Weighted Sum Calculation Research Articles

RETRACTED ARTICLE: Application of hardware system of temporal big data in enterprise human resource management in distributed environment

Distributed data collection systems are often used in many fields, such as industrial control, power systems, seismic monitoring, and ocean exploration. In a distributed data collection system, the time of data collection and the efficiency of data collection are all related to the accuracy of the data. Subsequent analysis and processing show that the distributed data collection system is a very important performance indicator: this article combines the timing factors of the timing rules in the dataset, and after normalization and expansion, the rules will be edited according to the effective voting rate. Perform a weighted sum calculation on the rules in the edited sub-database to obtain the final timing rules. Finally, the algorithm is implemented by pseudo code. With the rapid development of information technology, we have entered the era of big data. Big data has the characteristics of resource sharing, large storage capacity, and high speed, so it has been widely used in various fields. In the context of the rapid development of the modern economy, the human resources department has provided a relatively complete infrastructure system for many large companies. It can be seen that the work of the personnel department is gradually becoming an important factor in the development of the company. Appropriate implementation of big data in the company’s human resource model can integrate resources into the industry and the company, improving operational efficiency, and laying a solid foundation for the company’s sustainable and long-term development. This article first outlines the hardware system and company management of temporal big data in a distributed environment, discusses in detail the needs of human resource management in companies in the era of big data, and proposes a suitable human resource management model.

Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues.

Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM’s weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol’s ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner’s interacting scaffold is more flexible and adaptable.

Silicon nanodisk array with a fin field-effect transistor for time-domain weighted sum calculation toward massively parallel spiking neural networks

A nanodisk array connected with a fin field-effect transistor is fabricated and analyzed for spiking neural network applications. This nanodevice performs weighted sums in the time domain using rising slopes of responses triggered by input spike pulses. The nanodisk arrays, which act as a resistance of several giga-ohms, are fabricated using a self-assembly bio-nano-template technique. Weighted sums are achieved with an energy dissipation on the order of 1 fJ, where the number of inputs can be more than one hundred. This amount of energy is several orders of magnitude lower than that of conventional digital processors.

Improved neuron MOS-transistor structures for integrated neural network circuits

The neuron MOS transistor is a recently discovered device which is capable of executing a weighted sum calculation of multiple input signals and threshold operation based on the result of summation, thereby simulating the function of biological neurons. A comprehensive set of neuron test transistors has been designed, where a number of input gates are coupled capacitively to a floating gate, which controls the channel current. Integrated circuits for neural network applications have also been designed, based on the neuron MOS transistors. These circuits include neuron CMOS inverters and A/D and D/A converters. To increase the accuracy of the neuron MOSFET structures, calibration techniques are proposed and tested. All the test structures and circuits are implemented by using a standard 0.8 µm double-polysilicon CMOS technology. Attention was paid to saving of the layout area and reducing power consumption.

Superconductor based neuron single electron transistor

We have proposed a new functional superconductor single electron transistor called neuron superconducting single electron transistor (Neuron — SuSET), which simulates the function of biological neurons. The new transistor is capable of executing a weighted sum calculation of multiple input signals and threshold operation based on the results of the weighted summation, The basic structure and the variable threshold characteristics of the Neuron — SuSET are described based on the semiclassical model.

Two-ring systolic array network for artificial neural networks

The paper describes an efficient and fast systolic architecture for the implementation of artificial neural networks. The proposed architecture, TORAN (two-in-one ring array network), combines two-ring systolic array networks to perform two sets of weight-data pair multiplication operations in parallel. This increases the hardware by 40% but reduces by 50% the number of cycles to perform the weighted sum calculation compared with a single systolic ring-array network.

Efficient two-dimensional systolic array architecture for multilayer neural network

An efficient two-dimensional systolic array architecture for a multilayer perceptron neural network is presented. This structure can perform the weighted sum calculation in only N cycles, for an N × N network. A new technique is introduced to eliminate the problem of long connecting lines in the proposed systolic structure.