54 research outputs found

    Research on Coordinating Cloud Service Supply Chain Considering Service Disruption

    Get PDF
    The risk of the implementation of cloud service and the worry about the failure of projects or strategies caused by service disruption is an important reason of low adoption rates of the cloud service. Service disruption not only directly affects the cloud service free trial results, but also leads to compensation to the consumers. The coordination problem between a CFP (cloud function provider) and a CIP (cloud integration provider) in a cloud supply chain is investigated, in which service demand is determined by the application free trial. Coordination Contracts are discussed in two kinds of situations, linked respectively to the information symmetry and information asymmetry. The results show that the cost and risk-sharing coordination contracts we proposed can realize optimal supply chain performance, and Pareto improvement of supply chain members’ profits. Reducing the service disruption probability and improving the level of service reliability are the key to the free trial. Besides, the compensation cost allocation enhances the scalability of cost allocation. Through numerical exploration analysis, effectiveness of the model is demonstrated and some managerial insights are obtained

    Multi-agent hybrid mechanism for financial risk management

    Get PDF
    Purpose: The goal of this study was to propose the multi-agent mechanism to forecast the corporate financial distress. Design/methodology/approach: This study utilized numerous methods, namely random subspace method, discriminant analysis and decision tree to construct the multi-agent forecasting model. Findings: The study shows a superior forecasting performance. Originality/value: The use of multi-agent model to predict the corporate financial distress.Peer Reviewe

    An Intelligent Trade Matching System for B2B Marketplace

    Get PDF
    With the fast growth of B2B sales, an intelligent system is greatly useful for decreasing transaction cost and increasing market efficiency on electronic platforms. In order to improve the quality of transaction processing and customer experience, this paper proposes a knowledge-based system, which employs a Case-Based Reasoning (CBR) technique for trade matching in B2B marketplace as a substitute for the manual matching process. The system function and logical architecture are discussed. And the case repository is proposed to support this CBR approach where the case representation, case base indexing, case base decomposition and the dictionary are argued in details

    The influence of viscous liquid to the propagation of torsional wave in pipes

    Get PDF
    This paper deals with the torsional waves propagating in a pipe contacting with viscous liquid. The expression describing the movement of viscous liquid near the pipe is presented. Both the pipe is filled with and immersed in the liquid are considered and both the isotropic and transversely isotropic pipes are investigated. Numerical calculations were carried out to investigate the influence of viscosity of liquid on the dispersion and attenuation curves. The first torsional wave mode is investigated individually by varying the viscosity and the density of the liquid. It is concluded that the viscosity of the liquid has little influence on the dispersion curves, while its main effect is the attenuation. When the frequency and the thickness of the pipe are not large, the attenuation ratio between the two cases of pipe immersed in liquid and pipe filled with liquid is approximately a constant, which is determined by the ratio of inner radius and the thickness

    Physical-aware Cross-modal Adversarial Network for Wearable Sensor-based Human Action Recognition

    Full text link
    Wearable sensor-based Human Action Recognition (HAR) has made significant strides in recent times. However, the accuracy performance of wearable sensor-based HAR is currently still lagging behind that of visual modalities-based systems, such as RGB video and depth data. Although diverse input modalities can provide complementary cues and improve the accuracy performance of HAR, wearable devices can only capture limited kinds of non-visual time series input, such as accelerometers and gyroscopes. This limitation hinders the deployment of multimodal simultaneously using visual and non-visual modality data in parallel on current wearable devices. To address this issue, we propose a novel Physical-aware Cross-modal Adversarial (PCA) framework that utilizes only time-series accelerometer data from four inertial sensors for the wearable sensor-based HAR problem. Specifically, we propose an effective IMU2SKELETON network to produce corresponding synthetic skeleton joints from accelerometer data. Subsequently, we imposed additional constraints on the synthetic skeleton data from a physical perspective, as accelerometer data can be regarded as the second derivative of the skeleton sequence coordinates. After that, the original accelerometer as well as the constrained skeleton sequence were fused together to make the final classification. In this way, when individuals wear wearable devices, the devices can not only capture accelerometer data, but can also generate synthetic skeleton sequences for real-time wearable sensor-based HAR applications that need to be conducted anytime and anywhere. To demonstrate the effectiveness of our proposed PCA framework, we conduct extensive experiments on Berkeley-MHAD, UTD-MHAD, and MMAct datasets. The results confirm that the proposed PCA approach has competitive performance compared to the previous methods on the mono sensor-based HAR classification problem.Comment: First IMU2SKELETON GANs approach for wearable HAR problem. arXiv admin note: text overlap with arXiv:2208.0809

    Replacing Traditional Plastics with Biodegradable Plastics:Impact on Carbon Emissions

    Get PDF
    In recent years, a great deal of attention has been focused on the environmental impact of plastics, including the carbon emissions related to plastics, which has promoted the application of biodegradable plastics. Countries worldwide have shown high interest in replacing traditional plastics with biodegradable plastics. However, no systematic comparison has been conducted on the carbon emissions of biodegradable versus traditional plastic products. This study evaluates the carbon emissions of traditional and biodegradable plastic products (BPPs) over four stages and briefly discusses environmental and economic perspectives. Four scenarios—namely, the traditional method, chemical recycling, industrial composting, and anaerobic digestion—are considered for the disposal of waste biodegradable plastic product (WBBPs). The analysis takes China as a case study. The results show that the carbon emissions of 1000 traditional plastic products (plastic bags, lunch boxes, cups, etc.) were 52.09–150.36 carbon emissions equivalent of per kilogram (kg CO2eq), with the stage of plastic production contributing 50.71%–50.77%. In comparison, 1000 similar BPPs topped out at 21.06–56.86 kg CO2eq, approximately 13.53%–62.19% lower than traditional plastic products. The difference was mainly at the stages of plastic production and waste disposal, and the BPPs showed significant carbon reduction potential at the raw material acquisition stage. Waste disposal plays an important role in environmental impact, and composting and anaerobic digestion are considered to be preferable disposal methods for WBBPs. However, the high cost of biodegradable plastics is a challenge for their widespread use. This study has important reference significance for the sustainable development of the biodegradable plastics industry.</p

    Sequence Impedance Modeling and Analysis of Modular Multilevel Converter Considering DC Port Characteristics

    No full text
    The extensive deployment of Modular Multilevel Converters (MMCs) in AC/DC systems can lead to complex resonance issues. Impedance modeling forms the foundation for analyzing the stability of interconnected system. Existing investigations primarily address resonance concerns on the AC side of MMC. In the process of impedance modeling, the DC system is generally approximated as an ideal voltage source, thereby neglecting its dynamic impact on the impedance characteristic of MMC. Such simplification may result in inaccuracies within stability analysis findings. Based on the multi-harmonic linearization method, this paper introduces an impedance modeling approach that takes into account the characteristics of the DC port. Taking DC voltage control as a case study, the impedance model for the AC side of the MMC is established. The results of programming calculation and simulation measurement of sequence impedance are mutually verified. Finally, the MMC impedance responses determined by different DC port characteristics are analyzed. Significant alterations in MMC impedance characteristics are introduced by DC side resistance and capacitance, which may result in negative damping effects and a decrease in system stability margins. The DC side inductance has a relatively minor impact on MMC impedance responses. The analysis results show that the sequence impedance model of MMC considering DC port characteristics can improve the accuracy of stability analysis results

    Development and Fault Prediction of a New Operating Mechanism of HTPPM

    No full text
    In this article, we take a 126 kV single-break vacuum circuit breaker as the research object and study the application of high-energy-density PM motor in the high-voltage circuit breaker for the first time. The PM motor maintains maximum power density and torque density during the start-up phase. Note that most of the faults of high-voltage circuit breakers are mechanical faults. We designed a set of mechanical fault prediction systems for high-voltage circuit breakers. We present the prediction method of the opening and closing action curve of the high-voltage circuit breaker. It is inspired by Chaos Ant Colony Algorithm (CAS) and an optimized Long- and Short-Term Memory (LSTM) cycle neural network. We constructed the main structure of the neural network expert system and established the fault prediction model of the high-voltage circuit breaker, based on the LSTM cycle neural network, optimized by CAS. We used the improved least-square method to achieve the operation accuracy of the phase control switch. Finally, we completed the development and experiment of the prototype
    • …
    corecore