Miracle-dz/UniFAR-FTUs huggingface dataset:
Incorporation of Expert Knowledge for Learning Robotic Assembly Tasks

Autonomous learning of robotic manipulation tasks is a desirable proposition for the future of industrial manufacturing to increaseflexibility and reduce manual engineering effort. In particular assembly tasks that require contact-rich manipulation skills are challenging to accomplish with classical robotic control methods. The Reinforcement Learning (RL) framework provides a possibility to learn complex behaviors based on interaction with the environment. Although a lot of research has been done robotic assembly tasks remain a challenge for pure learning-based systems. In this paper we give an overview on grey-box learning approaches that integrate prior knowledge and learning based methods. Different dimensions of knowledge injection are identified, and knowledge representations are described. These representations are discussed in the context of industrial assembly processes to answer the question: how can process experts model their knowledge to boost RL approaches in the context of industrial assembly?
An Improved MPPT Technique Under Partial Shading Condition Using Simple P&O Algorithm

Partial shading (PS) affects the productivity of power generation in the photovoltaic system. During PS condition, PV panel shows multiple peaks in power-voltage (P- V) and current-voltage (I-V) characteristics, making conventional Maximum Power Point Tracking (MPPT) algorithms like Perturb and observe (P&O) method, Incremental conductance (I&C) method etc., fails to extract maximum power. Optimization based techniques like Particle swarm optimization (PSO), Cuckoo Search (CS) and Maximum power point scanning (MPPS) methods etc., can perform an active monitoring of maximum power under PS condition. Since, optimization-based systems are complex and time-consuming, in this paper an improved MPPT is proposed for tracking maximum power under PS condition. The Proposed MPPT method does not require any computational burden, making it more compatible for real time with good tracking efficiency. The proposed MPPT is verified for 3S configuration with various shading patterns showed satisfactory results with excellent dynamic behaviour and very high tracking efficiency.
Modelling of sweet gas flaring and the resultant gaseous emissions with their emission factors

Abstract Data from literature and a stoichiometric material balance model were employed to estimate associated emissions with flaring of sweet gas in Nigerian oil and gas companies. Emission factors were obtained using AP 42 formula. Results showed that thousands of tonnes, ranging from 6500 to 22,000 tonnes of natural gas were flared from 1997 to 2016. At flaring stack efficiencies of 97% and 98%, the associated emissions are: CH4, C2H6, C3H8, iC4H10, nC4H10, iC5H12, nC5H12, C6H14, C7H16, C8H18, C9H20, CO2, and N2 from unburnt natural gas and in addition to CO2, CO, N2, NO, NO2, H2O and H2 from incomplete combustion. At both flaring stack efficiencies, the amount of emissions from unburnt condition ranged from1,608 tonnes N2 to 9,146 tonnes CO2 all higher than any emission standards in the world, while the amounts of emissions from incomplete combustion ranged from 467,964 tonnes for CO2 the lowest to 2,476,011 tonnes for N2 the highest all higher than any emission standards in the globe. Emission factors of emissions from unburnt natural gas ranged from 0.000090 tonne/tonne for C10H22 to 0.026235 tonne/tonne for CH4 while those of the emissions from incomplete combustion ranged from 0.10285 tonne/tonne for H2 to 1.13137 for CO2 tonne/tonne. It was observed that thousands of tonnes of emissions are released into the atmosphere during flaring of sweet natural gas either at complete or incomplete combustion. It is recommended that flaring of natural gas should be reduced to a minimal level to safeguard the environment.
Antenna Design Using Modern Additive Manufacturing Technology: A Review

Three-dimensional (3D) printing technology is an area of research that has received great attention in the last decade and it is pointed out by many as the future of manufacturing. 3D printing can be described as an additive process that creates a physical object from a digital model, depositing materials layer by layer. The ability to quickly produce complex structures at a reduced cost and without wasting materials is the main reason why this additive manufacturing technique is increasingly being used instead of conventional manufacturing processes. 3D printing has been applied in several scenarios, including automotive, maritime and construction industry, healthcare, as well as in the antenna research field. This paper reviews the current state-of-the-art of 3D printed antennas. Firstly, an overview of 3D printing technology is presented and then a vast number of 3D printed antennas, categorized by their construction process, are described. Finally, the main advantages and some of the limitations of using 3D printing technology in the construction of Radio Frequency (RF) structures are presented.
Effect of soy consumption on liver enzymes, lipid profile, anthropometry indices, and oxidative stress in patients with non-alcoholic fatty liver disease: A systematic review and meta-analysis of clinical trials

The present systematic review and meta-analysis was conducted to investigate the effects of soy intake on liver enzymes, lipid profile, anthropometry indices, and oxidative stress in non-alcoholic fatty liver disease (NAFLD). A systematic search was undertaken in PubMed, Embase, Scopus, Web of Science, and Cochrane Library covering up to 10 January 2020. A fixed-effect or random-effects models were applied to pool mean difference (MD) and its 95 % confidence intervals (CI). Four clinical trials comprising 234 participants were included in the meta-analysis. Compared to the controls, alanine aminotransferase (ALT) levels (MD=-7.53, 95% CI=[-11.98, -3.08], P=0.001, I2=0.0 %), body weight (MD=-0.77, 95 % CI=[-1.38, -0.16], P=0.01, I2=36.9%), and the concentration of serum Malondialdehyde (MDA) (MD=-0.75, 95% CI=[-1.29, -0.21], P=0.007, I2=63.6%) were significantly changed following soy intake. Lipid profile was not significantly affected by soy intake. Moreover, no evidence of a significant publication bias was found. The present study suggests lowering effects for soy intake on ALT levels, body weight, and MDA in nonalcoholic liver patients. Therefore, further large-scale and well-designed clinical trials are needed to find conclusive findings.
Homogenization of Current Distribution in Parallel Connection of Interleaved Winding Layers of High-Frequency Transformers by Optimizing Distance between Winding Layers

The windings of high-frequency high-current transformers are required to reduce the proximity effect loss. Therefore, the Litz wire in parallel connection of interleaved winding layers is usually used as the winding of transformers. However, this can cause unbalanced AC current distribution, hindering effective reduction of the copper loss. This paper solves this problem by optimizing the distance between the winding layers based on the analytical principle called the extremum co-energy principle. The transformers used in the experiment to verify this method consists of three parallel-connected primary winding and two parallel-connected secondary winding, PQ core, thin polypropylene sheets for adjusting the distance between the winding layers. As a result, the copper loss can be recused by homogenizing the AC current distribution, and the effectiveness of this method has been clarified by experiments.
The Surgical Navigation System with Optical Position Determination Technology and Sources of Errors

Several causes of spatial errors in surgical navigation system have been investigated for several systems. These include identification error of the external features used for registration, geometrical distortion in the preoperative images, and tracking error of the surgical instruments. Another potentially important source of error is the patient head position detection retrospectively and concurrent with surgery. The visualization of target zone is carried out according to reference landmark points inside the center of the brain as well as an automatic algorithm for contour recognition (based on 3D segmentation and computer morphometry) was applied to edge the contour. Moreover, the optical navigation system was used to assess the accuracy of determination the position of the surgical instrument inside patient head construction using the “internal map” describing the anatomy of the brain. The major sources of errors occurring in the unit of position detection of the optical navigation system were considered. Furthermore, parts of the errors that could appear were simulated while operating the surgical navigation system. Eventually the research included the possibility of using different filters depending on natural source of noise that was also simulated and analyzed.
Inclination of Tech Stocks using Time Series Analysis and Prophecy of Returns using Recurrent Neural Network

The world of finance is undoubtedly volatile-the stock prices keep changing as each minute passes. This volatility can be understood and solved with modern technology i.e., the application of machine learning methods in amalgamation with financial analysis. First, a complete trend analysis is performed on the Closing Stock values of various MNC companies along with the total shares traded by them. Here, the issue of stock trading decision prediction is enunciated using Recursive Neural Networks (RNN) which incorporates Long-Short Term Memory (LSTM) algorithm. The keras library of python with the inclusion of layers is used. The usage of LSTM has proven to be one of the best methods of prediction as shown. The similarity between actual and predicted stocks is high and thus throws light on the success of the model. This can be a promising strategy for stock trading. The results thus obtained are depicted with the help of a few graphs that showcase the accuracy of the algorithm.
SIX SIGMA APPROACH TO IMPROVE SERVICE QUALITY AND A PRACTICE STUDY IN HOSPITALITY SECTOR

Six Sigma is one of the latest quality improvement initiatives that have gained popularity and recognition in many sectors worldwide. It is expected that Six Sigma approach will also be adopted in the tourism sector, ensuring high service quality. This study aimed at applying a Six Sigma model to improve the service process of a five-star hotel located in the city centre. A total of 357 samples with sufficient data quality were taken from 386 questionnaire forms presented to the hotel customers. The data obtained were analyzed with Six Sigma methods and tools. The five steps, which are the expansion of DMAIC methodology, consisting of define, measure, analyze, improve and control, were followed from among these methods. The current situation of the hotel was defined in the first phase; sigma level about the service quality was calculated in measuring phase; the measurements were interpreted, and proposed solutions for the problems regarding improvement activities were presented in the analyzing phase; and lastly, controls to be carried out to ensure the continuity of these improvements were explained. One of the aims of the study is to examine with the Servqual model, whether there is a difference between the quality of service expected and perceived by customers who purchase services from an accommodation facility. A questionnaire based on the Servqual model, which consists of tangibles, reliability, empathy, responsiveness and assurance dimensions, was designed and used as a data collection tool. According to the results obtained, it was found that the expectations of the customers regarding hotel services in general, could not be met. The sigma score of the hotel was calculated as 2,01. In the study, tangibles and reliability dimensions were found to be the lowest level of satisfaction among hotel customers.
A Bayesian Probabilistic TOA/AOA Hybrid Localization Algorithm in Multipath Environments

Cellular network-based positioning is a widely studied technology. It can be used to solve positioning problems when satellite signals cannot be received. TOA / AOA hybrid positioning is a reliable method in wireless positioning. However, due to multipath and NLOS propagation problems, the performance of the hybrid positioning algorithm is severely affected. Aiming at the above issues, we propose a TOA / AOA hybrid positioning algorithm based on the maximum posterior probability. By modeling the measurement error as a mixture Gaussian model, the algorithm accurately estimates the channel bias while calculating the user's position. Further, using the estimated channel information, the algorithm recalculates the user position to improve positioning accuracy. Compared with other positioning algorithms based on the Bayesian principle, the hybrid positioning method uses full of signal characteristics to estimate the transmitter position, so it can effectively reduce the impact of measurement errors. Simulation results show that the algorithm proposed in this paper effectively improves positioning accuracy.
3D Printing of Millimetre Wave and Low-Terahertz Frequency Selective Surfaces Using Aerosol Jet Technology

An investigation of the use of Aerosol jet 3D Printing of frequency selective surface for millimetre and low-THz applications is presented in this article. This 3D printing technique allows the fabrication of intricate details of the designs with high resolution. Band-stop and band-pass FSS are designed and tested. The band stop FSS consisted of a Square loop array that operated in the 26–28 GHz sub-millimetre band. This design is printed on glass substrate and can be used for deployment in windows. The bandpass FSS arrays consisted of simple slot elements arranged in a square lattice and operated at 125 GHz and 280 GHz. The slot arrays were printed on Kapton. Surface profiles demonstrated the uniformity and precision of this printing technique. Simulated and measured results compared well and offered good performances at both the millimetre wave and low-THz bands. The designs find applications in 5G and imminent 6G communications. This printing technique also provides environmentally friendly, rapid, and sustainable alternative for development of highly customised FSS which can be deployed to improve communications in buildings and in future Terahertz applications.
Typification and nomenclatural notes on twenty eight names of juno irises (Iridaceae) from Central and South Asia

Juno is one of the most disputable groups of the genus Iris s.l. (Iridaceae) and the largest part of its taxa are in need of typification. In the present paper, the nomenclatural types for 28 names (21 species, 7 varieties) of juno irises are designated. Most of the original material was collected from the Central Asian republics of the former Soviet Union (especially Uzbekistan and Tajikistan), and the other part was from South Asia (Afghanistan and Pakistan). Lectotypes are designated for 27 names, or a typification remark is provided. Among them, the previous first-step lectotypification of 2 names has been finalized, and 2 epitypes are designated for lectotypified names. A neotype is also designated for the name Iris tubergeniana .
Inductor Size Evaluation of an Electromagnetic Interference Filter for a Two-Level Power Factor Correction Rectifier Using Different Modulation Techniques

Wide band-gap semiconductors exhibit superior performance compared to their silicon counterparts, which has enabled converters to work at higher switching frequencies. However, a higher switching frequency means larger harmonics in the electromagnetic interference (EMI) range which may lead to the need for a larger EMI filter. This paper has essentially two main purposes: to find the impact of switching frequency (swept from 40 kHz to 300 kHz) on the size of EMI filter (especially the inductor part of it) and to evaluate the size using different modulation techniques. This paper shows that the effect of increasing the switching frequency on the size of EMI filter is not linear. The differential mode filter part of EMI filter results differently against the common mode filter part of it when the switching frequency and modulation technique changes. Therefore, depending on the application a suitable modulation and frequency can be picked to result an optimum filter. To support the method of the filter design, a filter is designed and built for a 5kW two-level power factor correction rectifier and the design is supported with EMI measurement on the unit.
Penciclovir-Resistance Mutations in the Herpes Simplex Virus DNA Polymerase Gene

Penciclovir is the active form of the orally available prodrug famciclovir, which is entering clinical use for herpesvirus infections. Like aciclovir, penciclovir is an acyclic guanosine analogue that is phosphorylated by viral thymidine kinase and whose triphosphate can inhibit viral DNA polymerase. We tested several well-characterized herpes simplex virus mutants with aciclovir-resistance mutations in the viral DNA polymerase gene for altered sensitivity to penciclovir. The mutants varied in their susceptibilities to penciclovir with one exhibiting 2-fold hypersensitivity, one marginal resistance and three about 3-fold resistance. Marker rescue and DNA sequencing analyses mapped the penciclovir-resistance mutation of one mutant, AraA r7, to a single base change that alters a glycine to a cysteine at residue 841 within conserved region III of α-like DNA polymerases. The results have implications for the mechanism of selective action of penciclovir, for the potential for development of resistance in the clinic, and for the substrate recognition properties of herpes simplex virus DNA polymerase.
A Deep Neural Network based disease detection scheme for Citrus fruits

One of the most significant factors is the quality evaluation of agricultural products in supporting their marketability and controlling waste management. To classify the fruits into healthy and defected class, deep learning algorithms have been implemented to perform citrus disease detection. This study aims to use the dense CNN algorithm to detect and provide an effective method for detecting the apparent defects of citrus fruit. Citrus fruit images are collected and put in two classes of good and damaged ones, to recognize and categorize the image dataset. Firstly, a dense CNN model was used without doing preprocessing and data augmentation on 150 images and achieved an accuracy of 67 percent but the proposed model has used data augmentation and pre-processing to enhance the CNN performance and have used 1200 images. Further, the proposed model is compared with the dense model where data augmentation and pre-processing techniques have not been used. The overall accuracy of the proposed model is 89.1%. The results show that techniques of data augmentation and preprocessing have delivered promising insights to estimate citrus fruit’s damages.
Mag-Barcode: Magnet Barcode Scanning for Indoor Pedestrian Tracking

In typical scenarios for indoor localization and tracking, it is essential to accurately track the pedestrians when they are crossing the connections of different spaces. In this paper, we propose a magnet barcode scanning-based solution for indoor pedestrian tracking. We assemble multiple magnet bars into magnet arrays as a unique magnet barcode, and deploy different magnet barcodes at different connections to label them. We embed an inertial measurement unit (IMU) into the pedes-trian‘s shoes. When the pedestrian crosses these connections, the magnetometer from the IMU scans the magnet barcode and recognize its corresponding ID. In this way, indoor pedestrian tracking can be regarded as a process of continuously scanning different magnet barcodes. By performing correlation analysis on these barcodes, the trace of pedestrian can be effectively depicted in the indoor map. To build a unique magnet barcode based on the magnet bar arrays, we provide an optimized structure for building the magnet barcode. To tackle the diversities of the pedestrian's gait traces in identifying the magnet barcode, we provide a generalized model based on the space axis for magnet barcode identification. As far as we know, this is the first work to use the magnet bar array to construct the magnet barcode for indoor pedestrian tracking. The real experiment results show that our system can achieve an average accuracy of 88.9% in identifying the magnet barcodes and an average accuracy of 93.1 % for indoor pedestrian tracking.
Text Mining-based Identification Model for Urban Rail Transit System Infrastructure Fault Analysis

A text mining based identification model for urban rail transit system infrastructure fault analysis is proposed for the Fault analysis and provide basis for fault prevention, control and diagnosis. The proposed model uses the Naive Bayes algorithm is used to segment the long text data which has been preprocessed, and identifies the cause of infrastructure fault. The analysis results are displayed clearly in the form of word clouds. The proposed model was experimentally verified by using 15 240 records of AFC equipment faults (defects) records from a certain urban rail transit group. in 2018.
Collision free motion planning for robots by capturing the environment

More and more industrial robots are being used in SMEs. Due to the lack of CAD models, they are rarely programmed using offline programming methods. With the methods presented in this paper, missing 3D maps of the environment can be flexibly created and even inexperienced operators can be enabled to program complex, collision-free motion sequences. In addition, already programmed sequences can be optimized by the downstream motion planning. The presented method also allows the omitting of intermediate points in complex sequences, which significantly reduces commissioning time and complexity.
Controller Cluster Optimization and Simulation of Satellite Network in Naval Battle Circumstance

In the naval battle circumstance, the satellite network consists of multi-controllers to form a cluster architecture. Frequent switching of the satellite network topology for ensuring consistency between the controller clusters gives rise to the excessive overhead of communication. To reduce the overhead, a new sparse method, SNS-TS (Sparse Network Snapshots based on Top-down and Sum-graphs), is proposed. Simulation experiments verify that SNS-TS method is more accurate than the typical method of GraphScope. SNS-TS method can effectively decrease the response time of satellite communication services, routing convergence time, transmission delay, packet loss rate and so on.
An adaptive robotic grasping with a 2-finger gripper based on deep learning network

In this paper, an adaptive and versatile robotic grasping system is presented that is able to manipulate manufactured objects in production factories with a 2-finger gripper. A pick and place scenario based on deep learning framework is implemented and is achieved based on the following main steps: detection of the manufactured objects in the global scene observed by a first RGB-D camera using a first deep learning network, estimation of the object pose using 2D bounding box coordinates and depth information, motion of the arm above the object in an approach pose using Kinematics and Dynamics Library (KDL), recognition of the object’s face using a second deep learning network and information coming from a second RGB-D camera setup on the arm wrist, decision on the optimal grasping mode (opening or closing the fingers), execution of the grasping action. The developed system is validated practically by experiments in real world settings using a mobile manipulator platform consisting of 6 DoF robot arm with a 2-finger gripper setup on a mobile robot equipped by two RGB-D cameras.