Optimization of Information System Resources ... - Herokuytanno.herokuapp.com/MasterThesis.pdf · A...
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A thesis submitted in partial satisfaction of
the requirement for the degree of
Master of Computer Science and Engineering
in the Graduate School of
the University of Aizu
Optimization of Information System
Resources Based on Information of System
User Behaviors
by
Yoshinobu Tanno
March 2012
iii
Contents
Chapter 1. Introduction...........................................................................................1
1.1. Related Works .....................................................................................................3
1.1.1 Green Computing…………………………………………………………………….3
1.1.2 Smart Grid…………………………………………………………………………….3
1.2. Goal of this research ...........................................................................................4
Chapter 2. Proposed Scheme …………………………………………….....................5
Chapter 3. Sample Implementation of the Proposal Scheme…………….………..6
3.1 Tracking Human Behavior …………………………………………………………...6
3.1.1 Sensors for Detecting Human ……………………………………………………...6
3.1.2 Implementation of the proposal Tracking Human Behavior...........................8
3.2. Electronic Power Controller............................................................................10
3.2.1. Management System for Display ………………………………………………..11
3.2.2. The Proposal Management System for Machine……………………………...12
Chapter 4. Performance Evaluation of the Proposal Scheme ..............................14
4.1. Research of Consumed Power……………..........................................................14
4.2. Preciseness of the Proposed Tracking Human Behavior…………………….....16
Conclusion ................................................................................................................17
Reference ..................................................................................................................18
iv
List of Figures
Figure 1 Passage of Electric Power Generation in Key Country……………2
Figure 2 Penetration of Personal Computer in Japan………………………..2
Figure 3 Goal of this research…………………………………………………….4
Figure 4 Sample in Human Tracking from Motion……………………………7
Figure 5 Images of Sensor Data in Kinect………………………………..…….8
Figure 6 Front Case Sample Data……………………………………………….10
Figure 7 Experiment of Environment for This System……………………….11
Figure 8 Sample Implementation of Machine Power Switching…………….12
Figure 9 The Rate of Working Time in Life Time of Japanese …….……...15
Figure 10 Comparing Power Consumption of Display………………………..15
List of Tables
Table 1 Accuracy of Human Body direction…………………………………….16
v
Acknowledgement
I would like to express my appreciation to Professor Takafumi Hayashi for his appropriate advice and diligent efforts in my research. And I also thank the members of our laboratory.
Abstract
Nowadays, consumed power increased with increasing computer usage.
Increasing dissipation power affects country that cannot supply stable
electric-power. Lack of stable power supply in a country damage on public
institution which needs to manage continuously in hospital and bank. There
are many methods to manage power supply but the existing method cannot
ensure stable power supply.
This research proposed a scheme for reducing power consumption because
increasing electric power is difficult to build electric generation plant. And this
research utilizes human behavior because it enables to detail control and it is
possible to reduce power optimally because computer can be integrated under
a proper management.
There are two core approaches to realize reducing power in this research.
First, managing power of personal computer structured system in display and
machine electric current source. This system used Windows, client server
model, wake on local area network, active directory, kinect-sdk-beta1 and can
realized to control power. Second, detecting human body direction back or
front used kinect sensor. The Kinect sensor can detect position of human parts
but cannot detect detail of human motion and pose. Therefore, a system to
detect human body direction was constructed and verified. The result
detecting human body direction back or front calculated 73.46±5%. Result of
these two shows realizable reducing power. In the future, human body
direction needs to ensure more flexible energy saving.
1
Chapter 1. Introduction
Power consumption increases is caused by spreading consumer electronics.
Moreover, the electronics will require more electronic power, because they will
be embedded with high performance computers. As a result, there is potential
of a power shortage. If the power shortage suddenly happened, people can be
injured from situations that machines such as medical instruments stop
working by no-supply electronic power. By the way, the total of supplying
electric power is depended on the amount of electricity generated from power
plants. Though their supply are fixed, the demand of electric power increase,
shown in Fig 1 [1]. And Fig 2 [2] shows the shipment of computers. Shipment
of this figure has increased in each of the years. The results, shows that
continue to increase it and increase power consumption. The problem is
practical from their conditions. So the technologies to manage power
consumption are required for avoiding the electric power crisis. There are two
means to avoid the crisis; increasing a supply of electric power or adopting
technologies to economize on electric power.
At first, avoiding the crisis is thought to be in increasing a supply of electric
power. Electric-generating capacity is decided by number of electric
generation plant. So, it’s growth increase in the supply of electric power. In
Japan produces the electric power through atomic power generation, thermal
power, hydropower generation, and so on. Atomic power generation overtops
other way to generate electricity, in contract damage of radiation leak
problems (Chernobyl, Three Mile Island, Fukushima). Thermal power is
superiority of electricity production along with atomic power, but fossil fuel
used. Therefore, the country has not fossil fuel needs to import it. So it is
difficult to supply electric power stationary for exhaustion and price
movements of fossil fuel. Hydraulic power is stable in the power production
unlike thermal power. But its electric production is less than atomic power
and thermal, it has the problem of destruction of nature. Each ways to
generate electricity have the problems and difficult to build the electric
generic plant.
2
Figure 1 Passage of Electric Power Generation in Key Country
Electric power generation of countries, China, Korea, India,
Brazil, Japan, America, France, Germany and Russia, from 1990 to 2009.
Vertical line shows percentage based on amount of power generation in 1990.
Figure 2 Penetration of Personal Computer in Japan
Figure 2 shows the saturation level of personal computer in Japan. (a) is the
saturation level in the number of households from 1987 to 2011. (b) is the level
which was measured from households excluded alone households.
(b)
(a)
3
1.1 Related Works
Adopting Technologies to Economize on Electric Power
There are two projects.
1.1.1 Green Computing
Green computing has been primarily minimizing power usage for datacenters
and technical equipment (such as desktops, projects) [3]. Green computing also
includes the goals of controlling and reducing the environmental footprint of computing
by minimizing the use and discharge of hazardous materials, conserving water and other
scarce resources, and reducing waste throughout the value chain [4]. To realize it, this
project virtualized machine resource and platform with VMware Server or Xen
and so on. Less number of machine, it is possible to reduce power. Other this
project refines parts of machine and machine for reducing power. For example,
there are reducing server named NX51 and cooling fan named Pulse Width
Modulation [5].
1.1.2 Smart Grid
Smart grid is a modern electric power-grid infrastructure for improved
efficiency, reliability, and safety, with smooth integration of renewable and
alternative energy sources, through automated control and modern
communication technologies [6]. Typical technology of smart grid is smart
metering system. Smart metering systems eliminate many labor-intensive
business processes, such as manual meter reading, field trips for service
connects and disconnects, on-demand reads, power outage and restoration
management, and other metering support functions. Smart metering systems
that continually communicate with smart meters are able to report loss of
voltage to an outage management system (OMS). A well-designed OMS
system can group and analyze customer calls using feeder models that provide
information about likely fault locations [7]. This information, in turn, is
passed along to crews that are working in the field to resolve fault conditions.
This used Google Power Meter and Microsoft Hohm [8].
4
1.2 Goal of This Research
It is not simply for Green computing to keep up the operational energy
consumption of computing equipment [9]. Because green computing has the
problem of economic, law, cycle of consumed power. And smart grid reduces
power what monitor and manage machine power. But there is a possibility of
high consumed power because smart grid need to improve monitor and
security [10] [11].
Both related approaches have disadvantage and could not keep pace human
action in real time. So this research proposed scheme which enables power to
manage automatically, more detail control than manually control regulation.
The goal of this research is illustrated in fig 3. Detecting detail of human
body direction in this research can develop novel method which individual
certification and motion recognition because detecting human body direction
used joint position X Y Z. A lot of join position data permit individual
certification and a lot of human body direction enables to recognize motion. If
this goal achieved, it is available various sectors in security, robot [15],
machine management, user customization, reducing power.
Figure 3 Goal of this research
Figure 3 shows goal of this research. Human body direction takes off
individual certification and cognition of action. These categories have an
impact in many sectors.
5
Capter2. Proposed Scheme
The proposed scheme can realize an effective power consumption
management based on human behavior. The proposed scheme requires only
small set of system which itself requires very small power consumption and
low management cost. The proposed scheme can manage the power
consumption of various systems by direct control with simple control. The
proposed scheme detects human pose using depth camera and manages
electronic consumption of devices connected to network. If the system
recognizes that the human does not use a particular device, the device is
changing suspension mode with orders from the system.
The proposed scheme uses an electronic connector embedded with systems
for turning on or off and depth camera for tracking human behavior. This
system only controls sensors in the connector for managing electronic power.
Therefore, target device of which a proposed scheme manages electronic power
is not need to embed with any other sensor.
Depth camera is a camera to measure distance of objects on its angle of view.
So it can recognize objects or human from a shape which can be formed from
group of objects on same distance.
A system which reduces amount of consuming electronic power is required to
reduce the number of mistaken movement decreases. Because one of most
consumption of electronic power is occurred when the device is starting,
amount of electricity consumption increases by turning on or off a device by
mistaken movement. In the case using time-based power manager, it cannot
detect which device is used or not. For example, that manager cannot
recognize behavior about watching display without no-input, but proposed
scheme can do using tracking human behavior with depth camera.
This research used tracing human behavior because it enables to detail
control.
Computer can supply and manage power in display controls inside of system.
And it is possible to reduce power optimally because computer can be
integrated management.
6
Capter3. Sample Implementation of
the Proposal Scheme
The proposed method consists of two function; tracking system of human
behavior and management system of electric power. This section explains
about constructed system. First, construction of tracking system is shown in
section 3.2. Then management system is shown in section 3.3.
3.1 Tracking Human Behavior
3.1.1 Sensors for Detecting Human
In this paper, Kinect sensor is used for tracking human behavior. This
sensor, which is made by Microsoft, consists of an infrared camera and an
infrared laser emitter for measuring distance. To measure the distance, it
measures depth as a triangulation process [12]. The laser source emits a single
beam which is split into multiple beams by a diffraction grating to create a
constant pattern of speckles projected onto the scene. This pattern is captured
by the infrared camera and is correlated against a reference pattern. The
reference pattern is obtained by capturing a plane at a known distance from
the sensor, and is stored in the memory of the sensor. The sensor using their
measuring method is classified in a depth camera. A RGB camera is also
equipped on a Kinect. So the sensor can provide information of depth and
camera image, simultaneously.
To tracking human behavior is required that the system can extract
human-posture from sensor data. For recognizing human posture, the
proposed method adopted a depth camera. One of popular method to tracking
walkers on station or street is a camera based method [13]. However, the
method using camera is difficult to recognize human-posture from images,
because it tracks walker behavior with gaps of before and after camera image.
On the other side, a depth camera is used to measure distances between the
cameras and objects and provides a depth map that is an aggregate of a result
about measuring distance. Therefore, the depth camera classifies recognized
object into human or others using the depth map. In other words, depth
camera can recognize human-body [14].
7
Fig 4 shows a result of detecting human silhouette in real time. Human
postures can be extracted from each image. Then the proposed method tracks
the behavior using a set of extracted postures. Kinect can detect RGB Image,
Depth Image, Skeletal Image, Position Data, shown in Fig 5. However, it is not
enough to detect human body direction, and the proposed method for tracking
required to detect human body direction.
Human State1 State2 State3 State4 State5
Motion
Figure 4 Sample in Human Tracking from Motion
Using sensor, human silhouette can be got continuously from motion. Frame
rate of this sensor is about 30fps in this research.
RGB Image (a) Depth Image (b)
8
Skeletal Image (c) Position Data (d)
Figure 5 Images of Sensor Data in Kinect
(a) Camera image. (b) Depth image. Each depth divided into color. (c)
Skeletal image measured from depth information (d) Axis information of body
parts. Kinect can estimate 20 parts of body from both camera image and depth
image. Then axis of each part is measured from the estimated result.
3.1.2 Implementation of the Proposal Tracking Human
Behavior
To detect human body direction back or front in a captured image, ten front
and back position data are used. These data and other sample data in 800
images and position data (Fig 6) are compared. Proposed scheme used
k-means algorithm because K-means is another popular clustering algorithm that has
been used in a variety of application domains, such as image segmentation and
information retrieval [16]. K-means can calculate distance of each object. And k of
value defined two, after got out except front and back body direction. To detect
back or front body direction, k-Nearest Neighbor method (k=3) because using
three points can be trilateration which raises probability of correct.
Detail of this method show following.
Definition
S: Sample case data
B: Back and Front case data
P: Human parts
D: Distance
First: sample data get out one data
9
Second: compare between got out x y z position data in human parts of 20
and back and front sample position data x y z (total 20 images data)
in human parts of 20 images in following
Third: sorted according to the 20 of the D in ascending order
Forth: get out top 3 data from sorted 20 of D
Fifth: image is judged as front or back by majority vote in top 3 of destination
data.
Repeat number of sample images from first to fifth.
This figure shows scrrenshot from running Kinect program in kinectSDK.
Back and Front sample based this data. Depth stream, Skeleton, Color
Video Stream is same in fig 5. (a) show frame rate.
Back Case Sample Data
(a)
10
Front Case Sample Data
Figure 6 Back Case Sample Data and Front Case Sample Data
This figure explain how used images data to detect human body direction.
These images are detected data of laboratory member and the author when
they open or close door in laboratory. Position data of these images is based
data to compare other images data.
3.2. Electronic Power Controller
More PC increase, more power increase. So this research implement to
reduce power system for PC related display and machine of power switching.
To implement, this system uses windows OS because Windows supported
Microsoft SDK and Kinect SDK. And it use Active Directory in machine
switching system because Active Directory can be managed other machine
efficiency. Therefore, existing machine also can be utilized in this system
because target device not need to embed with any other sensor for controlling
electronic power.
There are two systems for reducing electronic power. One is power of display.
Switching system. Two is power of machine switching system. This system
based environment of experiment for system shows Fig 7.
11
Figure 7 Experiment of Environment for This System
Figure 7 shows area to detect human data. If user goes to workspace, sensor
detects user and client run.
3.2.1. Management System for Display
While resting time is the user turns us back to display, display power switch
off. As a result, power can be reduced. When user in workspace Fig. 7 backs to
display, power of display is off.
Environment of usage
Machine: ProLiant ML 115 G5
Developing environment: Visual Studio
OS: Windows7 Professional 64 bit
Programing Language: C#
SDK: kinectSDK-v1.0-beta1-x64.msi
1 set a Kinect to machine
2 register back and front position data
3 execution of file
12
3.2.2. The Proposal Management System for Machine
It is possible for this system to control detail power which human cannot be
difficult to control. If user goes work space in as illustrated in Fig 7, the sensor
detects the user. When detecting user at first time, server sends start command to
client. While user works in work space, sensor continues to detect user. If user go
back home, sensor stops detecting user. Serve sends shutdown command to client
when Time of not detecting user is long time. This situation shows Fig 8.
Figure 8 Sample Implementation of Machine Power Switching
As a function of human body direction, Sensor in server sends shutdown or
run command to client machine flexibly.
Environment of usage
Machine: ProLiant ML 115 G5
OS: Windows7 Professional 64 bit (Client), Windows Server 2008 32bit (Server)
Developing environment: Visual Studio
Programing Language: C#
SDK: kinectSDK-v1.0-beta1-x64.msi
13
Client Server model
Server
1 install DNS server
2 install Active Directory Domain Server
3 add client machine name in Active directory
Client
1 set DNS address to server IP address
2 join server ’s domain
Setup power reduce system
1 server or other machine set Kinect
2 register back and front position data
3 execution of file
Machine power switching on System
If human is detected, server or other machine sends WoL (Wake on LAN)
command to client machine. WoL switches power in machine because WoL
send Magic Packet to Network card in machine. Magic packet is particular
packet, send mac address of client machine to broadcast sixteen [17]. Even if
machine shutdown, machine can be run because network card of client is wait
body direction. Everyone can use it because most of latest machine have this
function. Only WoL, Server not needs Active Directory.
Machine power switching off
To run remote machine, need to set sensor in server or other machine. To stop
client machine with this sensor, this server must have faculty of client’s
administrator. So this system needs active directory functions. Active
directory is a directory service made in Microsoft [18]. This server can be
structured client server model easily. Moreover this service is what server has
client’s administrator account. In fact server can send shutdown command to
client as administrator. This system run when Kinect is not detect any human
long time.
14
Capter4. Performance Evaluation of
the Proposed Scheme
4.1 Research of Consumed Power
To show proof of importance of reducing power, show electricity consumption
of a day in display because data of a day is vital unit. Used display is SHARP
LL-T1620H, to get apparent power used arduino shield named
WATTMETER2 and to get active power used smart outlet named FX-5204PS.
Apparent power is the product of effective current and effective voltage. Case
of direct current, apparent power and active power is same and consumed
power but case of alternating current, require active power because generate
wattles power. Both apparent power and active power shows in graph because
apparent power is likely to be used by personal electronics. To get value of
current and voltage in parallel, display link smart outlet and wattmeter2.
Average active power of display in all a day is 37.10W. Other apparent
power is 55.05W that product average of voltage in all a day (110.10V) with
smart outlet and average of current in all a day (0.5A) with arduino shield.
Apparent power is about 1321Wh that product 55.05W and 24 hours and
active power are about 890Wh that product 37.1W and 24 hours in
consumption of all a day. Other Apparent power is about 440Wh that product
55.05W and 8 hours and active power is about 296Wh that product 37.1W and
8 hours in consumption of saving power. 8 hours in saving power calculated
that purse data more than 50% in the rate of working person half-Hourly in
2010 [19] because data of more than 50% define general working time and
define general worker used display while this time. Value of saving power and
consumption power in all a day shows Fig 6.
Figure 6 of point is that compare consumption of saving power and all a day.
Active power reduces 594Wh and apparent power reduces 890Wh and shows
reducing power of 66%. Moreover general working time in Japanese is selected
as No.2 in 2011 [20]. General working time in Japan is longer nevertheless,
consumption power can reduce 66%. In fact, electric power saving is very
important and avail research.
15
Figure 9 The Rate of Working Time in Life Time of Japanese
This figure shows the number of persons which were working every 30
minutes [17]. The figure was a result that 4,905 peoples answered a
questionnaire of the Japan government.
Figure 10 Comparing Power Consumption of Display
This figure shows comparing power consumption between using saving and no
saving of electric power.
saving power
all a day
0
200
400
600
800
1000
1200
1400
activepower apparent
power
296 440
890
1330
saving power
all a day
(Wh)
16
4.2 Preciseness of the Proposed Tracking Human
Behavior
To manage power robustly, this research detected human body direction back
or front using k-means algorithm (k=2). To evaluate detected data of 800,
make a decision based on the following.
If shoulder and head looked to sensor, this image is front.
If shoulder and head looked to sensor against, this image is back.
If computer resulted data and my evaluation is same, computer of decision is
correct. Other is error. And to take a sample detected data, used following.
Definitions
n: Quantities of sampling
N: Largeness of parent population
e: maximum error
z: normal distribution matching fidelity
P: prospective rate of parent population
Number of evaluated data is randomness of 260 from 800 because 5% of
accident error can be allowance. This result is following. Total accuracy of
human body direction is 73.46 5%.
Table 1 Accuracy of Human Body direction
This table shows preciseness of human body direction. Total parameter is total
of category. Correct count is number that evaluated correct data in detecting
human behavior with k-means (k=2). Correct of probability is number that
dividing correct count by total value and add purse and minus five percent.
17
Capter5 Conclusion
Power consumption is increasing yearly because there is one factor what
shipment of computers also is increasing yearly. There exist projects which
include green computing and smart grid for this problem. But both projects
have each problem and not control power consumption which stands to human.
And so this research proposed scheme for reducing electricity power
appropriate to human body direction. This proposed system defined using
home but datacenter and office also can use it. If user’s behavior enabled to
control electric power in machine, it is possible for user to reduce electricity
without intercepting use of system. There are two main approaches of the
proposed scheme to realize reducing power.
First, managing power of personal computer structured system in display
and machine electric current source. This proposed system used windows,
client server model, wake on local area network, active directory,
kinect-sdk-beta1 and can realized to control power.
Second, detecting human body direction used kinect sensor. In the future,
human body direction needs to make energy saving more flexible. Low cost
and existing sensor can detect human position X Y Z but cannot detect human
body direction. So for detecting human sate, this research used 2-means
algorithm and k-Nearest Neighbor method (k=3) to compare between sample
position data of 10 back, front and other selected 273 at random in 800
position data to permit 5% error. In the result, calculated 73.46 5%.
Detecting human body direction in this research can develop a novel method
which individual certification and motion recognition because detecting
human body direction used joint position X Y Z. A lot of join position data
permit individual certification and a lot of human body direction enables to
recognize the human motion. If this goal achieved, it is available various
sectors in security, robot [14], machine management, user customization,
reducing power.
In the future, preciseness of recognizing human body direction needs to
increase because human body direction continues detecting in real time. And
not only human body direction of back and front but also left and right, skew
need to detect to make reducing power flexibility.
18
Reference [1] Community Data of Actual Condition, "PC Penetration in Household in
Japanese", http://www2.ttcn.ne.jp/honkawa/6200.html.
[2] Information Area of Electricity, "Section1 Environment of Energy in World
and Japanese",
http://www.fepc.or.jp/library/publication/pamphlet/nuclear/zumenshu/
[3] Robert Harmon, Haluk Demirkan, Nora Auseklis, Marisa Reinoso,”From
Green Computing to Sustainable IT:Developing a Sustainable Service
Orientation”, Proceedings of the 43rd Hawaii International Conference on
System Sciences – 2010.
[4] Auseklis, N., Demirkan, H. Hrmon, R. and Hefley, B., “Designing IT
Services for Sustainability and Business Values”, 2nd Annual International
Conference on Business and Sustainability: Designing Sustainability, October
15-17, 2008.
[5] ITpro Green IT Group of Repoters, ”Complete Understanding of Green IT
in Japanese”, June 19, 2008.
[6] Gungor, V.C., BinLu, Hancke, G.P, “Opportunities and Challenges of
Wireless Sensor Networks in Smart Grid”, IEEE TRANSACTIONS ON
INDUSTRIAL ELECTRONICS, VOL. 57, NO. 10, OCTOBER 2010.
[7] Cal LaPlace, “Realizing the smart grid of the future through AMI
technology” http://www.energyaxis.com/pdf/RealizingSmartGrid.pdf
[8] Tadahiro Gouda, “Smart Grid Textbook in Japanese”, February 24, 2011.
[9] David Wang,”Meeting Green Computing Challenges”,Electronics
Packaging Technlogy Conference, 2008. EPTC 2008. 10th.
[10] M. Amin and B. F. Wollenberg, “Toward a smart grid,” IEEE Power
Energy Mag., vol. 3, no. 5, pp. 34–41, Sep./Oct. 2005.
[11] R. Krishnan, “Meters of Tomorrow,” IEEE Power and Energy Magazine,
pp. 92–94, Mar. 2008.
[12] Norman Villaroman, Dale Rowe, Bret Swan, “Teaching Natural User
Interaction Using OpenNI and the Microsoft Kinect Sensor”,
http://sigite2011.sigite.org/wp-content/uploads/2011/10/session14-paper02.pdf
[13] Maged N Kamel Boulos, Bryan J Blanchard, Julio Montero, Aalap
Tripathy, Ricardo Gutierrez-Osuna,“Web GIS in practice X: a Microsoft Kinect
natural user interface for Google Earth navigation”, Kamel Boulos et al.
International Journal of Health Geographics 2011.
[14] K.Khoshelham,“ACCURACY ANALYSIS OF KINECT DEPTH DATA”,
http://www.isprs.org/proceedings/XXXVIII/5-W12/Papers/ls2011_submission_
19
40.pdf
[15] Wasim Menesi, Paul Vilchez, Mohammad Usman,”Natural User Interface
for Robot Armature Control”,
http://www.pvilchez.com/content/pdf/robonuiProposal.pdf
[16] Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schroedl,“Constrained
K-means Clustering with Background Knowledge”, Proceedings of the
Eighteenth International Conference on Machine Learning, 2001, p. 577-584.
[17] Wikipedia,”Wake-on-LAN”, http://ja.wikipedia.org/wiki/Wake-on-LAN
[18] Wikipedia,”ActiveDirectory”, http://ja.wikipedia.org/wiki/Active_Directory
[19] Toshiyuki Kobayashi, Emi Morofuji, Yoko Watanabe, Japanese Life
Time 2010 in Japanese ,
http://www.nhk.or.jp/bunken/summary/research/report/2011_04/20110401.pdf
[20] OECE,”Society at a Glance 2011 – OECD Social Indicators”
http://www.oecd.org/document/24/0,3746,en_2649_37419_2671576_1_1_1_374
19,00.html