A exhaust system consisting of a close-coupled Pd technology 32 in3 lightoff converter and Pt/Rh technology 170 in3 underfloor converter was vehicle-aged for 56000 miles on a vehicle equipped with a 3.8 l engine. Following this aging, the converters were taken off the vehicle and cut into 1″ thick sections along their axis and characterized for lightoff and warmed-up activity using a laboratory reactor to simulate vehicle exhaust. Each section was also analyzed for the quantity of oil additive poisons (phosphorus and zinc) deposited. Following this initial characterization, the phosphorus and zinc deposits were removed, and the sections were characterized again for lightoff and warmed-up activity. This procedure was used to qualitatively determine the relative contribution of oil additive poisoning and thermal sintering to the total activity deterioration as a function of axial position in the catalyst monoliths.
Analysis of the lightoff converter as taken from the vehicle showed a dramatic axial gradient in the lean and stoichiometric lightoff and warmed-up (600°C) performance for HC, CO and NOx, with most of the deterioration having taken place in the forward-most 1″ section of the converter, which was consistent with the gradient in the deposition of phosphorus (P) and zinc (Zn) in this converter. Comparison of these data sets with those obtained after removal of the P and Zn poisons indicates that most of the total deterioration of lean HC and CO activity can be attributed to P and Zn poisoning of the forwardmost 1″ section. When tested under stoichiometric conditions, most of the deterioration of HC activity is attributable to P and Zn poisoning, while most of the deterioration of CO and NOx activity is attributable to thermal deterioration. A similar activity and poison deposition gradient was detected in the underfloor converter, but to a smaller extent. 相似文献
We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification.
Practitioner Summary: Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide. 相似文献
ZigBee is the primary standard solution for wireless sensor networks, implementing the Ad hoc On‐Demand Distance Vector (AODV) protocol in the network layer and supported by the standard IEEE 802.15.4. This study is focused on mesh topologies and the critical problems encountered when AODV is executed in conjunction with the Carrier Sense Multiple Access with Collision Avoidance protocol. These problems are mainly related to the packet overhead required to carry out route creation. To perform preliminary experiments to be able to implement AODV in a real network, a new metric is proposed herein. This metric uses fuzzy logic to help in the decision‐making process. The objective of the fuzzy routine is to determine, during the route‐discovery process, the best node to forward request/reply packets, with the aim of reducing packet overhead and energy consumption. Moreover, minor changes are also added to the discovery procedure of AODV to improve the performance of the route‐creation process. This intelligent version of AODV has provided promising experimental results, greatly reducing the number of packets required, with the consequent energy saving while selecting the best nodes to be part of the routes. 相似文献
Geographic routing protocols use location information when they need to route packets. In the meantime, location information are maintained by location-based services provided by network nodes in a distributed manner. Routing and location services are very related but are used separately. Therefore, the overhead of the location-based service is not considered when we evaluate the geographic routing overhead. Our aim is to combine routing protocols with location-based services in order to reduce communication establishment latency and routing overhead. 相似文献