I. Machine learning can expand the scope of the Internet of Things
Numerous examples have already demonstrated how machine learning can be linked to specific gadgets that have garnered significant attention for the Internet of Things (IoT). Beyond mobile devices, self-driving cars, smart cities, and factories can also benefit from machine learning. As IoT products and services become more cost-effective, easier to market, and more readily consumed through the adoption of machine learning strategies, more consumers will flock to them, further expanding their reach globally.
The era of artificial intelligence is controversial, but it hasn't reached the point of Hollywood's doomsday predictions, and it has undeniably changed the world. In the next one or two decades, with the proliferation of billions of devices, advancements in algorithms and cost reductions for businesses and consumers will make machine learning even more indispensable. As more people register and log in on social media platforms, purchase smart devices, and commute in self-driving cars, the impact and even control of society by the Internet of Things will become even more powerful in the wondrous world of machine learning.
II. How to make good use of machine learning
Engineers around me often have a habitual way of thinking when doing related designs: first, they collect a large amount of data, then use tools to analyze features and generate models, and then use a large amount of data collection to correct and improve the models. This approach is probably the usual way of thinking for many designers. In general, it is required that the hardware acquisition devices provide as many parameters as possible, so as to enrich the input parameters of the algorithm model. Theoretically, this can better approximate the actual results. From a purely technical point of view, this is feasible. Moreover, as the sample size increases, by adopting the approach of machine learning, the final model and algorithm will become more and more accurate.
This design approach is highly efficient when ideal samples are available or sample data can be easily obtained. However, if the existing samples are not ideal, contain a large amount of interference, or the sample data is affected by human factors or must be collected manually, the traditional method loses its advantage. In this case, data preprocessing and necessary analysis are required, and several issues need to be addressed:
1. Predetermine several parameters that need to be studied. When selecting parameters, there should be certain basis and relevance. Taking the analysis of infrared signal output as an example, we can know from the publicly available information that infrared sensors are affected by temperature and spectral range, which will cause changes in the output curve. Therefore, temperature and spectral range are the parameters that need to be studied.
2. After determining the parameters, do not sample data for all parameters. Instead, sample data for a single parameter to thoroughly understand the influence of that single parameter on the final result, i.e., the weight. This can be represented by a function: Y = F(X), where Y is the result and X is the sample data parameter. The data processing includes basic operations such as smoothing, filtering, elimination, and insertion, which is the preprocessing process. After that, a basic function relationship or reference table can be generated to provide data support for the subsequent establishment of a large model.
3. After quantifying the influence of each data parameter, we obtain all the parameter reference tables and function correspondences. Using these existing results, we build the entire algorithm model and framework structure, design the weight of each data in the model, and finally form a preliminary machine learning model.
4. After completing the above three steps, we can finally improve the entire machine learning model by collecting large data samples, and ultimately achieve the previously set goal.
