Mathematical model for adaptive control system of ASEA robot at Kennedy Space Center

The dynamic properties and the mathematical model for the adaptive control of the robotic system presently under investigation at Robotic Application and Development Laboratory at Kennedy Space Center are discussed. NASA is currently investigating the use of robotic manipulators for mating and demating of fuel lines to the Space Shuttle Vehicle prior to launch. The Robotic system used as a testbed for this purpose is an ASEA IRB-90 industrial robot with adaptive control capabilities. The system was tested and it's performance with respect to stability was improved by using an analogue force controller. The objective of this research project is to determine the mathematical model of the system operating under force feedback control with varying dynamic internal perturbation in order to provide continuous stable operation under variable load conditions. A series of lumped parameter models are developed. The models include some effects of robot structural dynamics, sensor compliance, and workpiece dynamics

Improvement in the control aspect of laser frequency stabilization for SUNLITE project

Flight Electronics Division of Langley Research Center is developing a spaceflight experiment called the Stanford University and NASA Laser In-Space Technology (SUNLITE). The objective of the project is to explore the fundamental limits on frequency stability using an FM laser locking technique on a Nd:YAG non-planar ring (free-running linewidth of 5 KHz) oscillator in the vibration free, microgravity environment of space. Compact and automated actively stabilized terahertz laser oscillators will operate in space with an expected linewidth of less than 3 Hz. To implement and verify this experiment, NASA engineers have designed and built a state of the art, space qualified high speed data acquisition system for measuring the linewidth and stability limits of a laser oscillator. In order to achieve greater stability and better performance, an active frequency control scheme requiring the use of a feedback control loop has been applied. In the summer of 1991, the application of control theory in active frequency control as a frequency stabilization technique was investigated. The results and findings were presented in 1992 at the American Control Conference in Chicago, and have been published in Conference Proceedings. The main focus was to seek further improvement in the overall performance of the system by replacing the analogue controller by a digital algorithm

Towards multi-criteria cloud service selection

Cloud computing despite being in an early stage of adoption is becoming a popular choice for businesses to replace in-house IT infrastructure due to its technological advantages such as elastic computing and cost benefits resulting from pay-as-you-go pricing and economy of scale. These factors have led to a rapid increase in both the number of cloud vendors and services on offer. Given that cloud services could be characterized using multiple criteria (cost, pricing policy, performance etc.) it is important to have a methodology for selecting cloud services based on multiple criteria. Additionally, the end user requirements might map to different criteria of the cloud services. This diversity in services and the number of available options have complicated the process of service and vendor selection for prospective cloud users and there is a need for a comprehensive methodology for cloud service selection. The existing research literature in cloud service selection is mostly concerned with comparison between similar services based on cost or performance benchmarks. In this paper we discuss and formalize the issue of cloud service selection in general and propose a multi-criteria cloud service selection methodology

Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models

Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model's output, a straightforward approach is to incorporate past user data into the language model prompt, but this approach can result in lengthy inputs exceeding limitations on input length and incurring latency and cost issues. Existing approaches tackle such challenges by selectively extracting relevant user data (i.e. selective retrieval) to construct a prompt for downstream tasks. However, retrieval-based methods are limited by potential information loss, lack of more profound user understanding, and cold-start challenges. To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs. The summaries can be generated and stored offline, enabling real-world systems with runtime constraints like voice assistants to leverage the power of LLMs. Experiments show our method with 75% less of retrieved user data is on-par or outperforms retrieval augmentation on most tasks in the LaMP personalization benchmark. We demonstrate that offline summarization via LLMs and runtime retrieval enables better performance for personalization on a range of tasks under practical constraints.Comment: 4 pages, International Workshop on Personalized Generative AI (@CIKM 2023

Improving the Resilience of Wireless Sensor Networks Against Security Threats: A Survey and Open Research Issues

Wireless Sensor Network (WSN) technology has gained importance in recent years due to its various benefits, practicability and extensive utilization in diverse applications. The innovation helps to make real-time automation, monitoring, detecting and tracking much easier and more effective than previous technologies. However, as well as their benefits and enormous potential, WSNs are vulnerable to cyber-attacks. This paper is a systematic literature review of the security-related threats and vulnerabilities in WSNs. We review the safety of and threats to each WSN communication layer and then highlight the importance of trust and reputation, and the features related to these, to address the safety vulnerabilities. Finally, we highlight the open research areas which need to be addressed in WSNs to increase their flexibility against security threats

Efficacy of Green Cerium Oxide Nanoparticles for Potential Therapeutic Applications : Circumstantial Insight on Mechanistic Aspects

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Green synthesized cerium oxide nanoparticles (GS-CeO 2 NPs) have a unique size, shape, and biofunctional properties and are decorated with potential biocompatible agents to perform various therapeutic actions, such as antimicrobial, anticancer, antidiabetic, and antioxidant effects and drug delivery, by acquiring various mechanistic approaches at the molecular level. In this review article, we provide a detailed overview of some of these critical mechanisms, including DNA fragmentation, disruption of the electron transport chain, degradation of chromosomal assemblage, mitochondrial damage, inhibition of ATP synthase activity, inhibition of enzyme catalytic sites, disorganization, disruption, and lipid peroxidation of the cell membrane, and inhibition of various cellular pathways. This review article also provides up-to-date information about the future applications of GS-CeONPs to make breakthroughs in medical sectors for the advancement and precision of medicine and to effectively inform the disease diagnosis and treatment strategies.Peer reviewe

Carbon pricing and environmental response: A way forward for China’s carbon and energy market

Addressing the conflict between fossil fuel exploitation, usage, and greenhouse gas emissions is a top priority for China’s low-carbon socioeconomic development. Scalable Axisymmetric Matrix “a computerized general equilibrium model” is used to assess the impact of carbon tax policies on energy usage, carbon pollution, and macroeconomic drivers at reduction levels of 10%, 20%, and 30% of emissions. In the meantime, we examine the impact of various carbon tax recycling schemes in line with the tax neutrality concept. Although the carbon tax successfully reduces carbon emissions, we conclude that it will have a detrimental effect on the economy and social well-being. To cope with China’s increasing pollution emissions and ecological imbalances, the Chinese government promulgated the environmental protection tax law of the people’s Republic of China, which was officially implemented in 2018. Although carbon dioxide is not included in the Taxable Pollutants and Single Quantity Table attached to this law, China has almost reached a consensus on taxing carbon emissions. In 2021, the State Council of China issued the opinions on completely, accurately, and comprehensively implementing the new development concept and doing a good job in carbon peak and carbon neutralization, which made a comprehensive deployment to achieve the “double carbon” goal and improved the carbon tax policy and legal system, which is an essential part of it. Therefore, based on fiscal neutrality, an effective carbon tax recycling scheme can mitigate the adverse effects of its adoption. However, due to the current development in China’s energy-generating and transportation sectors, even minor steps can have huge effects on emissions with marginal economic implications

Automatic Online Evaluation of Intelligent Assistants

ABSTRACT Voice-activated intelligent assistants, such as Siri, Google Now, and Cortana, are prevalent on mobile devices. However, it is challenging to evaluate them due to the varied and evolving number of tasks supported, e.g., voice command, web search, and chat. Since each task may have its own procedure and a unique form of correct answers, it is expensive to evaluate each task individually. This paper is the first attempt to solve this challenge. We develop consistent and automatic approaches that can evaluate different tasks in voice-activated intelligent assistants. We use implicit feedback from users to predict whether users are satisfied with the intelligent assistant as well as its components, i.e., speech recognition and intent classification. Using this approach, we can potentially evaluate and compare different tasks within and across intelligent assistants according to the predicted user satisfaction rates. Our approach is characterized by an automatic scheme of categorizing user-system interaction into task-independent dialog actions, e.g., the user is commanding, selecting, or confirming an action. We use the action sequence in a session to predict user satisfaction and the quality of speech recognition and intent classification. We also incorporate other features to further improve our approach, including features derived from previous work on web search satisfaction prediction, and those utilizing acoustic characteristics of voice requests. We evaluate our approach using data collected from a user study. Results show our approach can accurately identify satisfactory and unsatisfactory sessions

Automatic Online Evaluation of Intelligent Assistants

Voice-activated intelligent assistants, such as Siri, Google Now, and Cortana, are prevalent on mobile devices. However, it is chal-lenging to evaluate them due to the varied and evolving number of tasks supported, e.g., voice command, web search, and chat. Since each task may have its own procedure and a unique form of correct answers, it is expensive to evaluate each task individually. This pa-per is the first attempt to solve this challenge. We develop con-sistent and automatic approaches that can evaluate different tasks in voice-activated intelligent assistants. We use implicit feedback from users to predict whether users are satisfied with the intelligent assistant as well as its components, i.e., speech recognition and in-tent classification. Using this approach, we can potentially evaluate and compare different tasks within and across intelligent assistants according to the predicted user satisfaction rates. Our approach is characterized by an automatic scheme of categorizing user-system interaction into task-independent dialog actions, e.g., the user is commanding, selecting, or confirming an action. We use the action sequence in a session to predict user satisfaction and the quality of speech recognition and intent classification. We also incorporate other features to further improve our approach, including features derived from previous work on web search satisfaction prediction, and those utilizing acoustic characteristics of voice requests. We evaluate our approach using data collected from a user study. Re-sults show our approach can accurately identify satisfactory and un-satisfactory sessions