http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Non-Linear Error Identifier Algorithm for Configuring Mobile Sensor Robot
Rajaram.P,Prakasam.P 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.3
WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.
Design of Mobile Sensor Robot using Non-Linear Fault Recognizer Algorithm
Rajaram.P,Prakasam Periasamy 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
For tracking the large scale environments, Wireless Sensor Network is an efficient tool. In such environment, restriction of battery life of the sensor networks is done because of the grouping of data, utilization of sensing, computation and communication. To determine this, a mobile robot is designed to recognize the data exist in the separated sensor networks and moved on to the sink. In new data collection technique, the outcome of the data grouping process is minimized as mobile robot can be employed in the restricted range. To improve the data collection in a varying environment, Non Linear Fault Recognizer (NLFR) algorithm has been introduced and designed in this paper to construct the robot using fault models that are non-linear. Experimental evaluation has been performed to calculate the outcome of the proposed NLFR and it has been examined that the proposed NLFR algorithm improves the fault correction rate upto 25% and efficiency upto 57%.
Real time Implementation of SHE PWM in Single Phase Matrix Converter using Linearization Method
P. Subha Karuvelam,M. Rajaram 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.4
In this paper, a real time implementation of selective harmonic elimination pulse width modulation (SHEPWM) using Real Coded Genetic Algorithm (RGA), Particle Swarm Optimization technique (PSO) and a new technique known as Linearization Method (LM) for Single Phase Matrix Converter (SPMC) is designed and discussed. In the proposed technique, the switching frequency is fixed and the optimum switching angles are obtained using simple mathematical calculations. A MATLAB simulation was carried out, and FFT analysis of the simulated output voltage waveform confirms the effectiveness of the proposed method. An experimental setup was also developed, and the switching angles and firing pulses are generated using Field Programmable Gate Array (FPGA) processor. The proposed method proves that it is much applicable in the industrial applications by virtue of its suitability in real time applications.
Real time Implementation of SHE PWM in Single Phase Matrix Converter using Linearization Method
Karuvelam, P. Subha,Rajaram, M. The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.4
In this paper, a real time implementation of selective harmonic elimination pulse width modulation (SHEPWM) using Real Coded Genetic Algorithm (RGA), Particle Swarm Optimization technique (PSO) and a new technique known as Linearization Method (LM) for Single Phase Matrix Converter (SPMC) is designed and discussed. In the proposed technique, the switching frequency is fixed and the optimum switching angles are obtained using simple mathematical calculations. A MATLAB simulation was carried out, and FFT analysis of the simulated output voltage waveform confirms the effectiveness of the proposed method. An experimental setup was also developed, and the switching angles and firing pulses are generated using Field Programmable Gate Array (FPGA) processor. The proposed method proves that it is much applicable in the industrial applications by virtue of its suitability in real time applications.
An Explainable Deep Learning Approach for Oral Cancer Detection
Babu P. Ashok,Rai Anjani Kumar,Ramesh Janjhyam Venkata Naga,Nithyasri A.,Sangeetha S.,Kshirsagar Pravin R.,Rajendran A.,Rajaram A.,Dilipkumar S. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
With a high death rate, oral cancer is a major worldwide health problem, particularly in low- and middle-income nations. Timely detection and diagnosis are crucial for efective prevention and treatment. To address this challenge, there is a growing need for automated detection systems to aid healthcare professionals. Regular dental examinations play a vital role in early detection. Transfer learning, which leverages knowledge from related domains, can enhance performance in target categories. This study presents a unique approach to the early detection and diagnosis of oral cancer that makes use of the exceptional sensory capabilities of the mouth. Deep neural networks, particularly those based on automated systems, are employed to identify intricate patterns associated with the disease. By combining various transfer learning approaches and conducting comparative analyses, an optimal learning rate is achieved. The categorization analysis of the reference results is presented in detail. Our preliminary fndings demonstrate that deep learning efectively addresses this challenging problem, with the Inception-V3 algorithm exhibiting superior accuracy compared to other algorithms.