<p>This paper proposed a Takagi-Sugeno fuzzy control method is used to build the model for current controlled of three phase photovoltaic inverter. The three phase inverter connected to grid with L filter. The DC link capacitor is splatted midpoint of three phase four wire and the DC link voltage in the input side of the inverter is considered as input variables value for maximum value of the reference current for the feedback controlled. The proposed of T-S fuzzy control method and hysteresis current control can solve and enhance the THD of the output current of the inverter. The T-S fuzzy is designed to monochrome controller makes the output currents are balanced and decrease the harmonic compensation of the full system. The Matlab simulations and test results show the THD, current and the voltage of the three photovoltaic inverter under T-S fuzzy controller method is current balancing efficiency. The control unit is capable of carrying out operation in good condition, dynamic characteristics stable and high quality.</p>

Nonstandard development of prison cell in any portion of the body is termed cancer lesions. Life duration of a tumour’s lesions can be enlarged by the primary detection of cancer. This work contracts with cataloguing of images depend on factors extracted from multiresolution analysis based on bee colony technique to enhance of investigative performance and decrease of unhealthy moles demises. From now this technique system goals to improve a portion of the current approaches and new measures to make available the accurate, fast and dependable automated analysis of skin lesions. This information is then fed to several well-known algorithms to obtain a skin cancer categorization. By this method, the segmentation step can be utilized to enhance the handling of the information and create preventive approaches against harm, thus decreasing the danger of skin cancer lesions. One of the most significant stages in dermoscopy image examination is the segmentation of melanoma. The experimental results suggest that the proposed method accomplished a higher performance compared to the ground truth images supported by skin cancer lesions’ dermatology. Investigational outcomes on the skin lesions databases designate that the bee colony prototypical acquires the utmost progressive performance. The factors of the scheme are estimated with accuracy, sensitivity and specificity.

Kinect sensor suggestions new viewpoints for the advance and application of inexpensive, portable and easy-to-use indication less motion capture skill. The goal of this work is to estimate accuracy of the Kinect cameras for full body motion investigation. This study developed an application that of using multiple depth and RGB Kinect sensors for that reasonable system that prepared with multi-depth of sensing was used in this work. Additional application confirmed the Kinect camera validity the evaluated of postural control and different images of biomedical for segmentation skin lesions. In this work, multi-depth assessment and segmentation are conjointly addressed using RGB input image under Median filter with post-processing. Compared with our algorithm outputs an organized-to-use highly suitable for creating 3D Kinect sensors with pre and post-processing steps. The multi-depth extracted image features have higher measurement and accuracy. The results are dealing out the depth and RGB picture with segmentation evaluation depend on feature extraction technique to enhance accuracy.

One of the damaging diseases among people in the world is skin cancer. Skin cancer leftovers an important scientific, clinical and public task. Swarm intelligence techniques (SITs) are new, improved and modern methods for optimization algorithms. Failure of detection in skin cancer images can be seen in SITs. This work proposes an efficient image and examines for some samples in this disease. The study presents a simple technique for a pre-processing and an automatic detection of SITs to make the needed analysis. This paper estimated all these various models using the PH2, Dermis, ISIC (2016, 2017, 2018) segmentation challenge dataset. The input images are improved for better processing than, the lesion sampling is segmented from the improved image by using Otsu thresholding and median filter operations. In the earlier studies, skin cancer is analyzed by means of several optimization algorithms. Now, the outcomes of the above algorithms were compared with the dice coefficient and it was demonstrated that the value of 97.35% which is nearer to manual segmentation. The accuracy the value of 98.58% when used for solving the same problem. To this end, a somewhat comprehensive analysis was showed to compare the effectiveness of many parameters’ combinations.

In recent years, the incidence of skin lesions has been one of the most rapidly increasing of all commonly occurring cancers. This deadliest form of melanoma must be detected early to be effectively treated. Because of the trouble and objectivity of human clarification, a significant research field has developed around the computerized examination of dermoscopy images. One reason to apply swarm intelligence systems is that an optimal solution can be advanced with a sensible computational application. This work introduces an artificial bee colony technique (ABC), distinctions, and applications. The planned ABC is a more suitable algorithm and one that requires smaller amounts of factors that need to be adjusted in comparison to other modern artificial swarm intelligence techniques (MASITs) for distinguishing unhealthy in skin tumor lesions. In these swarm's intelligence optimization algorithms have been positively executed for melanoma problems and provided extraordinary results guidance to better prediction and investigation of the skin cancer lesions. The experimental outcomes propose that the planned process proficient a developed accuracy associated to the ground truth (GT) used skin lesions' dermatology. So, we will be able to use these in a future study with different databases.