658 research outputs found
Arno Herzberg Collection 1930s-1992
No full textThe collection consists most entirely of newspapers clippings of Arno Herzberg’s articles. The articles deal with the Jewish situation in Germany in the 1930s, Israel and her problems with the outside world, Jewish holidays, and a small amount of articles dealing with economic issues, such as taxes. Other materials include a small amount of correspondence, manuscripts (all the manuscripts are photocopies lacking any annotations or remarks), and a memoir depicting the Hess family members between 1930 and the 1940s, including their imprisonment in the Bergen-Belsen concentration camp. There are no materials dealing with Arno Herzberg’s involvement with the Jewish Telegraphic Agency and very few materials pertaining to his primary profession (accounting.)Arno Herzberg was born in Germany in 1908. Between 1934 and 1937 he served as head of the Jewish Telegraphic Agency, an international news agency. Arno Herzberg left Germany in the late 1930s and settled in New York. He worked as a public accountant and published extensively on topics such as German Jews, Israel, and the Holocaust. He published most of his articles in the Jewish press. Arno Herzberg died in 2002Arno Herzberg was also the author of 'Der Kontingentsbegriff im Recht : (Beteiligungsziffern) eine wirtschaftsrechtliche Studie', published in 1932 and 'Saving Taxes through Capital Gains', 1957.digitize
Letter from Arno B. Cammerer to J. R. Eakin
No full textLetter from Arno B. Cammerer to J. R. Eakin describing the procedure for purchasing Bright Angel Trail
Letter from Arno B. Cammerer to Carl Hayden
No full textLetter from Arno B. Cammerer to Carl Hayden on building a Union Chapel in the Grand Canyon
Letter from Arno B. Cammerer to Carl Hayden
No full textLetter from Arno B. Cammerer to Carl Hayden informing him of the removal of the dynamite from Grand Canyon Village to a point near Rowe Well
Letter from Arno B. Cammerer to Carl Hayden
No full textLetter from Arno B. Cammerer to Carl Hayden regarding the storage of dynamite in Shoski Canyon. Written in red pencil at the top, "My dear Jesse, For your(?) further information, Jack
Practical deployment of reinforcement learning for building controls using an imitation learning approach
Get PDFThis paper addresses the critical need for more efficient and adaptive building control systems to maximise occupant comfort while reducing energy consumption. Our objective is to explore the practical application of model-free Deep Reinforcement Learning (DRL) in real-world building environments by developing a system that learns and adapts to changing conditions, beginning its operation by imitating an existing Rule-Based Control (RBC) system. This approach ensures initial reliability and performance while setting the stage for advanced learning capabilities. The methodology involves two distinct phases. Initially, the DRL controller mimics the behaviour of the RBC system, using imitation learning with behavioural cloning as a safe and efficient strategy to achieve baseline operational efficiency. Subsequently, the controller is implemented within a real building in an online learning setting. In this phase, the controller utilises real-time data to continuously refine its control policy, responding adaptively to occupant behaviours and external environmental conditions. To validate our approach, we conducted a comprehensive analysis, comparing the performance of our DRL controller against the baseline RBC controller, another RBC, and a PI (Proportional-Integral) controller implemented in a digital twin model of the real office environment. Energy consumption and temperature violations related to a temperature acceptability range are considered as metrics, providing a robust framework for assessing the effectiveness of our system. The results indicate that our DRL controller, supported by imitation learning, outperforms the two RBCs by reducing energy consumption by 40 % while reducing the cumulative sum of temperature violations by 43 % and 13 % with respect to the two RBCs. Although the PI controller ensures better performance in terms of temperature violations compared to DRL, it requires 45 % more energy than the proposed DRL controller due to its inherent inability to deal with multi-objective control problems. In conclusion, this paper demonstrates the feasibility and advantages of implementing advanced DRL techniques in real-world building control scenarios. Integrating imitation learning with a DRL controller offers a novel and effective way to enhance the scalability of DRL systems, expanding their application in buildings and driving significant improvements in energy efficiency
Letter from Arno B. Cammerer, Acting Director National Park Service, to Carl Hayden
No full textLetter from Arno B. Cammerer to Carl Hayden reiterating the safe nature of the dynamite storage
Letter from Arno B. Cammerer, National Park Service, to Carl Hayden
No full textLetter from Arno B. Cammerer to Carl Hayden regarding the storage of dynamite in Shoski Canyon
Comparison of two deep reinforcement learning algorithms towards an optimal policy for smart building thermal control
Get PDFHeating, Ventilation, and Air Conditioning (HVAC) systems are the main providers of occupant comfort, and at the same time, they represent a significant source of energy consumption. Improving their efficiency is essential for reducing the environmental impact of buildings. However, traditional rule-based and model-based strategies are often inefficient in real-world applications due to the complex building thermal dynamics and the influence of heterogeneous disturbances, such as unpredictable occupant behavior. In order to address this issue, the performance of two state-of-the-art model-free Deep Reinforcement Learning (DRL) algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), has been compared when the percentage valve opening is managed in a thermally activated building system, modeled in a simulated environment from data collected in an existing office building in Switzerland. Results show that PPO reduced energy costs by 18% and decreased temperature violations by 33%, while SAC achieved a 14% reduction in energy costs and 64% fewer temperature violations compared to the onsite Rule-Based Controller (RBC)
Letter from Arno B. Cammerer, U.S. National Park Service to Carl Hayden
No full textLetter from Arno B. Cammerer to Carl Hayden updating him in regards to the insurance claims by Roy James and M.J. Hanle
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