A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training

© 2019 IEEE. In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods

Locating the human eye using fractal dimensions

2001-2002 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

A method to enhance the deep learning in an aerial image

© 2017 IEEE. In this paper, we propose a kind of pre-processing method which can be applied to the depth learning method for the characteristics of aerial image. This method combines the color and spatial information to do the quick background filtering. In addition to increase execution speed, but also to reduce the rate of false positives

Chemical Composition and Biological Properties of Essential Oils of Two Mint Species

Purpose: To analyze the composition of essential oils of two types of mint as well as compare the antimicrobial, antioxidant and anti-inflammatory activities of the two oils.Methods: Peppermint (M. piperita L.) and chocolate mint (M. piperita L.) oils were obtained by steam distillation in a Clevenger-type apparatus. The chemical composition of the essential oils was determined by gas chromatography-mass spectrometry (GC/MS). The minimal inhibitory concentration (MIC) of the essential oils were determined by broth dilution method. The antioxidant activities of the oils were determined by 2, 2-diphenyl-1-picrylhydrazyl (DPPH)DPPH radical scavenging assay, β-Carotene-linoleic acid assay, andnitric oxide (NO) radical scavenging assay.Results: The two essential oils contain high levels of alcohol (43.47-50.10%) and terpene (18.55-21.07%) with the major compound being menthol (28.19-30.35%). The antimicrobial activity (minimum inhibitory concentration, MIC) of peppermint oil against E. coli, S. aureus and P. aeruginosa (0.15, 0.08, 0.92 %v/v, respectively) was stronger than that of chocolate mint (0.23, 0.09, 1.22 %v/v, respectively). In the anti-oxidant test including DPPH and β-Carotenelinoleic acid assays, peppermint oil showed superior antioxidant properties to chocolate mint oil (4.45 - 19.86 μl/mL). However, with regard to scavenging NO radical activity, chocolate mint oil exhibited higher activity than peppermint (0.31 and 0.42 μl/mL, respectively). Chocolate mint oil also exhibited higher anti-inflammatory activity than peppermint oil (0.03 and 0.08 μl/mL, respectively).Conclusion: The results obtained should help to clarify the functional applications of these folk herbs and their essential oils for aromatherapeutic healing and other folkloric uses.Keywords: Peppermint, Chocolate mint, Anti-microbial, Anti-oxidant, Anti-inflammator

Acupuncture Transmitted Infections

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A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

© 2016 IEEE. A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications

Robust Feature-Based Automated Multi-View Human Action Recognition System

© 2013 IEEE. Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets

Robust Facial Alignment for Face Recognition

© 2017, Springer International Publishing AG. This paper proposes a robust real-time face recognition system that utilizes regression tree based method to locate the facial feature points. The proposed system finds the face region which is suitable to perform the recognition task by geometrically analyses of the facial expression of the target face image. In real-world facial recognition systems, the face is often cropped based on the face detection techniques. The misalignment is inevitably occurred due to facial pose, noise, occlusion, and so on. However misalignment affects the recognition rate due to sensitive nature of the face classifier. The performance of the proposed approach is evaluated with four benchmark databases. The experiment results show the robustness of the proposed approach with significant improvement in the facial recognition system on the various size and resolution of given face images

In-Situ Hydrothermal Synthesis of Zinc Oxide Nanostructures Using Microheaters

A technique for in-situ hydrothermal synthesis of transversely suspended zinc oxide nanowires (ZnO NWs) using microfabricated heaters is presented. A number of issues relating to seed layer preparation, directed alignment, local heating control and the concentration of the synthesis solution are investigated for this method. It is shown that ZnO NWs can be synthesized and aligned from the ZnO seed surface to bridge two adjacent microheater elements. Moreover, hybrid ZnO nanotubes and nanorods are also synthesized by controlling the concentration of the synthesis solution employed. The crystalline structure of synthesized ZnO nanostructures are characterized by the transmission electron microscope (TEM) and selective area electron diffraction (SAED). Finally, ZnO NW devices based on proposed microheater synthesis approach are characterised for UV photoresponsivity demonstrating the potential of this approach to address practical device applications.Funding support by the WPI-AIMR, Tohoku University and by the Royal Society is gratefully acknowledged.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TNANO.2015.246807