An Energy-aware Routing Mechanism for Latency-sensitive Traffics

With the rapid development of Internet technology and enhanced QoS requirements, network energy consumption has attracted more and more attentions due to the overprovision of network resources. Generally, energy saving can be achieved by sacrificed some performance. However, many popular applications require real-time or soft real-time QoS performance for attracting potential users, and existing technologies can hardly obtain satisfying tradeoffs between energy consumption and performance. In this paper, a novel energy-aware routing mechanism is presented with aiming at reducing the network energy consumption and maintaining satisfying QoS performance for these latency-sensitive applications. The proposed routing mechanism applies stochastic service model to calculate the latency-guarantee for any given network links. Based on such a quantitative latencyguarantee, we further propose a technique to decide whether a link should be powered down and how long it should be kept in power saving mode. Extensive experiments are conducted to evaluate the effectiveness of the proposed mechanism, and the results indicate that it can provide better QoS performance for those latency-sensitive traffics with improved energyefficiency

An Adaptive Redundant Reservation Strategy in Distributed Highperformance Computing Environments

In distributed high-performance computing environments, resource reservation mechanism is an effective approach to provide desirable quality of service for large-scale applications. However, conventional reservation service might result in lower resource utilization and higher rejection rate if it is excessively applied. Furthermore, redundant reservation policy has been widely applied in many practical systems with aiming to improve the reliability of application execution at runtime. In this paper, we proposed an adaptive redundant reservation strategy, which uses overlapping technique to implement reservation admission and enable resource providers dynamically determine the redundant degree at runtime. By overlapping a new reservation with an existing one, a request whose reservation requirements can not be satisfied in traditional way might be accepted. Also, by dynamically determining the redundant degree, our strategy can obtain optimal tradeoff between performance and reliability for distributed high-performance computing systems. Experimental results show that the strategy can bring about remarkably higher resource utilization and lower rejection rate when using redundant reservation service at the price of a slightly increasing of reservation violations

Experimental and Numerical Vibration Analysis of Hydraulic Pipeline System under Multiexcitations

Pipeline systems in aircraft are subjected to both hydraulic pump pressure fluctuations and base excitation from the engine. This can cause fatigue failures due to excessive vibrations. Therefore, it is essential to investigate the vibration behavior of the pipeline system under multiexcitations. In this paper, experiments have been conducted to describe the hydraulic pipeline systems, in which fluid pressure excitation in pipeline is driven by the throttle valve, and the base excitation is produced by the shaker driven by a vibration controller. An improved model which includes fluid motion and base excitation is proposed. A numerical MOC-FEM approach which combined the coupling method of characteristics (MOC) and finite element method (FEM) is proposed to solve the equations. The results show that the current MOC-FEM method could predict the vibration characteristics of the pipeline with sufficient accuracy. Moreover, the pipeline under multiexcitations could produce an interesting beat phenomenon, and this dangerous phenomenon is investigated for its consequences from engineering point of view

SCDNet: Self-Calibrating Depth Network with Soft-Edge Reconstruction for Low-Light Image Enhancement

Captured low-light images typically suffer from low brightness, low contrast, and blurred details due to the scattering and absorption of light and limited lighting. To deal with these issues, we propose a self-calibrating depth network with soft-edge reconstruction for low-light image enhancement. Concretely, we first employ the soft edge reconstruction module to reconstruct the soft edge of the input image and extract the texture and detail information of the image. Afterward, we explore the convergence properties of each input via the self-calibration module to significantly improve the computational effectiveness of the method and gradually correct the inputs at each subsequent level. Finally, the low-light image is iteratively enhanced by an iterative light enhancement curve to obtain a high-quality image. Extensive experiments demonstrate that our SCDNet visually enhances the brightness and contrast, restores the actual color, and makes the image more in line with the characteristics of the human eye vision system. Meanwhile, our SCDNet outperforms the compared methods in some qualitative and quantitative metrics

Coke Oven Intelligent Integrated Control System

In order to improve the level of management and control of coke oven, the research on intelligent integrated control system is carried out. In modern advanced control system of coke oven, the control scheme of feedback combined with feed-forward, and control merged with management are widely utilized. The integrated management and control system of coke oven is introduced systematically, including the system model, production plan and management, heating control system, the model and method of evaluating temperature, intelligent combustion control and the pressure control gas collector of coke oven. It is pointed out that the integration of management and control develops towards the orientation of coke oven control system. Considering the complexity and importance of flue temperature control in coke oven heating process, the control method of combining the stopping heating time control with the heating gas flow adjustment is proposed, hybrid intelligent control models of flue temperature are buil

DAMNet: Dual Attention Mechanism Deep Neural Network for Underwater Biological Image Classification

Due to the complex background and biodiversity of underwater biological images makes the identification of marine organisms difficult. To solve these above problems, we propose a dual attention mechanism deep neural network for underwater biological image classification (DAMNet). Firstly, tthe proposed DAMNet uses multi-stage stacking to suppress the complex underwater background, and the multiple stacking can reduce the number of parameters of the model and improve the generalization ability. Secondly, the dual attention mechanism module is combined with the improved reverse residual bottleneck based on deep convolution to extract the feature information of underwater biological images from space and channel aspects to obtain better discrimination and feature extraction capability. Finally, the gravity optimizer is selected to update the model weights, and the exponential translation can improve the model’s convergence speed and learning rate. Extensive experiments on a dataset consisting of seven types of underwater biological images demonstrate that the DAMNet model has higher learning ability and robustness compared to the state-of-the-art methods. Our DAMNet model achieves 96.93% classification accuracy in all categories, which is at least a 2 percentage point improvement compared to other models

Facile and Controllable Ultrasonic Nebulization Method for Fabricating Ti3C2Tx‐Based Strain Sensor and Monitoring of Human Motion and Sound Wave

Abstract Flexible and wearable electronic devices hold great potential in electronic skins, health monitoring systems and soft robotics. Among them, flexible strain sensors with high performance are key components for wearable health monitoring devices. However, the facile and controllable preparation of highly sensitive sensors still faces significant challenges. By virtue of excellent conductivity of 2D transition metal carbids (MXenes), this work reports a facile and low‐cost fabrication strategy for large‐scale production of strain sensors. The sensitive layer is deposited on flexible interdigital electrodes by ultrasonic nebulization of Ti3C2Tx nanosheets. By controlling the nebulization time, different thicknesses of Ti3C2Tx films has a great influence on the performance of strain sensors. The Ti3C2Tx‐based strain sensor exhibits good sensing performances such as high GF (19.1) in the low strain range (≈0.25%–1.14%), short response time (0.7 s), and stable durability (over 1000 cycles). In practice, the potential applications of the strain sensor in sound frequency detection, human physiological signal monitoring and facial expression recognition are demonstrated. Finally, this work integrates the strain sensor with a miniaturized analyzer to assemble a wearable motion monitoring device for mobile healthcare. This study provides a facile strategy for fabricating flexible strain sensors in the field of wearable electronics