Intelligent Mine Periphery Surveillance using Microwave Radar

This paper deals with an intelligent mine periphery surveillance system, which has been developed by CSIR-Central Institute of Mining and Fuel Research, Dhanbad, India, as an aid for keeping constant vigilance on a selected area even in adverse weather conditions like foggy weather, rainy weather, dusty environment, etc. The developed system consists of a frequency modulated continuous wave radar, a pan-tilt camera, a wireless sensor network, a fast dedicated graphics processing unit, and a display unit. It can be spotting an unauthorized vehicle or person into the opencast mine area, thereby avoiding a threat to safety and security in the area. When an intrusion is detected, the system automatically gives an audio-visual warning at the intrusion site where the radar is installed as well as in the control room. The system has the facility to record the intrusion data as well as video footage with timestamp events in the form of a log. Further, the system has a long-range detection capability covering around 400 m distance with an integration facility using a dynamic wireless sensor network for deploying multiple systems to protect the extended periphery of an opencast mine. The field trial of this low-cost mine periphery surveillance system has been carried out at Tirap Opencast Coal Mine of North Eastern Coalfields in Margherita Area, Assam, India and it has proved its efficacy in preventing revenue loss due to illicit mining, unauthorized transportation of minerals, and ensuring safety and security of the mine to a great extent

Secure decision tree twin support vector machine training and classification process for encrypted IoT data via Blockchain platform

A secure decision tree twin support vector machine (DT-TSVM) multi-classification algorithm has been proposed in this paper for improving the reliability and security of the collected IoT data from multiple data providers. The multiclass secure DT-TSVM algorithm has been employed to train a machine learning model using the encrypted training dataset. The training dataset is collected via a blockchain platform. A blockchain method has been adopted to construct a secure and reliable distributed platform among dataset providers. The Paillier homomorphic cryptosystem has been applied for encrypting the IoT dataset. Then, the dataset has been recorded on the distributed ledger. The secure DT-TSVM algorithm's-based train model effectiveness has been compared with the other two available algorithms, namely the multiclass binary support vector machine (MBSVM) and one-to-one SVM algorithms. The experiment results showed that the privacy-preserving multiclass secure DT-TSVM-based model did not reduce the accuracy, but it increased the average precision and recall by 0.53% and 0.44% than MBSVM and 0.82% and 0.71% than one-to-one SVM, respectively. Further, the time consumption of data providers and data analysts did not change significantly with the increase of number of data provider

A proposed data acquisition system and algorithm for signal processing of moving-coil geophone’s output

The study of different types of vibrational and seismic movements is important for exploration in strata monitoring, machine health monitoring, earthquake detection, etc. In order to study these vibrational movements, it needs to be acquired first for analysis. Data acquisition using the seismic sensors is a challenging task. This paper presents a data acquisition system developed to acquire seismic signals from a moving-coil geophone. The paper also discusses a signal interpretation algorithm that is devised to perform automatic detection of a seismic event occurrence by separating through the waveform and non-waveform components in the sensor’s output using Gaussian naive Bayes classifier and Kernel density estimation technique. The proposed method is effective in the identification of a useful signal and identification of its nature of origin. Accuracy of the algorithm was 99% for the waveform classification. Sensitivity of the data acquisition system for the seismic sensors was 1.589 µm s–1. Further, the developed data acquisition system and the algorithm can be used in mines for seismological studies aimed at separating the vibration signal generated due to explosion and the one caused due to Earth’s tectonic and seismic activities

Perceptive Driving Assistant System for Opencast Mines During Foggy Weather

During harsh weather conditions, the presence of fog, dust in the environment degrades the image’s quality, which affects the visibility of drivers of heavy earth-moving machinery in opencast mines. Due to low visibility, mining operations cannot be carried out as drivers are easily prone to accidents. This paper proposes a technique that includes developing a vision enhancement system called perceptive driving assistant system for increasing visibility of real-time video of the road in front of the vehicle for operators of heavy earth-moving machinery at opencast mines during harsh weather conditions to overcome the problem. The system consists of high-quality Internet Protocol cameras and thermal cameras for real-time image processing and other well-defined devices, which is quite capable of enhancing the visibility of the image, outlining edges of the road, and detecting obstacles present on the path of operators for smooth driving and reducing threat of accidents. A high-speed graphical processing unit has been used for quality-performance parallel computing, which is well suited for real-time operations to empower fast real-time operations. The calculated frame per second (fps) of image enhancement, object detection, and edge detection is 17.91, 15.91, and 25.09 fps, respectively. The actual frame rate is 26.07 fps, and after applying the algorithm, the final frame rate is 19.65 fps. The calculated accuracy of the object detection model is 81.23%. Field trials indicate that the developed system has performed adequately during foggy weather

Artificial intelligent based smart system for safe mining during foggy weather

Opencast mining operations at hilly areas are usually affected during foggy weather due to the inability of drivers to operate heavy earth-moving machinery in low visibility conditions. This article deals with an intelligent vision enhancement system for continuing opencast mining operations during foggy weather. The system integrates hardware and software to provide multistage safety features that make it unique from existing systems. The system includes hardware like thermal cameras, high definition cameras, proximity radar, wireless devices, GNSS module, graphical processing unit, display unit, and so forth, and image processing software, namely real-time image stitching, image enhancement, and object detection using convolutional neural networks. The integrated system and algorithms display a 180° panorama field view of the vehicle's front using real-time video stitching. The front view after image processing, rear camera view, object detection through proximity radar, and real-time location of the vehicles on a 3D geo-tagged mine map by GNSS modules are displayed in four splitter windows on a touch screen fitted on the dashboard in front of the driver's seat. The driver can drive the vehicle by seeing the display screen during foggy weather. The output image of the developed image-processing algorithm has less distortion, better quality, and better depth perception than existing methods. Overall, there are significant improvements in the persistence of the color elements by 39.65%, contrast by 4.62%, and the corresponding entropy by 7.11% concerning the similar existing methods. The final system has been successfully tested in an opencast mine

Air quality modeling for impact evaluation of a mica, feldspar, and quartz mine in Nellore district, Andhra Pradesh, India

The dust emission from the mining area is the primary source of air pollution for the surrounding environment. This paper deals with the study of baseline air quality assessment and air pollution modeling exercise for a mica, feldspar, and quartz mine to predict the maximum dust concentration from the mine with and without control measures. Baseline PM10, PM2.5, SO2, and NO2 levels in the bufer zone of the planned mine site were found to be 53.1–79.5, 20.2–43.2, 16.6–31.2, and 21.2–50.1 mg m−3, respectively, and these values were lesser than the corresponding permissible limit of 100, 60, 80, and 80 µg m−3. The respective predicted PM10 and PM2.5 levels will be 73.9–97.1 and 31.9–44.2 mg m−3 without control measures, and 73.5–82.5 and 31.8–43.8 µg m−3 with control measures during operation of the mine. It is estimated that PM10and PM2.5 will remain below the permissible limit in the buffer zone of the mine. The paper suggests effective air pollution control measures, including a description of the developed smart dry fog dust suppression system and wirelessly controlled sprinkling system for applications at various dust emitting sources in the mining area

Intelligent driving system at opencast mines during foggy weather

The fog in mining operations minimises the visibility, preventing drivers from a clear view, causing accidents and vehicle collisions. This paper provides an intelligent driving system for heavy earthmoving machinery operators in opencast mines, including hardware and software. Hardware contains high definition and thermal cameras, a global navigation satellite system (GNSS), radar, laser light, wireless devices, graphical processing unit, touch screen, etc. The software covers image stitching, image enhancement, and convolution neural network-based object detection. The display dashboard is divided into four windows. Each window represents a different view, i.e. 180° panorama view of the driving lane, GNSS tracking map, proximity radar detection view, and rear thermal camera view. An additional colour transfer method has been used in the existing image stitching method to reduce misalignment and ghost effect in the panorama output. The proposed method outperformed the existing methods, namely contrast limited adaptive histogram equalisation (CLAHE) and dark channel prior (DCP). The proposed image enhancement technique has increased contrast, entropy, and colour average by 0.069, 0.43, and 13.96, respectively, than CLAHE, and 0.994, 0.43, and 42.07 than DCP. The accuracy of the object detection model is 97%, and the overall processing time of all the algorithms is 0.44949 seconds