Taguchi Method and Artificial Neuro-Fuzzy Inference System (ANFIS) based Validation of Enzyme Production: A review

Numbers of reports on enzyme production enhancements (from bacteria and fungi) are present in the literature by using One Variable at Time (OVAT) based optimization of medium components. OVAT strategy is not suitable for the cost-effective production of enzymes in lieu of modern statistical and artificially intelligent techniques like Response Surface Methodology (RSM), Taguchi Method and Artificial Neural Network (ANN) and Artificial Neuro-Fuzzy Inference System (ANFIS) etc. The Taguchi Method and ANFIS enzyme yield prediction results are in consonance with those produced by the RSM system and in fact are more closer to the actual enzyme yield. This shows the application of the proposed system in enzyme yield prediction given a set of parameter values

Design Optimization of the Aeronautical Sheet Hydroforming Process Using the Taguchi Method

The aluminium alloy sheet forming processes forging in rubber pad and diaphragm presses (also known as hydroforming processes) are simple and economical processes adapted to aeronautical production. Typical defects of these processes are elastic recovery, necking, and wrinkling, and they present di culties in control mainly due to property variations of the sheet material that take place during the process. In order to make these processes robust and unresponsive to material variations, a multiobjective optimization methodology based on the Taguchi method is proposed in the present study. The design of experiments and process simulation are combined in the methodology, using the nonlinear finite element method. The properties of sheet material are considered noise factors of the hydroforming process, the objective being to find a combination of the control factors that causes minimal defects to noise factors. The methodology was applied to an AA2024-T3 aluminium alloy sheet of 1 mm thickness stamping process in a diaphragm press. The results allowed us to establish the optimal pressure values, friction coeficient between sheet and block, and friction coeficient between sheet and rubber to reduce the elastic recovery variations and the minimal thickness before noise facts

Comparative analysis of text classification algorithms for automated labelling of quranic verses

The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as “Shahadah” (the first pillar of Islam) or “Pray” (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses

Point Pair Feature based Object Detection for Random Bin Picking

Point pair features are a popular representation for free form 3D object detection and pose estimation. In this paper, their performance in an industrial random bin picking context is investigated. A new method to generate representative synthetic datasets is proposed. This allows to investigate the influence of a high degree of clutter and the presence of self similar features, which are typical to our application. We provide an overview of solutions proposed in literature and discuss their strengths and weaknesses. A simple heuristic method to drastically reduce the computational complexity is introduced, which results in improved robustness, speed and accuracy compared to the naive approach

3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances

Unsupervised object modeling is important in robotics, especially for handling a large set of objects. We present a method for unsupervised 3D object discovery, reconstruction, and localization that exploits multiple instances of an identical object contained in a single RGB-D image. The proposed method does not rely on segmentation, scene knowledge, or user input, and thus is easily scalable. Our method aims to find recurrent patterns in a single RGB-D image by utilizing appearance and geometry of the salient regions. We extract keypoints and match them in pairs based on their descriptors. We then generate triplets of the keypoints matching with each other using several geometric criteria to minimize false matches. The relative poses of the matched triplets are computed and clustered to discover sets of triplet pairs with similar relative poses. Triplets belonging to the same set are likely to belong to the same object and are used to construct an initial object model. Detection of remaining instances with the initial object model using RANSAC allows to further expand and refine the model. The automatically generated object models are both compact and descriptive. We show quantitative and qualitative results on RGB-D images with various objects including some from the Amazon Picking Challenge. We also demonstrate the use of our method in an object picking scenario with a robotic arm

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Surface roughness modeling of CBN hard steel turning

Study in the paper investigate the influence of the cutting conditions parameters on surface roughness parameters during turning of hard steel with cubic boron nitrite cutting tool insert. For the modeling of surface roughness parameters was used central compositional design of experiment and artificial neural network as well. The values of surface roughness parameters Average mean arithmetic surface roughness (Ra) and Maximal surface roughness (Rmax) were predicted by this two-modeling methodology and determined models were then compared. The results showed that the proposed systems can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments modeling technique and artificial neural network can be effectively used for the prediction of the surface roughness parameters of hard steel and determined significantly influential cutting conditions parameters

Safety experiments for small robots investigating the potential of soft materials in mitigating the harm to the head due to impacts

There is a growing interest in social robots to be considered in the therapy of children with autism due to their effectiveness in improving the outcomes. However, children on the spectrum exhibit challenging behaviors that need to be considered when designing robots for them. A child could involuntarily throw a small social robot during meltdown and that could hit another person's head and cause harm (e.g. concussion). In this paper, the application of soft materials is investigated for its potential in attenuating head's linear acceleration upon impact. The thickness and storage modulus of three different soft materials were considered as the control factors while the noise factor was the impact velocity. The design of experiments was based on Taguchi method. A total of 27 experiments were conducted on a developed dummy head setup that reports the linear acceleration of the head. ANOVA tests were performed to analyze the data. The findings showed that the control factors are not statistically significant in attenuating the response. The optimal values of the control factors were identified using the signal-to-noise (S/N) ratio optimization technique. Confirmation runs at the optimal parameters (i.e. thickness of 3 mm and 5 mm) showed a better response as compared to other conditions. Designers of social robots should consider the application of soft materials to their designs as it help in reducing the potential harm to the head