Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Logic Controller for Efficient Energy Management of a PV System with HESS

With the rapid demand in the power system, the renewable energy sources (RES) have led to the higher penetration in the existing main grid. The proper control strategy for energy storage systems (ESS) have received a huge attention. The ESS is critical to maintain the correct power balance between the RESs and load demand. In this research, fuzzy logic control algorithm is implemented among other existing control algorithms to operate the battery and supercapacitor (SC) based hybrid energy storage system (HESS) in an optimal way. The battery is used to compensate the energy requirement during long duration, while the supercapacitor handles mainly the transient power fluctuations. This increases the battery life cycle due to limited stress on the battery. The proposed fuzzy inference system decides the optimal amount of charging/discharging of the HESS based on the state of charge (SOC) constraints of the battery and supercapacitor. MATLAB/Simulink is used to create and implement the microgrid test bench and fuzzy logic system (FLS). The simulation results substantiate the potential of FLS in the microgrid energy management in different modes of operation and load conditions.
Application of Fast Frequency Shift Measurement Method for INS in Navigation of Drones

Inertial navigation systems (INS) provide information about position and acceleration of a unmanned aerial vehicle (UAV) or airplanes. There are many types of INS, but most of them share the same principle of operation. We provide in this paper basic information in regard of accelerometers and gyroscopes; then, we demonstrate the effectiveness of our method based on computational experiments to measure the information from the INS. Finally we introduce a method for frequency shift detection and measurement based on pulse coincidence.
Basic Computer Vision Operators Hardware Approach Model

The development of vision sensors at a lower cost has prompted the creation of new applications that are based on the processing of images and video streams. The automatic control systems are increasingly being supported by sensors of vision and image processing to solve problems of classification, matching and tracking. Due to the nature of the control applications, vision operators are required to run on embedded systems, depending on the type of processing can be very demanding for sequential systems so it is necessary to execute these tasks directly on hardware with what is possible to improve the execution times of the operator. The use of embedded systems that include sequential regions and regions of programmable logic in the same chip and the new tools for development of high level systems HLS allows to carry functions to circuit to accelerate the execution times. The present work describes the results and evaluation architecture of synthesizing to circuits basic computer vision operators, encapsulated in an IPCore to guarantee its portability and re-usability. Proposing a procedure that allows to accelerate the tasks of more complex operations on images when executing parts of these tasks in hardware.
LiDAR Data Classification Using Spatial Transformation and CNN

Light detection and ranging (LiDAR) is a useful data acquisition technique, which is widely used in a variety of practical applications. The classification of LiDAR-derived rasterized digital surface model (LiDAR-DSM) is a fundamental technique in LiDAR data processing. In recent years, deep learning methods, especially convolutional neural networks (CNNs), have shown their capability in remote sensing areas, including LiDAR data processing. Traditional deep models empirically use a fixed neighborhood system as input to the network. Therefore, the weight and height of the input rectangle may not be optimal. In order to modify such handcrafted setting, a spatial transformation network is used here to identify optimal inputs. The transformed inputs are fed into a well-designed CNN to obtain the final classification results. Furthermore, morphological profiles are combined with spatial transformation CNN to further improve the classification accuracy. The proposed frameworks are tested on two LiDAR-DSMs (i.e., the Recology and Houston data sets). The experimental results show that the proposed models provide competitive results compared to the state-of-the-art methods. Furthermore, the proposed optimal input identification approach can also be found beneficial for other remote sensing applications.
Leveraging Hypergraph Random Walk Tag Expansion and User Social Relation for Microblog Recommendation

Recommending valuable contents for microblog users is an important way to improve users' experiences. As high quality descriptors of user semantics, tags have always been used to represent users' interests or attributes. In this work, we propose a microblog recommendation approach via hypergraph random walk tag expansion and user social relation. More specifically, microblogs are considered as hyperedges and terms are taken as hypervertexs for each user, and the weighting strategies for both hyperedges and hypervertexs are established. Random walk is performed on the weighted hypergraph to obtain a number of terms as tags for users. And then the tag similarity matrix and the user-tag matrix can be constructed based on tag probability correlations and weight of each tag. Besides, the significance of user social relation is also considered for recommendation. Moreover, an iterative updating scheme is developed to get the final user-tag matrix for computing the similarities between microblogs and users. Experimental results show that the algorithm is effective in microblog recommendation.
A 2-D Predistortion Based on Profile Inversion for Fully Digital Cartesian Transmitter

A novel 2-D digital predistortion (DPD) algorithm for fully digital Cartesian transmitter is proposed. The nonlinearity of the digital power amplifier in the digital transmitter necessitates DPD for linearization. In order to shorten the setup time of predistortion coefficients in the lookup table (LUT), a profile inversion-based method is adopted. Compared to the traditional adaptive iterative method, the profile inversion method for DPD in this paper shortens the setup time to $15~\mu \text{s}$ , which is possible for Long Term Evolution (LTE) time division duplex digital transmitter system to execute calibration within every frame. The algorithm is experimentally verified in a fully digital transmitter implemented in 65-nm general process CMOS technology with LUT size of 0.19 mm2, chip size of 2.7 mm2. The digital transmitter has achieved −35- and −32-dBc adjacent channel leakage ratio with 10-MHz LTE and 20-MHz 802.11g WiFi baseband feed in signals, respectively. The DPD improves the linearity of the transmitter by 21 dB for a 2-D constellation signal.
An Analog LO Harmonic Suppression Technique for SDR Receivers

A low-complexity analog technique to suppress the local oscillator (LO) harmonics in software-defined radios is presented. Accurate mathematical analyses show that an effective attenuation of the LO harmonics is achieved by modulating the transconductance of the low-noise transconductance amplifier (LNTA) with a raised-cosine signal. This modulation is performed through the bias network of a cascode device with a negligible increase in the LNTA noise figure. The proposed technique results in a notch at the third harmonic and at least 36 dB of attenuation at the fifth and the seventh harmonics. Experimental results in 130-nm CMOS and postlayout simulation results in 65-nm CMOS verify the proper functionality of the proposed technique and the accuracy of the proposed analyses.
Next Point-of-Interest Recommendation with Temporal and Multi-level Context Attention

With the prosperity of the location-based social networks, next Point-of-Interest (POI) recommendation has become an important service and received much attention in recent years. The next POI is dynamically determined by the mobility pattern and various contexts associated with user check-in sequence. However, exploring spatial-temporal mobility patterns and incorporating heterogeneous contextual factors for recommendation are challenging issues to be resolved. In this paper, we introduce a novel neural network model named TMCA (Temporal and Multi-level Context Attention) for next POI recommendation. Our model employs the LSTM-based encoder-decoder framework, which is able to automatically learn deep spatial-temporal representations for historical check-in activities and integrate multiple contextual factors using the embedding method in a unified manner. We further propose the temporal and multi-level context attention mechanisms to adaptively select relevant check-in activities and contextual factors for next POI preference prediction. Extensive experiments have been conducted using two real-world check-in datasets. The results verify (1) the superior performance of our proposed method in different evaluation metrics, compared with several state-of-the-art methods; and (2) the effectiveness of the temporal and multi-level context attention mechanisms on recommendation performance.
Study on Response of Knee Rehabilitation Actuator using PID Controller

In this paper, we present the results of the study of the response of the knee actuator rehabilitation when use pneumatic and Arduino UNO controller. The dynamics and transmission functions of the actuator are set, the parameters selected, and the simulation responded by Matlab Simulink software. Simulation results show fast response time and low overshoot. Designed and manufactured mechanical parts and control circuits for the actuator. Human experiments were carried out, with experimental results giving a low error between the set value and the actual value, the settings of the set angle and the set cycle were easy to set up and the LCD display parameters. Exercise results are graphically represented by the Matlab software.
Contact Force Analysis of RV Reducer

RV reducer is a commonly used transmission device in the industrial robot system. It incorporates two stage drive; the first stage uses involute planetary gear and the second stage uses cycloidal pin wheel. Due to the high working intensity and long working time of the robots, more and more attention has been paid to the stress analysis of all parts of RV reducer. Most studies just focus on the stress between cycloidal wheels and pins without considering the influence of the pin housing. In this paper, the FEM model contains a pin housing, forty pins, and two cycloidal wheels. The results show the area of most seriously stress concentration is the contact part of the cycloidal wheels with the eccentric shafts, and the contact force distribution of the pins is periodic, with the largest force appearing in two symmetrical positions.
Design and Simulation of an On-Board Integrated Charger using Cell Bypass Balancing Circuit for Electric Vehicles

Ahstract- This paper presents the design of an onboard integrated charger using cascaded converters and cell bypass balancing circuit for lithium-ion battery packs. Lithium-ion based serially connected battery cells experience imbalance in operation while charging and discharging. This limits the effective capacity utilization of the battery pack. Among all the active balancing circuits, cell/module bypass reconfiguration is considered to be quite efficient and fast, but introduces surge currents and reduces the system efficiency. This may also damage the components connected and reduce the battery life. In this paper we propose a modified circuit topology to overcome this problem. Simulations are carried out for 14 series connected cells each with nominal voltage of 3.7 V and capacity of 40Ahr. The results indicate considerable scope of application of the proposed approach for on-board electric vehicle applications. Index Terms-Surge current, SOC, Cell balancing.
Implementation of Deep Neural Networks for Industry Applications

Deep learning become a popular trend in current research and applications. Deep neural networks are important part of this trend. The paper shows the effect of neural network architecture on its power and capacity for solving complex, nonlinear problems. The problem has been analyzed using trigonometric, polynomial and digital approaches. Presented analysis show that the network capacity increases linearly with the network width and increases exponentially with the network depth.
Fault-Tolerant PMSG Direct-Drive Wind Turbines, using Vector Control Techniques with Reduced DC-Link Ratings

This paper presents a fault-tolerant permanent magnet synchronous generator (PMSG) drive for wind turbine systems, with the ability to handle power semiconductor open-circuit (O-C) faults in the full-scale back-to-back converter. The fault-tolerant topology consists of a five-leg converter, with a shared leg connected to a generator phase and to its corresponding grid phase. The main contribution of this paper consists on the development of an alternative vector-control-based pulse-width-modulation (PWM) control scheme for both machine-side converter (MSC) and grid-side converter (GSC), allowing to reduce the dc-bus voltage ratings. Moreover, a reliable fault diagnosis algorithm is also integrated, providing the information required to instantaneously trigger fault-tolerant remedial strategies, without any additional sensors. Simulation and experimental results are presented to validate the effectiveness of the proposed fault-tolerant PMSG drive.
Moving Horizon Estimator of PMSM N Onlinearities

Moving Horizon Estimators (MHE)solve an optimization problem to estimate unknown states or parameters based on a sequence of measurements containing disturbances and noise in nonlinear systems subject to input and state constraints. This research applies MHE to estimating the nonlinear behavior of interior-mount permanent magnet synchronous machines (IPMSM). The MHE estimates a disturbance term that reppresents the speed-dependent nonlinear terms of the machine model and can be interpreted as extended back-EMF. The formulation is based on a cost function that matches the measurements to the model formulation and an explicit regularization penalty is added. The term is interpreted as a gain that balances estimation accuracy with noise rejection. We demonstrate that our formulation can be solved in realtime and is effective in estimating the disturbance term on an experimental test bench both in terms of noise rejection and estimation accuracy.
Tablets from the Sippar library III. Two Royal Counterfeits

This article continues the publication of tablets from the library of the temple of Šamaš at Sippar begun in volume 52 of this journal. Presented here are the library's copies of two texts which have in common the fact that they are not what they purport to be: Maništūšu's cruciform monument, and a literary letter of Samsu-iluna. Both texts are clearly fictitious compositions which postdate their supposed royal authors by a long time. Their purpose was apparently to supply evidence of historical precedent. The invented evidence which these documents present as fact would be intended to substantiate or advance the claims of those who wrote them. While both tablets are duplicates of known texts, each adds to a greater or lesser degree to our knowledge of these texts, not least by filling lacunae and by confirming or rebutting the restorations of previous editors. The identification of a tablet from niche 8 B as a duplicate of the last column of a large Neo-Babylonian exercise tablet from Ur allows this text to be properly identified for the first time. It is one of several royal letters in Akkadian, some apparently genuine, others certainly bogus, which entered the scribal tradition and survive in late copies. This example purports to be from Samsu-iluna, the son and successor of Hammurapi of Babylon, to Enlil-nādin-šumi, a man whose several titles identify him as a very senior figure, and apparently a royal prince. As is the case with the Cruciform Inscription of Maništūšu, anachronisms in the text mark the composition out as later than it purports to be. Following the formulae which name the letter's addressee and sender the text itself opens with the phrase umma ana narê, an expression that also appears in a probably genuine letter of Nebuchadnezzar I.
An Approach to Dynamic Program Slicing Based on Modular Monadic Semantics

This paper presents a new dynamic slicing method,the modular monadic approach,which is based on modular monadic semantics.With the use of monad transformers,each representing a single notion of computation,the computation of program slicing can be abstracted as an entity that is independent of the language being analyzed,dynamic-slice monad transformer.Its definition and correctness proofs are provided in this paper.This paper also gives and illustrates dynamic slicing algorithm based on modular monadic semantics.The algorithm allows dynamic slices to be computed directly on abstract syntax,without recording an execution history or explicitly constructing intermediate structures,such as dependence graphs,in the corresponding monadic slicers.It is showed to be an appropriate approach to program slicing with excellent flexibility and modularity properties.
A Gaussian Model for Feature Selection in Protein Fold Recognition

Protein fold recognition is an important task to discover new biological functions of proteins. In this context, machine learning techniques have been used to protein fold recognition, stating this task as a classification problem. However, in many cases, the similarity of patterns to protein fold recognition becomes this process in a complex task, limiting the performance of the machine learning techniques. In this paper, we propose a feature selection method to support machine learning methods for protein fold recognition, using gaussian distributions in the process of features analysis. We cluster features by gaussian distributions. These clusters give information to reduce the dimensionality of the features. After that, we use baselines classifiers to protein fold recognition, using a well-known dataset for this task. The results suggest that the clustering and reduction of dimensionality of features using gaussian distribution can help to improve the accuracy of machine learning techniques on this task.
Combined PDM with Frequency-Temperature Profile Adaptation Control for Induction Metal Hardening

During metal hardening applications, the load parameter variation is inherently encountered. At temperature close to hardening, these parameters specifically the magnetic permeability and electrical resistivity vary. Therefore, a suitable control method is often required to allow the induction heating system operating under optimum conditions. This paper proposes on new approach dealing with the issue of implementing a frequency adaptation control without using the conventional phase-locked loop (PLL). This technique seems to outweigh the PLL in many respects particularly in induction metal hardening. This approach takes into account the equivalent electrical quantities with respect to the temperature of the workpiece during the treatment process. This strategy is combined with pulse density modulation (PDM) to conduct power control improvement which account for material resistivity increase. Applied to a full bridge series resonant inverter, the proposed method of frequency tracking is assessed through power evaluation through simulation and experimental tests during the operating frequency range of hardening process. The simulation model is performed using Matlab/Simulink. The obtained results show better performances with efficiency higher than 90%. The algorithm is implemented using an experimental setup for evaluation.
Thermo-mechanical reliability of sintered all-Cu electrical fine pitch interconnects under isothermal fatigue testing benchmarked against soldered and TLP-bonded SnAg3.5 joints

Cu sintering is one of the emerging technologies in the field of micro- and power electronics where operating temperatures higher than 150°C are required. At these temperatures, solder joints reach their limits due to high homologous temperatures. Hence, Cu sintered joints can serve as a substitute for these soft solder joints, being advantageous also with respect to thermo-dynamic stability, fatigue resistance, electrical conductance and cost. This paper addresses failure analysis of sintered (neck-based) All-Cu electrical interconnects (NEI) along with soldered SnAg3.5 and transient liquid phase bonded (TLPB) specimens which form an SnCu intermetallic (IMC) and are used in a homogenous Si-Si flip chip assembly for fine pitch interconnects. The SnAg3.5 solder serves as a benchmark for the NEIs and the TLPB joints. All the flip chip specimens were free of underfill material. The test samples were assembled on spring steel substrates using a Silicone-based adhesive for a low stress bond and then put under isothermal accelerated fatigue tests using 4-point bending at low-homologous temperatures (R.T.). The reliability investigation involves monitoring of electrical resistance as a failure indicator for interconnect fatigue. A failure criterion at 20% increase in resistance is defined to establish a correlation between the experimental failure times and resistance. The fatigue behaviour of the joints was also studied using Finite Elements analysis (FEA). The focus of the modelling was towards the behaviour of the critical joint. Cross-sections were prepared and analysed using optical microscopy and SEM to investigate the failure mode and mechanism.
FACE RECOGNITION from NON-FRONTAL IMAGES Using DEEP NEURAL NETWORK

Person recognition from pose-variant face images is a well addressed, yet challenging problem, especially for surveillance in a crowded place where the pose variation is large in the test set compare to the training set. Conventional feature extraction based face recognition techniques are not efficient enough to solve the problem. In this paper, a noble mechanism has been proposed to learn the training set consisting of few pose variant images and many frontal images of different persons using deep learning algorithms. At first, autoencoders are trained to build the templates for representing the pose variant training images. The left (45°) and right (+45°) templates cover all pose variations of test images from 90° to +90°. In the next step the convolution neural network (CNN) architectures are used in supervised mode for transforming the templates into person specific frontal images present in the training set. Left and right cluster of trained CNNs are obtained with respect to left and right templates. In the testing phase, the head-pose of the test image is estimated using collaborative representation based classifier (CRC) in order to select the appropriate cluster of CNN architectures for generation of the frontal image. The CNN architecture which provides the best match frontal image with the training set is recognized as the specific person. The matching score is measured using correlation coefficient and Frobenius norm. For a frontal test image if the matching score is below than the predefined threshold then the proposed method does not recognize the image. However, the training set has been updated by the unrecognized frontal test images for future recognition. The accuracy of the proposed method is around 99% when tested on CMU PIE database which is much higher in comparison to the existing face-recognition methods.