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Oilfield Sewage System Scheduling is a complicated, large-scale, nonlinear system problem with multiple variables. The complexity of the sewage system pipeline network connection grows along with the ongoing building of oilfield stations, and the shortcomings of the sewage system water quantity scheduling program based on human experience decision-making become increasingly apparent. The key to solving this problem is to realize the digital and intelligent scheduling of sewage systems. Taking the sewage system of an oil production plant in Daqing oilfield as the research object, the water scheduling model of the sewage system is established in this paper. Aiming at the complex nonlinear characteristics of the model, the Levy flight speed updating operator, the adaptive stochastic offset operator, and the Brownian motion selection optimization operator are established by taking advantage of the particle swarm optimization (PSO) and the cuckoo search (CS) algorithm. Based on these operators, a hybrid PSO-CS algorithm is proposed, which jumps out of the local optimum and has a strong global search capability. Comparing PSO-CS with other algorithms on the CEC2022 test set, it was found that the PSO-CS algorithm ranked first in all 12 test functions, proving the excellent solving performance of the PSO-CS algorithm. Finally, the PSO-CS is applied to solve a water scheduling model for the sewage system of an oil production plant in Daqing Oilfield. It is found that the scheduling plan optimized by PSO-CS has a 100% water supply rate to the downstream water injection station, and the total energy consumption of the scheduling plan on the same day is reduced from 879.95 × 106 m5/d to 712.84 × 106 m5/d, which is a 19% reduction in energy consumption. The number of water balance stations in the sewage station increased by 7, which effectively improved the water resource utilization rate of the sewage station. [ABSTRACT FROM AUTHOR]

With the continuous economic growth, the number of power customers has increased significantly, and consumers in the field of power marketing will inevitably have a credit crisis. In order to reduce the business risk of relevant departments and improve the risk prediction ability of the system, this paper evaluates and reviews the user credit system. In this paper, the basic structure of BP neural network is described firstly, and then, the traditional BP neural network model is optimized after analyzing its algorithm flow. Based on this point, this paper analyzes the characteristics of customers in the energy and electricity market in the research area, and referring to local experts who have been engaged in power sales for many years, this paper puts forward a new set of directly scored power system load forecasting index system and algorithm improvement scheme and discusses the evaluation of power market credit rating based on the credit evaluation suggestions of power customers. After establishing the judgment criteria, in this paper, the power load data of the target area are studied by empirical analysis method and select three different customers from the production area, commercial and residential areas and residential areas as cases to analyze the determination of their credit rating and then, discuss the results of regional power load forecasting. Finally, this paper puts forward a kind of power management method based on the user's credit rating, and in order to complete the modernization transformation of power management system and promote the market development. In this paper, after improving and optimizing the traditional BP neural network, it is applied to the power market to predict the target user credit system, so as to achieve the improvement of forecasting ability. [ABSTRACT FROM AUTHOR]

Precision marketing is faced with multiple levels of problems, such as pollution of the data environment and unscientific algorithms, which need to be sorted out urgently. Based on neural network technology, this paper constructs a neural network-based precision marketing model and focuses on data mining to study user churn prediction and user value enhancement, which are the two most important factors affecting marketing revenue. Moreover, this paper conducts an empirical test of the product strategy and market strategy adopted by big data precision marketing. According to the characteristics of the user population and the application scenarios of the product, this paper puts the corresponding precision marketing methods in a targeted manner and analyzes the performance of the model through experimental research. The research results show that precision marketing methods based on big data information platforms need to be more detailed and more comprehensive. At the same time, precision marketing methods need to correspond to the sensitive information characteristics of target users and consider the background and current situation of actual market execution to effectively play it role. [ABSTRACT FROM AUTHOR]

The feature extraction speed of the traditional athlete motion retrieval algorithm is slow, and it often takes dozens of minutes or even hours to analyze a video. The speed of this feature extraction obviously cannot meet the needs of big data video analysis. In response to these two problems exposed by Action Bank under large-scale data, this paper proposes to apply the template learning method based on spectral clustering to Action Bank, which replaces the cumbersome manual selection template step and is easy to generalize to different databases. Moreover, in view of the disadvantage of slow speed of extracting Action Bank features, this paper proposes a fast algorithm for accumulating Action Bank. In addition, this study uses the lookup table method instead of the time-consuming steps of the correlation distance calculation in template matching, which greatly accelerates the time of feature extraction. Finally, this study design experiments to analyze the performance of the algorithm. Through research, it can be seen that the algorithm of this study can be applied to athletes' sports retrieval and has certain recognition effects. [ABSTRACT FROM AUTHOR]

Humans have the ability to quickly identify their own environment, understand, judge and analyze it, which is one of the important reasons why human beings can survive in nature for a long time and gradually develop it into a prosperous society today. The key to human's ability to perceive and understand the environment lies in the ability to accurately find and identify objects, understand and describe visual scenes, and even express emotions on this basis. And if computers can realize automatic and accurate image recognition, and even understand the semantics of images correctly, it will surely improve and facilitate human life. Based on this, the author integrated and optimized the computer image recognition system and applied it in this paper. The core technology of the system is to improve the image algorithm, which can complete the training, testing and classification of target images. The experimental data are available. Choosing this algorithm to improve the learning and training of the data generated by the original image processing is more effective than directly training the original image. [ABSTRACT FROM AUTHOR]

With the progress of science and technology and the continuous deepening of audit information construction, audit has gradually separated from the traditional paper vouchers and inspection audit methods, and changed into the application of artificial intelligence, system information technology methods such as cloud technology and mobile Internet. block chain technology, as an advanced information technology, is highly valued by financial accounting experts and scholars due to its powerful data analysis methods and independent distributed architecture systems, which will also cause breakthrough changes to the audit industry. In terms of China's development, block chain technology has been successively used in commodity traceability, financing mode and other fields. In addition, it has become the backbone of the rapid technological, organizational and efficient changes of China's social system. [ABSTRACT FROM AUTHOR]

The Beluga Whale Optimization (BWO) algorithm is a meta-heuristic optimization algorithm proposed to simulate the life behavior of the beluga whale population, which has the advantages of simple design and easy implementation, less parameter adjustment and fast convergence. This paper proposed a white whale optimization algorithm (TWBWO) based on chaotic backward learning and water wave algorithm improvement to address the problems that the original white whale algorithm has insufficient exploration and exploitation ability in the middle and late stages in some cases, the diversity and solution accuracy are reduced, and it is easy to fall into local optimality. Further improve the computational accuracy and convergence speed of Moby Dick algorithm, enhance the global search and jump out of local optimum capability. Combining chaotic mapping and backward learning strategies improves the quality and diversity of populations and speeds up the convergence rate. The refraction operation of the water wave algorithm (WWO) is introduced to avoid the algorithm from repeatedly falling into local optima and to improve the computational accuracy of the algorithm. The experimental results show that the TWBWO algorithm is superior to the original algorithm and other classical algorithms in terms of convergence speed and solution accuracy as well as stability,with better performance and better finding ability. [ABSTRACT FROM AUTHOR]

Unmanned tractors under ploughing conditions suffer from body tilting, violent shaking and limited hardware resources, which can reduce the detection accuracy of unmanned tractors for field obstacles. We optimize the YOLOv8 model in three aspects: improving the accuracy of detecting tilted obstacles, computational reduction, and adding a visual ranging mechanism. By introducing Funnel ReLU, a self-constructed inclined obstacle dataset, and embedding an SE attention mechanism, these three methods improve detection accuracy. By using MobileNetv2 and Bi FPN, computational reduction, and adding camera ranging instead of LIDAR ranging, the hardware cost is reduced. After completing the model improvement, comparative tests and real-vehicle validation are carried out, and the validation results show that the average detection accuracy of the improved model reaches 98.84% of the mAP value, which is 2.34% higher than that of the original model. The computation amount of the same image is reduced from 2.35 billion floating-point computations to 1.28 billion, which is 45.53% less than the model computation amount. The monitoring frame rate during the movement of the test vehicle reaches 67 FPS, and the model meets the performance requirements of unmanned tractors under normal operating conditions. [ABSTRACT FROM AUTHOR]

Analyzing and understanding emotional expressions in user comments is a crucial and complex task in business. Conducting text sentiment analysis is of great significance. This paper constructs a novel DIBTBL model that integrates an extended sentiment dictionary, an improved swarm intelligence algorithm, and deep learning techniques to accurately capture and analyze user emotions. Firstly, this paper expands the sentiment dictionary to extract emotion feature words from the review text. Secondly, the BERT model embeds these emotional feature words and pre-processed text into high-dimensional semantic space to obtain richer semantic representations and improve sentiment classification performance. Then, the TextCNN-BiLSTM feature extraction model is established to balance the grasping ability of local and global features. Fourthly, this paper innovatively improves the swarm intelligence algorithm BWO to optimize the parameters of the TextCNN-BiLSTM. Finally, MLP is employed for sentiment classification. The experimental data is crawled from Ctrip, China’s largest hotel booking website. In the comparative experiment, the proposed model achieves a higher accuracy than TBL, DTBL, BTBL, and IBTBL by 2.94%, 2.44%, 1.64%, and 0.66%, respectively. In addition, we compare the proposed model with seven advanced models in the open dataset waimai_10k. The experimental results indicate that this model outperforms all the other models, with an average improvement in accuracy of 8.21%. The study offers precise insights into user sentiment, assisting companies in better understanding and meeting customer needs.

Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of twostage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed. [ABSTRACT FROM AUTHOR]

Featured Application: A new algorithm for rapid determination of SBS content in asphalt based on infrared spectroscopy was provided and Upgraded method for asphalt sample preparation in FTIR testing in this research. Infrared spectroscopy SBS asphalt-modifier content detection technology has been applied in practical projects, which can effectively ensure the quality of pavement engineering. If the composition ratio of modified asphalt is not clear, the accuracy of the test results will be affected, which is the disadvantage of conventional infrared spectroscopy detection methods. In this paper, the influence of different kinds of additives on the accuracy of the infrared spectrogram and SBS content test was analyzed, and the microscopic mechanism of the effect of additives on the accuracy of SBS modified asphalt content testing was uncovered. Meanwhile, the sample preparation technique was improved and the testing technique was optimized. Results demonstrate that the improved standard curve algorithm eliminates the effect of additives on test accuracy, and the effect of additives on test accuracy was eliminated by improving the standard curve algorithm. Without the addition of additives, the content of the modifier is accurately measured by simplifying the boundary conditions of the test technique. The preparation of samples by the dissolution method has more advantages than the tableting method and can be used as a new sample preparation technology. Improvements in sample preparation techniques and standard curve algorithms improve the safety, convenience, applicability, and accuracy of detection techniques. [ABSTRACT FROM AUTHOR]

In recent years, global climate change caused by carbon dioxide emissions has attracted more and more attention. Adjusting the energy mix by predicting energy demands is currently a more effective way to address climate issues and energy supply issues. Based on the panel data from 1999 to 2018 in China, this paper designed a new hybrid prediction model to predict the future electricity consumption of Chinese residents by double improvement of particle swarm optimization. By comparing with the BP neural prediction model without mixing and several BP neural prediction models with other improved and mixed forms, the results show that the BP neural network hybrid prediction model with DPSO-BP is more suitable for forecasting the electricity consumption of Chinese residents. At the same time, the prediction results of the DPSO-BP prediction model show that the annual electricity consumption of Chinese residents will increase from 9685 (100 million kWh) in 2018 to 13,171 (100 million kWh) in 2025 in the next 7 years. The research results provide a reference for future scholars in the design of algorithms and provide suggestions for the government to adjust energy and avoid severe power shortages or surpluses. [ABSTRACT FROM AUTHOR]

Intelligent Transportation System (ITS) is created in the context of the full development and progress of contemporary science and technology, aiming to help people make the best choice, drive the best vehicle, and control the best road network traffic. Intelligent vehicle positioning and navigation system is a high-tech integrated system that applies automatic vehicle positioning technology, geographic information system and database technology, computer technology, multimedia technology, and modern communication technology. In this paper, based on the research of GPS and GIS technology, by analyzing and comparing the advantages and disadvantages of different optimal path planning algorithms, we propose to optimize the A* algorithm by using the minimum heap sorting method for nodes and combining the twoway search and hierarchical search strategies to complete the improvement of the A* algorithm. The experimental results show that the path search speed of the improved algorithm has been significantly improved and the path design optimization is obvious. The improvement algorithm proposed in this paper is feasible and reasonable, which is good for upgrading the in-vehicle navigation system and relieving urban traffic congestion.

Vehicle positioning and vehicle identification of natural scene images are an important part of intelligent transportation systems and unmanned driving research. In current situation, there are still some problems in vehicle intelligent wireless positioning. In order to improve the intelligent wireless positioning efficiency of vehicles, based on the convolutional neural network, this research combines the concept of deep learning to carry out algorithm innovation in the research. Moreover, this paper combines the actual vehicle positioning problem points to collect data, simulates the vehicle positioning situation in a variety of complex situations, and designs a controlled test to verify. The results show that the algorithm of this study has certain effects, which can provide reference for subsequent related research and has certain practical significance. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The determination of the optimal measurement area of the articulated arm measuring machine belongs to the multi-dimensional function optimization problem under complex constraints. To realize high-precision measurement of low-precision articulated arm measuring machine, we analyze the working principle and error source of the measuring machine, and establish the optimization target model of the optimal measurement area in this paper. We propose a method for determining the optimal measurement area of an articulated arm measuring machine based on improved FOA. The basic FOA algorithm is improved, the historical optimal individual and population centroid information are added in the population iteration update process, and the fruit fly individuals in each iteration are directly used as the taste concentration judgment value, which increases cooperation and information sharing among fruit fly individuals, and improves the global optimization ability and stability of the algorithm. In the designated area of the measuring machine, we have carried out comparative experiments on the optimization results of improved FOA and basic FOA, ACO, PSO, AL-SC-FOA, LGMS-FOA, IPGS-FFO. Experimental results show that the improved FOA, ACO, PSO, and IPGS-FFO algorithms do not fall into local optimum, and the optimal measurement area determined by them is consistent with the optimization results of other algorithms, and is superior to other algorithms in convergence speed and stability, so it is more suitable for determining the optimal measurement area of articulated arm measuring machine. [ABSTRACT FROM AUTHOR]

English part-of-speech intelligent recognition is the scientific and technological basis for the development of intelligent speech systems. The difficulty in the current English speech recognition system lies in the recognition of English parts of speech. In order to improve the effect of English part-of-speech recognition, this study builds the language rules and morphological models of English morphological forms based on machine learning algorithms. Moreover, this study proposes a stemming extraction algorithm and a syllable division algorithm based on English characteristic rules. By studying basic phrases in English, this study analyzes the compositional structure of phrases, and determines the basic phrase structure and composition rules of English such as noun, verb, and adjective. In addition, this research studies the basic English phrase recognition algorithm based on the rule method and the analysis of basic phrase ambiguity resolution. Finally, this study designs a control experiment to analyze the performance of the algorithm proposed in this paper model and confirm the classification algorithm. The research results show that the algorithm proposed in this paper has a certain practical effect. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the flight time problem of Unmanned Aerial Vehicles (UAV), this paper proposes a set of energy management strategies based on reinforcement learning for hybrid agricultural UAV. The battery is used to optimize the working point of internal combustion engines to the greatest extent while solving the high power demand issues of UAV and the response problem of internal combustion engines. Firstly, the decision-making oriented hybrid model and UAV dynamic model are established. Owing to the characteristics of the energy management strategy (EMS) based on reinforcement learning (RL), which is an intelligent optimization algorithm that has emerged in recent years, the complex theoretical formula derivation is avoided in the modeling process. In terms of the EMS, a double Q learning algorithm with strong convergence is adopted. The algorithm separates the state action value function database used in derivation decisions and the state action value function-updated database brought by the decision, so as to avoid delay and shock within the convergence process caused by maximum deviation. After the improvement, the off-line training is carried out with a large number of flight data generated in the past. The simulation results demonstrate that the improved algorithm can show better performance with less learning cost than before by virtue of the search function strategy proposed in this paper. In the state space, time-based and residual fuel-based selection are carried out successively, and the convergence rate and application effect are compared and analyzed. The results show that the learning algorithm has stronger robustness and convergence speed due to the appropriate selection of state space under different types of operating cycles. After 120,000 cycles of training, the fuel economy of the improved algorithm in this paper can reach more than 90% of that of the optimal solution, and can perform stably in actual flight. [ABSTRACT FROM AUTHOR]

Oilfield Sewage System Scheduling is a complicated, large-scale, nonlinear system problem with multiple variables. The complexity of the sewage system pipeline network connection grows along with the ongoing building of oilfield stations, and the shortcomings of the sewage system water quantity scheduling program based on human experience decision-making become increasingly apparent. The key to solving this problem is to realize the digital and intelligent scheduling of sewage systems. Taking the sewage system of an oil production plant in Daqing oilfield as the research object, the water scheduling model of the sewage system is established in this paper. Aiming at the complex nonlinear characteristics of the model, the Levy flight speed updating operator, the adaptive stochastic offset operator, and the Brownian motion selection optimization operator are established by taking advantage of the particle swarm optimization (PSO) and the cuckoo search (CS) algorithm. Based on these operators, a hybrid PSO-CS algorithm is proposed, which jumps out of the local optimum and has a strong global search capability. Comparing PSO-CS with other algorithms on the CEC2022 test set, it was found that the PSO-CS algorithm ranked first in all 12 test functions, proving the excellent solving performance of the PSO-CS algorithm. Finally, the PSO-CS is applied to solve a water scheduling model for the sewage system of an oil production plant in Daqing Oilfield. It is found that the scheduling plan optimized by PSO-CS has a 100% water supply rate to the downstream water injection station, and the total energy consumption of the scheduling plan on the same day is reduced from 879.95 × 106 m5/d to 712.84 × 106 m5/d, which is a 19% reduction in energy consumption. The number of water balance stations in the sewage station increased by 7, which effectively improved the water resource utilization rate of the sewage station.

Intelligent network teaching system provides learners with abundant teaching resources and convenient, excellent and efficient learning environment. However, network teaching resources are widely distributed and difficult to centralize. Resource sharing has become a key problem to be solved in the network environment. The current research on online education resource recommendation mainly focuses on offline education, and there are few studies on online education resources. Based on this, this study studies the link prediction methods in online education and establishes appropriate models for online education. In the research, through improved analysis of traditional algorithms, an improved neural network path sorting algorithm based on path sorting method is proposed. At the same time, we use the path sorting algorithm based on random walk model and neural network-path sorting algorithm to realize the link prediction problem in the online learning knowledge base. In addition, the performance analysis of the algorithm is carried out by contrast method, and the performance comparison analysis is carried out by combining various common traditional recommendation algorithms with the research algorithm of this study. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the basic bat algorithm such as slow convergence, low convergence precision and weak stability, this paper des igned an improved bat algorithm based on Gaussian disturbance and exponential decreasing strategy (GDEDBA). It introduced the exponential decreasing strategy into the speed update formula, which made the algorithm enter local optimization quickly and perform a precise search. It added the constructed Gaussian disturbance term to the local new solution generation formula, then made the information exchange and study between all particles in the local new solution and the current global optimal particles, prevented falling into local optimum and increased population diversity. It designed the disturbance control factor to control the disturbance range of Gaussian disturbance, enhanced the stability of the algorithm. The simulation results of 15 classical test functions show that the optimization performance of improved algorithm is significantly improved, the convergence speed is faster, the solution accuracy is higher, and the stability is stronger. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When marine deep-towed multichannel seismic data are processed, the description of the receiving array geometry significantly impacts the quality of the imaging profile. Therefore, achieving a highly precise description of the receiving array geometry is very important for the fine imaging of such data. While basic particle swarm optimization (PSO) is known for its ease of implementation and efficiency, it often exhibits a low convergence accuracy. Consequently, the PSO algorithm is improved by modifying the inertia weight and incorporating Gaussian mutation. In combination with the actual motion of the towing streamer during surveys, a strategy for inheriting particle positions is introduced. When each seismic shot is solved sequentially, the results from the previous shot can serve as the initial particle positions for the next shot. The results indicate that this strategy achieves superior fitness values and outperforms the basic PSO algorithm. This method exhibits simplicity, rapid optimization, and a favorable solution quality, thereby offering a valuable approach to deep-towed array geometry inversion. It enhances the efficiency of deep-towed seismic data processing and serves as a reference for similar applications. [ABSTRACT FROM AUTHOR]

Abstract Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.

The appearance and development of cracks in the concrete bridge will seriously affect the safe use of bridge buildings. In order to better satisfy the crack detection requirement, this paper comes up with an image preprocessing scheme combining multiple adaptive filtering and contrast enhancement based on the image processing technology of concrete crack, which can improve the removal effect of background noise and obtain the characteristic vein information of tiny cracks. Then we designed a local adaptive algorithm of Otsu threshold segmentation and integrated with modified Sobel operator for removing isolated noise spots, so as to extract the crack edge information and improve the positioning accuracy of the crack boundary. Furthermore, according to the image feature of the bridge crack edge, the target crack is identified as well as classified and the feature data is calculated. The results of case analysis show that the data processing precision of the detection algorithm can reach 0.02mm, which can satisfy the actual engineering detection requirements of concrete bridge crack. [ABSTRACT FROM AUTHOR]

We present a Graphic Processing Units (GPU) implementation of non-Newtonian Hele-Shaw flow that models the displacement of Herschel-Bulkley fluids along narrow eccentric annuli. This flow is characteristic of many long-thin flows that require extensive calculation due to an inherent nonlinearity in the constitutive law. A common method of dealing with such flows is via an augmented Lagrangian algorithm, which is often painfully slow. Here we show that such algorithms, although involving slow iterations, can often be accelerated via parallel implementation on GPUs. Indeed, such algorithms explicitly solve the nonlinear aspects only locally on each mesh cell (or node), which makes them ideal candidates for GPUs. Combined with other advances, the optimized GPU implementation takes ≈ 2.5 % of the time of the original algorithm. [ABSTRACT FROM AUTHOR]

Highlights • A new pressure drop limit has been added into the optimization criteria. • The previous integrated optimization algorithm of NTCOC is improved. • The improved code has been applied on the HCSB blanket for CFETR Phase II. • The computational processes with two methods are compared. Abstract Blanket is the key component of Chinese Fusion Engineering Test Reactor (CFETR). In our previous work, considering that the radial arrangement of the internal functional zones has the greatest influence on the blanket performances and is also the most important basis of the 3D design, NTCOC, a Neutronics/Thermal-hydraulic Coupling Optimization Code which adopts 1D neutronics and 2D thermal-hydraulic simplified calculation models, has been independently developed for facilitating the radial build design and optimization of the CFETR helium cooled solid breeder (HCSB) blanket. Through setting three leading design requirements as the optimization criteria, this code has been applied on optimizing the radial build arrangement of a conceptual design of HCSB blanket for CFETR Phase I, which preliminarily verifies the reasonability of the code. However, recently, the design phase of CFETR has gradually changed from I to II, the fusion power of which will be quintuple to the previous I. This means that the internal nuclear power deposition in each blanket component under II operation condition will be much larger than the previous I. Therefore, much larger coolant inlet mass flowrate will be needed and more breeder units should to be inserted, both of which will make the total pressure drop of blanket for Phase II much larger than the previous I and even exceed the limit of CFETR HCSB blanket greatly. This can’t be sustained in CFETR, a power plant that should produce electricity. To solve this problem, in this paper, the previous integrated neutronics/thermal-hydraulic optimization algorithm is improved by adding the new pressure drop limit into the optimization criteria. Then the improved code has been applied on optimizing the radial build arrangement of a conceptual design of HCSB blanket for CFETR Phase II through constant and variable step length two methods. The calculation results verifies the feasibility and reasonability of the improved optimization algorithm preliminarily. [ABSTRACT FROM AUTHOR]

Because of the large mining thickness at one-time for extra thick seam using fully-mechanized top coal caving method and nearly shallow buried depth, the characteristics of surface movement and deformation particularity is obvious. Under this condition, its surface subsidence prediction has a certain degree of deviation with ihe actual situation. This paper analyzed the partic-ularity of surface movement and deformation for extra thick seam using fully-mechanized top coal caving method and nearly shallow buried depth of the test area. Considering thick-hard rock layer on surface subsidence control function,and based on layered elastic beam slab strata of subsidence control theory and random medium theory,the authors established a set of surface subsidence prediction model which is suitable for nearly shallow buried depth and extra thick seam using fully-mechanized top coal caving method. Practical application shows that degree of agreement for the prediction model algorithm with the experimental results is good,which is suitable for surface subsidence prediction application in extra thick seam mining using fully-mechanized top coal caving method. The prediction model algorithm preferably solved the problems such as rapid boundary convergence and unsymmetrical basin using probability integral method and improved the prediction accuracy of surface subsidence. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are being employed in a growing variety of fields for efficient monitoring and data collection about events that would be impossible with conventional computing platforms. WSNs are self-organizing networks that collects and processes important information via their resource-constrained sensor nodes and provide valuable information about happening events. The unattended nature of sensor nodes in these networks makes them vulnerable to a wide range of threats, and the resource limitations imposed on their nodes complicate things even more while monitoring the target regions. This study relies on an identity-based encryption (IBE) approach to protect these nodes' resources while ensuring that they collect valuable data from the target regions. IBE simplifies the key generation process in these networks and at the same time improves the network security up to a sufficient level. For this purpose, an identity-based encryption online/offline (IBOOE) algorithm is proposed as a more acceptable solution to the security concerns of these nodes. IBOOE improves on the previous algorithm, i.e., IBE, by splitting the encryption into two phases: offline and online. The offline phase is mainly responsible for complicated and resource-intensive operations, e.g. exponential operation and pairing operation. The online phase, on the other hand, is mainly responsible for relatively easy calculations such as integer multiplication or hash operation. The comparison of the proposed IBOOE against the state of the art work, i.e., IBE, clearly demonstrates the efficiency and effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

Introduction: It is essential to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random forest is an excellent machine learning algorithm even without any modification. We propose a new random forest weighting method and apply it to the gastric cancer patient data from the Surveillance, Epidemiology, and End Results (SEER) program. We evaluated the generalization ability of this weighted random forest algorithm on 10 public medical datasets. Furthermore, for the same weighting mode, the difference between using out-of-bag (OOB) data and all training sets as the weighting basis is explored.Material and methods: 110 697 cases of gastric cancer patients diagnosed between 1975 and 2016 obtained from the SEER database were included in the experiment. In addition, 10 public medical datasets were used for the generalization ability evaluation of this weighted random forest algorithm.Results: Through experimental verification, on the SEER gastric cancer patient data, the weighted random forest algorithm improves the accuracy by 0.79% compared with the original random forest. In AUC, macro-averaging increased by 2.32% and micro-averaging increased by 0.51% on average. Among the 10 public datasets, the random forest weighted in accuracy has the best performance on 6 datasets, with an average increase of 1.44% in accuracy and an average increase of 1.2% in AUC.Conclusions: Compared with the original random forest, the weighted random forest model shows a significant improvement in performance, and the effect of using all training data as the weighting basis is better than using OOB data. [ABSTRACT FROM AUTHOR]

Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.

This paper analyzes the features and mechanism of the B algorithm proposed by Vincent D. Blondel et al., and discusses the possible impact of nodes' property on community structure detection. Then it proposes an improved algorithm for the B algorithm by reconstructing the initial network and controlling node (community) merging process in order to obtain better modularity and the corresponding network partition. The community structure detection experiments on computer simulation networks and actual networks, show that the improved algorithm we proposed is reliable and effective, which achieves a better network partition with a larger modularity and has shorter computation time. [ABSTRACT FROM AUTHOR]

Infrared spectroscopy SBS asphalt-modifier content detection technology has been applied in practical projects, which can effectively ensure the quality of pavement engineering. If the composition ratio of modified asphalt is not clear, the accuracy of the test results will be affected, which is the disadvantage of conventional infrared spectroscopy detection methods. In this paper, the influence of different kinds of additives on the accuracy of the infrared spectrogram and SBS content test was analyzed, and the microscopic mechanism of the effect of additives on the accuracy of SBS modified asphalt content testing was uncovered. Meanwhile, the sample preparation technique was improved and the testing technique was optimized. Results demonstrate that the improved standard curve algorithm eliminates the effect of additives on test accuracy, and the effect of additives on test accuracy was eliminated by improving the standard curve algorithm. Without the addition of additives, the content of the modifier is accurately measured by simplifying the boundary conditions of the test technique. The preparation of samples by the dissolution method has more advantages than the tableting method and can be used as a new sample preparation technology. Improvements in sample preparation techniques and standard curve algorithms improve the safety, convenience, applicability, and accuracy of detection techniques.