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The document summarizes the initial ideas and final design of a gear system for a robotics project group consisting of 4 students. The group considered 3 options: an epicyclical gear system, a basic non-compound gear system, and a compound gear train. They ultimately selected the compound gear train because it allows for a better ratio with a smaller overall size while being less complex than an epicyclical system but more durable than a basic system. Key features of their compound gear train design include double thickness gears for strength, an anti-backlash system for consistency, and central mass positioning to prevent shaft bending.
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International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
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.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
5214-1693458878915-Unit 6 2023 to 2024 academic year assignment (AutoRecovere...
Bigdata of technology
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Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
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Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, SerbiaIEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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Creative journey pres
- 1. GEAR NIM
- 2. GROUP MEMBERS Jerimiah Flazza 9983333 • 4th Year Robotics and Mechatronics/Bachelor of Computer Science Rory Cottew 2081423 • 3rd Year Bachelor of Mechanical Engineering/Bachelor of Commerce Zifeng Li 7459386 3rd Year Mechanical Engineering Andrew Woodcock 9983295 • 4th Year Mechanical Engineering
- 3. INITIAL IDEAS Epicyclical Gears (Planetary) Pros: • It looks very elegant. • Have been told by past students that it can be very good if successful. • Compact design Cons: • Very high chance the system won't assemble properly. • System requires very precise manufacturing of parts for gears to properly function. • High failure rate. • For optimal performance it would require multiple planetary systems.
- 4. Basic non compound gear system Pros: • Super easy to make/design/assemble. • Very reliable. Cons: • Larger then a compound system. • Higher chance of backlash, equaling less durability. • Not very unique or elegant. • Not as much of a challenge. INITIAL IDEAS
- 5. FINAL DESIGN Compound gear train Pros: • Can get a better ratio with a smaller size overall. • More compact design. • Increases width instead on length. • Less technical then a planetary system. • Less complex shape. • Increased strength and durability. Cons: • More material required. • Although less technical then planetary, the thicker gearing could be tricky to assemble. • Therefore a longer assembly time.
- 6. FEATURES Double thickness of gears • Reduces the chance on individual teeth breaking. Anti-Backlash System • Creates more surfaces contact resulting in less backlash and keeps torque more consistent. Mass position • Having the mass connected in the center of the last shaft • Less chance for the shaft to bend and disrupt the gear connection.