Automated Intelligent Real-Time System For Aggregate Classification

This research focuses on developing an intelligent real-time classification system called NeuralAgg. Penyelidikan ini memfokuskan untuk membina sistem pengkelasan pintar secara masa nyata dipanggil NeuralAgg

Intelligent Rock Vertical Shaft Impact Crusher Local Database System

Aggregates are one of the major components in the concrete production. The aggregates output from the Rock Vertical Shaft Impact Crusher (RoR VSI), had been classified to six groups of shapes then divided further into two categories namely the high quality aggregates and the low quality aggregates. The characteristics of the aggregates such as shape, size and color, do play an important roles in the development of high strength concrete. In order to produce high quality aggregates, the system would need to be monitored and maintained continuously by analyzing the past and current data. Presently, there is no database system to store the images for the classified data. The conventional method of the aggregates is done manually which is slow, highly subjective and laborious. Therefore, a local database system is proposed to store information could help to overcome this problem. The images and aggregates’ recognition and classification data will be kept in order and it will have a simple and easy way of storing and retrieving information. The machine performance can be retrieved for any period of time by calculating the output for high quality aggregates out of total of agggregates produced. The shapes break down for all six recognizable shapes also can be displayed. These could help the engineer to monitor the system on output performance with continuous analysis, with shorter time. Other than that, the strength of the concrete can be determined by counting the number and the percentage of good quality of aggregate being used

Automated Intelligent real-time system for aggregate classification

Traditionally, mechanical sieving and manual gauging are used to determine the quality of the aggregates. In order to obtain aggregates with better characteristics, it must pass a series of mechanical, chemical and physical tests which are often performed manually, and are slow, highly subjective and laborious. This research focuses on developing an intelligent real-time classification system called NeuralAgg which consists of 3 major subsystems namely the real-time machine vision, the intelligent classification and the database system. The image capturing system can send high quality images of moving aggregates to the image processing subsystem, and then to the intelligent system for shape classification using artificial neural network. Finally, the classification information is stored in the database system for data archive, which can be used for post analysis purposes. These 3 subsystems are integrated to work in real-time mode which takes an average of 1.23 s for a complete classification process. The system developed in this study has an accuracy of approximately 87% and has the potential to significantly reduce the processing and/or classification time and workload

A system for alerting of a motion of a person

A system for alerting of a motion of a person using vibration method warning syste

Development Of Rubber Tire Gantry Crane (Prototype) Obstacles Avoidance Method Using A Single Sensor On Microcontroller Based

RTG crane is used to stack containers at the wharf. Because the size of the crane is too large and the operated at high place, it is nearly impossible for the operator to monitor the area under the crane. Currently, a pointing sensor system is installed but the crane still fails to stop if the obstacle is inside of the track due to low coverage of the pointing sensor system. In this paper, a new method is developed on a prototype system of RTG, to improve the current obstacle avoidance. The system is using is using servo motor to rotate an infrared sensor in order to cover the total area in the track. The results of new method is compared with the pointing sensor system and shown that the develop system by increasing the sensing area to three point or almost all the track area by rotating the infrared sensor 68” continuously and taking the possibility of obstacles outside the tracking area

Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network

This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1-score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks

Road markers classification using binary scanning and slope contours

Road markers guide the driver while driving on the road to control the traffic for the safety of the road users. With the booming autonomous car technology, the road markers classification is important in its vision segment to navigate the autonomous car. A new method is proposed in this paper to classify five types of road markers namely dashed, single, double, solid-dashed and dashed-solid which are commonly found on the two lane single carriageway. The classification is using unique feature acquired from the binary image by scanning on each of the images to calculate the frequency of binary transition. Another feature which is the slopes between the two centroids which allow the proposed method, to perform the classification within the same video frame period. This proposed method has been observed to achieve an accuracy value of at least 93%, which is higher than the accuracy value achieved by the existing method

Development of a quadruped crawling robot prototype

Although wheeled robots are commonly used, it has limited ability to move to any terrains at ease. They suffer from difficulties when travelling over uneven and rough terrains. Legged robots have an advantage over the wheeled robots in that they are suited for such situations. The implementation of legged robots normally requires many motors to move every joint in a robot leg. Additional motor will increase the construction cost, robot weight, and the demand for power supply. Moreover, robot simulation becomes more complex. This research is related to the design and development of a cost effective quadruped autonomous robot. The robot can moves according to a unique pattern using three servo motors as its actuator in each of its leg. The design of the robot is firstly made with CAD program and then the structure of the body and the leg is analyzed in order to find a conect balance and to make sure the servo motors are capable to move the robot. A prototype of the quadruped robot is fabricated and tested thoroughly. Experimental studies are carried out to test its stability issues when the robot moves. The robot is capable of moving forward, backward, turn left and turn right by crawling its way. A microcontroller is used as the brain of the robot assisted by two analog distance sensor for better obstacle sensing. It uses a rechargeable battery as the power supply for the microcontroller. The servo motors on the other hand are powered by another rechargeable battery. At the end of this research, a working prototype has been developed

Real-Time Video Road Sign Detection And Tracking Using Image Processing And Autonomous Car

Detection and monitoring of real-time road signs are becoming today's study in the autonomous car industry. The number of car users in Malaysia risen every year as well as the rate of car crashes. Different types, shapes, and colour of road signs lead the driver to neglect them, and this attitude contributing to a high rate of accidents. The purpose of this paper is to implement image processing using the real-time video Road Sign Detection and Tracking (RSDT) with an autonomous car. The detection of road signs is carried out by using Video and Image Processing technique control in Python by applying deep learning process to detect an object in a video’s motion. The extracted features from the video frame will continue to template matching on recognition processes which are based on the database. The experiment for the fixed distance shows an accuracy of 99.9943% while the experiment with the various distance showed the inversely proportional relation between distances and accuracies. This system was also able to detect and recognize five types of road signs using a convolutional neural network. Lastly, the experimental results proved the system capability to detect and recognize the road sign accurately