212 research outputs found
CORDIC algorithm and its applications
openThe CORDIC (Coordinate Rotation Digital Computer) algorithm is used for solving vast sets of functions such as trigonometric functions, hyperbolic functions and natural logarithms. This thesis is going to discuss how the algorithm works and its architecture implementation. It is also going to explore potential applications of the algorithm in digital communication systems, specifically for the realization of the DDS (Direct Digital Synthesis) and digital modulation.The CORDIC (Coordinate Rotation Digital Computer) algorithm is used for solving vast sets of functions such as trigonometric functions, hyperbolic functions and natural logarithms. This thesis is going to discuss how the algorithm works and its architecture implementation. It is also going to explore potential applications of the algorithm in digital communication systems, specifically for the realization of the DDS (Direct Digital Synthesis) and digital modulation
Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites
Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR)
based composites is a typical and crucial property in practical applications.
Previous studies show that the abrasion resistance can be calculated by the
multiple linear regression model. In our study, considering this relationship
can also be described into the non-linear conditions, a Multilayer Feed-forward
Neural Networks model with 3 nodes (MLFN-3) was successfully established to
describe the relationship between the abrasion resistance and other properties,
using 23 groups of data, with the RMS error 0.07. Our studies have proved that
Artificial Neural Networks (ANN) model can be used to predict the SSBR-based
composites, which is an accurate and robust process
Deep Task-specific Bottom Representation Network for Multi-Task Recommendation
Neural-based multi-task learning (MTL) has gained significant improvement,
and it has been successfully applied to recommendation system (RS). Recent deep
MTL methods for RS (e.g. MMoE, PLE) focus on designing soft gating-based
parameter-sharing networks that implicitly learn a generalized representation
for each task. However, MTL methods may suffer from performance degeneration
when dealing with conflicting tasks, as negative transfer effects can occur on
the task-shared bottom representation. This can result in a reduced capacity
for MTL methods to capture task-specific characteristics, ultimately impeding
their effectiveness and hindering the ability to generalize well on all tasks.
In this paper, we focus on the bottom representation learning of MTL in RS and
propose the Deep Task-specific Bottom Representation Network (DTRN) to
alleviate the negative transfer problem. DTRN obtains task-specific bottom
representation explicitly by making each task have its own representation
learning network in the bottom representation modeling stage. Specifically, it
extracts the user's interests from multiple types of behavior sequences for
each task through the parameter-efficient hypernetwork. To further obtain the
dedicated representation for each task, DTRN refines the representation of each
feature by employing a SENet-like network for each task. The two proposed
modules can achieve the purpose of getting task-specific bottom representation
to relieve tasks' mutual interference. Moreover, the proposed DTRN is flexible
to combine with existing MTL methods. Experiments on one public dataset and one
industrial dataset demonstrate the effectiveness of the proposed DTRN.Comment: CIKM'2
Entropy Optimization of Scale-Free Networks Robustness to Random Failures
Many networks are characterized by highly heterogeneous distributions of
links, which are called scale-free networks and the degree distributions follow
. We study the robustness of scale-free networks to
random failures from the character of their heterogeneity. Entropy of the
degree distribution can be an average measure of a network's heterogeneity.
Optimization of scale-free network robustness to random failures with average
connectivity constant is equivalent to maximize the entropy of the degree
distribution. By examining the relationship of entropy of the degree
distribution, scaling exponent and the minimal connectivity, we get the optimal
design of scale-free network to random failures. We conclude that entropy of
the degree distribution is an effective measure of network's resilience to
random failures.Comment: 9 pages, 5 figures, accepted by Physica
Optimization of network structure to random failures
Network's resilience to the malfunction of its components has been of great
concern. The goal of this work is to determine the network design guidelines,
which maximizes the network efficiency while keeping the cost of the network
(that is the average connectivity) constant. With a global optimization method,
memory tabu search (MTS), we get the optimal network structure with the
approximately best efficiency. We analyze the statistical characters of the
network and find that a network with a small quantity of hub nodes, high degree
of clustering may be much more resilient to perturbations than a random network
and the optimal network is one kind of highly heterogeneous networks. The
results strongly suggest that networks with higher efficiency are more robust
to random failures. In addition, we propose a simple model to describe the
statistical properties of the optimal network and investigate the
synchronizability of this model.Comment: 11 pages, 6 figures, accepted by Physica
Effects of Cutting Intensity on Soil Physical and Chemical Properties in a Mixed Natural Forest in Southeastern China
The mixed Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), Masson’s pine (Pinus massoniana Lamb.), and hardwood forest is a major forest type in China and of national and international importance in terms of its provision of both timber and ecosystem services. However, over-harvesting has threatened its long-term productivity and sustainability. We examined the impacts of timber harvesting intensity on soil physical and chemical properties 10 and 15 years after cutting using the research plots established with a randomized block design. We considered five treatments, including clear cutting and low (13.0% removal of growing stock volume), medium (29.1%), high (45.8%), and extra-high (67.1) intensities of selective cutting with non-cutting as the control. The impact on overall soil properties derived from principal component analysis showed increasing with a rise in cutting intensity, and the most critical impact was on soil nutrients, P and K in particular. Soil nutrient loss associated with timber harvesting even at a low cutting intensity could lead to nutrient deficits in this forest although most of the soil physical properties could be recovered under the low and medium intensities of cutting. These results indicate that clear cutting and the selective cutting of extra-high and high intensities should be avoided in this type of forest in the region
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