Mobilisation of arsenic from bauxite residue (red mud) affected soils: effect of pH and redox conditions

The tailings dam breach at the Ajka alumina plant, western Hungary in 2010 introduced ~1 million m3 of red mud suspension into the surrounding area. Red mud (fine fraction bauxite residue) has a characteristically alkaline pH and contains several potentially toxic elements, including arsenic. Aerobic and anaerobic batch experiments were prepared using soils from near Ajka in order to investigate the effects of red mud addition on soil biogeochemistry and arsenic mobility in soil–water experiments representative of land affected by the red mud spill. XAS analysis showed that As was present in the red mud as As(V) in the form of arsenate. The remobilisation of red mud associated arsenate was highly pH dependent and the addition of phosphate to red mud suspensions greatly enhanced As release to solution. In aerobic batch experiments, where red mud was mixed with soils, As release to solution was highly dependent on pH. Carbonation of these alkaline solutions by dissolution of atmospheric CO2 reduced pH, which resulted in a decrease of aqueous As concentrations over time. However, this did not result in complete removal of aqueous As in any of the experiments. Carbonation did not occur in anaerobic experiments and pH remained high. Aqueous As concentrations initially increased in all the anaerobic red mud amended experiments, and then remained relatively constant as the systems became more reducing, both XANES and HPLC–ICP-MS showed that no As reduction processes occurred and that only As(V) species were present. These experiments show that there is the potential for increased As mobility in soil–water systems affected by red mud addition under both aerobic and anaerobic conditions

2019 7TH INTERNATIONAL ISTANBUL SMART GRIDS AND CITIES CONGRESS AND FAIR (ICSG ISTANBUL 2019)

Nowadays, image processing and deep learning is used in industrial and non-industrial areas. Addition to this, smart cities are very popular trend for the researchers and r&d workers. In the smart city applications, researchers and r&d workers present solutions about traffic, health, security and energy problems in the cities. The smart city applications for the traffic are focused on proposing solutions about detecting traffic violations, congestions, park spot suggestion, public transportations etc. We propose a solution for detecting traffic stakeholders physical features based on image processing and deep neural classification. The mentioned traffic stakeholders are automobiles, buses, trucks, trailers, motorcycles and pedestrians. We detect contours from the traffic videos which appropriate size for these traffic stakeholders then we crop these contours from the video first. Then we use the deep image classifier model for classification with detected contours. Addition to this we calculate vehicles dimensional features based on the contour size and determine colors based on HSV features. We intend with this study providing physical features to the smart city workers and researchers for using these features in their applications which controlling violations, determining statistics and the other applications like mentioned. For this reason, we provide this solution with a web service application in the future

2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP)

Nowadays, many types of devices are controlled by electroselenography (EEG) signals. In the literature and in daily life, related studies with EEG controlled devices are increasing day by day. EEG based control applications are applied on many devices such as robot arm, robot, vehicle and unmanned aerial vehicle (UAV). EEG based control procedures usually involve taking, pre-processing, classifying EEG signals, and applying the resulting command to the controlled device. In this study, a performance analysis was carried out by examining the control application studies using EEG signals in the literature. In this analysis study, firstly all studies related to the subject in the literature are examined and the devices, methods, signal processing techniques and classification algorithms used in these studies are handled separately. Appropriate electrode selection for the type of device used in device control applications using EEG signals and type of interaction for command extraction from EEG signal appears to be an important step. In this respect, performance correlations between the types of EEG devices used in the literature studies and the electrode choices used in these studies were compared. Since there are a variety of preprocessing steps for EEG signals, this study provides comparisons based on EEG signal preprocessing techniques. Artificial neural networks (ANN), support vector machines (SVM) and K nearest neighbours (Knn) are used to classify the works in the literature. In this study, comparative studies based on classification methods used in literature studies are also included. As a result, in this study, the studies in the literature for the device control using the EEG signal are examined, compared, interpreted and evaluated, and the points to be considered in the designs to be performed in this area are given

2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK)

Fuzzy cognitive maps (FCM) is a method to update a given initial vector to obtain the most stable state of a system, using a neighborhood of weights between these vectors and updating it over a series of iterations. FCMs are modeled with graphs. Neighbor weights between nodes are between-1 and 1. Nowadays it is used in business management, information technology, communication, health and medical decision making, engineering and computer vision. In this study, a dynamic FCM structure based on Particle Swarm Optimization (PSO) is given for determining node weights and online updating for modeling of dynamic systems with FCMs. Neighborhood weights in dynamic FCMs can be updated instantly and the system feedback is used for this update. In this work, updating the weights of the dynamic FCM is a PSO based approach that takes advantage of system feedback. In previous literature suggestions, dynamic FCM structure performs the weight updating process by using rule-based methods such as Hebbian. Metaheuristic methods are less complex and more efficient than rule-based methods in such optimization problems. In the developed PSO approach, the initialise vector state of the system, the weights between the vector nodes, and the desired steady state vector are taken into consideration. As a fitness function, the system has benefited from the convergence state to the desired steady state vector. As a stopping criterion for PSO, 100 * n number of iteration limits have been applied for the initial vector with n nodes. The proposed method has been tested for five different scenarios with different node counts

An Approach for Online Weight Update Using Particle Swarm Optimization in Dynamic Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCM) is a method to update a given initial vector to obtain the most stable state of a system, using a neighborhood of weights between these vectors and updating it over a series of iterations. FCMs are modeled with graphs. Neighbor weights between nodes are between-1 and 1. Nowadays it is used in business management, information technology, communication, health and medical decision making, engineering and computer vision. In this study, a dynamic FCM structure based on Particle Swarm Optimization (PSO) is given for determining node weights and online updating for modeling of dynamic systems with FCMs. Neighborhood weights in dynamic FCMs can be updated instantly and the system feedback is used for this update. In this work, updating the weights of the dynamic FCM is a PSO based approach that takes advantage of system feedback. In previous literature suggestions, dynamic FCM structure performs the weight updating process by using rule-based methods such as Hebbian. Metaheuristic methods are less complex and more efficient than rule-based methods in such optimization problems. In the developed PSO approach, the initialize vector state of the system, the weights between the vector nodes, and the desired steady state vector are taken into consideration. As a fitness function, the system has benefited from the convergence state to the desired steady state vector. As a stopping criterion for PSO, 100 ∗ n number of iteration limits have been applied for the initial vector with n nodes. The proposed method has been tested for five different scenarios with different node counts. © 2018 IEEE

Multiple Object Tracking with Dynamic Fuzzy Cognitive Maps Using Deep Learning

Object tracking is the process of matching objects detected on image sequences onto image frames. There are different types of object tracking applications used for different scenarios. For example, if a single object is being traced on an image, this is a single object tracking application. Tracking multiple objects on an image is called multiple object tracking. Fuzzy cognitive maps, on the other hand, form the model of a system by using the features of a system and the relationships between these features. Here, the single object tracking process is a matching problem, so FCM assumes a classifier role. In conventional operations, FCMs use the same weight matrix for all initial concept values. This can reduce the performance of the solution that the FCM produces for the problem it tackles. The FCM structure we use here takes advantage of the dynamic learning of FCM weights with deep learning. The study was tested on different image sequences and the performance of the proposed method were very satisfactory. © 2019 IEEE