Reviewerly/exHarmony huggingface dataset:
A Quantitative Analysis and Planning Model for Strategic Management Resource Allocation Demand

Focusing on the support for strategic management link assessment, this paper presents an exploratory method for the formalized description and quantification of resource allocation demand. While addressing the needs for strategic management capability and quantitative analysis assessment, the methods, techniques and ways adopted in the automata theory are borrowed to analyze the problems. The basic connotations of resource allocation demand are studied with demand analysis and optimization. In this way, a "five-step method" is developed and proposed for resource allocation demand planning. This proposed method can meet the general requirements for scientific resource allocation demand planning. Therefore, it provides an effective way and solution to systematically integrate construction resources, scientifically change the way of allocation, and enhance the efficiency of resource utilization.
Implementation of improved RGBD 3D target detection model based on FPGA heterogeneous computing architecture

In order to solve the problems of low model accuracy, poor computing power, poor parallel ability and excessive power consumption in the deployment of RGBD based 3D target detection model at the embedded end, this paper first proposes an improved RGBD 3D target detection model based on ENet semantic segmentation model, which takes ENet as the semantic segmentation network, RGB image and depth information are fused to realize 3D target detection. Secondly, in order to apply the model at the edge, this paper constructs a lightweight network and cuts the network in the down-sampling stage of ENet model. Finally, this paper uses Xilinx ZCU104 as the hardware development kit, which takes FPGA as the auxiliary parallel operation unit and ARM as the main operation unit. It is a heterogeneous computing architecture with the ability to deal with complex operations. The architecture uses FPGA to accelerate the depth model in parallel, which improves the operation speed and reduces the power consumption. The test results of the model on ZCU104 are compared with other hardware. The results show that while ensuring the accuracy, the power consumption of the heterogeneous computing architecture used in this paper is 93% lower than that of Intel Xeon e5-2620 v4 CPU, the speed is 12 times higher, and the speed is more than 180 times higher than that of ARM Cortex-A53 commonly used at the edge.
Modeling Method for Photosynthetic Rate Based on Error Compensation ELM

Due to the complexity of crop photosynthesis, a single date-driven photosynthetic rate model is difficult to accurately reflect its actual changes, resulting in errors between the model output and the actual value, which is difficult to meet the needs of optimal control of greenhouse environment considering crop growth information. Therefore, a modeling method based on error compensation ELM is proposed to establish the photosynthetic rate model. In this method, on this basis of the initial model established by extreme learning machine (ELM), the Gaussian mixture model (GMM) is used to describe the error mean of photosynthetic rate model, and then realize the error compensation of the initial model. Finally, simulation results show the effectiveness of the proposed modeling method.
A Neural Component Analysis Aided Extreme Learning Machine Method for Power System Transient Stability Assessment

In recent years, the transient stability assessment (TSA) of power systems has attracted more and more scholars’ attention. However, the scale of the power system is gradually increasing and the corresponding characteristics are gradually increasing, which makes the accuracy of TSA greatly compromised. Therefore, it is not appropriate to directly use the obtained original data for the assessment. Considering that there are generally nonlinear characteristics in the data of the power systems, this paper adopts a recent neural network dimension reduction method to process the data first, and then performing extreme learning machine (ELM) on reconstructed data to realize TSA. The proposed TSA model can remove redundant and noisy information in the original data, thereby improving the accuracy of evaluation. Finally, the IEEE 10-machine, 39-bus New England simulation system is adopted to demonstrate the validity and superiority of the proposed model.
Implementation of an Algorithm for Dynamic Environment Mapping and Obstacle Avoidance With ROS

To solve the problem that indoor mobile robots always collide with obstacles during navigation, this paper proposes a dynamic environment mapping and obstacle avoidance algorithm with ROS. Simultaneous Localization and Mapping are realized through the Gmapping algorithm and the Cartographer algorithm respectively. Through comparison, it is found that the mapping effect of the Cartographer algorithm is more ideal. Apply A* algorithm to realize global path planning. Different from the traditional local obstacle avoidance algorithm, this paper applies the TEB algorithm to the Ackerman robot platform to achieve local path planning. A method of writing scripts to realize the multi-point cruise function of the robot is proposed and experiments are carried out in the simulation environment and the real environment respectively. Finally, experiments prove that the method applied in this paper makes the indoor mobile robot’s dynamic obstacle avoidance better.
Research on Gas Emergency Disposal Method Based on Knowledge Graph

When a gas accident occurs, relevant departments need to quickly determine an effective decision-making and disposal plan. However, most of the current gas accident emergency response plans are stored in text form. On-site disposal personnel need to consult a large number of texts to determine the corresponding disposal measures, and the timeliness is poor. In order to improve the reusability and flexibility of the gas emergency response plan stored in the form of text, and overcome the shortcomings of poor timeliness, this paper constructs a top-down and bottom-up gas emergency response knowledge graph. According to the classification of gas accident cases, a knowledge graph is constructed by combining TextRank keyword extraction, expert experience and semantic role labeling, and the feasibility of the method is verified by cases. This will be a promising decision-making method for gas emergency disposal.
Vehicle recognition based on spatial pyramid networks

At present, there are more and more monitoring facilities on the road, and the number of vehicle monitoring images increases exponentially, which undoubtedly poses a new challenge to vehicle recognition technology. In view of the complex background of road condition information, making effectively extract the vehicle feature information from the background is the key to process the monitoring data. In this paper, an RMSProp improvement algorithm is proposed by using Weber’s law to predict the noise of selected sample sets, and to adjust the decay rate with the adaptive method. This algorithm can not only effectively improve the accuracy of vehicle data recognition and reduce the possibility of local optimum, but also improve the convergence performance of neural network structure. Secondly, considering the complicated background of vehicle images in road monitoring and the mutual occlusion between vehicles, this paper proposed a feature extraction method named Max-weighted pooling and Imp-convNet-5 to further improve the performance of vehicle recognition. We combined the imp-convnet-5 model with the spatial pyramid model, and applied the Max-weighted pooling method into it to form a new vehicle recognition model. Finally, MNIST, Cifar-10 and UA-DETRAC datasets are used in this paper to test our proposed algorithm. Through experiments, it can be seen that the proposed model algorithm has made significant improvement in improving the recognition accuracy and reducing the training loss. It is proved that the proposed algorithm model is a reliable and effective recognition model.
Intelligent optimization of process parameters to corn straw pretreatment

For the optimization of pretreatment process parameters of corn straw, an intelligent optimization method is used, which combines the prediction model and the optimization algorithm. The support vector machine (SVM) prediction model for biogas production is constructed. And the optimal process parameters are obtained by grasshopper optimization algorithm (GOA) on the prediction model. Due to the difficulty of obtaining experimental data, the problem of small samples during the model training exists. So, the virtual samples are generated by uniform interpolation combined with GOA to improve the model prediction accuracy. Through the experiment of dual-frequency ultrasound combined with alkali pretreatment of corn stover fermentation, the effectiveness of virtual sample generation and the feasibility of intelligent process parameter search are verified.
Energy-Efficient Motion Planning and Control for Robotic Arms via Deep Reinforcement Learning

With the optimization of China’s energy supply structure, the issue of efficient energy use has attracted widespread attention from researchers [1]. The robotic arm, which is widely used in industrial production, comes with high energy usage. In order to reduce energy usage, we propose an energy-efficient motion planning and control method based on Reinforcement Learning (RL) to reduce energy consumption during operation. The motion planning and control policy is learned in a model-free manner using RL, making it effective even in complex industrial environments. The main goal can be divided into three parts: moving to the target location, obstacle avoidance, and energy saving. From this, a comprehensive and effective reward function is designed using a distance reward, a velocity reward, an obstacle avoidance reward, and an energy-saving reward. Using this reward function, a control policy is trained which balances the aforementioned goals. Finally, the algorithm is verified by using the trained policy to avoid obstacles in a simulation environment. Extensive experimental results have demonstrated the effectiveness of the algorithm proposed, and show that the learned control policy can make the robot move with less energy whilst avoiding obstacles.
Activity Image-to-Video Retrieval via Domain Adversarial Learning

With the rapid development of cross-modal retrieval, Activity Image-to-Video Retrieval (AIVR) aiming to retrieve videos containing similar activity as the query image, has attracted much attention. The core of AIVR is to learn motion information from the video to assist retrieval. In this paper, we present a novel Adversarial Activity Image-to-Video Retrieval (AAIVR) framework that acquires the video motion information by using domain adversarial learning. Domain adversarial learning is performed as an interplay between two processes. Firstly, representation disentanglement through encoders, tries to disentangle the video features into motion features and appearance features. Meanwhile, image appearance features from the query image are obtained. Then, we perform the image-to-video domain transfer to eliminate the modality inconsistency. A binary discriminator is designed to verify the transfer result. During testing, we conduct retrieval in both appearance and video feature spaces simultaneously. Extensive experimental results on two benchmark datasets show that the proposed AAIVR method significantly outperforms the state-of-the-art methods.
Research and Simulation on Aircraft Bidirectional Dynamic Track Search Algorithm

Aiming at the problem of dynamic programming algorithm costs too much time in 2-D environment, the bidirectional dynamic programming algorithm is proposed. The new algorithm utilizes the limited decision of single way dynamic programming algorithm and reduces the number of state points in calculation process to improve efficiency, then, the optimal intersection stage of bidirectional dynamic programming algorithm is proposed, the feasibility and global optimality of new algorithm are proved by mathematics. The simulation results show that the new algorithm can improve efficiency of aircraft track search.
Text Sentiment Analysis Based on Similarity and Cloud Model

Text sentiment analysis is an important research topic in natural language processing field. There are many problems in existing research methods due to semantic incoherence and independent topics. To address these problems, a comprehensive rating model is proposed in this paper for text sentiment analysis. It integrates topic similarity with cloud model theories. Firstly, LDA model is adopted to extract topic features. A sentiment revision algorithm based on cloud model is proposed to optimize the word sentiment polarities. Secondly, cosine similarity theory is introduced to measure the potential correlation of topics. Then, aspect segmentation algorithm is performed to obtain feature matrix and feature weight of reviews. Finally, feature parameters are optimized and the feature ratings of the reviews are concluded on the basis of above results.
Configurable Graphic Processing Unit Oriented OpenCL Kernel Performance Prediction Based on LSTM and Transformer

With the rapid development of big data and artificial intelligence, there is a huge demand for high performance computing. The heterogeneous computing is a running efficiently and cost effectively solution to combining the central processing unit, graphics processing unit (GPU), digital signal processor and other types of processing elements to reduce power consumption and improve capacity. The OpenCL is a common standard for programming heterogeneous devices. The OpenCL code can be applied for the different hardware equipments, but the code needs to be re-adjusted to change the parameter combination of the OpenCL kernel in order to achieve the best performance on the OpenCL device. However, there are thousands of combinations based on OpenCL kernel parameters, and testing all of them is going to consume huge time and resource costs. To solve this issue, the integration of bidirectional long short-term memory (LSTM) and the transformer with multi-head attention mechanism is proposed to predict the performance of the OpenCL kernel with various parameter combinations. In order to evaluate the availability and effects of the LSTM-Transformer deep learning model, the performance predictions of the parameterizable single-precision general matrix multiply GPU kernel with 241600 parameter combinations are implemented and verified. The sequence forecasting effects of the hybrid deep learning model are contrasted with those of the classical machine learning models, and the LSTM-Transformer model converges faster and has higher accuracy compared with other machine learning models.
A Hand-Eye Calibration Method based on Nonlinear Joint Optimization for RGBD Cameras

Hand-eye calibration is a basic research topic in the field of robotic vision. At present, most works focus on the hand-eye calibration of RGB cameras, while methods for calibration of RGBD cameras are relatively rare. Two popular kinds of modes for RGBD cameras are based on the stereo vision and the structured light, which both contain at least two cameras for generating the color image and the depth image, respectively. Motivated by this special structure feature, the paper proposes a novel hand-eye calibration method for RGBD cameras by integrating the vision system and the hand-eye robotic system into a whole model. Specifically, the traditional hand-eye calibration method is firstly used to obtain initial values of hand-eye matrices of the two cameras in the RGBD camera separately. Then, a nonlinear joint optimization algorithm is carried out to refine hand-eye matrices, in which the objective is to minimize the joint reprojection error of feature comers in images outputting by the two cameras in the RGBD camera. Thus, the optimized hand-eye calibration matrices can be finally obtained for both cameras. The proposed calibration approach is validated on the robotic hand-eye system, and the experimental results demonstrate that the presented algorithm greatly improves the calibration accuracy compared with the traditional methods.
Deep Convolutional Correlation Filter Learning Toward Robust Visual Object Tracking

Recently, convolutional neural network has been pervasively adopted in visual object tracking for its potential in discriminating the target from the surrounding background. Most of the visual object trackers extract deep features from a specific layer, generally from the last convolutional layer. However, these trackers are less effective, especially when the target undergoes drastic appearance variations caused by the presence of different challenging situations, such as occlusion, illumination change, background clutter and so on. In this research paper, a novel tracking algorithm is developed by introducing an elastic net constraint and a contextual information into the convolutional network to successfully track the desired target throughout a video sequence. Hierarchical features are extracted from the shallow and the deep convolutional layers to further improve the tracking accuracy and robustness. As the deep convolutional layers capture important semantic information, they are more robust to the target appearance variations. As for the shallow convolutional layers, they encode significant spatial details, which are more accurate to precisely localize the desired target. Moreover, Peak–Strength Context–Aware correlation filters are embedded to each convolutional layer output that produce multi–level convolutional response maps to collaboratively identify the estimated position of the target in a coarse–to–fine manner. Quantitative and qualitative experiments are performed on the widely used benchmark, the OTB–2015 dataset that shows impressive results compared to the state–of–the–art trackers.
The algorithm of coordinate calibration for multiple millimeter wave radars system

Localization and tracking of single millimeter wave radar usually make mistakes when outliers and virtual tracks are generated by multipath effect. Cooperation of multiple millimeter can effectively filter outliers and virtual tracks. However, each radar has its own coordinate system, they are different. The basis for cooperation of multiple radars is coordinate calibration. Thus, this paper proposed an algorithm to acquire the translation matrix and rotation angle which are used for coordinate calibration of multiple millimeter wave radars. Finally, numerical experiments validate that tracks generated by real target will coincide together after the optimal translation and rotation angle. On the contrary, if tracks are not generated by real target, they cannot coincide. This method can be used to filter tracks generated by multipath effect effectively.
Sequential Fast Covariance Intersection Fusion Kalman Filter For Multi-Sensor Networked Systems with Correlated Noises and Random Uncertainties

For the multi-sensor systems with correlated noises and random uncertainties, a new augmented state approach with fictitious noises and a Sequential Fast Covariance Intersection (SFCI) fusion Kalman filter are presented. These algorithms can compensate the mixed random uncertainties with the fictitious noises, which include the random one-step measurements and missing measurements, and decrease the large computational burden. Furthermore, the SFCI fusion algorithm overcomes the drawback of the Sequential Covariance Intersection (SCI) fusion, whose fusion result is sensitive to the fusion orders, and improves the accuracy and efficiency. The accuracy of the presented SFCI fusion Kalman filter is higher than that of each local estimator, less but close to that of the information fusion Kalman estimator weighted by matrices. Finally, the effectiveness of the SFCI fusion Kalman algorithm is verified by the simulation example.
A Combat Decision Support Method Based on OODA and Dynamic Graph Reinforcement Learning

Modern network centric joint combat greatly increases the complexity of war, and the demand for intelligent decision-making is becoming stronger. In this paper, a combat decision support method based on graph neural network and reinforcement learning is proposed. Based on OODA cycle theory, TSDI combat network is constructed, and the characteristics of nodes, OODA loops and network topology are extracted. A graph neural network GMQN which can adapt to dynamic network and dynamic action space is designed as the built-in neural network of reinforcement learning. The global joint action is obtained by combining GMQN and Kuhn-Munkras algorithm. Finally, the effectiveness and advantage of the proposed method are verified on the joint combat simulation platform.
An Improved Algorithm Design and Implementation Based on Unsupervised Learning in Computer Games

At present, the computer games with the intelligent algorithm optimization are always a popular research direction of the artificial intelligence. In this research field, the key concerned technologies and the important resolve are how to continue to gain the final winning rate constantly by independent identification realization, movement optimization and real-time update games based on the unsupervised learning algorithm in the computer chess games. According to the typical artificial intelligence application scene of computer games, an improved algorithm based on the unsupervised learning is designed and proposed in the paper. This algorithm uses alfa-beta search tree to select the best matching pattern and applies the concept of feature learning to make the image recognition innovatively. With the characteristics of the Checkers and the common search methods, it proposes a feature and pattern learning method based on the K-means clustering algorithm. By the experimental data and results, the improved algorithm proposed in the paper can gain and keep a high winning rate in the real games.
Research of Topic Detection Integrated with Correlation and Graph Analysis Theory

How to fast and effectively detect valuable topics and their developing trends from enormous texts has become a huge technical challenge. Topic detection is an important tool for implementing this task. Currently, most methods carried out their research under the assumption that topics were independent, which ignored inherent relationships between topics. In order to solve the above problems, topic correlation and graph analytical methods are integrated to establish a theoretical framework for topic detection. Firstly, correlated topic model is introduced to measure topic relevance and generate semantic term graph. Secondly, correlation graph algorithm is proposed in which the quantified topic correlation is adopted to extract co-occurrence relations. Co-occurrence term graph is obtained to realize the mining of important and hidden topics. Topic correlation is simultaneously used in analyzing semantic and co-occurrence relationships in our approach to discover meaningful topics and their changing trends. The simulation results verify the validity of the proposed theory.
Vision-based Adaptive Tracking Control of a Mobile Robot: Algorithms and Experimental Validation

This paper mainly investigates the problem of vision-based tracking control for a mobile robot. A RGB-D camera is used to obtain the image with depth information. The proposed visual tracking algorithm, namely the multi-feature fusion Kernel Correlation Filters (MF-KCF), enhances the tracking capability by adding image features and dynamically adjusting scale factor and model updating strategy. To handle the issues with unknown velocity of the moving target and field-of-view for the camera, an adaptive controller is designed by incorporating the prescribed performance bound (PPB) technique. Finally, an experimental platform based on ROS (Robot Operating System) is established which contains mobile base, visual sensor and Jetson TK1, etc. The MF-KCF algorithm and PPB-based adaptive controller are successfully applied to the actual experiment to verify the effectiveness of the adaptive vision-based tracking system.