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Sensor management is the integrated control of multiple sensors to provide enhanced and timely information to the fighter aircraft pilot. Artificial Intelligence techniques provide useful tools for building a sensor management system.

The car works on the asynchronous mechanism of artificial intelligence

Since the aircraft industry has a particularly high requirement for defect-free production of structural components, this paper presents a study on the online-detection of tool wear in automated drilling processes using a combination of external sensor technology and Artificial Intelligence methods. For this reason, a laboratory setup to conduct automatic drilling operations in fuselage material is introduced.

Abstract The use of data-driven algorithms for the integration or substitution of current production sensors is becoming a consolidated trend in research and development in the automotive field. Due to the large number of variables and scenarios to consider; however, it is of paramount importance to define a consistent methodology accounting for uncertainty evaluations and preprocessing steps, that are often overlooked in naïve implementations. Among the potential applications, the use of virtual sensors for the analysis of solid emissions in transient cycles is particularly appealing for industrial applications, considering the new legislations scenario and the fact that, to our best knowledge, no robust models have been previously developed.

Abstract Artificial intelligence-based computing systems like artificial neural networks (ANN) have recently found increasing applications in predicting complex chemical phenomena like combustion properties.

The consequent means towards improved enhancement of the safety of commercial vehicles will in future times require more and more electronic intelligence, in case a distinct optimization of the systems will not be possible with conventional means.

This year’s event will focus on how automated systems and artificial intelligence will impact the role of humans in aerospace systems.

An innovative IC development “merges” Hall effect sensing with the control circuitry, protective functions, and the high-current outputs necessary to power a new series of brushless dc (fan) motors. This proprietary and single-chip design replaces a separate Hall sensor IC and the various other components (many discretes) and provides the position and rotational sensing, the commutation circuitry, and switching functions required in a 2-phase, unipolar motor. Additionally, this custom IC includes several advantages and features not (usually) included in “discrete” designs: internal zener diode for limiting “flyback” voltage, output current limiting (with an option for further reduction), output enable (PWM speed control), and thermal shutdown. This. paper will highlight the functions, limits, merits, and potential for the “merging” of magnetic sensing and power.

The AFR control accuracy in the cold start is crucial to lowering emissions from IC-engine vehicles. An artificial UEGO “sensor” for estimating the real-time AFR during the engine cold start has been developed on the basis of a fuel-perturbation algorithm at Ford Scientific Research Labs. ...The AFR values calculated by the artificial UEGO sensor have been used in the closed-loop fuel control. Considering that the engine cold start AFR is an uncertain, non-linear problem, some other techniques for optimizing the input stimulation signal and the output-filtering model are integrated together with the fuel perturbation. ...Considering that the engine cold start AFR is an uncertain, non-linear problem, some other techniques for optimizing the input stimulation signal and the output-filtering model are integrated together with the fuel perturbation. This artificial sensor was realized and its performance was tested on a Ford vehicle for EPA75 cold 505 test.

The primary purpose of this investigation is to analyze the effects of vibrations on comfort and road holding capability of vehicles as observed in the variations of different parameters such as suspension springs, road roughness. Also, the problem of the design of experimental car suspension system for ride qualities using neural networks is presented. Two types of neural network are employed to analyze vibrations of experimental vehicle suspensions on different points. The road roughness is generated by using a pneumatic actuator with different strokes. The experimental and simulation results indicate that Radial Basis Neural Network more effective in the vibration isolation of the car body than Back Propagation Neural Network. The proposed neural network predictor could be used on all vehicle’s suspension vibration analysis.

Using these features the Artificial Neural Network (ANN) and J48 algorithms were trained and tested to classify the faults.

Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets.

Demand for predictive powertrain control is rapidly increasing with the recent advancement of Advanced Driving Assistance Systems (ADAS) and Autonomous Driving (AD). The full or semi-autonomous functions could be leveraged to realize better user acceptance as well as powertrain efficiency of the connected vehicle utilizing the proposed Drive Horizon. The sensors of automated driving provide perception of surrounding driving environment which is required to safely navigate the vehicle in real-world driving scenarios. The proposed Drive Horizon provides real-time forecast of driving environment that a vehicle will encounter during its entire travel. This paper summarizes the vehicle’s future speed prediction technique which is an integral part of Drive Horizon for optimized energy control of the vehicle. The prediction model has been developed that integrates information from multiple sources including vehicle GPS, traffic information and map data.

The technique employs near infrared Raman laser excitation at 785 nm, multivariate analysis, non-linear artificial neural networks and an offset spectra subtraction strategy. Aqueous humor glucose levels ranged from 37 to 323 mg/dL. ...Spectra generated from the aqueous humor were compared qualitatively to artificial aqueous samples and an excitation offset technique was devised to counteract broadband background noise partially obscuring the glucose signature. ...Feature extraction and data analysis were accomplished using second order Savitsky-Golay derivatives, linear multivariate analysis (partial least squares fit) and non-linear (artificial neural network) techniques. Predicted glucose levels correlated well with expected glucose concentration (R2= 0.98, n=32).

Technological Advances in ADAS (Advanced Driver Assistance Systems), AI (Artificial Intelligence) and AD (Autonomous Driving) there has been a demand of raw data transfer from Sensors like Radar, Camera, LiDAR, etc. because existing methods are unable to meet sensor fusion requirements of Level 5 AD. ...Advanced deep learning algorithms need raw data to extract complex features and fusion of data from sensors further helps to build algorithms that simulate human-like intelligence. Traditionally, the output of Radar is point cloud data [i.e. Range, Velocity, Angle] of the detected target which is the outcome of the computation done by the local radar.

Rapid progress is being made in the core artificial intelligence engines that form the basis of AV technology. However, without a quantum leap in testing and verification, the full capabilities of AV technology will not be realized.

Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. ...Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles.

This paper features a set of advanced technologies for autonomy and intelligence in advanced inspection systems of facility operations. These technologies offer a significant contribution to set a path to establish a system and an operating environment with autonomy and intelligence for inspection, monitoring and safety via gas and ambient sensors, video mining and speech recognition commands on unmanned ground vehicles and other platforms to support operational activities in the Cryogenics Test bed and other facilities and vehicles. ...These technologies offer a significant contribution to set a path to establish a system and an operating environment with autonomy and intelligence for inspection, monitoring and safety via gas and ambient sensors, video mining and speech recognition commands on unmanned ground vehicles and other platforms to support operational activities in the Cryogenics Test bed and other facilities and vehicles.

The proposed MSFD system employs the principles of artificial intelligence beyond simple comparison of data variance of the sensors. And then, its performance is further improved by using the temporal correlation information with an adaptive threshold scheme.

This transformation is driven by convergence of many advanced technologies such as advanced sensor and communication technologies, big data, advanced analytics, Artificial Intelligence (AI), robotics, additive manufacturing, virtual and augmented reality (VR/AR).