Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach for adapting a Cobot Workstation to Human Operator within a Deep Learning Camera

One of the major objectives of international projects in the field of Industrial Automation is to achieve a proper and safe human-robot collaboration. This will permit the coexistence of both humans and robots at factory shop floors, where each one has a clear role along the industrial processes. It’s a matter of fact that machines, including robots, have specific features that determine the kind of operation(s) that they can perform better. Similarly, human operators have a set of skills and knowledge that permits them to accomplish their tasks at work. This article proposes the adaptation of robots to the skills of human operators in order to implement an efficient, safe and comfortable synergy between robots and humans that are working at the same workspace. As a representative case of study, this research work describes an approach for adapting a cobot workstation to human operators within an installed deep learning camera on the cobot. First, the camera is used to recognize the human operator that collaborates with the robot. Then, the corresponding profile is processed and serves as an input to a module in charge of adapting specific features of the robot. In this manner, the robot can adapt e.g., to the speed of operation according to the skills of the worker or deliver parts to be manipulated according to the handedness of the human worker. In addition, the deep learning camera is used for stopping the process at any time that the worked leaves unexpectedly the workstation.
Distributed Network Behaviour Prediction Using Machine Learning and Agent-Based Micro Simulation

Distributed network behaviour is increasingly attracting huge attention both in academics and industrial initiatives, and most recently machine learning has been used in every possible field, leveraging its advantages.Typically, a distributed networking allows for the execution of distributed applications which results in complex behaviours among connected systems. This complexity in itself can create grey areas and vulnerabilities in the securities of these networks, therefore predicting the behaviour of these systems both at the macro and micro level has become essential. In the past most researchers have predicted network behaviour and network attack patterns by using aggregated data, but in this paper we focus on the application of machine learning at the individual user level such that the prediction of the individual network user behaviour pattern at the micro level becomes a substasive tool in creating a realistic agent based simulation of the whole distributed network, which in turn can serve as a test bed for predicting what-if scenarios such as network attacks on the target system or exposing vulnerabilities within the target system. The simulation result was validated by comparing the simulated interaction within the simulated network to the data logged in the server logs within the real life network system. This produced a correlation above 0.8, indicating a realistic model.
Secrecy Performance of Cooperative Cognitive Radio Networks Under Joint Secrecy Outage and Primary User Interference Constraints

In this paper, we investigate the secrecy performance of a Cooperative Cognitive Radio Network (CCRN) in the presence of an eavesdropper (EAV). The secondary users (SUs) are subject to three constraints which include peak transmit power level and interference limitation with respect to the primary user (PU) as well as secrecy outage constraints due to the EAV. Secrecy outage is achieved when the EAV cannot decode the targeted signal, but communications in the secondary network is still possible (non-zero capacity exists). Approximation expressions of the secrecy outage probability and the probability of non-zero secrecy capacity are derived to evaluate the secrecy performance. Monte Carlo simulations are provided to examine the accuracy of the derived approximation expressions. Based on this, power allocation policies for the SUs are derived, satisfying all the constraints while maximizing the secrecy performance as well as the quality of service performance of the secondary network. It can be concluded that with knowledge of the channel state information (CSI) of the EAV it is possible to calculate the optimal value for the secrecy outage threshold of the secondary user (SU) which in turn allows maximizing the secrecy performance. Most interestingly, our numerical results illustrate that the secrecy performance of the system is much improved when the parameters obtained using the CSI of the EAV are calculated optimally. Thence, the system can adjust the power allocation so that no eavesdropping occurs even without reducing quality of service (QoS) performance compared to a network without any EAV.
Transfer Leaming in Automatic Speech Recognition for Serbian

In automatic speech recognition systems the training data used for system development and data expected to be obtained during the practical use of the system do not have to fit each other perfectly, but other similar data may be available. Transfer learning can help to exploit such similar data for training in order to boost the speech recognizer’s performance in a certain domain. In this context, the paper presents the first application of transfer learning in speech recognition for the Serbian language. Several methods are proposed, with the goal of optimizing system performance on a specific part of the existing speech database for Serbian. The experimental results evaluated on a test set from the desired domain show significant improvement in both word error rate and character error rate.
Distributed Robust Energy Management of a Multi-Microgrid System in the Real-Time Energy Market

In this paper, a distributed robust energy management scheme for multiple interconnected microgrids (MGs) is developed. It aims to optimize the total operational cost of the MGs through energy trading with neighboring MGs and the main grid in the real-time energy market. Various uncertainties including renewable generation, load consumption, and buying/selling prices of the main grid are handled using an adjustable robust optimization technique. To keep consistent with the distributed nature of the multiple MGs, we propose a distributed adjustable robust optimal scheduling algorithm (DAROSA). Within the framework, each MG energy management system determines its own selling price and operation schedule via distributed communication of non-critical information with its neighboring MGs. Robust optimal scheduling and fair energy trading can be collectively achieved. A case study of a 4-MG system is conducted to validate the effectiveness of the proposed approach.
ADDA: Adaptive Distributed DNN Inference Acceleration in Edge Computing Environment

Implementing intelligent mobile applications on IoT devices with DNN technology has become an inevitable trend. Due to the limitations of the size of DNN model deployed onto end devices and the instability of wide-area network transmission, either End-only mode or Cloud-only mode cannot guarantee the reasonable latency and recognition accuracy simultaneously. A better solution is to exploit the edge computing, where the existing edge computing execution framework and offloading mechanism for DNN inference suffer unnecessary computational overheads and underutilized computing capacity of end and edge. To address these shortcomings, an adaptive distributed DNN inference acceleration framework for edge computing environment is proposed in this paper, where DNN computation path optimization and DNN computation partition optimization are taken into consideration. The evaluations demonstrate that our method can effectively accelerate the DNN inference compared to the state-of-the-art methods.
Developmental Stage Evaluation of AC/DC Distribution System Based on Fuzzy Recognition Network

In order to promote the consumption of renewable energy and meet the needs of multiple AC/DC load, it is necessary to define the evolution route and development form of AC/DC distribution system. A fuzzy neural network can evaluate fuzzy problems effectively by combining artificial neural network and fuzzy recognition theory. Firstly, the evaluation index system of developmental stage for AC/DC distribution system is constructed. Secondly, the fuzzy membership function of AC/DC distribution system for developmental stage evaluation is established by selecting the reference samples of each indicator. Thirdly, interpolation method is used to interpolate the reference samples and fuzzy output, so as to establish the corresponding relation between input and output. Finally, the developmental stage evaluation problem of AC/DC distribution system is transformed into fuzzy identification problem. On the basis of fully utilize the self-organizing and self-learning ability of neural network, the developmental stage of AC/DC distribution system is evaluated. The effectiveness of proposed index system and evaluation algorithm is verified by taking an actual AC/DC distribution system as an example. The evaluation results can accurately determine the development stage of AC/DC distribution system, and provide guidance for the construction of network framework and the consumption of renewable energy.
Occupancy Estimation in Semi-Public Spaces using Sensor Fusion and Context Awareness

Accurate occupancy estimation is of great importance in managing indoor comfort demands and energy costs in semi-public buildings like academic institutes, offices, etc. Large spaces with multiple occupant seating are common in such buildings. Sophisticated sensors used for occupancy detection and estimation in these spaces, like cameras and motion sensors, may prove expensive and intrusive. Contrarily, simple devices like a door sensor or a Passive Infrared (PIR) sensor, if employed individually, might not be accurate enough. In this work, we exploit the unique advantages of each of these low-cost, off-the-shelf sensors to propose a novel heuristic algorithm for occupancy estimation. We develop an analytical model to probabilistically derive the uncertainty associated with a door sensor. We propose an effective and unique way to enhance the capability of the door sensor by fusing it with a PIR sensor and context information in terms of occupancy memory of the space. The hardware test bed was deployed in an academic institute, that mimics a work space in semipublic building. The performance comparison of the sensors and proposed algorithm is presented in terms of probability of detection and errors.
Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory

The operations of power systems are becoming more challenging on account of the high penetration of renewable power generation, including photovoltaic systems. One method for improving the power system operation involves making accurate forecasts of day-ahead solar irradiation, enabling operators to minimize uncertainty in managing the balance between generation and load. To overcome the limitations of Long Short-term Memory (LSTM) in a one-dimensional forecasting problem, this work proposes a novel method in forecasting solar irradiation by encoding time-series data into images using the Gramian Angular Field and the Convolutional LSTM (ConvLSTM) network. The pre-processed data become a five-dimensional input tensor that is perfectly suitable for ConvLSTM. The ConvLSTM network uses convolution operations in its input-to-state transition and state-to-state transition. The network thus enables time-series forecasting by a feature-rich approach, which ultimately provides competitive forecasting performance despite the use of a small dataset. The proposed method was evaluated in day-ahead solar irradiation forecasting using a univariate dataset of Global Horizontal Irradiation (GHI) data from Fuhai in Taiwan. The proposed method was resampled using $5\times 2$ -fold cross-validation, and assessed using the Wilcoxon signed-rank test to determine the statistical significance of the result. It outperformed benchmark methods such as Autoregressive Integrated Moving Average (ARIMA), Convolutional Neural Network cascaded with Long Short-term Memory (CNN-LSTM), and LSTM cascaded with a fully-connected (FC) network.
Conceptual Framework for manufacturing data preprocessing of diverse input sources

The manufacturing industry today is experiencing a never seen increase in available data. These data compromise a variety of different formats, semantics, and quality. It is often distributed in different data sources, e.g. sensor data from the production line, environmental data or machine tool parameters. Coming from the field of application the paper will discuss, within a conceptual framework, the possibilities of how to integrate the diverse existing data-sources and how to pre-process the data with high quality using advanced outlier detection algorithms and developing reasonable outlier treatment values by applying machine-learning methods. The result will be validated with real manufacturing data from an automotive use-case.
A Hierarchical Update Buffer Management for 360-Degree Video Streaming

Virtual reality can provide users with immersive experience. However, because of its high requirement of bandwidth, it is still challenge to transmit 360-degree video. To overcome this issue, tile-based VR transmission strategy was proposed to reduce the data by dividing the 360-degree video into several tiles and streaming corresponding tiles according to user’s viewport. While the prediction accuracy is only guaranteed in a short time and it may cause frequent rebuffering when the network fluctuates, two-tier buffer system was proposed to tackle this problem. Nevertheless the two-tier method doesn’t consider the switching frequency of rate in user’s viewport. In this paper, we propose a novel hierarchical update buffer management method by jointly considering the probability of rebuffering and the switching frequency of rate in FoV to improve user’s QoE. The simulation results show that our proposed algorithm outperforms other methods in proving the smoothness of the viewing experience.
Variance Analysis of Signals from Four Electrode Electrochemical Sensors

This paper presents the signal accuracy analysis of a device for air quality monitoring that is equipped with electrochemical sensors with four electrodes from Alphasense Ltd. The device has been evaluated under several tests to see what is the hardware response without any sensors, how the variance of the signals differs with the addition of other sensors, not only electrochemical ones but also non-inverted infrared and light scattering ones, and also in the context of adding communication components for Orange LTE-M network. The purpose of the study is to create a complete air quality monitoring system and to prepare it for future calibration procedures.
Forest Distance Closeness Centrality in Disconnected Graphs

Ranking the relative importance of nodes is a fundamental issue in network science, especially in the analysis of social and information networks. A variety of importance metrics and related algorithms have been proposed. However, these previous measures either do not apply to disconnected networks or have some weakness when applied to disconnected networks. In this paper, we use forest distance to assess the importance of nodes in a graph, whether connected or disconnected. For a node in a graph, its forest distance is defined as the sum of forest distances from the node to all other nodes in the graph. To illustrate the discriminating power of forest distance, we first compute the exact forest distances for all nodes in the path graph, and show that the order of node importance given by forest distances is in complete agreement with our intuition. We then show that forest distance centrality has a better discriminating power than alternate metrics such as betweenness, harmonic centrality, eigenvector centrality, and PageRank. Also, we present a theoretically guaranteed estimation algorithm, which approximates the forest distances for all nodes in a graph in nearly linear time with respect to the number of edges. Finally, we perform extensive experiments on real-world networks, which show that our approximation algorithm is both efficient and accurate for large networks.
Transient Behavior Analysis of VSG-IIDG During Disturbances Considering the Current Limit Unit

The virtual synchronous generator (VSG) is of good prospect for the application of inverter-interfaced distributed generator (IIDG). Similar to conventional synchronous generators, power-angle instability of the VSG-IIDG may occur during faults. However, current studies generally adopt a simplified model and ignore the influence of voltage regulation and current limit in the VSG control. This paper studies the transient behavior mechanism of the VSG-IIDG based on the power-angle characteristics with and without current saturation. The terminal voltage fluctuation of the IIDG due to the voltage regulation of VSG control is also taken into consideration. The typical instabilities and their mechanism are revealed based on different operation conditions. The analysis is verified by simulations based on PSCAD/EMTDC.
TCP Multi-Stream Data Transfer using Multiple Network Interface Cards

In recent years, the significant increase in transmission control protocol and internet protocol (TCP/IP) network traffic has urged the importance of faster network systems. This study proposes a simultaneous TCP multi-streaming system to improve data transfer rate by integrating TCP streams over multiple IP domains. The system is implemented using a virtual network switch that processes the packet from multiple network interface cards (NICs); it also presents a possibility in software defined network, which attracts attention as a backbone of future networks as 5G networks. We utilized actual equipment for performing experiments and evaluations on a local network to measure the data transfer rate. Results confirmed an improvement in the communication rate.
IoT Protocols for Low-power Massive IoT: A Communication Perspective

The constantly growing number of resource-limited MTC (Machine-Type Communication) devices is challenging both the telecommunication operators as well as industrial companies to efficiently design communication technologies, protocols, and end-devices for data transmissions in the upcoming 5G and beyond wireless communication networks. As the communication technologies for mMTC (massive Machine Type Communications)/massive IoT (Internet of Things) attracted significant attention over the past years due to the extended communication range and improved lifetime of battery-operated devices, the debate turned from the communication parameters of available Low-Power Wide-Area (LPWA) technologies towards the implementation issues related to efficient data transmissions with respect to certain level of Quality of Service (QoS). In this regard, this paper aims to analyze already introduced communication protocols (i.e., TCP, UDP, CoAP (Constrained Application Protocol), and MQTT (Message Queuing Telemetry Transport)) possible to use for data transmission in massive IoT scenarios. Along with the highlights of the protocols in question, a practical implementation is done using the emerging cellular IoT standard, namely, NB-IoT (Narrowband IoT). Out of the obtained data, the focus is primarily given to the side-by-side comparison of protocols’ overheads and the subsequent data usage as the amount of transmitted data is deriving the monthly fees.
Improved Arrhythmia Detection from Electrocardiogram

Computerized detection and monitoring of arrhythmia, or irregular heartbeats using electrocardiogram (ECG) is a typical pattern recognition problem and has been under research from decades. The challenge is mainly with universality of features and their minimization. The proposed research shows an improved binary classification accuracy using deep autoencoder (DAE) neural network with a support vector machine (SVM) for six types of arrhythmic ECG beat recognition. It also compares the efficiency of feature extraction and classification of arrhythmic beats using principal component analysis (PCA), and discrete wavelet transform (DWT) combined with binary K-nearest neighbours (KNN) and SVM classifier. After filtering the raw ECG signal, the R-peaks were detected by a DWT based approach. The window around the QRS zone of each ECG beat was used for feature extraction and a reduced set of 40 features was fed to the binary classifiers. In the present work, six type of abnormalities viz. ‘A’, ‘F’, ‘L’, ‘R’, ‘V’, ‘f’ from MIT-BIH arrhythmia (mitdb) database were used for the evaluating the classifiers’ performance. Among the all six combination of proposed feature extraction and classification techniques, the average sensitivity (SE) and positive predictive value (PPV) achieved by DAE-SVM (DAE- KNN) were 99.99% and 99.98% (99.97% and 99.95%) respectively. The results are competitive with some published works.
Design and application of sensor measuring neutral DC current of transformer based on resistance sampling

The Hall sensors could be used in investigating the current of transformer neutral point. However, the closed-loop Hall sensor could not be installed live catenary while the open-loop Hall sensor could not work linearly with the temperature and be corrected online. In the paper, a new type of DC sensor is designed for monitoring the neutral DC current of transformer online and installing live catenary. The output characteristics of designed DC sensor are linear with the working temperature, and easily updated according to the temperature. The measurement error of designed DC sensor used in field measurement is less than 2% or 0.2A when investigating different amplitudes of DC current.
Reinforced Molecule Generation with Heterogeneous States

De novo molecular design and generation are frequently prescribed in the field of chemistry and biology, for it plays a critical role in maintaining the prosperity of the chemical industry and benefiting the drug discovery. In recent years, reinforcement learning-based methods leverage graphs to represent molecules and generate molecules as a decision making process. However, this vanilla graph representation may neglect the intrinsic context information with molecules and limits the generation performance accordingly. In this paper, we propose to augment the original graph states with the SMILES context vectors. As a result, SMILES representations are easily processed by a simple language model such that the general semantic features of a molecule can be extracted; and the graph representations perform better in handling the topology relationship of each atom. Moreover, we propose a framework that combines supervised learning and reinforcement learning algorithm to take a solid consideration of these two heterogeneous state representations of a molecule, which can fuse the information from both of them and extract more comprehensive features so that more sophisticated decisions can be made by the policy network. Our model also introduces two attention mechanisms, i.e., action-attention, and graph-attention, to further improve the performance. We conduct our experiments on a practical dataset, ZINC, and the experiment results demonstrate that our framework can outperform other baselines in the learning performance of molecule generation and chemical property optimization.