Klemen Kenda

Bio: Klemen Kenda is an academic researcher from Jožef Stefan Institute. The author has contributed to research in topics: Demand forecasting & Computer science. The author has an hindex of 6, co-authored 25 publications receiving 133 citations.

Groundwater Modeling with Machine Learning Techniques: Ljubljana polje Aquifer

Abstract: In this study a thorough analysis is conducted concerning the prediction of groundwater levels of Ljubljana polje aquifer. Machine learning methodologies are implemented using strongly correlated physical parameters as input variables. The results show that data-driven modelling approaches can perform sufficiently well in predicting groundwater level changes. Different evaluation metrics confirm and highlight the capability of these models to catch the trend of groundwater level fluctuations. Despite the overall adequate performance, further investigation is needed towards improving their accuracy in order to be comprised in decision making processes.

Streaming Data Fusion for the Internet of Things

Abstract: To achieve the full analytical potential of the streaming data from the internet of things, the interconnection of various data sources is needed. By definition, those sources are heterogeneous and their integration is not a trivial task. A common approach to exploit streaming sensor data potential is to use machine learning techniques for predictive analytics in a way that is agnostic to the domain knowledge. Such an approach can be easily integrated in various use cases. In this paper, we propose a novel framework for data fusion of a set of heterogeneous data streams. The proposed framework enriches streaming sensor data with the contextual and historical information relevant for describing the underlying processes. The final result of the framework is a feature vector, ready to be used in a machine learning algorithm. The framework has been applied to a cloud and to an edge device. In the latter case, incremental learning capabilities have been demonstrated. The reported results illustrate a significant improvement of data-driven models, applied to sensor streams. Beside higher accuracy of the models the platform offers easy setup and thus fast prototyping capabilities in real-world applications.

Mashups for the Web of Things

Abstract: The Web of Things (WoT) together with mashup-like applications is gaining popularity with the development of the Internet towards a network of interconnected objects, ranging from cars and transportation cargos to electrical appliances. In this chapter we provide a brief architectural overview of technologies which can be used in WoT mashups with emphasis on artificial intelligence technologies such as conceptualization and stream processing. We also look at data sources and existing WoT mashups. In the last part of the chapter we discuss the architecture and implementation of Videk, a prototype mashup for environmental intelligence.

Inductively Coupled RF Oxygen Plasma Studied by Spatially Resolved Optical Emission Spectroscopy

Abstract: Inductively coupled RF oxygen plasma generated by inductive coil wrapped around a linear glass tube is studied. Images of plasma properties in the linear reactor are presented. Plasma diagnostics was performed by spatially resolved optical emission spectroscopy.

IoT Middleware for Water Management

Abstract: Groundwater management is important for all urban systems. Thus, data needs to be available on request for various decision-makers and stakeholders. This article presents conceptual and implemented framework for collecting, analyzing and sharing of groundwater data for various purposes. It allows controlled access to data that is continuously collected from different feeds and transformed into a common format. With this approach, the latest data as well as historical records are always available for real-time queries and further analysis. The proposed system can be extended to cover other areas of data collection in the future.

A Visualized Botnet Detection System Based Deep Learning for the Internet of Things Networks of Smart Cities

Abstract: Internet of Things applications for smart cities have currently become a primary target for advanced persistent threats of botnets. This article proposes a botnet detection system based on a two-level deep learning framework for semantically discriminating botnets and legitimate behaviors at the application layer of the domain name system (DNS) services. In the first level of the framework, the similarity measures of DNS queries are estimated using siamese networks based on a predefined threshold for selecting the most frequent DNS information across Ethernet connections. In the second level of the framework, a domain generation algorithm based on deep learning architectures is suggested for categorizing normal and abnormal domain names. The framework is highly scalable on a commodity hardware server due to its potential design of analyzing DNS data. The proposed framework was evaluated using two datasets and was compared with recent deep learning models. Various visualization methods were also employed to understand the characteristics of the dataset and to visualize the embedding features. The experimental results revealed substantial improvements in terms of F 1-score, speed of detection, and false alarm rate.

Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System

Abstract: Advances in the Internet of Things (IoT) are helping to make water management smarter and optimizing consumption in the smart agriculture industry. This article proposes a new topology of sensor nodes based on the use of inexpensive and highly efficient components, such as water level, soil moisture, temperature, humidity, and rain sensors. Additionally, to guarantee good performance of the system, the used transmission module is based on LoRa LPWAN technology. The design of the main circuit board of the system is optimized by combining two layers and implementing software optimization. The overall sensor network is developed and tested in the research lab, and real farms can be controlled by users manually or automatically using the mobile application. Experimental results are produced by testing sensor and communication link effectiveness, and are subsequently validated in the field through a one-week measurement campaign.

A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China.

Abstract: Groundwater is unique resource for agriculture, domestic use, industry and environment in the Heihe River Basin, northwestern China. Numerical models are effective approaches to simulate and analyze the groundwater dynamics under changeable conditions and have been widely used all over the world. In this paper, the groundwater dynamics of the middle reaches of the Heihe River Basin was simulated using one numerical model and three machine learning algorithms (multi-layer perceptron (MLP); radial basis function network (RBF); support vector machine (SVM)). Historical groundwater levels and streamflow rates were used to calibrate/train and verify the different methods. The root mean square error and R2 were used to evaluate the accuracy of the simulation/training and verification results. The results showed that the accuracy of machine learning models was significantly better than that of numerical model in both stages. The SVM and RBF performed the best in training and verification stages, respectively. However, it should be noted that the generalization ability of numerical model is superior to the machine learning models because of the inclusion of physical mechanism. This study provides a feasible and accurate approach for simulating groundwater dynamics and a reference for model selection.