In this VIDEO example, we can see how easy it is to update the feature tree in OmniCAD. After creating the addendum, we import the STL files obtained from a simulator and apply the deformation, asking the system to automatically recreate the addendum from the new solid obtained after the deformation. https://lnkd.in/dZhk__Mf
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The most effective approach to evaluate object detection models is by executing inference on them. Therefore, in this demonstration tutorial, I will be comparing the recently launched YOLOv9 with its predecessor, YOLOv8 from Ultralytics. Full comparison available here: https://lnkd.in/dFKKHyRJ Joel Nadar Mohammad Zaid Mohammad amin Dehmolaee Muhammad Rizwan Munawar Harpreet Sahota 🥑 Andreas Welsch Utsav Desai Utsav Soi M Adnan Josie Jacobs Heerthi Raja H Alex Zap Musa Ceylan Youssef Hosni Nicholas Renotte Anisha Udayakumar Tarek Gasmi Peter Gostev #yolov9 #yolov8
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You gotta watch this thing. The main discussion and demo is awesome for TigerBeetle (A financial db written in Zig) However, the Q&A session afterwards about how the simulator works is fantastic, a heavier reliance in state machines and asserts. https://lnkd.in/eYituUeZ
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SimBricks relies on Loosely Coupled Simulator Processes for Parallelism and Easy Integration. This is our secret to scalably connect and integrate a range of independent and often incompatible simulators into a coherent whole. Read More: https://lnkd.in/eJQkP9sa #FullSystemSimulation #LargeScaleSimulation #VirtualPrototyping
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A compressed ConvNeXt model and CIFAR-10 dataset were used to test the effect of adversarial attacks using FGSM (Fast Gradient Sign Method). The accuracy dropped from 93% to 24%!!! and the below sample shows how the attack mislead the model to classify frog as a cat!! #artificialintelligence #adversarialattacks #convnext #fgsm
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SVA: Increasing clk by 25X kills sim time. Many properties are of this style: $rose(a) |-> ##[4000:6000] $fell(b) A potential solution: int count; always @(posedge clk) count<= count+1; task automatic t_b4000_6000(int range); int ct_now=count; @(engage b) am_amax: assert(count-ct_now <= range); endtask assert property(@(posedge clk) $rose(a) |-> (1, t_b4000_6000(6000-4000))); // ##[4000:6000] $fell(b) // another possibility property p; int ct_now; (@(posedge clk) ($rose(a), ct_now=count) |-> @(negedge b) count - ct_now <=(6000-4000); endproperty If the simulator puts the @(negedge b) event in the queue for something to do, I see less calculations here. https://lnkd.in/geM2zvHT
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🚀 Excited to introduce My Project: Traffic Speed Estimation with YOLOv8! in means of improving speed detection methods, I developed a system for traffic speed estimation using the YOLOv8 object detection model. This project leverages the UA-Detrac dataset and utilizes homogenous transformation for accurate speed calculations. Check out the video demonstration to see YOLOv8 in action! Your feedback is highly appreciated. data-prepreprocessing and training: https://lnkd.in/e5d6rfu3 #computervision #deeplearning #yolov8
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As many of you know, we had a major ResFracPro update that went out on November 4th for both the simulator and user interface. Highlights include: (a) the advanced ‘proppant transport from the well’ capabilities developed by Egor Dontsov for StageOpt have now been implemented in the ResFracPro simulator, (b) ‘libraries’ that allow you to save/load components of simulations (such as defined proppants, fluid models, etc.); this includes a pre-saved database of proppant and fluid types, (c) the ability to import fluid models from the Eclipse format and the Whitson format, (d) new wizards for: well shift, mesh refinement, and proppant immobilization, (e) the ability to prespecify line plot data for history matching, (f) gunbarrel plots showing proppant placement, fracturing, and fluid leakoff, (g) the ability to set the default VM size for running simulations on the server, and (h) many minor new options, bug fixes, and improvements. For ResFrac's November Office Hours, scheduled for TOMORROW, November 12th, at 9:00 a.m. Central, we will have a presentation with Mark McClure, who will go through some of the new features in ResFracPro. We'd love to have you join us! You can use the link in the comments to register.
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#CMSPaper 1283 studies if there are particles decaying halfway through traversing the CMS detector, instead of (almost) at the collision point. Such displaced vertices are an important aspect of the long-lived particles searches at the LHC. Shown: an estimate of the efficiency to see such a vertex (and the difference between simulation and real data for that signature) https://buff.ly/49wMECc
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⚡️ Curious how YOLOv11 ACCURACY compared to earlier models? I tested it in real-world scenarios—daylight, low-light, and even fast-moving objects. The results might SHOCK you! Watch the comparison and see for yourself. https://lnkd.in/gngCW-2p #YOLOv11 #YOLOv11Comparison
YOLOv11 vs YOLOv10 vs YOLOv9 vs YOLOv8 | A Deeper Detection Accuracy Comparison
https://www.youtube.com/
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With meticulous data annotation, I will infuse your machine learning and AI models with unparalleled accuracy and vitality. https://lnkd.in/dAAse5jv #Yankees #Bronny #Hasan #Polymarket #automation #modelling #computervision #aisurgery #data
Live inference with Ultralytics YOLO11 is Here! 🚀 The moment you've been waiting for has arrived—YOLO11 live inference is now available directly on our website! Experience real-time object detection, all without needing any downloads or setups. What's Included: ✅ Webcam inference: Detect objects live using your webcam. ✅ Video & image inference: Upload videos or images for instant results. ✅ Model options: Choose between YOLO11m, YOLO11s, or YOLO11n. ✅ Customizable options: Adjust confidence and IOU thresholds. Try it now ➡️ https://yolo11.com/
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