Panyapiwat Institute of Management

Mueang Nonthaburi, Thailand

Panyapiwat Institute of Management

Mueang Nonthaburi, Thailand
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Klinbun W.,Panyapiwat Institute of Management | Rattanadecho P.,Thammasat University
International Journal of Food Properties | Year: 2016

The dielectric properties (dielectric constant [ε′] and dielectric loss factor [ε″]) for three frozen meals (basil fried chicken, green curry with chicken, and congee with minced pork) were measured at frequency 2.45 GHz from –18 to 80°C. Thermal properties (thermal conductivity [k] and specific heat capacity [c]) of each food item were characterized using their composition for improving the modeling of microwave thawing process of frozen products. For all frozen meals, similar trends in the dielectric and thermal properties values were observed as a function of temperature. In all samples of frozen foods, the dielectric properties (ε′, ε″) rapidly increased with temperature for the range from –10 to 0°C. Thereafter, dielectric properties linearly increased with temperature for basil fried chicken and congee with minced pork but linearly decreased with temperature for green curry with chicken from 0 to 80°C. The dielectric properties data bases were used to calculate power reflected (Pr), power transmitted (Pt), dielectric loss tangent (tanδ), and penetration depth (dp). These parameters were related to the dielectric properties. The thermal conductivity values of all samples decreased with increasing temperature in the frozen stage and little changed after thawing. While the specific heat capacity values increased first and then do not change considerably. The temperature-dependent material properties can give insight into how the food product interacts with the incident electromagnetic radiation. © 2016 Taylor & Francis

Ahmad I.,Thammasat University | Jeenanunta C.,Thammasat University | Chanvarasuth P.,Thammasat University | Komolavanij S.,Panyapiwat Institute of Management
Food and Bioprocess Technology | Year: 2014

Application of genetic algorithm to optimize an artificial neural network (ANN) model for predicting end-of-storage quality parameters of frozen shrimp (Litopenaeus vannamei), which influence consumer purchase decisions, is demonstrated in this paper. Freezing rate (FR), thawing rate (TR), storage time, width, thickness, and length of frozen shrimp were measured and chosen as input variables to train the ANN against Commission International de l' Eclairage Color L*a*b* values, and textural properties (hardness, cohesiveness, and chewiness) as dependent variables. Experimentally obtained randomized data points (500) were used to develop the network, of which 20 % were used for testing the network, as an unseen environment. The developed genetic algorithm-artificial neural network (GANN) which included one hidden layer with 3-17 neurons successfully predicted color and textural values with correlation coefficient, R 2, of >0.9 and root mean square error (RMSE) of <1.6. The redness (a*) and cohesiveness took the longest training time and highest number of generations, as compared to the other parameters. Percent relative importance of input variables to output variables indicated that TR, FR, and storage time were the most important variables for the prediction of color and texture parameters. The results are compared with multiple linear regression (MLR) and ANN trained with backpropagation (BP) algorithm. The results indicate that the GANN model shows much better prediction, as compared to MLR and BP with smallest RMSE and highest R 2. © 2013 Springer Science+Business Media New York.

Klinbun W.,PANYAPIWAT Institute of Management | Rattanadecho P.,Thammasat University
International Communications in Heat and Mass Transfer | Year: 2016

Numerical study of fluid flow and heat transfer within two types of liquid (liquid two-layer and oil-water emulsion) when subjected to microwave energy are discussed. In order to obtain the simulation of microwave heating from room temperature, a 2D comprehensive model was integrated with electromagnetic field, incompressible laminar flow and heat transfer. The effects of layered configuration, layered thickness and dispersed fraction of emulsions were investigated. Temporal profiles obtained using fiber optic sensors at four discrete points were compared with the simulated temperature profiles. The simulated outlet temperatures of liquid had a good agreement with experimental data within the maximum prediction error of 5%. The theory presented in this paper can be effectively used to explain fluid transport during microwave heating system using the rectangular waveguide. © 2015 Elsevier Ltd.

Sanguansat P.,Panyapiwat Institute of Management
ECTI-CON 2015 - 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology | Year: 2015

In survey research, the offline paper-based questionnaire still necessary for data collection. Although the online survey is more convenient, internet system and portable device are needed for operating. That is not practical for some surveys. Furthermore, the paper-based questionnaire requires data entry by human which will take long time and easy to mistake. Therefore, this paper proposes the automated data entry by Optical Mark Recognition (OMR) with the proposed paper-based questionnaire. This questionnaire will input by scanner and the data will be displayed in the appropriate report for data analysis. However, the pattern of questionnaires is different for each survey. In this paper, the method for creating the questionnaire, which contains only closed-ended questions, is also proposed for user self-designed including the proper report of the output that will be opened or edited in the well-known spreadsheet software. According to the experimental results, the accuracy rate is high and suitable for real application that is the average of accuracy rate is 93.36 % for choice selection by three patterns of the markers. © 2015 IEEE.

Sanguansat P.,Panyapiwat Institute of Management
2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012 | Year: 2012

Generally, the dimension of feature vector in text classification depends on the number of words in the specific domain. Many documents of considered categories make it numerous. Therefore, the dimension of feature vector is very high that makes it consumes a lot of time and memory to process. Moreover, it is a cause of the small sample size problem when the number of available training documents is far smaller than the dimension of these feature vectors. This paper proposes the alternative technique of dimensionality reduction for the feature vector in two-dimensional manner by previously transforming the feature vector to the feature matrix and then using Two-Dimensional Principal Component Analysis (2DPCA) for reducing the dimension of this feature matrix. Based on 2DPCA, the original weighted term matrix is not necessary to store in the memory anymore because the scatter matrix of 2DPCA can be computed incrementally. The small reduction in matrix form impacts to the plenty of dimensionality reduction in vector form. From the experimental results on well-known dataset, the proposed method not only significantly reduce the dimensionality but also achieve the higher accuracy rate than the original feature space. © 2012 IEEE.

Mutchima P.,Suan Dusit Rajabhat University | Sanguansat P.,Panyapiwat Institute of Management
2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012 | Year: 2012

Determination of content importance is very important in achieving high quality classification. Term weighting schemes in text classification will be applied to classify videos by measuring importance of video contents. In other words, a video sequence can be treated as a document, and frames of a video are considered as words or terms which identify contents of a video. And to enhance the efficiency of video classification, this paper proposes a novel term weighting scheme, called the Term Frequency - Relevance and Non-relevance Frequency (TF-RNF) weighting. This technique can filter both relevant and non-relevant contents so as to reduce classification errors. Empirical evaluations of results show that the proposed technique significantly outperforms traditional techniques in sports video classification. © 2012 IEEE.

Pichpibul T.,Panyapiwat Institute of Management
Lecture Notes in Electrical Engineering | Year: 2015

This paper focuses on vehicle routing decision, a real-world problem proposed by a travel agency company operating in Chonburi, Thailand. The objective is to minimize the total number of vehicles used and the total travelling distance. Currently, the company’s planners have to spend hours on manually planning with spreadsheet software. Therefore, the decision-making software was developed by using an approach based on golden ball algorithm to increase the efficiency of the route assignment and especially suitable for the practical use. Computational results on a set of benchmark problems show that the proposed approach is competitive when compared it with the best existing approaches in the literature. Moreover, when the proposed approach is applied to solve the company’s problems, the obtained solutions were able to significantly reduce the total number of vehicles used and the total travelling distance for supporting the planners’ decision. © Springer-Verlag Berlin Heidelberg 2015.

Sanguansat P.,Panyapiwat Institute of Management
2016 8th International Conference on Knowledge and Smart Technology, KST 2016 | Year: 2016

This paper proposes the sentiment analysis system in Thai language. It aims to use for the three business types (Retail, Banking and Telecommunication) to monitor their brand image via social media. is the most popular online community in Thailand, which many customers posted the comments about their business. Normally, three sentiments must be identified (positive, negative and neutral), but four sentiments (positive, negative, neutral and need) are introduced in our proposed system because the need sentiment can be used for generating new business opportunities. The unsupervised deep learning feature extraction for text, called Paragraph2Vec, paragraph vector or Doc2Vec, was applied in this paper, compared to the classical TF-IDF. The experimental results show that our proposed method perform better than the baseline method. © 2016 IEEE.

Phiwma N.,Suan Dusit Rajabhat University | Sanguansat P.,Panyapiwat Institute of Management
International Arab Journal of Information Technology | Year: 2014

In this paper, we propose new methods for feature extraction and soft majority voting to adjust efficiency and accuracy of music retrieval. For our work, the input is humming sound which is sound wave and Musical Instrument Digital Interface (MIDI) is used as the reference song in database. A critical issue of humming sound are variation such as duration, sound, tempo, key, and noise interference from both environment and acquisition instruments. Besides all the problems of humming sound we have mentioned earlier, whether humming sound and MIDI in different domain which will make the difficulty for two domains to compare each other. However, to make these two in the same domain, we convert them into the frequency domain. Our approach starts from pre-processing by using features for note segmentation by humming sound. The process consists of four steps as follows: Firstly, the MIDI is already a sequence of pitch while the pitch in humming sound is needed to extract by Subharmonic-to-Harmonic (SHR). Subsequently, the extracted pitch can be used to calculate all above attributes and then multiple classifiers are applied to classify the multiple subsets of these features. Afterwards, the subset contain the multiple attributes, Multi-Dimensional Dynamic Time Warping (MD-DTW) is used for similarity measurement. Finally, Nearest Neighbours (NN) and soft majority voting are used to obtain the retrieval results in case of equal scores. From the experiments, to achieve 100% accuracy rate at the early top-n rank in retrieving, the appropriate feature set should consist of five classifiers.

Thanhikam W.,Panyapiwat Institute of Management
ISPACS 2013 - 2013 International Symposium on Intelligent Signal Processing and Communication Systems | Year: 2013

This paper proposes a wide-band noise reduction method using temporal accumulation and zero phase (ZP) signal. By replacing the ZP signal around the origin with the ZP signal in the second or latter period to get an estimated speech ZP signal, this method can reduce both stationary and non-stationary wide-band noises without a prior estimation of the noise spectral amplitude However, for very low SNR environment, reliable period estimation is difficult. This paper presents a study of period estimation in noisy speech based on temporal accumulated zero phase signal (TAZPS) representation. Since harmonic structure of speech changes much more slowly than noise spectrum, spectral peaks related to period harmonics would stand out over the noise through the accumulation. In this paper, we combine pitch estimation based on temporal accumulated ZP signal and noise suppressor. Simulation results show that the proposed noise reduction method further improves the SNR especially in a low SNR environment. © 2013 IEEE.

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