2,794 research outputs found
Complexity Analysis of Balloon Drawing for Rooted Trees
In a balloon drawing of a tree, all the children under the same parent are
placed on the circumference of the circle centered at their parent, and the
radius of the circle centered at each node along any path from the root
reflects the number of descendants associated with the node. Among various
styles of tree drawings reported in the literature, the balloon drawing enjoys
a desirable feature of displaying tree structures in a rather balanced fashion.
For each internal node in a balloon drawing, the ray from the node to each of
its children divides the wedge accommodating the subtree rooted at the child
into two sub-wedges. Depending on whether the two sub-wedge angles are required
to be identical or not, a balloon drawing can further be divided into two
types: even sub-wedge and uneven sub-wedge types. In the most general case, for
any internal node in the tree there are two dimensions of freedom that affect
the quality of a balloon drawing: (1) altering the order in which the children
of the node appear in the drawing, and (2) for the subtree rooted at each child
of the node, flipping the two sub-wedges of the subtree. In this paper, we give
a comprehensive complexity analysis for optimizing balloon drawings of rooted
trees with respect to angular resolution, aspect ratio and standard deviation
of angles under various drawing cases depending on whether the tree is of even
or uneven sub-wedge type and whether (1) and (2) above are allowed. It turns
out that some are NP-complete while others can be solved in polynomial time. We
also derive approximation algorithms for those that are intractable in general
NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series
As more connected devices are implemented in a cyber-physical world and data
is expected to be collected and processed in real time, the ability to handle
time series data has become increasingly significant. To help analyze time
series in data mining applications, many time series representation approaches
have been proposed to convert a raw time series into another series for
representing the original time series. However, existing approaches are not
designed for open-ended time series (which is a sequence of data points being
continuously collected at a fixed interval without any length limit) because
these approaches need to know the total length of the target time series in
advance and pre-process the entire time series using normalization methods.
Furthermore, many representation approaches require users to configure and tune
some parameters beforehand in order to achieve satisfactory representation
results. In this paper, we propose NP-Free, a real-time Normalization-free and
Parameter-tuning-free representation approach for open-ended time series.
Without needing to use any normalization method or tune any parameter, NP-Free
can generate a representation for a raw time series on the fly by converting
each data point of the time series into a root-mean-square error (RMSE) value
based on Long Short-Term Memory (LSTM) and a Look-Back and Predict-Forward
strategy. To demonstrate the capability of NP-Free in representing time series,
we conducted several experiments based on real-world open-source time series
datasets. We also evaluated the time consumption of NP-Free in generating
representations.Comment: 9 pages, 12 figures, 9 tables, and this paper was accepted by 2023
IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC
2023
Hybrid Job-driven Scheduling for Virtual MapReduce Clusters
It is cost-efficient for a tenant with a limited budget to establish a
virtual MapReduce cluster by renting multiple virtual private servers (VPSs)
from a VPS provider. To provide an appropriate scheduling scheme for this type
of computing environment, we propose in this paper a hybrid job-driven
scheduling scheme (JoSS for short) from a tenant's perspective. JoSS provides
not only job level scheduling, but also map-task level scheduling and
reduce-task level scheduling. JoSS classifies MapReduce jobs based on job scale
and job type and designs an appropriate scheduling policy to schedule each
class of jobs. The goal is to improve data locality for both map tasks and
reduce tasks, avoid job starvation, and improve job execution performance. Two
variations of JoSS are further introduced to separately achieve a better
map-data locality and a faster task assignment. We conduct extensive
experiments to evaluate and compare the two variations with current scheduling
algorithms supported by Hadoop. The results show that the two variations
outperform the other tested algorithms in terms of map-data locality,
reduce-data locality, and network overhead without incurring significant
overhead. In addition, the two variations are separately suitable for different
MapReduce-workload scenarios and provide the best job performance among all
tested algorithms.Comment: 13 pages and 17 figure
Robust Optimization Design of Bolt-Shotcrete Support Structure in Tunnel
The uncertainty of rock and soil parameters is one of the key problems to limit the stability of tunnel support structure. Based on this, a robust optimization design method is proposed to reduce the sensitivity of support system to the uncertainty of rock and soil parameters. By defining the design parameters, noise factors and system response, a robust design system for bolt-shotcrete support structure is established. The non-dominant solutions of system robustness and support cost consist of the Pareto Front, then an knee point recognition method is designed to further filter all non-dominant solutions and determine the only optimal solution. The robust optimization design of the bolt-shotcrete support structure is carried out with a tunnel as the engineering background. The results show that the method can not only improve the stability and adaptability of the supporting structure, but also reduce the economic cost to the greatest extent, which provides a reference for the optimization design of other geotechnical engineering supporting structures
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?
Anomaly detection is the process of identifying unexpected events or
ab-normalities in data, and it has been applied in many different areas such as
system monitoring, fraud detection, healthcare, intrusion detection, etc.
Providing real-time, lightweight, and proactive anomaly detection for time
series with neither human intervention nor domain knowledge could be highly
valuable since it reduces human effort and enables appropriate countermeasures
to be undertaken before a disastrous event occurs. To our knowledge, RePAD
(Real-time Proactive Anomaly Detection algorithm) is a generic approach with
all above-mentioned features. To achieve real-time and lightweight detection,
RePAD utilizes Long Short-Term Memory (LSTM) to detect whether or not each
upcoming data point is anomalous based on short-term historical data points.
However, it is unclear that how different amounts of historical data points
affect the performance of RePAD. Therefore, in this paper, we investigate the
impact of different amounts of historical data on RePAD by introducing a set of
performance metrics that cover novel detection accuracy measures, time
efficiency, readiness, and resource consumption, etc. Empirical experiments
based on real-world time series datasets are conducted to evaluate RePAD in
different scenarios, and the experimental results are presented and discussed.Comment: 12 pages, 5 figures, and 9 tables, Proceedings of the 35th
International Conference on Advanced Information Network-ing and Applications
(AINA 2021
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series
Anomaly detection is an active research topic in many different fields such
as intrusion detection, network monitoring, system health monitoring, IoT
healthcare, etc. However, many existing anomaly detection approaches require
either human intervention or domain knowledge, and may suffer from high
computation complexity, consequently hindering their applicability in
real-world scenarios. Therefore, a lightweight and ready-to-go approach that is
able to detect anomalies in real-time is highly sought-after. Such an approach
could be easily and immediately applied to perform time series anomaly
detection on any commodity machine. The approach could provide timely anomaly
alerts and by that enable appropriate countermeasures to be undertaken as early
as possible. With these goals in mind, this paper introduces ReRe, which is a
Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time
series. ReRe employs two lightweight Long Short-Term Memory (LSTM) models to
predict and jointly determine whether or not an upcoming data point is
anomalous based on short-term historical data points and two long-term
self-adaptive thresholds. Experiments based on real-world time-series datasets
demonstrate the good performance of ReRe in real-time anomaly detection without
requiring human intervention or domain knowledge.Comment: 10 pages, 9 figures, COMPSAC 202
A Wideband Printed Directional Antenna Array with Impedance Regulating Load
We proposed a broadband directional antenna array working at mobile communication frequency band, which achieves a relative bandwidth of 50.7%. This binary antenna array is fed by two branches of the balanced microstrip. To enhance the antenna bandwidth, we introduced a section of loading metal strip. The antenna prototype has a S11 lower than −10 dB within the 1.5 GHz to 2.52 GHz frequency band, particularly from 2.01 GHz to 2.50 GHz, the S11 is lower than −15 dB. The gain varies with relatively small variation within the working band, which is 5.4 dBi to 8.7 dBi
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