Integrates artificial intelligence into cameras to optimize Ground Services and create a safe working environment.
Personal Protective Equipment Control
Limited Areas Control
Vehicle Controls
Operation Time Control
Fod Control
Airport return management is a critical process that enables aircraft to take off and land on time and efficiently. With the increasing demand for air travel, airports are under pressure to optimize their return processes to accommodate more flights and reduce delays. Artificial intelligence (AI) can play an important role in improving returns management by providing real-time data analysis and decision-making capabilities.
One possible use case for AI in aircraft fallback management is the optimization of gate assignments. Traditional gate assignment methods rely on manual decision making and can be prone to errors and delays. AI-powered door assignment algorithms can analyze real-time data such as flight schedules, weather conditions and aircraft maintenance status to determine the most efficient door assignments. This can result in shorter turnaround times and improved flight schedules.
Another use case for AI in aircraft fallback management is estimating maintenance issues. Traditional maintenance forecasting methods rely on scheduled inspections and manual inspections. However, AI-powered maintenance forecasting algorithms can analyze sensor data and flight data to predict potential maintenance issues before they occur. This can result in reduced downtime and increased safety.
AI can also be used to optimize the fueling and loading process. Traditional refueling and loading methods rely on manual decision making and can be prone to errors and delays. AI-powered algorithms can analyze real-time data such as fuel consumption, flight schedules and cargo weight to determine the most efficient refueling and loading schedule. This can result in shorter turnaround times and improved flight schedules.
Overall, the use of artificial intelligence in aircraft return management can increase efficiency, reduce delays and increase safety. It can also provide real-time data and decision-making capabilities to help airports optimize their return processes and meet increasing air travel demand.
Airlines
For airlines, time is money. As long as the plane is flying, money is lost in the sense of waiting on the ground while performing a money-saving operation. We believe there are two different approaches when considering shorter turnaround times for airlines.
Time gained first can be used to compensate for minutes of delay. This is a fairly simple approach, using the value of minutes of delay, which is considered to be approximately $100 per minute. This means that a delay of 5 minutes per flight can be compensated, with a benefit of $500 per flight.
A second approach is to look at aircraft utilization rates. 5 minutes gained on a single spin does not equal 5 more minutes of flight time. Even gaining 5 minutes on all turns at a station does not guarantee additional flight addition. However, some aircraft cannot be prepared for the next flight in one day due to time constraints. Gaining 5 minutes on each turn during the day can make additional flights possible. If these gains are made across the network, flight plans can be optimized to take advantage of short turnaround times across the network. The exact value of additional flights is very different for each airline, but revenues are added for additional flights. Also, the cost of these additional flights is marginally lower due to the spread of all airline fixed costs across more flights.
Airport
The main benefit of shorter turnaround times for airports is shorter booth occupation time, which can lead to higher booth utilization rates. The same logic applies in the case of aircraft utilization rates: each minute reduction cannot be used for stand occupation; however, the gains made throughout the airport during the day can be used with gate planning optimization.
In our gate optimization study for a major hub airport in Europe, we found that saving 5 minutes per turn resulted in 3 fewer booths being used during morning rush hour. This means that the airport basically has 3 additional, highly valuable aircraft stands. The value of this result can be calculated as the opportunity cost of building 3 additional stands. Alternatively, we can evaluate the additional income that these stands will generate for the airport.
Assuming that each will host 1 flight during the airport rush hour, this means that the 3 stands will operate 3 additional flights per day or 1,095 additional flights per year. The aviation and commercial revenue of an average flight (on average between narrow-body and wide-body flights) is around $10,000 for a flight (based on our internal calculations using public aviation fares and expenditure per passenger).
Power Transmission Line Component Control
Pole, Isolator, Traverse Control
Arc Horns Arc Guard Ring Control
Inspection of Rusty Damaged Defective Components
Low Suspended - Severed Energy Cables
Leakage Line Cable Detection
Control of Fasteners
Powerline inspection using artificial intelligence (AI) drones can provide significant benefits in terms of cost, safety and efficiency. The use case will include using a drone equipped with cameras and other sensors such as thermal imaging or lidar to inspect power lines for damage or defects. The AI system will be trained to identify and classify different types of damage, such as broken wires or damaged insulators, and generate a detailed report of the findings.
The drone would fly along the power line, capturing images and other data as it went. The AI system will analyze the data in real time, identify potential problems and highlight them on a map or other visual display. This allows the operator to quickly and easily identify areas that require further inspection or repair.
Using drones for power line inspection can significantly reduce the need for manual inspections, which can be both time-consuming and dangerous. Drones can safely access areas that are difficult or impossible for humans to reach, such as high towers or remote locations. The use of artificial intelligence can also increase the accuracy and consistency of the audit process by reducing the risk of missed or overlooked defects.
In addition to identifying and classifying damage, the
AI system can also be used to predict potential failures by analyzing patterns in data and identifying trends or anomalies. This will allow utilities to proactively plan maintenance and repairs and reduce the risk of power outages or other problems.
Overall, the use of drones and artificial intelligence for powerline surveillance can provide a cost-effective and efficient solution to protect and improve power grid infrastructure. It can increase safety and reduce downtime, while providing valuable information on the condition of power lines, helping utilities make informed decisions about maintenance and repair.
Afforestation Control
The difficulty of measuring the distance of trees to power transmission lines
Manual measurement
Difficulty of controlling distances in areas where transportation is difficult
The use of drone technology and artificial intelligence algorithms takes vegetation management further. This technology involves using drones equipped with AI algorithms to monitor and maintain vegetation in a given area.
An example of this technology is the monitoring of power lines and transmission towers. Equipped with drones, cameras and AI algorithms, it can fly over power lines and transmission towers, detecting any vegetation growing too close to the equipment. This information can then be used to plan vegetation maintenance and pruning, reducing the risk of power outages from vegetation coming into contact with equipment.
In general, the use of drones with AI algorithms in vegetation management increases the efficiency and effectiveness of vegetation management operations. This saves costs and provides a significant improvement in safety and environmental outcomes.
AI-powered drone technology is also widely used in the agricultural sector. This technology is used to improve the efficiency of agricultural operations by monitoring the health of vegetation and soil. AI algorithms can detect plant diseases or pests by making detailed analyzes of vegetation on images taken by drones. In this way, farmers can prevent harvest losses by intervening early and make their agricultural operations more efficient.
In addition to this, drone technology is also used in natural disasters. In particular, in situations such as floods or fires, drones with AI algorithms can be used to assist response teams. With aerial images, drones can more quickly and accurately scan flood or fire areas. In this way, response teams can better understand the extent and rate of spread of the incident and plan their response accordingly.
As a result, AI-powered drone technology is a technology that can be used in many areas. It can be used in areas such as vegetation management, agriculture, disaster response, and increase the efficiency and effectiveness of operations. This can provide benefits such as cost savings, improved safety and environmental outcomes.
Product Quality Control
Detection of defects in products at the time of production
Faulty and flawless products tracking
Managing the production operation
In the manufacturing industry, quality control is a crucial element to ensure that manufactured products meet required standards and specifications. With the advancement of technology, companies are turning to artificial intelligence (AI) to improve their quality control processes.
One use case of artificial intelligence in product quality control is to use image recognition to detect defects in products. Using cameras and machine learning algorithms, the AI system can analyze images of products and identify defects such as scratches, dents or irregular shapes. This enables fast and accurate detection of defects, reducing the need for manual inspection.
Another use case is to use artificial intelligence to predict potential defects in the manufacturing process. By analyzing data from previous production runs and identifying patterns, the AI system can predict when a defect is likely to occur and take preventive measures to prevent it. This can help improve overall product quality and reduce costs associated with defective products.
In addition, AI can be used to optimize the production process by analyzing data from production runs and identifying areas for improvement. This can include identifying bottlenecks in the production line, increasing efficiency and reducing waste.
Overall, the use of artificial intelligence in product quality control can significantly improve the efficiency and accuracy of the process while reducing the costs associated with defective products. It allows companies to produce higher quality products, increase customer satisfaction and increase market competitiveness.
Artificial intelligence can be used in the manufacturing industry not only for product quality control, but also to increase efficiency and optimize processes. For example, AI-powered robotic systems can be used to move, pack or load materials and products on production lines. This reduces human error in the production process, enabling faster and more accurate production.
In addition, artificial intelligence can be used to optimize energy use in the manufacturing process. For example, it can provide suggestions to increase energy efficiency by analyzing the working times, energy consumption and efficiency of the machines in the production lines.
Finally, AI-powered supply chain management can be used to optimize material sourcing, production and distribution. This enables better decisions in inventory management and logistics processes and reduces costs and time throughout the supply chain.
In general, artificial intelligence offers great advantages to companies by providing more efficient, accurate and cost-effective production in the manufacturing industry. AI-powered manufacturing can help businesses increase profitability, improve sustainability and environmental efficiency, and increase customer satisfaction.
Bench Break-Shift Control
Controlling Personal Protective Equipment Control in Selected Areas with Artificial Intelligence Support, integrated into cameras
Identification and tracking of employees through cameras, reporting and recording break times
Detection of the people at the counter in the units by means of cameras, recording the working hours with the support of Artificial Intelligence
Shift management and job security are vital for any organization to ensure employee well-being and maintain an efficient work environment. Today, with the development of artificial intelligence technology, these areas have become more developable.
Artificial intelligence in worker shift management offers many use cases. For example, AI-powered shift scheduling software can analyze employee availability, skills, and preferences to create an optimized schedule that maximizes productivity while keeping employee well-being in mind. By taking into account factors such as employee fatigue and burnout, this software can prevent schedule conflicts and ensure that employees do not work overtime.
Another use case of artificial intelligence in occupational safety is artificial intelligence-assisted monitoring systems. These systems can analyze data from cameras, sensors and other devices to detect potential safety hazards in the workplace. For example, an AI-powered monitoring system that can detect a gas leak or fire can alert the appropriate personnel to take action. Additionally, AI-powered monitoring systems can analyze worker behavior to detect and prevent potential safety hazards, such as workers not wearing appropriate worker protective equipment.
Overall, the use of AI in worker shift management and safety can provide organizations with valuable insights and improve the overall safety and well-being of workers. By harnessing the power of AI, organizations can make data-driven decisions that optimize employee productivity and safety. Thus, employees can work in a healthier, safer and more productive work environment.
Smart Education Recommendation System
Provides staff development
Provides personal development
Performs analyzes and reports
Employee Development: The system is used by companies to identify the training needs of their employees based on their job roles and performance. It uses artificial intelligence to analyze the employee's performance data, job requirements, and previous training history to recommend relevant training programs. This helps the company invest in the right training programs and improve the overall performance of its employees.
Career Development: The system is used by individuals to identify the education they need to advance in their careers. It uses artificial intelligence to analyze an individual's skills, qualifications, and work history to recommend relevant training programs. This helps the individual identify the training they need to achieve their career goals and increase their chances of promotion.
Compliance Training: The system is used by organizations to ensure that their employees comply with relevant regulations and laws. It uses artificial intelligence to analyze the employee's job role and previous training history to recommend relevant compliance training. This helps the organization stay compliant and avoid costly penalties.
Personal Development: The system is used to determine the training that individuals need for their personal development. It uses artificial intelligence to analyze an individual's interests and goals to suggest relevant training programs. This helps the individual identify the training they need to achieve their personal growth goals and improve their overall well-being.
These systems enable employees and individuals to be more successful in the business world by helping to develop their talents and skills. In addition, these systems help companies stay competitive by helping employers identify and invest in the right training programs for their employees.
The advantages of these systems include increasing productivity, reducing operating costs and increasing employee satisfaction. In addition, thanks to these systems, it is ensured that employers and employees are more conscious and knowledgeable about training and development.
However, the success of these systems is directly proportional to the collection and analysis of the right data. Therefore, it is important for companies and individuals to correctly use and interpret the data provided by these systems. In addition, the use of these systems should be carefully considered in terms of the privacy and protection of personal data of individuals and employees.
As a result, AI-based training systems are an important tool that helps identify and recommend the training needed to achieve success in business and individual careers. The correct use of these systems helps employers and employees improve their performance and helps companies stay competitive, while also contributing to the personal development of individuals.
