基于RT-Thread的液压支架监控分站的设计

为提高煤矿生产的安全性和效率,降低生产成本,设计了基于嵌入式实时操作系统RT-Thread的液压支架监控分站。该分站通过CAN总线将综采工作面液压支架的工作数据上传到主站并接收主站下达的指令;此外,通过支架控制器可以实现对液压支架的远程控制。软件设计利用信号量和事件集进行线程间通信同步;同时采用多线程优先级抢占调度机制,实现任务的并发运行。实验证明,该分站具有精度高、可靠性强和实时响应等特点,达到工程实际应用需求。

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

Security Threads and IoT Security

One of the technologies that have attracted the most attention recently across a variety of applications is the Internet of Things (IoT). The Internet of Things (IoT) is the combination of sensor, embedded computing, and communication technologies. The goal of the Internet of Things is to provide seamless services to anything, everywhere, at any time. The internet of things (IoT) technologies plays a vital role everywhere after the internet and information and communication technology, ushering in the fourth disruptive technology revolution (ICT). For real-time processing, communication, and monitoring, the smart items are linked together through wired or wireless connections. Implementing the IoT system presents security and privacy challenges since IoT devices are incompatible with current security standards based on tradition. This paper discusses IoT security strands, mitigation strategies, and privacy issues. This study’s major objective is to get more knowledge about security threats, mitigation techniques, and privacy concerns in IoT devices. The authors also mentioned a few cutting-edge technologies that can address general security problems. This study’s major objectives are to find research gaps in IoT security and match solution paradigms. The advent and rapid growth of the Internet of Things (IoT), which offers innumerable benefits, facilities, and applications including smart grids, smart homes, smart cities, and intelligent transportation systems (ITS), have an impact on everyone’s life. However, the deployment and use of sensing devices exposes IoT-based systems and applications to many security flaws and attacks. Furthermore, the lack of standardization brought on by the diversity of devices and technologies makes integrating security in the IoT a severe problem. The purpose of this review paper is to highlight the numerous security threats, challenges, and attacks that IoT-enabled applications face.

Using multi-threads to hide deduplication I/O latency with low synchronization overhead

Data deduplication,as a compression method,has been widely used in most backup systems to improve bandwidth and space efficiency.As data exploded to be backed up,two main challenges in data deduplication are the CPU-intensive chunking and hashing works and the I/O intensive disk-index access latency.However,CPU-intensive works have been vastly parallelized and speeded up by multi-core and many-core processors;the I/O latency is likely becoming the bottleneck in data deduplication.To alleviate the challenge of I/O latency in multi-core systems,multi-threaded deduplication (Multi-Dedup) architecture was proposed.The main idea of Multi-Dedup was using parallel deduplication threads to hide the I/O latency.A prefix based concurrent index was designed to maintain the internal consistency of the deduplication index with low synchronization overhead.On the other hand,a collisionless cache array was also designed to preserve locality and similarity within the parallel threads.In various real-world datasets experiments,Multi-Dedup achieves 3-5 times performance improvements incorporating with locality-based ChunkStash and local-similarity based SiLo methods.In addition,Multi-Dedup has dramatically decreased the synchronization overhead and achieves 1.5-2 times performance improvements comparing to traditional lock-based synchronization methods.

Improved Scatter Search Algorithm for Multi-skilled Personnel Scheduling of Ship Block Painting

This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Roller Screw and Threaded Chain Speed Reducer: An Experimental Evaluation

We are developing a speed reducer that can be considered a transformation of a worm gear reducer: the worm is replaced by an inverted roller screw, and the gear is replaced by a threaded chain drive. This configuration lessens wear, increases load capacity, and improves efficiency. The threaded chain consists of nut-shaped links. This paper presents the results of tests carried out on a prototype with a reduction ratio of 46.

GIS-Based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) Techniques to Derive Flood Risks Management on Rice Productivity in Gishari Marshland

Floods are phenomenon with significant socio-economic implications mainly for human loss, agriculture, livestock, soil loss and land degradation, for which many researchers try to identify the most appropriate methodologies by analyzing their temporal and spatial development. This study therefore attempts to employ the GIS-based multi-criteria decision analysis and analytical hierarchy process techniques to derive the flood risks management on rice productivity in the Gishari Agricultural Marshland in Rwamagana district, Rwanda. Here, six influencing potential factors to flooding, including river slope, soil texture, Land Use Land Cover through Land Sat 8, rainfall, river distance and Digital Elevation Model are considered for the delineation of flood risk zones. Data acquisition like Landsat 8 images, DEM, land use land cover, slope, and soil class in the study area were considered. Results showed that if the DEM is outdated or inaccurate due to changes in the terrain, such as construction, excavation, or erosion, the predicted flood patterns might not reflect the actual water flow. This could result unexpected flood extents and depths, potentially inundating rice fields that were not previously at risk and this, expectedly explained that the increase 1 m in elevation would reduce the rice productivity by 0.17% due to unplanned flood risks in marshland. It was found that the change in rainfall distribution in Gishari agricultural marshland would also decrease the rice productivity by 0.0018%, which is a sign that rainfall is a major factor of flooding in rice scheme. Rainfall distribution plays a crucial role in flooding analysis and can directly impact rice productivity. Oppositely, another causal factor was Land Use Land Cover (LULC), where the Multivariate Logistic Regression Model Analysis findings showed that the increase of one unit in Land Use Land Cover would increase rice productivity by 0.17% of the total rice productivity from the Gishari Agricultural Marshland. Based on findings from these techniques, the Gishari Agricultural Marshlands having steeped land with grassland is classified into five classes of flooding namely very low, low, moderate, high, and very high which include 430%, 361%, 292%, 223%, and 154%. Government of Rwanda and other implementing agencies and major key actors have to contribute on soil and water conservation strategies to reduce the runoff and soil erosion as major contributors of flooding.

基于RT-Thread的可信软件栈的设计和实现

针对基于Linux和TCG软件栈(Trusted computing group Software Stack,TSS)的复杂性问题,提出一种轻量级的可信软件栈。分析了TSS的基本结构与TSS在嵌入式系统的局限,总结出基于嵌入式系统的可信软件栈设计需求,设计出软件栈命令调用的机制和软件栈的结构。此外,分析了TSS密钥管理缓存算法,在flash中定义一块密钥槽空间,方便密钥管理中直接访问,阐述密钥生成的逻辑过程,实现面向嵌入式系统的可信软件系统。经实验验证,该软件栈可以结合RT-Thread实时系统实现基本的可信计算功能。

基于Multi-WHFPN与SimAM注意力机制的版面分割

作为OCR的预处理工作,版面分割技术越来越受到学术界和工业界重视。针对版面分割中遇到的检测速度慢、目标区域边界不准确以及细小目标易遗漏等问题,提出了YOLOv7-MSY模型。此模型首先借鉴残差连接思想,提出了Multi-WHFPN网络结构。它采用可训练的权重参数,突出特征融合过程中特征重要性,并添加了小目标检测头,从而提升对小目标的检测性能;其次,引入SimAM注意力机制,可以在不增加额外参数的基础上在3D维度评估特征权重,以增强重要特征,抑制无效特征;最后,使用YEIOU来代替原模型中的定位损失函数,提升了模型的收敛速度与回归精度。在江苏省档案馆提供的数据集上进行实验对比,YOLOv7-MSY对目标区域边界检测更加敏感,对细小目标的检测效果更好。YOLOv7-MSY的mAP@.5达到了0.871,相较于原YOLOv7模型提高了7.84%。该模型的版面分割的效果优于其他类型的版面分割算法,具有良好的泛化性能,并且版面分割速度处于较高水平。

COMSOL Multiphysics在锂离子电池中的应用

作为一种具有前景的能量存储系统,锂离子电池需要进一步提高能量密度、功率密度、可靠性和循环稳定性,以满足不断增长的大型能源存储、电动汽车和便携式电子设备需求。当前对锂离子电池的实验研究仍然面临多个挑战,这些挑战包括电解液的导电性和安全性、高能量负极的沉积-剥离机制的优化、高能量正极的循环电压和容量维持、高电流条件下的界面极化和容量释放,以及在极端电流-温度-针刺条件下的热失控管理等问题。这些问题涉及到电-化-力-热等多个场的耦合作用,需要进行协同优化处理。COMSOL Multiphysics提供了一种可行的工具,通过求解多物理场耦合的连续方程,能够同时考虑载流子浓度、电流密度、电-化学势、温度、应力/应变和几何形态等综合信息的演化。本文概述了该工具在锂离子电池的电解液、负极和正极设计等方面的研究,并聚焦于多场耦合对电池性能的综合影响、多场耦合模拟方法以及理论模拟与实验表征的结合。最后,本文对理论与实验联合研究中的多场和多尺度问题进行了展望。

基于RT-thread的精确时间同步系统设计

分布式智能采集终端需要一致的时间基准,用于多种数据和事件的时间标记。为满足智能采集终端对高精度时间同步的需求,选择IEEE 1588精确时间同步协议实现时间同步;设计基于纯国产32 bit双核微控制器HPM6750和以太网收发器RTL8211FS(I)-VS的精确时间同步系统,选用国产开源嵌入式实时操作系统RT-thread操作系统,实现在物理层获取时间戳来运行IEEE 1588协议;构建测试平台对该时间同步系统进行主从时钟同步精度测试。测试结果表明,设计在物理层获取时间戳时达到的时间同步精度在±250 ns以内,与在数据链路层获取时间戳时达到±10μs的时间同步精度相比较,有效提高了时间同步精度,可广泛应用于对时间同步精度有较高要求的智能采集终端。

Profile of Multidrug Resistant Bacteria in Bukavu Hospitals and Antimicrobial Susceptibility to Escherichia coli, Pseudomonas aeruginosa, Proteus mirabilis and Staphylococcus aureus

Objective: To evaluate the spread of Multidrug-Resistant (MDR) bacterial infections in Bukavu hospitals and test antimicrobial susceptibility patterns of some isolates to usual marketed antibiotics. Methods: The prevalence of MDR strains was determined by using general antimicrobial susceptibility data collected from 3 hospital laboratories. The susceptibility of some isolates to usual antibiotics was processed by agar diffusion method with standard E. coli ATCC8739 and standard antibiotics discs as controls. The tested antibiotics were ampicillin, ceftriaxone, gentamicin, chloramphenicol and ciprofloxacin. Results: At the 3 hospitals, 758 tests were realized in urine, pus, stool, FCV, blood, LCR, split and FU specimens;46 strains were unidentified and 712 strains were identified. Of 712 identified strains, 223 (31.4%) were MDR or XDR strains including Escherichia coli, Klebsiella pneumoniae, Enterobacter, Proteus mirabilis, Salmonella enterica, Pseudomonas aeruginosa, Citrobacter freundii, Morganella morganii, Enterococcus faecalis and E. faecium, Neisseria gonorrohoae, Staphylococcus aureus, coagulase-negative, staphylococci, Streptococcus pneumoniae and Streptococcus pyogenes. Of the infected patients, 36 (21.5%) children were under 16 years and 188 (78.5%) adults were predominately women (58.5%). The susceptibility test showed that all strains but S. aureus were resistant to ampicillin and amoxicillin and ciprofloxacin. Gentamicin, ceftriaxone, and chloramphenicol remain partially active (27% - 80%) against P. mirabilis, E. coli and P. aeruginosa. The resistance is more likely related to strain mutation than to pharmaceutical quality of the antibiotics prescribed. Conclusion: Both data from hospital laboratories and in vitro post-testing findings confirmed the ongoing elevated prevalence of MDR strains in Bukavu. The causes of antibiotic misuse and socio-economic determinants of the phenomenon of resistance should be scrutinized in order to take adequate strategies in the prospective of establishing an effective control system against this threat to overall health. The results of this work on MDR profiles have various implications for the management of infectious diseases. It provides indicators for the surveillance of antimicrobial resistance, practical guidelines for antibiotic susceptibility testing in biomedical laboratories, and guidance for antibiotic therapy.

Contribution of GIS to Soil Landscape Mapping by Multi-Criteria Analysis Using Weighting: The Case of the Square Degrees of M’Bahiakro (Centre) and Daloa (Centre-West) in Ivory Coast

As part of the drive to improve coffee and cocoa production in Ivory Coast, studies are carried out to identify soils that are favourable for these crops. It is therefore necessary to orientate soil investigations based on reliable criteria that best discriminate soil cover. With this in mind, this study is being carried out to help improve survey methods by mapping soil landscapes. It uses GIS and weighted multicriteria analysis. To do this, satellite images were processed and the geological map of the square degrees of M’Bahiakro and Daloa was reclassified. The results show that relief is the main factor in soil landscape differentiation, with respective weights of 0.58 and 0.67 for the forest and pre-forest zones. In contrast, the weight of geological formation in soil landscape differentiation remains low (0.05 for the forest zone and 0.07 for the pre-forest zone). The criteria used on the base of aggregation sum methods have made it possible to formulate soil landscape mapping prediction functions according to agro-ecological environments in the humid intertropical zone. This is essential for the orientation of soil survey work. Nevertheless, other comparative methods, such as the coding mapping method, could provide elements for discussion to validate the models.

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

Multi-Agent模式下的城市暴雨内涝应急决策方法研究

为厘清应对暴雨内涝灾害动态决策过程中决策主体、决策、决策方案等决策要素间的不确定关系,提出1种多主体(Multi-Agent)和贝叶斯决策网络(BDN)相结合的应急决策方法。首先分阶段构建“主体-任务”可视化网络,分析暴雨内涝灾害各应急阶段的主要任务和参与的决策主体;在考虑到决策要素间的动态不确定性可能造成决策风险的前提下,运用Multi-Agent和BDN方法探究各决策要素间的影响关系,以便进行方案集优选。研究结果表明:该方法具有实用性和现实意义,研究结果可为城市暴雨内涝灾害的应急决策提供理论参考。

Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.

基于长短时记忆神经网络的Multi-GNSS卫星钟差建模预报

针对卫星钟差预报中二次多项式模型存在易受噪声干扰、预报精度不高的问题,构建一种基于长短时记忆神经网络的multi-GNSS卫星钟差预报模型,并分析不同卫星系统、不同钟类型基于不同建模方案的模型精度。为验证该模型的有效性和可行性,利用LSTM模型、QP模型、QP-LSTM模型分别基于12 h和24 h钟差序列进行建模,预报1 h、3 h、6 h、12 h钟差。结果表明,LSTM模型建模24 h、预报1 h精度最高。multi-GNSS卫星钟差LSTM预报模型中Galileo系统精度最高,其次为BDS-2系统和GPS系统,GLONASS系统精度最低,精度分别为0.018 ns、0.069 ns、0.133 ns、0.242 ns。不同原子钟预报精度不同,氢原子钟预报精度优于铷原子钟、铯原子钟。LSTM神经网络模型预报精度相较于QP-LSTM模型提升27%,相较于QP模型提升36%。

MiniGUI-Threads与MiniGUI-Lite下多串口通信的设计与实现

MiniGUI是目前较成熟的嵌入式Linux系统的图形用户界面,具有小巧、可配置和可移植等特点。MiniGUI可以编译成两个截然不同的运行模式:MiniGUI-Threads模式适用于实时性高,功能相对简单的系统,比如工控仪器等;而MiniGUI-Lite模式多用于功能丰富,结构复杂的系统,如PDA等信息产品。文章介绍了如何在这两个运行模式下设计并实现多个串口的实时双工通信,最后还指出了两种模式下实现串口监听程序和同步问题方面的差别。