Brosur FTS.pptx
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This document provides specifications for portable kidney loop filtration systems with triple, double, or single vessel filter options. The triple vessel system filters fluid through three stages: a particulate filter, followed by two particulate and water separator filters. It can filter fluids like gasoline, diesel, and oils at flow rates up to 250 liters per minute. The document also provides pump specifications and dimensions for a single vessel SKL-50 portable filtration system.
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The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
artificial intelligence and data science contents.pptx
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Software Engineering and Project Management - Introduction, Modeling Concepts...
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Data Driven Maintenance | UReason Webinar
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Mechanical Engineering on AAI Summer Training Report-003.pdf
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Applications of artificial Intelligence in Mechanical Engineering.pdf
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
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- 1. Filter & Vessel Spec ( Triple Vessel Filter ) PORTABLE KIDNEY LOOP FILTRATION ( Triple Vessel ) Flow rate up to 250 LPM Inlet Outlet Stage 1 Stage 2 Stage 3 Acc : 2 inch NPT : 2 inch NPT : Particulate filter ( mesh filter 20 mic.–washable ) : Particulate & Water Separator Filter : Particulate & Water Separator Filter : Double Pressure gauge on each vessel, sampling point for taking fuel sample For use : gasoline, diesel, biodiesel and industrial fluids such as lubrication oils, hydraulic oils, and many solvent Pump Spec Flow rate Max. Pressure Volt/Hz Type/Merk Flowmeter : 60 LPM / 960 RPM : 6 Bar : 3 phase or 1 phase / 50 Hz ( With Panel ) : Gear Pump Koshin : Fill - rate cap. up to 150 LPM Real time particle counter (optional ) Include rubber hose 5 mtr ( inlet & outlet ) Common Aplication : Kidney Loop Filtration Flushing & cleaning sump tank Diesel or biodiesel Fuel treatment Transformer Oil Compressor Oil Fluid Transfer DESIGN FEATURES : Housing coated carbon steel, Mobile Cart Moveable and easy to connection 1st Vessel Element : Mesh Filter/Particulate Filter 2nd Vessel element : Particulate & Water Separator Filter 3 rd Vessel Elemen t: Particulate & Water Separator Filter
- 2. PORTABLE KIDNEY LOOP FILTRATION ( Double Vessel ) Filter & Vessel Spec ( Double Vessel Filter ) Flow rate up to 250 LPM Inlet Outlet Stage 1 Stage 2 Acc : 2 inch NPT : 2 inch NPT : Particulate filter ( mesh filter 20 mic.–washable ) : Particulate & Water Separator : Double Pressure gauge on each vessel, sampling point for taking fuel sample For use : gasoline, diesel, biodiesel and industrial fluids such as lubrication oils, hydraulic oils, and many solvent Pump Spec Flow rate Volt/Hz Type : 60 LPM / 960 RPM : 3 Phase or 1 phase / 50 Hz ( with panel ) : Koshin Gear Pump Flowmeter : Fill -rate cap. up to 150 LPM Real time particle counter (optional ) Include rubber hose 5 mtr ( inlet & outlet ) Common Aplication : Kidney Loop Filtration Flushing & cleaning sump tank Diesel or biodiesel Fuel treatment Transformer Oil Compressor Oil Fluid Transfer DESIGN FEATURES : Housing coated carbon steel, Mobile Cart Moveable and easy to connection 1st Vessel Element : Mesh Filter / Coalescer Filter 2nd Vessel element : Partikulat filter / Separator filter
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