Electronic control/display interface technology

An effort to produce a representative workstation for the Space Station Data Management Test Bed that provides man/machine interface design options for consolidating, automating, and integrating the space station work station, and hardware/software technology demonstrations of space station applications is discussed. The workstation will emphasize the technologies of advanced graphics engines, advanced display/control medias, image management techniques, multifunction controls, and video disk utilizations

Artificial Intelligence and Statistics

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and research. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors' collaborative research

Information theoretic approach to interactive learning

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.Comment: 6 page

Sensor-less maximum power extraction control of a hydrostatic tidal turbine based on adaptive extreme learning machine

In this paper, a hydrostatic tidal turbine (HTT) is designed and modelled, which uses more reliable hydrostatic transmission to replace existing fixed ratio gearbox transmission. The HTT dynamic model is derived by integrating governing equations of all the components of the hydraulic machine. A nonlinear observer is proposed to predict the turbine torque and tidal speeds in real time based on extreme learning machine (ELM). A sensor-less double integral sliding mode controller is then designed for the HTT to achieve the maximum power extraction in the presence of large parametric uncertainties and nonlinearities. Simscape design experiments are conducted to verify the proposed design, model and control system, which show that the proposed control system can efficiently achieve the maximum power extraction and has much better performance than conventional control. Unlike the existing works on ELM, the weights and biases in the ELM are updated online continuously. Furthermore, the overall stability of the controlled HTT system including the ELM is proved and the selection criteria for ELM learning rates is derived. The proposed sensor-less control system has prominent advantages in robustness and accuracy, and is also easy to implement in practice

Conceptual design of a lightweight machine with variable control for texturing on concrete surfaces

In Peru, constructions are commonly built with cement and bricks, then proceed to decorate and embellish the walls using texturing. Currently, in Peru, this process is done in a traditional way where generally support tools are used such as trowels, rollers, and brushes, among others. As this process is done manually, the texturing is of lower aesthetic quality. Due to its geographic location, Peru is one of the countries that most often applies cement in its constructions, which makes it the most accessible and suitable material for applying textures to walls. Currently, there is no specific machine for texturing, so the conceptual design of a lightweight machine for texturing on concrete surfaces was proposed using the VDI 2221 design methodology, integrating a variable speed control system and display of parameters in real-time

Atmega328P-based X-ray Machine Exposure Time Measurement Device with an Android Interface

The purpose of this study was to design an X-ray microcontroller-based ATmega328P microcontroller exposure time measurement device. That can be done by integrating an X-ray detection circuit, analog signal conditioner, ATmega328P microcontroller and Bluetooth module HC-05 to display and control the measurement results on mobile phones Android. The benefits of this research are expected to be able to increase knowledge and expertise in the field of radiology instruments through X-ray machine parameter measurement techniques and assist technicians to calibrate the X-ray exposure time parameters

A novel flux-controllable vernier permanent-magnet machine

Session GR: Permanent Magnet Special Motors (Poster Session): GR-01By artfully integrating the vernier structure and the DC field winding together, a novel flux-controllable vernier permanent-magnet (FCVPM) machine is proposed, which has the merits of offering high torque output at low speed operation and the flux weakening control at high speed operation. The key is to use the flux modulation poles to modulate the high speed rotating field of the armature winding and the low speed rotating field of the PM rotor, thus achieving the gear effect for high torque output. Also, the DC field winding is specially incorporated into the stator embedded slots, which can effectively weaken the airgap flux density, thus realizing the flux weakening control at high speed operation. By using the time-stepping finite-element-method, the basic characteristics, low speed and high speed operation performances of the machine are analyzed, which verifies the validity of the machine design. © 2011 IEEE.published_or_final_versionThe IEEE International Magnetic Conference (INTERMAG2011), Teipei, Taiwan, 25-29 April 2011. In IEEE Transactions on Magnetics, 2011, v. 47 n. 10, p. 4238-424

Lithium-ion battery digitalization: Combining physics-based models and machine learning

Digitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the design and development phase. Accurate physics-based models play a crucial role in the digitalization of lithium-ion batteries by providing an in-depth understanding of the system. Unfortunately, the high accuracy comes at the cost of increased computational cost preventing the employment of these models in real-time applications and for parametric design. Machine learning models have emerged as powerful tools that are increasingly being used in lithium-ion battery studies. Hybrid models can be developed by integrating physics-based models and machine learning algorithms providing high accuracy as well as computational efficiency. Therefore, this paper presents a comprehensive review of the current trends in integration of physics-based models and machine learning algorithms to accelerate the digitalization of lithium-ion batteries. Firstly, the current direction in explicit modeling methods and machine learning algorithms used in battery research are reviewed. Then a thorough investigation of contemporary hybrid models is presented addressing both battery design and development as well as real-time monitoring and control. The objective of this work is to provide details of hybrid methods including the various applications, type of employed models and machine learning algorithms, the architecture of hybrid models, and the outcome of the proposed models. The challenges and research gaps are discussed aiming to provide inspiration for future works in this field