DPC Model Research Articles

Energy efficient IPC based dual compression for endurance enhancement of NVRAM as main memory in embedded devices

AbstractThe Non‐Volatile Random Access Memory (NVRAM) is recently identified as the most upcoming main memory technology in Embedded and Internet of Things (IoT) systems due to its appealing qualities like zero static power consumption and great memory cell density. On the other hand, most NVRAMs have low write endurance due to workload‐induced write variance, leading to more write activity at a few blocks of memory than the other blocks. Improving the endurance of NVRAM on embedded architectures is very critical due to the limited constraints of Embedded architectures. The main goal of this paper is to address the complexity of NVRAM designs, with a focus on embedded boards, and to create a prototype that enhances NVRAM endurance using a compression technique. Furthermore, this framework is not particular to a single NVRAM device. An Instruction Per Cycle based Dynamic Pattern Compression (IPC_DPC) model is developed to address NVRAM's high write latency and low endurance. The primary goal of the proposed IPC_DPC model is to minimize the energy utilization and latency of heavy workloads during the execution by pre‐determining the workload's instruction cycles. The IPC_DPC model initially divides the Low IPC and High IPC workloads and then compresses the workloads dynamically based on their IPC values compared to the threshold factor. As a result, IPC_DPC improves the compression ratio and reduces the write latencies associated with traditional compressors, significantly reducing the write activity and improving the lifetime of NVRAM. We implemented the proposed IPC_DPC model using GEM5 with a Raspberry Pi board and took IoMT, MiBench, and EEMBC benchmarks for evaluation. Compared to FPC, BDI, FNW, and DFPC models, the proposed IPC_DPC model is shown to be more efficient in terms of compression ratio (56.05%) and energy reduction (17%).

Control and Performance Analysis of Variable Speed Pump-Controlled Asymmetric Cylinder Systems under Four-Quadrant Operation

Since the energy crisis, the further development of a variable speed pump-controlled hydraulic system driven by an electric machine has attracted increasing attention during the past few years. As a response to this, an innovative double pump-controlled asymmetric cylinder system (DPC) and its control method are proposed in this study. The purpose of this study is to investigate the performance of two variable speed pump-controlled systems for asymmetric cylinders under a four-quadrant operating condition, in comparison with single pump control (SPC). The four-quadrant operating principles of the two systems are analyzed by simulation. Simulation models for both systems are introduced, and a position control method is proposed, with a tracking differentiator and speed feedforward, plus proportional-integral-derivative (PID) for four-quadrant operation. The DPC model was validated with the measurement of a crane. The simulations using the validated model were performed with a position reference and a varying load (four-quadrant operating arm of an excavator). The results demonstrated that the velocity fluctuation is eliminated by using the DPC instead of the SPC, and the position control performance of the DPC is better compared to the SPC, although the energy efficiency decreases slightly. Hence, the proposed DPC and its position control method are feasible for the four-quadrant operation of asymmetric cylinders.

Modeling the powder compaction process by an integrated simulation and inverse optimization method

A method of integrated modeling using the inverse optimization was proposed to simulate the compaction process of metal powders in this work. By using the present work, one can alleviate the time consuming and expensive experiment, and the material parameters can be determined more accurately. The constitutive parameters of the modified Drucker-Prager Cap model were identified using finite element simulations in ABAQUS- iSIGHT integrated environment. The downhill simplex optimization method has been used to predict the material parameters in a way to minimize the differences between experimental and simulation result of uniaxial powder compaction. The accuracy of inverse parameters has been validated as the powder Distaloy AE, while the sensitivity of the plasticity parameters of the DPC model has been discussed. The results show that the ejection process cannot be fully simulated when the plastic parameters are assumed to be constants, while for the case that adopting plastic parameters changed with relative density can show a good agreement with the experimental data. Finally, the parametric module of Python was applied to simulate the compaction of industrial part. The integrated modeling using inverse optimization method was verified again in compaction of a typical multi-level gear component. And the numerical results show that the errors of the maximum and the minimum relative density are about 5% and 4% respectively.

Nonperturbative renormalization group for the diffusive epidemic process.

We consider the Diffusive Epidemic Process (DEP), a two-species reaction-diffusion process originally proposed to model disease spread within a population. This model exhibits a phase transition from an active epidemic to an absorbing state without sick individuals. Field-theoretic analyses suggest that this transition belongs to the universality class of Directed Percolation with a Conserved quantity (DP-C, not to be confused with conserved-directed percolation C-DP, appearing in the study of stochastic sandpiles). However, some exact predictions derived from the symmetries of DP-C seem to be in contradiction with lattice simulations. Here we revisit the field theory of both DP-C and DEP. We discuss in detail the symmetries present in the various formulations of both models. We then investigate the DP-C model using the derivative expansion of the nonperturbative renormalization group formalism. We recover previous results for DP-C near its upper critical dimension d_{c}=4, but show how the corresponding fixed point seems to no longer exist below d≲3. Consequences for the DEP universality class are considered.

Implementation of a Collaborative Document Processing in the Cloud

Document processing is one of the most widely used and well developed With the recent fast development of high-speed internet and distributed computing, it is possible to move document processing to web-based, and even cloud-based. The initial benefits of moving office documents into cloud for small and medium sized are business cost saving for buying, maintaining, and upgrading both software and hardware. However, the most significant advantage of doing is to enable users of real-time collaborative editing on a shared cloud-based document. Therefore, moving office applications into cloud is an inevitable trend for the development of office application. A novel efficient document-processing model (DPC) in the cloud is proposed. Detailed description and functioning of this model is briefly discussed first in this paper. Next, we implemented the DPC model in the Google cloud through the Google App Engine. Our cases testing verified the proposed DPC model enabling users to process their office document collaboratively by a proper granularity in cloud.

Hysteresis loss in vector Preisach models

PurposeThe magnetic modeling of materials has been focused on computing magnetization and forces in devices. With increasing efforts to make energy efficient devices, attention must now be paid to hysteresis losses in magnetizing processes. The purpose of this paper is to discuss the pertinent parameters that determine these losses and a method of identifying them.Design/methodology/approachA vector model was used that is designed to compute losses correctly, to focus on hysteresis losses in models that compute the same magnetization, but have different losses.FindingsThe model computes the same cyclic losses as expected.Research limitations/implicationsThe analysis of this paper is limited to isotropic media, but it can be generalized to anisotropic media. The accuracy of the Della Torre, Pinzaglia and Cardelli (DPC) model is also limited.Practical implicationsOnce the model has been validated, the designer can minimize the instantaneous as well as the average dissipation. This will permit the design of more reliable devices.Originality/valueThe DPC model used in this work has not been fully explored previously.

Experimental Verification of the Deletion and Congruency Properties in Si-Fe Magnetic Steels

This work presents an experimental procedure to verify if a model of vector hysteresis, the DPC model (Della Torre, Pinzaglia, Cardelli), based on the Preisach theory, can be identified for a given material. The identification can be achieved only if the material has a magnetic behavior in agreement with the properties of congruency and deletion which characterize the model. Finally, we show some results obtained on a sample of commercial nonoriented magnetic steel.

Numerical Implementation of the DPC Model

This paper discusses the numerical implementation of a new vector hysteresis model, called the Della Torre, Pinzaglia and Cardelli (DPC) model, introduced as general hysteresis vector model for both isotropic and anisotropic magnetic materials. The present implementation is a 2-D one that uses any kind of hysteron described by the DPC formulation. Here some examples of numerical computation using Stoner-Wohlfarth-type hysterons are presented. The results computed by the model, applied to some basic vector hysteresis configurations, are in agreement with experimental behavior and model properties.

Magnetization dependent vector model and single domain nanostructures

In this paper we present a phenomenological approach computing the behavior of a cobalt particle by the Della Torre–Pinzaglia–Cardelli (DPC) model. The identification of the model is realized using a suitable numerical procedure and a set of data obtained using a micromagnetic numerical model based on Brown’s equations. In order to simplify the identification, a class of hysterons, which describes the magnetization process of a single domain in agreement with the Stoner and Wohlfarth theory, is used. The accuracy of the reconstructed magnetization cycles using the DPC model and the save of the computation time with respect to the micromagnetic numerical model are shown.

A Real-Time Neuronal Model of Classical Conditioning

A new neuronal model of classical conditioning is proposed. The model, called the delay-producing connections (or DPC) model, is an extension of Klopf's (1988) drive-reinforcement model and Sutton and Barto's (1981) model. The DPC model makes two contributions: lt represents the trace of each conditioned stimulus (CS) by a differential equation; and it replaces each CS in the activation rule by its CS trace, which is assumed to be equal to the CS eligibility. The DPC model reproduces the usual shape of a conditioned response, the curve of efficacy of conditioning as a function of the interstimulus interval (ISI), the dependence of the optimal ISI on CS duration, the extinction of a conditioned response [even for long-lasting CSs as opposed to Klopf's (1988) model], and several other properties of classical conditioning.