The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian. We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth. Software is available via the open source image analysis system TINA Vision, www.tina-vision.net. Supplementary data are available at Bioinformatics online.

Small particles contaminated in hard disk drives (HDD) potentially cause damage to the device, leading to data loss. Hard disk industries, therefore, pay attention to identifying the types and sources of these contaminants. However, expensive analytical procedures are required for precise identification when testing samples are relatively small and scarce. A traditional tool, Raman spectroscopy, provides spectra with poor signal-to-noise ratios when dealing with sub-micron particles. Hence, human experts find noisy Raman spectral identification a real burden. In this study, we proposed a practically applicable pipeline, consisting of a denoising autoencoder with the spectral gradient correlation for the classification task followed by the novel validation step based on an ensemble of CNN models to remove the predictions with low certainty. In the experiments, three different backbone models for denoising autoencoders are studied, including multilayer perception (MLP), convolutional neural network (CNN), and U-Net. While the ensemble model consists of eight different CNN models that act as independent machine experts, whose votes indicate agreement with the correlation approach. When less agreement is observed, the sample is said to be unidentified and rejected from the classification task. With our validation step, the results bestow exceptionally high classification accuracy of 0.965, 0.955, and 0.976 for spectra undergoing our proposed pipeline with MLP, CNN, and U-Net autoencoder denoising models, respectively. This highlights the effectiveness of our proposed pipeline in practical application

Seksan Laitrakun, Somrudee Deepaisarn, Sarun Gulyanon, Chayud Srisumarnk, Nattapol Chiewnawintawat, Angkoon Angkoonsawaengsuk, Pakorn Opaprakasit, Jirawan Jindakaew, and Narisara Jaikaew. Journal of information and communication convergence engineering 2023;21:208-15. https://doi.org/10.56977/jicce.2023.21.3.208