Loose-end defect on the tip of the cigarette is a common problem found on production lines. Large companies use computerized sophisticated machines to detect and reject it automatically. However, the same solution can not be implemented in small or home industries because the investment cost is too high. This paper presents the development of simple electronic instrumentation to detect loose-end defects in cigarettes. It relies on the signal acquisition of a photoelectric transceiver mechanism that is converted and processed digitally to relatively quantify the difference between the good and defect. Motor-driven cigarette rotator device was built specially to test the performance of the instrumentation system. Loose-end defects are made artificially by removing 0.2 gr tobacco from the tip of a normal cigarette. The average quantifying relative value was found above 58.5%, this indicates that the system has good contrast properties. At the speed of 10,000 cigarettes/min, the average accuracy is 90.5%, and at 17,500 cigarettes/min, the average accuracy is 65.3%.
The problem of garbage is not a new thing from the past until now, garbage is still the center of attention and a prolonged problem because of the various impacts of the problems that are caused and felt. Many factors are the cause of the increasing amount of garbage every day, including the lack of self-awareness about maintaining cleanliness and disposing of garbage in its place. This study aims to develop an IoT-based garbage collection robot. The method in developing this garbage collection robot is using the Fuzzy Mamdani method. The IoT-based garbage collection robot is designed using the Arduino Uno microcontroller as well as the Node MCU ESP 8266, HC-SR04 sensor, DHT11 sensor, SG90 Servo motor, DC motor and L298N motor driver. The way this robot works is to detect the distance with the HC-SR04 sensor as a distance data input then processed by the Arduino microcontroller and then the SG90 servo motor will move to clamp the garbage automatically and for the robot using control via a smartphone to be directed to the destination garbage. The results of this study are that the robot can clamp or pick up garbage with a distance of less than 5 cm and can control the speed of a DC motor using the fuzzy Mamdani method.
Abstract— Tanks have an important role in protecting a region in the event of a battle. The tank's cannon is a very reliable weapon in warfare. However, the cannon's precision while aiming and firing targets is a concern. A control system must be created to increase the cannon's stability and precision. The state feedback control technique with a full-state observer is the control system that can manage cannon disturbances. The control system is built around three DC motors, each of which operates the cannon’s x, y, and z axes. Then performed tests for each axis at an angle of 90 degrees, The state feedback control with a full state observer can produce outstanding performance, with the time required for the cannon to reach the target angle was 0.51 seconds, and the cannon system had 0% overshoot.
The development of UAV technology has reached the stage of implementing artificial intelligence, control, and sensing. Cameras as UAV data inputs are employed to ensure flight safety, search for missing persons, and disaster evacuation. Human detection using cameras while flying is the focus of this article. The application of human detection in pedestrian areas using aerial image data is used as the dataset in the deep learning input process. The architectures discussed in this study are YOLOv5 and YOLOv8. The precision, recall, and F1-score values are used as comparisons to evaluate the performance of these architectures. When both architecture performances are applied, YOLOv8 outperforms YOLOv5. The achieved performance of YOLOv8 is a precision of 84.62%, recall of 75.93%, and F1-score of 79.98%.
The utilization of lettuce leaves as a nutritious dish has made its demand relatively high. This research presents a moisture control method to optimize seed growth for lettuce. The prototype system has been developed using Arduino Mega, DF Robot soil moisture humidity sensor, and DC spraying pump for testing three different control scenarios, the open loop control system (time-based), the Proportional control system, and the combination of open loop and Proportional control method as an alternative. The results show that each control method can achieved the desired moisture of rockwool at 60%. Also, the combination of open loop and Proportional control can lessen energy usage of the system by up to 51% compared to the common open loop method.
