Daily Life of Engineering Student

Conference Paper: Mobile Robot Localization via Unscented Kalman Filter

2020-03-16T17:03:00.000+07:00

Mobile Robot Localization via Unscented Kalman Filter

https://ieeexplore.ieee.org/document/9034570

Abstract:
Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically, the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.

Published in: 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

Date of Conference: 5-6 Dec. 2019

Date Added to IEEE Xplore: 16 March 2020

ISBN Information:

Electronic ISBN: 978-1-7281-4520-4

USB ISBN: 978-1-7281-4519-8

Print on Demand(PoD) ISBN: 978-1-7281-4521-1

INSPEC Accession Number: 19455535

DOI: 10.1109/ISRITI48646.2019.9034570

Publisher: IEEE

Conference Location: Yogyakarta, Indonesia, Indonesia

Conference Paper: Optimized Altitude Control for Quadrotor UAV in Virtual Environment

2019-12-23T19:47:00.000+07:00

Conference Paper

Abstract:
Quadrotors offer many interesting features and have great potential to be used in many social applications. Unfortunately, due to its nature as an underactuated system, it can be made stable by itself. Hence, such a control strategy is required. This paper presents the optimized PID controller with Firefly Algorithm (FA) for quadrotor in virtual environment. The results verify that the optimized PID controller succeeds to regulate the altitude of quadrotor in the real physics-like of virtual environment.

Date of Conference: 24-25 July 2019

Date Added to IEEE Xplore: 23 December 2019

ISBN Information:

Electronic ISBN: 978-1-7281-1655-6

USB ISBN: 978-1-7281-1654-9

Print on Demand(PoD) ISBN: 978-1-7281-1656-3

DOI: 10.1109/ICOIACT46704.2019.8938586

Conference Location: Yogyakarta, Indonesia, Indonesia

Conference Paper: Smoothed A-star Algorithm for Nonholonomic Mobile Robot Path Planning

2019-12-23T19:46:00.000+07:00

Conference Paper

Abstract:
There are various path planning methods for mobile robots and one of them is A-star algorithm. It serves fairly well for looking the path accurately. Unfortunately, the conventional A-star algorithm may have some sharp turns. It causes problems for nonholonomic mobile robots to track the path. In this paper, we proposed a scheme to make A-star algorithm smoother. The smoothed path is easier for nonholonomic mobile robots to be tracked. We conduct simulation in the virtual environment that covers the physical behavior of mobile robot. Simulation and experiment verify that the smoothed A-star algorithm can be implemented for nonholonomic mobile robots.

Published in: 2019 International Conference on Information and Communications Technology (ICOIACT)

Date of Conference: 24-25 July 2019

Date Added to IEEE Xplore: 23 December 2019

ISBN Information:

Electronic ISBN: 978-1-7281-1655-6

USB ISBN: 978-1-7281-1654-9

Print on Demand(PoD) ISBN: 978-1-7281-1656-3

DOI: 10.1109/ICOIACT46704.2019.8938467

Conference Location: Yogyakarta, Indonesia, Indonesia

Conference Paper: State of Charge Estimation for Lithium Polymer Battery using Kalman Filter under Varying Internal Resistance

2019-12-23T19:42:00.000+07:00

Conference Paper

Abstract:
Battery Management System (BMS) is necessary in order the batteries to work properly. One important item in BMS is State of Charge (SOC), which indicates charge level of the batteries and belongs to internal states of battery. Practically speaking, the internal states of battery can not be measured directly. Thus, SOC has to be estimated. Moreover, it is possible that its internal resistance changes while the battery is being used. In this paper, nominal value of the internal resistance is acquired from parameter identification by utilizing experimental setup for lithium polymer battery. Subsequently, through simulation, the internal resistance is set to vary around its nominal value and Kalman Filter is utilized to estimate the SOC. The estimated SOC from Kalman Filter is then compared to one from Observer and Coulomb Counting under the same condition for verification purpose. Simulation verifies that Kalman Filter performs better than Observer and Coulomb Counting for SOC estimation under varying internal resistance.

Published in: 2019 International Conference on Information and Communications Technology (ICOIACT)

Date of Conference: 24-25 July 2019

Date Added to IEEE Xplore: 23 December 2019

ISBN Information:

Electronic ISBN: 978-1-7281-1655-6

USB ISBN: 978-1-7281-1654-9

Print on Demand(PoD) ISBN: 978-1-7281-1656-3

DOI: 10.1109/ICOIACT46704.2019.8938490

Conference Location: Yogyakarta, Indonesia, Indonesia

Conference Paper: Quadrotor Proportional-Derivative Control on SO(3) with Input Saturation

2019-08-08T19:38:00.000+07:00

Conference Paper

Abstract:
The aim of this research is to present a model of a quadrotor Unmanned Aerial Vehicle (UAV) with input saturation, as well as its corresponding Proportional-Derivative (PD) control. PD control offers ease of design, simplicity, and reliability for the second-order system. A good example of the second-order system is quadrotor. Quadrotor is an underactuated system and it can not be stabilized mechanically. In order to stabilize its behavior along the desired state with input saturation, PD control algorithm on Special Orthogonal-3 (SO(3)) is proposed. The effectiveness of the proposed PD control algorithm can be verified by numerical simulation. Its result shows that the proposed PD controller quickly convergent to the desired attitude, although under input saturation.

Published in: 2018 12th South East Asian Technical University Consortium (SEATUC)

Date of Conference: 12-13 March 2018

Date Added to IEEE Xplore: 08 August 2019

ISBN Information:

Electronic ISBN: 978-1-5386-5094-3

Print on Demand(PoD) ISBN: 978-1-5386-5095-0

INSPEC Accession Number: 18888143

DOI: 10.1109/SEATUC.2018.8788855

Conference Location: Yogyakarta, Indonesia, Indonesia

IEEE Keywords
- PD control ,
- Rotors ,
- Unmanned aerial vehicles ,
- Reliability ,
- Actuators ,
- Mathematical model ,
- Attitude control
INSPEC: Controlled Indexing
- attitude control ,
- autonomous aerial vehicles ,
- control system synthesis ,
- helicopters ,
- PD control ,
- stability
INSPEC: Non-Controlled Indexing
Author Keywords
- Quadrotor ,
- Unmanned Aerial Vehicle ,
- PD control ,
- input saturation ,
- Special Orthogonal-3

Communicating vessels for low-cost water level monitoring

2019-04-12T09:27:00.003+07:00

EPIC 2018
AIP Conference Proceedings, Volume 2088, Issue 1, 10.1063/1.5095275

Free Published Online: 29 March 2019

Communicating vessels for low-cost water level monitoring
AIP Conference Proceedings 2088, 020023 (2019)
https://doi.org/10.1063/1.5095275
Advanced industrial technology in engineering physics

There are many factors cause error of the reading of resistive sensors for monitoring water level influenced by several factors. One of them is the shape of the water tank. This study aims to improve the reading of resistive sensors to the shape of water tanks, regardless its shapes and volumes. It can be achieved by utilizing communicating vessels. The resistive-based on voltage divider sensor, in the form of two conductive wires dipped in water. This water level measurement uses STM32F103C8T6 as a data processing control. It serves a better capability for the sensor to read water levels in two tanks of different shapes and volumes. The resistive sensor can read the water level with an average error of 1.11 percents.

https://aip.scitation.org/doi/abs/10.1063/1.5095275

Catatan Singkat Kalman Filter

2019-01-06T09:10:00.002+07:00

Nama Kalman filter diambil dari nama Rudolf Emil Kálmán, orang yang menggagas dan mempopulerkan metode prediksi ini. Kalman filter digunakan pada misi peluncuran Apollo ke bulan, dan sejak itu Kalman filter menjadi kian populer dan diterapkan diberbagai bidang seperti pada pesawat terbang, kapal selam, dan juga untuk peluncuran misil.

Bahkan Wall street menggunakan Kalman filter untuk memprediksi perkembangan pasar. Diterapkan juga di bidang robotika, IoT (Internet of Things) sensor, dan juga perangkat laboratorium instrumentasi.

Secara umum, Kalman Filter dapat dibagi menjadi prediction model dan correction model untuk suatu dalam sistem. Pada prediction model terdapat noise proses. Sementara itu, correction model melakukan update dari suatu nilai yang diperkirakan (estimated value) beserta dengan noise pengukuran.

Prediction Model
Diasumsikan bahwa actual state pada saat $k$ merupakan hasil update dari state sebelumnya saat $(k-1)$
\begin{equation} \mathbf{x}_k = \mathbf{F}_k\mathbf{x}_{k-1} + \mathbf{B}_k\mathbf{u}_k + \mathbf{w}_k, \label{eq:kalman} \end{equation}
dengan $\mathbf{F}_k$, $\mathbf{B}_k$, dan $\mathbf{w}_k$ masing-masing merupakan state transition, control-input, dan noise proses.

A priori estimated state, $\mathbf{\hat{x}}_{k|k-1}$, dan error covariance, $\mathbf{P}_{k|k-1}$
\begin{eqnarray} \mathbf{\hat{x}}_{k|k-1} {}&=&{} \mathbf{F}_k\mathbf{\hat{x}}_{k-1|k-1}+\mathbf{B}_k\mathbf{u}_k,\\ \mathbf{P}_{k|k-1} {}&=&{} \mathbf{F}_k\mathbf{P}_{k-1|k-1}\mathbf{F}_k^{\mathsf{T}}+\mathbf{Q}_k, \label{eq:apriori} \end{eqnarray}
dengan $\mathbf{Q}_k$ merupakan covariance untuk noise proses.

Correction Model
Sementara itu, untuk pengukuran
\begin{equation} \mathbf{z}_k = \mathbf{H}_k\mathbf{x}_k+\mathbf{v}_k, \label{eq:measurement} \end{equation}
dengan $\mathbf{H}_k$ merupakan model pengukuran dan $\mathbf{v}_k$ adalah noise pengukuran.

Sebelum melakukan update, perlu diidentifikasi residu awal
\begin{equation} \mathbf{\tilde{y}}_k = \mathbf{z}_k - \mathbf{H}_k\mathbf{\hat{x}}_{k|k-1}, \label{eq:residu_prefit} \end{equation}
dan covariance residu
\begin{equation} \mathbf{S}_k = \mathbf{R}_k + \mathbf{H}_k\mathbf{P}_{k|k-1}\mathbf{H}_k^{\mathsf{T}}, \label{eq:residu_covariance} \end{equation}
dengan $\mathbf{R}_k$ merupakan covariance dari noise pengukuran.

Selanjutnya perlu diketahui nilai Kalman gain
\begin{equation} \mathbf{K}_k = \mathbf{P}_{k|k-1}\mathbf{H}_k^{\mathsf{T}}\mathbf{S}_k^{-1}. \label{eq:kalman_gain} \end{equation}
Dengan demikian, update dari estimated state dan error covariance
\begin{eqnarray} \mathbf{\hat{x}}_{k|k} {}&=&{} \mathbf{\hat{x}}_{k|k-1} + \mathbf{K}_k\mathbf{\tilde{y}}_k,\\ \mathbf{P}_{k|k} {}&=&{} \left(\mathbf{I}_{2 \times 2}-\mathbf{K}_k\mathbf{H}_k\right)\mathbf{P}_{k|k-1}\left(\mathbf{I}_{2 \times 2}-\mathbf{K}_k\mathbf{H}_k\right)^{\mathsf{T}}\nonumber\\ {}&&{}+\mathbf{K}_k\mathbf{R}_k\mathbf{K}_k^{\mathsf{T}}. \label{eq:posteriori} \end{eqnarray}

Sehingga, update nilai residu
\begin{equation} \mathbf{\tilde{y}}_{k|k} = \mathbf{z}_k-\mathbf{H}_k\mathbf{\hat{x}}_{k|k}. \label{eq:post_residu} \end{equation}

Digimon World Re:Digitize PSP CWCHEATS

2019-01-04T16:41:00.000+07:00

Digimon World Re:Digitize PSP CWCHEATS

Berikut ini adalah daftar cheats untuk game Digimon World Re:Digitize untuk handheld PSP.

_C0 SP Skill Meter is always Critical
_L 0x10058194 0x00004000
_L 0x1005819C 0x00004000
_C0 Digvolution Chart 100% L+R
_L 0xD0000005 0x10000300
_L 0x2037c974 0xffffffff
_L 0x2037c978 0xffffffff
_L 0x2037c97c 0xffffffff
_L 0x2037c980 0xffffffff
_L 0x2037c984 0xffffffff
_C0 Digimon Instant Death - Kill your own Digimon.
_L 0x2037C5EC 0x00000000
_C0 Digimon Never Die
_L 0x2037C5EC 0x0098967F
_C0 Bit Max
_L 0x2037B260 0x05F5E0FF
_C0 All Skills Unlocked
_L 0x2037C654 0xFFFFFFFE
_L 0x2037C658 0x1FFFFFFF
_C0 SP Max (Finisher Gauge)
_L 0x6037E708 0x00000064
_L 0x00000001 0X000001B4
_C0 99 Card Possession
_L 0x8037D7AD 0x00BE0001
_L 0x00000000 0x00000063
_C0 Immediate Digivolution L + R + X
_L 0x2037C5F0 0x0000FFFF
_L 0xD0000000 0x10004300
_L 0x2037C5F0 0x00000000
_L 0xD0000000 0x10002200
_L 0x2037C5F0 0x00000000
_C0 HP 9999
_L 0x1037C150 0x0000270F
_L 0x1037C15C 0x0000270F
_C0 MP 999
_L 0x1037C158 0x000003E7
_L 0x1037C15C 0x000003E7
_C0 ATK 200
_L 0x1037C160 0x000000C8
_C0 DEF 200
_L 0x1037C162 0x000000C8
_C0 SPD 200
_L 0x1037C164 0x000000C8
_C0 BRAIN 200
_L 0x1037C166 0x000000C8
_C0 ATK 500
_L 0x1037C160 0x000001F4
_C0 DEF 500
_L 0x1037C162 0x000001F4
_C0 SPD 500
_L 0x1037C164 0x000001F4
_C0 BRAIN 500
_L 0x1037C166 0x000001F4
_C0 ATK 999
_L 0x1037C160 0x000003E7
_C0 DEF 999
_L 0x1037C162 0x000003E7
_C0 SPD 999
_L 0x1037C164 0x000003E7
_C0 BRAIN 999
_L 0x1037C166 0x000003E7
_C0 HP 9999 During Battle
_L 0x103F5754 0x0000270F
_C0 MP 9999 During Battle
_L 0x103F575C 0x0000270F
_C0 Infinite Hp / Mp
_L 0x6037E708 0x00000004
_L 0x00000002 0x00000170
_L 0x10000174 0x00000000
_L 0x6037E708 0x00000004
_L 0x00000002 0x00000178
_L 0x1000017C 0x00000000
_C0 Time Operation - ?
_L 0xD0000000 0x10008010
_L 0x200227B4 0x24A50200
_L 0xD0000000 0x10008020
_L 0x200227B4 0x00A22821
_L 0xD0000000 0x10008040
_L 0x200227B4 0x24A5FE00
_C0 Happiness Max
_L 0x2037C170 0x42C80000
_C0 Discipline Max
_L 0x2037C174 0x42C80000
_C0 Curse Max
_L 0x0037C178 0x00000010
_C0 Small Recovery (Restores 500 HP) x 99
_L 0x1037B774 0x00000001
_L 0x0037B778 0x00000099
_C0 Medium Recovery (Restores 1500 HP) x 99
_L 0x1037B774 0x00000002
_L 0x0037B778 0x00000099
_C0 Large Recovery (Restores 5000 HP) x 99
_L 0x1037B774 0x00000003
_L 0x0037B778 0x00000099
_C0 Super Recovery (Restores Full HP) x 99
_L 0x1037B774 0x00000004
_L 0x0037B778 0x00000099
_C0 MP Floppy (Restores 500 MP) x 99
_L 0x1037B774 0x00000005
_L 0x0037B778 0x00000099
_C0 Medium MP Floppy (Restores 1500 MP) x 99
_L 0x1037B774 0x00000006
_L 0x0037B778 0x00000099
_C0 Large MP Floppy (Restores 5000 MP) x 99
_L 0x1037B774 0x00000007
_L 0x0037B778 0x00000099
_C0 Double Floppy (Restores 1500 HP & MP) x 99
_L 0x1037B774 0x00000008
_L 0x0037B778 0x00000099
_C0 Recovery Floppy (Cures abnormal status like confusion, paralysis, slow, poison, and liquid crystallisation) x 99
_L 0x1037B774 0x00000009
_L 0x0037B778 0x00000099
_C0 Omnipotent Floppy (Cures abnormal status, restores 1500 HP & MP) x 99
_L 0x1037B774 0x0000000A
_L 0x0037B778 0x00000099
_C0 Protection Floppy (Raises resistance against status changes) x 99
_L 0x1037B774 0x0000000B
_L 0x0037B778 0x00000099
_C0 Restore Floppy (When used before the Death Counter reaches 0, restores 1500 HP & MP) x 99
_L 0x1037B774 0x0000000C
_L 0x0037B778 0x00000099
_C0 Super Restore Floppy (When used before the death counter reaches 0, restores Full HP & MP) x 99
_L 0x1037B774 0x0000000D
_L 0x0037B778 0x00000099
_C0 Bandage (Used to treat wounds, also sometimes cures illness) x 99
_L 0x1037B774 0x0000000E
_L 0x0037B778 0x00000099
_C0 Bandage DX (Always cures injuries and illness. A great plaster) x 99
_L 0x1037B774 0x0000000F
_L 0x0037B778 0x00000099
_C0 Offence Plugin (In-Battle: Boosts Offence a little) x 99
_L 0x1037B774 0x00000010
_L 0x0037B778 0x00000099
_C0 Defence Plugin (In-Battle: Boosts Defence a little) x 99
_L 0x1037B774 0x00000011
_L 0x0037B778 0x00000099
_C0 High speed Plugin (In-Battle: Boosts Speed a little) x 99
_L 0x1037B774 0x00000012
_L 0x0037B778 0x00000099
_C0 Universal Plugin (In-Battle: Boosts Offence, Defence, and Speed a little) x 99
_L 0x1037B774 0x00000013
_L 0x0037B778 0x00000099
_C0 Offence Plugin S (In-Battle: Boosts Offence a lot) x 99
_L 0x1037B774 0x00000014
_L 0x0037B778 0x00000099
_C0 Defence Plugin S (In-Battle: Boosts Defence a lot) x 99
_L 0x1037B774 0x00000015
_L 0x0037B778 0x00000099
_C0 Highspeed Plugin S (In-Battle: Boosts Speed a lot) x 99
_L 0x1037B774 0x00000016
_L 0x0037B778 0x00000099
_C0 Autopilot (Use it to return to File City quickly) x 99
_L 0x1037B774 0x00000017
_L 0x0037B778 0x00000099
_C0 Offence Chip (A special chip that increases your Digimon's Offence) x 99
_L 0x1037B774 0x00000018
_L 0x0037B778 0x00000099
_C0 Defence Chip (A special chip that increases your Digimon's Defence) x 99
_L 0x1037B774 0x00000019
_L 0x0037B778 0x00000099
_C0 Brains Chip (A special chip that increases your Digimon's Brain) x 99
_L 0x1037B774 0x0000001A
_L 0x0037B778 0x00000099
_C0 Speed Chip (A special chip that increases your Digimon's Speed) x 99
_L 0x1037B774 0x0000001B
_L 0x0037B778 0x00000099
_C0 HP Chip (A special chip that increases your Digimon's HP) x 99
_L 0x1037B774 0x0000001C
_L 0x0037B778 0x00000099
_C0 MP Chip (A special chip that increases your Digimon's MP) x 99
_L 0x1037B774 0x0000001D
_L 0x0037B778 0x00000099
_C0 Devil Chip A (An evil looking chip which will greatly increase your Digimon's Offense and Brain. But...) x 99
_L 0x1037B774 0x0000001E
_L 0x0037B778 0x00000099
_C0 Devil Chip D (An evil looking chip which will greatly increase your Digimon's Defense and Speed. But...) x 99
_L 0x1037B774 0x0000001F
_L 0x0037B778 0x00000099
_C0 Devil Chip E (An evil looking chip which will greatly increase your Digimon's HP and MP. But...) x 99
_L 0x1037B774 0x00000020
_L 0x0037B778 0x00000099
_C0 Portable Potty (A portable toilet. Not very comfortable) x 99
_L 0x1037B774 0x00000021
_L 0x0037B778 0x00000099
_C0 Training Manual (A Method for an efficient Training has been written on this Manual. Works already by only possessing it) x 99
_L 0x1037B774 0x00000022
_L 0x0037B778 0x00000099
_C0 Rest Pillow (A fluffy pillow which boosts the recovery effect while your Partner sleeps) x 99
_L 0x1037B774 0x00000023
_L 0x0037B778 0x00000099
_C0 Amulet Charm (The enemy Digimon will avoid this odorous charm. Possessing the opposite item will negate this effect) x 99
_L 0x1037B774 0x00000024
_L 0x0037B778 0x00000099
_C0 Enemy Bell (A device that sends an enemy Digimon attracting signal. Possessing the opposite item will negate this effect) x 99
_L 0x1037B774 0x00000025
_L 0x0037B778 0x00000099
_C0 Health Sandals (When walking slowly they reduce your Partners Fatigue) x 99
_L 0x1037B774 0x00000026
_L 0x0037B778 0x00000099
_C0 Meat (Harvested from the field. A lot of Digimon like to eat it) x 99
_L 0x1037B774 0x00000027
_L 0x0037B778 0x00000099
_C0 Giant Meat (In case you want to eat a lot. This big meat fits the desire) x 99
_L 0x1037B774 0x00000028
_L 0x0037B778 0x00000099
_C0 Sirloin (This superb meat makes your heart jump only by looking) x 99
_L 0x1037B774 0x00000029
_L 0x0037B778 0x00000099
_C0 Super Carrot (Although this is a carrot, the real sort is unknown. When eaten, it boosts the Training Effect of MP, Offense and Brain) x 99
_L 0x1037B774 0x0000002A
_L 0x0037B778 0x00000099
_C0 Cherry Radish (A special radish harvested from a Tropical Island. Nourishing, boosting the Training Effect of HP, Speed and Defense) x 99
_L 0x1037B774 0x0000002B
_L 0x0037B778 0x00000099
_C0 Stringy Grass (Grass growing in the Digital World. Reduces Fatigue) x 99
_L 0x1037B774 0x0000002C
_L 0x0037B778 0x00000099
_C0 Digimushroom (A mushroom that grows in the Digital World. Will make stomach a bit full) x 99
_L 0x1037B774 0x0000002D
_L 0x0037B778 0x00000099
_C0 Ice Mushroom (A mushroom that grows strong under the snow. Digimon that eat it will become more disciplined) x 99
_L 0x1037B774 0x0000002E
_L 0x0037B778 0x00000099
_C0 Deluxe Mushroom (This deluxe mushroom increases all talents of your Digimon) x 99
_L 0x1037B774 0x0000002F
_L 0x0037B778 0x00000099
_C1 Digipine (An umbrella shaped pine. The components inside will increase the effect of training for all stats) x 99
_L 0x1037B774 0x00000030
_L 0x0037B778 0x00000099
_C0 Smiling Apple (Its overflowing sweetness makes this apple smile. To be given when In a bad mood) x 99
_L 0x1037B774 0x00000031
_L 0x0037B778 0x00000099
_C0 Energy Lemon (This lemon showered in electromagnetic waves. Contains ingredients that are healthy for Digimon) x 99
_L 0x1037B774 0x00000032
_L 0x0037B778 0x00000099
_C0 Gold Acorn (A gold acorn. Can be eaten or sold for a high price in shops) x 99
_L 0x1037B774 0x00000033
_L 0x0037B778 0x00000099
_C0 Heavy Strawberry (A giant strawberry that resembles a wrestler Digimon. With only one of these, your stomach becomes full) x 99
_L 0x1037B774 0x00000034
_L 0x0037B778 0x00000099
_C0 Divine Chestnut (A very sweet chestnut. With its melting sweetness it recovers all HP) x 99
_L 0x1037B774 0x00000035
_L 0x0037B778 0x00000099
_C0 Super Veggie (A legendary Digimon-shaped plant. When not screaming, it restores all MP) x 99
_L 0x1037B774 0x00000036
_L 0x0037B778 0x00000099
_C0 Edible Cactus (This edible cactus satisfies the hunger, but also makes you lose weight) x 99
_L 0x1037B774 0x00000037
_L 0x0037B778 0x00000099
_C0 Orange Banana (A mysterious Orange Banana. Recovers HP & MP effectively) x 99
_L 0x1037B774 0x00000038
_L 0x0037B778 0x00000099
_C0 Strength Fruit (This fruit is said to grant you strength when eaten. Increases your Digimon's Offence slightly) x 99
_L 0x1037B774 0x00000039
_L 0x0037B778 0x00000099
_C0 Tough Fruit (This fruit is said to make your body strong when eaten. Increases your Digimon's Defense slightly) x 99
_L 0x1037B774 0x0000003A
_L 0x0037B778 0x00000099
_C0 Quick Fruit (This fruit is said to grant you fast feet when eaten. Increases your Digimon's Speed slightly) x 99
_L 0x1037B774 0x0000003B
_L 0x0037B778 0x00000099
_C0 Wisdom Fruit (This fruit is said to make you wise when eaten. Increases your Digimon's Brain slightly) x 99
_L 0x1037B774 0x0000003C
_L 0x0037B778 0x00000099
_C0 Robust Fruit (This fruit is said to increase your physical power heavily when eaten. Increases your Digimon's maximum HP slightly) x 99
_L 0x1037B774 0x0000003D
_L 0x0037B778 0x00000099
_C0 Peace Fruit (This fruit is said to lift your spirits when eaten. Increases your Digimon's maximum MP slightly) x 99
_L 0x1037B774 0x0000003E
_L 0x0037B778 0x00000099
_C0 Digi Anchovy (This small fish is everywhere. Fills the stomach only a little. You can feed it to other fishes) x 99
_L 0x1037B774 0x0000003F
_L 0x0037B778 0x00000099
_C0 Digi Snapper (An easy-to-catch fish. Fills the stomach when eaten. Seems to have an ordinary taste) x 99
_L 0x1037B774 0x00000040
_L 0x0037B778 0x00000099
_C0 Digi Trout (A square shaped fish. Its eggs are said to be excellent. Not only fills the stomach, but also raises the discipline) x 99
_L 0x1037B774 0x00000041
_L 0x0037B778 0x00000099
_C0 Black Digi Trout (A very scary fish that eats other fishes. When eaten, you can taste the remains of other fishes in the stomach of this fish) x 99
_L 0x1037B774 0x00000042
_L 0x0037B778 0x00000099
_C0 Digi Catfish (A big mouth and a majestic beard characterise this fish. The Digi Catfish is said to be the cause of the mysterious earthquake) x 99
_L 0x1037B774 0x00000043
_L 0x0037B778 0x00000099
_C0 Digi Sea bass (A legendary fish said to be the god of fish. It is said to grant immortality when eaten) x 99
_L 0x1037B774 0x00000044
_L 0x0037B778 0x00000099
_C0 Digi Medaka (A small fish coming in swarms. You can also feed other fishes with it) x 99
_L 0x1037B774 0x00000045
_L 0x0037B778 0x00000099
_C0 Digi Goldfish (A rare fish. Although they are often ornamental, you can of course eat them as well.It is said to prefer a certain fish) x 99
_L 0x1037B774 0x00000046
_L 0x0037B778 0x00000099
_C0 Digi Devil Carp (A very powerful fish. Makes your Digimon full, also increases its power) x 99
_L 0x1037B774 0x00000047
_L 0x0037B778 0x00000099
_C0 Digi Centaury (A rare fish. When eaten it makes your digimon full, also makes it happy) x 99
_L 0x1037B774 0x00000048
_L 0x0037B778 0x00000099
_C0 Digi Salmon (A fish that lives in a cold place. It has a delicious taste, and makes your Digimon happy as well) x 99
_L 0x1037B774 0x00000049
_L 0x0037B778 0x00000099
_C0 Digi Loach (A small fish that grew a beard. When eaten it has a good taste, also raises discipline greatly) x 99
_L 0x1037B774 0x0000004A
_L 0x0037B778 0x00000099
_C0 Digi Eel (A slender fish with a slick body. Digimon eat it at festival times. It is quite delicious) x 99
_L 0x1037B774 0x0000004B
_L 0x0037B778 0x00000099
_C0 Digi Arowana (An ancient fish with a colourful body. It is said to taste delicious and that the Digimon who eats it will become stronger) x 99
_L 0x1037B774 0x0000004C
_L 0x0037B778 0x00000099
_C0 Digi Pirarucu (This fish boasts itself with being the biggest fish on file island) x 99
_L 0x1037B774 0x0000004D
_L 0x0037B778 0x00000099
_C0 Moldy Meat (Likely to make your Digimon sick when eaten. A very dangerous meat) x 99
_L 0x1037B774 0x0000004F
_L 0x0037B778 0x00000099
_C0 Happy Mushroom (No one knows what will happen. A dangerous mushroom) x 99
_L 0x1037B774 0x00000050
_L 0x0037B778 0x00000099
_C0 Chain Melon (Instead of rotting, this Melon took chains of illusion. It is said to grant longevity when you eat it) x 99
_L 0x1037B774 0x00000051
_L 0x0037B778 0x00000099
_C0 unused items replace first item x 99
_L 0x1037B774 0x00000052
_L 0x0037B778 0x00000099
_C0 unused items replace first item x 99
_L 0x1037B774 0x00000053
_L 0x0037B778 0x00000099
_C0 Giant Starfish (The lovely Starfish MarineAngemon was looking for. Cute...?) x 99
_L 0x1037B774 0x00000054
_L 0x0037B778 0x00000099
_C0 Tiny Starfish (The lovely Starfish MarineAngemon was looking for. Cute...?) x 99
_L 0x1037B774 0x00000055
_L 0x0037B778 0x00000099
_C0 Spiky Starfish (The lovely Starfish MarineAngemon was looking for. Cute...?) x 99
_L 0x1037B774 0x00000056
_L 0x0037B778 0x00000099
_C0 Round Starfish (The lovely Starfish MarineAngemon was looking for. Cute...?) x 99
_L 0x1037B774 0x00000057
_L 0x0037B778 0x00000099
_C0 Mellow Starfish (The lovely Starfish MarineAngemon was looking for. Cute...?) x 99
_L 0x1037B774 0x00000058
_L 0x0037B778 0x00000099
_C0 Bony Starfish (The lovely Starfish MarineAngemon was looking for. Cute...?) x 99
_L 0x1037B774 0x00000059
_L 0x0037B778 0x00000099
_C0 Giant Crystal (Probably cut from a harm. A giant crystal.) x 99
_L 0x1037B774 0x0000005A
_L 0x0037B778 0x00000099
_C0 Ordinary Pebble (Found at common places, an ordinary pebble) x 99
_L 0x1037B774 0x0000005B
_L 0x0037B778 0x00000099
_C0 Green Digimushroom (A green Digimushroom. It is said to be more delicious than the Digimushroom, but many Digimon don't care for it) x 99
_L 0x1037B774 0x0000005C
_L 0x0037B778 0x00000099
_C0 Very Dirty Item (An item that is unspeakably dirty) x 99
_L 0x1037B774 0x0000005D
_L 0x0037B778 0x00000099
_C0 RE Floppy (Restores HP steadily for 30 seconds) x 99
_L 0x1037B774 0x0000005E
_L 0x0037B778 0x00000099
_C0 Unused items replace first item x 99
_L 0x1037B774 0x0000005F
_L 0x0037B778 0x00000099
_C0 unused items replace first item x 99
_L 0x1037B774 0x00000060
_L 0x0037B778 0x00000099
_C0 unused items replace first item x 99
_L 0x1037B774 0x00000061
_L 0x0037B778 0x00000099
_C0 unused items replace first item x 99
_L 0x1037B774 0x00000062
_L 0x0037B778 0x00000099
_C0 unused items replace first item x 99
_L 0x1037B774 0x00000063
_L 0x0037B778 0x00000099
_C0 Giant Claw (Forces Rookie Digimon to digivolve into Greymon) x 99
_L 0x1037B774 0x00000064
_L 0x0037B778 0x00000099
_C0 Flame Shard (Forces Rookie Digimon to digivolve into Meramon) x 99
_L 0x1037B774 0x00000065
_L 0x0037B778 0x00000099
_C0 Blazing Wings (Forces Rookie Digimon to digivolve into Birdramon) x 99
_L 0x1037B774 0x00000066
_L 0x0037B778 0x00000099
_C0 Devil Wings (Forces Rookie Digimon to digivolve into Devimon) x 99
_L 0x1037B774 0x00000067
_L 0x0037B778 0x00000099
_C0 Righteous Mane (Forces Rookie Digimon to digivolve into Leomon) x 99
_L 0x1037B774 0x00000068
_L 0x0037B778 0x00000099
_C0 Pure White Clothes (Forces Rookie Digimon to digivolve into Angemon) x 99
_L 0x1037B774 0x00000069
_L 0x0037B778 0x00000099
_C0 Mythic Beast Wing (Forces Rookie Digimon to digivolve into Airdramon) x 99
_L 0x1037B774 0x0000006A
_L 0x0037B778 0x00000099
_C0 Steel Helmet (Forces Rookie Digimon to digivolve into Kabuterimon) x 99
_L 0x1037B774 0x0000006B
_L 0x0037B778 0x00000099
_C0 Sea Serpents Blood (Forces Rookie Digimon to digivolve into Seadramon) x 99
_L 0x1037B774 0x0000006C
_L 0x0037B778 0x00000099
_C0 Mythril Coat (Forces Rookie Digimon to digivolve into Garurumon) x 99
_L 0x1037B774 0x0000006D
_L 0x0037B778 0x00000099
_C0 Dreadful Horn (Forces Rookie Digimon to digivolve into GeoGreymon) x 99
_L 0x1037B774 0x0000006E
_L 0x0037B778 0x00000099
_C0 Sun Seed (Forces Rookie Digimon to digivolve into Sunflowmon) x 99
_L 0x1037B774 0x0000006F
_L 0x0037B778 0x00000099

Python OpenCV Analisis Histogram

2018-12-23T12:31:00.001+07:00

Pagi hari menulis research paper ditemani dengan satu gelas kopi panas. Meski menjelang Natal dan tahun baru, sepertinya tidak ada tanda-tanda libur bagi engineer. Nasib.

Sebagai sambilan dan juga refreshing, tulisan catatan ringan di blog yang kian absurd. Untuk artikel blog kali ini akan dibahas mengenai seri artikel dasar image processing dengan OpenCV dan Python. Pada bagian ini dibahas singkat mengenai histogram.

Suatu histogram memberikan garis besar informasi mengenai intensitas distribusi dari suatu image. Histogram menampilkan plot nilai pixel, disebut bin, mulai dari 0 hingga 255, untuk 8-bit, pada sumbu X dan jumlah pixel yang terkait di sumbu Y dari plot tersebut.

Beberapa hal yang perlu dipersiapkan, tentu saja OpenCV dan Python, berikutnya asumsikan file 'image.jpg' berada di satu direktori dengan file Python.

Script pertama, menampilkan histogram dari file 'image.jpg' tersebut.

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('image.jpg',0)
histogram = np.bincount(img.ravel(),minlength=256)
print(histogram)
plt.hist(img.ravel(),256,[0,256]);
plt.title('Histogram')
plt.xlim([0,256])
plt.show()

Berikut hasil histogram yang pertama.

Script kedua, menampilkan histogram RGB.

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('image.jpg')
color = ('b','g','r')
for i,col in enumerate(color):
   histr = cv2.calcHist([img],[i],None,[256],[0,256])
   plt.plot(histr,color = col)
   plt.xlim([0,256])
plt.legend(('Blue','Green','Red'), loc = 'upper right')
plt.title('Histogram')
plt.show()

Terdapat channel [0], [1], dan [2], untuk RGB. Sedangkan bila hanya menampilkan histogram gray scale cukup [0]. Berikut hasilnya.

Script ketiga, menggunakan mask untuk menentukan region dari image yang hendak dianalisis. Terkadang dalam beberapa kasus, seseorang hanya tertarik bagian tertentu dari suatu image. Dengan kata lain, bagian lainnya diabaikan dan tidak dianalisis. Langsung saja berikut script untuk masking.

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('image.jpg',0)
# Create a mask
mask = np.zeros(img.shape[:2], np.uint8)
mask[100:300, 100:400] = 255
masked_img = cv2.bitwise_and(img,img,mask = mask)
# Calculate histogram with mask and without mask
# Check third argument for mask
hist_full = cv2.calcHist([img],[0],None,[256],[0,256])
hist_mask = cv2.calcHist([img],[0],mask,[256],[0,256])
plt.subplot(221), plt.imshow(img, 'gray')
plt.title('Full Image')
plt.subplot(222), plt.imshow(mask,'gray')
plt.title('Mask')
plt.subplot(223), plt.imshow(masked_img, 'gray')
plt.title('Masked')
plt.subplot(224), plt.plot(hist_full), plt.plot(hist_mask)
plt.legend(('Full','Masked'), loc = 'upper right')
plt.title('Histogram')
plt.xlim([0,256])
plt.show()

Berikut hasilnya.

Bagian pertama selesai, lanjut ke bagian selanjutnya.

MATLAB: Root Locus

2018-12-16T08:07:00.001+07:00

Daily Life of Engineering Student, catatan kali ini tentang root locus MATLAB.
Berikut ini adalah script MATLAB untuk plot root locus.

clear all, close all, clc;

%% Transfer function

num = 1;
den = [1 4 3];
sys = tf(num,den);
% the plant
Kp = 100;
Ki = 50;
Kd = 25;
PID = tf([Kd Kp Ki],[1 0]);
% PID controller

%% Closed-loop system

CL = feedback(PID*sys,1);
% Closed-loop system

t = 0:0.01:5;
% time interval

figure(1)
step(sys);
hold on;
step(CL,t);
legend('Initial plant','Closed-loop system');
stepinfo(CL)
% plot the step response in get the information
%
%        RiseTime: 0.0883
%    SettlingTime: 0.1625
%     SettlingMin: 0.9038
%     SettlingMax: 0.9938
%       Overshoot: 0
%      Undershoot: 0
%            Peak: 0.9938
%        PeakTime: 0.2934

[R,K] = rlocus(CL);
% R complex root locations for the gains K

figure(2)
rlocusplot(CL)
pole(CL)
% root locus plot
%
% -24.9525
%   -3.4701
%   -0.5775

Berikut adalah plot root locus dan step response.

Lihat juga tentang.

Kendali state space MATLAB.

MATLAB: State Space Transfer Function

2018-12-12T09:40:00.001+07:00

Berikut ini adalah script MATLAB untuk merancang transfer function suatu plant dari model state space.

clear all, close all, clc;

%% The transfer function

A = [1 2; 0 3]
% state matrix

B = [1; 2]
% input vector

C = [1 0]
% output vector

D = zeros(size(C,1),size(B,2))
% feedforward

%% Verify observability and controllability

Om = obsv(A,C)

Cm = ctrb(A,B)

if rank(Om) == size(A,1)
    'It is observable'
else
    'It is not observable'
end

if rank(Cm) == size(A,1)
    'It is controllable'
else
    'It is not controllable'
end

%% The transfer function

[num,den] = ss2tf(A,B,C,D)

sys = tf(num,den)

eigenValues = eig(A)
% The eigen values are 3 and 1

poles = pole(sys)
% 3 and 1

Lihat juga tentang.

Kendali state space MATLAB.

MATLAB: Control Loop

2018-12-08T10:23:00.000+07:00

Catatan singkat Daily Life of Engineering Student tentang control loop di MATLAB. Berikut ini adalah script MATLAB untuk merancang controller berdasarkan kondisi loop yang diinginkan, loop shaping.

clear all, close all, clc;

%% The transfer function

P = tf([1],[1 0 2 5])
% the plant
% P =
%         1
%   -------------
%   s^3 + 2 s + 5

pole(P)

desiredLoop = tf([10],[1 0])
% desired loop
%   10
%   --
%   s

K = desiredLoop/P
% Gain
% K =
%
%   10 s^3 + 20 s + 50
%   ------------------
%           s

%% Closed-loop system

CL = feedback(series(K,P),1)

t = 0:0.01:5;

step(P,t), grid
hold on;
step(CL,t)
legend('Plant', 'Closed-loop system')

Berikut ini adalah step response dari plant dan closed-loop.

Lihat juga tentang.

Kendali state space MATLAB.

MATLAB: Bode State Space

2018-12-07T09:25:00.000+07:00

Berikut ini adalah script MATLAB untuk Bode plot diagram state space.

clear all, close all, clc;

%% Define the system

A = [1 0 2;
    2 1 3;
    4 2 1]
% state matrix

B = [1; 0; 0]
% input vector

C = [1 0 0]
% output

D = zeros(size(C,1),size(B,2))
% feedforward

%% Verify the controllability and observability

Cm = rank(ctrb(A,B))
% controllability matrix

Om = rank(obsv(A,C))
% observability matrix

if Cm == size(A,1)
    'It is controllable'
else
    'It is not controllable'
end

if Om == size(A,1)
    'It is observable'
else
    'It is not observable'
end

%% Bode plot diagram

sys = ss(A,B,C,D)
% state space system

margin(sys)

Berikut ini ialah Bode plot diagram.

Lihat juga tentang.

Kendali state space MATLAB.

Untuk penjelasan tentang Bode plot dapat dilihat pada artikel berikut.

MATLAB: Bode Plot Diagram

2018-12-06T18:48:00.000+07:00

Berikut ini adalah script MATLAB untuk analisis closed-loop dengan Bode plot diagram.

clear all, close all, clc;

%% Define the system

num = 1;
den = [1 3 5 7];
P = tf(num,den);
% define the plant

Kp = 3;
Ki = 5;
Kd = 7;

K = tf([Kd Kp Ki],[1 0])
% define the PID controller

CL = feedback(series(K,P),1)
% closed-loop system (CL)

%% Bode analysis

[Gm1,Pm1,Wcg1,Wcp1] = margin(P)
% Gain margin and Phase margin of the plant
% Gm1 = 8.0004
% Pm1 = inf

[Gm2,Pm2,Wcg2,Wcp2] = margin(CL)
% Gain margin and Phase margin of CL
% Gm2 = Inf
% Pm2 = 93.3738

bode(P),grid
hold on
bode(CL)
legend('Plant','Closed-loop system')

Berikut ini ialah Bode plot diagram.

Lihat juga tentang.

Kendali state space MATLAB.

Untuk penjelasan tentang Bode plot dapat dilihat pada artikel berikut.

MATLAB: Linear-Quadratic Regulator Design for State Space Systems

2018-12-05T17:25:00.000+07:00

Berikut ini adalah script MATLAB untuk LQR (Linear-Quadratic Regulator) controller.

clear all, close all, clc;

%% Define the system

A = [1 0 2;
    2 3 0;
    1 2 3]
% the state matrix

B = [1; 0; 0]
% the input vector

eig(A)
% the eigenvalues are
% 4.7511 + 0.0000i
% 1.1244 + 1.0251i
% 1.1244 - 1.0251i
% it is not stable

%% LQR design

Q = eye(size(A))

R = 1

K = lqr(A,B,Q,R)
% K =
%    14.5461   24.4004   41.9475

%% State feedback controller

sys = ss(A-B*K, eye(size(A)), eye(size(A)), eye(size(A)))

eig(sys.a)
% the eigenvalues are
% -4.7742 + 0.0000i
% -1.3860 + 1.1477i
% -1.3860 - 1.1477i
% it is stable

x0 = [5; 5; 5]
% initial state

t = 0:0.01:5;
% the time duration

x = initial(sys,x0,t);
% response of the state-space
% with initial condition x0
% and time duration t

x1 = [1 0 0]*x';
x2 = [0 1 0]*x';
x3 = [0 0 1]*x';
% the state variables

%% Plot the state variables

subplot(3,1,1); plot(t,x1), grid
title('Response to initial condition')
ylabel('x1')

subplot(3,1,2); plot(t,x2), grid
ylabel('x2')

subplot(3,1,3); plot(t,x3), grid
ylabel('x3')
xlabel('t (seconds)')

Berikut ini ialah plot response dari state variable.

Lihat juga tentang.

Kendali state space MATLAB.

MATLAB: Pole-Placement State Space

2018-12-04T11:29:00.000+07:00

Berikut ini adalah script MATLAB untuk state feedback controller.

clear all, close all, clc;

%% Define the system

A = [1 0 2;
2 3 0;
1 2 3]
% the state matrix

B = [1; 0; 0]
% the input vector

eig(A)
% the eigenvalues are
% 4.7511 + 0.0000i
% 1.1244 + 1.0251i
% 1.1244 - 1.0251i
% it is not stable

%% The pole-placement

desiredPoles = -0.5*ones(3,1)
% desired poles = [-0.5; -0.5; -0.5]

K = acker(A,B,desiredPoles)
% determine the gain K
% based on the Ackermann's formula
% yields
% K = [8.5000 13.0156 12.7188]

%% State feedback controller

sys = ss(A-B*K, eye(size(A)), eye(size(A)), eye(size(A)))

eig(sys.a)
% the eigenvalues are
% -0.5000 + 0.0000i
% -0.5000 + 0.0000i
% -0.5000 - 0.0000i

x0 = [0.1; 0.1; 0.1]
% initial state

t = 0:0.01:25;
% the time duration

x = initial(sys,x0,t);
% response of the state-space
% with initial condition x0
% and time duration t

x1 = [1 0 0]*x';
x2 = [0 1 0]*x';
x3 = [0 0 1]*x';
% the state variables

%% Plot the state variables

subplot(3,1,1); plot(t,x1), grid
title('Response to initial condition')
ylabel('x1')

subplot(3,1,2); plot(t,x2), grid
ylabel('x2')

subplot(3,1,3); plot(t,x3), grid
ylabel('x3')
xlabel('t (seconds)')

Berikut ini ialah plot response dari state variable.

Lihat juga tentang.

Kendali state space MATLAB.

MATLAB: Transform State Space Transfer Function

2018-12-03T21:09:00.000+07:00

Berikut ini adalah script MATLAB untuk mengubah dari state space menjadi transfer function.

clear all, close all, clc;

%% State space representation
A = [-11 -18;
    1 0]
% the state matrix

B = [1; 0]
% the input vector

C = [0 1]
% the output vector

D = zeros(size(C,1),size(B,2))
% the feedforward

%% Verify the controllability and observability

Cm = ctrb(A,B)
% Controllability matrix

Om = obsv(A,C)
% Observability matrix

if rank(Cm) == size(A,1)
    'It is controllable'
else
    'It is not controllable'
end

if rank(Om) == size(A,1)
    'It is observable'
else
    'It is not observable'
end

%% Transform into transfer function

[num,den] = ss2tf(A,B,C,D)
sys = tf(num,den)
pole(sys)
% yields
% s = -9
% s = -2

step(sys)
% plot the step response

Berikut ini adalah step response dari sistem tersebut.

Lihat juga tentang.

Kendali state space MATLAB.

MATLAB: State Space Observability and Controllability

2018-12-02T21:04:00.000+07:00

Berikut ini adalah script MATLAB untuk menguji apakah sistem observable juga controllable.

clear all, close all, clc;

%% Define the matrices

A = [1 2;
    7 2]
% A the state matrix

B = [1; 1]
% B the input vector

C = [1 0]
% C the output vector

D = zeros(size(C,1), size(B,2))
% D the feedforward

eig(A)
% eigenvalues of A
% -2.2749
% 5.2749
% it is not stable

%% Verify the controllability and observability

Cm = ctrb(A,B)
% Controllability matrix

Om = obsv(A,C)
% Observability matrix

if rank(Cm) == size(A,1)
    'It is controllable'
else
    'It is not controllable'
end

if rank(Om) == size(A,1)
    'It is observable'
else
    'It is not observable'
end

Lihat juga tentang.

Kendali state space MATLAB.

BMS: Passive balancing of battery lithium polymer using shunt resistor circuit method

2018-12-01T23:02:00.000+07:00

Passive balancing of battery lithium polymer using shunt resistor circuit method

Published Online: 21 July 2016
AIP Conference Proceedings 1755, 090011 (2016);
https://doi.org/10.1063/1.4958529

Abstract
In this study, the system was designed for monitoring the voltage condition of each battery’s cell. The method used in this study is by using passive shunt resistor balancing method. An electronic circuit was designed in order to balance the value of the able voltage at the battery cells using resistors and then to remove the excess voltage. The result showed that the electrical circuit was capable to balance the voltage of each cell. Based on experiment with several values of load, it is can concluded that 0.1C rate of discharge has the best performance, because it does not affect battery voltage characteristic significantly which leads to better sensor reads.

https://aip.scitation.org/doi/abs/10.1063/1.4958529

Lihat juga mengenai artikel battery management system lainnya.

BMS: The comparison of RBF NN and BPNN for SOC estimation of LiFePO4 battery

2018-12-01T23:00:00.000+07:00

The comparison of RBF NN and BPNN for SOC estimation of LiFePO4 battery

Published Online: 21 July 2016
AIP Conference Proceedings 1755, 090010 (2016);
https://doi.org/10.1063/1.4958528

Abstract
State of Charge (SOC) defined as the percentage of remaining capacity relative to the maximum capacity of the battery. In Battery Management Systems (BMS), SOC is an important variable. In this paper will describe comparison between Backpropagation Neural Networks (BPNN) and Radial Basis Function Neural Network (RBF NN) method for SOC estimation of LiFePO4 battery. BPNN and RBF NN have good characteristics to solve the nonlinear problem. We used discharge and Urban Dynamometer Driving Schedule (UDDS) as training data and testing data. In this research, architecture of BPNN are input layer, one hidden layer with 8 neurons and one output layer. Then architecture of RBF NN are input layer, one hidden layer with 2 neurons and output layer. The experiment used LiFePO4 battery with capacity 2200 mAh, with nominal voltage 4.2 volt. The actual SOC used coloumb counting which are 0 and 1. In this study shows that BPNN and RBF NN can be applied for SOC estimation in LiFePO4 of battery. Both of method have different charcteristics to give output in the network. Applying BPNN can make network more accurate but need more time for iteration. Then implementation RBF NN to estimate SOC is more efficiency in time. It means that network not needs more time for iteration.

https://aip.scitation.org/doi/abs/10.1063/1.4958528

Lihat juga mengenai artikel battery management system lainnya.

BMS: Backpropagation neural network models for LiFePO4 battery

2018-12-01T22:59:00.000+07:00

Backpropagation neural network models for LiFePO4 battery

Published Online: 21 July 2016
AIP Conference Proceedings 1755, 090009 (2016);https://doi.org/10.1063/1.4958527

Abstract
Neural Networks have been used in system control, medicine, pattern recognition and business. The backpropagation neural network (BPNN) appear to be most popular and have been widely used in many applications. BPNN is a supervised learning technique for training multilayer feedforward neural networks. The gradient or steepest descent method is used to train a BPNN by adjusting the weights. The purpose of update numerical weights is minimize error of network between target and output. In this paper, focus with BPNN modeling with data battery for training and testing. We used discharge and Urban Dynamometer Driving Schedule (UDDS) as training data and testing data, respectively Architecture of BPNN consist of input layer, hidden layer and output layer. The otherhand, using BPNN has problem to define amount of hidden neurons. In this study, we used current or voltage as input in input layer, one hidden layer with 8 neurons and one output layer. We used Levenberg-Marquardt algorithm to get fast iteration when computation. The experiment used 2200 mAh of LiFePO4 battery. Result of this research show that Mean Squared Error (MSE) value when current as input and voltage as target is 0.021135 with regresion is 0.626. Then MSE value when voltage as input and current as target 0.029925 with regresion is 0.5213. In this study relationship between voltage and current battery is nonlinear.

https://aip.scitation.org/doi/abs/10.1063/1.4958527

Lihat juga mengenai artikel battery management system lainnya.

BMS: Fault detection design and simulation based on battery modelling

2018-12-01T22:56:00.000+07:00

Fault detection design and simulation based on battery modelling

Published Online: 21 July 2016
AIP Conference Proceedings 1755, 090008 (2016);
https://doi.org/10.1063/1.4958526

Abstract
In electric vehicles, a lithium polymer battery is usually adopted as the main energy storage battery for charging and discharging processes. To manage the battery, Battery Management System (BMS) is needed in the effective and efficient way in order to assure the operation of an EV successful. Failure in the battery means failure in the whole system. In fact, most of the battery damage are found due to the failure of error detection on the battery’s sensors or actuators. Therefore, it requires fault detection on the battery which can work accurately. In this research, the implementation of equivalent circuit models of Lithium Polymer batteries has been done in the Matlab Simulink model to determine the working limit of the voltage and current so that any fault can be detected and isolated, such as overcurrent and overvoltage. The simulation results show that the error detection can be done accurately so that the battery can work optimally.

https://aip.scitation.org/doi/abs/10.1063/1.4958526

Lihat juga mengenai artikel battery management system lainnya.

Informasi Beasiswa Kuliah Luar Negeri dan Internship 2019

2018-12-01T21:56:00.000+07:00

Bagi para pencari beasiswa, berikut beberapa tawaran menarik beasiswa luar negeri untuk awal tahun 2019.

Hokkaido University Summer Program 2019, Japan (Fully Funded)
Deadline: 28 February 2019
Info: https://bit.ly/2QRrjPv

StuNed Program Short Course Scholarship for Indonesian Citizen 2019, Netherlands
Deadline:27 January 2019
Info: https://bit.ly/2rxz9Q2

Korea University International Summer Campus 2019, South Korea (Partial Funded)
Deadline: 15 May 2019
Info: https://bit.ly/2Qt4Clu

ACU Summer School 2019 at University of Mauritius [Fully Funded]
Deadline: 13 January 2019
Info: https://bit.ly/2KS8UN4

CERN Senior Fellowship Programme 2019 [Fully Funded] in Switzerland
Deadline: 04 March 2019
info: https://bit.ly/2RxU0y3

ETH Internship 2019 [Fully Funded] Summer Internship in Switzerland
Deadline: 31 December 2018
Info: https://bit.ly/2Pif2Po

UTSIP Japan Summer Internship 2019 Kashiwa [Fully Funded]
Deadline: 31 January 2019
Info: https://bit.ly/2FW8TsL

University of Tokyo Summer Internship Program Japan 2019 /UTokyo Amgen Scholars Program 2019 [Fully Funded]
Deadline: 01 February 2019
Info: https://bit.ly/2QxMYvR

Asian Graduate Student Fellowships 2019, Singapore
Deadline: 15 December 2019
Info: https://bit.ly/2UhFufx

CERN Junior Fellowship Programme 2019 [Fully Funded] in Switzerland
Deadline: 04 March 2019
Info: https://bit.ly/2EdduoB

UNIL Summer Internship in Switzerland 2019 [Fully Funded]
Deadline: 20 January 2019
Info: https://bit.ly/2AU1OmD

CERN Short Term Internships in Switzerland 2019 [Fully Funded] in Switzerland
Deadline: Different Deadlines for Each Program
Info: https://bit.ly/2QCrQVw

CERN Openlab Summer Program 2019 [Fully Funded] in Switzerland
Deadline:31 January 2019
Info: https://bit.ly/2APE1nS

UTRIP Summer Internship in Japan 2019 at University of Tokyo [Fully Funded]
Deadline: 10 January 2019
Info: https://bit.ly/2AJYGtu

Japan Internship Program 2019 at Okinawa Institute of Science and Technology [Fully Funded]
Deadline: Next Deadline is 28 February 2018
Info: https://bit.ly/2UaRnnC

ASEAN-INDIA RESEARCH TRAINING FELLOWSHIP (AI-RTF), India
Deadline: 31 December 2018
Info: https://bit.ly/2DVi4ai

Future Global Leader Fellowship (Fully Funded), USA
Deadline: 31 January 2019
Info: https://bit.ly/2QwtvMa

Fully Funded Masters and Training Programmes in Belgium (150 Scholarships)
Deadline: 11 January 2019 and 08 February 2019
Info: https://bit.ly/2AIyGid

Young Leaders Access Program 2019 Leadership Program, USA (Middle East)
Deadline: 02 January 2019
Info: https://bit.ly/2PewNPk

2019 MEPI Student Leaders Program,USA [Fully Funded]
Deadline: 31 December 2018
Info: https://bit.ly/2KLzo2V

Young Leaders Access Program 2019, USA (All Country)
Deadline: 02 January 2019
Info: https://bit.ly/2RvdwuX

The New dance WEB Scholarship Program for EU and Non-EU Students in IMPULSTANZ
Deadline: 9 January 2019
Info: https://bit.ly/2DVxr33

Fully Funded for Journalist Fellowship in University of Oxford
Deadline: 11 February 2019
Info: https://bit.ly/2Aen6v7

Dari daftar beasiswa kuliah luar negeri tersebut beberapa fully funded, dibiayai penuh. Selain beasiswa juga ada internship yang cukup menarik.

MATLAB: Basic to Control

2018-12-01T17:00:00.001+07:00

Berikut ini adalah script MATLAB untuk dasar kendali dengan PID.

clear all, close all, clc;

%% Define the plant

P = tf([1],[1 0 -9]);
% transfer function of the plant
% P =
%      1
%   -------
%   s^2 - 9
pole(P)
% yields s = 3 and s = -3
% thus the plant is not stable

%% The PID controller

Kp = 90;
Ki = 60;
Kd = 30;
% taking into Laplace form
% K = Kd*s + Kp + Ki/s

K = tf([Kd Kp Ki],[1 0]);
% PID controller
% K =
%
%   30 s^2 + 90 s + 60
%   ------------------
%           s
CP = series(K,P);
% the open-loop equals to:
% CP = K*P

CL = feedback(CP,1);
% closed-loop (CL) feedback of the system
pole(CL);
% yields
% s = -27.0919 + 0.0000i
% s = -1.4540 + 0.3170i
% s = -1.4540 - 0.3170i

%% Verify the stablility of CL

if isstable(CL,1)
    'Closed-loop is stable'
else
    'Closed-loop is not stable'
end

%% Step response of the system

t = 0:0.1:5;

step(CL,t)

Closed-loop system tersebut stabil. Berikut ini adalah step-response.

Lihat juga tentang.

Kendali state space MATLAB.

Membahas H-Index dan Self Citations

2018-12-01T11:19:00.000+07:00

Sambut hari baru dengan semangat dan kopi.

Catatan engineer absurd kali ini membahas tentang h-index atau disebut juga Hirsch index. Bagi para peneliti dan dosen, tidak hanya di Indonesia, tapi di penjuru dunia, h-index digunakan untuk mengukur tingkat produktivitas dan pengaruh publikasi penelitiannya. Metode ini diajukan oleh Jorge E. Hirsch seorang pakar fisika dari UC San Diego

Baik, kata kuncinya adalah produktivitas dan pengaruh, sehingga h-index berdanding lurus dengan keduanya. Seorang dosen dengan produktivitas publikasi yang tinggi belum tentu memiliki h-index yang tinggi pula, perlu dilihat berapa banyak kutipan (citations) dari publikasinya.

Baiklah, ambil saja kasus seperti berikut.

Seorang dosen dengan h-index 1, maka dia minimal harus memiliki 1 publikasi dan 1 kutipan.
Seorang dosen dengan h-index 4, maka dia minimal harus memiliki 4 publikasi dengan masing-masing memiliki 4 kutipan (citations), sehingga minimal ia memiliki 16 kutipan.
Seorang dosen dengan h-index 20, paling tidak dia telah menghasilkan 20 karya publikasi ilmiah yang telah dikutip masing-masing sebanyak 20 kali, dengan demikian minimal karyanya telah dikutip sebanyak 400 kali.
Seorang dosen dengan 1 publikasi namun memiliki 10 kutipan, dia hanya memiliki h-index 1. Ingat faktor produktivitas dan pengaruhnya.
Dengan demikian bisa saja seorang dosen dengan jumlah kutipan yang lebih rendah memiliki h-index yang lebih tinggi dibandingkan dosen lainnya dengan kutipan yang lebih banyak.
Singkatnya, bila seorang dosen ingin memiliki h-index sebanyak $n$, maka ia harus memiliki $n$ buah karya publikasi, dengan masing masing dikutip sebanyak $n$ kali. Total kutipan minimal ialah $n^2$ kali.

Berikut ilustrasi, berapa jumlah kutipan (citations) untuk mencapai h-index tertentu.

Dengan sumbu x ialah jumlah publikasi, sumbu y jumlah kutipan paling sedikit yang diperlukan untuk mencapai h-index tertentu, dan sumbu z ialah h-index. Perlu diketahui bahwa h-index ini dianggap cukup penting, selain menjadi kebanggaan beberapa jenis orang, h-index juga menjadi pertimbangan untuk pengajuan dana hibah penelitian. Tidak hanya itu, QS membuat ranking universitas di dunia, beberapa kriteria penilaiannya ialah jumlah publikasi, kutipan (citations) per fakultas, serta jumlah penulis yang terafiliasi dengan universitas tersebut.

Hal ini memicu beberapa peneliti dan dosen melakukan hal yang tidak terpuji, yakni manipulasi jumlah kutipan dengan self citations. Self citations pada dasarnya adalah mengutip hasil karya yang telah dipublikasikan oleh seorang dosen atau peneliti di karya publikasinya selanjutnya. Tentu saja publikasi yang dikutip harus sesuai atau merupakan lanjutan penelitian sebelumnya. Namun sayangnya, beberapa oknum dosen atau peneliti melakukan self citations yang berlebihan, terpicu h-index yang tinggi. Mereka melakukan self citations secara berlebihan, dan tidak jarang pula yang dikutip tidak terkait dengan karya publikasinya yang baru.

Melihat dari sisi lain, self citations juga terjadi saat publisher suatu journal ilmiah memuat artikel yang mengutip artikel dari journal tersebut. Tidak salah bila dilakukan secara alami dan kaidah ilmiah, namun beberapa publisher ada yang 'memaksa' para penulis untuk setidaknya mengutip sejumlah artikel dari journal mereka.

Self citations bukan hal yang haram, itu masuk domain etis dari seorang peneliti. Hanya saja, bila ketamakan dan gengsi dibiarkan, hal itu akan merusak kaidah penelitian. Pada hakekatnya, ilmu pengetahuan harus bermanfaat dan memudahkan hidup umat manusia, bukan digunakan untuk ketamakan segelintir orang saja.

Catatan kali ini cukup absurd dan sok bijak.