Air compressors are vital in industries, supplying compressed air for industrial applications while ensuring efficiency reduced energy loss, maintenance costs and reliability is critical. These compressors are affected by environmental factors and component health when operating under cyclic conditions. This work presents a data-driven approach to optimize the screw compressor efficiency using feature engineering. Feature selection methods like correlation analysis and Elastic Net identified informative sensors, while feature extraction techniques such as curvature analysis and peak detection quantified deviations of features. To reduce the effort on comparison and faster implementation, an Artificial Neural Network (ANN) Regression with a custom loss function predicts outlet pressure RMSE difference and cycle duration, achieving R-squared scores of 0.97 and 0.99 respectively. The analysis found optimal cycle durations of 193s, 108s, and 94s for high, low, and medium pressures, with RMSE deviations of 0.2, 0.07, and 0.10. The proposed method enables monitoring, alerting users to operational variations for improved compressor efficiency.

Image processing workflows typically consist of a series of different functions, each working with “image” inputs and outputs in an abstract sense. However, the specific in-memory representation of images differs between and sometimes within libraries. Conversion is therefore necessary when integrating functions from several sources into a single program. The conversion process forces users to consider low-level implementation details, including data types, color channels, channel order, minibatch layout, memory locations, and pixel intensity ranges. Specifically in the case of visual programming languages (VPLs), this distracts from high- level operations. We introduce im2im, a Python library that automates the conversion of in-memory image representations. The central concept of this library is a knowledge graph that describes image representations and how to convert between them. The system queries this knowledge graph to generate the conversion code and execute it, converting an image to the desired representation. The effectiveness of the approach is evaluated through two case studies in VPLs. In each case, we compared a workflow created in a basic blockbased VPL with the same workflow enhanced using im2im. These evaluations show that im2im automates type conversions and eliminates the need for manual intervention. Additionally, we compared the overhead of using explicit intermediate representations versus im2im, both of which avoid manual type conversions in VPLs. The results indicate that im2im generates only the necessary conversions, avoiding the runtime overhead associated with converting to and from intermediate formats. A performance comparison between the step-by-step approach used by im2im and a single-function approach demonstrates that the overhead introduced by im2im does not impact practical usability. While focused on block-based VPLs, im2im can be generalized to other VPLs and textual programming environments. Its principles are also applicable to domains other than images. The source code and analyses are available via GitHub.

Jupyter Notebook [1] is an open source, interactive computing platform widely used in the scientific computing and artificial intelligence community [2], [3], [4], [5]. The popularity of the platform is a consequence of the generated single notebook document combining source code, markdown, and visualizations (Fig. 1). This makes the platform ideal for tasks such as data analysis and scientific image processing, where repeatability and transparency of analysis tasks are just as important as functionality and performance. However, the obligatory use of code is an obstacle to acceptance of the platform in scientific communities where programming is not generally taught in the curriculum. Consequently, many experimental communities rely on manual image processing using graphical user interfaces [6], [7], [8]. The obvious disadvantages are the lack of repeatability, transparency, and precision in image processing and data analysis tasks. To solve these issues, we propose to extend Jupyter Notebook with visual programming cells. In each visual programming cell, users can create the program by assembling graphical nodes that represent computational instructions, and the textual program is automatically generated and executed by the environment. Cells will support version control aware serialization and deserialization. The core innovation of our proposed work lies in a change of workflow and the adaption of a jupyter-based workflow in experimental communities that have no culture of working with source code. The system can be adapted to multiple applications and domains by integrating new node types. We hereby present an early version of the system and provide one use case from microscopy image processing to demonstrate the integration of existing non-Python software.