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Facing the challenge that the weld fatigue life prediction results are not accurate because the quasi-static method does not consider the natural frequency of the structure, the paper, upon the correction of time-domain loading based on the harmonic response analysis, proposed a new method for fatigue life prediction of welds within a new time-domain. The history of time domain loading at the air springs was obtained by the Mechanical Simulation, while the history of frequency domain loading was obtained upon the Fourier transformation. The harmonic response analysis was carried out on a car body to obtain the frequency response coefficient at key nodes of the car body, then the time-load history was corrected, and finally the fatigue life of key welds was obtained by the quasi-static method and the structure stress method. The result shows that, the fatigue life of the car body is 1.65e5 km obtained by the correction method, which is more conservative and takes full consideration of the impact of the structure's natural frequency, providing a new concept for future researches of life prediction.

Extra charge will be generated and deposited into the cell when an energetic ion strikes the SOI SRAM. The sensitive regions will collect the deposited charge. This may upset the logic values stored in SRAM. The Geant4 is a very useful tool to simulate the process above. The GDML (Geometry Description Markup Language) file can be used to model the geometry of device or circuit in Geant4. In the paper, we used the GDML file to construct the 3-D geometry model of SOI SRAM for the simulation of heavy ion irradiation. And the Single Event Upset (SEU) Cross-Section is used to estimate the hardening levels of SRAM.

Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5%∼8.8% increase in energy consumption.

Data collection is one of the major traffic pattern in wireless sensor networks, which requires regular source nodes to send data packets to a common sink node with limited end-to-end delay. However, the sleep latency brought by duty cycling mode results in significant rise on the delivery latency. In order to reduce unnecessary forwarding interruption, the state-of-the-art has proposed pipeline scheduling technique by allocating sequential wakeup time slots along the forwarding path. We experimentally show that previously proposed pipeline is fragile and ineffective in reality when wireless communication links are unreliable. To overcome such challenges and improve the performance on the delivery latency, we propose Robust Multi-pipeline Scheduling (RMS) algorithm to coordinate multiple parallel pipelines and switch the packet timely among different pipelines if failure happens in former attempts of transmissions. RMS combines the pipeline features with the advantages brought by multi-parents forwarding. Large-scale simulations and testbed implementations verify that the end-to-end delivery latency can be reduced by 40% through exploiting multi-pipeline scheduled forwarding path with tolerable energy overhead.

By exploiting reception diversity of wireless network links, researchers have shown that opportunistic routing can improve network performance significantly over traditional routing schemes. However, recently empirical studies indicate that we are too optimistic, i.e. diversity gain can be overestimated if we continue to assume that packet receptions of wireless links are independent events. For the first time, this paper formally analyzes the opportunistic routing gain under the presence of link correlation considering the loss of DATA and ACK packets. Based on the model, we introduce a new link-correlation-aware opportunistic routing scheme, which improves the performance by exploiting the diverse uncorrelated forwarding links. Our design is evaluated using simulation where we show (i) link correlation leads to less diversity gain, (ii) and with our link-correlation-aware design; improvement can be gained. We also provide a unique model to generate strings of randomly correlated receptions.

Flooding in low-duty-cycle wireless sensor networks is very costly due to asynchronous schedules of sensor nodes. To adapt existing flooding-tree-based designs for low-duty-cycle networks, we shall schedule nodes of common parents wake up simultaneously. Traditionally, energy optimality in a designated flooding-tree is achieved by selecting parents with the highest link quality. In this work, we demonstrate that surprisingly more energy can be saved by considering link correlation. Specifically, this work first experimentally verifies the existence of link correlation and mathematically proves that the energy consumption of broadcasting can be reduced by letting nodes with higher correlation receive packets simultaneously. A novel flooding scheme, named Correlated Flooding, is then designed so that nodes with high correlation are assigned to a common sender and their receptions of a broadcasting packet are only acknowledged by a single ACK. This unique feature effectively ameliorates the ACK implosion problem, saving energy on both data packets and ACKs. We evaluate Correlated Flooding with extensive simulations and a testbed implementation with 20 MICAz nodes. We show that Correlated Flooding saves more than 66% energy on ACKs and 15%–50% energy on data packets for most network settings, while having similar performance on flooding delay and reliability.

In order to produce kiwifruit juice, by single factor and orthogonal test, according to the juice yield, preservation rate of Vc, changes of juice color and aroma as the index, using pectinase and pulp enzyme dealed with kiwi fruit pulp to explore a better technology. The results showed that two enzymes both could improve the yield of kiwi fruit juice substantially. Pectinase could increase more yield of kiwi fruit juice, but the enzymolysis condition was intense, the content of Vc and juice color and aroma was influened bigger; Pulp enzyme enzymolysis condition was mild, the whole index is better, the optimized yield conditions: enzymolysis temperature 30 °C, add enzyme quantity 0.5 mL·kg – 1, enzymolysis time 2h, the yield could achieve to 0.852mL·g – 1 and the preservation rate of Vc achieved 67%. Pulp enzyme is more suitable for the production of kiwifruit juice.

Vehicular ad hoc networks (VANETs) represent promising technologies for improving driving safety and efficiency. Due to the highly dynamic driving patterns of vehicles, it has been a challenging research problem to achieve effective and time-sensitive data forwarding in vehicular networks. In this paper, a Shared-Trajectory-based Data Forwarding Scheme (STDFS) is proposed, which utilizes shared vehicle trajectory information to address this problem. With access points sparsely deployed to disseminate vehicles' trajectory information, the encounters between vehicles can be predicted by the vehicle that has data to send, and an encounter graph is then constructed to aid packet forwarding. This paper focuses on the specific issues of STDFS such as encounter prediction, encounter graph construction, forwarding sequence optimization and the data forwarding process. Simulation results demonstrate the effectiveness of the proposed scheme.

This paper proposes a data forwarding scheme called Trajectory-based Statistical Forwarding (TSF), tailored for the data delivery from infrastructure nodes (e.g., Internet access points) to moving vehicles in vehicular networks. To our knowledge, this paper presents the first attempt to investigate how to effectively utilize the packet destination vehicle’s trajectory for such an infrastructure-to-vehicle data delivery. This data delivery is performed through the computation of a target point based on the destination vehicle’s trajectory that is an optimal rendezvous point of the packet and the destination vehicle. TSF forwards packets over multi-hop to a selected target point where the vehicle is expected to pass by. Such a target point is selected optimally to minimize the packet delivery delay while satisfying the required packet delivery probability. The optimality is achieved analytically by utilizing the packet’s delivery delay distribution and the destination vehicle’s travel delay distribution. Through theoretical analysis and extensive simulation, it is shown that our design provides an efficient data forwarding under a variety of vehicular traffic conditions.

This paper proposes a Trajectory-Based Data Forwarding (TBD) scheme, tailored for the data forwarding in light- traffic vehicular ad-hoc networks. State-of-the-art schemes have demonstrated the effectiveness of their data forwarding strategies by exploiting known vehicular traffic statistics (e.g., densities and speeds) in these vehicular networks. These results are encouraging, however, further improvements can be made by taking advantage of the growing popularity of GPS-based navigation systems. This paper presents the first attempt to investigate how to effectively utilize vehicles" trajectory information in a privacy-preserving manner. In our design, the trajectory information is combined with the traffic statistics to improve the performance of data forwarding in road networks. Through theoretical analysis and extensive simulation, it is shown that our design outperforms the existing scheme.

Due to the enormous amount of data in DNA sequences to be processed, the computational complexity and speed are important issues to be considered. In this paper, a new integrative method is presented for predicting protein coding regions. We first establish a Takagi-Sugeno fuzzy model to identify the first nucleotide of a codon in coding regions, then the time-frequency characteristics of the output values of the model are analyzed by short-time Fourier transform. The position of coding regions can be exactly identified and the computating time is greatly reduced. Simulation results show the proposed method is more effective for coding regions prediction than the existing coding region discovery tools.

In road networks, sensors are deployed sparsely (hundreds of meters apart) to save costs. This makes the existing localization solutions based on the ranging be ineffective. To address this issue, this paper introduces an autonomous passive localization scheme, called APL. Our work is inspired by the fact that vehicles move along routes with a known map. Using binary vehicle-detection timestamps, we can obtain distance estimates between any pair of sensors on roadways to construct a virtual graph composed of sensor identifications (i.e., vertices) and distance estimates (i.e., edges). The virtual graph is then matched with the topology of road map, in order to identify where sensors are located in roadways. We evaluate our design outdoor in Minnesota roadways and show that our distance estimate method works well despite of traffic noises. In addition, we show that our localization scheme is effective in a road network with eighteen intersections, where we found no location matching error, even with a maximum sensor time synchronization error of 0.3 sec and the vehicle speed deviation of 10 km/h.

An adaptive inverse controller for nonlinear systems is designed by using RBF neural network. The controller consists of a RBF identification network and a RBF control network. An optimization algorithm is proposed for redundant number of hidden units of familiar RBF neural network, and the approach combines the rival penalized competitive learning (RPCL) and the improved regularized least squares (IRLS) to provide an efficient procedure for constructing a minimal RBF neural network that generalizes very well. The RPCL adjusts centers, while the IRLS estimates the connection weights. The effectiveness of the proposed controller is illustrated through a simulated application to a nonlinear system.