless LAN.