Solubility measurements are essential in various research and industrial fields. With the automation of processes, the importance of automatic and real-time solubility measurements has increased. Although end-to-end learning methods are commonly used for classification tasks, the use of handcrafted features is still important for specific tasks with the limited labeled images of solutions used in industrial settings. In this study, we propose a method that uses computer vision algorithms to extract nine handcrafted features from images and train a DNN-based classifier to automatically classify solutions based on their dissolution states. To validate the proposed method, a dataset was constructed using various solution images ranging from undissolved solutes in the form of fine particles to those completely covering the solution. Using the proposed method, the solubility status can be automatically screened in real time by using a display and camera on a tablet or mobile phone. Therefore, by combining an automatic solubility changing system with the proposed method, a fully automated process could be achieved without human intervention.

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The acid-assisted photolysis of the trimethyl lock on the surface was characterized by mass spectrometry.

Deep neural networks (DNNs) have successfully classified EEG-based brain-computer interface (BCI) systems. However, recent studies have found that welldesigned input samples, known as adversarial examples, can easily fool well-performed deep neural networks model with minor perturbations undetectable by a human. This paper proposes an efficient generative model named generative perturbation network (GPN), which can generate universal adversarial examples with the same architecture for non-targeted and targeted attacks. Furthermore, the proposed model can be efficiently extended to conditionally or simultaneously generate perturbations for various targets and victim models. Our experimental evaluation demonstrates that perturbations generated by the proposed model outperform previous approaches for crafting signal-agnostic perturbations. We demonstrate that the extended network for signal-specific methods also significantly reduces generation time while performing similarly. The transferability across classification networks of the proposed method is superior to the other methods, which shows our perturbations' high level of generality. Our code is available for download on https://github.com/AIRLABkhu/Generative-Perturbation-Networks.