Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of the Control Strategy on the Efficiency of SynRM Based Small-Scale Wind Generators

Improvement of the energy efficiency of wind generation installations can significantly reduce their payback period, especially for small-scale wind turbines in regions where wind streams are highly unstable. Meanwhile, electric machines are still the main source of losses in such systems. The most effective Permanent Magnet Synchronous Motors (PMSMs) substantially depend on the availability and prices of rare-earth magnets, as well as proper thermal and flux operating conditions. A good alternative in terms of efficiency and reliability for PMSM can be a Synchronous Reluctance Motor (SynRM). However, high nonlinearity of SynRM’s magnetic circuits hampers the improvement of the control performance, which can be crucial not only for accuracy of torque curve MPPT tracking, but also for the efficiency of the inverter-motor systems as a whole. This paper provides a description of the offline characterization procedure for SynRM motors and comparison of main control laws in terms of inverter-motor efficiency. The methods for calculation of maximum torque-per-ampere (MTPA), maximum torque-per-volt (MTPV), maximum power factor (MPF) and maximum torque-per-loss (MTPL) current trajectories are presented. Efficiency performance of the listed control techniques were tested and analyzed on a 4 kW REEL SynRM motor for application with a torque-curve MPPT regulator of a general small-scale wind turbine.
Dynamic Adaptive Hybrid Impedance Control for Dynamic Contact Force Tracking in Uncertain Environments

The importance of robot contact operation control has been increasing recently due to a need for robots to interact more with the outside world. However, traditional robot compliance control cannot take both transient contact force overshoots and steady-state force tracking error problems into account. To address this problem, this paper aims to design a dynamic adaptive hybrid impedance (DAHI) controller to deal with dynamic contact force tracking in uncertain environments (e.g., polishing scenarios). Under the premise of analyzing the transient response and steady-state error in the hybrid impedance control (HI) and adaptive hybrid impedance (AHI) control, the DAHI control, which combines the advantages from HI and AHI control, is applied to improve the performance of AHI controller. The main goal of such a controller is to avoid force overshoots in the contact stage while maintaining force tracking error in the dynamic tracking stage. The proposed controller is capable of adapting its update rate parameter online in order to track a reference force in uncertain environments. Besides, it does not require any modeling or estimation of an environment’s dynamics or the robot’s dynamics. The simulation and experimental results both show the achieved control performance. The results have also been compared with the previous control methods.
A Digital Neuromorphic Hardware for Spiking Neural Network

The neuromorphic hardware with a non-von Neumann architecture has the advantage of highly-parallel and low-power. In this paper, a digital neuromorphic core with 1024 neurons, 1024 axons and a $1024\times 1024$ synaptic crossbar is designed, and the scalable network could be implemented based on the 2D mesh network on chip (NOC) architecture. The transformed deep spiking neural network (SNN) models can be mapped to our hardware directly, and show good application results. At the case of the full firing rate, the average power of a spike is 2.76E-08J, and for some image recognition tasks, the hardware power consumption is at the milliwatt level.
Discovering the Relationship between Travel Behavior and Land use: A Case Study of Beijing, China

The interaction between travel behavior and land use has drawn much attention for decades. Instead of satellite images, this paper aims to establish a link between human mobility and urban structure using massive taxi data and POI data based on real world. First, the 24-dimensional drop-off/pick-up temporal difference vector (DPTDV) is proposed and used to identify travel patterns. Then, the study area is classified into different clusters according to the hourly variation of the difference between drop-offs and pick-ups. Further, twelve POI features and two indicators are extracted to explore the land use characteristics corresponding to each specific travel pattern. Meanwhile, the relationship between travel activity and land use intensity is linearly fitted. The results from this case study will allow us to have a deeper understanding of urban dynamics, help alleviate traffic congestion and provide guidance for smarter urban construction.
A Novel Highly Reliable 12T SRAM Bitcell Design

This paper presents a novel highly reliable dual port 12T static random access memory (SRAM) bitcell. Compared with the state-of-the-art soft-error-tolerant bitcells and the traditional 6T, the proposed 12T exhibits much larger read noise margin (RSNM), and also saves 85.4% read access time on average, making it much suitable for high-speed highly reliable applications.
Computer-Aided Document Indexing System

An enormous number of documents is being produced that have to be stored, searched and accessed. Document indexing represents an efficient way to tackle this problem. Contributing to the document indexing process, we developed the Computer-Aided Document Indexing System (CADIS) that applies controlled vocabulary keywords from the EUROVOC thesaurus. The main contribution of this paper is the introduction of the special CADIS internal data structure that copes with the morphological complexity of the Croatian language. CADIS internal data structure ensures efficient statistical analysis of input documents and quick visual feedback generation that helps indexing documents more quickly, accurately and uniformly than by manual indexing.
R-STAN: Residual Spatial-Temporal Attention Network for Action Recognition

Two-stream network architecture has the ability to capture temporal and spatial features from videos simultaneously and has achieved excellent performance on video action recognition tasks. However, there is a fair amount of redundant information in both temporal and spatial dimensions in videos, which increases the complexity of network learning. To solve this problem, we propose residual spatial-temporal attention network (R-STAN), a feed-forward convolutional neural network using residual learning and spatial-temporal attention mechanism for video action recognition, which makes the network focus more on discriminative temporal and spatial features. In our R-STAN, each stream is constructed by stacking residual spatial-temporal attention blocks (R-STAB), the spatial-temporal attention modules integrated in the residual blocks have the ability to generate attention-aware features along temporal and spatial dimensions, which largely reduce the redundant information. Together with the specific characteristic of residual learning, we are able to construct a very deep network for learning spatial-temporal information in videos. With the layers going deeper, the attention-aware features from the different R-STABs can change adaptively. We validate our R-STAN through a large number of experiments on UCF101 and HMDB51 datasets. Our experiments show that our proposed network combined with residual learning and spatial-temporal attention mechanism contributes substantially to the performance of video action recognition.
Neutron-Irradiation Effects on ZnO Nanostructure

Nuclear radiation effects on nanostructured semiconductors are concerned for the nano-detectors in space applications. In this paper, the neutron-irradiation effects on the ZnO nanowires and nanoflakes grown by chemical vapor transportation were investigated. After 10 $^{15}$ n/cm$^{2}$ neutron irradiation, the ZnO nanowires maintained their structures with their diameter decreased, while the ZnO nanoflakes changed their structures into nanoparticles, which indicates that the ZnO nanowire structure is more stable than the nanoflake structure for the neutron irradiation. The neutron irradiation causes a red shift of the near band emission peak for the irradiated ZnO nanowires, which is probably due to the generation of shallow defects, and a blue shift for the ZnO nanoflakes, which is consistent with the structure transformation of the nanoflakes.
Strategic Bidding in Electricity Markets: A Power Grid Partitioning Approach

By bidding strategically, a producer firm owning several generators aims to maximize its profit by submitting optimum bids to the wholesale electricity markets. For a bulk power producer that influences the price of electricity, i.e. a price-maker producer, strategic bidding problem appears as a highly non-convex and challenging optimization problem. The computation time of state-of-art solution method for strategic bidding problem increases linearly as the number of random scenarios or scheduling horizon increases. However the computation time of state-of-art approach increases significantly as the size of power grid increases. This paper proposes a partitioning method that when combined by the state-of-art solution method results in considerable reduction in computation time.
Adaptive Web Services Brokering

Web services provide a means to architect and operate large-scale distributed information systems. However, syntactic and semantic differences among Web services complicate their interoperability and service composition. Brokers can facilitate their interoperability by providing discovery and mediation services, yet existing approaches are impractical for dynamic applications. We address this limitation by formulating three discovery tasks as supervised learning tasks. In particular, we apply textual case-based and decision tree induction approaches to these tasks and investigate the use of multiple representations. We evaluate their performance in a broker that discovers and mediates requests and responses for meteorological and oceanographic data. Our evaluations show that, for our evaluation tasks, classifiers learned by either approach can effectively perform service discovery in a broker.
Image Fusion Method Based on Structure-Based Saliency Map and FDST-PCNN Framework

Image fusion has become an active and promising research topic in image processing. It provides an effective way to combine several source images to form a composite image with more detailed information than any one of the source images. An FDST-PCNN framework, which integrates finite discrete shearlet transform (FDST) with pulse-coupled neural network (PCNN), is proposed to possess a higher ability enhance fusion effects. We first propose a structure-based saliency (SBS) map to enhance the clear and important features in one image. The SBS map combines the depth information with the saliency information and could be a good representation of the most essential information of the source images. After multi-scale decomposition by the FDST, the SBS map of the source images and the modified-spatial-frequency of the subbands are both utilized to tune the PCNN neuron response and determine the fused coefficients in each subband. The experimental results on multi-focus and multi-sensor images verify the effectiveness of our proposed fusion method. Compared with other PCNN-based fusion methods, the proposed method achieves significant improvement in preserving detailed edge information and improving overall visual performance.
A Novel Approach to Detect Malware Variants Based on Classified Behaviors

An application programming interface (API) is an excellent feature since it is a procedure call interface to an operating system resource. Behavior features based on API play an important role in analyzing malware variants. However, the existing malware detection approaches have a lot of complex operations on construction and matching. Graph matching is an NP-complete problem and is time-consuming because of computational complexity. To address these issues, a promising approach is proposed to construct the classified behavior features from different malware families. In the proposed approach, a classified behavior feature consists of a kernel object (an API call parameter) and a series of operations (an API trace). Besides, a classified behavior graph (CBG) is represented as a number by hash to reduce workload and matching time. Subsequently, multiple machine learning classifiers are used for system classification. In particular, to verify the efficiency of our approach, we perform a series of experiments with different families. The experiments on 1220 malware samples show that the true positive rate is up to 88.3% and the false positive rate keeps within 3.9% by the support vector machine (SVM).
Single-Ended Sub-threshold 9T SRAM Cell With Ground Cut-Off

As supply voltage scales down, the leakage energy starts to dominate the total power. This paper presents a single ended 9T SRAM bit cell which operates at subthreshold voltages. With design techniques like feedback cutting schemes and ground cut-off both read ‘1’ and write ‘0’ power reduced by nearly 50% in contrast to the reference 8T SRAM cell. The write ‘0’ timing is 30% lesser than the 8T cell at low supply voltages with stable read and write operations for voltages as low as 200mV. The functionality of the cell has been validated at various process corners.
FVEC feature and Machine Learning Approach for Indonesian Opinion Mining on YouTube Comments

Mining opinions from Indonesian comments from YouTube videos are required to extract interesting patterns and valuable information from consumer feedback. Opinions can consist of a combination of sentiments and topics from comments. The features considered in the mining of opinion become one of the important keys to getting a quality opinion. This paper proposes to utilize FVEC and TF-IDF features to represent the comments. In addition, two popular machine learning approaches in the field of opinion mining, i.e., SVM and CNN, are explored separately to extract opinions in Indonesian comments of YouTube videos. The experimental results show that the use of FVEC features on SVM and CNN achieves a very significant effect on the quality of opinions obtained, in term of accuracy.
Improvement of Cluster Importance Algorithm with Sentence Position for News Summarization

Text summarization is one of the ways to reduce large document dimension to obtain important information from the document. News is one of information which usually has several sub-topics from a topic. In order to get the main information from a topic as fast as possible, multi-document summarization is the solution, but sometimes it can create redundancy. In this study, we used cluster importance algorithm by considering sentence position to overcome the redundancy. Stages of cluster importance algorithm are sentence clustering, cluster ordering, and selection of sentence representative which will be explained in the subsections below. The contribution of this research was to add the position of sentence in the selection phase of representative sentence. For evaluation, we used 30 topics of Indonesian news tested by using ROUGE-1, there were 2 news topics that had different ROUGE-1 score between using cluster importance algorithm by considering sentence position and using cluster importance. However, those 2 news topics which used cluster importance by considering sentence position have a greater score of Rouge-1 than the one which only used cluster importance. The use of sentence position had an effect on the order of sentence on each topic, but there were only 2 news topics that affected the outcome of the summary.
A Sentiment-Aware Trading Volume Prediction Model for P2P Market Using LSTM

With the development of Internet lending, the use of peer-to-peer (P2P) as a new financial credit model has been increasing in China. However, this rapid development has led to a major potential risk. A few P2P enterprises operate well in the beginning but close within a short period because of the suspension of business, fraud, illegal fundraising, and blind expansion. Effective supervision of the P2P industry is an urgent problem. Trading volume reflects the operation stability of P2P platforms. Hence, predicting the volume of the P2P market is an important research topic. This paper first analyzes the trading data of a P2P platform. It is found that the sentiment of investor comments is related to the trading volume of the P2P platform. Then, we use the TextCNN model to classify the sentiment of investor comments and obtain the time series of changes in sentiment. It is verified that the time series of change in sentiment and the P2P volume index has statistical causality and a strong correlation. This paper proposes a model that uses the trend of change in investor sentiment to predict P2P trading volume. This model uses the historical time series change in investor sentiment, the P2P volume index, and WeekDay characteristics to predict future P2P trading volume. The experimental results show that the proposed model is better than a few existing baseline methods. Compared with baseline regression, the Pearson coefficient of the predicted and actual values of the proposed model is increased by 13.26%, the mean squared error is decreased by 27.62%, and the R-squared value is increased by 28.48%.
Monitoring Walking Devices For Calorie Balance In Patients With Medical Rehabilitation Needs

This article presents a measurement system intended to monitor human footsteps. The goal is to guide the user's medical rehabilitation incorrect use to prevent dangerous situations and maximize comfort. Indicators of risk in question: the imbalance of movement in motor coordination and the number of calories to be burned for medical rehabilitation patients by balancing the patient's needs. Measurements are made by placing the sensor on one of the patient's legs. The main sensor accelerometer used in this study. The measurement system comes with an application link that allows patients and therapists to monitor the activity. Calibration and experimental results are presented in this article
An Overview of Network Slicing for 5G

Besides conventional mobile broadband communication service, 5G is envisioned to support various new use cases from vertical industries. These new scenarios bring diverse and challenging requirements, such as a broader range of performance, cost, security protection, and mobility management. The one-size-fits-all design philosophy applied in existing networks is not viable any more. Slicing a single physical network into several logical networks customized to different unique requirements has emerged as a promising approach to fulfill such divergent requirements in a sustainable way. In this article, we provide a comprehensive survey of 5G network slicing. We first present the driving forces and the concept of network slicing. Then related key enabling technologies, including network function virtualization and modularization, dynamic service chaining, management and orchestration are discussed. The latest progress in 3GPP standardization and industry implementation on 5G network slicing is presented. Finally, the article identifies several important open issues and challenges to inspire further study toward a practical network slicing enabled 5G system.
Photovoltaic Power System with Ancillary Service Capability Based on Instantaneous Power

In this paper, a photovoltaic system connected to the grid with the capability of providing ancillary service of harmonic compensation based on the instantaneous power theory is studied. For this, the control strategy uses the inverter, that interfaces the PV with the grid, to operate injecting the power from the PV and additionally functioning as an active power filter. One of the advantages of the proposed strategy is that the compensation is performed without the need of detection of the harmonic components present in the current and a phase locked loop, which reduces the computational effort. Other advantages of this method are a simple algorithm, good dynamic response and a low number of sensors. For implementation the measured grid power is used as control variable. The control is based on the power balance between the grid and the inverter, and is carried out in αβ coordinated system. A perturb and observe (P&O) algorithm is used for maximum power tracking, along with a control algorithm for dc bus voltage. To validate the study, results were obtained in three different scenarios.
An Inclusive Survey on Array Antenna Design for Millimeter-Wave Communications

The enormous growth of wireless data traffic in recent years has made the millimeter-wave (mm-wave) technology as a good fit for high-speed communication systems. Extensive works are continuing from the device to system, to the radio architecture, to the network to support the communication in mm-wave frequency ranges. To support this extensive high data rate, beam forming is found to be the key-enabling technology. Hence, an array antenna design is an extremely important issue. The beam-forming arrays are chosen to achieve the desired link capacity considering the high path loss and atmospheric loss at mm-wave frequencies and also to increase the coverage of the mm-wave communication system. There are diverse design challenges of the array due to the small size, use of large numbers of antennas in close vicinity, integration with radio-frequency (RF) front ends, hardware constraints, and so on. This paper focuses on the evolution and development of mm-wave array antenna and its implementation for wireless communication and numerous other related areas. The scope of the discussion is extended on the reported works in every sphere of mm-wave antenna array design, including the selection of antenna elements, array configurations, feed mechanism, integration with front-end circuitry to understand the effects on system performance, and the underlying reason of it. The new design aspects and research directions are unfolded as a result of this discussion.
Ontologies and Bigram-based approach for Isolated Non-word Errors Correction in OCR System

In this paper, we describe a new and original approach for post-processing step in an OCR system. This approach is based on new method of spelling correction to correct automatically misspelled words resulting from a character recognition step of scanned documents by combining both ontologies and bigram code in order to create a robust system able to solve automatically the anomalies of classical approaches. The proposed approach is based on a hybrid method which is spread over two stages, first one is character recognition by using the ontological model and the second one is word recognition based on spelling correction approach based on bigram codification for detection and correction of errors. The spelling error is broadly classified in two categories namely non-word error and real-word error. In this paper, we interested only on detection and correction of non-word errors because this is the only type of errors treated by an OCR. In addition, the use of an online external resource such as WordNet proves necessary to improve its performances.