AUTOMATIC SPEED VARIATION SYSTEM IN AUTOMOBILE LINKED WITH DOORS

The sudden accidents often occur when passengers traveling on the footpath of public transport at high speed. The sudden fall from footsteps at that speed may cause serious risk to passengers. In this paper, we present a passengers safety system that has two major features, one is to detect the passengers present in the footpath of the vehicle and the other one is to maintain the speed of the vehicle based on the detection of passengers in the footpath. The present work used IOT system that contains an ultrasonic sensor to detect passengers in a given area. Based on the sensor output of detection of passengers, the door of public transport will automatically open/close and also it will maintain or increase the speed of the bus based on the results. The development of this IOT system also implements an IEEE 802.11 technology to transmit the signal from public transport to mobile apps. The monitoring and controlling mobile application are developed which can receive/transmit the data from/to the controller of the system by using IEEE 802.11. This mobile application shows the status of detection of passengers, door status, and also the real-time speed of the public transport. Keywords – Passenger safety, IoT, Monitoring system, Wireless Communication.

Implementasi Kendali Keseimbangan Gerak Two Wheels Self Balancing Robot Menggunakan Fuzzy Logic

Self balancing robot is a two-wheeled robot that only has two fulcrums so that this robot is an unbalanced system. Therefore, a control system that can maintain the stability of the robot is needed so that the robot can keep in standing position. This study aims to design a self-balancing robot and its control system which improves the robot's performance against the maximum angle of disturbance that can be overcome. The control system used is based on fuzzy logic with 9 membership functions and 81 rules. The control system is applied to the ESP-32 microcontroller with the MPU-6050 sensor as a feedback position of the robot and DC motor as an actuator. Complementary filters are added to the MPU-6050 sensor readings to reduce noise to obtain better robotic tilt angle readings. The improvement of this research compared to previous research based on fuzzy is the addition of the number of membership functions from 7 to 9 and the embedding of a complementary filter on the MPU-6050 sensor output reading. The result shows that the designed self balancing robot which has dimensions of 10cm x 18cm x 14.5cm can cope with the maximum disturbance angle up to 17.5⁰.

Detection and Early Warning of Toxic Gases Based on Semiconductor Wireless Sensors

This paper studies a semiconductor wireless sensor system, which is composed of a semiconductor wireless sensor sampling circuit, gas-sensitive signal alarm and wireless transmitting circuit, and wireless radio frequency signal receiving circuit. The system is suitable for wireless monitoring of hydrogen fluoride gas in chemical plants. The hydrogen fluoride gas sensor is designed, integrated, and classified according to the polarity and size of the sensor output signal. The signal processing circuit of the sensor output signal is made with an integrated design. This paper developed a simulation experimental system for the wireless monitoring network characteristics of toxic hydrogen fluoride gas and completed the monitoring system’s sensor characteristic calibration and accuracy comparison simulation experiment, the communication distance test experiment of the communication system, and the research experiment on the influence of environmental humidity on the sensor characteristics of the monitoring system. In terms of software, the workflow of network nodes has been optimized. Since the structure of the wireless sensor network is not exactly the same in different application fields, the toxic gas monitoring system based on wireless sensor networks must focus on extending the network’s life cycle. Without affecting the normal operation of the system, distributed compressed sensing can greatly extend the service life of the system. Therefore, this subject combines the compressed sensing technology developed in recent years with the air monitoring system for the processing of transmission data, in order to achieve the purpose of further reducing the energy consumption of the system. The simulation experiment demonstrated that the lmF neural network combined with gas sensor array technology can realize qualitative identification, quantitative analysis of single gas, and quantitative analysis of mixed combustible gas. The research work in this area also provides a new way to further combine the miniature hydrogen fluoride gas sensor unit with sensor technology, integrate the hydrogen fluoride gas sensor unit and the electronic tag, and expand the wireless application of the gas sensor.

Sistem Keseimbangan Robot ERISA Pada Bidang Miring Menggunakan Kontrol PD dan Sensor Fusion

The world of robotics is currently developing; many robots are created to help humans work in carrying out daily activities. Various types of robots have been created, one of which is a type of robot that resembles a human body (humanoid). Humanoid robots are developing in many countries, including Indonesia. In its development, the walking technique is a major factor in making humanoid robots. Humanoid robots have the ability to walk like humans by balancing their body positions while walking, so they don’t fall. In maintaining this balance, a tilt detection system for the robot’s position and a balancing system is needed when the robot is about to fall. So, to overcome this problem, implementation is carried out using the sensor fusion method at the output of the sensors that are used to minimize noise or interference with the sensor output value. The accuracy of the sensor output value on the position angle tilt detector can help the robot provide a balancing act. By implementing a PD control system and sensor fusion consisting of a Kalman filter and a complementary filter, the robot was successfully carried up and down the plane to a maximum slope of 12 °.

Prediction and Forecasting of Air Quality Index in Chennai using Regression and ARIMA time series models

Air is one of the most fundamental constituents for the sustenance of life on earth. The meteorological, traffic factors, consumption of non-renewable energy sources, and industrial parameters are steadily increasing air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of air quality in our environment needs to be monitored continuously. The Air Quality Index (AQI), which indicates air quality, is influenced by several individual factors such as the accumulation of NO2, CO, O3, PM2.5, SO2, and PM10. This research paper aims to predict and forecast the AQI with Machine Learning (ML) techniques, namely linear regression and time series analysis. Primarily,Multi Linear Regression (MLR) model, supervised machine learning, is developed to predict AQI. NO2, Ozone(O3), PM 2.5, and SO2 sensor output collected from Central Pollution Control Board (CPCB) – Chennai region, India feed as input features and optimized AQI calculated from sensor's output set as a target to train the regression model. The obtained model parameters are validated with new and unseen sensor output. The Key Performance Indices(KPI) like co-efficient of determination, root mean square error and mean absolute error were calculated to validate the model accuracy. The K-cross-fold validation for testing data of MLR was obtained as around 92%. Secondly, the Auto-Regressive Integrated Moving Average (ARIMA) time series model is applied to forecast the AQI. The obtained model parameters were validated with unseen data with a timestamp. The forecasted AQI value of the next 15 days lies in a 95 % confidence interval zone. The model accuracy of test data was obtained as more than 80%.

A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features

This study proposes a novelty-classification framework that applies to structural health monitoring (SHM) and sensor output validation (SOV) problems. The proposed framework has simple high-dimensional features with several advantages. First, the feature extraction method is extensively applicable to instrumented structures. Second, the high-dimensional features’ utilization alleviates one of the main issues of supervised novelty classifications, namely, imbalanced datasets and low-sampled data classes. Recurrent Neural Networks are employed for the classification of high-dimensional features. Furthermore, generative adversarial networks (GAN) are trained with low-sampled data classes’ high-dimensional features for generating new data objects. The generated data objects are combined with the initial training set for improving classification results. The proposed framework is studied on two SHM and SOV datasets. The SHM dataset has twenty-one data classes, with a total test accuracy of 99.60% compared to another study with 88.13% accuracy. The SOV classification shows improved results with a mean accuracy of 96.5% compared to three other studies with mean accuracy values of 93.5%, 92.97%, and 71.1%. Furthermore, the integration of GAN’s generated data objects with low-sampled classes improved those classes’ mean F1 score from 44.77% to 64.58% and from 73.39% to 90.84% on SOV and SHM case studies, respectively. The integration of GAN-generated data objects with the initial low-sampled data classes for accuracy improvement shows more potential in the SHM dataset than the SOV case, which can be due to the signal pattern-based labeling logic of SOV datasets.

Investigation of sensitive element for pressure sensor based on bipolar piezotransistor

The article translated from Russian to English on pp. 691-693 (please, look down). The paper summarizes results of investigation of high-sensitivity MEMS pressure sensor based on a circuit containing both active and passive stress-sensitive elements: a differential amplifier utilizing two n-p-n piezotransistors and for p-type piezoresistors. A comparative analysis of a sensor utilizing this circuit with a pressure sensor based on traditional piezoresistive Wheatstone bridge and built on the same mechanical part is provided. MEMS pressure sensor with the differential amplifier (PSDA) has sensitivity of S = 0.66 mV/kPa/V, which exceeded the sensitivity of the element with piezoresistive Wheatstone bridge (PSWB) by 2.2 times. The sensitivity increase allows for the following sensor improvements: die size reduction, increase of diaphragm mechanical strength while keeping high pressure sensitivity, and simplifying requirements to external processing of the pressure sensor output signal. There are two main challenges related to the use of PSDA-based pressure sensors: strong dependence of output signal on temperature and higher than in PSWB noise reducing the dynamic range of the device to 10 3. The article describes methods of addressing these problems. The temperature dependence of sensor output signal can be minimized with help of an offset thermal compensation circuit and by eliminating metallization at the thin part of the diaphragm. The noise can be minimized by reducing the thickness of the active base region of the transistor. Circuit analysis with software NI Multisim shows that sensitivity of PSDA-based pressure sensor can be increased 2.3 times by circuit optimization.

Determination of the Concentration of Ultrafine Aerosol Using an Ionization Sensor

As public awareness of the threats related to ultrafine aerosols increases, there is a growing need for inexpensive, real-time exposure assessment devices. In this work, the well-established technology used in the smoke detector with a radioactive source was tested in laboratory conditions to check its suitability for determining the number concentration of the ultrafine aerosol. It has been shown that the sensor output changes linearly with the change of diesel soot concentration in the range up to 8.3 × 105 particles cm−3. The sensor has also been shown to be able to detect rapid changes in aerosol concentration. Empirical equations describing the influence of air velocity, temperature, relative humidity, and pressure on the sensor output were determined. The collected results confirm that the ionization sensor can be used to assess ultrafine aerosol exposure, although additional engineering work is required to increase the resolution of the output signal measurement and to compensate for the effects of weather conditions. The presented method can be used for concentration monitoring and risk assessment in environmental engineering, materials engineering, cosmetics industry, textiles, sports, chemical, mining, energy, etc.

Development of a Wearable Mouth Guard Device for Monitoring Teeth Clenching during Exercise

Teeth clenching during exercise is important for sports performance and health. Recently, several mouth guard (MG)-type wearable devices for exercise were studied because they do not disrupt the exercise. In this study, we developed a wearable MG device with force sensors on both sides of the maxillary first molars to monitor teeth clenching. The force sensor output increased linearly up to 70 N. In four simple occlusion tests, the trends exhibited by the outputs of the MG sensor were consistent with those of an electromyogram (EMG), and the MG device featured sufficient temporal resolution to measure the timing of teeth clenching. When the jaw moved, the MG sensor outputs depended on the sensor position. The MG sensor output from the teeth-grinding test agreed with the video-motion analysis results. It was comparatively difficult to use the EMG because it contained a significant noise level. Finally, the usefulness of the MG sensor was confirmed through an exercise tolerance test. This study indicated that the developed wearable MG device is useful for monitoring clenching timing and duration, and the degree of clenching during exercise, which can contribute to explaining the relationship between teeth clenching and sports performance.