The microstructure and properties of powder metallurgy high-speed steel prepared by hot isostatic pressing with different heat treatments have been studied. The microstructure, phase composition, effect of quenching and tempering parameters, fracture morphology, and mechanical properties of the sample are discussed in detail. The H-HSS sample presents the characteristics of the powder prior to the particle boundary and consists of carbide and ferrite, in which the carbides are fine and evenly dispersed without segregation. The bending strength and hardness of the H-HSS sample are 3112 MPa and 56.3 HRC, respectively. The Q-HSS sample is mainly composed of martensite, residual austenite, and carbides. With the increase in quenching temperature, the grain size of the matrix gradually grows, and the small carbide particles dissolve into the matrix, causing an increase in carbide size and a decrease in quantity. The bending strength and hardness of the Q-HSS sample quenched at 1210 °C achieve the maximum values of 3114 MPa and 68.8 HRC, respectively. After tempering, the martensite is transformed from a quenched lath shape to a needle shape, the residual austenite content decreases, and secondary carbides precipitate from the matrix, resulting in a secondary hardening. The T-HSS sample that is quenched at 1120 °C followed by tempering at 550 °C for 20 min has the best bending strength of 4355 MPa. However, the T-HSS sample that is quenched at 1240 °C followed by tempering at 550 °C for 120 min has a maximum hardness value of 69.5 HRC. The fracture mode of Q-HSS sample is brittle fracture, and the fracture mechanism is cleavage fracture. After tempering, the fracture mechanism of the T-HSS sample presents a transitional fracture mode between the cleavage fracture and micropore aggregation fracture.

Two-dimensional transition metal carbides/nitrides (MXenes) are emerging members of the two-dimensional material family, obtained by removing the A layer of the MAX phase through methods such as liquid-phase etching. This article summarizes the structure and properties of MXenes, as well as several preparation methods, including etching with hydrofluoric acid and fluoride salts, alkali-based etching, electrochemical etching, Lewis acid molten salt etching, and direct synthesis. Due to their unique two-dimensional structure and surface chemistry, MXenes exhibit good metallic conductivity, hydrophilicity, excellent flexibility, and ion intercalation properties, showing great potential in the research and application of supercapacitors and attracting widespread attention. The combination of MXene with other types of materials, including polymers, metal hydroxides, metal oxides, and carbon materials, takes advantage of composites to improve energy storage performance and shows great potential in the research and application of supercapacitors. This article provides a detailed summary of MXene composite materials and capacitor performance and introduces the research progress of MXene materials in the field of supercapacitor energy storage applications, aiming to provide references for the preparation of high-performance MXene supercapacitor electrode materials.

In this paper, a direct method of adaptive input shaping algorithm for a harvesting mechanical arm clamps the tomato bunches is proposed to achieved zero residual vibration. The traditional input shaping would lose its vibration suppressing function when the system parameter changed during mechanical arm's load varied. The adaptive input shaping algorithm based on recursive least square method (RLS) requires no system identification. The residual vibration of output signal is used as the input of the algorithm to calculate the impulse time and amplitude of shaper. An adaptive forgetting factor updating algorithm is proposed to improve the control performance in variable load condition. The experimental results show that the adaptive forgetting factor input shaper greatly reduces the residual vibration.

When the harvesting mechanical arm clamps the tomato bunches and puts them into the picking basket, the acceleration and deceleration movement of the mechanical arm makes the tomato bunches swing in the moving process, which is easy to break the fruit stalks and cause them to fall off. First, in order to accurately track the desired position of the mechanical arm, the PID control was designed and implemented. Then the tomato stem is approximated as a flexible connecting rod with tomatoes as the load. Due to the different quality of the bunches picked each time, the system model changes with the load. The traditional input shaping algorithm could not suppress the vibration of tomato. Therefore, the adaptive input shaping control method is adopted to realize double closed-loop control and achieve the purpose of vibration suppression. The simulation results show that the algorithm has good suppression ability of end vibration and satisfactory control effect of manipulator displacement under variable load.

In order to solve the bionic quadruped robot movement control problems, a improved method with particular attention on toe trajectory is proposed. The kinematic equation of legs is established through motion analysisJoint angle functions are solved by inverse kinematics employing legs phases in typical gait planning and the displacement for rods of hydraulic cylinders by mechanism. The toe trajectory is calculated to ensures the smoothness of the robot moving. Experimental results show that the robot walks stably on the even surface, the undulation of body has small amplitude, and the design of prototype is rational and efficient.

The event-driven programming model has been proposed to efficiently process iterative applications and incremental applications. In clusters based the event-driven model, applications are structured as a series of short triggers, each of which will be invoked when associate events are trigged. And framework assigns a newly submitted trigger to a node where the relevant datasets set. Unfortunately it may lead to load imbalance because associate events may occur by chance. Numerous triggers in a node may be simultaneously invoked but other nodes have no triggers running. To the end, we provide TSSA, a new two steps event-driven for the event-driven clusters to maximize improve the utilization of node resources. Our results indicate TSSA performs well, and minimizes total execution time of applications.

基于多元智力理论,细化学生学习过程中的各个环节,制定量化考核标准,构建了多元化-全过程大学物理实验课程学生成绩量化评价体系.该评价系统注重学习过程考查和学生能力评价,突出平时考核,旨在更科学、客观地评价学生的学习成绩,引导学生自主学习,提高学生实验的积极性和主动性,促进学生科学实验素养形成,切实提高实验教学的质量.

Due to cluster resource competition and task scheduling policy, some map tasks are assigned to nodes without input data, which causes significant data access delay. Data locality is becoming one of the most critical factors to affect performance of MapReduce clusters. As machines in MapReduce clusters have large memory capacities, which are often underutilized, in-memory prefetching input data is an effective way to improve data locality. However, it is still posing serious challenges to cluster designers on what and when to prefetch. To effectively use prefetching, we have built HPSO (High Performance Scheduling Optimizer), a prefetching service based task scheduler to improve data locality for MapReduce jobs. The basic idea is to predict the most appropriate nodes for future map tasks based on current pending tasks and then preload the needed data to memory without any delaying on launching new tasks. To this end, we have implemented HPSO in Hadoop-1.1.2. The experiment results have shown that the method can reduce the map tasks causing remote data delay, and improves the performance of Hadoop clusters.

本文设计了一种可调节压缩比的斯特林热机装置,可实现斯特林热机的压缩比(最大体积与最小体积的比值)动态调节以及压强(p)-体积(V)曲线的实时监测.阐述了轨尺在双缸双活塞斯特林热机中实现动态调节的机理,编写了采集热机工作过程中压强和体积的Labview程序.通过调节轨尺完成压缩比的动态调节,演示了斯特林热机在不同压缩比条件下的工作状态,分析了压缩比影响斯特林热机性质的机理.该装置制作简单,可使用酒精灯作为热源,用于物理演示实验教学.

HBase is a popular distributed Key/Value storage system based on the idea of BigTable. It is being used in many data-centers, such as Facebook and Twitter, for their portability and scalability. For the system, low-latency and large storage is expected when used in industry. However, it is time consuming when retrieving one column via another one. Many technologies were considered to solve the problem. One approach is to add secondary index for HBase such as hindex, which achieves high performance in retrieving. Unfortunately, when one column is of limited kinds of data, secondary index cannot reduce storage consumption when accelerating the retrieval. In this paper, we present a novel design of HBase to reduce storage consumption as well as accelerating the retrieval in the above situation. We design an enumeration system for HBase and provide an interface to create enumeration for specific column in tables. Our performance evaluation reveals that it achieves 2.27x improvement in retrieval and 12x reduction in storage compared with non-enumeration in HBase.

With the development of cloud computing and internet, e-Commerce, e-Business and corporate world revenue are increasing with high rate. These areas require scalable and consistent databases. NoSQL databases such as HBase has been proven to scalability and well performance on cloud computing platforms. However, the inevitable special data with few increment and frequent access leads to hotspot data and unbalanced accessing distribution between data storage servers. Due to their properties, these data often cannot be stored in multiple tables. Some storage nodes become the bottleneck of the distributed storage system, therefore, it becomes difficult to improve the performance by increasing the number of nodes which severely limits the scalability of the storage system. In order to make the performance of the cluster increases with the size of the cluster simultaneously, we devise a new distributed database storage framework to solve those issues mentioned above by changing the storage and read-write mode of the hotspot data. This structure guarantees that the hotspot data will not aggregate in the same storage node, as it guarantees that the data is not too hot in a single storage node. We implement the scalable database based on Apache HBase, which achieve almost double performance of throughput considering heavy read-write pressure situation only with double reading substites. Besides, heavy load node owing to hotspot data will no longer present in the new distributed database.

With the rapid development of cloud computing, more and more applications need to process large amount of data on clusters. Different types of data processing frameworks in cloud have appeared, such as MapReduce, Spark and Percolator. These frameworks are used to a certain type of data processing. In this paper, we provide processing framework called Wave, which is designed for bulk data processing, incremental computing and iterative processing with a uniform application interface. Wave is an event driven data process model for semi-structured data of distributed systems. Programmers use events and trigger reactions to process the data. Wave provides simplified API for users to implements parallel programs on cluster. Programs running in Wave are automatically parallelized and executed on cluster synchronously. Wave uses an implicit mechanism to synchronize the parallel program's execution without any user specification.

The recent next-generation sequencing machines generate sequences at an unprecedented rate, and a sequence is not short any more called read. The reference sequences which are aligned reads against are also increasingly large. Efficiently mapping large number of long sequences with big reference sequences poses a new challenge to sequence alignment. Sequence alignment algorithms become to match on two big data. To address the above problem, we propose a new parallel sequence alignment algorithm called Bwasw-Cloud, optimized for aligning long reads against a large sequence data (e.g. the human genome). It is modeled after the widely used BWA-SW algorithm and uses the open-source Hadoop implementation of MapReduce. The results show that Bwasw-Cloud can effectively and quickly match two big data in common cluster.

With the coming concept of 'big data', the ability to handle large datasets has become a critical consideration for the success of industrial organizations such as Google, Amazon, Yahoo! and Facebook. As an important Cloud Computing framework for bulk data processing, Hadoop is widely used in these organizations. However, the performance of MapReduce is seriously limited by its stiff configuration strategy. Even for a single simple job in Hadoop, a large number of tuning parameters have to be set by users. This may easily lead to performance loss due to some misconfigurations. In this paper, we present an adaptive automatic configuration tool (AACT) for Hadoop to achieve performance optimization. To achieve this goal, we propose a mathematical model which will accurately learn the relationship between system performance and configuration parameters, then configure Hadoop system based on this mathematical model. With the help of AACT, Hadoop is able to adapt the hardware and software configurations dynamically and drive the system to an optimal configuration in acceptable time. Experimental results show its efficiency and adaptability, and that it is ten times faster compared with default configuration.

Hadoop is a popular framework that provides easy programming interface of parallel programs to process large scale of data on clusters of commodity machines. Data intensive programs are the important part running on the cluster especially in large scale machine learning algorithm which executes of the same program iteratively. In-memory cache of input data is an efficient way to speed up these data intensive programs. However, we cannot be able to load all the data in memory because of the limitation of memory capacity. So, the key challenge is how we can accurately know when data should be cached in memory and when it ought to be released. The other problem is that memory capacity may even not enough to hold the input data of the running program. This leads to there is some data cannot be cached in memory. Prefetching is an effective method for such situation. We provide a unbinding technology which do not put the programs and data binded together before the real computation start. With unbinding technology, Hadoop can get a better performance when using caching and prefetching technology. We provide a Hadoop framework with unbinding technology named unbinding-Hadoop which decide the map tasks' input data in the map starting up phase, not at the job submission phase. Prefetching as well can be used in unbinding-Hadoop and can get better performance compared with the programs without unbinding. Evaluations on this system show that unbinding-Hadoop reduces the execution time of jobs by 40.2% and 29.2% with WordCount programs and K-means algorithm.

In principle,using Hall effect to detect current; in structure,using differential magnetic circuit for temperature correction and CMOS technology for a higher degree of integration; in precision,using STM32 MCU to achieve linearity adjustment and temperature compensation; by adding shielding layer to reduce electromagnetic interference. It has CAN bus transmission functions,while achieving docking with wireless sensor networks( WSNs) at the same time,and has short-range wireless transmission capability,which makes the new type intelligent sensors can able to adapt to a variety of high-low temperature environment better. Experiments show that measuring range of sensor can reach ± 100 A,precision can reach 0. 5 %,and CAN bus communication can be achieved,while wireless transmission can be realized at range of 0 ~ 50 m at the same time.

本文介绍了利用无线Wi-Fi通信技术、电化教学设备和学生手机、平板电脑、笔记本电脑等无线终端,设计开发了一套可以增进师生在课堂上沟通与交流的智能教学评估系统.系统已经在多门课程中进行了使用测试,结果表明:系统满足了师生的不同需求,各个功能运行稳定、可靠,为提高课堂教学效率、开展互动教学与研究型教学提供了一种可行的电子化的方法.

With the development of cloud computing, more and more applications are moving to a distributed fashion to solve problems. These applications usually contain complex iterative or incremental procedures and have a more urgent requirement on low-latency. Thus many event-driven cloud frameworks are proposed. To optimize this kind of frameworks, an efficient strategy to minimize the execution time by redistributing work- loads is needed. Nowadays, load balance is a critical issue for the efficient operation of cloud platforms and many centralized schemes have already been proposed. However, few of them have been designed to support event-driven frameworks. Besides, as the cluster size and volume of tasks increases, centralized scheme will lead to a bottleneck of master node. In this paper, we demonstrate a decentralized load balancing scheme named DLBS for event-driven cloud frameworks and present two technologies to optimize it. In our design, schedulers are placed in every node for independently load-monitoring, autonomous decision-making and parallel task-scheduling. With the help of DLBS, master frees from the burden and tasks are executed with lower latency. We analyze the excellence of DLBS theoretically and proof it through simulation. At last, we implement and deploy it on a 64-machine cluster and demonstrate that it performs within 20% of an ideal scheme, which are consistent with simulation results.

The event-driven programming model has been proposed to efficiently process iterative applications and incremental applications. In clusters based the event-driven model, applications are structured as a series of triggers, each of which will be invoked when associate events are trigged. And framework assigns a newly submitted trigger to a node where the relevant datasets set. Unfortunately it may lead to load imbalance because associate events occur by chance. Numerous triggers in a node may be simultaneously invoked but other nodes have no triggers running. Jobs composed of short, sub-second triggers present a difficult balancing challenge. To the end, we design DLBer, a new dynamic load balancing algorithm for the event-driven clusters to maximize improve the utilization of node resources.

Due to cluster resource competition and task scheduling policy, some map tasks are assigned to nodes without input data, which causes significant data access delay. Data locality is becoming one of the most critical factors to affect performance of MapReduce clusters. As machines in MapReduce clusters have large memory capacities, which are often underutilized, in-memory prefetching input data is an effective way to improve data locality. However, it is still posing serious challenges to cluster designers on what and when to prefetch. To effectively use prefetching, we have built HPSO (High Performance Scheduling Optimizer), a prefetching service based task scheduler to improve data locality for MapReduce jobs. The basic idea is to predict the most appropriate nodes to which future map tasks should be assigned and then preload the input data to memory without any delaying on launching new tasks. To this end, we have implemented HPSO in Hadoop-1.1.2. The experiment results have shown that the method can reduce the map tasks causing remote data delay, and improves the performance of Hadoop clusters.