Optimal analog wavelet bases construction using hybrid optimization algorithm

An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and the least-squares error criterion, a general framework for designing optimal analog wavelet bases is established, which is one of difficult nonlinear constrained optimization problems. Then, to solve this problem, a hybrid algorithm by combining chaotic map particle swarm optimization (CPSO) with local sequential quadratic programming (SQP) is proposed. CPSO is an improved PSO in which the saw tooth chaotic map is used to raise its global search ability. CPSO is a global optimizer to search the estimates of the global solution, while the SQP is employed for the local search and refining the estimates. Benefiting from good global search ability of CPSO and powerful local search ability of SQP, a high-precision global optimum in this problem can be gained. Finally, a series of optimal analog wavelet bases are constructed using the hybrid algorithm. The proposed method is tested for various wavelet bases and the improved performance is compared with previous works.Peer reviewedFinal Published versio

An Intelligent Auxiliary Vacuum Brake System

The purpose of this paper focuses on designing an intelligent, compact, reliable, and robust auxiliary vacuum brake system (VBS) with Kalman filter and self-diagnosis scheme. All of the circuit elements in the designed system are integrated into one programmable system-on-chip (PSoC) with entire computational algorithms implemented by software. In this system, three main goals are achieved: (a) Kalman filter and hysteresis controller algorithms are employed within PSoC chip by software to surpass the noises and disturbances from hostile surrounding in a vehicle. (b) Self-diagnosis scheme is employed to identify any breakdown element of the auxiliary vacuum brake system. (c) Power MOSFET is utilized to implement PWM pump control and compared with relay control. More accurate vacuum pressure control has been accomplished as well as power energy saving. In the end, a prototype has been built and tested to confirm all of the performances claimed above

A flow disturbance estimation and rejection strategy for multirotors with round-trip trajectories

This paper presents a round-trip strategy of multirotors subject to unknown flow disturbances. During the outbound flight, the vehicle immediately utilizes the wind disturbance estimations in feedback control, as an attempt to reduce the tracking error. During this phase, the disturbance estimations with respect to the position are also recorded for future use. For the return flight, the disturbances previously collected are then routed through a feedforward controller. The major assumption here is that the disturbances may vary over space, but not over time during the same mission. We demonstrate the effectiveness of this feedforward strategy via experiments with two different types of wind flows; a simple jet flow and a more complex flow. To use as a baseline case, a cascaded PD controller with an additional feedback loop for disturbance estimation was employed for outbound flights. To display our contributions regarding the additional feedforward approach, an additional feedforward correction term obtained via prerecorded data was integrated for the return flight. Compared to the baseline controller, the feedforward controller was observed to produce 43% less RMSE position error at a vehicle ground velocity of 1 m/s with 6 m/s of environmental wind velocity. This feedforward approach also produced 14% less RMSE position error for the complex flows as well

Investigation of air transportation technology at Princeton University, 1991-1992

The Air Transportation Research Program at Princeton University proceeded along six avenues during the past year: (1) intelligent flight control; (2) computer-aided control system design; (3) neural networks for flight control; (4) stochastic robustness of flight control systems; (5) microburst hazards to aircraft; and (6) fundamental dynamics of atmospheric flight. This research has resulted in a number of publications, including archival papers and conference papers. An annotated bibliography of publications that appeared between June 1991 and June 1992 appears at the end of this report. The research that these papers describe was supported in whole or in part by the Joint University Program, including work that was completed prior to the reporting period

Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

An Innovative Take on Filtering Carbon Dioxide Through CryoCapture

Overview (Air Mover): Carbon dioxide plays an important role in the earth's ecosystem; the lives of many organisms are based on the balancing of this gas. Plants and animals need it for survival however, an excess of carbon dioxide can also end the organism’s life. The production of the gas mostly comes from the combustion of fossil fuel, power plants, big industries, vehicles, and processes involving natural gasses. One of the most known issues of carbon dioxide pollution is global warming. The greenhouse gas essentially traps heat in the atmosphere, increasing the global temperature. The methodology provided is an innovative solution towards the creation of an environmentally friendly carbon dioxide filter. Current air filtration systems are restricted to industrial environments limiting the ability to filter the air. Due to the large noise and low range of operation of axial fans the filtration systems need controlled environments for longevity. The paper presents a versatile air mover that can be mounted onto multiple surfaces due to its low profile and bracket mounts. Furthermore, the usage of a diagonal fan inside of a PVC pipe allows for a durable system that can operate at high efficiency and low noise. The main challenge in designing the air mover was figuring out how to quantify the scalability of the device and what parameters could be changed in order to make the device more viable. The designs most prominent feature are the inclusion of a modular enclosure that can be adapted to multiple areas and environments while withstanding harsh conditions due to the PVC piping that can be coated with a diagonal fan for high volumetric flow rates and pressure differential for versatility in environments the device is placed in as well as efficiency. Overview (Carbon Storer): The Civil and Environmental Engineering team is responsible for finding a cost effective and sustainable way to transport, store and recycle the carbon caught in the air from the Carbon Catcher designed by the other engineering teams. In the team’s design, the Carbon Catcher will reduce the harmful emissions in the air by capturing CO2, store it and then utilize it in another industry which will reduce the need to mine for more raw materials which would thus further reduce the pollution emitted into the environment. Our plan is to recycle the carbon emitted from a factory and utilize it in CO2 dry ice. It's the Civil and Environmental Engineers’ job to find a way to connect a sustainable solution with a solution that improves the public’s quality of life. There are many industries that pollute immense amounts from the mining of raw material or the emission of pollutants. The team wants to show industries that the economic solution can also be the sustainable solution. Overview (Membrane) The team’s solution focuses on the use of cryogenic carbon capture, a method in which the selective freezing points of the gaseous components of air are used to separate out carbon dioxide. For this process, the team will be utilizing a 4 step filtration process. First, the flue gas will be run through a particulate filter to catch all macroscopic particles that may be present within the air. Afterwards, the gas is then passed through a dehumidifier where a majority of water content will be extracted. Following this, The gas was then run through a long pipe and progressively cool it down to the freezing point of carbon dioxide. Finally, the filtered gas is extracted, and a bubbler is used to separate the solid carbon dioxide. The carbon dioxide is then compressed and recycled around the feed pipe to help in the cooling process. Along the process of this design, the team encountered problems finding the optimum materials for temperatures this low. As well, coming up with a way to eliminate heat transfer from the outside posed a huge problem. Through the experience, the team was able to gain a greater view of what benefits and drawbacks must be balanced, along with the economic interest that comes with designing an efficient process. Unlike how most designs are focused, It was understood that using a membrane only provided so much creativity when it came to filtration. As a result, the team researched other successful methods and arrived at utilizing cryogenics to filter. Goal Research to provide a single solution to remove levels of carbon dioxide in the immediate atmosphere, transport it to a storage mechanism, and find a way to recycle it. Powerful research is required to ensure effective methodologies, material usage, and flexible scalability of the overall device. This particular team seeks to find an alternative separation process to membrane filtration, the efficacy of which has not been demonstrated beyond the scale of a laboratory

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time