Applying graph matching techniques to enhance reuse of plant design information

利用过程拓扑结构迁移图神经网络用于软测量建模

Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies

针对标准软测量模型因固定输入结构无法跨工厂迁移的问题，提出拓扑感知图神经网络方法，将工厂建模为图以灵活适配不同传感器网络，实现多工厂间软测量模型迁移，提升跨场景复用性。

将基于XAI的无监督知识发现应用于污水处理厂的运行模式：一个真实案例——AQUAVALL污水处理厂

Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP

本文提出并测试了一种基于可解释人工智能（XAI）的无监督知识发现方法，用于从AQUAVALL污水处理厂的大量历史数据中提取少数几个可解释的运行模式，并在低维空间中展示相关变量和设施单元。

Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants

Deep learning techniques involving image processing and data analysis are constantly evolving. Many domains adapt these techniques for object segmentation, instantiation and classification. Recently, agricultural industries adopted those techniques in order to bring automation to farmers around the globe. One analysis procedure required for automatic visual inspection in this domain is leaf count and segmentation. Collecting labeled data from field crops and greenhouses is a complicated task due to the large variety of crops, growth seasons, climate changes, phenotype diversity, and more, espe…

SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments

Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds. Modernized factories have advanced process control algorithms such as model predictive control (MPC). However, there is little exploration of applying deep reinforcement learning to control manufacturing plants. One of the reasons is the lack of high fidelity simulations and standard APIs for benchmarking. To bridge this gap, we develop an easy-to-use library that includes five high-fidelity simulation environments: BeerFMTEnv, ReactorEnv, AtropineEnv, PenSimEnv and mAbEnv,…

通过同时学习进行正则化：植物分类的案例研究

Regularization Through Simultaneous Learning: A Case Study on Plant Classification

本文提出同时学习正则化方法，结合辅助数据集与目标数据集UFOP-HVD，通过定制损失函数提升CNN泛化能力，准确率提高5-22个百分点，并在小样本下表现高效。

Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks

Several papers have recently contained reports on applying machine learning (ML) to the automation of software engineering (SE) tasks, such as project management, modeling and development. However, there appear to be no approaches comparing how software engineers fare against machine-learning algorithms as applied to specific software development tasks. Such a comparison is essential to gain insight into which tasks are better performed by humans and which by machine learning and how cooperative work or human-in-the-loop processes can be implemented more effectively. In this paper, we present …