Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient forecasting model technique for river stream flow in tropical environment

ABSTRACT Monthly stream flow forecasting can provide crucial information on hydrological applications including water resource management and flood mitigation systems. In this statistical study, time series and artificial intelligence methods were evaluated according to implementation of each time-series technique to find an effective tool for stream flow prediction in flood forecasting. This paper explores the application of water level, rainfall data and input time series into three different models; linear regression (LR), auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN). The performances of the models were compared based on the maximum coefficient of determination (R2) and minimum root means square error (RMSE). Based on the results the ANN model presents the most accurate measurement, with the R2 value of 0.868 and 18% RMSE. The present study suggests that ANN is the best model due to its ability to recognise times series patterns and to understand non-linear relationships.
A Software Platform for Noise Reduction in Sound Sensor Equipped Drones

A flying drone provides multiple video capturing options for filming videos. Since a noise is generated by the propellers and rotors of a drone, the quality of sound in the recorded video is quite low. Large drones are used singly in missions while small ones are used in formations or swarms. The small drones are proving to be useful in civilian applications. These are effective with multiple drones. Consideration of small drones for the applications, such as group flight, entertainment, and signal emission lead to deployment of networked drones. To solve the noise problem and develop group display applications, a software platform for these issues in networked drones is proposed. Noise reduction combines the active noise control and spectral subtraction. In addition, drones form group displays for an entertainment and displaying application. We develop a small-scale testbed to measure the service quality of proposed platform. The experimental results show that the proposed noise reduction produces a speech signal with up to 67.5% similarity to the original signal. It outperforms active noise control and spectral subtraction with the similarities of 53.1% and 39.6%, respectively. We see that drone formation can form a group display to show messages effectively.
Textile teacher students’ collaborative design processes in a design studio setting

The purpose of the present article was to analyse textile teacher students’ collaborative designing of a functional 3D textile puzzle for visually impaired children. The data collection took place across three sessions of collaborative designing: defining design constraints, visualisation and building a mock-up. Twelve first-year university-level students training to become textile teachers participated in the study, and they worked in four teams with three students in each team. We were interested in the nature of their design process and how kernel design ideas were created and transformed during the collaborative design process. The analysis focused on the teams’ design activities and content logs of the video data. The video-recorded data were segmented in 2-minute intervals using INTERACT video analysis program. Each segments were classified according to 7 observable design activities. This provided a macro level analysis for all design activities during each design session and data for further analysis of different orientations of teams. The results indicated that all teams engaged in progressive design processes and were able to create unique and practical design solutions. The design processes turned out to be a problem driven in nature for two teams whereas the others engaged in a solution-driven design process.
Data-Driven Modeling Based on Two-Stream ${\rm{\lambda }}$ Gated Recurrent Unit Network With Soft Sensor Application

Data-driven soft sensors, estimating the pivotal quality variables, have been widely employed in industrial process. This paper proposes a novel soft sensor modeling approach based on a two-stream <inline-formula><tex-math notation="LaTeX">${\rm{\lambda }}$</tex-math></inline-formula> gated recurrent unit (<inline-formula><tex-math notation="LaTeX">$TS - {\rm{\lambda }}$</tex-math></inline-formula> GRU) network. First, factors <inline-formula><tex-math notation="LaTeX">${{\rm{\lambda }}_1}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${{\rm{\lambda }}_2}$</tex-math></inline-formula> are implemented to alter the linear constraint existing in the original GRU unit, enriching the information passing through. Then, a two-stream network structure is designed, equipped with some advanced network parameter adjustment techniques, such as batch normalization and dropout rate, to learn diverse features of the various process data. Finally, the learned features from the two streams are fused and a supervised learning regression layer is employed to decrease the error between the output and label. The application in melt viscosity index estimation for a real polymerization industrial process has demonstrated that the proposed <inline-formula><tex-math notation="LaTeX">$TS - {\rm{\lambda }}$</tex-math></inline-formula> GRUs algorithm for soft sensor modeling is more accurate and promising than other existing methods.
Detection and Tracking of Moving Obstacles (DATMO): A Review

Summary Working with mobile robots, prior to execute the local planning stage, they must know the environment where they are moving. For that reason the perception and mapping stages must be performed previously. This paper presents a survey in the state of the art in detection and tracking of moving obstacles (DATMO). The aim of what follows is to provide an overview of the most remarkable methods at each field specially in indoor environments where dynamic obstacles can be potentially more dangerous and unpredictable. We are going to show related DATMO methods organized in three approaches: model-free, model-based and grid-based. In addition, a comparison between them and conclusions will be presented.
Strength reduction of sewing threads during high speed sewing in an industrial lockstitch machine

Reports on investigations into the mechanism of sewing thread strength reduction. Results indicate that structural damage, namely, structural openness and pull‐out of fibre ends from the surface are the major cause of strength reduction and that the fibre strength reduction is found to be only marginal compared to the thread strength reduction. Cotton threads exhibit higher strength loss owing to their poor abrasion resistance. Comparatively shorter fibres in cotton threads are also found to be responsible for the higher strength reduction.
Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives

Increasing demand to strengthen energy security has increased the importance of natural gas sweetening and biogas upgrading processes. Membrane-based separation of carbon dioxide (CO2) and methane (CH4) is a relatively newer technology, which offers several competitive advantages, such as higher energy-efficiency and cost-effectiveness, over conventional technologies. Recently, the use of graphene-based materials to elevate the performance of polymeric membranes have attracted immense attention. Herein, we do not seek to provide the reader with a comprehensive review of this topic but rather highlight the key challenges and our perspectives going ahead. We approach the topic by evaluating three mainstream membrane designs using graphene-based materials: (1) nanoporous single-layer graphene, (2) few- to multi-layered graphene-based stacked laminates, and (3) mixed-matrix membranes. At present, each design faces different challenges, including low scalability, high production cost, limited performance enhancement, and the lack of robust techno-economic review and systematic membrane design optimization. To help address these challenges, we have mapped out a technology landscape of the current graphene-based membrane research based on the separation performance enhancement, commercial viability, and production cost. Accordingly, we contend that future efforts devoted to advancing graphene-based membranes must be matched by progress in these strategic areas so as to realize practical and commercially relevant graphene-based membranes for CO2/CH4 separation and beyond.
Performance Test of MicroAeth® AE51 at Concentrations Lower than 2 μg/m3 in Indoor Laboratory

It is difficult to find information about how the microAeth® AE51 performs, in spite of its versatility for about a decade in various research fields such as black carbon measurements and personal exposure studies. Stimulated by this, we conducted real-time tests for indoor aerosol in order to provide performance characteristics toward proper usage. We calculated the attenuation (ATN) using the reference signal together with the sensing signal to compare it with the ATN recorded in a microAeth® AE51. Performance was evaluated under extremely low concentration through the zero test, using filtered clean air. Ten-day-long continuous measurements were done for both indoor aerosol and filtered particle free air to examine the feasibility of microAeth® AE51 in an indoor use. Generally, MicroAeth® AE51 exhibited excellent performance, though it showed relatively low performance under some conditions. Noise was intensified while it was directly connected to a power adaptor. Another issue includes the occurrence of negative concentrations for extremely clean air. The noise amplification turned out to be related to a power source independent on the internal battery, and it was able to be removed by post-processing. Uncertainty analysis was carried out to better understand the origin of unwanted noise. Technical perspective of a proper usage will be addressed with regard to what will play a role for a long-term monitoring.
Hierarchical Distributed Voltage Optimization Method for HV and MV Distribution Networks

The increasing penetration of distributed photovoltaics (PVs) has brought great challenges to the security and stability of distribution networks (DNs), especially voltage violation. Current voltage optimization methods rarely consider the mutual voltage support between high voltage (HV) and medium voltage (MV) DNs, which may cause PV generation losses in extreme scenarios. Limited by the convergence conditions of common distributed optimization algorithms, mixed integer optimization problem can hardly be calculated in a distributed manner. This paper tries to exert the mutual voltage support capability of HV and MV DNs and realize optimal operation of continuous and discrete voltage regulation devices. Thus, a hierarchical distributed voltage optimization method for HV and MV DNs is proposed in this paper. It is assumed that HV and MV DNs are operated by different control systems and real-time communication between them is available. First, the voltage optimization models of HV and MV DNs are developed to minimize the total cost of voltage adjustment and network losses, which optimize the dispatch of on-load tap changers, reactive power compensators, feeder switches, and distributed PVs. Then the generalized Benders decomposition is applied to solve the mixed integer optimization model in a master-slave distributed manner and the accuracy of LinDistFlow equation is improved through the compensation of peak voltage and boundary power flow in MV DNs. Finally, the effectiveness of proposed method is demonstrated via simulation tests in Jinzhai DNs of China. The results of proposed optimization method are very close to those of global centralized optimization method.
Specifying the Waveforms for the Calibration of CISPR 16-1-1 Measuring Receivers

Ensuring the measuring receiver's compliance with the specifications given in CISPR 16-1-1 is mandatory. This is achieved through traceable calibration of the instrument regarding its standard requirements. Incidentally, the receiver's response to pulses of the weighting detectors is the most challenging aspect that must be calibrated. The calibration method requires generating broadband pulses of a given impulse area and at certain repetition frequencies. Such pulses must deliver a known quasi-peak level and have a flat spectrum over the frequency band under consideration. Although the standard states the tolerable uncertainties for the pulse characteristics, it does not stipulate their exact waveform. This void is significant because it hinders the reproducibility of the calibration method, forbids to completely describe the CISPR 16-1-1 pulse generator, and truncates the traceability chain. This paper proposes a parametric model for pulsed waveforms that are compliant with the requirements established for the calibration of the receivers’ response to pulses. The model is numerically solved and evaluated for producing a waveform vector that can be transformed into a reference voltage signal using arbitrary function generators. The validity of the proposed waveforms is experimentally verified in terms of its time domain characteristics and its frequency spectrum.
The masses and radii of HD 186753B and TYC7096-222-1B : the discovery of two M-dwarfs that eclipse A-type stars

We present observations of two new single-lined eclipsing binaries, both consisting of an Am star and an M-dwarf, discovered by the Wide Angle Search for Planets transit photometry survey. Using WASP photometry and spectroscopic measurements we find that HD 186753B has an orbital period of P=1.9194 days, a mass of $M=0.24\pm0.02~M_{\odot}$ and radius of $R=0.31^{+0.06}_{-0.06}~R_{\odot}$; and that TCY7096-222-1B has an orbital period of P=8.9582 days, a mass of between 0.29 and 0.54 $M_{\odot}$ depending on eccentricity and radius of $R=0.263^{+0.02}_{-0.07}~R_{\odot}$. We find that the Am stars have relatively low rotational velocities that closely match the orbital velocities of the M-dwarfs, suggesting that they have been “spun-down” by the M-dwarfs.
Combining Lean and Agile Project Management in a Multi-Project Environment: Case Study in a Retail Company

Both the Last Planner System (LPS) and Scrum have been suggested as suitable planning and control methods for dealing with complex project environments. However, most previous studies have investigated the use of those methods for planning and control in single projects, in general managed separately from other projects. This paper reports the results of an investigation which aims to propose a planning and control model for managing construction projects in a multi-project environment. Using Design Science Research (DSR) as a methodological approach, an empirical study has been carried out in a fashion retailer company from Brazil. The model has been built by using a research strategy similar to Action Research. These are the main findings so far: (a) the nature of the project management activities demand a different planning and control approach, compared to what is normally found in relation to planning and control design or construction; and (b) there are challenges on the systematic use of performance measures to support learning and decision-making. These initial conclusions will serve as a basis for incorporating improvements in the model.
Peningkatan Life Skills Warga Belajar melalui Kursus Komputer di PKBM Cipta Cendekia Kota Tangerang

The purpose of this study is to know the efforts, outcomes, supporting factors and inhibiting the increase of citizen life skills through computer courses. The research approach used is descriptive qualitative with data obtained from interviews, observation, documentation, written test and practice test. Sources of research data obtained from tutors, study residents, and managers. Data were analyzed using source triangulation, method, and theory. The results showed that the tutor's efforts in improving the computer skills of citizens learning the B and C package run well and successfully. Course participants get results with cognitive, affective and skills skills that help course participants find work and have a business. Supporting factors are the high level of willingness of studying citizens, adequate facilities and infrastructure, media and learning methods are quite good, as well as aid funds from the APBD is quite helpful. Inhibiting factors are lack of recruiting, damaged computers and fewer number of participants, limited allocation of APBD funds, and lack of tutors.
Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture

Remaining useful life (RUL) prediction has been increasingly considered in many industrial fields for the reliability and safety of their systems. As a data analysis tool of deep learning, deep convolutional neural network (CNN) shows great potential for RUL prediction. This paper proposes an intelligent RUL prediction method based on a double-CNN model architecture. Given the powerful feature extraction capability of CNN, the proposed method is fed with original vibration signals with no need to resort to any feature extractor, which can also retain the useful information in maximum. The prediction includes two stages: first, incipient fault point is identified by the first CNN model and a proposed “3/5” principle; then, the second CNN model is constructed for RUL prediction. In practice, RULs of identical components are different from each other, which poses a major challenge in RUL prediction. To overcome this problem, an intermediate reliability variable is first calculated in this paper, instead of directly predicting the RUL value. Then, a mapping algorithm is proposed to map reliability to RUL. To demonstrate the effectiveness of the proposed method, data of four tests of bearing degradation are utilized for RUL prediction. Compared with state-of-the-art methods, the proposed method shows higher prediction accuracy and robustness. The prediction results and evaluation indexes demonstrated the effectiveness and superiority of the proposed method.
HyPeRM: A hybrid personality-aware recommender for movie

Recommendation systems aim to provide end users with suggestions about items, social elements, products or services that are likely to be of their interests. Most studies on recommender systems focus on finding ways to improve the recommendations, including personalizing the systems based on details such as demographics, location, time and emotion, among others. In this work, a hybrid recommender system, namely HyPeRM, is presented, which uses users' personality traits along with their demographic details (i.e. age and gender) to improve the overall quality of recommendations. The popular Big Five personality trait measurement scale was used to gauge users' personalities. HyPeRM was evaluated using two metrics, that is, Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA). Both metrics revealed that HyPeRM outperformed the baseline model (i.e. one without user's personality) in terms of the recommendation accuracies. The study shows that user recommendations can be further enhanced when their personality traits are taken into consideration, and thus their overall search experience can be improved as well.
Enhanced Microcontroller Interface of Resistive Sensors Through Resistance-to-Time Converter

Systems using microcontrollers make the interface of a transducer with the digital world much easier. They build smart, lucid, compact, cheap, and less power consuming electronic interfaces. Limited acquisition time is critical for many industrial applications. Single-element resistive sensors are used on a large scale. An attempt has been made to reduce the acquisition time of the measuring system compared to the existing work which converts the change in sensor resistance to the change in time with three charge–discharge cycles. Efforts have been made in this paper to reduce this to two charge–discharge cycles. The proposed work also compensates the errors due to lead wire resistance, the change in lead wire resistance, port pin resistances, and varying ambient temperature with reduced acquisition time. The mean of absolute errors, standard deviation, integral squared error (ISE), nonlinearity, and hysteresis of the proposed method have been computed. Simulation and experimentation study of the proposed system provided encouraging results. The mean of absolute errors and standard deviation of the proposed method in the simulation were 0.07 and 0.08 $\Omega $ , respectively. Furthermore, during experimentation, the values of the mean of absolute errors and standard deviation were 0.11 and 0.11 $\Omega $ , respectively. ISE was found to be 0.09 and 0.19 in simulation and experimentation, respectively. The proposed two-cycle method has an acquisition time of only 60% of the three-cycle method with no reduction in performance.
Gait analysis of a transfemoral amputee with a hydraulic polycentric knee prosthesis

During the stance phase, full extension of the prosthetic knee is allowed by a posterior centre of rotation (de Vries 1995). During the swing phase, the knee flexion depends on the spring, on the hydraulic unit and on the inertia of the prosthesis (Tsai and Mansour 1986; Fitzlaff and Heim 2002). Gait analysis was performed with a trough knee amputee wearing a polycentric knee. The kinematic model of the knee was combined with the pressure measurement of the hydraulic unit in order to analyse the influence of the knee on the gait for different walking speeds.
Dynamic properties of façade frame scaffoldings

The paper presents data on natural frequencies and damping parameters of scaffoldings. The analyzes were made mainly on the basis of acceleration measurements made on scaffoldings on construction sites. These studies were supplemented by numerical analyzes, which allowed to show changing of the natural frequencies of the scaffoldings as the effect of the structure exploitation. Finally, it was found that the scaffoldings are structures with low natural frequencies ranging from 1.0 Hz to 4.0 Hz and a logarithmic decrement of damping at 0.06 in most cases, however differences can be observed with the values as low as 0.03.
MPLS Virtual Private Networks

• Finding Feature Information, on page 1 • Prerequisites for MPLS Virtual Private Networks, on page 1 • Restrictions for MPLS Virtual Private Networks, on page 2 • Information About MPLS Virtual Private Networks, on page 4 • How to Configure MPLS Virtual Private Networks, on page 9 • Configuration Examples for MPLS Virtual Private Networks, on page 18 • Additional References, on page 21 • Feature Information for MPLS Virtual Private Networks, on page 21
Comparative study of multi‐objective finite set predictive control methods with new max–min strategy applied on a seven‐level packed
 U
 ‐cell inverter

This article studies the design and implementation of multi-objective predictive control (MO-PC) of a grid-connected seven-level packed U-cell (PUC7) inverter for minimising the line current total harmonic distortion (THD) and capacitor voltage error simultaneously. The weighting factor method is usually used as a simple method for solving the control problem in the literature. However, there are some difficulties and shortcomings in the calculation of weighting factors. Here, max–min selection strategy with together priority is adopted to reduce these deficiencies and improves the system performance. The switch model of the PUC inverter is derived and then applied in designing the MO-PC for grid-connected applications, where a controlled active or reactive power is injected into the utility. A comparative study among three strategies of weighting factor, fuzzy decision-making and max–min selection is performed to distinguish the proposed method superiority. Experimental results are given to validate the practicality of the applied controller in regulating the line current and capacitor voltage of the grid-connected PUC7 inverter.