Miracle-dz/UniFAR-FTUs huggingface dataset:
Two stage market model in microgrid using cooperative game theory

For micro grids with renewable energy sources, the main goal is to optimize the energy usage in a particular area based on the prediction of energy consumption and production. However, prediction error cannot be evaded and it causes various problems to operate microgrid system. To solve these problems, we are going to propose two-stage energy operation model in a local microgrid system. In addition, by applying cooperative game theory with the Shapley-value algorithm, revenue and payment are determined in real-time period based on the actual contribution of individual prosumer. Numerical anaylsis shows that our suggested approach can reduce peak load more than 16% and reduce the total charge over 5% compared with the case of non-cooperative energy market.
Exponential Synchronization of Chaotic Lur'e Systems via Event-Triggered Control

This study deals with the synchronization problem for master-slave chaotic Lur'e systems (CLS). In order to reduce the communication burden, a novel adaptive event-triggered control (ETC)mechanism is developed. Different from the existing event-triggered schemes, the parameter of the event-trigger in this paper is adaptively adjusted. Moreover, the proposed event-triggered scheme exploits the advantage of the continuous-time measurements and ensures a minimum inter-event time. A special Lyapunov functional is employed to reduce the conservative of the synchronization criterion. Correspondingly, the desired event-triggered parameters are obtained by a linear matrix inequalities (LMIs)approach. Simulation results of Chua's circuit is provided to illustrate the effectiveness of the proposed techniques.
Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images

Noise level estimation is a challenging area of digital image processing with a variety of applications, including image enhancement, image segmentation, and feature extraction. In this paper, an adaptive estimation of additive white Gaussian noise level based on the singular value decomposition (SVD) of images is proposed. The proposed algorithm aims to improve the performance of noise level estimation in the SVD domain at low noise levels. An initial noise level estimate is used to adjust the parameters of the algorithm in order to increase the accuracy of noise level estimation. The proposed algorithm exhibits the ability to adapt the number of considered singular values and to accordingly adjust the slope of a linear function that describes how the average value of the singular value tail varies with noise levels. Although, for each image, the proposed algorithm performs the noise level estimation twice in two distinct stages, the singular value decompositions are only performed in the first stage of the algorithm. The experimental results demonstrate that the proposed algorithm improves the noise level estimation at low noise levels without a significant increase in computational complexity. At noise level $\sigma = 15$ , the improvements in the mean square level are about 39% at the expense of slightly higher additional computational time.
Plug-in Electric Vehicle Supported DVR for Fault Mitigation and Uninterrupted Power Supply in Distribution System

Now a days the electric vehicles usages is gaining popularity because of the pollution caused by existing and conventional IC engines. Electric vehicle integration to the distribution grid is increased at a faster rate because it can act as power backup to the grid. Thereby reducing the peak load and filling the valley point. This paper proposes the power backup of EV is utilized as an UPS to Software Company as well as used to support the Dynamic Voltage Restorer (DVR) to mitigate the fault occurring in the distribution system. It is known that most of the software engineers own a Electric car. The Batteries in the car is connected to the charging point (or) grid with monitoring of State of Charging (SOC) facilities in the parking area of company. When the Renewable power (solar energy) is available, the batteries will be charged to hundred percentage of SOC. Then excess power from PV will connected to load as well as grid. When the electrical power supply cutoff the car batteries will act as a battery bank of UPS and support to the critical load with condition based Allowable SOC. The total capacity of the batteries depends upon the no of cars available at a particular shift in a day. The entire system is modeled using MATLAB SIMULINK, the real time controllers are done by Raspberry pi development board and the results prove the feasibility of the proposed idea.
Artificial Tactile Sensor Structure for Surface Topography Through Sliding

Tactile sensors mimicking the human sense of touch have been studied and various technologies for the sensing of external stimuli have been suggested as well. Humans detect certain external stimuli and become aware of related sensations, such as roughness or smoothness. Among the various physical parameters, surface information is the most informative type of perception to impart these sensations onto an electromechanical system, such as an android robot or a smart phone. Here, an array sensor, which uses a sliding method for the precise perception of surface information, such as shapes and structures, is demonstrated. The suggested array sensor design with the excellent dynamic response of a piezoelectric material results in enhanced spatial resolutions with sliding motions and detects variable sliding speeds. A soft material was employed to the sensor to enhance the capability of shape distinction. Color mapping was applied to translate surface patterns into visual images. The reconfigured surface information had high accuracy compared to actual information. The demonstrated sliding speed, pattern detection, and shape detection capabilities as well as the higher spatial resolutions allow the sensor to be utilized as an artificial tactile sensor.
Budget and Deadline Aware e-Science Workflow Scheduling in Clouds

Basic science is becoming ever more computationally intensive, increasing the need for large-scale compute and storage resources, be they within a High Performance Computer cluster, or more recently within the cloud. In most cases, large scale scientific computation is represented as a workflow for scheduling and runtime provisioning. Such scheduling become an even more challenging problem on cloud systems due to the dynamic nature of the cloud, in particular, the elasticity, the pricing models (both static and dynamic), the non-homogeneous resource types, the vast array of services, and virtualization. This mapping of workflow tasks on to a set of provisioned instances is an example of the general scheduling problem and is NP-complete. In addition, we also need to ensure that certain runtime constraints are met - the most typical being the cost of the computation and the time which that computation requires to complete. In this article, we introduce a new heuristic scheduling algorithm, Budget Deadline Aware Scheduling (BDAS), that addresses eScience workflow scheduling under budget and deadline constraints in Infrastructure as a Service (IaaS) clouds. The novelty of our work is satisfying both budget and deadline constraints while introducing a tunable cost-time trade off over heterogeneous instances. In addition, we study the stability and robustness of our algorithm by performing sensitivity analysis. The results demonstrate that overall BDAS finds a viable schedule for more than 40000 test cases accomplishing both defined constraints: budget and deadline. Moreover, our algorithm achieves a 17.0-23.8 percent higher success rate when compared to state of the art algorithms.
Literature & photography interactions, 1840-1990 : a critical anthology

Literature greeted photography warmly when the daguerreotype was invented in 1839, and they have had a close relationship ever since. This remarkable book traces comprehensively for the first time the give and take between these sister arts by gathering writings about photography and photographs by and of writers from England, Europe, and the United States over the last century and a half. Included are selections by avid amateur photographers (such as Lewis Carroll, Emile Zola, August Strindberg, George Bernard Shaw, Eudora Welty, and Jack London, among numerous others), professional photographers writing about literary matters (Nadar on Balzac, Stieglitz on Stein, Man Ray on Hemingway), and collaborators explaining their work (Henry James and Coburn, Steinbeck and Capa, Capote and Avedon). Most selections are illustrated with photographs and documented by notes which help map this rich field.
Eosinophilic esophagitis in pediatric age, state of the art and review of the literature

Eosinophilic esophagitis (EoE) is a chronic immune-mediated relapsing disease caused by eosinophilic infiltration of the esophageal mucosa which is normally lacking these cells. EoE belongs to the group of the so called Eosinophilic Gastrointestinal Disorders (EGIDs). From a rare and unusual disease, EoE has become an emerging entity and in recent years its incidence and prevalence have increased all over the world, also in children. The pathogenesis is very complex and still not completely clear. Esophageal disfunction symptoms (e.g. dysphagia and food impaction) represent the typical manifestation of EoE and this condition could be difficult to recognize, more in pediatric age than in adults. Moreover, symptoms can often overlap with those of gastro-esophageal reflux disease (GERD), leading to a delayed diagnosis. EoE is often related to atopy and an allergological evaluation is recommended. Untreated EoE could provoke complications such as strictures, esophageal rings, narrowing of the esophagus. Diagnosis is confirmed by the demonstration in biopsy specimens obtained through upper endoscopy of eosinophilic inflammation (>15 for high powered field) of the esophageal mucosa and other histological features. Other tests could be useful not specifically for the diagnosis, but for the characterization of the subtype of EoE. Since EoE incidence and knowledge about physiopathology and natural history have increased, the goal of the review is to provide some helpful tools for the correct management in pediatric age together with an overview about epidemiology, pathogenesis, clinical, diagnosis and treatment of the disease. (www.actabiomedica.it)
The Controllable Eddy Current Brakes with Permanent Magnets: Basic Concept

The use of permanent magnets (PMs) in eddy current brakes (ECBs) increases efficiency because no energy is consumed by an excitation system of the brake. The main drawback of this case is no possibility to change braking torque of the ECBs in operating mode. This paper proposes a way for torque control in the ECBs with PMs. The torque control is realized by means of mechanical movements of an inner salient pole core that provides small reluctance in “On” position and very high reluctance in “Off' position. The basic concept and approximate torque calculations have been done.
EMPROF: Memory Profiling Via EM-Emanation in IoT and Hand-Held Devices

This paper presents EMPROF, a new method for profiling the performance impact of the memory subsystem without any support on, or interference with, the profiled system. Rather than rely on hardware support and/or software instrumentation on the profiled system, EMPROF analyzes the system's EM emanations to identify processor stalls that are associated with last-level cache (LLC) misses. This enables EMPROF to accurately pinpoint LLC misses in the execution timeline and to measure the cost (stall time) of each miss. Since EMPROF has zero "observer effect", so it can be used to profile applications that adjust their activity to their performance. It has no overhead on target machine, so it can be used for profiling embedded, hand-held, and IoT devices which usually have limited support for collecting, and limited resources for storing, the profiling data. Finally, since EMPROF can profile the system as-is, its profiling of boot code and other hard-to-profile software components is as accurate as its profiling of application code. To illustrate the effectiveness of EMPROF, we first validate its results using microbenchmarks with known memory behavior, and also on SPEC benchmarks running a cycle-accurate simulator that can provide detailed ground-truth data about LLC misses and processor stalls. We then demonstrate the effectiveness of EMPROF on real systems, including profiling of boot activity, show how its results can be attributed to the specific parts of the application code when that code is available, and provide additional insight on the statistics reported by EMPROF and how they are affected by the EM signal bandwidth provided to EMPROF.
Robot-based Shoe Manufacturing System

This paper presents robot-based shoe manufacturing systems for the upper and sole manufacturing process. For upper manufacturing process, we propose a robot path generation algorithm for upper roughing and cementing by industrial robot manipulators, where working trajectory on upper is acquired by using 3D digitizer. For the sole manufacturing process, we use an industrial robot manipulator with a cementing tool and a 3D scanning system. We propose a trajectory generation algorithm for sole cementing to transform 3D information of sole into 6D information of robot. We have verified the effectiveness of the proposed robot based upper and sole manufacturing systems experimentally.
Novel Skeleton-based Action Recognition Using Covariance Descriptors on Most Informative Joints

Human action recognition has attracted much attention of research community in recent years due to its large application domain such as human-robot interaction, gaming, surveillance, etc. Action recognition can be implemented in either single-modal (color, depth, skeletal) or multi-modal schemes. Our research focuses on human action recognition based on skeletal information. In literature, a Temporal Hierarchy of Covariance Descriptors on 3D Joints (Cov3DJ) was proposed to exploit information from time dimension of skeletal data. Since each joint has a certain level of engagement in an action, another approach aims at selecting joints which are most informative for action recognition. In this paper, a novel framework, named as Covariance Descriptor on Most Informative Joints (CovMIJ), is proposed to benefit from the simplicity of representation via covariance descriptor and noise immunity by using only Most Informative Joints (MIJ) for action recognition. Extensive experiments between CovMIJ, Cov3DJ and state-of-the-art Res-TCN (Temporal Convolutional Neural Networks with Residual Units) on two public datasets (MSR-Action3D and CMDFALL) show the efficiency of our proposal. On MSR-Action3D dataset, accuracy of CovMIJ achieves 93.6% while that of Cov3DJ is only 90.53%. Improvement is found on CMDFALL dataset with accuracy of 64.72% for CovMIJ compared to 61.34% for Cov3DJ. On CMDFALL dataset, CovMIJ with F1 score of 62.5% outperforms the deep learning network Res-TCN with F1 score of 39.38%.
Association and Dissociation of Optimal Noise and Input Impedance for Low-Noise Amplifiers

For magnetic resonance imaging (MRI) receive coil arrays, an ideal low-noise amplifier (LNA) is noise matched while exhibiting a high-input reflection coefficient of unity or slightly higher. For this purpose, we present a design approach allowing to manipulate the optimal noise impedance and input impedance. The method is based on noise and S-parameters, hence technology independent. As an example, the method is used to design an LNA for MRI receive coil arrays operating at 32.1 MHz. The design demonstrates the highest coil decoupling published so far of 54 dB. The measured noise figure of 0.44 dB is also better than other published designs. The measured gain is 22 dB with a 1-dB compression point of −14.5 dBm. The power consumption is 81 mW. We expect this method to enable better MRI receive coil array designs resulting in lower examination time and cost due to higher quality images.
Review and Selection Strategy for High-Accuracy Modeling of PWM Converters in DCM

Among various modeling methods for DC-DC converters introduced in the past two decades, the state-space averaging (SSA) and the circuit averaging (CA) are the most general and popular exhibiting high accuracy. However, their deduction approaches are not entirely equivalent since they incorporate different averaging processes, thus yielding different small signal transfer functions even under identical operating conditions. Some research studies claimed that the improved SSA can obtain the highest accuracy among all the modeling methods, but this paper discovers and clearly verifies that this is not the case. In this paper, we first review and study these two modeling methods for various DC-DC converters operating in the discontinuous conduction mode (DCM). We also streamline the general model-deriving processes for DC-DC converters, and test and compare the accuracy of these two methods under various conditions. Finally, we provide a selection strategy for a high-accuracy modeling method for different DC-DC converters operating in DCM and verified by simulations, which revealed necessary and beneficial for designing a more accurate DCM closed-loop controller for DC-DC converters, thus achieving better stability and transient response.
Estimation of Viewed Image Categories via CCA Using Human Brain Activity

This paper presents a method to estimate viewed image categories via canonical correlation analysis (CCA) using human brain activity measured by functional magnetic resonance imaging (fMRI). The proposed method enables estimation of image categories that a subject viewed by using only the subject’s brain activity. Specifically, the proposed method calculates the projection matrices that enable direct comparison between human brain activity and images that subjects viewed through CCA. After projecting the human brain activity and the viewed images on the same latent space, k-Nearest Neighbor (k-NN) is performed to estimate the viewed image categories from only human brain activity. Through the projection matrices, the proposed method can increase training data for k-NN even if a large number of pairs of human brain activity and images cannot be prepared. Experimental results for ten subjects show the effectiveness of the proposed method.
Towards Bilateral Upper-Limb Rehabilitation after Stroke using Kinect Game

This paper presented a game-based rehabilitation of the upper limb after stroke. We designed and developed a game for supporting stroke patients to have an exercise their arms, and the game had functions for recording their playing and showing a performance report. The performance report can infer the progress of bilateral uppper-limb rehabilitation and use for comparing among patient cases. This is because the game used a Kinect device to detect the arm movements in aspect of precision and speed.
Combining Fuzzy Set – Simple Additive Weight and Comparing With Grey Relational Analysis For Student’s Competency Assessment In The Industrial 4.0

The labor market will have many challenges between quality labor supply and demand as well as the labor structure in the context of the industrial 4th. When automation will replace people in variuous areas of the economy, students will have to adapt quickly to changes in production that otherwise would be redundant. One of the most important problems is to choose of branch for appropriate to their abilities. Generally, determining these abilities is so complex process. Not managing this process completely or managing it badly causes some hardships in student’s life and in consequence of their educational life. This paper presents an integrated approach that is combination of the fuzzy set theory and the simple additive weight (SAW) method in term of student competency assessment. Our results show the better in sensitivity, specificity, this compared with grey relational analysis (GRA) method.
Terahertz Field Localization Using Slot Dielectric Waveguide Implemented in Silicon-On-Glass (SOG) Technology

Slot dielectric waveguide implemented in Si-OnGlass (SOG) technology is presented for field localization at the THz frequency range. The waveguide characteristics are studied using simulations. Moreover, waveguide dimensions are optimized to maximize the field localization. The presented structure can be a platform for highly sensitive THz sensors and high performance THz nonlinear devices.
Rapid detection of extra‐intestinal pathogenic Escherichia coli multi‐locus sequence type 127 using a specific PCR assay

Purpose. Members of the ST127 uropathogenic E. coli (UPEC) clone have a high virulence potential and are also highly virulent in insect infection models. However, strains of this lineage are reported in relatively low numbers in many studies. ST127 strains are also usually widely susceptible to antibiotics and, consequently, their true prevalence may be under‐recognized as they will be eradicated during empirical therapy. A genuine concern is the possibility that members of this highly virulent lineage will acquire resistance, leading to a more serious threat. The aim of this study was to design and validate a PCR assay specific to ST127. Methodology. Genomic sequences obtained from various UPEC isolates from the leading clones were used in comparative genomic analyses to allow identification of highly discriminatory sequences specific to E. coli ST127. The fliC (flagellin) and a homologue of the upaG (autotransporter adhesin) gene were identified as meeting our criteria and were used to develop a multiplex PCR assay. A total of 143 UPEC isolates representing 99 different MLST clones from three locations (North West and South West England and Riyadh, Saudi Arabia) were used to validate the PCR assay. Results. The multiplex PCR readily identified all 29 E. coli ST127 isolates but, equally importantly, produced no false positives with representatives of any of the other 98 STs tested. Conclusion. We report the design and validation of a specific multiplex PCR for the rapid and reliable identification of ST127, which can be used for enhanced surveillance for this high‐risk clone.
An Attentive Neural Sequence Labeling Model for Adverse Drug Reactions Mentions Extraction

Adverse drug reactions (ADRs) are a main cause of morbidity and mortality in patients. Extracting mentions of ADRs from the health-related text has important applications in biomedical research. Existing work mainly utilizes feature-based pipeline methods or neural network models that use only word embeddings as input features. These methods either require many efforts to design task-specific features or suffer misclassification on those words, which have not been seen before. Therefore, we propose an end-to-end neural sequence labeling model that labels words in an input sequence with ADRs membership tags. In addition to word-level embeddings, we also adopt character-level embeddings and combine them via an embedding-level attention mechanism. Through such an attention mechanism, our model can dynamically determine how much information to utilize from a word- or character-level component. In addition, we use the intermediate output of the model as an auxiliary classifier and combine it with the final output of the model to improve the overall performance. We evaluate different architectures on two ADRs labeling datasets. One is an ADRs-related Twitter corpus that includes many informal vocabularies and irregular grammar, and the other is a biomedical text extracted from PubMed abstracts with many professional terms and technical descriptions. Our model achieves approximate match F1 scores of 0.844 and 0.906 for ADRs identification on the Twitter and PubMed datasets, respectively. It presents the state-of-the-art performance on both the datasets. Our system is completely end-to-end, requires no task-specific feature engineering or hand-crafted features, and thus can be generalized to a wide range of sequence labeling tasks.