Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Online Reviews on Online Hotel Booking Intentions

To explore the impact of online reviews on hotel booking intentions, this study reviews relevant literature studies, designs a measurement scale, and issues questionnaires for investigation. A total of 234 questionnaires were issued, 205 questionnaires were usable. The survey response rate was 87.60%. Then, the descriptive analysis, validity analysis, reliability analysis, and regression analysis were conducted on the data through statistical software SPSS19.0. The results show that online reviews have a significant positive effect on hotel booking intentions. It is expected that this study can enrich relevant studies and provide practical value and guiding significance to hotel owners under the background of Internet+.
Clutter-Aware MIMO-OFDM Based Target Detection: Algorithm Design and Experimental Test

Wireless sensing aided by well-established communication systems is an important research direction in the area of integrated sensing and communications (ISAC). In this work, we investigate the clutter-aware target detection based on the commonly used multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) communication framework. Specifically, under the premise that the clutter covariance matrix is unknown, we develop two generalized likelihood ratio test (GLRT) based detection methods for the scenarios with unknown and known noise variance, respectively. We adopt practical fifth-generation (5G) new radio (NR) frames to perform both simulation and experiment, which show that the proposed methods yield remarkable performance gains compared to the traditional energy detector. Furthermore, when the noise variance is known, the proposed detector exhibits near-optimal performance.
Evaluating a New Configuration for Medium-Voltage Drive Systems in a Mining Plant

Large AC motors are ubiquitous in the mining industry, and these often require medium-voltage (MV) converters. Generally, long cables provide the connection between the variable speed drive (VSD) and the motor, which increases problems involving high frequency currents and overvoltages. These undesired phenomena are attenuated by large, heavy and expensive inverter output filters. As an alternative solution to such problems, the authors have proposed, in previous works, the separation of the frequency converter into its rectifier and inverter units, where the rectifier remains in its original location and the inverter is placed beside the motor, thus establishing a DC transmission link through the long cable. Such a configuration naturally (i) mitigates the high-frequency phenomena, (ii) reduces the voltage drop along the cable, and (Hi) can result in substantial copper savings. Until the present moment, the evaluations performed on the proposed topology was aimed at low voltage mining applications, which have shown excellent results. However, since MV drive systems are common in the mining industry, the proposed drive topology should also be analyzed considering this application. In this way, this paper evaluates the copper and energy savings obtained through this alternative drive topology by means of a case study involving three MV motor drive systems situated in a mining plant.
PV–Battery Integrated Multiport Solid State Transformer in Distribution Systems for Curtailing the Grid Power and Improving Carbon Neutrality

Solid-state transformer (SST) based power conversion solutions are getting increasing attention these days as they can potentially replace the conventional low-frequency transformer. Using power converters, the SST offers flexibility to control the voltage, current, and frequency. This paper proposes a PV-battery integrated multiport solid-state transformer for curtailing the grid power and improving carbon neutrality in power grids. The load is catered mainly by solar PV and battery, while the grid supplies power only when solar PV and battery cannot meet the load demand in low-voltage distribution networks. The MVac grid (11 kV, 50 Hz) is connected to a dc bus using a cascaded front-end rectifier (FER) and a multi-active bridge (MAB) converter. The PV and battery ports are integrated to the same dc bus using another triple-active bridge (TAB) converter. The dc bus voltage is converted to LVac (400 V, 50 Hz) using an active neutral point clamped (ANPC) multilevel inverter with a three-phase, four-wire output. The performance of the system is substantiated using simulation results.
Applying Additive Manufacturing to Engineering Education

This paper outlines the incorporation of education and training in digital fabrication and material processing within a master's engineering program, involving a group of 20 students. The program utilized additive manufacturing, specifically 3D printing, to provide students with practical knowledge in part design, manufacturing, and performance assessment. The study focused on fused deposition modeling (FDM) for the fabrication of a robotic arm, where students evaluated the performance of its components. The installation of drive motors and provision of relevant software were also integral parts of the study. A survey conducted before and after the course aimed to track the learning experience and obtain feedback from participating students. The findings showed that incorporating digital production, self-directed learning, and peer collaboration had a notable positive effect on engagement and learning outcomes.
Emotion Personalization with Machine Learning using EEG Signals and Dry Electrodes

Personalization is essential in enhancing the performance of machine learning models in brain-computer interfaces (BCIs) for emotion recognition, specifically in valence and arousal classification. In this work, we address the challenge of personalizing BCI models utilizing a wireless consumer non-invasive electroencephalogram (EEG) device with dry electrodes. Our research investigates the effectiveness of three machine learning algorithms in classifying valence and arousal: k-Nearest Neighbors (k-NNs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs). To achieve personalization, we adopt an incremental learning approach by progressively incorporating high-quality subject data during model training that are taken from the ground-truth. We compare the performance of the models before and after personalization. The results demonstrate significant improvements in valence and arousal classification accuracy through personalization, with the personalized models outperforming the non-personalized models by up to 27.82% and 28.80%, respectively.
A Novel Multigain Resonant Switched Capacitor DC-DC Converter with Reduced Ratings for High Power Applications

Switched Capacitor Converter (SCC) as replacements for conventional inductor-based switched mode supplies in the interface of renewable systems are of significant interest owing to their benefits such as high energy density, compact size, and elimination of high volume inductors from their design. However, most SCCs operate at a fixed gain, and the closed-loop control of an SCC is a tedious task. Hence, an easily controllable variable gain SCC is of much interest. Converters acting as an interface to renewable systems tend to operate at high gain and medium power levels where the device ratings have to be brought down to reduce the converter’s size and cost. This work proposes a novel SCC topology with features such as multigain, reduced rating of the components, and zero current switching (ZCS). The converter achieves six gain settings with both step-up and step-down operations incorporated within it. All the switched capacitors in the converter are rated for just the input side voltage, and the active switch ratings are also brought down. The design equations and criteria for the selection of various components are established in the work. The analysis is validated with a set of simulation results.
Design and Implementation of Object Detection Model for UAV Aerial images Based on TS-YOLOv8

The main task of UAV aerial object detection is to accurately detect and locate objects using rectangular boxes in aerial images or videos. Due to factors such as shooting height and angle, small objects and overlapping objects account for a large proportion of aerial photography objects. Current UAV aerial photography object detection models may encounter missed detection issues when detecting these two types of objects. In response to the above issues, this paper proposes a TS-YOLOv8 UAV aerial photography object detection model. This model adds a tiny object detection layer on the basis of YOLOv8n to solve the problem of missed detection of small objects. Secondly, the Soft-NMS algorithm is used to optimize the candidate boxes of YOLOv8n to solve the problem of missed detection of mutually occluding objects. The experimental results on the VisDrone2019 dataset show that the proposed TS-YOLO unmanned aerial vehicle aerial photography object detection model can effectively solve the problem of missed detection of small and mutually occluding objects.
Applying Random Forest Algorithm to Predicting the Stock Price Trend of IC Design Companies

IC design companies play an important role in Taiwan's semiconductor industry and provide a critical pillar for global semiconductor development. This study takes IC design stocks as the research target, and proposes a stock price prediction method based on the random forest (RF) algorithm. The RF algorithm classifier trains the prediction model through parameter adjustment, and then through feature screening to improve the accuracy rate. It generates the final result of predicting the stock price. The experimental data collected closing price data for input materials and technical indicators as feature values. The data is obtained from XQ Global Winner, and the research period is daily data from 2015/01/05 to 2022/12/30. The results found that after screening the model features, the stock price prediction trend can significantly improve the accuracy and AUC, and improve the model's explanatory power.
Passive Sliding Mode Control Strategy for Modular Multilevel Matrix Converters

Modular Multilevel Matrix Converters (M3C) are applicable for high voltage and high-power occasion. When the linear proportional-integral (PI) control in double-αβ coordinate system is used, the controlled quantities are still AC quantities, which need to be regulated by many parameters and have high harmonic content, and when the nonlinear passivity-based control (PBC) in double-dq coordinate system is used, all the controlled quantities are converted into direct flow, and the control structure is simpler. To address the issue that passive control is difficult to ensure a good dynamic response of the system, this paper provides a passive sliding-mode control policy based on M3C, which applies the passive controller based on Euler-Lagrange (EL) model to the internal loop current management, and then introduces the sliding-mode control (SMC) to availably address the issue that the passive control is too dependent on the direct current, and the control structure is relatively simple. effectively address the issue that passive control is too dependent on system parameters, making the control system have stronger anti-interference ability and dynamic response ability.
Measuring urban sprawl in China by night time light images

In the process of urbanization, a phenomenon called “urban sprawl” usually occurs. This phenomenon may exaggerated the negative effects of urbanization on environment, public and social health, energy efficiency, and maintenance of farmland. Therefore, the understanding of this phenomenon is urgently required for us to achieve sustainable development. This study proposed a group of night time lights (NTL) indicators of urban sprawl, which intend to use the distribution of lightness to quantify urban sprawl. These measures are proved to be efficient in describing urban sprawl. In addition, they are consistent and easy calculating, making comparison analysis easy to be done. These indicators are used to study urban sprawl in China during the year 2000 to 2010, the results show that in the last ten years, metropolitan areas in the northern part of China have undergone a more sprawl-like urban growth compared with other parts of China.
A Tenant Side Compilation Solution for Cloud FPGA Deployment

We propose a DecryptStrapper and Dually Lever-aged Deployment (DS+DLD), a protocol to authenticate tenant-side design checks for trusted cloud FPGA deployment. Current methods require tenants to trust the cloud FPGA provider with their designs for inspection and allow for no method to confirm the confidentiality of the design check process (without Trusted Execution Environments which have a performance overhead). As a result, there is a clear need for a protocol that allows both tenants and providers to cooperate when deploying FPGA configuration files in a trusted way. DS+DLD works by combining the hash of a compiled bitstream with the hash of the design rule or virus scan results, preventing tenants from uploading bitstreams with falsified results while also never exposing an unencrypted bitstream to the Cloud FPGA Provider (CFP). The result is sent along with an encrypted version of the bitstream to the cloud provider, who recovers the hash of the bitstream and forwards the encrypted bitstream to a Root-of-Trust on the FPGA to program. This Root-of-Trust, known as DecryptStrapper (DS), checks the hash provided by the CFP against the recovered bitstream to confirm they match before programming the FPGA. This ensures that both parties are satisfied with the design in a process we call Dually Leveraged Deployment, forming DS+DLD. As DS+DLD does not use true Trusted Execution Environments (TEEs), the compilation overhead is minimal. We find that our method, while marginally slower than others that fail to provide the same level of coverage, manages to outperform the most similar method, completing 1.06x-3x faster when including compile-time and up to 1. 5x faster when not, while also addressing a larger amount of potential vulnerabilities.
Enhanced Short-term Reactive Energy Demand Forecasting by Employing Seasonal Decomposition and Multi-Model Approach

The forecasting of reactive energy demand is an essential task for power system operators as it enables effective planning and management of reactive energy sources. This work proposes a short-term reactive energy forecasting method using seasonal decomposition and a multi-model approach. The main concept of seasonal decomposition is to decompose the time series into trend, seasonal, and residual components. Subsequently, the multi-model approach is used to forecast different components, and each model will be selected based on the components' characteristics. Different models are proposed, including linear regression (LR), convolutional neural network (CNN), and eXtreme Gradient Boosting (XGB) to forecast the reactive energy components. The LR, CNN, and XGB are employed to forecast the trend, seasonality, and residual components, respectively. Moreover, a real-time dataset consisting of the transformer's actual interval meter readings and performance metrics validated the proposed method's effectiveness.
Performance Modeling and Scalability Analysis of Stream Computing in ESSPER FPGA Clusters

ESSPER is a flexible and scalable FPGA cluster system designed for reconfigurable HPC, with customizability, scalability, and interoperability with existing HPC systems. The inherent characteristics of ESSPER make it a superior alternative for achieving highly scalable computing solutions, particularly for compute-intensive applications such as numerical simulations and machine learning. However, constraints in communication bandwidth or latency can impact performance and scalability. This paper builds upon a previous performance model study to analyze performance modeling and scalability of streaming computation using a 1D ring of cascaded FPGAs with high-speed, low-latency communication links in ESSPER. For evaluation and verification, we implemented a simple vector addition on ESSPER. The Result indicates that measured performance metrics closely align with predictions from the performance model. This model can also be used to estimate performance for other ESSPER applications.
A Sine Wave-Shaped Spring to Enhance the Compactness of Series Elastic Actuators

In this paper, we discuss the design methodology of a novel torsional spring, embedded in a series elastic actuator (SEA) for portable upper-limb exoskeleton applications. Starting from a simple beam model, a class of design candidates is examined through theoretical formulation and finite element (FE) simulations. We found out that a sine wave shape is the best compromise to achieve the desired compliance for a safe use of the exos. Besides, this novel design reveals to reduce the encumbrance of the joint with respect to previous architectures, for fixed weight. The spring was manufactured in a 3D-printed metal with main dimensions - $\phi$ diameter 98 mm, 18 mm of width - and weight - 0.34 kg. Experiments have been performed on the SEA to validate the joint characteristic in the torque-angle plane. Results are promising since linearity is confirmed and the experimental stiffness coincides with the theoretical estimate. In particular, the joint is validated for operation up to a deflection of 13.75° under a nominal torque of 9 Nm. Therefore, the proposed elastic joint is suitable for integration in physical human-robot interactions.
Optimization of Corona Triode Polarization by 2D Electrostatic Mapping

Traditional methods for characterizing piezoelectric materials typically involve analyzing their electromechanical response, focusing on coefficients such as the piezoelectric strain coefficient d33. In this study, an alternative approach is proposed to evaluate the piezoelectric response by examining the surface charge accumulated in the tested sample following a corona polarization process. Through the use of an electrostatic voltmeter, two influential factors affecting the piezoelectric response were investigated: the number of needles employed during the polarization and the presence of a grid interposed between the high-voltage needles and the specimen.
A Simple and Efficient Design of 2DOF Controllers with Application to the Speed Regulation of Electrical Motors

This paper proposes a design procedure for a two degree of freedom (2DOF) controller applied to first order plants. The feedforward controller is comprised here by a simple proportional gain, and the feedback action is provided by a proportional-integral controller. The proposed design procedure provides the control gains that allow to the closed-loop system a first order transfer function response, demanding from the control designer the choice of only one parameter: the frequency, p, of the closed-loop pole. The limit for the control signal, $\bar{U}$, is also taken into account in the 2DOF design, allowing operation without control signal saturation. The proposed 2DOF controller is applied to the speed regulation and current regulation of a permanent magnet synchronous motor. For a comparison, a conventional proportional-integral controller (without the feedforward action) is obtained by a specialized function in MATLAB, and the results show the superiority of the 2DOF controller, which guarantees operations without overshoot and without control signal saturation.
Model to Increase the Productive Efficiency in the Plastic Manufacturing Sector

One of the problems in the plastic manufacturing sector is the low level of efficiency, this through defective products, which forges the exploration of tactics to control this index. In this context, the present investigation proposes a model of improvement of the plastic accessories productive processes, which will provide an increase in productive efficiency. After the analysis through the Visual Stream Mapping (VSM), Overall Equipment Effectiveness (OEE) and Failure Mode and Effect Analyses (FMEA) of the current situation, a low level of efficiency of the production of plastic accessories and their respective reasons were diagnosed. For which, the Total Productive Maintenance (TPM), 5S and Jidoka tools will be implemented in the production processes in order to reduce problems or anomalies.
Human Action Recognition Method Based on Bag-of-Words Model

The dictionary constructed by the traditional bag-of-words model is unstable and ignores the order of word vectors. When it is used for human action recognition, the recognition effect is unstable, especially for reverse action recognition. Aiming at these problems, a method of action classification based on space-temporal joint frequency histogram is proposed. Firstly, the key angle information of the limb is extracted, and the inter-frame difference of the key angle is used as the time feature descriptor. Then, according to the characteristics of joint movement, the human body is divided into five main active parts, and each part constructs a stable time word bag and a space word bag respectively. Then the joint frequency histograms of each part are merged to represent the action sequence. Finally, the action classification is realized by using support vector machine (SVM). Experiments are carried out on a challenging dataset-UTKinect dataset. The results show that compared with the traditional bag-of-words model and some existing methods, this method can effectively improve the accuracy of action recognition.
Enhancing Efficiency and Delivery Performance Through Optimization of Machine Scheduling in Pre-emptive Parallel Manufacturing Systems

The current study investigates the integration of unrelated parallel machine scheduling and inventory (WIP) buffer allocation in the production process using Mixed Integer Linear Programming (MILP). Industrial variables like pre-emption and job batching are considered to make it more realistic. The experiment showed that buffer stock is essential for managing unpredictability and meeting customer demand. The findings highlight the significance of the current schedule process with batching of jobs and buffer stock as a crucial mechanism for handling and reducing the negative impact of disruptions in the manufacturing process. Additionally, it also finds pre-emptive job locations and tardy jobs, both of which are crucial for on-time delivery. The CPLEX 12.8 solver is used to analyze the MILP model, which gives the integer results in a suitable time.
Comparison of Two Video-Based Methods for Knee Joint Angle Measurement: A Preliminary Study

The measurement of the range of motion of human lower limb joints is of fundamental importance for biomechanical assessment. To date, there are numerous video-based methodologies for joint angle analysis, such as vision systems with passive markers and vision systems using deep learning algorithms. This study conducted a comparative analysis between two video-based measurement methods for analyzing the knee angle in the sagittal plane in locomotion activities. A healthy adult subject was involved in this study. Two motor conditions were analyzed: slow walking (3.6 km/h) and fast walking (5,0 km/h) on a treadmill. Two RGB cameras were used, one placed sagittally to the subject and the other at 45°. A 6-camera optoelectronic system was used as the gold standard. The knee angle in the sagittal plane was analyzed using a deep learning method (TC-Former) and a passive marker method (Blob Analysis). The results for slow walking on the treadmill show a relative error expressed in percentages of 14.7±11.0 and 7.5±3.5 for the first and second methods respectively. The results for the fast walking task show a relative error of 20.0±7.7 and 8.9±4.0 for the first and second methods, respectively.