Showing papers in "International Journal of Engineering - Transactions C: Aspects in 2018"

Subcritical Water Extraction of Bioactive Compounds from Ginger (Zingiber officinale Roscoe)

Abstract: Ginger is one of the commonly used spices that has been exhibited to have pharmaceutical activities. These therapeutic properties are mainly attribute to gingerols and shogaols. To extract these bioactive compounds, subcritical water extraction (SWE) was employed as a green method. The influence of adding co-solvents, temperature, retention time and particle size on extraction yield were investigated. In addition the impact of ultrasonic and enzyme pretreatment was studied. Enzyme-assisted SWE with 2% ethanol as co-solvent and using ginger having particle size of 1mm, operated at 130°C and 20 bars for 30 min, is approved as optimized condition. At this condition, the total obtained polyphenol content and the concentration of total gingerols and shogaol were 5325 µg GAE/g dried ginger and 2990.5 µg bioactive/g dried ginger, respectively. Pretreatment of ginger powder with α-amylase prior to SWE, resulted in a 2.8 and 2.22 folds increase in total polyphenol content and concentration of total gingerols and shogaol, respectively. SEM analysis was conducted to evaluate the effect of pretreatment on morphology of ginger and analyze the extraction process.

Rolling Bearing Fault Analysis by Interpolating Windowed DFT Algorithm

Abstract: This paper focuses on the problem of accurate Fault Characteristic Frequency (FCF) estimation of rolling bearing. Teager-Kaiser Energy Operator (TKEO) demodulation has been applied widely to rolling bearing fault detection. FCF can be extracted from vibration signals, which is pre-treatment by TEKO demodulation method. However, because of strong noise background of fault vibration signal, it is difficult to extract FCF with high precision. In this paper, the improved algorithm of rolling bearing fault diagnosis is analyzed. Based on the envelope analysis by TKEO demodulation, it combines zero padding technique and the Improved Iterative Windowed Interpolation DFT (IIWIpDFT) algorithm to correct demodulated signal. Experimental result shows that the proposed algorithm decreases Root Mean Square Error (RMSE) of FCF(inner race) form about 2Hz~5.5Hz to about 0.5Hz for short data length, the same treatment also decreases RMSE form about 1.1Hz~3Hz to about 0.4~0.5Hz for longer data length in most cases. Meanwhile, the RMSE of FCF (outer race) improved 2.3 to 84.5% as compared to the application of traditional TEKO demodulation alone.

A Queuing Model for Stochastic Location¬-inventory Problem with Waiting Cost Considerations

Abstract: This paper presents a three-level supply chain model which includes single supplier, several distribution centers and sets of retailers. For this purpose, by adopting the queuing approach, a mixed nonlinear integer programming model is formulated. The proposed model follows minimizing the total cost of the system by determining: 1) the number and location of distribution centers between candidated ones; 2) the possibility of allocating each of the retailers to the distribution centers; 3) the amount of retailers demand; and 4) the policy of distribution centers. In the proposed model, the cost of waiting in queue is also considered. In order to make the problem more realistic, we consider uncertain demand and lead-time, which follow Poisson and Exponential distributions, respectively. Hence, we apply continuous-time Markov process approach to obtain the amount of annual ordering, purchase and inventory. Then, the results are used to formulate the location-inventory problem. Finally, the proposed model is solved using GAMS software version 24.1.3.

Voice-based Age and Gender Recognition Based on Learning Generative Sparse Models

Abstract: Voiced-based age detection and gender recognition are important problems in the telephone speech processing to investigate the identity of an individual. In this paper, a new gender and age recognition system is introduced based on the generative incoherent models learned using sparse non-negative matrix factorization and the atom correction step as a post-processing method. The proposed classification algorithm includes training step to provide the appropriate trained atoms for each data class and also the test phase to assess the classification performance. Since the classification accuracy depends highly on the selected features, the Mel-frequency cepstral coefficients are employed to train basis for the better representation of the voice structure. These bases are learned over the data of male and female speakers using non-negative matrix factorization with the sparsity constraint. Then, atom correction is carried out using an energy-based algorithm to decrease the coherence between different categories of the trained dictionaries. In the sparse representation of each data class, the atoms related to other sets with the highest energy are replaced with the lowest energy bases if the reconstruction error does not exceed from a specified limit. The experimental results showed that the proposed algorithm performs better than the earlier methods in this context especially in the presence of background noise. doi: 10.5829/ije.2018.31.09c.08

A New Approach Applying Multi-objective Optimization using a Taguchi Fuzzy-based for Tourist Satisfaction Management

Abstract: The paper describes the usage of the fuzzy Mamdani analysis and Taguchi method to optimize the tourism satisfaction in Thailand. The fuzzy reasoning system is applied to pursue the relationships among the options of a tour company in order to be used in Taguchi experiments as the responses. In this research, tourism satisfaction is carried out using L18 Taguchi orthogonal arrays on parameters such as budget, duration, hotel-choices, travel-options inside the country and theme of the travel are analyzed for one output objective as satisfaction. The output of the fuzzy reasoning system is used as an input in the response of each experiment in Taguchi method. But, the improvement is used for the mean de-fuzzified output in the same experiment. The result is estimated using Taguchi-Fuzzy application and if companies focus on the selected options, it is most probable to achieve more than 90 percent of satisfaction.

Re-configuration of the Relief Network Considering Uncertain Demand and Link Failure in an Earthquake: A Multi-stage Stochastic Programming

Abstract: Disasters inevitably trigger far-reaching consequences affecting all living things and the environment. Therefore, top managers and decision-makers in disaster management seek comprehensive approaches to evaluate facilities and network preparedness in dealing with the response phase of predicted disaster scenarios in terms of number of casualties, costs, and unmet demands. In this regard, previous studies on the preparedness phase have often been limited to the location of eligible facilities without considering other important factors such as current assets, entities and configuration. Thus, the present study proposes a reconfiguring and repositioning model in order to simultaneously assess whether existing support bases should remain, be consolidated or phased out as well as whether new support base facilities should be established and subsequently supply and demand requirements considered. In the proposed model, in addition to considering a scenario tree for destruction and demands, network links affected by the intensity of disaster events are also evaluated. Furthermore, in order to increase reliability, the destruction of network links takes into account that link failures give rise to vulnerability in related links. In the proposed model, multi-stage stochastic programming has been implemented on various real destruction and demand scenarios. The results indicate definite advantages in the re-positioning or reconfiguring model compared with current configurations. Moreover, the superior capability of the applied solving approach versus one of the traditional approaches is also appraised.

Developing the Inventory Routing Problem with Backhauls, Heterogeneous Fleet and Split Service

Abstract: One of the most important points in a supply chain is customer-driven modeling, which reduces the bullwhip effect in the supply chain, as well as the costs of investment on the inventory and efficient transshipment of the products. Their homogeneity is reflected in the Inventory Routing Problem, which is a combination of distribution and inventory management. This paper expands the classical Inventory Routing Problem based on the Multiple Delivery Strategy along with one of the functionalities of routing problem, namely, "backhauls", with a priority consideration for linehaul customers. Then it has been modeled in the form of a problem with Multi-period, multi-product, and multi- vehicle planning horizons in which stock out is not allowed. Moreover, for an optimal use of the vehicle capacity to serve the linehaul and backhaul customers, this study adopted a “split service” problem to the model, which also increases the complexity of the problem. First, considering the above-mentioned assumptions, a new mathematical model is proposed in the form of mixed integer programming for the problem defined in this paper. Then, since the stated problem can be considered among the non-deterministic polynomial-time hard, an efficient meta-heuristic genetic algorithm is provided for solving it. At the end, the numerical results obtained by this algorithm are analyzed using the randomized test problems. The result shows that by adopting a split service approach, 70% of the test problems will demonstrate cost reduction.