Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Detection of Hands and Objects in Egocentric Videos, for Ambient Assisted Living Applications

The need for technology assisted (or ambient assisted) living is increasing all the time as the population ages and the number of people with dementia and other conditions impairing memory and cognitive ability increases. In such applications, amongst others, it is necessary to identify and assess potentially hazardous situations. These include scenarios involving a person's hands and their interactions with various objects. In this paper, we describe our novel approach to identify human hands and objects in videos of people performing a variety of everyday tasks. We compare the performance of our method using different strategies with that of other state of the art approaches. We conclude that, when the proposed approach takes advantage of a pre-trained model, hand detection is performed accurately (94%), providing reliable information for assisted living applications.
Impact of Electric Vehicles on the Expansion Planning of Distribution Systems Considering Renewable Energy, Storage, and Charging Stations

Energy storage systems (ESS) have adopted a new role with the increasing penetration of electric vehicles (EV) and renewable energy sources (RES). EV introduce new charging demands that change the traditional demand profiles and RES are characterized by their high variability. This paper presents a new multistage distribution expansion planning model where investments in distribution network assets, RES, ESS, and EV charging stations are jointly considered. The charging demand necessary for EV transportation is performed using a vehicle model based on travel patterns. The variability associated with RES along with the demand requires the incorporation of uncertainty, which is characterized through a set of scenarios. These scenarios are generated by the ${k}$ -means++ clustering technique that allows keeping the correlation in the information of the uncertainty sources. The resulting stochastic program is driven by the minimization of the present value of the total expected cost including investment, maintenance, production, losses, and non-supplied energy. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program, which can be solved by commercial software. Numerical results are presented for an illustrative 54-node test system.
Using Balancing Terms to Avoid Discrimination in Classification

From personalized ad delivery and healthcare to criminal sentencing, more decisions are made with help from methods developed in the fields of data mining and machine learning than ever before. However, their widespread use has raised concerns about the discriminatory impact which the methods may have on people subject to these decisions. Recently, imbalance in the misclassification rates between groups has been identified as a source of discrimination. Such discrimination is not handled by most existing work in discrimination-aware data mining, and it can persist even if other types of discrimination are alleviated. In this article, we present the Balancing Terms (BT) method to address this problem. BT balances the error rates of any classifier with a differentiable prediction function, and unlike existing work, it can incorporate a preference for the trade-off between fairness and accuracy. We empirically evaluate BT on real-world data, demonstrating that our method produces tradeoffs between error rate balance and total classification error that are superior and in only few cases comparable to the state-of-the-art.
Modeling, Control, and Stability of Smart Loads Toward Grid of Nanogrids for Smart Cities

Low inertia power generation units make islanded microgrids and nanogrids more vulnerable to voltage and power fluctuations. Smart loads are a possible solution to suppress voltage and power fluctuations in islanded nanogrids. Since smart loads utilize inverters with short-time responses, their dynamics would have a considerable effect on the dynamics and stability of nanogrids, Therefore, the dynamics of smart loads play a significant role for stability analysis of these systems. This paper analyzes the dynamic behaviors of smart loads in nanogrids. Furthermore, a state-space model is developed for smart loads. The stability of smart loads is studied using the developed model, along with circuit simulations. The case study simulations are provided to verify the performance of utilizing smart loads in mitigating voltage and power fluctuations in islanded nanogrids.
A Behavior Profiling Model for User Authentication in IoT Networks based on App Usage Patterns

Access to Internet of Things (IoT) devices is, in most cases, achieved remotely through end-user devices such as smartphones. However, these devices are susceptible to theft or loss, and their use by unauthorized users could lead to unauthorized access to IoT networks, consequently allowing access to user information. Due to the inherent weaknesses in many authentication approaches, such as knowledge-based authentication, as well as the complications involved in employing them for continuous and implicit authentication, focus has turned to a consideration of behavioral-based authentication. As most access to IoT devices is achieved through end-user devices, a variety of information can be extracted and utilized for continuous authentication without requiring further user intervention. As an example, the ability to continuously retrieve application usage profiles and sensor data on such devices strengthens the argument for employing behavioral-based mechanisms for continuous user authentication. Behavioral techniques that are user-friendly and non-intrusive can be utilized in the background to continuously and transparently verify users. This paper discusses behavioral-based authentication mechanisms with regard to security and usability. It then presents an authentication model that verifies users with an average F-measure of 96.5%. Overall, the preliminary results are promising and show the effectiveness and usability of the proposed model.
MuVAN: A Multi-view Attention Network for Multivariate Temporal Data

Recent advances in attention networks have gained enormous interest in time series data mining. Various attention mechanisms are proposed to soft-select relevant timestamps from temporal data by assigning learnable attention scores. However, many real-world tasks involve complex multivariate time series that continuously measure target from multiple views. Different views may provide information of different levels of quality varied over time, and thus should be assigned with different attention scores as well. Unfortunately, the existing attention-based architectures cannot be directly used to jointly learn the attention scores in both time and view domains, due to the data structure complexity. Towards this end, we propose a novel multi-view attention network, namely MuVAN, to learn fine-grained attentional representations from multivariate temporal data. MuVAN is a unified deep learning model that can jointly calculate the two-dimensional attention scores to estimate the quality of information contributed by each view within different timestamps. By constructing a hybrid focus procedure, we are able to bring more diversity to attention, in order to fully utilize the multi-view information. To evaluate the performance of our model, we carry out experiments on three real-world benchmark datasets. Experimental results show that the proposed MuVAN model outperforms the state-of-the-art deep representation approaches in different real-world tasks. Analytical results through a case study demonstrate that MuVAN can discover discriminative and meaningful attention scores across views over time, which improves the feature representation of multivariate temporal data.
Study of an in-line fiber Mach-Zehnder interferometer with peanut-shape structure for refractive index sensing

A peanut-shape structure based in-line fiber Mach–Zehnder interferometer (MZI) was fabricated by commercial available fusion splicer. The power distribution of the modes in the device was calculated and measured experimentally. The in-line fiber MZIs with the different arm lengths (30 and 100mm) were exploited for refractive index (RI) measurement. The highest RI sensitivity of 67.953 nm/RIU was achieved within the measured RI range of 1.33–1.36.
Odd/Even Differential Image Steganography Approach

By leveraging the concept of steganography, secret messages between parties can be hidden in such cover medium such as: audio, image, or text. The most common medium to hide the messages is the image. However, one of the required factors when implementing an image steganography technique is how this technique resists image modifications in a way that doesn’t affect the secret message. Therefore, this paper is devoted to create a new image steganography algorithm with an attempt to provide robustness and capacity characteristics. This algorithm is built based on odd/even pixels subtraction algorithm. In this method, cover image pixels which are using to hide information behind, are chosen randomly. For extracting, secret bits are extracted with inverting the embedding algorithm. Least Significant Bits (LSB) algorithm is used as a baseline in implementing and evaluating the performance of the proposed technique.
Real time realization of highly reliable Cascaded Full-bridge Interleaved Buck Inverter based APF using TIFLC id-iq Control Strategy

This paper proposes a multilevel cascaded full-bridge interleaved buck inverter (CFBIBI) based active power filter using Type 1 fuzzy logic controller (T1FLC) and multicarrier PWM id-iq control scheme with no shoot-through phenomenon. The conventional cascaded full-bridge inverter is comprised of multiple numbers of modular inverter cells and every single modular inverter cell consists of two power devices in each leg, however it still suffers from the shoot-through phenomenon with decreased reliability. As no shoot-through occurrence in each modular full-bridge interleaved buck inverter of its cascaded multilevel inverter, there is copious improvement in the reliability as likened to other cascaded inverters. Additionally, the dead time introduction does not necessitate in the full bridge interleaved buck inverter, as in conventional, which easily pushes the theoretical limit of duty cycle, and hence full energy transfer takes place to the load by pulse width modulation (PWM). The performance of multilevel cascaded full-bridge interleaved buck inverter (CFBIBI) along with the application as a shunt active power filter (APF) has been described in detail for the different voltage source condition. Simulation and OPAL-RT experimental results are presented for the validation of this multilevel cascaded full-bridge interleaved buck inverter based shunt active power filter.
An Identification Approach for the Data-Driven SIR in the PnP Monitoring and Control Architecture

Aiming at establishing reliable and flexible data-driven designs of process monitoring and control systems, this paper presents the latest study on the identification of the data-driven realization of the stable image representation (SIR) in the plug-and-play process monitoring and control architecture (PnP-PMCA). The core of this study is the identification of the multiplication operators from the reference signal to the control input and the system output measurement. This work is essential to the future research on the data-driven PnP process monitoring and control system designs. The correctness and the effectiveness of the proposed identification approach have been verified and demonstrated through randomly generated system and designed closed-loop
Application of Fuzzy Cognitive Maps to Multi-Step Ahead Prediction of Electricity Consumption

This paper is devoted to the multi-step ahead prediction of multivariate time series with the use of fuzzy cognitive maps. The aim of the analysis is to predict the electricity consumption in a household. The learning process was performed with the developed Structure Optimization Genetic Algorithm (SOGA). A comparison with the well-known method for fuzzy cognitive maps learning: the Real-Coded Genetic Algorithm was done. The obtained results show that the SOGA algorithm enables simplifying the fuzzy cognitive map by selection of the most significant concepts and connections and forecasting the three next steps of electricity consumption with satisfactory accuracy.
Programmable Node in Software-Defined Wireless Sensor Networks: A Review

Wireless Sensor Networks (WSN) and the Internet of Things play a critical role in many applications ranging from monitoring, tracking and surveillance, social enhancement and many more. Although WSN is used for applications above, still there are some challenges it faces. A few of the challenges faced by WSN include the inability to withstand a large number of sensor nodes deployed in a heterogeneous system leaving the WSN system unmanaged. So recently, there is a huge interest to utilize Software-Defined Networking (SDN) in WSN with more focus on the architecture, routing protocols, topology discovery, SDN controllers, etc. However, without an SDN-enabled sensor node, these systems/models will not be able to operate efficiently and reduce the complication of network configurations and management. Thus, this paper caters the design and development of SDN- enabled sensor node that is applicable to different applications and allowing functions of different processes within the WSN to run efficiently and reduce the costs, improving energy efficiency, scalability and render a system with multiple of functional sensors.
A Survey of Pedagogical Affordances of Augmented and Virtual Realities Technologies in loT - Based Classroom

Education has become a crucial issue all over the world due to its contribution to societies' development. This article emphasizes the increasing interest in enhancing the learning and teaching process in a smart classroom. Based on that, we investigate the affordances of augmented reality (AR) and virtual reality (VR) technologies in educational sector. Our objective is to discern the main potential uses of these technologies to help teachers to transmit knowledge efficiently and effectively, and assist students to comprehend and retain it. In fact, these technologies could enhance education if they are well integrated into the educational activities. This article highlights the affordances of AR/VR technologies, and lists the cases where these technologies facilitate teaching and learning in smart classrooms.
Gradient Boosting Decision Tree and Random Forest Based Partial Discharge Pattern Recognition of HV Cable

As a typical “white box” pattern recognition method, the traditional Decision Tree (DT) method, which is easy to be interpreted and trained, has been widely applied in the Partial Discharge (PD) pattern recognition of high voltage cables. However, the disadvantages of the DT method are significant, including the limited recognition accuracy, poor anti-interference ability and poor generalization ability, etc. In order to overcome the challenges, Gradient Boosting Decision Tree (GBDT) and Random Forest (RF) based PD pattern recognition methods are presented in the paper. Firstly, 3500 PD samples are obtained based on the PD testing of five types of artificial defects of ethylene-propylene (EPR) cables in the high voltage lab. Secondly, PD data pre-processing is carried out and 34 types of PD features are extracted from the raw data. Thirdly, the principles of GBDT and RF based PD pattern recognition are presented in details. Finally, the 3500 PD samples are applied to evaluate the GBDT and RF based PD pattern recognition methods, which are compared with the traditional DT method. The results show that the PD pattern recognition accuracies of RF, GBDT and DT are 89.7%, 90.4% and 84.3% respectively. GBDT and RF based PD pattern recognition methods show remarkable advantages, compared with the DT method, which are both applicable for industrial application of PD based condition monitoring of high voltage cables.
Wavelet-Based 6-DOF Visual Tracking System for Miniature Aerial Vehicle

This paper develops a new wavelet-based visual tracking system for miniature aerial vehicle (MAV) system. The visual signals for visual tracking system is formulated based on using wavelet coefficients. The design uses multiresolution interaction matrix with half and details image to relate the time-variation of wavelet coefficients with the velocity of the MAV and controller. The design is evaluated on a quadrotor MAV system to demonstrate the effectiveness of the wavelet based 6-DOF visual tracking system without using image processing unit. The evaluation results show that the MWT based linear controller can provide accuracy and efficiency without using image processing unit provided that the system does not contain any uncertainty.
A Distributed Voltage Controller for Medium Voltage Grids with Storage-Containing Loads

In this paper, a set-up consisting of a medium voltage grid with connected water distribution network as a load is investigated. The water distribution network contains water reservoirs that can be used as storages to support the voltage control by adjusting the electric power consumption to keep the voltages in the required limitations. At the same time the electric power and water demand are satisfied. The considered set-up is derived from a real-world set-up in the city of Kaiserslautern, Germany, where the power grid and water distribution grid are operated by the same operator. A distributed model predictive controller for the voltage control is designed in general form and thus the concept be applied to other loads containing storages. The controller is tested in simulations.
Cooperative Search of Multiple Robots with a Distributed Algorithm

This paper presents a cooperative algorithm for multiple robots that move in 3-D unknown environment performing coverage and search tasks. We assume some significant constraints, i.e., each agent carried an onboard vision-based sensor facing downward to task area and has a limited communication radius. Based on the coverage control and probability map updated model, we proposed a combination method considering the tradeoff between probability information and Euclidean distance to obtain an optimal robots deployment. This algorithm is illustrated by the simulation and effectively make a group of robots searches multiple static targets under the coordination mechanism.
Semi-Supervised Approach for Recovering Traceability Links in Complex Systems

Building a complex system requires the collaboration of different stakeholders. They work together to model the system keeping in mind the requirements described in specification documents. This complexity induces a large volume of requirements and models, i.e., artefacts that will be subject to frequent changes during the project lifetime. Since the artefacts are correlated with each other's, each change has to be rigorously propagated. Identifying traceability links between system's artefacts is then a critical step to reach this goal. In Information Retrieval domain, many approaches have been already proposed to cope with traceability issues. Their main drawback is they introduce an important amount of false positive links making the traceability links validation phase time consuming and error-prone. In this paper, we propose an approach that identifies traceability links with a reduced amount of false positive links ranging from 20% to 30% while raising the amount of true links identified up to 70%. The approach consists of three main steps: 1) we measure syntactical and semantic similarities between pairs of artefacts by combining the use of four major Information Retrieval techniques; 2) using these similarity measures, we identify the most likely true and false links and we build the so called training data set; 3) this training data set and the four IR techniques are used as input of a predictive model in order to classify between true and false links leading ultimately to a reduced amount of false positives. The output is given in the form of a confidence measure that will help the modeller validating the traceability links. We evaluated our approach using four well-known public case studies. Each one comes with a clear identification of true traceability links which allowed us to compare with the outcome of our approach and validate its effectiveness.
Secondary Active Rectifier Control Scheme for a Wireless Power Transfer System with Double-Sided LCC Compensation Topology

This paper proposes a secondary control method using an active rectifier for a double-sided LCC tuned wireless power transfer system (WPT) to reduce the communication requirements of the system. As the active rectifier regulates the output power with a constant primary coil current, the secondary circulating time interval is adjusted while maintaining a unity power factor. First, circuit modelling and sensitivity analyses for the voltage gain, input impedance, coil current rating and power transfer are investigated with a design example of 20kW WPT. Subsequently, the control scheme is developed by considering the four operation modes with the phase difference as the control variable. To prove the effectiveness of the proposed control method, a simulation model is built, and the results demonstrate good dynamic performance with high accuracy for voltage regulation. Compared with the conventional secondary control, it improves the performance of the system along with a well-developed converter gain by maintaining the equivalent load resistive and reduced circulating time intervals, which further improves the system efficiency. The proposed system also eliminates the need for an additional de/de converter on the secondary side.
Adaptive Tracking Control for a Boost DC – DC Converter: A Switched Systems Approach

Power converters (Buck, Boost, Buck-Boost, multilevel converters) have many applications such as in renewable energy, power supply for computers, etc. The converters are controlled by switches to transfer the energy from a power source to a load and these systems need to be considered as a switched system class. This work develops a backstepping technique to design an adaptive tracking control law for a non-autonomous switched systems. This method is then applied to the DC-DC boost converter under the consideration of the uncertainty of the parameters in the model and arbitrary switchings. Based on this model, an adaptive tracking control scheme is design to allow the output voltage tracks a desired voltage when the load is changed. The theoretical analysis and simulation results point out the good effectiveness of the proposed adaptive technique control for the DC – DC converter.
State-of-Charge Co-estimation of Li-ion Battery based on on-line Adaptive Extended Kalman Filter Carrier Tracking Algorithm

Li-ion batteries as a source of energy in electric vehicles (EV) and hybrid electric vehicles (HEV) are receiving more attention with the worldwide demand for energy conservation and environmental protection. In this paper, an improved State-of-Charge (SOC) co-estimation algorithm based on the second-order RC equivalent circuit model is proposed. Firstly, Forgetting Factor Recursive Least Squares (FFRLS) algorithm is adopted to realize on-line parameter identification of the model. Secondly, SOC is estimated with identified parameters by adaptive extended Kalman filter carrier tracking (AEKF) algorithm based on innovations and residuals. The results of two discharge experiments in different conditions show that the co-estimation algorithm has a higher estimation accuracy, convergence speed and robustness compared with off-line AEKF SOC estimation algorithm, which is more suitable for on-line estimation of electric vehicle SOC.