The occurrence of intelligent building fires causes huge economic losses to the country and society, and even people’s safety. It is necessary to accurately assess the degree of intelligent building fire risk so that the fire emergency management department can make scientific decisions. In this paper, a trustworthy classification model for intelligent building fire risk is proposed, which provides a scientific and reasonable model supporting the classification assessment of intelligent building fire risk. The model integrates Bayesian Network (BN) and software trustworthy computing approach. BN is used to calculate the risk value of attributes that describe the fire risk situation of the intelligent building from 7 profiles. Based on the fire risk attribute values, trustworthy computing is adopted to classify the fire risk into 5 ranks which indicates the severity degree of building fire risk: the higher the rank is, the greater the harm is. Taking the Shanghai Jing’an 11.15 fire as an example, the result confirms that the method proposed in this paper has good theoretical significance and practical value. In addition, we compare our method with 3 fire risk assessment methods in the reference. The comparisons illustrate that the trustworthy classification model proposed in this paper is more comprehensive, rational, and scientific.

A Trustworthy Classification Model for Intelligent Building Fire Risk

Presently most of the robots do repeat works or jobs which are considered as more difficult for human beings. Also, it is very dangerous for the workers to work in the hazardous environments which can be overcome by industrial robots. Intelligent bots can sense, learn, monitor, control, and can-do high-quality work continuously without breaks. This action was done by merging the human benefits and physical systems with intelligent cyber systems. To monitor and detect the faults in the manufacturing industries a blue tooth based intelligent inspection robot is designed in this paper. The robot’s control action is developed by block designer of MIT app-inventor in the android phone along with blue-tooth connection, and the robot can be operated and controlled by phone. This smart inspection-robot has four wheels which can move and inspect the manufacturing products inside the factories. The proposed robot has camera and is connected through blue tooth. The speed and position of the robot are inserted into the processor which controls the robot smoothly and automatically. First the controller application is developed by designer and block editor of MIT app inventor. Then it is installed on the android phone. Now the Arduino UNO-microcontroller with blue tooth is entrenched with android application will communicate with the robot and finds the fault accurately by personal image classifier. The personal image classifier will detect the cracks and holes in big and small size, also detects corrosion and indicate the damages percentage in any manufactured products which is very difficult or cannot view through human naked eyes. So, the reviewer sends the workers immediately to the correct place and the damages can be repaired at low cost at right time. At last, the proposed mobile robot has been controlled and are performed and success experimentally of the coordination between the Android application and the mobile robot are demonstrated.

Optimization of an Intelligent and Survey Robot for Inspection and Fault Diagnosis Mechanism in the Manufacturing Sector

In the context of increasing the workload of nurses in the hospitals and also to reduce the human intervention, a real-time operated medical assistive robot is proposed here. It is a line following robot that can do the medicine food delivery to the patients, inform emergency alerts from the patients to the nurse and disinfecting the ward. The nurse can control it, and it will take the role of a nurse in the patient’s ward. The design is such a way that it can function only in the wards arranged with the line following the path. Line following is feasible with the IR sensors and the DC motors with L293D driver. Raspberry Pi monitors the state of the robot and also retrieves the medicine information. Time settings of food, medicine delivery and disinfection of the ward are done by the nurse. A carrier with LED-connected slots will carry the food and medicines of each patient. When the robot reaches the destination, the corresponding LED will be ON which indicates that particular patient’s slot. During emergencies, by pressing the emergency switch in the robot, the patients can notify it to the nurse through the Blynk mobile application. A servo motor will be there for disinfecting the ward. Since, in the present scenario, the value of social distancing is more, this robot will create a great impact on society. Experimental results show that its features not only help the nurse, but are also useful for the patients and their bystanders as well. 

Real-Time Operated Medical Assistive Robot

The emerging trend of automobile connectivity with and without internet in accordance with the radical growth of traffic, technologies should be appropriate to solve the issues related to speeding negligence of the driver, instant recovery of the accident-prone vehicle and causality within the golden window time. One of the challenges encountered in the recovery and timely assistance, is time of the incident and scare population. This article elaborates on a hybrid technique incorporating state of the art technology to detect and prevent traffic and automobile mishap. This is achieved by interface Hall effect magnetic sensor to detect the speeding, MQ2 gas sensor to keep a count of the oxygen level comfortable frequency, added on objective is the flame sensor to identify and caution the driver and the authorized personnel in case of fire in order to prevent disaster. Accelerometer MMA 7761 is incorporated in the model to identify the axis tilt, Piezoelectric sensor acts as a safety device, on occurrence of an accident, the enormous strain endured is converted to an electrical signal which symbolizes accurate of an incident. To maintain the safety distance between the vehicles, an ultrasonic sensor is installed to indicate any violation and thereby preventing the collision which tend to happen unexpectedly. A prototype is made incorporating all the sensors with Arduino, Wi-fi module microcontroller and the information collected is transferred to cloud which is accessible universally through all modes.

Vehicle Mishap Prevention and Detection using IOT and mesh topology implied node to node Communication

Pipeline inspection is important to ensure the safe operation of pipelines. Obtaining the location of an underground pipeline is a prerequisite for most inspection technologies. Existing pipeline location methods can find a pipeline’s location, but they require multiple measurements and cannot be used by automatic inspection robots. In this paper, a tunnel magnetoresistance (TMR)-sensor-array-based pipeline location method is proposed to solve this problem. Firstly, a detection probe is designed using a TMR sensor array. It is calibrated by the improved ellipsoid fitting method to measure the magnetic field around the pipeline accurately. Secondly, a relative pipeline-position-locating method is proposed by detecting the phases of the magnetic induction signals at different frequencies. Thirdly, a three-dimensional pipeline location method is proposed. The horizontal and vertical distances and the angle between the pipeline and the probe are calculated by measuring the magnetic induction amplitude. Finally, a simulation model and a test platform are established, and the experimental results illustrate that, by adopting the TMR array, the three-dimensional pipeline location method can locate a pipeline in real time in three dimensions with good accuracy.

https://www.mdpi.com/2071-1050/15/12/9816/pdf?version=1687251401

TMR-Array-Based Pipeline Location Method and Its Realization

The provision of resources via pipeline is critical in a modern and urban lifestyle. Because this is a man-made structure, many flaws occur, resulting in numerous deadly injuries. As a result, humans created and created numerous devices that aid in the detection of such flaws and errors and one of these devices is the robot. This robot could detect water tubes leakage, gas pipes leakage and corrosion in the pipes with its camera, and the robot can move in all directions by using omni-directional wheels and can also enter the pipes. In this paper a mobile robot with vex microcontroller and Arduino microcontroller with pixy camera is designed to detect and display the fault in gas pipes and water tubes.

Leak Detection and Corrosion Identification in Water Tubes, Gas Pipes by Mobile Robot

The Internet of Things based Smart Sensing and Alarm System with Autonomous Guiding Robots for Efficient Fire Emergency Evacuation System was designed and developed to provide immediate monitoring and alerts, allowing for efficient emergency evacuation with robotic assistance. The project includes two types of robots: a robot guide and a robot transporter. The NodeMCU-controlled Vex Navigating Robot directs people to the closest and most appropriate safety exits, while the Remote-Controlled Arduino-based Robot Transporter shuttles restrained or elderly people. The system employs an Internet of Things-based microcontroller to read and process data from sensors that detect temperature, humidity, and the number of people present in each room. This data is sent to the Cloud server and shown in the Blynk Mobile App. When a fire occurs, an alert signal using GSM and GPS technologies is sent to the fire station, the building health and safety team, and the autonomous guiding robot that replaces the Fire Warden, informing them of the incident's location and possible casualties. In this system, the robot system plays an important role in the evacuation process, assisting in the avoidance of delays and the saving of lives. The Autonomous navigating Robot was tested in a Floor plan layout with 9 rooms. The sensing unit was fixed in all rooms and the Robot in front of BS109 is tested with 3 different sources of fire, one in BS108, BS103 and BS 110. The robot successfully chose the appropriate exit path of path2 when the source of fire is in BS103 and BS110 and path1 when source of Fire is BS108. Testing of the algorithm is done with robot in front of Bs104, BS109, BS110 with 3 different source of fire. Transporting Robot is tested with remote controlled unit and is found to successfully transport people up to the weight of 80kgs with less than 2% deviation from its destined angle of travel.

S.V. Tresa Sangeetha

Papers

Leak Detection and Corrosion Identification in Water Tubes, Gas Pipes by Mobile Robot

IoT based Smart Sensing and Alarming System with Autonomous Guiding Robots for Efficient Fire Emergency Evacuation