A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.

Study on Multi-stream Heat Exchanger Network Synthesis with Parallel Genetic/Simulated Annealing Algorithm

The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one optimum and computational difficulty for traditional algorithms to find the global optimum. Compared with deterministic algorithms, evolutionary computation provides a promising approach to tackle this problem. In this paper, a mathematical model of multi-stream heat exchangers network synthesis problem is setup. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee et al., non-isothermal mixing is supported. As a consequence, nonlinear constraints are resulted and nonconvexity of the objective function is added. To solve the mathematical model, an algorithm named GA/SA (parallel genetic/simulated annealing algorithm) is detailed for application to the multi-stream heat exchanger network synthesis problem. The performance of the proposed approach is demonstrated with three examples and the obtained solutions indicate the presented approach is effective for multi-stream HENS.

High-Performance Transmission Mechanism Design of Multi-Stream Carrier Aggregation for 5G Non-Standalone Network

Multi-stream carrier aggregation is a key technology to expand bandwidth and improve the throughput of the fifth-generation wireless communication systems.However,due to the diversified propagation properties of different frequency bands,the traffic migration task is much more challenging,especially in hybrid sub-6 GHz and millimeter wave bands scenario.Existing schemes either neglected to consider the transmission rate difference between multistream carrier,or only consider simple low mobility scenario.In this paper,we propose a low-complexity traffic splitting algorithm based on fuzzy proportional integral derivative control mechanism.The proposed algorithm only relies on the local radio link control buffer information of sub-6 GHz and mmWave bands,while frequent feedback from user equipment(UE)side is minimized.As shown in the numerical examples,the proposed traffic splitting mechanism can achieve more than 90%link resource utilization ratio for different UE transmission requirements with different mobilities,which corresponds to 10%improvement if compared with conventional baselines.

输入串联输出并联型三电平双有源桥变换器功率与电压平衡控制策略

针对三电平半桥型输入串联输出并联(ISOP)型双有源桥(DAB)变换器的各模块参数差异而导致的模块功率不均衡传输问题,该文首先分析多模块功率不平衡原因,然后提出基于三重移相(TPS)调制的串联侧功率平衡策略。针对ISOP系统多模块间参数差异问题,提出多模块参数差异估计方法,该方法无需增加额外的电流或电压传感器,通过注入强制分量作为扰动而采集反映模块差异的特征量变化,对控制量进行补偿实现多模块功率平衡。仿真和实验结果表明,在多种扰动条件下,所提功率平衡方法可实现样机串联侧均压,在输出电压24V工况和10~435W功率范围下,系统在所提控制策略下能明显提高串联侧的电压平衡,工作效率达到90%以上。

Submanifolds with Parallel Mean Curvacture

In this paper, we consider a class of submanifolds with parallel mean curvacture vector fields. We obitain the suffitient conditions that the above submanifolds is of tatall umbilical and that its codimension is decrease.

Parallel Dispatch:A New Paradigm of Electrical Power System Dispatch

Modern power systems are evolving into sociotechnical systems with massive complexity, whose real-time operation and dispatch go beyond human capability. Thus,the need for developing and applying new intelligent power system dispatch tools are of great practical significance. In this paper, we introduce the overall business model of power system dispatch, the top level design approach of an intelligent dispatch system, and the parallel intelligent technology with its dispatch applications. We expect that a new dispatch paradigm,namely the parallel dispatch, can be established by incorporating various intelligent technologies, especially the parallel intelligent technology, to enable secure operation of complex power grids,extend system operators' capabilities, suggest optimal dispatch strategies, and to provide decision-making recommendations according to power system operational goals.

Hazard-evaluation-oriented Moving Horizon Parallel Steering Control for Driver-Automation Collaboration During Automated Driving

Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization.The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation,the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner.The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.

Multi-Stream CNN-Based Personal Recognition Method Using Surface Electromyogram for 5G Security

As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal recognitionmethod used for granting permission is a password-basedmethod,which causes security problems.Therefore,personal recognition studies using bio-signals are being conducted as a method to access control to devices.Among bio-signal,surface electromyogram(sEMG)can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation.Furthermore,as an advantage,sEMG can be conveniently measured from arms and legs.This paper proposes a personal recognition method using sEMG,based on a multi-stream convolutional neural network(CNN).The proposed method decomposes sEMG signals into intrinsic mode functions(IMF)using empirical mode decomposition(EMD)and transforms each IMF into a spectrogram.Personal recognition is performed by analyzing time–frequency features from the spectrogram transformed intomulti-streamCNN.The database(DB)adopted in this paper is the Ninapro DB,which is a benchmark EMG DB.The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%,compared with the singlestream CNN using the spectrogram of raw sEMG.

Performance Evaluation Methods for Multi-stream Plate-Fin Heat Exchanger

Mathematical model of cross type multi-stream plate-fin heat exchanger is established.Meanwhile,mean square error of accumulative heat load is normalized by dimensionless,and the equations of temperature-difference uniformity factor are improved.Evaluation factors above and performance of heat exchanger are compared and analyzed by taking aircraft three-stream condenser as an example.The results demonstrate that the mean square error of accumulative heat load is common result of total heat load and excess heat load between passages.So it can be influenced by passage arrangement,flow inlet parameters as well as flow patterns.Dimensionless parameter of mean square error of accumulative heat load can reflect the influence of passage arrangement to heat exchange performance and will not change dramatically with the variation of flow inlet parameters and flow patterns.Temperature-difference uniformity factor is influenced by passage arrangement and flow patterns.It remains basically unchanged under a certain range of flow inlet parameters.

基于自适应幂级数初始点的电力系统全纯嵌入潮流并行计算

在利用全纯嵌入潮流计算方法求解电力系统潮流时,若平启动的电压幂级数初始点远离系统潮流解实际值,则易导致计算量增大,且在求解各节点电压的过程中,传统基于中央处理器(CPU)串行计算架构的计算耗时较长。提出一种基于自适应幂级数初始点的全纯嵌入潮流并行计算方法。根据系统节点导纳矩阵和平衡节点电压自适应调整系统全纯嵌入潮流的幂级数初始点,构建基于自适应幂级数初始点的全纯嵌入潮流计算模型,以加快电压幂级数的收敛。借助多核CPU计算平台实现各节点电压Padé近似的并行化,以提高系统潮流方程的求解效率。不同规模测试系统的仿真结果表明,所提方法可实现电力系统全纯嵌入潮流的高效、准确求解。

Multi - weighted tree based query optimization method for parallel relational database systems

The author investigates the query optimization problem for parallel relational databases. A multi - weighted tree based query optimization method is proposed. The method consists of a multi - weighted tree based parallel query plan model, a cost model for parallel qury plans and a query optimizer. The parallel query plan model is the first one to model all basic relational operations, all three types of parallelism of query execution, processor and memory allocation to operations, memory allocation to the buffers between operations in pipelines and data redistribution among processors. The cost model takes the waiting time of the operations in pipelining execution into consideration and is computable in a bottom - up fashion. The query optimizer addresses the query optimization problem in the context of Select - Project - Join queries that are widely used in commercial DBMSs. Several heuristics determining the processor allocation to operations are derived and used in the query optimizer. The query optimizer is aware of memory resources in order to generate good - quality plans. It includes the heuristics for determining the memory allocation to operations and buffers between operations in pipelines so that the memory resourse is fully exploit. In addition, multiple algorithms for implementing join operations are consided in the query optimizer. The query optimizer can make an optimal choice of join algorithm for each join operation in a query. The proposed query optimization method has been used in a prototype parallel database management system designed and implemented by the author.

A Parallel Platform for Web Text Mining

With user-generated content, anyone can De a content creator. This phenomenon has infinitely increased the amount of information circulated online, and it is beeoming harder to efficiently obtain required information. In this paper, we describe how natural language processing and text mining can be parallelized using Hadoop and Message Passing Interface. We propose a parallel web text mining platform that processes massive amounts data quickly and efficiently. Our web knowledge service platform is designed to collect information about the IT and telecommunications industries from the web and process this in-formation using natural language processing and data-mining techniques.

真空联合堆载预压法在海陆交互相软土地基中应用

我国沿海诸省大量存在着大面积的海陆交互相软土,在这类地基上修建工程,不经过处理或处理不当,会引起工程质量的降低甚至较为严重的工程事故。以广州南沙新区明珠湾区江灵北路(西段)软基处置工程为例,通过分析真空联合堆载预压法加固软土地基后路堤填筑过程中软基变形特点,评价处置方法的加固效果及适用性。研究结果表明:真空联合堆载预压法能够加速软土地基的沉降速率、提升施工期的总沉降量、降低工后沉降速率;同时,该方法能够有效维持膜下真空度水平。该方法在海陆交互相软土地基中应用效果显著,卸载前固结度可达94%,工后沉降量小。

基于岩层运动并行计算系统的锚杆单元原理、实现及巷道支护效果分析

在自主开发的适于模拟连续介质向非连续介质转化或非连续介质进一步演化的岩层运动并行计算系统(基于拉格朗日元与离散元耦合连续-非连续方法)中,引入了一种能反映轴向和剪切变形的锚杆单元,形成了锚杆模块。2个锚杆节点与之间的构件组成锚杆单元,其本质是在任一种覆盖(将1个四边形单元离散成2个三角形子单元,离散方式有2种,即2种覆盖)中,锚杆节点相对于子单元的位置保持不变。控制循环由CPU执行,密集的计算任务由GPU执行,锚杆模块与其他模块同时执行以获得较高的计算效率。考察了拉拔试验中不同位置锚杆单元轴力的演化规律。随着拉拔的进行,距离拉拔端一定距离内的锚杆单元轴力呈振荡上升趋势,从围岩对锚杆作用的角度对此进行了解释。通过将目前的锚杆轴力分布与基于Mindlin问题的位移解推导出的理论结果进行对比,验证了锚杆单元的正确性。为了研究锚杆单元对巷道的支护效果,以巨野矿区某轨道大巷为背景建立力学模型,得到了有无锚杆支护条件下巷道围岩的变形—开裂—运动过程。研究发现,锚杆单元对于阻碍巷道围岩变形、控制裂纹发展和改善应力分布具有良好的效果,这是由于锚杆长度范围内围岩围压增大导致了围岩承载力提高。目前的锚杆支护条件下的结果(巷道附近破坏区内岩石单元之间的明显裂纹使围岩发生了较大膨胀)比传统连续方法的结果更符合实际。

基于GPU的宽带信号干涉仪测向算法实现

在应用干涉仪算法测向时,需要对多个接收信号进行逐一处理,而且测向精度往往与本地样本库数据量正相关,因此当需对大量信号进行高精度测向时,常规的计算平台难以保证算法的实时性。针对以上问题,提出一种优化的干涉仪算法,能够在降低样本库数据量的同时保持较高的测向精度,并探索了一种基于GPU并行计算的算法实现方式。经计算机仿真验证,基于GPU实现的优化算法,可将执行速度提升至CPU平台的十余倍。

元件参数对RC串并联网络选频特性的影响

本文分析元件参数对RC串并联网络选频特性的影响问题,研究上限截止频率、下限截止频率、通频带、品质因数等反映选频性能指标的参数与元件参数之间的关系以及幅频特性的不对称现象,给出定量描述的计算公式,并在Multisim仿真软件系统中进行仿真实验验证。目的是探索提高RC串并联网络选频性能时元件参数的合理选择方法,具有理论和工程实际意义。

并联机床插补算法与原理性插补误差预估

以一种可实现 3平动自由度并联机床为例 ,提出粗、精插补策略与算法 ,并构造出由PVT和虚实非线性映射造成的原理性插补误差的预估模型。结果表明 ,若机床在中低速下运行 ,则原理性插补误差对精度的影响可忽略不计。

近海型含煤岩系沉积学及层序地层学研究进展

层序地层学是近20年来发展起来的一门新的方法学科,并在聚煤作用分析中得到广泛应用。作者就近海型含煤岩系沉积学研究历史以及煤系层序地层学研究方法及有关问题进行探讨,认为近海环境的聚煤作用实际上是海平面(基准面)上升过程中发生的,同时提出煤层厚度受泥炭堆积速率与可容空间增加速率的控制:靠陆一侧冲积平原和三角洲平原沉积环境中,厚煤层主要出现在最大海泛面位置;而靠海一侧障壁—潟湖或碳酸盐岩台地沉积环境中,厚煤层主要出现在初始海泛面的位置;但就整个三级复合层序来说,层序中厚度最大、分布最广的煤层主要分布于可容空间增加速率最大的最大海泛面附近的位置。对于中国晚古生代近海型煤系中常见的"根土岩—煤—石灰岩"序列,聚煤作用发生于海相石灰岩"滞后时段",即在海侵之后、海相石灰岩层真正沉积下来之前的时段,这一时段可容空间增加速率与泥炭堆积速率平衡,有利于聚煤作用发生。

渤海湾西岸BQ1孔揭示的沉积环境与海面波动

渤海湾西岸BQ1孔位于歧口凹陷区内,孔深95.6m,揭露了晚更新世以来比较连续的沉积。通过对109个样品鉴定和环境分析,共分辨出Ⅱ、Ⅱ、Ⅲ三个海相层及其内部的海面波动。每个海相层内均有前后2次海侵高峰和中间的海退。在全新世海侵层中共区分出9次海面波动,分辨率达500～800a级；第Ⅲ海相层存在5次海面波动,与深海O同位素的5a、5c、5e分期的峰值大体对应。在海相层中,海陆过渡相层位占较大比例,其中的石膏结晶和黄铁矿晶簇是该相的重要指相矿物。所见的Ⅰ、Ⅱ、Ⅲ海相层皆为平均海平面下数米的浅海沉积。