It was not worked when tried to import It have not showed any xlsx file only mat files shown

This dataset isn't published elsewhere. You can just cite this website: apmonitor.com/pds/index.php/Main/SoilClassification

Optimization algorithms for aircraft routing are essential for ensuring efficient flight operations, minimizing costs, and maximizing safety. Mixed Integer Linear Programming is a popular method for scheduling optimization. Nonlinear Programming is used for trajectory optimization. Here is a recent review paper: academic.oup.com/iti/article/doi/10.1093/iti/liad026/7459776

​@@apmthanks for the answer? Do you think more complex approaches like Reinforcement Learning or Model Predictive Control could work? I'm not sure what to do when they don't give a solution.. e.g. you could plan ahead using RL but if there would be a crash in the future what would I do?

​@@apmAnd is nonlinear optimization fast enough if I have many agents?

@@zemanntill MPC uses linear or nonlinear programming to calculate the new trajectory. The speed depends on the number of equations and degree of nonlinearity. RL can be useful if you have a simulated environment where it can learn from the trials. RL is typically slow, but iteratively improves.

@@alaaosama7679 websockets communicate over TCP/IP. WebSockets in Python do not inherently require the Internet to work. WebSockets are designed to establish a full-duplex communication channel over a single TCP connection, which can be used on local networks as well as over the Internet. Whether they need the Internet depends on the specific application and the endpoints involved in the communication. If both the WebSocket client and server are on the same local network or even the same machine, the Internet is not required.

You can have 2 inputs to the system with [u,d] and one output [y].

Sure, see Exercise 2: apmonitor.com/do/index.php/Main/LSTMNetwork

MQTT has a broker that serves as a database: apmonitor.com/dde/index.php/Main/MQTTProtocol MQTT is often used for devices that are intermittently connected and where fault tolerance is needed. OPC UA has server where values are stored: apmonitor.com/dde/index.php/Main/OPCTransfer OPC is used in industrial automation. Modbus has a server / client function: apmonitor.com/dde/index.php/Main/ModbusTransfer It is an older standard that is fast and reliable.

Here is more information on underdamped, critically damped, and overdamped second order systems: apmonitor.com/pdc/index.php/Main/SecondOrderSystems

Great idea - it just adds an additional input parameter to the function, but makes the code more modular and reusable for other applications.

That's an interesting observation and likely another way to enforce a positive output constraint. Many of the machine learning packages also allow constraints to be specified now: www.tensorflow.org/api_docs/python/tf/keras/constraints

Sure, here is a complete series of tutorials with an Arduino device for real-time MPC. apmonitor.com/do/index.php/Main/AdvancedTemperatureControl Lab F is linear MPC, the easiest to implement.

You could estimate mass flow from the accumulation rate of the coolant. If you can put the condenser on a weight scale, it would give you a weight versus time that would translate into flow. It may not be practical if the condenser accumulator is built into an existing structure.

@apm oh thanks alot sir

The Teltonika FMB920 can transmit data using LTE or via a local gateway using a Raspberry Pi. For LTE, ensure the micro-SIM is active and configure the device with network and MQTT settings to send data directly to an MQTT broker over the internet. Alternatively, use a Raspberry Pi as a local gateway by enabling Bluetooth on both devices for local data transfer, and configure the Raspberry Pi to send this data to an MQTT broker using installed MQTT client software. This setup not only allows flexibility based on connectivity and mobility needs but also the possibility of integrating additional processing capabilities through the Raspberry Pi.

It is created when you train the model.

@@apm OK, got it, Thanku

The software is Python Gekko. Source code is available at apmonitor.com/me575/index.php/Main/LimestoneSlurry

@@apm thank you 🙏

Elementary Principles of Chemical Processes 3rd Edition by Richard M. Felder and Ronald W. Rousseau - unfortunately, I don't remember the problem number.

Elementary Principles of Chemical Processes 3rd Edition by Richard M. Felder and Ronald W. Rousseau

Time-Sensitive Networking (TSN) is important in many applications. I typically use MODBUS when speed is important. It is an older standard, but specializes in high-speed numerical data.

Great observation. Setting a bound or rearranging the equation to avoid divide-by-zero is often effective.

The EV_TYPE=2 option is a squared error objective. See the code at apmonitor.com/do/index.php/Main/EstimatorObjective It is also possible to specify a custom objective instead of using the build-in types using `m.Minimize()` function. Additional examples are shown at apmonitor.com/do/index.php/Main/DynamicEstimation

Here is an example with MIMO control. apmonitor.com/do/index.php/Main/TCLabF and a simple example: apmonitor.com/wiki/index.php/Apps/LinearStateSpace (see Gekko example at the end).

@@apm ok. Thanks a lot

Yes, no problem on deploying it as a web service. Just open the firewall on port 443 to not block the connection.

The easiest way to create a Neural Network model is to use scikit-learn or TensorFlow for fitting and then import the model into Gekko as shown here: gekko.readthedocs.io/en/latest/ml.html Gekko uses max2 functions for ReLU because they are more efficient to solve than the max3 or if3 functions with binary variables. Search for "relu" in the gekko ML.py file for more details: github.com/BYU-PRISM/GEKKO/blob/master/gekko/ML.py on how it translates scikit-learn and tensorflow models to Gekko.

Your method works for initialization. An alternative is to use STATUS=1 and .value=np.random.randn() without needing to use .FSTATUS and .MEAS

@@apm Thank you for your guidance! I In case I want to train my neural network with a constrained loss function, Can, using TensorFlow model for fitting and import it into Gekko, work??? If yes, how then should I use constrained optimization during model training?

@@apm Thank you very much !

@@user-su5eu5ts2z you can add constraints in TensorFlow: www.tensorflow.org/api_docs/python/tf/keras/constraints/Constraint and then import that fit model in Gekko. Gekko is more flexible with different types of constraints, but TensorFlow is built for larger datasets. Once the model is fit, those constraints are no longer required to make inferences.

The error message "The requested operation has no match to return." (BadNoMatch) from the Python OPC UA package generally indicates that the operation you attempted to perform did not find a valid target or match in the server's address space or data structures.

In MQTT (Message Queuing Telemetry Transport), topics are used as the routing information for messages, and clients subscribe to these topics to receive messages that interest them. However, MQTT does not have a built-in feature to discover topics because it is designed to be lightweight and simple. Topics are typically determined by the application design or the configuration of the MQTT broker and its clients. If you're working with an MQTT broker and you don't know the topics, here are a few approaches you can take to discover or manage topics: Broker Configuration and Logs: Check Broker Configuration: If you have access to the MQTT broker, you can check its configuration files. Some brokers might list the topics that have been configured or have defaults. Broker Logs: Some MQTT brokers log details about connected clients and their subscriptions. By examining these logs, you might be able to identify active topics. Contact System Administrator or Developer: If you're working in a managed environment, the system administrator or the developer who set up the MQTT broker might have a list of topics or documentation about the topic structure.

Yes, no problem on Spyder. Here is a way to display real-time plots: In the Spyder IDE menu bar select Tools, Preferences, IPython Console, Graphics, Backend=Automatic. This will allow real-time plots to display as a new window instead of inline in the IPython console. Here is additional help: apmonitor.com/pdc/index.php/Main/ArduinoSetup

@@apm Thank you so much for providing great content! I appreciate your work!

Sure, the file location is shown at 0:48 in the video

Set `stream=True`. Here is example code: github.com/ollama/ollama-python