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Electromagnetic Environment
Published in Cary R. Spitzer, Uma Ferrell, Thomas Ferrell, Digital Avionics Handbook, 2017
In AIMS, the more dramatic step of making hardware monitoring active on every CPU clock cycle was taken. All computing and input–output (I/O) management resources are lockstep compared on a processor cycle-by-cycle basis. All feasible hardware soft or hard faults are detected. In this approach, if a soft or hard fault event occurs, the processor module is immediately trapped to service handlers, and no data can be exported. In past systems, the latency between such an event and eventual detection (or washout) was the real culprit. The corrupted data would propagate through computations and eventually affect some output. To recover, drastic actions (reboots or rearms) were often necessary. In AIMS, critical functions such as displays (because the flight crew could “see” hiccups) have a “shadowing” standby computational resource. The shadow sees the same input set at the same time as the master self-checking pair. If the master detects an event, within nanoseconds the faulty unit is blocked from generating outputs. The Honeywell SAFEbus® system detects the loss of output by the master and immediately passes the shadow’s correct data for display.
Three-Phase Power Flow and Harmonic Analysis
Published in Antonio Gómez-Expósito, Antonio J. Conejo, Claudio A. Cañizares, Electric Energy Systems, 2018
Wilsun Xu, Julio García-Mayordomo
In addition to harmonics, a distorted waveform may also contain interharmonics. Interharmonics are those components whose frequencies are not integral multiples of the fundamental frequency. A waveform that contains interharmonics is not periodic on a cycle-by-cycle basis. As a result, the one cycle-based DFT analysis shown earlier is not effective to analyze interharmonics. The subject of interharmonics is beyond the scope of this book.
Three-Phase Power Flow and Harmonic Analysis
Published in Antonio Gómez-Expósito, Antonio J. Conejo, Claudio Cañizares, Electric Energy Systems, 2017
Wilsun Xu, Julio García-Mayordomo
In addition to harmonics, a distorted waveform may also contain interharmonics. Interharmonics are those components whose frequencies are not integral multiples of the fundamental frequency. A waveform that contains interharmonics is not periodic on a cycle-by-cycle basis. As a result, the one cycle-based DFT analysis shown earlier is not effective to analyze interharmonics. The subject of interharmonics is beyond the scope of this book.
Application of area traffic control using the max-pressure algorithm
Published in Transportation Planning and Technology, 2020
S. A. Ramadhan, H. Y. Sutarto, G. S. Kuswana, E. Joelianto
With increasing personal mobility and the rapid growth in the number of vehicles, congestion has become a common occurrence. Traffic signal control is one of the key techniques for influencing the effectiveness of traffic flow in urban networks that enables conflicting traffic to flow through intersections using the timing of green/red light periods. Finding the best possible cycle time over many intersections to decrease congestion overall and to increase throughput by means of the network is challenging. In general, there are two types of control for signal control: static/fixed time control and vehicle-actuated control. The former comprises the optimization of the cycle time, offset and split of green times. This can be optimized in a coordinated or isolation way (Gartner, Little, and Gabbay 1975). In contrast, the later uses online measurements from detectors to optimize signal timings on a cycle-to-cycle basis in real-time. Some examples of this type of controller: SCOOT (Hunt et al. 1981), UTOPIA (Mauro and DiTaranto 1990) and RHODES (Mirchandani and Head 2001). SCATS (Lowrie 1982) is one of the examples of a combination of fixed time control and vehicle-actuated control. Each algorithm has its own advantages and disadvantages, so it cannot be determined which is the best control system.
SCoPTO: signalized corridor management with vehicle platooning and trajectory control under connected and automated traffic environment
Published in Transportmetrica B: Transport Dynamics, 2021
Actuated control is one of the widely used traffic signal control paradigms (Cesme and Furth 2014). It serves traffic flows at different approaches based on dynamic traffic demands on a cycle-by-cycle basis. For the major-minor type of intersections, previous studies indicate that extending the current green phase for imminently arriving platoons on the major road is an effective strategy (Jiang, Li, and Shamo 2006; He, Larry Head, and Ding 2012). This platoon-based green time extension grants the priority to the major road platoons some additional green time, allowing upcoming platoons to pass the intersection during the extended green time at the expense of reasonable additional delay imposed on the minor road traffic. Green time extension has been proved as a simple but effective approach to improve traffic performance at signalized intersections and corridors (Balke, Dudek, and Urbanik 2000; Liao and Davis 2007; Ekeila, Sayed, and Esawey 2009; Hu, Park, and Emily Parkany 2014). and we adopt this method to strike a balance between the delay on major and minor roads. Most current (human-driven vehicle) platoon recognition methods rely on fixed detectors and/or statistical methods, and it is difficult to track the platoons due to highly dynamic traffic conditions (Gaur and Mirchandani 2001). Once the platoons enter the segment between the upstream detectors and stop bar, it is hard to collect their real-time vehicular information and their approaching trajectories can only be estimated. The accuracy of the estimation may vary significantly under different traffic conditions due to the stochastic behaviors of human-driven vehicles, therefore affecting the effectiveness of platoon-based signal control strategies.
A probe-based demand responsive signal control for isolated intersections under mixed traffic conditions
Published in Transportation Letters, 2023
Himabindu Maripini, Lelitha Vanajakshi, Bhargava Rama Chilukuri
Yulianto (2003) developed an adaptive fuzzy logic signal controller for mixed traffic conditions with a high proportion of two-wheelers. Maximum queue length and average occupancy rate from video image processing were used as input values and weight values were derived for each signal group using the fuzzy rule base. Green splits for next cycle were decided based on these weights on a cycle-by-cycle basis. Nuli and Mathew (2013) developed TRASCR-C (TRaffic Adaptive Signal Control using Reinforcement learning – Corridor) and showed it to be better than a vehicle actuated control. However, the traffic dynamics of the arriving traffic volume and its fluctuations were not considered in the study. An ATCS named ‘Composite Signal Control Strategy’ (CoSiCoSt) was developed as a reactive rule-based system by Center for Development of Advanced Computing (CDAC), Thiruvananthapuram, India (G. George 2016). The evaluation reported an improvement of 2% to 12% in average travel speed and reduction in average delay by 11% to 30%. However, it is yet to evolve as a comprehensive model because of its complicated detector configurations and layouts (Kovvali, Ganji, and Rao 2016). Mishra et al. (2022) developed an ATCS for mixed traffic conditions that computes optimal cycle length such that it minimizes the congestion while maximizing throughput. The congestion scores were quantified using the time of arrival data acquired from Google traffic APIs and compared with the historical average congestion scores to adjust the cycle length. Though the algorithm mitigates congestion using shorter cycle times, the computational challenges associated with real-time implementation and quantitative evaluation of its performance are not discussed.