The PVC Stripping Process Predictive Control Based on the Implicit Algorithm

According to the nonlinear and parameters time-varying characteristics of stripper temperature control system, the PVC stripping process Generalized Predictive Control based on implicit algorithm is proposed. Firstly, supporting vector machine is adopted to dynamically modelize for the stripper temperature; Secondly, combining with real-time model linearized of nonlinear model, a predictive model is linearized for real-time online correction. Then, the implicit algorithm is used for optimal control law. Finally, the simulation results show that the algorithm has excellent validity and robustness of temperature control of the stripper

Lumped-Parameter Model and Nonlinear DSSI Analysis

A 2-.degrees-of-freedom discrete model with 8 constant lumped parameters is developed to equivalently simulate frequency-dependent dynamic impedances of the elastic halfspace. The equations of motion for the nonlinear dynamic soil-structure interaction (DSSI) analysis are established in the time domain and then nonlinear seismic responses of the coupling system are predicted by the proposed iterative procedure. Based on numerical results for three typical shear-type structures, effects of the shear stiffness of underlying soils and different ground motions on dynamic responses are examined

DEVELOPMENT AND VALIDATION OF AN INSTRUMENT TO MEASURE INDIVIDUAL LEVEL ERP ASSIMILATION

Case evidence has shown the important role of individual level assimilation of ERP technology in realizing the business value of implemented ERP systems. However, empirical research in this area has been constrained by the lack of a validated scale for measuring individual level ERP assimilation. This study address this limitation by first theoretically conceptualizing three key dimensions through a multi-case study and then following a rigorous development process to validate a formative measurement instrument for individual level ERP assimilation. The findings show that individual level ERP assimilation consists of width, depth, and innovation, and the proposed measurement instrument is reliable and meets the validity requirements

Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance

Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus, they are useful to enable energy-efficient hardware inference. However, exploiting spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. In this work, we propose an FPGA-based convolutional SNN accelerator called Skydiver that exploits spatio-temporal workload balance. We propose the Approximate Proportional Relation Construction (APRC) method that can predict the relative workload channel-wisely and a Channel-Balanced Workload Schedule (CBWS) method to increase the hardware workload balance ratio to over 90%. Skydiver was implemented on a Xilinx XC7Z045 FPGA and verified on image segmentation and MNIST classification tasks. Results show improved throughput by 1.4X and 1.2X for the two tasks. Skydiver achieved 22.6 KFPS throughput, and 42.4 uJ/Image prediction energy on the classification task with 98.5% accuracy.Comment: Accepted to be published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 202

UNDERSTANDING INDIVIDUAL LEVEL ERP ASSIMILATION FROM A SOCIAL NETWORK PERSPECTIVE: A MULTI-CASE STUDY

Prior research on ERP assimilation has primarily focused on influntial factors at the organizational level. In this study, we attempt to extend our understanding of individual level ERP assimilation from the perspective of social network theory. We designed a multi-case study to explore the relations between ERP users´ social networks and their levels of ERP assimilation based on the three dimensions of the social networks. We gathered data through interviews with 26 ERP users at different levels in five companies. Qualitative analysis was used to understand the effects of social networks and individual interactive learning. We found that user social networks play a significant role in individual level ERP assimilation through interactive learning behaviours among users. We also found five key factors that facilitate users´ assimilation of ERP knowledge: homogeneity (age, position and rank), tie content (instrumental and expressive ties), tie strength, external ties, and centrality. Our research has significant implications for managing assimilation of ERP systems and improving users´ ERP assimilation level in organizations

COMPREHENSIVE UNDERSTANDING THE INHIBITORS AND ENABLERS OF KNOWLEDGE TRANSFER IN ERP ASSIMILATIONS: A MULTI-CASE STUDY

In the enterprise resource planning (ERP) assimilation process, organizations are increasingly concerned about how to ensure employees to have sufficient ERP knowledge effectively. However, limited attention has been directed toward examining knowledge transfer in the assimilation stage systematically. In particular, a significant omission is to understand the key enablers and inhibitors of employees’ learning intention who may receiver knowledge passively in the mandatory setting. We employed a multi-case study method in this exploratory research by interviewing 33 ERP users at all levels in nine big-size firms in China. Results of this analysis suggested that causal ambiguity in new systems, incumbent system habit, and technostress significantly undermined recipients’ learning intentions. Meanwhile, perceived management support, relation embeddedness, and symbolic adoption were key determinants of increased their learning intentions. This study is arguably the first that attempts to look into passive knowledge transfer phenomenon in some depth, and extends prior researches in ERP lifecycle by shedding light on the joint influences of enablers and inhibitors in ERP assimilation context

Effect of non-invasive ventilator in combination with tiotropium bromide on pulmonary function and sleep quality of patients with chronic obstructive pulmonary disease complicated with obstructive sleep apnea-hypopnea syndrome

Purpose: To study the influence of non-invasive ventilator and tiotropium bromide on pulmonary function and sleep quality of patients with chronic obstructive pulmonary disease (COPD) combined with obstructive sleep apnea-hypopnea syndrome (OSAHS).Methods: One hundred and twenty patients with COPD-OSAHS were selected and randomly assigned to control group (CG) and treatment group (TG), with 60 subjects in each group. Non-invasive ventilator therapy was used in both groups, based on conventional therapy, while tiotropium bromide was added in TG. Treatment effectiveness in the two groups was evaluated and compared.Results: Total effectiveness was significantly higher in TG than in CG. Post-therapy arterial oxygen saturation (SaO2) and oxygen partial pressure (PaO2) were increased, while partial pressure of carbon dioxide (PaCO2) and lactic acid (Lac) were decreased in both groups (p < 0.05). Post-treatment values of indices of lung function, viz, forced expiratory volume (FEV1), forced vital capacity (FVC) and FEV1/FVC ratio were higher than the corresponding pre-treatment levels, and also values were significantly higher in TG than in CG (p < 0.05). Average sleep time, apnea and hypopnea index (AHI) and mechanical ventilation time of TG were less than those of CG. There were lower levels of Creactive protein (CRP), procalcitonin (PCT) and interleukin-17 (IL-17) in TG than in CG. During the treatment, no obvious adverse reaction was seen in both groups.Conclusion: Non-invasive ventilator, in combination with tiotropium bromide, is more effective in the treatment of COPD-OSAHS than the use of non-invasive ventilator alone. However, further clinical trials are required before its adoption in clinical practice