The Research of Periodic Solutions of Time-Varying Differential Models

We have studied the periodicity of solutions of some nonlinear time-varying differential models by using the theory of reflecting functions. We have established a new relationship between the linear differential system and the Riccati equations and applied the obtained results to discuss the behavior of periodic solutions of the Riccati equations

Using Analytical Hierarchy Process Methods in Cash Holding and Corporate Working Capital Management: an Asian Perspective

Cash holding behavior is an important financial behavior of the company, which reflects the company\u27s financial strategy and management strategy. What are the factors that affect corporate cash holdings? How does the change in these factors affect the change in corporate cash holdings? Starting from two aspects of national macroeconomic environmental factors and microeconomic environmental factors, this paper tries to analyze these factors and how they affect corporate cash holdings. Working Capital Management is an important part of the company\u27s financial management, which is closely related to the value creation of the company. What is the content of working capital management? How to manage the working capital effectively? This paper expounds the contents of working capital management from the perspective of process management, and expounds how to effectively manage working capital from the perspective of situational management. To link these two areas ids the goal of this study

Annealing evolutionary parallel algorithm analysis of optimization arrangement on mistuned blades with non-linear friction

This paper sets up a lumped parameter model of engine bladed disk system when considering the nonlinear friction damping based on mistuned parameters which is obtained from the blade modal experiment. A bladed arrangement optimization method, namely annealing evolutionary algorithm with tabu list is presented which combines the local search ability of SA (simulated annealing) and the global searching ability of GA (genetic algorithm) introducing tabu list as the search memory list. Parallel TAEA (tabu annealing evolutionary algorithm) is presented based on CUDA (Compute Unified Device Architecture) combining GPU (Graphics Processing Unit) and its performance is analyzed. The results show that optimization based on CUDA framework can improve computing speed. At the same time using optimization results can reduce the amplitude of forced vibration response of bladed disk system and make it in the range of allowable engineering

Semantic Communications with Explicit Semantic Base for Image Transmission

Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications. However, the knowledge forming and synchronizing mechanism that enables semantic communication systems to extract and interpret the semantics of information according to the communication intents is still immature. In this paper, we propose a semantic image transmission framework with explicit semantic base (Seb), where Sebs are generated and employed as the knowledge shared between the transmitter and the receiver with flexible granularity. To represent images with Sebs, a novel Seb-based reference image generator is proposed to generate Sebs and then decompose the transmitted images. To further encode/decode the residual information for precise image reconstruction, a Seb-based image encoder/decoder is proposed. The key components of the proposed framework are optimized jointly by end-to-end (E2E) training, where the loss function is dedicated designed to tackle the problem of nondifferentiable operation in Seb-based reference image generator by introducing a gradient approximation mechanism. Extensive experiments show that the proposed framework outperforms state-of-art works by 0.5 - 1.5 dB in peak signal-to-noise ratio (PSNR) w.r.t. different signal-to-noise ratio (SNR)

Dynamic UAV Swarm Collaboration for Multi-Targets Tracking under Malicious Jamming: Joint Power, Path and Target Association Optimization

In this paper, the multi-target tracking (MTT) with an unmanned aerial vehicle (UAV) swarm is investigated in the presence of jammers, where UAVs in the swarm communicate with each other to exchange information of targets during tracking. The communication between UAVs suffers from severe interference, including inter-UAV interference and jamming, thus leading to a deteriorated quality of MTT. To mitigate the interference and achieve MTT, we formulate a interference minimization problem by jointly optimizing UAV's sub-swarm division, trajectory, and power, subject to the constraint of MTT, collision prevention, flying ability, and UAV energy consumption. Due to the multiple coupling of sub-swarm division, trajectory, and power, the proposed optimization problem is NP-hard. To solve this challenging problem, it is decomposed into three subproblems, i.e., target association, path plan, and power control. First, a cluster-evolutionary target association (CETA) algorithm is proposed, which involves dividing the UAV swarm into the multiple sub-swarms and individually matching these sub-swarms to targets. Second, a jamming-sensitive and singular case tolerance (JSSCT)-artificial potential field (APF) algorithm is proposed to plan trajectory for tracking the targets. Third, we develop a jamming-aware mean field game (JA-MFG) power control scheme, where a novel cost function is established considering the total interference. Finally, to minimize the total interference, a dynamic collaboration approach is designed. Simulation results validate that the proposed dynamic collaboration approach reduces average total interference, tracking steps, and target switching times by 28%, 33%, and 48%, respectively, comparing to existing baselines.Comment: 14 pages, 17 figure

Reducing Transport Latency for Short Flows with Multipath TCP

Multipath TCP (MPTCP) has been an emerging transport protocol that provides network resilience to failures and improves throughput by splitting a data stream into multiple subflows across all the available multiple paths. While MPTCP is generally beneficial for throughput-sensitive large flows with large number of subflows, it may be harmful for latency-sensitive small flows. MPTCP assigns each subflow a congestion window, making short flows susceptible to timeout when a flow only contains a few packets. This condition becomes even worse when the paths have heterogeneous characteristics as packet reordering occurs and the slow paths can be used with MPTCP, causing the increased end-to-end delay and the lower application Goodput. Thus, it is important to choose the appropriate subflows for each MPTCP connection to achieve the good performance. However, the subflows in MPTCP are determined before a connection is established, and they usually remain unchanged during the lifetime of that connection. To address this issue, we propose DMPTCP, which dynamically adjusts the subflows according to application workloads. Specifically, DMPTCP first utilizes the idea of TCP modeling to estimate the latency on the path under scheduling and the data amount sent on the other paths simultaneously, and then decides the set of subflows to be used for certain application periodically with the goal of reducing completion time for short flows and achieving a higher throughput for long flows. We implement DMPTCP in a Linux server and conduct extensive experiments both in NS3 and in Linux testbed to validate its effectiveness. Our evaluation shows that DMPTCP decreases the completion time by over 46.55% compared to conventional MPTCP for short flows while increases the Goodput up to 21.3% for long-lived flows

Reducing Transport Latency for Short Flows with Multipath TCP

Multipath TCP (MPTCP) has been an emerging transport protocol that provides network resilience to failures and improves throughput by splitting a data stream into multiple subflows across all the available multiple paths. While MPTCP is generally beneficial for throughput-sensitive large flows with large number of subflows, it may be harmful for latency-sensitive small flows. MPTCP assigns each subflow a congestion window, making short flows susceptible to timeout when a flow only contains a few packets. This condition becomes even worse when the paths have heterogeneous characteristics as packet reordering occurs and the slow paths can be used with MPTCP, causing the increased end-to-end delay and the lower application Goodput. Thus, it is important to choose the appropriate subflows for each MPTCP connection to achieve the good performance. However, the subflows in MPTCP are determined before a connection is established, and they usually remain unchanged during the lifetime of that connection. To address this issue, we propose DMPTCP, which dynamically adjusts the subflows according to application workloads. Specifically, DMPTCP first utilizes the idea of TCP modeling to estimate the latency on the path under scheduling and the data amount sent on the other paths simultaneously, and then decides the set of subflows to be used for certain application periodically with the goal of reducing completion time for short flows and achieving a higher throughput for long flows. We implement DMPTCP in a Linux server and conduct extensive experiments both in NS3 and in Linux testbed to validate its effectiveness. Our evaluation shows that DMPTCP decreases the completion time by over 46.55% compared to conventional MPTCP for short flows while increases the Goodput up to 21.3% for long-lived flows

Giant magneto-birefringence effect and tuneable colouration of 2D crystals' suspensions

One of the long sought-after goals in manipulation of light through light-matter interactions is the realization of magnetic-field-tuneable colouration, so-called magneto-chromatic effect, which holds great promise for optical, biochemical and medical applications due to its contactless and non-invasive nature. This goal can be achieved by magnetic-field controlled birefringence, where colours are produced by the interference between phase-retarded components of transmitted polarised light. Thus far birefringence-tuneable coloration has been demonstrated using electric field, material chirality and mechanical strain but magnetic field control remained elusive due to either weak magneto-optical response of transparent media or low transmittance to visible light of magnetically responsive media, such as ferrofluids. Here we demonstrate magnetically tuneable colouration of aqueous suspensions of two-dimensional cobalt-doped titanium oxide which exhibit an anomalously large magneto-birefringence effect. The colour of the suspensions can be tuned over more than two wavelength cycles in the visible range by moderate magnetic fields below 0.8 T. We show that such giant magneto-chromatic response is due to particularly large phase retardation (>3 pi) of the polarised light, which in its turn is a combined result of a large Cotton-Mouton coefficient (three orders of magnitude larger than for known liquid crystals), relatively high saturation birefringence (delta n = 2 x 10^-4) and high transparency of our suspensions to visible light. The work opens a new avenue to achieve tuneable colouration through engineered magnetic birefringence and can readily be extended to other magnetic 2D nanocrystals. The demonstrated effect can be used in a variety of magneto-optical applications, including magnetic field sensors, wavelength-tuneable optical filters and see-through printing.Comment: 10 pages, 4 figures. Nature Communications, 2020, Accepte