Pengembangan Embedded Device Berbasis PLC untuk Simulator Rejection System dengan Penambahan Human Machine Interface

Changes to the simulator rejection system from the previous research were carried out by replacing all sensors, drive motors, PLC systems, and adding HMI systems. The objectives in this research, namely (i) changing and developing a rejection system simulator, creating a ladder-based program structure and configuring HMI systems and (ii) measuring the performance of the simulators. Rejection system simulator is fabricated and reassembled, ladder-based syntax into PLCs and HMI is also configured, and observing the performance is done through the HMI layer. The results of programming is carried out through (i) providing software for PLCs, (ii) programming the PLC system, (iii) compiling and uploading programs from PC to PLC, (iv) configuring PLC and HMI via ethernet, and (v) compiling and uploading the program structure from PC to HMI. The performance for observing the condition of the bottle cap through the HMI is observed when (i) synchronization between the simulator system and the HMI-assisted PLC control, (ii) the reading of the sensors installed on the simulator, and (iii) the rotation process of the rejector arm. Overall, the rejection system simulator with a PLC-based assisted by HMI can be used as a process simulation against the implementation of the rejection system

Compressed Firmware Classification Based on Extra Trees and Doc2Vec

Firmware formats vary from vendor to vendor, making it difficult to track which vendor or device the firmware belongs to, or to identify the firmware used in an embedded device. Current firmware analysis tools mainly distinguish firmware by static signatures in the firmware binary code. However, the extraction of a signature often requires careful analysis by professionals to obtain it and requires a significant investment of time and effort. In this paper, we use Doc2Vec to extract and process the character information in firmware, combine the file size, file entropy, and the arithmetic mean of bytes as firmware features, and implement the firmware classifier by combining the Extra Trees model. The evaluation is performed on 1,190 firmware files from 5 router vendors. The accuracy of the classifier is 97.18%, which is higher than that of current approaches. The results show that the proposed approach is feasible and effective.

RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

COVID-19 Authorized Entry with Face Mask Detection using Raspberry Pi

There are no effective face mask detection applications in the current COVID-19 scenario, which is in great demand for transportation, densely populated places, residential districts, large-scale manufacturers, and other organizations to ensure safety. In addition, the lack of big datasets of photographs with mask has made this task more difficult. With the use of Python programming, the Open CV library, Keras, and tensor flow, this project presents a way for recognizing persons without wearing a face mask using the facial recognition methodology. This is a self-contained embedded device that was created with the Raspberry Pi Electronic Development Board and runs on battery power. We make use of a wireless internet connection using USB modem. In comparison to other existing systems, our proposed method is more effective, reliable, and consumes significantly less data and electricity

Embedded Device Berbasis PLC pada Miniatur Konveyor untuk Pengoperasian Simulator Rejection System

A PLC-based embedded device on a miniature conveyor machine for operating a rejection system has been designed and constructed. The research objectives, namely (i) design and manufacture of an integrated system, (ii) making program structure based on a ladder diagram, and (iii) measuring the performance of an integrated system. Integrated system assembled from a miniature of the conveyor machines and belt installation, and installing the dc motor, while the rejection system assembly by placing several sensors, installing stepper motor, and wiring to the PLC system. Programming based on ladder diagram carried out by determining algorithms, compiling the ladder diagram, addressing the input/output, and compiling and uploading the program from PC to PLC. The performance of the integrated system was observed when (a) observation during synchronization, (b) observations of sensor readings while the rejection system simulator is operating, and (c) observation and measurement of the processing time of the rejection arm. Overall results have been obtained in the form of a PLC-based embedded device for the rejection system simulator on observations of the condition of the bottle caps for beverage packaging. Based on the overall observation, the PLC-based embedded device has functioned to operate the rejection system can be implemented at a manufacturing scale.