Miracle-dz/UniFAR-FTUs huggingface dataset:
A Two-Stage Stochastic Dispatch for Power Systems Considering Renewable Energy Integrated into System Reserve

The development and utilization of renewable energy is an essential development strategy to cope with the energy crisis. Based on the wind power prediction information, wind power can be integrated into the system reserve to balance the operating economy of thermal power units and renewable energy consumption. Thus, the reserve dispatching model on the basis of the two-stage stochastic optimization model is established in this paper to deal with the uncertainty of wind output. Thus, the reserve dispatching model on the basis of the two-stage stochastic optimization model is established in this paper to deal with the uncertainty of wind power. Numerical results on an advanced IEEE 24-bus system demonstrate the effectiveness of the proposed model. The results show that the operating schedule of wind power incorporated into the reserve can alleviate the anti-peaking shaving characteristics and improve wind power utilization.
SemAudio: Semantic-Aware Streaming Communications for Real-Time Audio Transmission

Deep learning (DL) enabled semantic communications have been developed to improve the offline communication efficiently and intelligently by exploring the semantic information, while constraining their applications in real-time online scenarios. In this work, we propose SemAudio, the first DL-based streaming semantic communication system for real-time audio processing. To better extract the semantic features of the audio signal, SemAudio employs the Transformer-XL due to its potential to capture long-distance dependency. Moreover, the system works based on a chunk-based mask attention strategy to enable real-time streaming. By incorporating the novel Transformer-XL and chunk-wise approach, SemAudio can effectively learn and extract semantic features from real-time audio data. Furthermore, to alleviate the channel distortion and attenuation, the semantic and channel encoder/decoder are jointly designed by minimizing the mean error in both time and frequency domains rather than the merely time domain. The extensive experimental results suggest that our proposed SemAudio outperforms the traditional communications. Besides, the proposed SemAudio compromises the quality and latency to meet real-time requirements, which obtains satisfactory performance with significantly higher accuracy and lower latency under multiple channel conditions for real-time audio communication.
Age-oriented Access Control in GEO/LEO Heterogeneous Network for Marine IoRT

Satellite communication is regarded as a promising technique for providing connectivity in remote areas, which creates opportunities for data collection and transmission in marine Internet-of-Remote-Things (IoRT) networks. Most existing investigations in the field of satellite access control focus on communication throughput and transmission delay. However, the freshness of information and the heterogeneous satellite networks are rarely considered. To this end, we first present a satellite-based marine IoRT system, where a GEO/LEO heterogeneous network is considered to harness the full potential of existing satellite systems, and the age-of-information (AoI) is introduced to characterize the freshness of the status update information generated by IoRT devices. Then, an optimal age-oriented access control problem is formulated to maintain the freshness of information in the long term. We transform this non-convex sequential decision problem into a model-free Markov Decision Process (MDP) problem and solve it by leveraging the deep reinforcement learning (DRL) framework. Simulation results show that the proposed strategy significantly outperforms the state-of-the-art ones in terms of long-term AoI performance. Moreover, the proposed strategy could make cooperative access decisions and obtain an excellent trade-off between satellites on different layers.
Optimal Power Sharing Control with Stability Enhancement for Islanded Microgrids

This paper proposes a novel evolutionary algorithm-based approach to optimize power sharing control of distributed generators (DGs) in islanded microgrids. The proposed approach is inspired by PID control, where the P-frequency term ensures the proportional power sharing among DGs, the I-frequency term avoids the frequency excursion, and the D-frequency term promotes better dynamic performance. First, this paper defines the objective of PID control, which is contributed by minimizing the power control cost and frequency overshot during transients. The objective is achieved by employing a particle-swarm-optimization (PSO) technique to seek for optimal settings of PID control. The PSO-PID power sharing control enables to minimize the control cost with more stable power sharing performance and stronger adaptability under different operating conditions. Simulation tests are carried out which prove the validity of this approach and its outperforming in the enhancement on control stability margin, compared to the conventional PID control.
A Compact Broadband Circularly Polarized Wide-Slot Antenna With Axial Ratio Bandwidth Encompassing LTE 42 and LTE 43 Standards of 5G Mid-Band

This study presents a compact broadband wide-slot antenna with broadband left-hand circular polarization compatible with both LTE 42 and LTE 43 standards of 5G mid-band applications. The proposed antenna is fabricated on an FR4 dielectric substrate with overall dimensions of $0.41\lambda _{o}\times 0.36\lambda _{o}\times 0.02\lambda _{o}$ , where $\lambda _{o}$ is the free space wavelength at the resonant frequency of the antenna. The antenna ground plane is etched to form a square radiating slot with a pair of rectangular ground stubs that are diagonally placed inside the slot. On the other side of the antenna, the feed line is loaded by horizontal and vertical stubs to improve the coupling between the feed line and the square slot. To generate a circular polarization, the feeding stubs cooperate with the pair of rectangular ground stubs to excite the radiating slot of the antenna at two different feeding points whose currents have approximately equal amplitude and 90° phase shift. The measured impedance bandwidth (BW) of the proposed wide-slot antenna is 16.2% (580 MHz along the band 3.3-3.88 GHz), while the observed axial ratio bandwidth (ARBW) is 12.2% (440 MHz in the 3.4-3.84 GHz band). The measured gain values are found to be larger than 2.5 dB along both standards of the 5G mid-band applications.
Music Classification Using Fourier Transform and Support Vector Machines

Information retrieval from music is an active research area in computer science. In this paper, we perform a music classification by genre using the subset of the characteristics of the music signal. Features based on magnitude, pitch, and tempo have been found to be informative for classifying musical pieces by genre. We group the features into these categories. These features are calculated from the Fourier transform’s magnitude spectrum. By analyzing the data and exploring it, we develop knowledge about features that can be used for classification, and finally using an information ranking classifier to select the best feature. Finally, Support Vector Machines had the best performance with an accuracy of 81.85% when classifying Spotify music into 20 genres.
GRU-Attention based TD3 Network for Mobile Robot Navigation

In this paper, we propose a goal-oriented navigation reinforcement learning network called GRU-Attention based TD3 network, which takes lidar measurements, the distance between agent and target, and yaw toward the target as state inputs. The policy in the network outputs continuous actions consisting of forward velocity and yaw angular velocity. Our proposed network can perform obstacle-avoidance navigation without prior knowledge of the environment. We train our network in a simulation environment. To show that our proposed network is better in navigation tasks, we compare its performance with two other networks: the pure TD3 network and the GRU-based TD3 network in several different simulation worlds. The experiments show that the mobile robot with our proposed network can bypass the obstacles safely and arrive at the goal positions as fast as possible. The supplementary video is given at: https:// youtu.be/HkqUZSsT5a0. The implementation is made open source at: https://github.com/Barry2333/ DRL_Navigation. git.
Exact Complexity Certification of a Standard Branch and Bound Method for Mixed-Integer Linear Programming

Model predictive control (MPC) with linear cost function for hybrid systems requires the solution of a mixed-integer linear program (MILP) at each sampling time. The branch and bound (B&B) method is a commonly used tool for solving mixed-integer problems. In this work, we present an algorithm to exactly certify the computational complexity of a standard B&B-based MILP solver. By the proposed method, guarantees on worst-case complexity bounds, e.g., the worst-case iterations or size of the B&B tree, are provided. This knowledge is a fundamental requirement for the implementation of MPC in a real-time system. Different node selection strategies, including best-first, are considered when certifying the complexity of the B&B method. Furthermore, the proposed certification algorithm is extended to consider warm-starting of the inner solver in the B&B. We illustrate the usefulness of the proposed algorithm by comparing against the corresponding online MILP solver in numerical experiments using both cold-started and warm-started LP solvers.
A Multi-Agent Deep Reinforcement Learning based Voltage Control on Power Distribution Networks

Due to the high penetration of renewable energy in the distribution network, the exponential increase in the amount of data collected and variables makes it difficult for centralized control methods to achieve real-time voltage regulation. Besides, hardware conditions (e.g., communication equipment) limit its application in practice. Therefore, a model-free multi-agent deep reinforcement learning (MADRL) voltage control strategy is developed in this paper. The proposed MADRL control strategy is carried out with a framework of centralized training and distributed execution. We apply deep deterministic policy gradient algorithm to help each agent control its corresponding PV inverter in a distributed manner. However, during the training process, the agent could observe other agents' information to improve training. The simulation on a 33-bus distribution network is carried out to illustrate the effectiveness of the proposed method, and its superiority is also validated by comparing with traditional methods.
Finite-time boundedness and extended passive analysis for fuzzy Markov jump systems with saturation

In this work, the problem of extended passive-based finite-time control is studied for Takagi-Sugeno fuzzy Markov jump systems with actuator saturation over a finite horizon. The focus is on the design of a mode-dependent non-fragile controller such that the resulting closed-loop system is stochastically finite-time bounded and extended passively. Specifically, by using the mode-dependent Lyapunov-Krasovskii functional technique and Abel lemma-based finite sum inequality, a sufficient condition is established to ensure the finite-time bounded of the addressed systems. Finally, the feasibility and effectiveness of the proposed design are verified and demonstrated by a simulation example.
--------------------------------------------------------------------------------------------------------------------------------------Impact and Employee Innovative Behaviour in the Nigerian Telecommunication Sector

: The purpose of this research was to discover the core of the link that exists between Impact, a psychological empowerment component, and employee innovative behaviour. The study was carried out in the telecommunications industry of Nigeria's south-south region, with a questionnaire serving as the major data collection method. The study looked at how measures like idea creation, concept development, and idea execution were used to evaluate employee creative behaviour. Respondents were picked from a pool of 310 employees from telecommunications businesses. The data was evaluated descriptively as well as inferentially. In the descriptive analysis, means, standard deviations, and percentages were employed, while the Spearman Rank Order Correlation Coefficient (rho) and regression analysis were used in the inferential analysis. According to the findings, impact had a strong positive and significant association with idea creation, as well as a good and significant relationship with concept development, but not with idea implementation. As a result, the data indicated, among other things, that employees will exhume psychological stability if the assigned activity is regarded as effective and appreciated.
Evaluation of Three Land Surface Temperature Products From Landsat Series Using in Situ Measurements

Three operational long-term land surface temperature (LST) products from Landsat series are available to the community until now, i.e., U.S. Geological Survey (USGS) LST, Instituto Português do Mar e da Atmosfera (IPMA) LST, and China University of Geosciences (CUG) LST. A comprehensive assessment of these LST products is essential for their subsequent applications (APPs) in energy, water, and carbon cycle modeling. In this study, an evaluation of these three Landsat LST products was performed using in situ LST measurements from five networks [surface radiation budget (SURFRAD), atmospheric radiation measurement (ARM), Heihe watershed allied telemetry experimental research (HiWATER), baseline surface radiation network (BSRN), and National Data Buoy Center (NDBC)] for the period of 2009–2019. Results reveal that the overall accuracies of CUG LST with bias [root-mean-square error (RMSE)] of 0.54 K (2.19 K) and IPMA LST with bias (RMSE) of 0.59 K (2.34 K) are marginally superior to USGS LST with bias (RMSE) of 0.96 K (2.51 K). The RMSE of USGS LST is about 0.3 K less than IPMA/CUG LST at water surface sites and is about 0.4 K higher than IPMA/CUG LST at cropland and shrubland sites. As for tundra, grassland, and forest sites, the RMSEs of three Landsat LST products are similar, and the RMSE difference among three Landsat LST products is < 0.18 K. Considering the close emissivity estimates over water surface in these three LST data, USGS LST has a better performance in atmospheric correction over water surface compared with IPMA/CUG LST. For land surface sites, the RMSE of LST increases initially and then decreases with land surface emissivity (LSE) for three Landsat LST products. This indicates that the emissivity correction has a large uncertainty for moderately vegetated surface with emissivity ranging from 0.970 to 0.980. Underestimated emissivity for USGS LST at vegetated sites leads to overestimation of LST, which could have led to the higher bias and RMSE compared with IPMA/CUG LST. For the LST retrievals for the three different sensors [i.e., Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and thermal infrared sensor (TIRS)] onboard the Landsat satellite series, the accuracies are consistent and comparable, which is beneficial for providing long-term and coherent LST.
Learning a Parallel Network for Emotion Recognition Based on Small Training Data

Speech emotion recognition (SER) classifies speech into emotion categories such as “happy”, “sad”, and “angry”. Speech emotion recognition has attracted more and more attention in recent years as a challenging pattern recognition task, but its performance is limited by the amount of training data. In this paper, we propose a parallel network consisting of a CNN and a Transformer that receives two types of inputs. The Convolutional Neural Network (CNN) accurately recognizes emotions from the speech data using a mel-spectrogram feature. The transformer uses Multi-Attention from Mel-Frequency Cepstrum Coefficient (MFCC) to realize the extraction of emotional semantic information in a sequence. Experiments are carried out on the Ryerson Audio-Visual Database of Emotion Speech and Song (RAVDESS) dataset. The results demonstrate the effectiveness of the proposed method and show significant improvement over previous results with fewer data and less training time without data augmentation.
Passive Human Tracking Using One Pair of Commodity WiFi Devices with Unknown Locations

Some published WiFi-based passive human tracking systems have achieved sub-meter accuracy. However, they require the location of WiFi devices to be known in advance for passive human tracking, which limits their application in practical indoor scenarios. In this paper, we propose WiSen, a novel passive human tracking system using one pair of commodity WiFi devices with unknown locations. First, we introduce a signal power model for human-related signal extraction and multi-dimensional parameter estimation. Due to low-resolution parameter estimates and noise, we further design a confidence-aware-based path pruning method that combines the distribution of path parameters from successive windows to select reliable paths of interest. Before that, we adopt a data augmentation method to increase the number of available paths to learn parameter distributions better. Then, we statistically estimate the transmitter's location using a kernel density estimation method and ultimately yield the user's location using an improved Gaussian Sum filter approach. We validate the performance of WiSen in real-life indoor environments. The experimental results show that WiSen can realize the sub-meter level accuracy for passive human tracking and device localization.
UCL: Unit Competition of Layers for Streaming Tasks in Heterogeneous Networks

Partitioning and offloading the deep neural network (DNN) model over multi-tier computing units have been recently proposed to shorten the inference time. However, the state-of-the-art cannot adapt to large-scale offloading problems for streaming tasks because of its exponential complexity. Besides, as an essential kind of DNNs, the offloading of grouped con-volutional neural networks (GCNNs) has not been explored yet. Motivated by the above facts, in this paper, we concentrate on the offloading of chained DNNs (CDNNs) and GCNNs for streaming tasks. Consider a typical heterogeneous network consisting of various computing units, the user equipment (UE) publishes computation-intensive and delay-sensitive streaming DNN tasks while computing units accomplish them collaboratively. To mini-mize the delay of processing the task stream, DNN layers should be offloaded to appropriate units, which is the streaming-task multi-unit (STMU) problem. To tackle this problem, we formulate a non-cooperative potential game called unit competition of layers (UCL). The theoretical analysis proves the existence of the Nash equilibrium (NE), and the corresponding algorithm with linear complexity is developed to achieve the NE. Finally, extensive experiments demonstrate that UCL outperforms the state-of-the-art significantly in large-scale scenarios while maintaining similar performance on small-scale tasks.
A new perspective on robust performance for LQG control problems

This paper considers a class of discrete time, linear, stochastic uncertain systems defined in terms of a nominal Gaussian state-space model; the uncertainty is described by a relative entropy tolerance for each time increment of the dynamic model. For this class of systems, a problem of worst-case robust performance analysis with respect to a quadratic cost functional is solved. The solution takes the form of a risk-sensitive cost with a time-varying risk-sensitive parameter. Finally, a numerical example is presented to illustrate the methodology.
Requirements Engineering for the Development of Disruptive Systems Engineering Innovations

System development processes today are characterized by a certain flow or structure that they follow. Most of these processes begin with the definition of requirements or needs that are introduced by the stakeholders. Yet, the effects and actual satisfaction of the requirements occurs further down the line with a significant time delay. This discrepancy makes achieving the objectives of a development project difficult, which can be especially critical when it comes to products that are supposed to or have the potential to disrupt a market or sector. To address these issues, the paper at hand shows a concept based on a disruptive technology framework that ties attributes of these technologies to the requirement definition and elicitation. By connecting the potential for disruption to the requirements, instead of analyzing the circumstances retroactively, specific aspects can be evaluated regarding their impact and sensitivity. The understanding that this impact analysis yields helps to set up and guide a system development for disruption to alleviate issues that might reduce the system’s potential. The concept is shown using the example of new vaccine developments.
A Systematic Review on Control Strategies of Three Stage Generator for More Electric Aircraft Power System

Modern aircraft adopt a three-stage generator power system with variable frequency. It is difficult to ensure the dynamic performance of this system because of its complex architecture, and designing a controller for this system is also challenging as well. Therefore, this study reviews and briefly discusses a number of control strategies for three-stage generators used in AC and DC power systems for aircraft applications under a variety of speed and load variations. The performance of different strategies is evaluated based on settling time and percentage overshoot in steady and transient states; their results are summarized in Table I.
Engagement Analysis Using DAiSEE Dataset

With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant's engagement level in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves development of robust models that can capture facial micro-emotions efficiently. For the development of an Engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and have a gold standard annotation for engagement prediction. Earlier research done using DAiSEE dataset involved training and testing standard models like CNN based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195 and 0.866 for predicting boredom level, engagement level, confusion level and frustration level respectively.
Dynamic-Connected Hybrid Precoding for MIMO-OFDM Systems with Low-Resolution Phase Shifters

To realize the gain guaranteed by the massive multiple-input multiple-output (MIMO) technology with a balanced tradeoff between the performance improvement and the hardware complexity, systems based on dynamic-connected hybrid analog and digital architectures are considered. To provide a more practical design, we in this paper investigate the hybrid precoder design for MIMO-OFDM systems with dynamic-connected architecture and low-resolution phase shifters and propose a design that is suitable for the systems with and without the subarray structure. Simulation results show that our proposed design can have the performance that is almost identical to the state-of-the art designs with the infinite-resolution phase shifters.