1 Electrooptic Logic AND Gate Using a Single Microring Resonator M. Rakib Uddin*, Hafizuddin Helmi, Nur’azmina Lingas and Zainidi Haji Abdul Hamid Electrical and Electronic Engineering Programme Area, Faculty of Engineering Universiti Teknologi Brunei (UTB), Gadong, Brunei Darussalam *[email protected]Abstract - In this paper, an electrooptic logic AND gate is demonstrated using a single microring resonator. The main principle of the logic operation is the resonant shift due to the electro-optic effect which is responsible to change the effective index of the device material. The logic operation is justified by simulation with gate output in the domain of optical spectrum as well as timing diagrams. Extinction ratio of about 30 dB was recorded from the gate output spectrum. Timing diagrams test was performed with a 10 Gbps digital input signal and a clear AND output was achieved. Keywords- Silicon photonics; micro-ring resonator; Photonic AND gate. I. INTRODUCTION Photonic technology in computing is important for future high speed computers and communications [1], [2]. In optical signal processing, like other photonic devices and circuits, optical logic gates have also received considerable attention [3]-[5]. In the field of photonic computer and communications, the logic units are the main building blocks and in communication networks they can enable many advanced functions such as all-optical bit-pattern recognition, all-optical packet header and payload separation, all-optical bit error rate monitoring, all- optical label swapping, all-optical packet drop and so on. Microring resonator has potential applications in photonic computing. In references [6], [7] electro- optic logic gates are demonstrated using microring resonator; however, they used two cascaded rings to achieve the basic logic functions. In this paper, we propose a quite simple circuit using a single mirroring resonator instead of two cascaded rings to get logic AND logic functions. We design, simulate and demonstrate the results of logic AND gate. The gate is designed using a single micro ring resonator. The gate operation is verified by the optical spectrums as well as by the waveforms of 10 Gb/s data inputs. II. PRINCIPLE OF OPERATION A micro-ring resonator with external voltage applied exhibits a property of resonant shift [8], [9]. These changes can be achieved by various methods and amongst all methods, the convenient and instant method is the electric fields passing through the materials which in turn causes an electro-optic effect, i.e., the material index changes and then the resonance is shifted. Using this behaviour, logic AND gate function is demonstrated. The schematic of the logic circuit using a single ring along with the symbol and the truth table of AND gate is shown in Fig. 1. Optical Input Optical Gate Output Gate Inputs (Electrical) A B A B Output 0 0 0 0 1 0 1 0 0 1 1 1 A B Output (a) (b) (c) Fig. 1 The schematic of the logic circuit using a single ring along with the symbol and the truth table of AND gate. Fig. 1 (a) is the schematic diagram of the single bus waveguide ring resonator. The single bus waveguide microring resonator has only one straight waveguide which has two ports including input port and throughput port. The straight waveguide is closely located with the ring waveguide. The diameter of the ring is dependent on the requirements of the device. The gap between the straight waveguide and the ring is also an important parameter. The coupling coefficient is a function of the gap. The waveguide dimensions are also depends on the design requirements. The typical width of the waveguide is 500 nm and the height of the waveguide is 220 nm. The gap is about 100 nm. An electrical excitation is applied to the ring resonator which is shown by “A, B” in the schematic diagram in Fig. 1 (a). The symbolic diagram of the AND gate is shown in Fig. 1 (b). The truth table of the logic AND functions are shown in Fig. 1 (c). The definition of AND logic is
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1
Electrooptic Logic AND Gate Using a
Single Microring Resonator
M. Rakib Uddin*, Hafizuddin Helmi, Nur’azmina Lingas and Zainidi Haji Abdul Hamid
Electrical and Electronic Engineering Programme Area, Faculty of Engineering
Universiti Teknologi Brunei (UTB), Gadong, Brunei Darussalam
future high speed computers and communications [1], [2]. In optical signal processing, like other photonic devices and circuits, optical logic gates have also received considerable attention [3]-[5]. In the field of photonic computer and communications, the logic units are the main building blocks and in communication networks they can enable many advanced functions such as all-optical bit-pattern recognition, all-optical packet header and payload separation, all-optical bit error rate monitoring, all-optical label swapping, all-optical packet drop and so on. Microring resonator has potential applications in photonic computing. In references [6], [7] electro-optic logic gates are demonstrated using microring resonator; however, they used two cascaded rings to achieve the basic logic functions. In this paper, we propose a quite simple circuit using a single mirroring resonator instead of two cascaded rings to get logic AND logic functions. We design, simulate and demonstrate the results of logic AND gate. The gate is designed using a single micro ring resonator. The gate operation is verified by the optical spectrums as well as by the waveforms of 10 Gb/s data inputs.
II. PRINCIPLE OF OPERATION
A micro-ring resonator with external voltage
applied exhibits a property of resonant shift [8], [9]. These changes can be achieved by various methods and amongst all methods, the convenient and instant method is the electric fields passing through the
materials which in turn causes an electro-optic effect, i.e., the material index changes and then the resonance is shifted. Using this behaviour, logic AND gate function is demonstrated. The schematic of the logic circuit using a single ring along with the symbol and the truth table of AND gate is shown in Fig. 1.
Optical Input Optical Gate Output
Gate Inputs (Electrical)
A B
A B Output
0 0 0
0 1 0
1 0 0
1 1 1
A
B Output
(a)
(b) (c)
Fig. 1 The schematic of the logic circuit using a single ring along with the symbol and the truth table of AND gate.
Fig. 1 (a) is the schematic diagram of the single bus waveguide ring resonator. The single bus waveguide microring resonator has only one straight waveguide which has two ports including input port and throughput port. The straight waveguide is closely located with the ring waveguide. The diameter of the ring is dependent on the requirements of the device. The gap between the straight waveguide and the ring is also an important parameter. The coupling coefficient is a function of the gap. The waveguide dimensions are also depends on the design requirements. The typical width of the waveguide is 500 nm and the height of the waveguide is 220 nm. The gap is about 100 nm. An electrical excitation is applied to the ring resonator which is shown by “A, B” in the schematic diagram in Fig. 1 (a). The symbolic diagram of the AND gate is shown in Fig. 1 (b). The truth table of the logic AND functions are shown in Fig. 1 (c). The definition of AND logic is
the output is logic HIGH only if the inputs are logic HIGH, otherwise the outputs are logic LOW which is shown in the truth table in Fig. 1 (c).
The optical micro ring resonator used for the demonstration in Fig. 1 has a frequency of resonance at λr and with external voltage the resonant shift to λr’. The gate function is based on the resonant shift. The phenomena of the resonant shift is shown in Fig. 2 schematically for different gate voltages of AND operations. When an optical CW light is sent to the ring resonator input port with the wavelength of λr, it gives an optical throughput output with low power, which can be classified as optical logic ‘0’. When sufficient electrical supply is sent to the resonator input port, the ring resonator output optical power increases to the point it can be considered as logic ‘1’.
In Fig. 2 (a), it is shown schematically that for the voltage inputs 0, 0; the resonant shift is not sufficient; So that the optical output is low and is it considered as logic “0”. In Fig. 2 (b), it is shown that for the voltage inputs either 0, 1; or 1, 0; the resonant shift is not sufficient too; So that the optical output is low and is it considered as logic “0”. Whereas when both the electrical inputs are high (1, 1), the resonant shift is sufficient which is shown in Fig. 2 (c). So that the optical output is high enough which can be considered as logic “1”. This is the schematic of logic AND gate operation. In the figure (Fig. 2), the wavelengths λr and λr’ represents the original and the shifted wavelength, respectively.
Fig. 2 The schematic of optical spectrum for logic AND function.
Fig. 2 (a) shows the low logic output when both the electrical logic inputs are low, i.e., both are 0s. In this case, both the light wavelengths are located at the same position which means the measured light intensity at the original wavelength and the wavelength due to two electrical inputs A, B as LOW are same as logic LOW. Fig. 2 (b) shows the schematic of logic low output when either inputs is 0 or 1. If only one input is high, it is not enough to shift the resonance enough to get a higher optical power at the output. So, the output is logic low. In this case both the original resonant wavelength and the wavelength due the either A or B logic HIGH are located closely so that the intensity of the light at throughput port is still logic LOW. Fig. 2 (c) shows the 1, 1 operation. When both the electrical inputs are high, the resonance is shifted enough so that the output is high enough to consider the logic high, ‘1’. In this case it is shown that the original resonant wavelength and the wavelength due to the excited A, B are located at different location which makes sure that the light intensity at the original resonant point is high enough compare to the other three cases. This high intensity is
considered as logic HIGH. In all three figures in Fig. 2 the two dotted lines represent the two logic inputs. Base on this principle, we design our logic device in the simulation environment using a commercially certified software called “Lumerical (www.lumerical.com). In the software, there are four modules. We used the module, “INTERCONNECT” to design and simulated the logic operation of the AND gate based on a single photonic microring resonator. The justification of the logic operation in spectrum domain as well as using timing diagrams are conducted using this software which will be described in the following sections with output results. The design of light source, the ring resonator, electrical signals and the optical spectrum analyser were performed using the same “INTERCONNECT” environment.
with optical spectrum. A commercially certified simulation software called “Lumerical Solutions” [https://www.lumerical.com/] was used to simulate the logic function. The spectrum results in Fig. 3 show
that the optical intensity is LOGIC HIGH only when both the inputs are LOGIC HIGH which is the truth of AND gate. For four conditions (00, 01, 10, 11), the output intensities are -18 dB, -23 dB, -18 dB and 10 dB. 10 dB is logic high whereas -18 or lower dBs are logic low. So, output is LOGIC HIGH when
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-60
-40
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0
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Outp
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wer
(d
Bm
)
Wavelength (nm)
0,0
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-80
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0
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Outp
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wer
(d
Bm
)
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0,1
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Outp
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1,0
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Po
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(d
Bm
)
Wavelength (nm)
1,1
≈ -23dBm
≈ 10dBm
≈ -18dBm
≈ -18dBm
Fig. 3 Simulation results: AND logic output in spectrum domain.
4
Fig. 4 Simulation results: AND logic output timing
diagrams.
both inputs are high which is the AND gate truth
function. The spectrum results in Fig. 3 were
measured by the simulation and recorded using the
optical spectrum analyser. The input light was set to a
single wavelength light source to fit a certain resonant
wavelength. Fig 4 shows the timing diagrams of the
AND gate outputs simulated by the software. The
AND gate output was recorded using the through port
of the ring resonator. The timing diagrams also
verifies the AND operation. In Fig. 4, “A” and “B” are
the input waveforms and “AND” is the output. In the
figure it is shown that when inputs are 0, 0, the output
is “0. When inputs are 1, 0, or 0, 1, the output is
also“0”. When both inputs are 1, 1, the output is “1”
which are the truth functions of AND gate.
IV. CONCLUSION
In this paper, we have simulated and demonstrated a
novel scheme of digital micro-photonic logic gate
based on the opto-electronic effect in silicon photonic
micro ring resonator. The AND logic functions were
verified by both the optical spectrum and digital data
signal at the rate of 10 Gbps. Both the optical
spectrum and the digital waveforms, proved the AND
logic operation using a single micro ring resonator. It
is noted that the optical micro-ring resonator can be a
promising micro device for photonic logic design.
Since the AND gate is the basic logic gate, it can be
the basis for the micro-photonic digital building
blocks for future optical computers and
communication applications.
ACKNOWLEDGEMENT
The authors gratefully acknowledge use of Graduate
Studies and Research Office Grant [UTB/GSR/1/2016
(1)] at Universiti Teknologi Brunei (UTB), Brunei
Darussalam.
REFERENCES
[1] H. J. Caulfield and S. Dolev, “Why future supercomputing requires optics,” Nat. Photon., vol. 4, pp. 261–263, 2010. [3] D. A.
B. Miller, “The role of optics in computing,” Nat. Photon., vol. 4, p.
406, 2010. [2] D. A. B. Miller, “The role of optics in computing,” Nat. Photon.,
vol. 4, p. 406, 2010.
[3] H. Yoo H. J. lee, Y. D. Jeong, and Y. H. Won, “All-optical logic gates using absorption modulation of an injection-locked Fabry-
Perot laser diode,” in proc. Photonics in Switching, Greece, Oct. 16-
18, 2006. [4] M. R. Uddin, J. S. Lim, Y. D. Jeong and Y. H. Won, "All-optical
digital logic gates using single-mode Fabry-Perot laser diodes,"
Photonics Technology Letters, IEEE, vol. 21, no. 19, 2010. [5] G. Berrettini, A. Simi, A. Malacarne, A. Bogoni, and L. Poti,
“Ultrafast integrable and reconfigurable XNOR, AND, NOR, and
The use of magnetic motor to generate electricity ever since in
the 18 century. In most cases, external resources such as hydro
and wind are needed to power the magnetic motor before an
induce electricity can be produced. In this study, a magnetic
motor is design to self-rotate it’s rotor by naturally repulsion
and the attraction of magnetic field by arranging the
magnets into Halbach array. The self-rotation magnetic
motor sometime it is known as a machine that produce
“Free Electric Energy”. Based from the results the rotor is
rotating at the constant speed that produced the torque
that lead to the development of mechanical power.
Keywords: Induction Motor, Electrical Energy,
Halbach Array, Magnetic Motor.
I. INTRODUCTION
The term “free energy” is not maybe a gas station
giving away gas however this is not the case for Nikola
Tesla where he was the first one to identify “radiant
energy” where energy harvesting the Sun. Nikola Tesla is
the key researcher in free energy theories and invented
most of the free energy devices. Tesla introduced two free
energy theories. The earlier is known as Crooke’s
radiometer and later as “cosmic-ray motor” which he
claimed to be “thousands of times more powerful” as
compare to Crooke’s radiometer. Tesla’s free energy
concept was patented in 1901 as an “Apparatus for the
Utilization of Radiant Energy.” In 1932, Tesla claimed has
successful harnessed the cosmic rays. The radiant energy
receiver stored static electricity obtained from the air and
converted it to a usable form [1, 2]. However, Tesla’s free
energy are not from the magnetic motor generator that
produce the electricity.
In this study, a free energy is created from permanent
magnet motor without utilizes resources from outside such
as burning fossil fuels namely coal, petroleum and natural
gas [3] to induced voltage. The free energy comes from the
naturally repulsion and the attraction of magnetic field that
creates the motion of electric motors. This self-running
electric motors is attached to a turbine motor shaft which
resulting an induced voltage.
The term, "Free Energy" is widely used and
often abused in the industry. Many believe no such
thing of free energy, or whatsoever machine capable to
generate energy out of nothing. In others word, there are
no such things of “perpetual motion machine” that can do
work continues indefinitely without utilizing external.
II. MAGNETIC MOTOR
The first magnetic motor was first deployed in 1880 is
a direct current (DC) magnetic motor when direct current
was the only source of power, until Nikola Tesla invented
the alternative current (AC) magnetic motor in 1889 where
in 1886 the starting era of AC power system in the world.
The induction energy from AC magnetic motors need
external resources for operations such as hydro dams to
and windmills. With the exception of solar
power, 95% of the induction of electricity in the
world comes from electromagnet -based power
generating systems by setting magnets into
motion while wrapping windings of magnet wire
around the magnet to induce electricity. Since
than the research development of an AC magnetic
motor growth has never stop.
The configuration of the magnets in the self -
running magnetic motor in this study using
Halbach array. The simplest Halbach array
configuration as shown in figure 1 is creating
strong magnetic field at one side while cancelling
the field to near zero on the others side of the
array. The magnetic field lines of the Halbach
array is shown in figure 2.
Figure 1: Simple Halbach array configuration
Strongest side of magnetic field
Canceled side of magnetic field
11
Figure 2: The magnetic field lines of Halbach array [4]
The main advantages of using Halbach array where the
magnetic field strength produced is very strong and
increases the efficiency of the magnetic circuit as compare
with others arrays configurations. However, the main
drawback of such configuration it’s difficult to assemble
and the magnets are arranged in a direct or quasi-direct
repelling condition that will act to demagnetize their
neighboring magnets. The Halbach array configuration
major applications of the one-sided flux distribution
ranging from the simple refrigerator magnet to much
complicated application deployed in the Maglev train
(magnetic levitation).
The general description of the Halbach array
configuration magnetization pattern was given by [5], a
simple superposition of two trigonometric functions as
shown in equations 1 & 2
where λ denotes the wavelength and the magnetization
amplitude.
III. SELF-RUNNING MAGNETIC MOTOR DESIGN
The self-running magnetic motor is designed with 3 layers
as shown in figure 3. The basis of the design is based on
[6]. The most inner magnets consists of 10 magnets, the
middle magnets is made of 14 magnets and the outer
magnets is comprises of 21 magnets. The magnets field
arrangements follow Halbach array [7]. The middle and
the outer layers are the stator whereas the most-inner layer
act as rotor. The inner radius of the magnets is at 4.1cm,
the middle magnets radius at 6.4cm and outer magnets
radius at 1.7cm with an air-gap of 0.3cm between the
magnets. The whole radius of the design is at 12.5cm. The
magnets material use “neodymium (NdFeB) n52” is the
strongest permanent magnet commercially available in the
market. The property of the NdFeB n52 magnet is shown
in Table 1 and it is made from an alloy of neodymium, iron
and boron.
Table 1: Property of NdFeB n52 magnet Remanance (Br) Coersive
Force Hcb (Hc)
Intrensic
Coersive
Force Hcj (Hj)
Maximum
Energy Product
(BH) Max
mT G K A/M Oe K A/M Oe KJ/m³ MGOe
1430 14300 796 10000 876 11000 398 50
Figure 3: Self-running magnetic motor with
Halback magnetic field directions
Figure 4 shows the 3D design of the self-running
magnetic motor
Figure 4: The 3D design of self-running
magnetic motor
IV. SIMULATION RESULTS
The self-running magnetic motor design is tested using
Finite Elements simulations tool. The Finite Elements
FEMM4.2 is an open source magnetic motor that provide
wide range of possibilities to simulate the design. Figure 5
shows the magnetic field strength of the self-running
magnetic motor obtained from FEMM4.2
Figure 5: The magnetic field strength
12
Figure 6 shows the simulation results of relative
centrifugal force (RFC) produces by the self-running
magnetic motor. It is notice from the graph for 360 turn it of the self-running magnetic motor produces two cycles response. This is due that the rotor have ten magnets where the arrangement is set into two sets of Halbach array. Each set of Halbach array results in one complete cycle.
Figure 6: RFC waveform
To confirm that the rotor is rotating at a constant speed 10
cycles of 360 were simulated and the results are shown in
in Figure 7 and 8. Constant responses were recorded in
figure 7 showing that the rotor is rotating at a constant
speed.
Figure 7: RCF responses of 10 cycles
Figure 8 shows the harmonics of the 10 cycles. The
harmonics response of the 10 cycles shows the same as of
figure 6. This shows the design of the self-running
magnetic motor the rotor is rotating at the same frequency.
Torque is another important parameter that can be
measured from the simulation. Figure 9, shows the torque
response as expected it’s produced two cycles from 360
turn from the self-running magnetic motor as can be seen in figure 6 of RCF.
Figure 8: RCF Harmonics of the 10 cycles.
Figure 9: Torque response form simulation
From equation (3) the revolution per minute (rpm) of
the rotor in the self-running magnetic motor can be
calculated. The rpm is needed as it is part of the equation
(4) to find the mechanical power.
where
RCF = relative centrifugal force,
r = centrifugal radius in mm
From (3) the rpm can be plotted as shown in figure 10.
13
Figure 10: Revolution per minute (rpm) response
Once torque is obtained the mechanical power can be
calculated by using the equation shown in (4):
The main objective of this study and the most important is
the capability of the self-running magntic motor to induce
electricity. In normal cases the efficiency of the generator
are working around 90% to produce electrical power.
Hence, the electrical power can be derived from
mechanical power as shown in equation (5). The
comparison output of the mechanical power versus
electrical power are as shown in figure 11.
Electrical Power = 0.9 x Mechanical Power (5)
Figure 11: Mechanical power from self-running
magnetic motor
V. CONCLUSION
From the study, it can be concluded it is possible to
induce electricity from self-running magnetic motor.
However, this primarily finding will needs further
investigation before a prototype can be developed.
Abstract— Power Systems are prone to damage due to
overcurrent which is a result of faults like ground faults, line
faults, short circuit etc. To minimize the damage caused by these
faults suitable protection systems must be in place. The
protection systems consist of a primary system and a backup
system with proper coordination between the two systems (i.e.
their tripping time) in order to ensure proper clearance of faults
in minimum time. In this paper, the optimization of overcurrent
relays which are used primarily as backup systems against these
faults are analyzed. The application of Ant Colony Optimization
and Two Phase Simplex Algorithm in radial system is done to
obtain the time multiplier settings of the relays. This enables us to
achieve proper coordination between the overcurrent relays in
the network.
Index Terms— Ant Colony Optimization, Protection, Radial
Distribution, Swarm Intelligence, Two Phase Simplex Method.
I. INTRODUCTION
Electric Power is transmitted to the consumers from
generation centers through distribution systems. The electric power needs to be transmitted at low voltage levels to minimize loss and is stepped down at substation. Using primary feeders this stepped down electric power is fed to the distribution transformers. The type of distribution used depends on location and economics, but it is easier to coordinate current based devices if they are in a radial network [1-4].
A radial network consists of one power source and group of customers in series. All the customers are affected if there is a power failure. In order to minimize the damage to the system and interruption of power supply the importance of reliable protective systems is paramount and this is done keeping in mind that the occurrence of abnormalities in power systems is unavoidable [5-10]. Distribution systems in general, have two lines of defense a primary protection system and a backup system. The primary system acts as the first line of defense against faults the backup system comes into play in case of failure of the primary system [11-14]. The backup system would operate only after a certain period of time known as Coordination Time Interval (CTI) in order to give a chance to the primary system to operate [15-17]. The primary system consists of overcurrent relays. With the help of current and voltage transformers, the relays detect faults. The settings
of the relay must be done in a way that the relay located closest to the fault should have the minimum time of operation. Nevertheless, complex situations may arise which can lead to the faulty operation of relays. Hence, optimum coordination between relays is necessary [18].
This paper implements the use of Swarm Intelligence
Algorithm namely Ant Colony Optimization and Two Phase
Simplex Optimization to find the optimum Time Multiplier
Settings (TMS) of the relays in order to ensure minimum time
of operation of relays.
II. OVERCURRENT RELAY COORDINATION OF A
TWO-BUS RADIAL SYSTEM
There are two types of overcurrent relays: directional and non-directional relays. As Directional overcurrent (DOC) relays, do not require coordination with the relays behind them, they are preferred over the non-directional relays.
Figure1 : A radial two bus system
A radial feeder with two sections and feeders is shown in Fig 1. For a fault at F, relay R2 will be the first to operate. R2 operates after 0.1 sec time after the inception of the fault in order to protect the relay from transient current surges in the relay. Relay R1 should operate after a fixed time interval, CTI, which is equal to the sum of operation time of circuit breaker at bus 2, overshoot time of relay R2 and 0.1 sec. Similarly, these conditions can be expanded to larger networks. Using these constraints, the system is formulated as a Linear Programming Problem (LPP) and solution is obtained using the Two Phase Simplex and Ant Colony Optimization Algorithm. In this manner, we obtain the TMS and time of operation of relays R1 and R2.
25
A. Problem Formulation
DOC relays require two main parameters to operate
namely relay current settings and the time TMS [8]. Relay
settings depend on the maximum load current in the feeder.
TMS is obtained by minimizing the objective function [9-15]:
n
Min z = Σ topi (1)
i=1
where,
topi operating time of the primary relay i, for a fault at i
under the following constraints [2]:
B. Bounds on Operating Time –
topimin topi topimax (2)
where,
topimin the minimum time required for operation of
the relay at i for fault at ‘i’.
topimax time required for operation of the relay at i
for a fault at ‘i’.
C. Coordination Time Criteria –
Coordination time is the minimum time required
between operation of two relays [2].
tbopi -- topi ≥ Δt (3)
where,
tbopi - the operating time of the backup relay i, for a
fault at ‘i’.
Δt - the coordination time interval (CTI) [2].
D. Relay Characteristics –
Normal inverse definite minimum time (IDMT)
characteristics are assumed for all relays [2,5].
𝛼= 𝜆 (4)
(𝑃𝑆𝑀)𝛾−1
where,
λ is 0.14and is 0.02.
Plug multiplier setting (PSM) is given by
PSM = If (5)
CT ratio x Relay Setting
where,
If is the fault current (in A).
topi = λ ∗ (TMS) ∗ ((PSM)γ– 1) −1 (6)
i.e. topi = α(TMS) (7)
Substituting (7) in (1) gives the objective function as:
n
Min z = Σ αi(TMS)i (8)
i=1
The value of TMS is hence determined.
III. TWO PHASE SIMPLEX ALGORITHM
The two phase method is used to solve a linear
programming problem. It is used to retain optimality while
bringing the primal set of equation back to feasibility. It is
useful for re-optimizing a problem after a constraint is added
into a problem or some parameters of the same are changed so
that the previous optimal basis remains no longer feasible [4].
The algorithm is [4, 5]:
A. Algorithm
1. Start.
2. Try and convert the linear programming problem in
maximization form.
3. Check if all constraints are in ≥ form, if not then
convert them into the same.
4. Introduce slack variables to remove inequalities and
transform them into equalities.
5. Create a table by considering artificial coefficients as
basis variables.
6. Initialize the Cq values of non basis variables by
comparing it from the given equation.
7. Now fill up the table by entering all the values of
basis as well as non basis variables and also the RHS
column form the given constraint equations.
8. The Zq values of all the non basis variables are
calculated by the summation of product of cost and
the corresponding non basis values there after
calculate the values of Zq-Cq.
9. Now check whether all the values of Zq-Cq are
positive or not, if so then stop the process.
10. The column having most negative value of Zk-Cq is
taken as key column and the corresponding column
26
variable will be treated as the one that enters the
basis.
11. The values in the RHS column are divided by the
values in the corresponding key column for each row.
The row having minimum such values will be taken
as key row. The values obtained if found to be
negative will not be considered as key row.
12. The row having minimum value obtained from the
previous step, the variable corresponding to that row
leaves the basis.
13. The element corresponding to key row and key
column is taken as pivot element.
14. Make pivot element as one and make the
corresponding elements as zero by using row
transformation method in order to obtain a modified
table.
15. Develop the next improved solution by repeating the
process till all Zq-Cq becomes non-negative.
16. Repeat the same process for the second phase
iterations with the only difference in costs that are
taken as original coefficients of objective function,
until all the values of Zq-Cq becomes non negative.
17. The right hand side (RHS) column values obtained
in the final step gives the optimized solution.
18. end
IV. ANT COLONY OPTIMIZATION ALGORITHM
The Ant Colony Optimization(ACO) Algorithm uses the behavior of forging ants to determine the optimum solution of a problem. While foraging for food ants tend to distribute over an area to speed up the process. To indicate a path has been explored, each ant secrets pheromones while travelling. Thus the pheromone concentration for the most travelled path increases when the ants find the food source and the paths begin to overlap. As more ants follow the path with the highest pheromone concentration the pheromone in the other paths begin to evaporate with time. Thus, they compute the optimal path.
In this paper we applied the traditional ACO algorithm to solve an LPP in the continuous domain by thorough the method of recursive discretization. We used the constraints of the problem to define the boundaries of our solution. The algorithm is explained below.
A. Algorithm
1. Create Initial population of ants;
2. Set the boundaries of space for search using
constraint equations.
3. Discretize continuous domain into clustered points.
At first large size clusters are formed.
4. Spawn the ants at random location in space.
5. While(discretization factor > Set Value)
6. for i=1:n (all n ants)
7. Calculate the desirability of ants next location using
cost function and pheromone presence.
8. Move ant to most desirable point and increment the
pheromone value of that point.
9. end if
10. Ants cannot move from presents location.
11. Choose point with least cost value.
12. Define new space.
13. Decrement discretization.
14. end
First a fixed population of ants are spawned in a space defined by the constraints. The ants begin foraging by moving from one point to another by determining the desirability of the point and choosing the point with maximum desirability. The desirability of a point is determined by the function
Pm,n = (ταm,n)*(ηβ
m,n) (9)
Σ(ταm,n)*(ηβ
m,n)
Where m,n are the x and y coordinates of a point.
τ amount of pheromone on that point and α is the factor which controls the influence of pheromone.
η desirability of point based on cost function defined by the equation to be maximized and β is the factor which controls the influence of the cost function.
After moving to a point the ant updates the pheromone value of the point using the equation.
τmn = (1-ρ)τmn + ΣΔτkmn (10)
where
ρ pheromone evaporation rate
τkmn amount of pheromone deposited by the kth ant.
Δτkmn is calculated by the formulae,
Δτkmn = Q (11)
Lk
where
Q constant factor
Lk cost of the kth ant’s tour which in this case is the value of the cost function.
At the end of each iteration the minimum points of
the function are determined and the point which gives the least
value is defined as the new space for next iteration with an
decrease in the discretization factor.
V. RESULTS
2-bus Radial system
27
Consider the 2-bus radial system shown in Figure 1. It includes a 220 kV, 100 MVA source (also taken as the base kV and base MVA of the system). The CTI for the relay is taken as 0.57 s. The maximum fault current just beyond relay R1 is 2108A and beyond R2 is found to be 1703 A. Using the equations (2) and (4) the values of α are calculated and tabulated as shown in Table 1. Here we assume the upper limit of the TMS of both relays as 1.2 and the lower
R2 being the primary relay operates first when the fault occurs at F. Let R2 operate 0.2 s after the fault inception to ensure that it does not operate for current surges. Relay R1 should operate after the CTI, which equals to the sum of operating time of circuit breaker (CB) at bus 2, overshoot time of relay R1 and 0.2 sec.
TABLE 1
Relay Constants
FAULT LOCATION RELAY RA- Ap
CONSTANTS RELAY RB- Ap
CONSTANTS
JUST BEYOND A 3.21
JUST BEYOND B 7.38 3.57
Let x1 and x2 be TMS values of relay R1 and R2 respectively.
3.57*x1 – 3.57*x2 ≥ 0.57 (12)
Subject to the constraint,
3.21*x1 ≥ 0.2 (13)
And
3.57*x1≥ 0.2 (14)
The upper limit is taken at 1.2.
For (ACO) we set the discretization factor to 1 at first
and then decrement by one tenth of the original value for each
iteration for 5 such iteration to get our value our minimum
point to an accuracy of 10-5. A population is chosen of 50 ants
to start with as this helps to arrive at a minimum point faster.
The results of ACO are compared with the Two Phase
Simplex Algorithm and are tabulated as shown in Table 2.
Table 2 TMS values of relays
TMS of Relays
X1 X2
Two
Phase
simple
x
.215 .056
ACO 0.11574 0.05851
VI. INFERENCE
To optimize the TMS, two algorithms namely Ant
Colony Optimization and Two Phase simplex method have
been compared. It can been deduced that using ACO, the
optimization is higher and more effective
VII. CONCLUSION
The protection of distribution system from
overcurrent faults is very important for power system
protection engineers. OC relays are predominantly used in
radial distribution networks and are expected to identify and
isolate faults instantly. This paper compares the effectiveness
of Swarm Intelligence Algorithm ACO and Two Phase
Simplex Algorithm in finding the optimized solutions for time
multiplier setting and time of operation of relays. These
algorithms can also be conveniently extended to larger
distribution networks. Moreover, to better understand
distribution networks and to perceive its ability to minimize
and isolate faults, other optimization can be undertaken to
obtain case specific results.
REFERENCES
1. O.V.G. Swathika, and S. Hemamalini, “Prims-Aided Dijkstra Algorithm for
Adaptive Protection in Microgrids,” IEEE Journal of Emerging and Selected
Topics in Power Electronics, vol. 4(4), pp.1279-1286, 2016.
2. O.V.G.Swathika, A. Das, Y. Gupta, S. Mukhopadhyay, and S. Hemamalini,
“Optimization of Overcurrent Relays in Microgrid Using Interior Point
Method and Active Set Method,” In Springer Proceedings of the 5th
International Conference on Frontiers in Intelligent Computing: Theory and
Applications, pp. 89-97, 2017.
3. A. Gupta, O.V.G. Swathika, and S. Hemamalini, “Optimum Coordination
of Overcurrent Relays in Distribution Systems Using Big-M and Dual
Simplex Methods,” In IEEE Computational Intelligence and Communication
Networks, pp. 1540-1543, 2015.
4. O.V.G. Swathika, S. Mukhopadhyay, Y. Gupta, A. Das, and S.
Hemamalini, “Modified Cuckoo Search Algorithm for Fittest Relay
Identification in Microgrid,” In Springer Proceedings of the 5th International
Conference on Frontiers in Intelligent Computing: Theory and Applications,
pp. 81-87, 2017.
5. O.V.G. Swathika, S. Hemamalini, “Review on Microgrid and its Protection
Strategies,” International Journal of Renewable Energy Research, vol. 6(4),
pp.1574-1587, 2016.
6. O.V. Gnana Swathika, Indranil Bose, Bhaskar Roy, Suhit Kodgule, and S.
Hemamalini, “Optimization Techniques Based Adaptive Overcurrent
Protection in Microgrids,” Journal of Electrical Systems, Special Issue 3, vol.
10, 2016.
7. O.V. Gnana Swathika, and S. Hemamalini. Adaptive and Intelligent
Controller for Protection in Radial Distribution System. In Springer Advanced
28
Computer and Communication Engineering Technology, vol. 362, pp. 195-
209, 2016.
8. M. Sukumar Brahma and A. Adly Girgis, “Development of Adaptive
Protection for Distribution Systems With High Penetration of Distributed
Generation,” IEEE Transactions on Power Delivery, vol.19, pp. 56-63, 2004.
9. C. Vassilis Nikolaidis, Evangelos Papanikolaou, and S. Anastasia
Safigianni. “A Communication-Assisted Overcurrent Protection Scheme for
Radial Distribution Systems with Distributed Generation,” IEEE Transaction
on Smart Grid, 7(1), 114-123, 2016.
10. H.H. Zeineldin HH, E. F. El-Saadany, and M.M.A. Salama. “Distributed
Generation Micro-Grid Operation: Control and Protection,” In Power Systems
Conference: Advanced Metering, Protection, Control, Communication, and
Distributed Resources, pp. 105-111, 2006.
11. P. Prashant Bedekar, R. Sudhir Bhide, and S. Vijay Kale, “Optimum
Coordiantion of Overcurrent relays in Distribution system using Dual Simplex
Method”, In IEEE International Conference on Emerging Trends in
Engineering and Technology, pp. 555-559, 2009.
12. Y.G. Paithankar, and S.R. Bhide, “Fundamentals of Power System
Protection,” Prentice Hall of India Private Limited, New Delhi, 2007.
13. Badri Ram, and D.N. Vishwakarma, “Power System Protection and
Switchgear,” Tata McGraw Hill Publishing Company Limited, New Delhi,
2008.
14. B. K. Manohar Singh, B. K. Panigrahi and A. R. Abhyankar, “Optimal
Overcurrent Relay Coordination in Distribution System”, In IEEE
International Conference on Energy, Automation, and Signal, pp. 1-6, 2011
15. K. Deb.”Optimization for Engineering Design –Algorithms and
Examples,” Prentice Hall of India Private Limited, New Delhi, 2006.
16. P.P. Bedekar, and S.R. Bhide, “Optimization of multivariable nonlinear
functions using genetic algorithms,” In IEEE International Advance
Computing Conference, 2009.
17. R. Madhumitha, P. Sharma, D. Mewara, O.V.G. Swathika, and S.
Hemamalini, “Optimum Coordination of Overcurrent Relays Using Dual
Simplex and Genetic Algorithms”, In IEEE International Conference on
Computational Intelligence and Communication Networks, pp. 1544-1547,
2015.
18. Chao-Rong Chen, Cheng-Hung Lee, and Chi-Juin Chang, “Optimal
Overcurrent Relay Coordination in Power Distribution System Using a New
Approach”, International Journal of Electrical Power & Energy Systems,
45(1), pp.217-222, 2012.
29
An Improved Rules-based Control of Battery Energy
Storage for Hourly Power Dispatching of
Photovoltaic Sources
M. A. Jusoh and M. Z. Daud School of Ocean Engineering, Universiti Malaysia Terengganu,
Fig. 9 Eye diagram at 8 Gbps based on Fig.8 configuration. (Red-Traditional
Binary Signaling, Blue- CHS)
Based on existing research, the common-mode noise will
increase at conductor one in a nibble-to-nibble configuration.
However, with symmetric Novel Coplanar routing the issue is
eliminated based on the edgeside and broadside routing scheme
as illustrated in Fig. 10.
Fig. 10 Novel Coplanar Routing for nibble-to-nibble configurations
This configuration provides a better Return on Technology
Investment (ROTI) compared to traditional routing that will be
shown in the next research paper. Good eye opening has been
observed based on Fig.10 edge-side nibble-to-nibble
configuration as illustrated in Fig. 11 with 2 mil spacing
between signals.
43
Fig. 11 Eye diagram for edgeside nibble-to-nibble configuration at 8 Gbps data rate, ten-inch channel length and 2 mil trace spacing. (Red- Traditional Binary
Signaling, Blue-CHS)
Based on the above analysis, the concept can be extended to
stripline routing as shown in Fig. 12, which demonstrates that
this concept is feasible for this analysis except that the reference
signals need to be added between the common-mode signals.
The stripline configuration shows less crosstalk since the fields
will fringe in the substrate area, which is close to a
homogeneous configuration. In the Novel Coplanar routing
scheme, termination is not an issue and length mismatch can be
supported for up to 100 mils between the signal traces.
Fig. 12 Coplanar Strip approach on stripline nibble-to-nibble routing
In existing research, CHS shows the capability to support the
CHS 3D Novel routing scheme, and can potentially grow an
infinite n number of nibbles vertically or horizontally. There is
the potential that the Novel Coplanar Routing concept can be
applied to 3D CHS Novel routing by eliminating the reference
layer and thus achieving 2X higher bandwidth per volume.
V. CONCLUSION
The CHS concept paves the way for introducing new routing
schemes that will offer higher bandwidth per volume compared
to traditional routing with no design changes required on the
CHS scheme. This paper shows that with this new routing
scheme, the routing density about doubles compared to CHS 3D
Novel routing and CHS microstrip routing. Traditional routing
is not recommended for high speed designs due to high
crosstalk from reflection, and the existing crosstalk solution
does not help reduce the routing density compared to the CHS
method. Nevertheless, this novel routing scheme is relatively
new and further investigation is required to understand the
advantages and sensitivity of the CHS concept. In addition,
proofs of concept need to be conducted in the future. Besides
that, Return on Technology Investment (ROTI) is a new
concept that will be discussed in the next research paper, which
will include the advantages of new routing schemes such as
Novel Coplanar and CHS 3D Novel routing compared to
traditional routing.
ACKNOWLEDGMENT
Special thanks to Professor Dr. Paul G. Huray the author of
the books, Maxwell’s Equations and The Foundations of Signal
Integrity, Dr. Femi Oluwafemi, Stephen H. Hall from Intel,
USA and Tom McDonough from USC for their valuable
guidance throughout this research.
REFERENCES
[1] C. Sreerama, “Novel crosstalk mitigation solutions for high-speed
interconnects to maximize bus band-width and density,” 8th annual signal
integrity symposium, Penn State Harrisburg, PA, Apr. 4, 2014, pp. 1. [2] F. Broyde and E. Clavelier, “A new method for the reduction of crosstalk
and echo in multiconductor interconnections,” IEEE Trans. Circuits Syst.
I, Reg. Papers, Vol. 52, pp. 405-416, Feb. 2005. [3] C. R. Paul, “Analysis of multiconductor transmission lines,” 2nd ed., New
York, NY, Wiley-Interscience, 2007.
[4] S. H. Hall, H. L. Heck, “Advanced signal integrity for high-speed digital designs,” 1st ed., Hoboken, NJ, Wiley-IEEE Press, 2009.
[5] C. Yongjin, H. Braunisch, K. Aygun, and P. D. Franzon, “Analysis of
inter-bundle crosstalk in multimode signaling for high-density interconnects,” ECTC, Lake Buena Vista, FL, 2008, pp. 664-668.
Azniza Abd Aziz received her PhD. In electrical engineering
(signal integrity) from University of South Carolina. Her
current research interest includes Signal Integrity solutions for
high-speed data design. She was an Advanced Signal Integrity
Engineer in Intel, Penang, Malaysia and Senior Signal Integrity
Engineer in Hewlett Packard Enterprise, California, USA with
almost 10 years experiences working on designing, validation
desktop, mobile and server platforms. Currently, she is lecturer
at USM, Malaysia.
Chaitanya Sreerama is a staff hardware engineer at Intel Labs
in Hillsboro, OR. She received her B.S degree in electronics and
communication engineering form JNTU, India in 2001, M.S
degree in electrical engineering (numerical & computational
electromagnetics) from Clemson University in 2004, and PhD.
degree in electrical engineering (signal integrity) from
University of South Carolina in 2014. She has been working at
Intel since 2004, and her areas of expertise include EMI, RFI,
and Signal Integrity. In addition to patents (14) and publications
(4), she has been awarded the 2010 Intel Achievement Award
for her contributions to the company.
44
A Grounded Capacitance Multiplier Based on CCII
J. Vavra Univ. of Defence, Dept. of Electrical Engineering, Kounicova 65, 662 10 Brno, Czech Republic
orthosis, that have been used with patients who have CTS and
that its application has been effective [9]. Emerging
technologies such as Virtual Reality, VR, are beginning to be
applied in this type of disorders, allow to develop different
interactive scenarios capable of creating a simulation that
involves all the senses, in real time in the form of digital
images and sounds, giving the sensation of presence in this
virtual environment [6], [10-11]; there are applications that are
already being applied as is the case of the Mobile Mixed
Reality System (MoMiReS): a 3D game based on smartphones
that generates physical interactions with a wireless glove that
lead to a set of repetitive movements to rehabilitate problems
originated in the wrist of the hand, with games specifically for
the treatment of CTS, achieving high system approval ratings
[12-13].
In this context, the article proposes the development of a
virtual tool, in which interactive games are implemented in
which the rehabilitation exercises are allowed, for the CTS,
using robots that allow interaction in the proposed games with
the aim of completing rehabilitation to reduce the discomfort
caused by this syndrome.
This article is divided into 6 Sections including the
Introduction and References. Section 2 present the Problem
formulation, in which the traditional rehabilitation exercises
are described in a fast way; describes of the development of
the proposal in Section 3; in section 4 the usability analysis of
the proposed tool is presented and finally the conclusions are
detailed in Section 5.
II. PROBLEM FORMULATION
The CTS are considered as an occupational disease, some
56
authors [14] consider that the etiology of the CTS is largely
structural, genetic and biological, and that environmental and
occupational factors, such as the repetitive use of the hand,
generate this syndrome. Some of the work factors that have
been better related to the development of STC are those that
cause an increase in pressure in the carpal tunnel due to
inadequate estimation of the load on the hands. Examples of
tasks related to the STC stand out the specific position of the
hand during the performance of the task (dorsiflexion flexion,
extension and substitute), the resistance to overcome with the
fingers the grip and possession of an object, the pressure on
the hand, the repetitive movements and the work with
vibratory tools. These factors are frequently observed in the
work of people employed in meat processing, assembly of
sub-assemblies, packaging of products, or employees such as
supermarket cashiers and people who work with computers. Among the traditional exercises used as therapy to reduce the CTS are: i) Extend and stretch the wrists and fingers out, ii) Stretch both wrists forward and relax the fingers, iii) Make a frozen fist and turn to the left and iv) With your fist frozen, gently bend each wrist down. [15]
Figure 1. Extension and Stretching Exercises
Many of the traditional rehabilitation exercises can be performed on an individual basis, so that people suffering from this syndrome lose interest in carrying out rehabilitation exercises in a continuous manner, not complying with the time of the rehabilitation period indicated.
For the above, we propose a virtual tool that helps to
perform the rehabilitation exercises in a different way, through
interactive games in which these exercises can be done in an
entertaining way using robots as haptic devices that help in
carrying out the exercises and allow to record the speeds and
scopes of the movements, with which you can generate
historical evolution of the realization of them
III. PROPOSAL DESCRIPTION
The bilateral interaction between the patient and the virtual
environment is carried out through a spherical robot which is
controlled and manipulated by the patient while performing
the rehabilitation tasks determined by the virtual work
environment. The development of the virtual environment will
allow the spherical robot to emit an input signal to the virtual
environment that is analyzed to indicate whether the patient
successfully fulfilled the defined task by increasing the
complexity of the treatment, otherwise the instructions of the
rehabilitation process are repeated. In Fig. 2, the block
diagram proposed for the development of the virtual
environment is shown, which is divided into five parts.
System inputs, the input devices of the carpal tunnel
rehabilitation treatment system allow the capture of signals to
be interpreted and perform a predetermined action. The
devices used as inputs are: i) GearVR, this virtual device
allows the immersion of the patient in the virtual environment,
stimulating him to perform the rehabilitation treatment
proposed by the specialist; ii) Robot Sphero, this electronic
device allows bilateral interaction between the patient and the
virtual interface. Through the manipulation of the robot the
information is sent to fulfill the proposed rehabilitation task
while the virtual environment closes the control loop feeds the
patient through the robot Sphero the movement that must be
executed to perform the proposed task.
Figure 2. Block diagram of the virtual environment
Outputs: he system consists of electronic devices that emulate
movements, environments, sounds, among others; these output
devices are: virtual reality helmet, audio speakers and the
spherical robot that executes the movements according to the
fulfillment of the patient's rehabilitation treatment.
Virtual environment: the virtual reality environment is
developed in order to motivate the patient to carry out the
rehabilitation treatment proposed by the specialist, according
to the evolution of the patient, the complexity of the treatment
57
is increased allowing to record a record of compliance and
advances of the patient; the virtual environment was
developed on the Unity 3D platform, where it has the
respective programming of scripts that allow interacting with
the inputs and outputs of the system.
Control stage: the proposed control for the rehabilitation
treatment provides the patient's interaction in the virtual
environment through the feedback of position and speed by
means of the robot Sphero. The Sphero robot is capable of
sending and receiving signals with respect to the reference
system ( ),R X Y Fig. 3; As the patient manipulates the robot in
a coordinated and cooperative way, he will receive the
positions directly XP , YP and orientation 𝜓 allow for
monitoring in the execution of the treatment in order to
improve the rehabilitation process.
Figure 3. Reference system of Sphero Robot
Virtualization, the virtual rehabilitation environment provides
the patient with the necessary information, so that the
fulfillment of the tasks is understandable and friendly, i.e., the
environments created have a high level of immersion and
interaction with the patient. Immersion considers visual,
auditory and correction signals XF , YF , these are the ones in
charge of motivating and helping the patient in fulfilling the
tasks; while the interaction is effected through the Sphero
robot that sends the position XP , and orientation 𝜓 to the
virtual environment.
For the rehabilitation treatment of the carpal tunnel, is work in
two scenarios illustrated in Fig. 4, i) Safe box scenario,
instruction is presented that allows to open the safe for the
purpose that the patient moves from right to left the wrist; ii)
Labyrinth scenario. the exercises performed allow to stretch
the release of the pressure exerted by the median nerve.
(a) Safe box scenario
(b) Labyrinth scenario
Figure 4. Rehabilitation Scenarios
IV. METHODOLOGIC AND ANALYSIS
This section presents the methodology for the application of
interactive games using simulated instructions and actions,
using virtual reality and the Sphero robot, which are oriented
towards patients with STS depending on the intensity level of
the syndrome. The games present a series of exercises where it
is possible to control hand movement and record patient
information such as: i) intensity of movement ii) direction of
movement iii) duration of the exercise. Thus, is possible to
analyze the progress of rehabilitation, as well as the patient's
acceptability to the use of virtual environments as an
alternative treatment for STS. Depending on the specialist's
recommendations, two types of games have been proposed:
A. Game of Safe box
The game shows a safe that must be opened, entering the
correct combination, for this purpose the instructions to be
followed by the patient are indicated, i.e. position and
orientation. Each time a task is completed, the results are
displayed to record the progress of therapy. The game
interface is shown in the Fig4.
(a) Instructions
58
((b) Turn 30o to the left
(b) Turm 60o to the rigth
(c) Turm 55o to the left
(d) Error in instructions
Figure 5. Environment Game of Safe box
In this game, the patient must hold the Sphero simulating the
wheel to make movements from left to right, with many
repetitions given randomly, thus is possible to improve the
mobility of tissues that are being rehabilitated.
B. Game of Labyrinth
Applying an interactive virtual environment, it is proposed to
use the Sphero to mobilize within a labyrinth, in this game
obstacles and rewards are presented, so that in the course the
patient can be gaining points each time he catches a group of
coins, while if he collides with an obstacle will be penalized
returning to the initial location (see Fig.5).
Figure 6. Environment Game Labyrinth
While the game is running, the patient makes slight
movements from left to right and back and forth, if the patient
deviates from the predetermined path or collides with an
obstacle, the Sphero robot will send a correction signal that is
generated by means of force feedback.
Finally, to establish the interest in these games, a usability test
was carried out with two groups of people: i) patients (Qp)
who have undergone CTS treatment, and ii) physiotherapists.
For the first group, 4 CTS patients were considered who
59
combined traditional rehabilitation with two rehabilitation
sessions using the virtual tool. The second group included 3
physiotherapists, who interacted with the games so that they
could evaluate the experience in relation to the environment
and familiarization with the application. Fig. 6 shows the
results obtained for the questions posed with ten being the
highest weighting and zero the lowest weighting.
Table 1: Questions to evaluate the usability of the virtual
environment
Questions
Qp1. How familiar are you with handling devices that allow
immersion in virtual environments?
Qp2. Is the management of virtual environments easy?
Qp3. Is the execution of the games simple and intuitive?
Qp4. Are the limitations given by external noise (light, depth)
imperceptible?
Qp5. Does the equipment used cause no discomfort?
Qd1. Are patients motivated with this type of tool to perform
rehabilitation therapies?
Qd2. Does the system facilitate obtaining information about
the progress of rehabilitation?
Qd3. Does the incorporation of new games facilitate the
development of exercise routines?
Qd4 Does the system facilitate the detection of errors in
rehabilitation?
Qd5. Can the system be implemented for rehabilitation due to
similar traumas?
Figure 7. Questions results
In Fig. 6, it can be observed that both patients and
physiotherapists have acceptance for the use of this type of
exercise games to perform CTS therapies, also show the
possibility that they can be applied for the recovery of other
types of trauma.
V. CONCLUSION
In this work, a virtual system has been developed to
quantify the value of the CTS rehabilitation treatment variable
through a Sphero robot. The Sphero robot accepts linear and
angular displacements as inputs, while as outputs it provides
force and torque, which are used for system feedback
according to the type of rehabilitation movement the patient
performs.
The results obtained in the virtual environment implemented
for people suffering from STS show the efficiency of the
proposed system; the system developed was tested with
patients of different gender and age, under the supervision of a
physiotherapist who verified the validity of the movement, and
the results obtained were counteracted with traditional
therapies.
The system presents two series of virtual exercises, one in
which repetitive movements are performed to strengthen the
tissues and measure the patient's hand's ability to move; and
the other game presents a playful environment, giving the
patient greater security since it participates in the therapy as a
game and not as a therapeutic procedure, this method aims to
change the usual position of the hand or generate shocks that
help reduce pain or discomfort produced as a symptom.
REFERENCES
[1] McGaghie, W., Issenberg, B., Cohen, E., et al.: Does Simulation-based
Medical Education with Deliberate Practive Yield Better Results than Traditional Clinical Education? A Meta-Analytic Comparative Review
of the Evidence, Journal HHS Public Access, vol. 86, pp. 706-711, 2012.
[2] Sucher, B., Schreiber, A.: Carpal Tunnel Syndrome Diagnosis, Physical Medicine & Rehabilitation Clinics, vol. 25, pp. 229-247, 2014
[3] Huisstede, B., Hoogvliet, P., Franke, T., Randsdorp, M., and Koes, B.,
Carpal Tunnel Syndrome: Effectiveness of Physical Therapy and Electrophysical Modalities. An Updated Systematic Review of
Randmized Controlled Trials, Archives of Physical Medicine and
Rehabilitation, 2017 [4] Stocker, R. L., Macheiner, A.: Capitate Non-Union: One of the Causes
of Carpal Tunnel Syndrome, Handchirurgie, Mikrochirurgie, plastische
Chirurgie: Organ der Deutschsprachigen Arbeitsgemeinschaft fur Handchirurgie, vol. 48, no 3, p. 171-174, 2016.
[5] Rosales, R. S., Martin-Hidalgo, Y., Reboso-Morales, L., & Atroshi, I.: Reliability and construct validity of the Spanish version of the 6-item
CTS symptoms scale for outcomes assessment in carpal tunnel
syndrome. BMC musculoskeletal disorders, vol. 17, pp.115-125, 2016. [6] Fernández-de-las-Peñas, C., Fernández-Muñoz, J. J., Palacios-Ceña, M.,
Navarro-Pardo, E., Ambite-Quesada, S., & Salom-Moreno, J.: Direct
and indirect effects of function in associated variables such as depression and severity on pain intensity in women with carpal tunnel
syndrome, Pain Medicine, vol. 16, pp. 2405-2411, 2015.
[7] Della Fina, V., Cera, R.: Protecting the Rights of People with Autism in the Fields of Education and Employment, Ed Springer-Verlag (2015).
[8] Ennis-Cole, Demetria L.: Technology for learners with autism spectrum
disorders, Ed. Springer, 2015.
[9] Al S., Sarah, Hussain Al D.: "Descriptive characteristics of children with
autism at Autism Treatment Center, KSA, Vol. 151. Physiology &
behavior 151 pp 604-608, 2015. [10] Quevedo, W., Ortiz, J.S., Velascco, p., Sánchez, J., Álvarez, M., Rivas,
D., and Andaluz, V.H.: Assistance System for Rehabilitation and
Valuation of Motor Skills, Sugmente Reality, Virtual Reality, and Computer Graphics, Lecture Notes in Coputer Science, pp. 166-174,
2017.
[11] Huang, X., Naghdy, F., Naghdy, G., Du, H., and Todd, C.: The Combined Effects of Adaptive Control and Virtual Reality on Robot-
0
2
4
6
8
10
12
Qp1 Qp2 Qp3 Qp4 Qp5 Qd1 Qd2 Qdt3 Qd4 Qd5
Average Standard deviation
60
Assisted Fine Hand motion Rehabilitation in Chronic Stroke Patients: A
Case Stud, Journal of Stroke and Cerebrovascular Diseases, 2017
[12] Bernardini, S., Porayska-Pomsta, K., & Smith, T. J.: ECHOES: An
intelligent serious game for fostering social communication in children with autism, Vol. 264. Information Sciences, 264, pp. 41-60, 2014.