Miracle-dz/UniFAR-FTUs huggingface dataset:
Interfacing an Electric Vehicle to the Grid with Modular Conversion Unit: A Case Study of a Charging Station and its Control Framework

In response to vehicle-to-grid (V2G) application, promising solutions for fast charging stations of electric vehicles (EV) are still demanding with considerations of their interfaced requirements on the AC distribution network. An integrating periodic loads with different energy storage technologies to the common DC bus is strong alternative to the existing infrastructures. This paper addresses a key role of rectification topology in fast charging stations with respect to higher scalability, redundancy and its capacity to compensate unbalanced loads or total harmonic distortion (THD) at the point of common coupling (PCC). It is achieved through the operational performance of the Modular Multilevel Converter (MMC) deployed to transfer AC power generated by medium-voltage grid to common DC bus feeding multiple EV batteries. The developed control framework is constructed by two-layer configuration, in which a first-layer finite control set-model predictive control (FCS-MPC) methodology provides an initial controlled variables to regulate the rectifier module voltages and currents at the connected loads. The second-layer iterative controller is embedded into an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) algorithm for handling nonlinearities and updating weighting factors. The control objectives are improving the computational burden with variables constraints, lowering the dc ripples and grid current harmonic contents. The feasibility assessment of load-leveling approach is also studied when large energy storage systems are in operation. To prove a functionality of the proposed design, numerical simulations are conducted and some results are presented.
An Image Encryption Technique using Sine Curve

In this article parametric curve based a new image encryption method is proposed. Here, to obtain the encrypted image, sine curves (sin(a, w, ¢, t) = a * sin(¢ + 27rwt)) have used in both confusion and diffusion phases. In different steps, different sine curves have used, with varying the parameters and this provide more security to the proposed method. The result of the proposed method, performance under different statistical analysis and sensitivity analysis established that proposed method provides a secure and efficient way for image encryption.
Zero-Shot Learning: An Energy Based Approach

Zero-shot learning deals with the problem when the training domain and the test domain have different class sets of image instances. To tackle the problem of some classes in the test data never appeared in the training set, a most popular approach is to map both images and classes in a common space under the embedding based framework. Nevertheless, most embedding based models suffered from the semantic loss problem. Furthermore, the expressive power is limited by representing classes and images as mere points. To tackle these problems, in this paper, we propose an Energy-Based Zero-shot Learning model (EBZL) to encode the association between class attributes and input images for zero-shot learning. EBZL is composed of two parts. The first part is a variational autoencoder that reduces the input dimension of images with representative hidden representations. By feeding the hidden representations as the input of the second part, the second part works as the energy function part based on the deep Boltzmann machine. Specifically, we adapt tradition deep Boltzmann machine to a supervised setting without changing its property as an undirected probabilistic graphic model, which helps to preserve semantic integrity and circumvents semantic loss problem. We further utilize variational inference techniques and mean-field approximation to reduce time complexity in model training process. Finally, extensive experimental results on several real-world datasets clearly show the effectiveness of our proposed method.
On-line Parameter Identification and Self-Commissioning of Current Controller for Servo Motor Drives Considering Time Delay in Both Modeling and Control

The main theme of this paper is to propose an online parameter identification and self-commissioning of current controller for servo motor drives as time delay is taken into consideration. The advantages of considering time delay include increase of current controller bandwidth and easy to achieve delay compensation. The electrical parameters and time delay required for self-commissioning of current controller are identified on-line through injecting signal and real-time Fast Fourier Transform (FFT) under standstill. The current controller gains are determined based upon the magnitude optimal method as the delay is considered while retaining the maximum phase margin. The experimental results will be shown, which are derived from DSP-controlled PMSM servo drives. With the proposed parameter identification method, in which time delay identification is included, the measured bandwidth of current loop is improved as compared with that without such consideration. These results confirm the effectiveness and claims of the proposed method.
First Insights on the Electromagnetic Design of Axial-Flux Synchronous-Reluctance Maschine

This paper presents a new topology of synchronous reluctance machine, based on the axial-flux concept (AF-SynRM). As case study, an electric drive used in a cooling fan module for vehicles is considered, but the applications field can be much wider. At first, the existing/reference radial flux Interior Permanent Magnet Synchronous Motor (IPMSM) has been modeled by means of Finite Element Method (FEM) to check the electromagnetic behavior, (i.e. flux linkage and torque). The next step consists in the design and optimization of a concentrated winding, magnet-free, radial flux Synchronous Reluctance Machine (SynRM). Following that, the newly proposed AF-SynRM is benchmarked against the both magnet-based and magnet-free radial flux (RF) counterparts. As the reference machines are employing a fractional slot concentrated winding, the same is done for the AF-SynRM, even though superior behavior is expected when considering its distributed variant. Results obtained during the 3D FE-based optimization are reported highlighting the main challenges needed to be addressed (e.g. higher torque ripple and q-axis inductance). Being a motor topology that is rather complex regarding its manufacturability, possible directions and their problems are discussed.
Feature Regions Segmentation Based RGB-D Visual Odometry in Dynamic Environment

A novel RGB-D visual odometry method for dynamic environment is proposed. Majority of visual odometry systems can only work in static environments, which limits their applications in real world. In order to improve the accuracy and robustness of visual odometry in dynamic environment, a Feature Regions Segmentation algorithm is proposed to resist the disturbance caused by the moving objects. The matched features are divided into different regions to separate the moving objects from the static background. The features in the largest region which belong to the static background are used to estimate the camera pose finally. The effectiveness of our visual odometry method is verified in a dynamic environment of our lab. Furthermore, an exhaustive experimental evaluation is conducted on benchmark datasets including static environments and dynamic environments compared with the state-of-art visual odometry systems. The accuracy comparison results show that the proposed algorithm outperforms those systems in large scale dynamic environments. Our method tracks the camera movement correctly while others failed. In addition, our method can give the same good performances in static environment. Experiments demonstrate that the proposed RGB-D visual odometry can obtain accurate and robust estimation results in dynamic environments.
Intelligent Electronic Devices With Collaborative Intrusion Detection Systems

This paper proposes the new concept of intelligent electronic devices (IEDs) with built-in distributed intrusion detection systems. The proposed IEDs have the capabilities to monitor and detect anomalies and abnormal behaviors of the host system of IED and IEC 61850-based messages, e.g., sampled values and generic object oriented substation event. The proposed IEDs collaborate with neighboring IEDs to make an accurate decision and find the origin of cyber attacks. The commodity embedded system has been used to verify the performance of the proposed intrusion detection system with power system protection functions of IEDs. The results show that the proposed mitigation methods work accurately and efficiently with protection functions, e.g., overcurrent and distance protections, on the embedded board.
A Novel Method for Summarization and Evaluation of Messages from Twitter

Users of microblogs such as Twitter publish messages about many topics including real world events. Patterns can be identified and used to classify events from these messages. It is challenging to summarize the event related messages concisely to get important information about the event. In this paper, we propose a novel method of event summarization that uses the frequently occurring sets of words and features of events related messages in a cluster. For effective evaluation of system summaries, we created a set of extensive reference summaries from manual summaries. These reference summaries were used to evaluate various sum-marisers using standard ROUGE based metrics. We compared our summarizer with many baselines on a set of 50 manually labelled and extended event summaries. In our experiments, the proposed summarizer outperformed other summarizers on ROUGE-1, ROUGE-2, and ROUGE-L.
Fast Tucker Factorization for Large-Scale Tensor Completion

Tensor completion is the task of completing multi-aspect data represented as a tensor by accurately predicting missing entries in the tensor. It is mainly solved by tensor factorization methods, and among them, Tucker factorization has attracted considerable interests due to its powerful ability to learn latent factors and even their interactions. Although several Tucker methods have been developed to reduce the memory and computational complexity, the state-of-the-art method still 1) generates redundant computations and 2) cannot factorize a large tensor that exceeds the size of memory. This paper proposes FTcom, a fast and scalable Tucker factorization method for tensor completion. FTcom performs element-wise updates for factor matrices based on coordinate descent, and adopts a novel caching algorithm which stores frequently-required intermediate data. It also uses a tensor file for disk-based data processing and loads only a small part of the tensor at a time into the memory. Experimental results show that FTcom is much faster and more scalable compared to all other competitors. It significantly shortens the training time of Tucker factorization, especially on real-world tensors, and it can be executed on a billion-scale tensor which is bigger than the memory capacity within a single machine.
Sentimentalizer: Docker container utility over Cloud

In this computer era, the most interesting thing is to determine the human opinion using machines. Humans use opinions for conveying their response on a host of things to others. With the increasing popularity and availability of enriching opinion mediums such as personal blogs, forum discussions, online review sites, and micro blogging sites like Twitter, there are new challenges and opportunities for using this information to understand and analyze the sentiments of others. However, web texts usually seem noisy and represent significant issues at the lexical as well as the syntactic level. In this paper, lightweight Docker container is employed over cloud as a utility for sentiment analysis using the four popular classification approaches. It analyzes the reviewer's comment on a product across multiple websites. The analyzed information can be used as a recommendation for the product to a customer. The evaluation process on NLTK benchmark movie review dataset is performed with accuracy, computational cost and resources utilization. The computational analysis shows that our proposed approach meets the requirements of the real time applications over Cloud.
Integrating Direct and Indirect Load Control for Congestion Management in LV Networks

With the energy transition, capacity challenges are expected to occur more frequently in low-voltage (LV) distribution networks. In the literature, several direct and indirect load control methods have been suggested as solutions to alleviate network congestion. Direct methods involve the network operator directly controlling appliances at the households, while indirect methods aim to motivate end-users to shift their consumption through price changes. In this paper, the direct and indirect methods are combined into an integrated approach, making use of the advantages of both methods. An agent-based architecture is adopted so that distributed and computational intelligence can be combined to ensure a smooth coordination among the actors. A sensitivity-based curtailment scheme is used to incorporate the unbalanced loading condition of the LV networks. The efficiency of the proposed integrated approach is investigated through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 94% reduction in congestion by the integrated approach, while maintaining the required levels of supply in the network.
Heterogeneity Reduction for Data Refining Within Ontology Learning Process

The (semi-)automated integration of new information into a data model is a functionality which is required in cases when input documents are extensive and therefore a manual integration difficult or even impossible. We proposed the solution combining the ontology learning process with information acquisition from the Web (web mining). This approach offers a robust way how to integrate even previously unknown information disregarding target application or domain. The solution deals with facilitating identification of input data among existing concepts or with the definition of a new concept. The proposed solution was experimentally verified on the integration of an excel document containing spare parts and Ford Supply Chain Ontology.
A Review of Machine Learning Algorithms for Web Page Classification

The internet contains a large amount of data, to exploit it a special treatment of its content should be done, and a particular web information system should be developed. As a critical step of those systems, the web page classification is proposed. In this paper, we present the characteristics of web page classification, we reviewed the different machine learning algorithms used to categorize web pages. Finally, we track some assumptions from the studied methods.
Deadbeat Predictive Direct Power Control of Neutral-Point-Clamped Converter Based Active Front End Rectifier for More Electric Aircraft Applications

In this paper, a deadbeat predictive direct power controller (DPC) is proposed for neutral-point-clamped (NPC) converter based active front end (AFE) rectifier for more electric aircraft (MEA) applications. In the proposed AFE rectifier, the input filter inductance value and size are remarkably reduced by employing 3L-NPC converter, which leads to notable increase the power density of the attained AFE rectifier. Moreover, the novel deadbeat predictive DPC is proposed to achieve constant switching frequency, continuous sinusoidal input current, unity PF as well as fast transient response in presence of wide frequency variation of AC power supply source. The performance and feasibility of the proposed deadbeat DPC for 3L-NPC based AFE rectifier are verified by the simulation results.
A Coarse-to-Fine Method for Cloud Detection in Remote Sensing Images

In this letter, a coarse-to-fine unsupervised method is proposed for cloud detection in remote sensing images. First, the color, texture, and statistical features of the remote sensing images are extracted with the color transform, dark channel estimation, Gabor filtering, and local statistical analysis methods. Then, an initial cloud detection map can be obtained by performing the support vector machines (SVM) on the stacked features, in which the SVM is trained with a set of samples automatically labeled by processing the dark channel of the original image with several thresholding and morphological operations. Finally, guided filtering is used to refine the boundaries in the initial detection map, which further improves the cloud detection accuracy. Experiments performed on several real remote sensing images demonstrate that the proposed method show better detection performances with respect to several recently proposed cloud detection methods in terms of both quantitative and visual comparisons.
Deep Reinforcement Learning with Knowledge Transfer for Online Rides Order Dispatching

Ride dispatching is a central operation task on a ride-sharing platform to continuously match drivers to trip-requesting passengers. In this work, we model the ride dispatching problem as a Markov Decision Process and propose learning solutions based on deep Q-networks with action search to optimize the dispatching policy for drivers on ride-sharing platforms. We train and evaluate dispatching agents for this challenging decision task using real-world spatio-temporal trip data from the DiDi ride-sharing platform. A large-scale dispatching system typically supports many geographical locations with diverse demand-supply settings. To increase learning adaptability and efficiency, we propose a new transfer learning method Correlated Feature Progressive Transfer, along with two existing methods, enabling knowledge transfer in both spatial and temporal spaces. Through an extensive set of experiments, we demonstrate the learning and optimization capabilities of our deep reinforcement learning algorithms. We further show that dispatching policies learned by transferring knowledge from a source city to target cities or across temporal space within the same city significantly outperform those without transfer learning.
Ensuring Privacy Protection of the Users of E-commerce Systems

In the light of the development that are daily done in the internet and wireless networks, E-commerce systems received a lot of attention, where it can makes our life easily. In such systems, the customer is forced to send his/her order based on his real location. After the performing the order, the customer pays using his/her own Visa card. However, the information contained in the Visa card and those included in the sent order may reveal sensitive information about the customer. Therefore, the privacy of the customer must be protected when dealing with the Ecommerce systems. In this paper, we introduce a system that ensures the privacy of the E-commerce systems’ users. Specifically, we introduce a novel fragmentation based technique (FBT) to protect the privacy of the information included in the Visa cards. In regards to the information included in the sent order, we propose two approaches: Adopted k-Anonymity Approach (AkA), which protects the Identity of the customer and Smart Location Transformation (SLT) to protect the location privacy of the customer. Our proposed approaches showed a higher privacy protection and better performance in relative to the previous approaches.
Collective Human Behavior in Cascading System: Discovery, Modeling and Applications

The collective behavior, describing spontaneously emerging social processes and events, is ubiquitous in both physical society and online social media. The knowledge of collective behavior is critical in understanding and predicting social movements, fads, riots and so on. However, detecting, quantifying and modeling the collective behavior in online social media at large scale are seldom unexplored. In this paper, we examine a real-world online social media with more than 1.7 million information spreading records, which explicitly document the detailed human behavior in this online information cascading system. We observe evident collective behavior in information cascading, and then propose metrics to quantify the collectivity. We find that previous information cascading models cannot capture the collective behavior in the real-world and thus never utilize it. Furthermore, we propose a generative framework with a latent user interest layer to capture the collective behavior in cascading system. Our framework achieves high accuracy in modeling the information cascades with respect to popularity, structure and collectivity. By leveraging the knowledge of collective behavior, our model shows the capability of making predictions without temporal features or early-stage information. Our framework can serve as a more generalized one in modeling cascading system, and, together with empirical discovery and applications, advance our understanding of human behavior.
Haptic Rendering for Time-Variant System Based on FDTD Method Considering Realtime Discretization

In this paper, we present a method to rapidly discretize a continuous time-variant system in real time. Three discretization methods are compared. The proposed method does not calculate for discretization, and therefore, the calculation cost is 1/100 of that of the conventional zero-order hold model. The presented method has high stability in higher order systems and the position and size of the internal parameters are determined for each sampling time. The effectiveness of the Finite-Difference Time-Domain(FDTD) method is demonstrated by comparing responses with a continuous system by using simulations. By experiments, we reproduce the haptic sensations based on the time-variant environmental model expressed by Tustin approximation and FDTD method.
Grammatical Framework Programming With Multilingual Grammars

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An Integrated Model for Crime Prediction Using Temporal and Spatial Factors

Given its importance, crime prediction has attracted a lot of attention in the literature, and several methods have been proposed to discover different aspects of characteristics for crime prediction. In this paper, we propose a Clustered Continuous Conditional Random Field (Clustered-CCRF) model which is able to effectively exploit both spatial and temporal factors for crime prediction in an integrated way. In particular, we observe that the crime number at one specific area is not only conditioned on its own historical records but also has high correlation to crime records from similar areas. Therefore, we propose two factors: an auto-regressed temporal correlation and a feature-based inter-area spatial correlation, to measure such patterns for crime prediction. Further, we present a tree-structured clustering algorithm to discover high similar areas based on spatial characteristics to improve the performance of our proposed model. Experiments on real-world crime dataset demonstrate the superiority of our proposed model over the state-of-the-art methods.