Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions

The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpag

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

Urban geo big data

The paper deals with the general presentation of the Urban GEO BIG DATA, a collaborative acentric and distributed Free and Open Source (FOS) platform consisting of several components: local data nodes for data and related service Web deploy; a visualization node for data fruition; a catalog node for data discovery; a CityGML modeler; data-rich viewers based on virtual globes; an INSPIRE metadata management system enriched with quality indicators for each dataset.Three use cases in five Italian cities (Turin, Milan, Padua, Rome, and Naples) are examined: 1) urban mobility; 2) land cover and soil consumption at different resolutions; 3) displacement time series. Besides the case studies, the architecture of the system and its components will be presented

CropSight: A scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management

Background: High-quality plant phenotyping and climate data lay the foundation of phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations, at different scales. Findings: CropSight is a PHP and SQL based server platform, which provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralised data management. Data transfer and annotation are accomplished automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSight system, which synchronise daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted. Conclusions: As a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future

Top-down sustainability transitions in action: How do incumbent actors drive electric mobility diffusion in China, Japan, and California?

In explaining how socio-technical transitions occur, prevailing theories focus on bottom-up processes driven by new entrants, diverse actors and open-ended exploration in small, protected niches. Incumbent firms are frequently portrayed as hampering change, while managerial strategies using traditional public policy instruments remain understudied. Addressing this bias, we examine strategies used by networks of incumbent state and industry actors in China, Japan and California to accelerate the production and diffusion of battery-electric or hydrogen-powered vehicles. We build a comprehensive framework that systematically marries mechanisms of industrial transformation described in developmental-state literature with theories of socio-technical change from transitions scholarship. We then use a vast dataset of secondary documents and interviews to examine the principal strategies employed in each country, identifying variations over two phases of technological diffusion. Findings reveal that the incumbent actor networks in each country have collectively employed multiple but similar strategies. Yet closer inspection of specific policy instruments, such as regulations and performance-based incentives, along with ambitions to phase out vehicles with internal combustion engines, reveals differences across cases. We explain these by considering different motivations for each country’s transition and influencing socio-political conditions. Our study contributes to the enrichment of future transitions research in at least two ways. Theoretically, by integrating literature on transitions and developmental states, we deepen understanding of how incumbent state and market actors can attempt to drive socio-technical change. Empirically, our analysis provides important evidence for understanding the strategies driving top-down transitions outside northern Europe, and the conditions affecting instrument choice

Moneyball Sentencing

Sentencing is a backward- and forward-looking enterprise. That is, sentencing is informed by an individual’s past conduct as well as by the criminal justice system’s prediction of the individual’s future criminal conduct. Increasingly, the criminal justice system is making these predictions on an actuarial basis, computing the individual’s risk of recidivism according to the rates of recidivism for people possessing the same group characteristics (e.g., race, sex, socio-economic status, education). The sentencing community is drawn to this statistical technique because it purportedly distinguishes with greater accuracy the high-risk from the low-risk, and thereby allows for a more efficient allocation of sentencing resources, reserving incarceration for the truly dangerous and saving the low-risk from needless penal attention. Despite these asserted benefits, risk-assessment tools are exogenous to the theories of punishment, the very foundation for sentencing in Anglo-American jurisprudence. This Article reviews the legality and propriety of actuarial predictive instruments, using these theories and governing constitutional and statutory law as the touchstone for this analysis. This Article then applies these normative and legal principles to seventeen major characteristics that may comprise an offender’s composite risk profile. It argues that risk-assessment instruments are problematic for three reasons: they include characteristics that are prohibited by constitutional and statutory law; subject the individual to punishment for characteristics over which the individual has no meaningful control; and presume that the individual is a static entity predisposed, if not predetermined, to recidivate, thereby undermining individual agency and betting against the individual’s ability to beat the odds

ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid

An all ambient, room-temperature processed solar cell from a bare silicon wafer

大気中かつ室温での太陽電池の作製を実現 --低コストで簡便に太陽電池の製造が可能に--. 京都大学プレスリリース. 2023-03-15.Solar cells charging forward: Realizing the potential of creating silicon-based photovoltaics at room temperature. 京都大学プレスリリース. 2023-04-11.Solar cells are a promising optoelectronic device for the simultaneous solution of energy-resource and environmental problems. However, their high cost and slow, laborious production process so far severely hinder a sufficient widespread of clean, renewable photovoltaic energy as a major alternative electricity generator. This undesirable situation is mainly attributed to the fact that photovoltaic devices have been manufactured through a series of vacuum and high-temperature processes. Here we realize a PEDOT:PSS/Si heterojunction solar cell fabricated only in ambient and room-temperature conditions from a plain Si wafer, with an over-10% energy conversion efficiency. Our production scheme is based on our finding that PEDOT:PSS photovoltaic layers actively operate even on highly doped Si substrates, which substantially mitigates the condition requirements for electrode implementation. Our approach may pave the way for facile, low-cost, high-throughput solar cell fabrication, useful in various fields even including developing countries and educational sites