Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: (1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and (2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort

CST09 - Analysis and countermeasure for Sulfide Stress Cracking of Centrifugal compressor

Case StudyIn case of copressors operated in evironment of wet hydrogen sulide, Sulfide Stress Craking could act critical mechanism of impeller failure. This case study deal with what make sulfide stress cracking and how to take a countermeasure to prevent recurrence of sulfide stress craking, based on 2 cases of impeller failure from petroleum process

Luminescent Organic Barcode Nanowires for Effective Chemical Sensors

Chemical materials are sometimes harmful to the environment as well as humans, plants, and animals. Thus, high-performance sensor systems have become more important in the past few decades. To achieve pH scale sensing in nanosystems, we applied luminescence polymer nanowires with alumina oxide template method with electrochemical polymerization. We made polymer nanowire barcode by alternately stacking poly(3-methylthiophene) (P3MT) and poly(3,4-ethylenedioxythiophene) (PEDOT) in a nanoporous template. After polymerization, a hydrofluoric acid solvent was used to remove the template, and, for changing the pH scale, we used sodium hydroxide. We measured optical properties of each part of barcode using Raman scattering and photoluminescence and confirmed that only P3MT was changed by alkali treatment

Reduction in the Migration Activity of Microglia Treated with Silica-Coated Magnetic Nanoparticles and their Recovery Using Citrate

Nanoparticles have garnered significant interest in neurological research in recent years owing to their efficient penetration of the blood–brain barrier (BBB). However, significant concerns are associated with their harmful effects, including those related to the immune response mediated by microglia, the resident immune cells in the brain, which are exposed to nanoparticles. We analysed the cytotoxic effects of silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)] in a BV2 microglial cell line using systems toxicological analysis. We performed the invasion assay and the exocytosis assay and transcriptomics, proteomics, metabolomics, and integrated triple-omics analysis, generating a single network using a machine learning algorithm. The results highlight alteration in the mechanisms of the nanotoxic effects of nanoparticles using integrated omics analysis