Miracle-dz/UniFAR-FTUs huggingface dataset:
LadderBot: A Requirements Self-Elicitation System

[Context] Digital transformation impacts an ever-increasing amount of everyone's business and private life. It is imperative to incorporate user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by users that are novices at contributing requirements to IS development projects. [Objective] We need to develop RE systems that are capable of assisting a wide audience of users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. [Research Method] We present the prototypical self-elicitation system "LadderBot". A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human (expert) interviewer's capability to rephrase questions and provide assistance in the process. An experimental study is proposed to evaluate LadderBot against an established questionnaire-based laddering approach. [Contribution] This work-in-progress introduces the chatbot LadderBot as a tool to guide novice users during requirements self-elicitation using the laddering technique. Furthermore, we present the design of an experimental study and outline the next steps and a vision for the future.
Rest-to-Rest Trajectory Planning for Underactuated Cable-Driven Parallel Robots

This article studies the trajectory planning for underactuated cable-driven parallel robots (CDPRs) in the case of rest-to-rest motions, when both the motion time and the path geometry are prescribed. For underactuated manipulators, it is possible to prescribe a control law only for a subset of the generalized coordinates of the system. However, if an arbitrary trajectory is prescribed for a suitable subset of these coordinates, the constraint deficiency on the end-effector leads to the impossibility of bringing the system at rest in a prescribed time. In addition, the behavior of the system may not be stable, that is, unbounded oscillatory motions of the end-effector may arise. In this article, we propose a novel trajectory-planning technique that allows the end effector to track a constrained geometric path in a specified time, and allows it to transition between stable static poses. The design of such a motion is based on the solution of a boundary value problem, aimed at a finding solution to the differential equations of motion with constraints on position and velocity at start and end times. To prove the effectiveness of such a method, the trajectory planning of a six-degrees-of-freedom spatial CDPR suspended by three cables is investigated. Trajectories of a reference point on the moving platform are designed so as to ensure that the assigned path is tracked accurately, and the system is brought to a static condition in a prescribed time. Experimental validation is presented and discussed.
Effect of Magnetization Curve Model and Winding Connection Mode on Magnetically Saturated Controllable Reactor Based on ANSYS

In order to guide the selection of magnetization curve model in modeling and provide useful information for the application of winding connection mode in structure design, it is necessary to analyze and know the effect of different curve models and winding modes on a magnetically saturated controllable reactor (MSCR). The paper analyses the features of each winding mode and the relationship between parameters under different winding modes. The 3D finite element model of MSCR with different winding modes and B-H curve models is established by the field-circuit coupling method based on ANSYS/Maxwell. Comparing the simulation results with different B-H curve models and winding modes, it is found that the B-H curve model affects the current harmonic components to some extent. For a MSCR with separately excited winding mode, it is more suitable for the condition of the ultra-high voltage, its winding loss is obviously greater than that of a self-excited MSCR under the same load condition, but its working current is smaller.
Experimental Verification of Security Measures in Industrial Environments

Industrial Control Security (ICS) plays an important role in protecting Industrial assets and processed from being tampered by attackers. Recent years witness the fast development of ICS technology. However there are still shortage of techniques and measures to verify the effectiveness of ICS approaches. In this paper, we propose a verification framework named vICS, for security measures in industrial environments. vICS does not requires installing any agent in industrial environments, and could be viewed as a non-intrusive way. We use vICS to evaluate the effectiveness of classic ICS techniques and measures through several experiments. The results shown that vICS provide an feasible solution for verifying the effectiveness of classic ICS techniques and measures for industrial environments.
A High Step-down DC-DC Converter with Matrix-Transformer and Wide Voltage Gain for Vehicle Power Supply Applications

A two-stage high step-down DC-DC converter with matrix-transformer is presented for vehicle power supply applications. Wide voltage gain is achieved by the front-end Boost converter and down-stream phase-shifted full-bridge converter. With the novel two-stage structure, the input voltage range of the down-stream full-bridge converter is narrowed significantly, which helps to reduce the current stress of primary switches and the voltage stress of the secondary switches. Devices with better performances can be chosen and high efficiency power conversion is achieved with the presented topology. Furthermore, the matrix-transformer is used in full-bridge converter to handle the large output current, which is beneficial for simplifying the design and processing of the transformer. Meanwhile, the current sharing of transformer windings will not be considered. Operation principles and characteristics of this converter are analyzed in detail. Experimental results are provided to evaluate the effectiveness of the presented solutions.
Model Predictive Control Based Data-Driven Retrofit Controller for Network Systems

In this paper, we propose a data-driven retrofit controller design method based on the robust model predictive control. Retrofit control is a distributed design of decentralized controllers for a large-scale networked system. By using the retrofit control paradigm, the damping performance of the subsystem of interest can be improved by only relying on the local information of the subsystem. In this paper, we propose the extension of the existing method by utilizing the approximate model of the environment through the system identification approach. Then, by using the robust model predictive control framework, we formulate an optimization problem which produces a retrofit controller that complies to the constraints under the presence of the interconnection signal noise. We show that although the retrofit control framework only requires the local information, the approximate model of the environment will improve the damping performance even with a certain degree of inaccuracy. Lastly, the effectiveness and robustness of the proposed method are demonstrated through numerical simulation.
Improved Negative Sequence current Injection Balancing Method considering Zero Sequence Voltage for cascaded H-bridge STATCOM

Cascaded H-bridge STATCOM is widely adopted to regulate the power quality in power grid and industrial application. Considering the existence of zero-sequence voltage which cannot be neglected in the simulation model, this paper proposed an improved clustered voltage balancing strategy based on negative-sequence current injection. The proposed method takes the zero-sequence voltage into consideration and provide better balancing performance. Simulation and experimental results verify the effectiveness of proposed control strategy.
Economic Model Predictive Control for Energy Systems in Smart Homes

In this paper we present simulations of Economic Model Predictive Control (EMPC) for control of the energy system in a smart home. The energy system in a smart home consists of a stationary battery, photo voltaic solar cells on the roof, a heat pump for heating, and an electrical vehicle battery that is charged. Using weather forecasts, thermal comfort and driving profiles, the EMPC coordinates this energy system and its interaction with the external energy system by purchasing and selling electricity at prices that are announced in advance. The EMPC is a linear program with soft constraints for the output constraints and an objective function that represents the cost of energy. In contrast to existing methods, the key novelties in the present paper is the use of multi-level soft constraints and an objective function that accounts not only for the cost of energy used during the prediction horizon but also for the value of energy stored at the end of the prediction horizon. We demonstrate by simulation, that these novelties are important for well-behaved closed-loop performance of the EMPC.
Wireless Sensor Network Prototype to Monitor the Condition of Holding Furnaces in the Aluminum Casting Plant

Currently in the industry, higher production levels are required every day, for this reason, it is essential that the machinery used for production is always in optimal conditions which will consequently increase the production efficiency. Due to this, the continuous monitoring of the machinery's condition has become a strong strategy to maximize production time. Due to the growing demand to monitor technologies, this document proposes the development of a prototype that allows monitoring the condition of maintenance furnaces used in a foundry plant to keep melted aluminum at a temperature between 600°c and 620°c. The electrical current consumed by the resistors that maintain the temperature in melted aluminum is measured through a Wireless Sensors Network (WSN). Through this network, it is possible tomonitor this type of machinery continuously and remotely, allowing awareness of the industry's wellbeing thanks to the application of the Industrial Internet of Things.
Internet Advertising Investment Analysis Based on Beijing and Jinhua Signaling Data

With the popularization of the internet, the media of advertising tends to diversify. Compared to traditional advertising methods, internet advertising is well received for its convenient and fast propagation mode. However due to its mandatory and invasive property, the publicity effect of internet advertising is greatly reduced. To make the delivery of Internet advertising more directional and efficient, we try to analyze the behavioral preferences of netizens from Beijing and Jinhua when using different Apps, and then conclude some rational delivery strategies for internet advertising investors. We use Multi-state Models (MSM) to construct an online Apps transfer model for netizens, which will help to get some intuitive conclusions. After that, basing on online apps transfer model, we adopt entropy method, hierarchical clustering, and Tucker decomposition to mine the potential behavioral preferences of netizens. Finally we provide some suggestions and references with the internet advertisers.
Assessment of The Alignment Maturity Level of Business and Information Technology at CV Jaya Technology

This research discusses the assessment of alignment maturity level between business and information technology on organizations. Empirical study made on CV Jaya Technology to assess the alignment by using different area as: communications area, competency/value measurement area, governance area, partnership, scope and architecture, skills. An assessment of the level of IT maturity and business strategy is carried out at CV Jaya Technology Indonesia. The technique of collecting data using a questionnaire to the management appointed according to their authority, amounting to 5 respondents. The results of the study show that the alignment maturity level of business and IT strategy alignment at CV Jaya Technology is at level 3 with an average value of 3.32, called Established Focused Process.
Online Parameter Identification Based on MTPA Operation for IPMSM

This paper proposes a model-based forecasting method to estimate electrical parameters of an Interior Permanent Magnet Synchronous Motor(IPMSM) using Maximum Torque Per Ample(MTPA) control strategy. Parameter identification technology is practical in the field of motor control, and the model-based forecasting method proposed in this paper, is practical due to its simplicity and obtaining fast convergency simultaneously. In addition, parameters identified by the algorithm can be updated and used in control strategies automatically in real time, which also enhances the control performance. Finally, the model-based forecasting method put forward in this paper is verified on a prototype of an IPMSM by simulation.
A Data-Driven Detection System for Predicting Stress Levels from Autonomic Signals

This paper introduces and details a detection system for continuous monitoring of psychological stress. The formulation of classes relating to varying levels of stress intensity is described. This is necessary for determining the therapy needed to alleviate the associated effects, particularly in population groups suffering from chronic and mental illnesses such as diabetes and depression. The data-driven detection system mainly comprises kernel principal component analysis for dimensionality reduction, and nearest neighbour classifier for supervised learning to determine the associated stress intensity. We evaluate the generalised stress detection system using a 3-fold cross validation and a test set comprising an independent subject. We obtain a 0.66 F1-score with a precision of 0.70 and a recall of 0.67 over 4 classes of stress: no stress, low stress, moderate stress, and high stress.
TIMiTIC: A C++ based Compact Thermal Simulator for 3D ICs with Microchannel Cooling

CFD simulation of liquid-cooled integrated circuits is very time-consuming. However, in the case of ICs with straight integrated microchannels, the flow is very regular and very often, the CFD simulation is not needed to correctly predict the hydrodynamic and thermal behaviour. In this paper, we present a thermal simulator, written in C++, which significantly reduces the thermal simulation time of such chips, while maintaining acceptable accuracy. For a typical test chip we have observed the average relative error 3.7 % for the worst analysed steady-state case, while the simulation time was only 10 ms, compared to 20 minutes for full CFD simulation.
A Low-Power, High Reliable Data Collection Scheme for Wireless Sensor Networks

In wireless sensor networks (WSNs), the process of collecting information from sensor nodes is one of the most important key applications. The data collection process in a WSN should meet several requirements, such as high reliability, low power consumption and so forth. In this paper, we introduce a low-power, high-reliability data collection scheme in WSNs using a state-of-the-art IEEE 802.14. 4e MAC protocol, which is Time Slotted Channel Hopping (TSCH) in combination with the utilization of the feather nodes. Feather nodes are the nodes which have no child nodes. Our proposed method firstly detects all the feather nodes in the network, it then runs a lightweight scheduling mechanism on those nodes. By giving extensive practical experiments on the FIT IoT-LAB platform, we successfully develop a low-power-TSCH, high-reliability data collection scheme. Results in different testbed scenarios show that our scheme achieved good performance, compared to existing models using pure TSCH. That is, the end-to-end delivery ratio achieves 99.99% and the energy consumption of radio listening is 6 and 2 times lower than baseline and Orchestra schemes, respectively.
Fault Diagnosis of Motor Bearing Based on Speed Signal Kurtosis Spectrum Analysis

Bearing failure is one of the most common types of motor faults. For the electrical diagnostic methods of permanent magnet synchronous motor(PMSM) bearing faults, the existing literatures are mostly based on the motor current signal, but subject to the limitations of sampling frequency and other factors, the current method can guarantee the analysis effect only through external signal acquisition such as oscilloscope. Considering the relatively high resolution of encoder in PMSM, proposed a diagnosis method based on motor speed with kurtosis spectrum analysis which can promote the impact component extraction to improve the signal-to-noise ratio(SNR). Compared with current method, it can promote diagnosis stability and reduce costs. In this paper, the integrated model of motor and bearing is constructed, then the simulation experiment is carried out based on the speed method. Experiments based on speed and current signals are carried out to make comparison, and the mature vibration method is used as reference. The results show that although the speed method is affected by the speed domain and load torque, the diagnostic strategy combining the rotational speed signal and the kurtosis spectrum can basically reach the effect of vibration method and has a good full-speed versatility, which is obviously better than the current signal.
Land Cover Classification for Satellite Images Through 1D CNN

Land cover classification of satellite imagery can provide significant information for many applications, including surface analysis, environmental monitoring, building reconstruction, etc. Land cover classification has been generally performed using unmixing-based or shallow/deep learning approaches, among which the unmixing-based approaches suffer from stability issues due to the complex intrinsic properties of the data, deep learning-based approaches like 2D CNN requires large labeled training set which is often unavailable in satellite images and small ground truth collection leads to spatial discontinuities (as shown in Fig. 1), making 2D CNN approaches unviable. In this paper, we first propose a 1D convolution neural network-based framework applied to each pixel in the spectral domain where we extract descriptive local features for improved classification. Experimental results demonstrate superior classification accuracy through comparison with traditional unmixing-based and neural network methods using just limited number of training samples.
Experimental Identification of the Induction Machine Frame Convection Coefficient for Varying Ventilator Speeds

Thermal modeling of induction machines is becoming increasingly important to improve the operational efficiency and performance. Lumped-Parameter Thermal Networks are both flexible and suitable for online use, but their parameters must be well identified to obtain accurate temperature predictions. The convection coefficient between the motor frame and the environment is a particulary difficult parameter to obtain analytically because it summarizes complex fluid flows across the motor. In this paper, the frame convection coefficient and frame convection resistance of a 5.5 kW induction machine is determined experimentally for different external ventilator speeds. It is shown that the average frame convection coefficient increases for increasing ventilator speeds, whereas the convection resistance is inversely proportional to the fan speed. Furthermore, the local convection coefficient changes as function of the axial frame position due to a decreasing local air speed further away from the fan end.
Online Rotor Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Tooth Flux

Demagnetization and eccentricity are common rotor faults of permanent magnet synchronous motors, which are important factors affecting the reliability of the motor and the whole drive system. Therefore, monitoring and diagnosing these faults online is important. A mathematical model of stator tooth flux under demagnetization and eccentricity faults is established to solve this problem and analyze the influence of demagnetization and eccentricity on the spatiotemporal distribution of stator tooth flux. Moreover, a method that extracts rotor fault characteristics and formulates the corresponding fault diagnosis is proposed. The proposed diagnosis method is analyzed and verified through simulation and experimentation, and results show that the proposed method can efficiently diagnose and differentiate between demagnetization and eccentricity faults. The proposed method can provide an important basis for the operation and maintenance of the motor, assist in the reduction of loss due to faults, and improve the reliability of the motor and the entire system.
Coordinate Checkpoint Mechanism on Real-Time Messaging System in Kafka Pipeline Architecture

The real-time messaging system is the critical thing in computing based on time-critical decision making in many organizations. In the real-time messaging system, fault tolerance is the key challenge of developing reliability requirements. Apache Kafka is a popular framework for consuming data stream into the processing platforms. However, there are many challenges in the replication process because of a server failure in Apache Kafka. To develop fault tolerance in Apache Kafka, this paper focuses on defining a fixed checkpoint interval to reduce the recovery time and lost messages in server failure. Then, we measure the cost of checkpointing, cost of rollback and total time cost of overheads due to the fixed checkpointing intervals. The system shows the drawback of real-time processing depend on the number of lost messages on various partitions and server failure processes. The experimental results emphasize the checkpoint interval method to reduce recovery time and lost messages. Conforming to the experimental results, the performance of the total time cost with checkpoint interval saves time approximately 30% than without checkpoint interval.
UAV Assisted Ground User Localization

The use of aerial platforms, also called unmanned aerial vehicles (UAVs), as flying BSs has risen dramatically in recent years owing to their various benefits and practical applications. The UAVs can provide high reliable wireless communications for ground devices as well as enhance the capacity and the coverage of wireless networks by effectively complementing the conventional terrestrial communication infrastructures. These vehicles, with their adaptive altitude, can enable LoS transmission links for ground devices more probably. Due to their agility and mobility, they can be quickly and efficiently deployed to handle communication and localization needs in several unexpected and temporary scenarios including the natural disaster, traffic congestions, stadiums and concerts. More importantly, the UAVs, through their mentioned abilities, can increase the number of observation points, without the requirement for additional hardware, and hence can achieve localization with a small number of devices. Remarkably, albeit the exciting great potential of UAV based localization, little work has been reported in the available literature. In line with this, in this work, we study a line-of-sight (LoS)/non-LoS (NLoS) mixed signal power model based localization solution relying on least square algorithm for aerial networks. The probabilistic propagation model and its localization performance are investigated through simulations.