Distributed Path Planning Classification with Web-based 3D Visualization using Deep Neural Network for Internet of Robotic Things

Internet of Robotic Things (IoRT) distributes heterogeneous intelligences among devices and platforms. A distributed control of a three-degree-of-freedom (3-DOF) robot manipulator is integrated with web-based 3D visualization. An asynchronous protocol was utilized to broadcast kinematic data of a 3-DOF robot manipulator between platforms. However, kinematic data computed using inverse kinematic equations directly cannot identify the singularity issue of robot manipulator. Singularity avoidance required to prevent robot component or joint from damage. Therefore, this study proposed a deep neural network approach as a classification-based of manipulator robot path planning to avoid singularity issues. Deep neural network (DNN) was trained in 12 minutes, 52 seconds in 500 iterations. Training accuracy measured with value 96,23 percent, validation accuracy measured with value 96,13 percent, and testing accuracy measured with value 96,48 percent Additionally, 3 DOF manipulator robot web-based 3D visualization was made using Web Graphics Library (WebGL). The distributed platform was tested successfully and can distribute and classify 2352 motions per second

Robot Lengan 4 Derajat Kebebasan Menggunakan Tampilan Antarmuka Pengguna Berbasis Arduino Uno

Kemajuan teknologi dalam bidang robotika pada saat ini sudah memasuki era modernisasi serta perkembangannya memasuki era baru dan sudah serba otomatis untuk sebuah pendidikan dan teknologi. Robot lengan pada dunia industri berpengaruh sangat besar, misalnya dapat membuat waktu lebih efisien dan dapat mengurangi biaya dimana yang sebelumnya harus membayar gaji karyawan. Berdasarkan hasil pengujian sinkronasi gerak antar robot lengan dan slider pengendali didapatkan hasil bahwa semakin besar nilai derajat pergesaran slider maka lebar pulsa sinyal PWM semakin melebar dan tegangan keluaran dari pin mikrokontroler Arduino Uno semakin besar dengan nilai rata – rata tegangan yang dihasilkan sebesar 115,03 mV. Robot dalam keadaan standby hanya membutuhkan daya sebesar 0,9 Watt dan saat beroperasi membutuhkan data sebesar 3,94 Watt. Pada motor servo ini, terdapat beberapa kekurangan, dimana salah satunya adalah tidak akuratnya dalam melakukan pengukuran sudut, dapat dihitung bahwa selisihnya sebesar 13% atau sejauh 7,3 derajat

Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.  

SELF-COLLISION AVOIDANCE OF ARM ROBOT USING GENERATIVE ADVERSARIAL NETWORK AND PARTICLES SWARM OPTIMIZATION (GAN-PSO)

Collision avoidance of Arm Robot is designed for the robot to collide objects, colliding environment, and colliding its body. Self-collision avoidance was successfully trained using Generative Adversarial Networks (GANs) and Particle Swarm Optimization (PSO). The Inverse Kinematics (IK) with 96K motion data was extracted as the dataset to train data distribution of  3.6K samples and 7.2K samples. The proposed method GANs-PSO can solve the common GAN problem such as Mode Collapse or Helvetica Scenario that occurs when the generator  always gets the same output point which mapped to different input  values. The discriminator  produces the random samples' data distribution in which present the real data distribution (generated by Inverse Kinematic analysis).  The PSO was successfully reduced the number of training epochs of the generator  only with 5000 iterations. The result of our proposed method (GANs-PSO) with 50 particles was 5000 training epochs executed in 0.028ms per single prediction and 0.027474% Generator Mean Square Error (GMSE)

Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.  

PERANCANGAN SCORE BOARD DAN TIMER MENGGUNAKAN LED RGB BERBASIS ARDUINO DENGAN KENDALI SMART PHONE ANDROID

Smart Phone merupakan salah satu kecanggihan teknologi dibidang telekomunikasi yang didalamnya terdapat fitur-fitur yang dapat mempermudah pekerjaan manusia. Banyak sekali jenis smart phone  diantaranya adalah smart phone dengan OS Android. Smart phone Android merupakan smart phone yang mudah penggunaannya, baik untuk keperluan bisnis, pendidikan, hiburan dan lain-lain. Dengan media komunikasi, pertukaran informasi, pertukaran data dan sebagaginya akan terasa lebih mudah dan cepat. Kemajuan teknologi tersebut tentunya belum dapat memenuhi kebutuhan jasmani seseorang khususnya dalam bidang olahraga. Namun kehadirannya mampu mendorong kemudahan dalam bidang olahraga tersebut. Misalnya, penggunaan sistem penskoran dan timer yang menggunakan seven segment sehingga dapat digunakan pada kondisi indoor ataupun outdoor. Score board dan timer digunakan guna mempermudah juri atau wasit menentukan score dan waktu pertandingan pada beberapa cabang olahraga. Karena diketahui setiap cabang olahraga mempunyai peraturan yang berbeda prihal mengenai sistem penskoran dan waktu nya. Hasil dari penelitian ini adalah menghasilkan suatu score board dan timer menggunakan LED RGB yang dapat dikontrol melalui smart phone android. Score board dan timer yang dibuat mampu digunakan dalam beberapa cabang olahraga seperti basket, badminton, footsal dan volley

SELF-LEARNING OF DELTA ROBOT USING INVERSE KINEMATICS AND ARTIFICIAL NEURAL NETWORKS

As known as Parallel-Link Robot, Delta Robot is a kind of Manipulator Robot that consists of three arms mounted in parallel. Delta Robot has a central joint constructed as an end-effector represented as a gripper. An Analysis of Inverse Kinematic (IK) used to convert the end-effector trajectory (X, Y) into rotations of stepper motors (ZA, ZB and ZC). The proposed method used Artificial Neural Networks (ANNs) to simplify the process of IK solver. The IK solver generated the datasets contain motion data of the Delta robot. There are 11 KB Datasets consist of 200 motion data used to be trained. The proposed method was trained in 58.78 seconds in 5000 iterations. Using a learning rate (α) 0.05 and produced the average accuracy was 97.48%, and the average loss was 0.43%. The proposed method was also tested to transfer motion data over Socket.IO with 115.58B in 6.68ms

Defect classification of radius shaping in the tire curing process using Fine-Tuned Deep Neural Network

The curing process or vulcanization process is the final stage of the tire manufacturing process, where the properties of the tire compound change from rubber-plastic material to become elastic by forming cross-links in its molecular structure. The green tire is formed in the curing process, which is placed on the bottom mould. The inside of the green tire surrounds the bladder. The top mould will close to carry out the next curing process. In closing the mould, there is a shaping process of forming a green tire placed on the bladder and given a proportional pressure. Improper or abnormal radius shaping results cause seventy percent of product defects. This paper proposed abnormal detection of radius shaping in the curing process using Fine-tuned Deep Neural Network (DNN). Several DNN models have been examined to analyze an optimized DNN model for abnormal detection of radius shaping in the curing process. The fine-tuned DNN architecture has been exported for the curing system. The DNN was trained with a training accuracy of 97.88%, a validation accuracy of 95%, a testing accuracy of 100%, and a loss of 4.93%

Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds

Impact of Moving Sign (Running Text) Implementation at PKBM Wiyata Utama

The running information display board or Running Text is one of the information media or digital publications comprised of an ordered pattern of Light Emitting Diode (LED) lights, and each LED has a coordinate point that determines which LED position is on or off. This LED light is available in a range of colors, including red, yellow, green, blue, white, and blended hues. This running text is often used in Office Buildings, School Buildings, Shopping Buildings, and other locations where the general public must be informed. At this community service, running text has been installed in the PKBM Wiyata Utama school environment in Kembangan Utara, West Jakarta, which is suitable for school-related information media such as education level, school name, and school event