KUMAR CHANDRA JAGILAM

Innovative professional with a strong track record in developing and implementing advanced algorithms to address complex business challenges and drive operational excellence. Expert in leveraging machine learning techniques and data analysis to deliver actionable insights and optimize decision-making processes. Proven ability to design and deploy scalable solutions, conduct thorough data preprocessing, and enhance model performance to support data-driven strategies and achieve impactful business results.

• PYTHON FOR DATA SCIENCE - Provided by 360DigTMG powered by IBM developer skill network, Issued on 20 march , 2021
• DATA SCIENCE USING PYTHON AND R PROGRAMMING - Issued by 360DigITMG on Jan 31, 2021

Project: Solar Panel Defect Detection

• Objective: Developed an AI/ML model to differentiate and detect defects in solar panels.

Responsibilities:

• Data Collection: Gathered and compiled a comprehensive dataset of solar panel defects, including various types and conditions.
  Data Labeling: Performed detailed labeling of defect images to create a high-quality training dataset.
  Model Development: Designed and trained machine learning models to identify and classify defects with high accuracy.
  Model Optimization: Conducted rigorous testing and evaluation of the model; employed fine-tuning techniques to enhance accuracy and performance.
  Outcome: Achieved significant improvements in defect detection accuracy, contributing to better quality control and maintenance of solar panels.

Project: Internal Defect Detection in Wind Turbine Blades

• Objective: Develop a machine learning model to detect internal defects in wind turbine blades.
• Data Collection: Acquired a substantial dataset of images showing internal defects in wind turbine blades.
• Data Labeling: Performed detailed labeling of images to ensure high-quality training data.
• Model Development: Created and trained a machine learning model to identify and classify internal defects.
• Model Testing: Evaluated model performance and applied AI/ML techniques to enhance accuracy and reliability.
• Outcome: Achieved significant improvements in defect detection, contributing to more effective maintenance and quality control.

Project: PPE Kit Detection for Site Workers

• Objective: Develop a machine learning model to identify whether site workers are equipped with necessary PPE kits (helmets, shoes, vests).
• Data Collection: Collected a large dataset of images featuring various PPE kits and site workers.
• Data Labeling: Labeled images with classes indicating the presence or absence of helmets, shoes, and vests.
• Model Training: Developed and trained a machine learning model to recognize PPE kit usage accurately.
• Accuracy Improvement: Continuously refined the model and applied optimization techniques to enhance detection accuracy.
• Deployment: Deployed the trained model on a server to provide real-time PPE compliance monitoring for safety managers and company personnel.
• Outcome: Enabled automated recognition of PPE kit compliance, improving safety oversight and enforcement on-site.

Project: Autonomous Solar Panel Cleaning Robot

• Objective: Develop an autonomous robot for cleaning solar panels using AI/ML and computer vision techniques.
• Technology Used: Leveraged OpenCV library and Python for developing the computer vision and autonomous driving capabilities.
• Development: Wrote Python code to enable the solar cleaning robot to navigate and clean solar panels autonomously.
• AI/ML Integration: Implemented machine learning models to enhance the robot’s ability to detect and avoid obstacles while ensuring effective cleaning.
• Outcome: Successfully created a fully autonomous solar panel cleaning rover, improving maintenance efficiency and reducing manual labor requirements.

Project: Autonomous Navigation Rover with ROS

• Objective: Build an autonomous rover capable of navigating and mapping environments.
• Technology Used: Utilized ROS (Robot Operating System) for system integration and control.
• Mapping: Employed LiDAR technology for area mapping to enable the rover to understand and navigate its environment.
• Computer Vision: Integrated OpenCV library for real-time visual processing and obstacle detection.
• Sensors: Incorporated additional sensors to enhance navigation accuracy and environmental awareness.
• Outcome: Successfully developed a navigation rover that autonomously maps and navigates environments, demonstrating advanced capabilities in autonomous robotics and sensor integration.