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Copyright of e-Education Research is the property of Northwest Normal University 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.)

This paper intends to solve the problems of cumbersome modification of station data in the traditional interlocking table structure and low efficiency in route search based on interlocking table, and to improve the convenience of data storage in the interlocking system and the efficiency of station route search. It presents a station model using an adjacency list structure, and on this basis, a new route search model using an improved depth first search algorithm. The proposed station model based on adjacency list can reduce the difficulty of station data maintenance, and improve the efficiency of route search when used together with an improved depth first search algorithm. [ABSTRACT FROM AUTHOR]

In the railway construction process, there are such problems as limited means of train positioning, lack of information exchange and multi-party joint control, and poor eff ect of personal safety protection. To solve these problems, this paper proposes a railway construction protection system, which builds on high-precision positioning using the Beidou system, and utilizes such technologies as 4G communication and secondary radar communication, to achieve train location tracking and train approach early warning. In terms of train approach early warning, the proposed system utilizes the multiple-redundancy early warning technology, which combines secondary radar projection with Beidou system projection for early warning. The multiple-redundancy early warning technology enables the detection of train approach in the construction area and the subsequent generation of early warning information even when only one early warning mechanism is effective due to such reason as the presence of tunnels, which ensures the reliability of system functions. [ABSTRACT FROM AUTHOR]

If there are trains with the same train number in the CTC system, the wireless route notice may be sent to a wrong train, which will cause confusion to the driver. To solve this problem, this paper proposes a solution to improve the wireless route notice transmission by the CTC system in GSM-R network covered sections. The proposed solution obtains the locomotive or EMU operation plan from the locomotive or EMU dispatching system through the CTC system. Then, it determines the leading locomotive's locomotive number or the EMU's train set number after matching with the actually received wireless train number information, to ensure that the CTC system sends the wireless route notice to the correct train. Thus, it can effectively prevent the incorrect transmission of wireless route notice by the CTC system without changing the wireless route notice transmission logic in the CTC system. [ABSTRACT FROM AUTHOR]

Aiming at problems such as high interclass similarity and large intraclass scale changes in Lugol's chromoendoscopy (LCE) images, this paper proposes a method for the detection of multiple esophageal diseases, which is based on Sparse R-CNN and equipped with multi-scale detail enhancement pyramid network (MDEPN) structure. In order to solve the problems of information loss and semantic difference in feature pyramid network (FPN) structure of Sparse R-CNN, the MDEPN structure firstly uses Gabor modulated convolution module to enhance the features of different scales, and uses Gabor's strong attention to direction and scale to improve the expression ability of texture information in the feature map. Secondly, the directional channel pooling module is used to extract the local directional similarity and the correlation between local and global features of different scale features, so as to reduce the semantic differences in the fusion of different scale features. After testing on a self-built dataset of multiple esophageal LCE lesions, the accuracy of mAP0.50 is 65.0%, 2.4 percentage points higher than that of the benchmark model Sparse R-CNN, and higher than other major detection methods. In addition, the designed MDEPN module can be integrated into other detection models as an independent structure to improve performance, and has certain versatility. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) identification is critical for the effective and healthy development of PV industry. PV recognition is hampered by the complex background and variable shape and color of PV panels in high-resolution remote sensing images. This paper proposes a method for accurately extracting photovoltaic land from high-resolution remote sensing images. The encoder and decoder functions in this network combine multi-layer features to combine rich semantic data. Important spatial and channel properties are captured using the global convolution and the dual attention mechanism, while some lost channel data are recovered using the channel fusion module. The proposed method can effectively solve the problems of photovoltaic panel blurred edges and adhesion. Experiments on open PV datasets show that the IoU of the proposed method in PV01, PV03, and PV08 is 87.02%, 92.98%, and 88.43%, respectively, when compared to U-Net, SegNet, DeepLabv3, and DeepLabv3+. Experimental results show that the proposed method can achieve high accuracy segmentation of photovoltaic panels in high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

In order to meet the downstream water demand in the open channel water transfer project, this paper considers it necessary to formulate the control scheme in advance. The design of feed forward control scheme is often complicated, and there are many influencing factors, which make it difficult for dispatchers to make decisions quickly. Based on the storage compensation method, this paper applies feed forward control to the section from Diaohe control gate to Jialihe control gate of the middle route of South-to-North Water Transfer Project. Based on the calculation results of storage compensation, the relationship between the initial flow, water diversion flow and feed forward control time is analyzed, and a fast calculation formula of feed forward control time with single and double variables is established. The influence of different factors on the downstream water level deviation of storage compensation control is explored, and the functional relationship between the initial flow, water diversion flow and the maximum deviation of the downstream water level is obtained. In order to solve the problem that the deviation of the downstream water level under the original storage compensation control is too large in the extreme working condition of engineering operation, the feed forward control of the channel pool is carried out by using the secondary storage compensation rule, which effectively reduces the downstream water level deviation. [ABSTRACT FROM AUTHOR]

In order to address common problems that occur when training personnel responsible for receiving and departing trains using the CBI simulation training system, a fault setting panel for receiving and departing trains is added to the existing CBI training equipment. This addition allows for fault setting and meets the training needs of the personnel. This paper proposes a design for the fault setting panel and for implementing related circuits by examining the training items for addressing the faults of the fixed signal interlocking equipment in stations and based on the working principle of TYJL-ADX CBI equipment. The experiments show that the method of adding the fault setting panel not only satisfies the "fail-safe" design principle but also can effectively solve the common problems in the simulation training system. This method is highly versatile and worth applying to other training equipment. [ABSTRACT FROM AUTHOR]

In order to investigate the dynamic stress characteristics of runner and distributor of pump-turbine, this paper establishes the solid domain models of stay vanes, guide vanes and runner, and carries out the FSI-based computation of runner and distributor. The calculation results show that the stress concentration at the "T" junction between the runner blade and crown is related to the vortex development state at the runner inlet the dynamic and static interference between runner and guide vanes is the main source of pressure pulsation in the vaneless zone, where the main frequency is the blade passing frequency ; the dynamic stress of guide vanes and runner is generally high because the pump-turbine is easily involved in the unstable area while working under smaller GVO (guide vanes opening) or low head conditions. The dynamic stress of guide vanes is greater than that of the runner owing to its constraint guidance effect on water flow, and increased flow rate is beneficial to reducing the dynamic stress of the runner. So for the purpose of improving the dynamic stress of runner, the measures could be ime, in order to solve the limitation of Shapley value method in solving the problem of joint scheduling gain allocation of multi-stakeholder cascade power stations, the paper introduces the idea of aggregation dimension reduction. The idea of the model is as follows : the increment of the cascade power generation benefits before and after the joint dispatching of the upstream power station is taken as the compensation benefit. Firstly, four different objective allocation methods are used to calculate the characteristic parameter indexes of the power station to form the basic decision matrix of TOPSIS. The TOPSIS principle is used to obtain the relative satisfaction of the power station to each allocation method. At the same time, the coalition game theory is used to calculate the power generation benefits of all possible coalition modes of the power station, and the compensation benefit allocation results based on Shapley value are obtained. Finally, the allocation coefficient obtained by TOPSIS principle is combined with the allocation coefficient obtained by Shapley value method to form a comprehensive allocation weight, which is applied to the compensation benefit allocation of eight-stage power station in the upper reaches of the Heihe River. The engineering application example shows that the method not only considers the marginal contribution of the union power station to the whole and the satisfaction degree of each power station, but also takes into account the difference of the characteristic index of the power station, and reasonably quantifies and distributes the power generation compensation benefit among the power stations. The allocation result is fair and reasonable and easy to be accepted by all parties, which can promote the power stations at all levels in the basin to participate in the joint dispatching so as to achieve the goal of maximizing the overall benefit. It has an important reference significance for the distribution of compensation benefits of a large number of cascade hydropower stations. [ABSTRACT FROM AUTHOR]

In view of the large number of joint power stations in the basin but the existence of disputes in the distribution of benefits, a more reasonable method of benefit distribution for cascade power stations is proposed. Based on Shapley value method and TOPSIS method, considering the contribution of power stations and the relative satisfaction of power stations with different objective allocation methods, this paper establishes a compensation benefit allocation model of cascade power stations based on TOPSIS-Shapley value method. At the same time, in order to solve the limitation of Shapley value method in solving the problem of joint scheduling gain allocation of multi-stakeholder cascade power stations, the paper introduces the idea of aggregation dimension reduction. The idea of the model is as follows : the increment of the cascade power generation benefits before and after the joint dispatching of the upstream power station is taken as the compensation benefit. Firstly, four different objective allocation methods are used to calculate the characteristic parameter indexes of the power station to form the basic decision matrix of TOPSIS. The TOPSIS principle is used to obtain the relative satisfaction of the power station to each allocation method. At the same time, the coalition game theory is used to calculate the power generation benefits of all possible coalition modes of the power station, and the compensation benefit allocation results based on Shapley value are obtained. Finally, the allocation coefficient obtained by TOPSIS principle is combined with the allocation coefficient obtained by Shapley value method to form a comprehensive allocation weight, which is applied to the compensation benefit allocation of eight-stage power station in the upper reaches of the Heihe River. The engineering application example shows that the method not only considers the marginal contribution of the union power station to the whole and the satisfaction degree of each power station, but also takes into account the difference of the characteristic index of the power station, and reasonably quantifies and distributes the power generation compensation benefit among the power stations. The allocation result is fair and reasonable and easy to be accepted by all parties, which can promote the power stations at all levels in the basin to participate in the joint dispatching so as to achieve the goal of maximizing the overall benefit. It has an important reference significance for the distribution of compensation benefits of a large number of cascade hydropower stations. [ABSTRACT FROM AUTHOR]

This paper proposes a scenario-based safety analysis method for fully automatic train operation systems. All the operation scenarios of fully automatic train operation systems are modeled, and the influencing factors in each scenario are identified, to quickly identify the possible operational risks and avoidance measures in each operation scenario of fully automatic operation systems. The eff ectiveness assessment of such measures is made on the basis of the residual risks. Thus, a series of protection measures through human intervention are obtained for various operating scenarios of fully automatic systems, which solves the problem that the systems provided by multiple suppliers cannot be analyzed as a whole, and improves the operation safety of fully automatic systems. [ABSTRACT FROM AUTHOR]

At present, the quality inspection of chemical fiber products mainly relies on the naked eye recognition of inspectors, which has strong instability, in addition, traditional image recognition technology needs to set specific model detection according to specific defects, and the versatility is weak. Therefore, a model based on Convolutional Block Attention Module and VGG with modified fully connected layer (CBAM-VMFC) for image recognition of chemical fiber cake defects was proposed. In order to classify and identify qualified images, large areas of tripwires filament, paper tube breakage and small areas of hairiness images were classified and recognized. Firstly, the sample imbalance problem was solved by oversampling; Secondly, the traditional three fully connected layers were adjusted to two fully connected layers of size 256 and 1, and the Dropout method was added to prevent overfitting; Finally, the CBAM module was added between the convolutional layer and the fully connected layer to obtain the features of interest based on the spatial domain and channel domain. The results show that this method can effectively reduce the amount of memory calculation by 440 MB, accelerate the convergence speed of the model, and effectively identify the appearance defects of fiber cake with an accuracy rate of 97. 5%, which meets the needs of industrial applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Currently, machine learning-based detectors are widely used to handle millions of Android malware, but they often suffer from poor anti-adversarial attack abilities. The research on the adversarial examples generation method of malware is helpful to promote the development of the field of malware detection. The adversarial examples generation technology in the black-box scenario is more in line with the real environment, but the effect is not good compared with the white-box scenario. To solve this problem, this paper proposes a black box malware adversarial examples generation method based on SNGAN, migrating the SNGAN approach to the malware domain from the image domain, adversarial examples are generated by the network of generator and replace detector iterative training, the training process is stabilized by spectral normalization. The proposed method can cheat the machine learning detectors by adding disturbance to the existing malware. Experimental results show that our method can avoid detection effectively from a variety of machine learning classifiers, and the feasibility and portability of the method are verified. [ABSTRACT FROM AUTHOR]

Tchebyscheff map is a typical chaotic map and the correlation functions play a key role in the study of its statistical properties. This paper aims at the solutions of a class of exponential Diophantine equations arising in the calculation of correlation functions of the Tchebyscheff map. To solve the equation, we firstly reduce it to a new Diophantine equation with strictly increasing exponentials and nonzero coefficients. Then we introduce the definition of "block" and classify the reduced equations based on the number of blocks constructing the equation, thus transform the problem of solving the equation to solve the new equations constructed by blocks. Finally we solve the equations constructed by one block and two blocks and exemplify the application of the main results. [ABSTRACT FROM AUTHOR]

On the basis of in-depth investigation of the problems existing in the signal simulation panel in the field signal construction, this paper analyzes the typical problems encountered in the use of the signal simulation panel in the training base, and puts forward the solution of "software + hardware" intelligent simulation panel for railway signal to solve the common problems such as poor versality, simple function and cannot be reused. The software is developed to realize free modification, drawing and configuration of station and yard plan; the hardware of simulation outdoor signal equipment is developed and expansion slots are reserved to improve the versatility of the equipment. Through application verification, it can meet the needs of the field signal construction simulation test or training base to replace outdoor station and yard signal equipment to carry out indoor interlocking practical training teaching, and has certain promotion value. [ABSTRACT FROM AUTHOR]

In order to solve the problem of insufficient analytical capabilities, application depth, and data innovation arising from the large amount of business data accumulated in the organization of heavy-haul railway transportation, and promote the application of big data technology in the field of heavy haul railway transportation, this paper proposes a design solution for a big data analytics platform in the field of heavy-haul railway transportation. Based on an analysis of the characteristics of heavy-haul railways, a framework for the platform is presented, which utilizes a hybrid architecture combining Hadoop and MPP (Massively Parallel Processing). The study also designs the application and technical architecture of the big data platform, and discusses the key technologies and business applications of the platform. Currently, the construction and operation of the heavy-haul railway big data analytics platform based on this design solution have been implemented [ABSTRACT FROM AUTHOR]

In response to the current demand for intelligent detection in rail transit, we need to strengthen the support role of technological innovation in rail transit, and apply new technologies to the field of subway construction and operation in a forward-looking manner to improve operation and maintenance capabilities. In view of the limited methods of current section condition monitoring and the low efficiency and poor accuracy of traditional manual measurement methods, this paper elaborates in detail on the use of different intelligent detection methods to solve the following problems: the hidden defects of ring network cables are difficult to detect; the field strength of the wireless communication system in subway tunnels cannot be continuously measured; the impact of foreign object intrusion in subway tunnels is significant. This study can improve measurement accuracy and efficiency, detect and solve abnormal problems in advance, and improve the safety of section equipment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 reinforcement learning, the policy gradient algorithm often needs to model the continuous-time process as a discrete-time process through sampling. To model the problem more accurately, it improves the sampling frequency. However, the excessive sampling frequency may reduce the training efficiency. To solve this problem, this paper proposed action stable updating algorithm. This method calculated the probability of action repetition using the change of the output of the policy function, and randomly repeated or changed the action based on this probability. This paper theoretically analyzed the performance of this method. This paper evaluated the performance of this method in nine different environments and compared it with the existing methods. This method surpassed existing methods in six of these environments. The experimental results show that this method can improve the training efficiency of the policy gradient algorithm in continuous-time problems. [ABSTRACT FROM AUTHOR]

To solve the decision problem in which the evaluations are multi-granular intuitionistic linguistic sets, this paper proposed a TODIM method based on relative entropy and binary entropy. Firstly, this method defined relative entropy and binary entropy of the intuitionistic linguistic sets to measure the difference and uncertainty of decision information. Secondly, it built an expert weighting model on the basis of relative entropy and binary entropy, and proposed attribute weighting models under the scenarios where the subjective weight was completely known, partially known and completely unknown. Finally, in order to gather the information of multi-granularity group decision information, this paper developed a multi-granular intuitionistic linguistic weighted arithmetic average (MIL-WAA) operator. The example analysis shows that this method can better measure the uncertainty and difference of decision information, and it considers the limited rationality behavior of decision-makers, so it is reasonable and effective. [ABSTRACT FROM AUTHOR]

The fine-grained event detection task at the sentence level aims at identifying and classifying triggers. To solve the problem of over-smoothing and lack of dependency type information in existing event detection methods, this paper proposed an event detection method based on dependency information and graph convolutional network (GCN）. This method firstly used bi-directional long short-term memory (Bi-LSTM) networks to encode the sentence, and constructed a multi-order syntactic graph and a dependent syntactic graph based on the dependency analysis. Then it used GCN to aggregate the sentences’ dependency information, which effectively utilized the multi-hop information and the dependent type information. On automatic content extraction (ACE) dataset, the proposed method achieved 81.7% and 78.6% F1 values in the two subtasks of trigger identification and classification. Results show that the proposed method can capture event information in sentences more effectively, and improve the effect of event detection. [ABSTRACT FROM AUTHOR]

Road traffic accidents occur frequently and seriously in the accident-prone spots. In order to improve the safety and efficiency of road traffic, it is necessary to find the location of accident-prone points. The existing density clustering algorithm needed to set the number of center points and was easy to expand the clustering range when identifying traffic accident-prone points, this paper proposed the limit cluster expansion and adaptive search clustering by fast search and find of density peaks (LA-CFDP) algorithm to solve these problems. LA-CFDP algorithm automatically determined the number of center points by increasing the restriction condition of center points, and introduced the parameter expansion factor to limit the cluster expansion range, so as to improve the adaptability and accuracy of the algorithm for accident-prone point identification. The case analysis on the 2019 traffic accident data set of four cities in United Kingdom shows that the Sihouette coefficient of the clustering results of LA-CFDP algorithm reaches 0.72～0.92, and the Davies-Bouldin index (DBI) are all reduced to below 0.37. The clustering results accord with the definition and characteristics of accident-prone spots, and can provide reliable basis for the management of accident-prone spots. [ABSTRACT FROM AUTHOR]

Regular path query is a technique of using regular expressions to query graph data, usually uses finite state automata to achieve query matching. The matching result of the existing regular path query method is a sequence of vertex pairs, and the structure of the graph is not fully preserved. In order to solve this problem, this paper proposed a method of structured regular path query for graph data. By structuring constraints between different sequences, the query results could be transformed from paths to subgraphs. For this purpose, the method firstly defined a structured regular path query language, and then designed a structured query parsing and a matching algorithm based on this structure. Experimental results on simulated and real datasets verify the influence of network size on query speed. This paper compared the proposed method with the control group. Experimental results show that the proposed method can realize structured query under the premise of satisfying regular expression constraints. [ABSTRACT FROM AUTHOR]

In the recommendation system, the user’s ratings are often affected by the rating context, that is, the user’s previous ratings of some items will affect the objectivity of his rating of the current item. Sparse linear method treats user ratings affected by context as the same as other ratings when calculating item similarity. However, this partial ratings cannot objectively reflect the similarity between items. To solve the above problems, this paper proposed a recommendation algorithm combining rating context and item similarity based on sparse linear method. It divided the algorithm into three stages.The first stage used weighted ratings to calculate the item’s nearest neighbor for feature selection.In the second stage, it used the rating error weight to reduce the fitting of the ratings affected by the context of the algorithm model, and trained the item similarity matrix. In the third stage, it predicted the ratings according to the user’s ratings and the item similarity, and finally sorted the predicted ratings to complete the item recommendation. Experiments were conducted on four datasets of MovieLens, it used mean average precision (MAP), mean reciprocal rank (MRR) and normalized discounted cumulative gain (NDCG) to evaluate the effectiveness of the algorithm. The experimental results show that the fusion rating context will further improve the accuracy of item similarity and thus improve the performance of recommendation. [ABSTRACT FROM AUTHOR]

In order to solve the problem of bypassing detection methods for DNS covert channel（DCC） traffic deformation strategies, this paper proposed a DCC detection method based on traffic slice. In this method, the traffic of the experimental environment was firstly divided into batches based on the sliding window, and then obtained the traffic slice based on the aggregation of each host-end IP. Each slice contains DNS packets and Web packets belonging to the same host in a short time span. Then, for the DNS data volume, DNS request behavior, DNS response behavior, and association behavior correlation behavior with WEB of DNS in the slice, it implement DCC-detection-oriented feature engineering and create detection models on this basis. Comparative experiments show that the detection accuracy of the constructed DCC detection model on the conventional DCC traffic datasets is 99.83%,and the false positive rate is only 0.08%.On the deformed DCC traffic datasets with six types of different traffic deformation strategies, the detection ability of the DCC detection model is more than 95% on average, which is better than the other detection schemes. It proves that the proposed method is effective in dealing with DCC traffic deformation. Moreover, the proposed method, which can effectively detect DCC communication within a single traffic slice, is a detection method with good real-time performance. [ABSTRACT FROM AUTHOR]

To solve the problem of inaccurate description and unreliable relation inference in group activity recognition, this paper focused on constructing a scene relationship graph for three aspects: individual, group, and scene, and proposed a scene relationship graph network（SRGN） for group activity recognition. This method included a feature extraction module, a scene relation graph inference module, and a classification module. The feature extraction module extracted individual features, group features, and scene features by convolutional neural network. To fully explore the impact of scene on individual and group descriptions, the scene relation graph inference module learnt individual features and group features by building individual-scene and group-scene relationship graphs in a two-branch framework. Scene graph inference took into account the influence of individual on group and introduced a cross-branch module. It used the classification module to classify individual features and group features for prediction. The experimental results show that the group recognition accuracy of the proposed method on volleyball and collective activity data sets is improved by 1. 1% and 0. 5%,respectively. It verifies the validity of the scene graph in describing individual feature and group feature. [ABSTRACT FROM AUTHOR]

To address the shortcomings of the object detection model in the training process, such as the allocation of positive and negative samples without considering the aspect ratio of the ground-truth box and the poor adaptability to different distributions of objects, this paper proposed the ratio-prior and loss-aware assignment（RLA） algorithm. RLA didn’t change the structure of the original detection model, firstly it selected an equal proportion of the central sampling area based on the aspect ratio of the ground-truth box, then calculated the integrated loss of the anchor points, considered the actual distribution of objects within the ground-truth box, and finally distinguished between positive and negative samples by means of a dynamic loss threshold. The algorithm solved the problems of poor adaptability and difficulty in selecting the best positive samples based on IoU allocation, and the sample allocation for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample allocation algorithms, the algorithm outperforms the MS COCO dataset by 1. 66% over the AP of the baseline FCOS, and 0. 76% and 0. 24% over the AP of the ATSS algorithm and the PAA algorithm respectively when the model structure is the same, demonstrating the effectiveness of the RLA algorithm. [ABSTRACT FROM AUTHOR]

Copyright of China Harbour Engineering is the property of Editorial Office of China Harbour Engineering 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.)

By solving the problems such as the shortage of drainage power in inland rivers and the difficulty of construction land, the integrated gate pump with compact structure and small floor area has received much attention. Among them, the integrated gate pump with larger diameter has a short application time and few examples in China. This paper takes the Salt River Pump Gate Project as the background, uses the three-dimensional parametric calculation model, carries on the design optimization analysis of the door blade thickness, the plate thickness and other parameters, and the reinforcement analysis of the weak parts of the wall pipe and web. At the same time, the key problems such as vibration and anticorrosion of the gate pump are analyzed and demonstrated. The results show that the thickness of the door blade should not increase too much, and the panel and web structure with large separation should be properly strengthened at the same time to control the resonance frequency domain. When the ratio of gate width to height is less than 0.6 and the ratio of the pump outer diameter to gate width is close to 0.5, the plate thickness combination in the range of 12-16 mm should be used. Transverse, longitudinal and diagonal ribs should be considered at the same time for wall pipe reinforcement. Through a dynamic simulation analysis, the design of Salt River Pump Gate can avoid the resonance of the integrated pump gate, and has a certain safety margin. [ABSTRACT FROM AUTHOR]

Copyright of China Surfactant Detergent & Cosmetics is the property of China Surfactant Detergent & Cosmetics 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.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays 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.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays 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.)

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security 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 problems of low accuracy, poor generalization ability and large number of parameters in the diagnosis model of COVID-19 based on deep learning, a lightweight siamese architecture network SMViT (siamese masked vision transformer) for COVID-19 diagnosis based on ViT (vision transformer) and siamese network is proposed. Firstly, a lightweight strategy of cyclic substructure is proposed, which uses multiple subnets with the same structure to make a diagnosis network, thereby reducing the number of network parameters. Secondly, masked self-supervised pre-training model based on ViT is proposed to enhance the potential feature expression ability of the model. Then, in order to effectively improve the diagnostic accuracy of the diagnosis model of COVID-19, and improve the poor generalization ability of the model under small samples, this paper constructs the twin network SMViT. Finally, the ablation experiment is used to verify and determine the structure of the model, and the diagnostic performance and lightweight capacity of the model are verified through comparative experiments. Experimental results show that, compared with the most competitive ViT-based diagnostic model, the Accuracy, Specificity, Sensitivity and F1 scores of this model on the X-ray dataset have increased by 1.42%, 4.62%, 0.40% and 2.80% respectively, and the Accuracy, Specificity, Sensitivity and F1 scores on the CT image dataset have increased by 2.16%, 2.17%, 2.05% and 2.06% respectively. The SMViT model has strong generalization ability for small sample size datasets. Compared with ViT, SMViT model has smaller parameters and higher diagnostic performance. [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.)

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.)

In order to solve the problems of low efficiency of manual cartoning and large area of production line in the winery, a continuous vertical cartoner was designed. Vertical ring layout was adopted in the machine, wine bottles and wine boxes can be directly packed without dumping after sorting equidistant; the tower multi-station layout was adopted in cartoning module, and the 8-head cartoning actuator was independent of each other for non-stop cartoning; the mode of grabbing can reduce the impact of the cartoning process to realize the smooth cartooning of wine bottles. On this basis, the contour line of the cylindrical cam was designed according to the cartoning process, and the cylindrical cam was optimized by curve fitting method, and the simulation analysis of the cartoning mechanism was carried out. The results show that the acceleration curve of the boxing actuator is relatively smooth and the structure is reasonable, which meets the cartoning requirements. The proposed cartoner effectively improves the production efficiency and provides a new idea for wine bottle boxing. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

Track circuit is an important device to reflect the train occupancy status and transmit the train control information. During the retrofitting of the normal-speed railways, there has been the problem of mismatch between track circuits due to the high running speed of the single-locomotive train, the delay in equipment response, and the delay in relay action. This paper proposes the method of slow-operating circuit using capacitors and resistors or equipment replacement to solve this mismatch problem. The proposed method has been applied during the retrofitting of the track circuits of Rongjiawan Station of Beijing-Guangzhou Railway, and has been proven to effectively solve the problem of mismatch between track circuits and such ensuing problems of the interlocking system as fail to unlock normally, forget to unlock, and report track unoccupied in the presence of train. [ABSTRACT FROM AUTHOR]

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This paper proposed an improved RRT（Rapidly-Explorative Random Tree） algorithm to solve the problem that the traditional target offset RRT was easy to fall into local optimum in the environment where there were many obstacles such as yarn racks, placing platforms and other packages during the package handling process. The collision detection model was established by geometric envelope method, and the distance coefficient g was added to keep a safe distance between the end of the manipulator and the obstacle; A random action selection strategy was proposed to select the expansion action before each expansion of the tree; A weakening target offset strategy was proposed to offset the extension direction according to the position of Xgoal relative to the Xnear and Xrand line; According to the vertical distance limit method and cubic spline interpolation, the initial path was optimized, and finally a smooth path was obtained. Through simulation comparison with RRT algorithm, M-RRT algorithm, RRT algorithm with added gravity coefficient and Improved RRT * algorithm, the maximum decrease in path length was 23.3%, the maximum decrease in time was 82.5%, and the distance from obstacles was always more than 50 mm. The results show that the algorithm improves the efficiency and safety of the manipulator handling. [ABSTRACT FROM AUTHOR]