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The EASE software suite provides system designers and consultants with an invaluable set of tools for all aspects of professional practice, from detailed, realistic modeling and simulation of venue acoustics and sound system performance to informative and engaging client presentations, as well as professional data assessment and verification. EASE Standard and its smaller brother EASE JR, along with the optional modules AURA and EARS, take the guesswork out of system design, help eliminate costly mistakes and reduce installation time. They assist designers in learning and growing by graphically displaying accurate predictions of real-world acoustics. EASE models are an ideal way to explore options and to evaluate what works and what doesn’t work – before the virtual venue becomes a job site and changes are time-consuming and expensive. Skyrim ps3 modded game save download. Key benefits of EASE simulation: • Convince customers with calculations by the industry standard electro-acoustical simulation software.
• Be sure to meet the requirements for SPL, STI and other demands with minimum effort. • Become aware of obstacles that would remain hidden without simulation. • Analyze and solve acoustical problems before they arise. • Save time and money by avoiding multiple iterations on site. • Make use of the largest and highest quality loudspeaker database to find the optimal solution. Learn more about the! EASE – Industry Standard for Acoustical Simulation of Rooms Since the 1990´s, EASE has been setting the worldwide standard for acoustic simulation both inside rooms and in open areas.
Rooms can be defined using a CAD module, absorption coefficients can be assigned to surfaces, and sound sources as well as listener positions can easily be added to the model. These data can then be used to generate an exact simulation of reverberation times, speech intelligibility and other acoustical parameters even before the room itself is built.
Aerospace systems are expected to remain in service well beyond their designed life. Consequently, maintenance is an important issue. A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. Simple structural elements, consisting of flat aluminum plates of AL 2024-T3, were subjected to increasing static tensile loading. As the loading increased, designed cracks extended in length, releasing strain waves in the process.
Strain wave signals, measured by acoustic emission sensors, were further analyzed in post-processing by artificial neural networks (ANN). Several experiments were performed to determine the severity and location of the crack extensions in the structure.
ANNs were trained on a portion of the data acquired by the sensors and the ANNs were then validated with the remaining data. The combination of a system of acoustic emission sensors, and an ANN could determine crack extension accurately. The difference between predicted and actual crack extensions was determined to be between 0.004 in. And 0.015 in. With 95% confidence.