Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Reward-Based Reinforcement Learning for Multi-Agent Pursuit and Evasion

The reinforcement learning is studied to solve the problem of multi-agent pursuit and evasion games in this article. The main problem of current reinforcement learning for multi-agents is the low learning efficiency of agents. An important factor leading to this problem is that the delay of the Q function is related to the environment changing. To solve this problem, a probabilistic distribution reward value is used to replace the Q function in the multi-agent depth deterministic policy gradient framework (hereinafter referred to as MADDPG). The distribution Bellman equation is proved to be convergent, and can be brought into the framework of reinforcement learning algorithm. The probabilistic distribution reward value is updated in the algorithm, so that the reward value can be more adaptive to the complex environment. In the same time, eliminating the delay of rewards improves the efficiency of the strategy and obtains a better pursuit-evasion results. The final simulation and experiment show that the multi-agent algorithm with distribution rewards achieves better results under the setting environment.
Kinematic Analysis of Stewart Platform using MATLAB

Stewart platform is one of the most documented and researched-on parallel robot today. It has uses ranging from military tanks, flight simulators and so on. This Stewart Platform has 6 degrees of freedom, consisting of an upper platform and a lower base connected by six extensible linearly actuated legs. This paper involves analysis of inverse kinematic equations which lead to precise calculation of the length of each leg required for a given orientation and position of the platform. Then a MATLAB script is coded with the equations and we get the result with user input values. Finally, some future scopes of this paper are discussed.
A Low-Profile Wideband Wide-Scanning 1-D Tightly Coupled Dipole Array With Metal Walls

A low-profile wideband wide-scanning 1-D (one-dimensional) TCDA (tightly coupled dipole array) is proposed in this paper. To apply the TCDA design concept to 1-D array, two metal walls are loaded in open side of the array, which aims at providing the boundary conditions similar to traditional TCDAs. The simulated results show that the infinite array achieves a 3:1 impedance bandwidth for VSWR<2.2 at broadside, and VSWR<3 for scanning up to 50°. The total profile of the proposed array is only 0. 15λlow (λlow is the wavelength at the lowest frequency).
An Image Inpainting Method Considering Edge Connectivity of Defects

In this paper, we propose a new image inpainting method that improves the line distortion and unnatural coloration in the restoration area observed in the conventional methods. Many deep learning inpainting methods have been proposed in recent years. However, these conventional methods have problems with distorted lines and unnatural colors in the restoration area. We solve the problems of the reconstruction of distorted lines and unnatural color simultaneously by applying edge generator from EdgeConnect to DeepFill v2. Through evaluation experiments, we show that the proposed model is better than the conventional methods in PSNR and SSIM of images with clear color boundaries and complex edges.
Crowd Counting Based on CSI and Convolutional Neural Network

With the rapid development of people-centered internet of things technology, crowd counting has attracted more and more attention and plays an important role in smart home and public security management. This paper present a crowd counting system based on WiFi Channel State Information (CSI) and Convolutional Neural Network (CNN). The system uses the convolutional neural network to process input data and achieves a recognition accuracy of 97% in a six people environment. We analyze the influence of distance on recognition accuracy and discuss the data processing methods with the other two neural networks. Furthermore, we compare this system with the other system in the similar parameters and validate the effectiveness of our system in crowd counting using CSI.
Supercritical Fluid Extraction-Oxidation Technology to Remediate PCB-contaminated Soils/Sediments: An Economic Analysis

The remediation of PCB polluted soils/sediments, left to classical technologies such as incineration and bioremediation, is an overwhelming problem, given that vast amounts of spilled PCBs still exist in the environment after nearly three decades of PCB production ban. Other innovative, alternative cleanup technologies demonstrated that a better solution might exist. A two-step remediation process proposed for real-world PCB-contaminated soils/sediments, based on supercritical fluid extraction (SCFE) and supercritical water oxidation (SCWO), is economically analyzed in this paper. The base case of the process consists of three 2.5-m 3 extractors operated in a 30-min cycle and a 29-m-long SCWO tubular reactor (0.385 m i.d.), which can treat yearly 36,000 m 3 of contaminated materials. To assess the commercial potential and the economic feasibility of the proposed process, capital and operational costs are analyzed and the results show total costs of the process in the range of $142-174/m 3 sediment. Comparison with other alternative technologies suggests that the SCFE/SCWO process is economically feasible and attractive for the remediation of real-world PCB highly contaminated soils/sediments.
Research on Optimal Control of Fractional Order PIλDμ Parameters of SCR Denitrification System

Selective Catalytic Reduction (SCR) is the most widely used and most mature denitrification technology in thermal power plants in my country. In view of the strong interference characteristics in the SCR denitrification system, this paper applies the fractional order PIλDμ controller to the outer loop control of the denitrification system. Because the fractional order PIλDμ controller has many parameters and the adjustment process is complicated and cumbersome, this paper proposes an Optuna optimization algorithm with CMA-ES sampler. This algorithm introduces the sampling principle of the CMA-ES algorithm into Optuna, and uses the strong parameter search ability of CMA-ES to determine the parameters of the fractional order PIλDμ controller. The experimental results show that the fractional order PIλDμ controller has good tracking, anti-interference and robustness in the denitrification control system of thermal power plants.
Shaped Power Pattern Synthesis of Linear Arrays with Dynamic Range Ratio Control

Semi-definite relaxation (SDR) is applied to synthesize a shaped power pattern for a linear antenna array with accurate sidelobe and null control. The dynamic range ratio (DRR) control is also incorporated into the SDR-based synthesis formulation. Two examples of synthesizing flat-top and cosecant-squared patterns with both sidelobe and null upper bounds are conducted to validate the effectiveness of this method. Results show that the SDR method can generate the desired shaped power patterns with good performance, and the DRR control incorporated into the SDR method can help to reduce the power loss for the shaped pattern synthesis.
A Method for Surface Detect Classification of Hot Rolled Strip Steel based on Xception

Hot rolled strip steel is an important raw material for automobile, home appliance and other manufacturing industries, and the quality of its surface and plate shape has a vital impact on the products produced by end users. In actual industrial production, different measures should be taken for different kinds of strip steel surface defects. Therefore, it is of great significance to classify the surface defects of hot rolled strip accurately. An improved method based on Xception algorithm is presented. The algorithm can classify the hot rolled strip defects and is more suitable for the imbalance between categories to some extent. Compared with 91.18% of the original Xception algorithm, the classification accuracy of the improved algorithm reached 93.87% on the hot rolled strip defect dataset. The improved scheme solves the problem of unbalanced dataset samples to a certain extent and improves the classification accuracy of dataset significantly.
High-Input-Impedance Amplifiers Design for Dry-Electrode Biopotential Acquisition: A Review

Wearable health monitoring devices are becoming more and more popular due to their improved user comfort and long-term measurement capability. Dry-electrode based interfaces are often limited by skin-electrode impedance, leading to signal attenuation and CMRR degradation. In order to solve these problems, the analog front-end amplifier must be designed with ultra-high input impedance. To achieve this target while maintaining balanced overall performance, it is necessary to investigate both optimized amplifier architectures and input-impedance boosting techniques. This paper reviews the key limiting factors to realize an ultra-high input impedance biopotential amplifiers, dominated by on-chip and off-chip parasitic capacitance from both circuit and system perspectives. We summarize and compare a number of high-impedance biopotential amplifier architectures, as well as various impedance boosting techniques, as the guidelines to develop the next generation dry-electrode biopotential signal acquisition ASICs and systems.
Customized Structured Product Design for Carbon Emission Rights: Based on the Application of "Cross-border Wealth Management" in the Greater Bay Area

In recent years, under the tide of China's financial innovation, structured products have become a popular financial product in China, developing rapidly in a short period of time. Therefore, this paper focus on the needs of both sides of the transaction, and presents the customizable structured product for carbon emission rights for the first time in China. This paper analyses the characteristics and current development of carbon emission rights and structured products, which leads to the design of an innovative structured product prototype, proposing the basic information and combination of products. Among a series of pricing methods, the optimal Monte Carlo pricing method is selected and improved by the control variables technique to accurately price the specific product. Also, Monte Carlo method is carried out through MATLAB to predict and simulate the price trend of the underlying carbon emission rights of the structured product in multiple paths and to complete the pricing of the structured product. Finally, the paper concludes with recommendations for this structured product. For issuers, they should pay attention to annual hotspots and product innovation, reduce homogeneity and provide personalized services, thus increasing the attractiveness of the product. For investors, they should pay attention to the various return scenarios that may exist at the end of the product period and choose a financial product that takes into account their own risk appetite.
Quadra-OAM-Mode SIW Slot Antenna Using Circularly Traveling Wave Resonator Structure

This paper presents an SIW slot antenna using two circular ring resonators with traveling wave inside radiating beams with orbital angular momentum (OAM) mode numbers ±3 and ±4. Two separated SIW circular resonators with phase variation of 6π and 8π along with two 90° hybrid couplers are introduced to obtain four OAM mode radiation. Analytical formulas are demonstrated to derivate the feeding port position and other parameters of the antenna, whose feasibility is verified in simulations using HFSS.
Coexistence Between IMT-2020 and EESS(PASSIVE) in the 24 GHz Band

The 24.25-27.5 GHz frequency band identified for International Mobile Telecommunications (IMT) in the World Radiocommunications Conference 2019 (WRC-19) has generated concerns in the weather data prediction community [1], once the use of this band for future 5G system could impact the weather forecasts due to unwanted emissions of this new system. In this paper, we re-run baseline simulations between IMT-2020 and Earth-Exploration Satellite Service (passive) - EESS (passive) in the 24 GHz that supported WRC-19 decisions on IMT unwanted emissions limits to protect the EESS (passive). Additionally, we evaluate a new coexistence scenario, treated as sensitivity analysis, evaluating the impact of different elements spacing in Advanced Antennas Systems (AAS) expected to be used in real 5G millimeter wave deployments. Results show that the potential interference in the EESS (passive) increases for beamforming antennas that implement separation distance between elements higher than λ/2 in the vertical plane.
Passive UHF RFID Yarn For Temperature Sensing Applications

This paper presents an Ultra High Frequency (UHF) Radio Frequency IDentification (RFID) temperature sensor integrated into a textile yarn. The tag system consists of a thermistor of which the resistance is sensitive to temperature variation. The theoretical working principle is detailed and validated using electromagnetic simulation. The UHF RFID temperature sensor is fabricated using the E-Thread® technology. Experimental results demonstrate the potential of the sensor for detecting a temperature variation comprised between $25^{\circ}\mathrm{C}$ and $70^{\circ}\mathrm{C}$ through a variation of the reading distance between 4.5 meters to 6.2 meters.
Automated Model Transformation in modeling Digital Twins of Industrial Internet-of-Things Applications utilizing AutomationML

In recent years, the manufacturing industry sector has undergone major changes. Better known by the term Industry 4.0, this trend describes the transformation of centrally managed production systems towards decentralized value creation networks. As this leads to a strong increase regarding complexity, engineering such current or future manufacturing systems becomes a difficult task. An example for such a challenging engineering process is the selection of technical implementations fulfilling all requirements and performing the needed functions. In order to support this process, model-based systems engineering (MBSE) and its expressions like model-driven architecture (MDA) proved to be promising approaches. However, semi or fully automated model transformations, which could deal with the aforementioned issue, are still yet widely unexplored in the industrial area. Therefore, this paper introduces an approach transforming the logical architecture of a system into its technical implementation by utilizing AutomationML. As the goal is to perform this step automatically, additional tool support is provided by developing specific software. The results of this approach are thereby evaluated by a real-world case study, which is applied according to the concepts of the Reference Architecture Model Industrie 4.0 (RAMI 4.0).
Deep Reinforcement Learning Based Wireless Resource Allocation for V2X Communications

The shortage and low utilization of air-interface spectrum resources have always been the bottleneck of the development of vehicle-to-everything (V2X) communications. In this paper, we investigate the issues of resource blocks (RBs) sharing and vehicle transmission power allocation under orthogonal frequency division multiplexing (OFDM) technology to improve the utilization of spectrum resources. In order to meet the high data rate of vehicle-to-infrastructure (V2I) links and the high reliability of vehicle-to-vehicle (V2V) links, the optimization problem is defined as maximizing a weighted utility function that can jointly represent the different requirements of two types of V2X links. Considering the high mobility of vehicle environments, we propose an online distributed multiagent reinforcement learning (MARL) method to solve the above non-convex optimization problem and design the three elements of reinforcement learning. Simulation results demonstrate that this method can effectively improve V2X networks performance under the dynamic channel environment.
Frequency Domain Analysis and Convolutional Neural Network Based Modulation Signal Classification Method in OFDM System

Automatic Modulation Classification (AMC) is widely used in many aspects and occupies a critical position in non-cooperative communication. Recently, deep learning (DL) based AMC algorithms attract more and more attention due to the outstanding performance in modulation recognition. In this paper, we propose a novel convolutional neural network (CNN)-based AMC method that employs frequency domain analysis (FDA) pre-processing and $l_{2}$ regularization for the orthogonal frequency division multiplexing (OFDM) systems. Different from traditional algorithms, the proposed algorithm is superior in high accuracy of the classification even at low signal-to-noise ratios (SNRs) owing to pre-processing by FFT. Moreover, the adoption of $l_{2}$ regularization effectively suppresses overfitting. Simulation results are given to illustrate that our proposed method has an evidently advantage over traditional methods in classifying BPSK, 4PSK, 8PSK and 16QAM modulation techniques.
A call for more explainable AI in law enforcement

The use of AI in law enforcement raises several significant ethical and legal concerns. One of them is AI explain- ability principle, which is mentioned in numerous national and international AI ethical guidelines. This paper firstly analyses what AI explainability principle could mean with relation to AI use in law enforcement, namely, to whom, why and how the explanation about the functioning of AI and its outcomes needs to be provided. Secondly, it explores some legal obstacles in ensuring the desired explainability of AI technologies, namely, the trade secret protection that often applies to AI modules and prevents access to proprietary elements of the algorithm. Finally, the paper outlines and discusses three ways to mitigate this conflict between the AI explainability principle and trade secret protection. It encourages law enforcement authorities to be more proactive in ensuring that Face Recognition Technology (FRT) outputs are explainable to different stakeholder groups, especially those directly affected.
Optimized Incremental Selection Hybrid Decode-Amplify-Forward Relaying Cooperation for Average Transmission Power Reduction

To improve the spatial diversity gain and reduce the average transmission power in cooperative communications, a power-optimized channel status information (CSI) aware incremental selection hybrid decode-amplify-forward (ISHDAF) scheme with destination scheduling is proposed. In the optimized scheme, three cooperative modes of amplify-forward (AF), decode-forward (DF), and source retransmission are combined to increase the diversity gain. In addition, it reduces the average transmission power by lifting the probability of using lower-power relay cooperation. Simulation results show that the proposed scheme obtains a total power gain of 0.8w at outage probability of 10−1 when compared with that of the existing incremental selection decode-forward (ISDF) scheme. Under the same performance, when the relay distance factor ratio is 0.9 and the total system power is 10w, the proposed scheme after power allocation outperforms the traditional ISHDAF scheme by about 1w average transmission power.
The Effect of Interruption Modality on Primary Task Resumption

The majority of empirical papers investigating the effect of interruption modality on primary task resumption have been grounded in Multiple Resource theory; this theory stresses the benefits of cross-modal information presentation. Alternatively, Altmann and Trafton's (2002) Memory for Goals theory suggests that maintaining an association between the suspended primary task goal and relevant environmental cues is critical to the task resumption process. Using reaction time and eye movement measures, the theoretical predictions of these two frameworks were empirically examined to determine whether interruption modality influences primary task resumption.
Lessons learnt from deploying an IoT sensing system for e-Agriculture in South Africa

One of the initiatives contributing to meeting the agricultural needs of farmers, particularly in the underserved communities in resource-constrained environments, is the Leap Agri project. This paper presents one of the outcomes from the Leap Agri project, specifically an Internet of Things (IoT) system which consists of Leap Agri Farmer and Leap Agri Sensor applications, as well as the lessons learnt during the initial stages of the prototyping of the sensing system in a South African environment. The paper sought to provide an answer to the research question which is “what are the lessons learnt during the early stage of the adaptation of an IoT-based mobile system for e-Agriculture in a South African environment? ”. The observed experience of assembling the IoT sensing system is described in this paper. From the observations, several lessons are deduced to inform future exploration of the Leap Agri applications and the sensing box.
Dynamics Modeling and Control of Humanoid Robot Arm with 7DOF Actuated by Pneumatic Artificial Muscles

The robotic arm driven by pneumatic artificial muscle has a configuration similar to that of a human arm, and has essential compliant characteristics, which has important research significance for the development of Tri-Co (coexistence-collaboration-cognitive) robots. In order to realize the real-time motion control of the humanoid robotic arm, it is necessary to model the kinematics and dynamics of the robotic arm. In this paper, the Modified Denavit-Hartenberg (MDH) method is used to model the forward kinematics of the humanoid robotic arm, and the Newton-Euler Dynamics method is used to model the dynamics of the humanoid robotic arm. Research shows that the trajectory of the human arm in free space satisfies the Logistic function, and the Logistic function is used as the curve of each joint of the humanoid robotic arm. According to the established model, the computed torque control scheme is used to realize the control of the humanoid robotic arm. Simulation experiments show that each joint of the humanoid robotic arm can accurately track the desired joint angle curve.
$H_{\infty}$ Performance Robustness Analysis of Fractional-Order Systems with Structured Perturbations

This paper considers the $H_{\infty}$ performance robustness analysis of fractional-order systems with structured perturbations. Based on the assumption that the nominal fractional-order systems satisfies $H_{\infty}$ performance, innovative conditions for checking whether the fractional-order systems with structured perturbations preserve the $H_{\infty}$ performance are given. Then, the explicit robustness bounds of preserving the $H_{\infty}$ performance of fractional-order systems with structured perturbations are established. The results are derived by using $\mu$-analysis. The final two numerical examples are provided to illustrate the obtained methods.
The Participation and Position of Chinese Equipment Industry in Global Value Chains

This paper uses the value-added method to measure the participation and position index of China's equipment industries in global value chains (GVC) based on the World Input Output Tables from 2000-2014. The results illustrated that the participation of China's equipment industries in GVC have a slow upward trend during this period, and has a higher index compared to other countries. Furthermore, the composition between China with six representative countries show foreign value-added is higher than the indirect value-added, and the position index is always negative during that period. This means that a large percentage of intermediates is used to export Chinese equipment industries to manufacture final products. The negative position index indicates Chinese equipment industries remaining in the downstream of GVC, where Chinese equipment industries are mainly in at the lower level of production processes with low value created