Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Challenges for Process Mining in Human-Centered Industrial Environments

Operators in industrial manufacturing environments are under pressure to cope with increasing flexibility and complexity of work. The automation of manufacturing requires operators to adopt new techniques and shifts the focus from low-complexity repetitive tasks to dealing with the execution of high-complexity tasks in cooperation with machines. The emergence of wearable technologies makes it possible to equip operators with miniaturized sensors that may be used to determine the physical and mental stress experienced by operators. Process mining technologies are suited to analyze such sensor data in the context of the manufacturing process with the ultimate goal of improving the operator's well-being through re-organization of work and the work place. However, the storage and processing of such highly personalized data comes with many privacy challenges. Whereas there are many potential benefits, such as improve the work environment, there are also many justified reasons for operators to oppose the processing of their data. Apart from employee concerns, data protection regulations, such as EU GDPR (Europe's General Data Protection Regulation), imposes many compliance challenges for the design of a process mining systems dealing with personal data. We contribute an analysis of the privacy challenges of using process mining on data recorded from sensorized operators in human-centered industrial environments. Guided by privacy research and the regulation imposed by the GDPR, we describe guidelines for privacy in process mining systems.
A Holistic Framework for Enhancing Privacy Awareness

Home users face increasingly higher risks of privacy loss and struggle with the difficult task of protecting large volumes of personal information. Most privacy research assumes that users have uniform privacy requirements. The main problem with this approach is that research has also shown that users have different privacy attitudes and expectations based upon a variety of factors, including (but not limited to) gender, age and education level. Privacy therefore can mean different things in different contexts, to different people at different times. For example, some uses are less concerned regarding the sharing and use of their location information while others will be very concerned. Therefore, it is important to factor these requirements in to a privacy-awareness model that can enhance user’s awareness and make more informed decisions to reduce their specific degree of exposure. The quantity and range of sensitive information also requires approaches that give users back the control over their data. Therefore, prioritization of privacy-related information based on an individual user basis should be utilised to ensure relevant and timely notification about privacy-related information that is important to the user. This paper presents a critical analysis of the current state of the art and proposes a novel mobile-based architecture to provide users with effective and usable privacy protection.
A Semi-Automatic Video Labeling Tool for Autonomous Driving Based on Multi-Object Detector and Tracker

In recent years, the development of autonomous cars has gained great momentum due to the vast advances in deep learning technique for recognizing and tracking objects on the roads. To apply the deep learning technique, a large set of properly annotated videos are normally needed to train the neuron network model. However, the process of annotating videos is very time-consuming and tedious, and currently it relies mainly on human. Automating this process is actually a chicken-and-egg problem: we need a perfect object detection and tracking tool to annotate the videos so as to train a perfect object detection and tracking algorithm. A viable alternative is to annotate the videos using a less perfect tool and then correct the results manually. In this paper, we introduce such a semi-automatic video labeling tool for autonomous driving. Our tool is based on the open-source video annotation system VATIC. A multi-object detector and tracker is first used to annotate the video. Possible errors in the labeling of the objects are identified and then presented to human annotators to produce correct annotations. Experiments on labeling test videos show that our tool can complete the annotation task faster, while maintaining the same quality as a human annotator. The proposed tool is modified from the open-source tool VATIC and is available from Github.
Towards QoS-Aware Service-Oriented Communication in E/E Automotive Architectures

With the raise of increasingly advanced driving assistance systems in modern cars, execution platforms that build on the principle of service-oriented architectures are being proposed. Alongside, service oriented communication is used to provide the required adaptive communication infrastructure on top of automotive Ethernet networks. A middleware is proposed that enables QoS aware service-oriented communication between software components, where the prescribed behavior of each software component is defined by Assume/Guarantee (A-G) contracts. To enable the use of COTS components, that are often not sufficiently verified for the use in automotive systems, the middleware monitors the communication behavior of components and verifies it against the components A/G contract. A violation of the allowed communication behavior then triggers adaption processes in the system while the impact on other communication is minimized. The applicability of the approach is demonstrated by a case study that utilizes a prototype implementation of the proposed approach.
Research on Grid-connected Strategy of Distributed Generation Dominated by on -grid Electricity Price (July 2018)

The access of high permeability and large-scale distributed generation makes the operating environment of distribution network extremely badly. To solve the problem of safe and reliable access to distribution networks with large-scale distributed energy sources, based on the on-grid price, this paper deduces the demand information of power distribution network by analyzing and comparing it; establishes the Markov decision process model, defines the reward and punishment mechanism of different behaviors; introduces particle swarm optimization algorithm to optimize the power flow calculation process and improve convergence rate; proposes the control strategy of distributed energy access to the distribution network, and maximizes the economic benefits of distributed energy under the premise of ensuring the highest priority of power quality, and provide more effective means to solve the problem of active distribution network dissipating distributed energy.
Real-time Twitter Sentiment Analysis using 3-way classifier

Sentiment analysis or opinion mining is a critical issue where a huge amount of information related to user's opinion widespread in all counties in the world. This paper presents an online system for real-time twitter sentiment analysis and classification. The proposed system helps users to enter the query and get a graphical representation of the tweets polarity. Out of various classification algorithms, Simple Voter and Naïve Bayes algorithms have been used to classify tweets. The obtained results show that the accuracy of the system is efficient using Naïve Bayes classifier.
Industrial LoRa: A Novel Medium Access Strategy for LoRa in Industry 4.0 Applications

Among the Low-Power Wide Area technologies available today, LoRa is one of the most successful, as it enables robust long-distance low-power communications in the unlicensed sub_GHz Industrial, Scientific and Medical band. A number of solution providers and network operators show much interest in this technology for Industry 4.0 applications. In addition, LoRa represent a promising candidate for boosting future Industrial Internet of Things networks and services. However, the current standardized LoRaWAN Medium Access Control (MAC) protocol over LoRa does not fully cope with the requirements of industrial networks, in particular as far as the scheduling of real-time data flow is concerned. To overcome this limitation, this paper proposes Industrial LoRa, a novel medium access strategy for LoRa that enables the implementation of industrial wireless networks for Industrial Internet of Things and Industry 4.0 applications. The paper describes the proposed MAC scheme and the advantages it provides. A simulative assessment shows the performance of the proposed approach in a realistic scenario that is compliant with the requirements of soft real-time applications in Industry 4.0.
Multi-Reservoir Echo State Network for Proton Exchange Membrane Fuel Cell Remaining Useful Life prediction

In this paper, a Multi-Reservoir Echo State Network is used to estimate the Fuel Cell degradation, and its remaining useful lifespan. It proposes a methodology for predicting the fuel cell output voltage evolution with time. Echo State Network is a powerful Artificial Intelligence tool for time series predicting which main characteristics is the use of a reservoir of neurons, randomly created, instead of hidden layers such as for Artificial Neural Networks. Only the output layer is optimized by a multi-linear regression, resulting in a time reduced training phase. This leads to a possible increase of the reservoir size to preserve, even improve, its accuracy. However, the bottleneck linked to the use of this tool lies in its architecture optimization. This paper proposes a way to overcome the echo state network parameters optimization process by using a Multi-Reservoir Echo State Network. Then a comparison between an Echo State Network optimized algorithm and a Multi-Reservoir Echo State Network for fuel cell RUL prediction is proposed. In order to have a good prediction of the FC lifetime, an innovative approach based on the Multi-Reservoir Echo State Network is developed and validated using experimental data.
Integrating periodically CL-TWE MZI electro-optic modulator based on InP-MQW optical waveguides

In this paper, we reported the design, fabrication and preliminary measurements of an integrating periodically capacitive-loaded travelling wave electrode (CL-TWE) Mach- Zehnder interferometer (MZI) modulator based on InP-based multiple quantum well (MQW) optical waveguides. The device configuration includes an optical MZI, a DC electrode, two phase electrodes, a U-electrode and an I-electrode. It was fabricated by standard photolithography, ICP dry etching, electroplating and etc. Measurement results show that the EO modulator has a propagation loss of about 9.4 dB/cm and an electric bandwidth of larger than 40 GHz.
A Variable Step Size P&O MPPT Algorithm for Three-Phase Grid-Connected PV Systems

The main objective of this paper is to propose a variable step size Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm for two-stage three-phase grid-connected photovoltaic (PV) systems. The previous-stage Boost circuit achieves the maximum power point tracking control. The inverter side of the rear-stage realizes the stability of the DC bus voltage and the control of the grid-connected current. By adopting a novel variable-step size P&O algorithm, a fixed step size is adopted when the distance is far from the maximum power point, and the step size is reduced by half every time the working point crosses the peak point, until the final step is smaller than the set threshold and the operating point is stable at the maximum power point. The proposed method effectively solves the problem of slow tracking speed and voltage disturbance near the power point in the conventional fixed-step P&O. In the inverter side, the use of double closed-loop control can ensure the stability of the DC bus voltage and achieve unit power factor grid-connected control. Finally, the proposed algorithm is applied to the two-phase three-phase grid-connected simulation model established in MATLAB. The simulation results show that the proposed method shortens the tracking time by 0.08s and eliminates the power oscillation phenomenon near the maximum power point compared to the fixed step P&O.
Modeling and Evaluation of Mobility Management in mmWave Cellular Networks

5G cellular networks will enable new differentiated services and applications to support industry like healthcare, energy, automobile and security. These services will massively increase the number of attached devices like mobiles, sensors, and actuators, resulting in sharp increase in mobile traffic volume. To cater high traffic volume and to fulfill strict quality of service requirements, 5G networks are expected to handle complex scenarios to provide seamless mobility and better resource utilization. In this paper, we propose mobility management algorithm for target cell selection and provide a methodology to evaluate its performance under varying conditions in mmWave framework. We use discrete time Markov chain (DTMC) and Markov decision process (MDP) to model radio access network controller and user equipment respectively. Probabilistic model checking (PMC) is used to do performance evaluation of the algorithm. The results show that our proposed algorithms adapts to network changes and responds successfully to mobility management process execution. With the help of PMC, we also gain useful insights regarding critical network parameters which can be used to achieve optimal resource utilization and to enhance user perceived quality of service (QoS).
Consideration of Landscape Recognition for Topological Localization

In this paper, we considered the localization methods which recognizes landscape. In order to realize localization technique by recognizing landscape like a human, we recognize the landscape from objects arrangement in view. In this paper, we consider landscape recognition by SVM and segmentation method by deep learning. As results of object recognition by SVM and segmentation, the recognition rate was higher in the method by segmentation. In addition, from the experiment of localization by landscape recognition at multiple locations, we clarified the problem when applying this method to localization.
LTCL-Filter Active-Damping Design Considerations for Low-Switching-Frequency Grid-Tied VSCs

Most electronic devices are connected to the grid by using power electronics converters and, in order to reduce the filter size and cost, novel filter topologies are being proposed in the literature. For high-power applications, LTCL filters are an attractive solution since the size of inductors can be reduced while maintaining a low switching frequency. However, this type of filter requires additional passive damping elements unless an active damping strategy is included in its control system. In this paper, the hardware and control system design of a VSC connected to the grid via a LTCL filter is addressed, with emphasis on the damping problem. It is shown that adequate damping can be provided by applying two different strategies. The first one is by changing the hardware of the converter (without additional passive elements), while the second one is by adding a compensator in series with the current controller. The proposed design methodology and control system improvements were tested in a simulation of a 15 kW VSC connected to the grid via a LTCL filter.
Transfer Hawkes Processes with Content Information

Hawkes processes are widely used for modeling event cascades. However, content and cross-domain information which is also instrumental in modeling is usually neglected. In this paper, we propose a novel model called transfer Hybrid Least Square for Hawkes (trHLSH) that incorporates Hawkes processes with content and cross-domain information. We also present the effective learning algorithm for the model. Evaluation on both synthetic and real-world datasets demonstrates that the proposed model can jointly learn knowledge from temporal, content and cross-domain information, and has better performance in terms of network recovery and prediction.
Branch Energy Control of the Three-Phase to Single-Phase Direct AC-AC Modular Multilevel Converter Under Equal Frequency Operation Condition

For medium- to high-voltage ac-ac conversion applications, direct ac-ac modular multilevel converters (MMCs) show advantages on power density compared to indirect ac-ac (ac-dc-ac) MMCs. However, direct ac-ac MMCs suffer from branch energy divergence when the operation frequencies of the input and the output are equal. This paper investigates the three-phase to single-phase direct ac-ac MMC under equal frequency operation condition and proposes a novel control strategy to balance its branches' energies. The effectiveness of the control strategy is validated by the simulation results.
Improving Hosting Capacity of Rooftop PVs by Quadratic Control of an LV-Central BSS

High integration of rooftop photovoltaic (PV) plants in distribution systems leads to new technical challenges: reverse-active power and voltage rise in low-voltage (LV) and medium-voltage (MV) grids. These challenges limit the maximum amount of power can be produced by PVs in LV and MV grids, called the hosting capacity (HC). Battery storage systems (BSSs) have been used in many studies to decrease the reverse power and improve the HC by controlling the active power. However, the influence of a central BSS on the HC can be greatly improved by using a quadratic power control, simultaneous active, and reactive power control, and by selecting of the optimal battery size, the converter size, and the place of the central BSS. The effectiveness of the quadratic power control was not seen in previous works due to the fact that grids with one level of voltage without modeling of MV/LV transformers were simulated. This paper develops a method to select the optimal size of the battery and converter unit as well as the optimal place of an LV-central BSS having an optimal quadratic power control. The simulation results show considerable effects of the optimal selection of an LV-central BSS on the HC improvement.
Fusion of Multirate Measurements for Nonlinear Dynamic State Estimation of the Power Systems

With the increasing availability of sensors, power system dynamic state estimation (PSDSE) is going to play a critical role in the reliable and efficient operation of power systems. The real-time measurements in today’s power grid are obtained through various types of sensors having different sampling rates, e.g., the traditional SCADA systems with low sampling rates (generally 0.5–2 samples per second), and different groups of phasor measurement units having high sampling rates (usually 30–60 samples per second). We propose a multi-rate multi-sensor data fusion-based PSDSE framework to utilize the measurements coming from sensors with two different sampling rates. The continuous time-domain nonlinear dynamical and measurement equations are discretized at appropriate sampling periods to obtain two discrete models. Two separate estimators are developed using these models. State information of the intermediate time steps of the estimation having coarser sampling period is evaluated using model-based prediction. These two estimations are optimally combined or fused using Bar–Shalom–Campo formula. The proposed algorithm tracks the dynamic states successfully during transient events such as faults. The method is demonstrated by using the standard IEEE-9, 39, 57, and 118 bus systems. The fusion-based state estimator is shown to perform better than the individual state estimators.
Design and Planning of a Multiple-Charger Multiple-Port Charging System for PEV Charging Station

Investment of charging facilities is facing deficit problems in many countries at the initial development stage of plug-in electric vehicles (PEVs). In this paper, we study the charging facility planning problem faced by a PEV charging station investor who aims to serve PEV customers with random behaviors and demands (but follow a series of predicted distributions) with lower economic costs of both charging facilities and practical operation. First, we design a multiple-charger multiple-port charging (MCMP) system which provides the capability of scheduling a limited quantity of chargers to serve more PEVs. Second, we further develop a general two-stage stochastic programming model to plan the MCMP system inside a PEV parking lot with consideration of coordinated charging. Third, we classify MCMP stations according to three different connecting patterns between chargers and parking spaces. Fourth, the general planning model is simplified (as a reference) based on the classification. Finally, we complement our analysis through case studies.
Distribution Preserving Multi-task Regression for Spatio-Temporal Data

For many spatio-temporal applications, building regression models that can reproduce the true data distribution is often as important as building models with high prediction accuracy. For example, knowing the future distribution of daily temperature and precipitation can help scientists determine their long-term trends and assess their potential impact on human and natural systems. As conventional methods are designed to minimize residual errors, the shape of their predicted distribution may not be consistent with their actual distribution. To overcome this challenge, this paper presents a novel, distribution-preserving multi-task learning framework for multi-location prediction of spatio-temporal data. The framework employs a non-parametric density estimation approach with L2-distance to measure the divergence between the predicted and true distribution of the data. Experimental results using climate data from more than 1500 weather stations in the United States show that the proposed framework reduces the distribution error for more than 78% of the stations without degrading the prediction accuracy significantly.
Proportional-Integral and Proportional-Resonant Based Control Strategy for PUC Inverters

In this paper, a proportional-integral (PI) and proportional-resonant (PR) based control strategy is proposed for packed-U-cell (PUC) inverters. In the conventional PI-PI based control strategy, while the first PI regulates the DC capacitor voltage, the second PI regulates the AC load current. However, it is shown that the second PI cannot guarantee zero tracking error in the load current. The main reason of this comes from the fact that PI controllers are not able to achieve zero tracking error for the AC signals. Also, in an attempt to reduce the tracking error by tuning PI gains distorts the load current. In this study, a PI-PR based control strategy is obtained by replacing the second PI by a PR controller. The performances of both PI-PI and PI-PR control methods have been compared. It is shown that the load current to tracks its reference in all circumstances provided that the inverter current reference is generated accurately. Computer simulations are conducted to show the steady-state and dynamic performances of both control methods.
Two Level Based Privacy Protection Approach for Internet of Things Users in Cloud Computing

recently, privacy protection in IoT received high attention to protect the personal information of the users. The risks that threaten the privacy of IoT users maybe happened at the data stored on the server of the cloud or at the queries sent by the users, where analysis could be applied by an attacker. With this paper, we propose a double level-based approach in order to protect the privacy of IoT users. We acted at the level of the server, where anonymity based component is installed on the server to protect the query. In addition, we acted with the user machine level, where a transform technique is used in order to ensure the privacy of the location of the IoT users. Our approach gave better performance compared to previous approaches.