Engineering AI Systems: A Research Agenda

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this paper, we provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that we have studied. The main contribution of the paper is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.Comment: 8 pages, 4 figure

Characterization of the concentration and distribution of urban submicron (PM1) aerosol particles

[[abstract]]Concentrations of ambient particulate matter mass were measured in terms of PM1 and PM1–10 for 8 months between 2000 and 2001, at a sampling site in the urban area of the city Kaohsiung, Taiwan. The data from 20 samples were closely studied for any seasonal phenomena that affected air pollution patterns. The PM1 and PM1–10 concentration variations differed in the examined patterns. This seemed to indicate that the ambient coarse (PM1–10) and submicron (PM1) particles were being contributed by different sources. On average for the 8-month samples, 52±20% of the PM10 was made up of PM1. The PM1-to-PM10 ratio was observed to vary between summer and winter, it being higher in the summer (approximately 62% in summer and 48% in winter). The correlation (r2) between the PM1/PM10 ratio and the parameters showed that there was no significant correlation between the PM1/PM10 ratio, PM concentrations, and the average and maximum wind speeds. Both emission activities and meteorological conditions are important when considering airborne pollutant concentrations. Based on the evaluation of the data obtained in this study, the contribution to the concentration level of the PM and the ratio at the sampling site could have depended upon meteorological parameters and also the formation of PM, i.e. the formation of secondary aerosols. Results from recent studies (J. Air Waste Manage. Assoc. 51 (2001) 489; Atmos. Environ. 36 (2002) 1911) at this same study site supported that combustion sources and secondary aerosols played significant roles in the formation of ambient submicron (PM1) aerosol particles in the urban area

Dry deposition velocities as a function of particle size in the ambient air

[[abstract]]The atmospheric particle mass size distribution (0.1–100 μ m) and dry deposition flux were measured simultaneously with a wide range aerosol classifier (WRAC) and a smooth greased surface. Microscopic techniques were used to size the particles collected on the deposition surface and generate mass size distributions of deposited particles. All the depositional mass size distributions peaked (interval with the largest mass) between 30 and 100 μm in diameter. Deposition velocities were calculated by dividing the size segregated particle flux by the airborne particle concentration for each of the 10 WRAC stage intervals. Experimentally determined dry deposition velocities for atmospheric particles in the size range of 5–80 μm in diameter were greater than predictions made with the Sehmel-Hodgson deposition velocity model developed from wind tunnel experiments, particularly at higher wind speeds. A multistep method was used to calculate total and cumulative deposition fluxes with the Sehmel-Hodgson model. Calculated and measured fluxes were within 10% for low wind speeds but differed by up to 50% for higher wind speeds. The results also show that particles > 10 μm in diameter dominate atmospheric dry deposition

Characterization of the major chemical species in PM2.5 in the Kaohsiung City, Taiwan

[[abstract]]The concentrations and characteristics of the major components in ambient fine particles in the urban city of Kaohsiung, Taiwan were measured and evaluated. PM2.5 samples were collected using a dichotomous sampler from November 1998 to April 1999 and analyzed for water-soluble ion species using ion chromatography and for carbonaceous species using an elemental analyzer. It was found that SO42−, NO3−, and NH4+ dominated the identifiable components, and occupied 42.2% and 90.0% of PM2.5 mass and total dissolved ionic concentrations. Carbonaceous species (organic and elemental carbon) accounted for 20.8% of PM2.5. The secondary aerosol formed through the NO2/SO2 gas-to-particle conversion was estimated based on the sulfur/nitrogen oxidation ratio (SOR/NOR), i.e., sulfate sulfur/nitrate nitrogen to total sulfur/total nitrogen. The average SOR and NOR values were 0.25 and 0.07 for PM2.5. The high SOR and NOR values obtained in this study suggested that there existed a secondary formation of SO42− from SO2 along with NO3− from NO2 in the atmosphere. The secondary organic carbon formed through the volatile organic compound gas-to-particle conversion was estimated from the minimum ratio between organic and elemental carbon obtained in this study, and was found to constitute 40.0% of the total organic carbon for PM2.5 (6.6% of the particle mass). The results obtained in this study suggest that the formation of secondary aerosols due to conversion from gaseous precursors is significant and important in urban locations

Regeneration of ferrous EDTA in an SO{sub 2}/NO{sub x} scrubber system by electrochemical methods

The decay of iron-chelate absorbent used in an wet scrubber system Of SO{sub 2}/NO{sub x} is regenerated by electrochemical cell. The concept of electrochemical regeneration of iron*EDTA along with the kinetics information is described in this paper. Electrode materials, electrolyte concentration, applied current level and the operating potential range of the regeneration are the decisive parameters to affect the cell performance