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This paper summarizes eight types of error of terminology in the patent text in the output of Machine Translation from English into Chinese, including term being mistranslated as a verb, term being mistranslated as a common noun, term being redundantly translated, term being mistranslated as a homophone, term being mistranslated as a wrong term, term being mistranslated due to Chinese expression, term being mistranslated without initial, and term being mistranslated due to wrong acronym. These errors can be solved by the translator before Machine Translation and the translator can identify and correct these errors by pre-editing of the source text.

In this paper, we analyze a spatial 3-degree-of-freedom cable-driven robot with a point-mass end-effector suspended by 3 cables. The kinematic and dynamic behaviour of this robot was investigated in previous works, in particular, the possibility of taking advantage of inertia forces on the end-effector to enlarge the robot workspace. Here, we propose to use this highly dynamic robot as a robotic sling, in order to perform throwing operations: the end-effector uses a gripper to carry an object, and the gripper opens at a suitable instant in order to launch the object towards a given target point. The mathematical model and results from numerical simulations are presented .

With the gradual development of electronic transformer and smart substation, the detection technology of electric energy meter with digital input has become a bottleneck problem in the development of digital substation. At present, most of the digital input watt-hour meters in digital substations adopt off-line detection or no detection, and off-line detection has no corresponding traceability support. Based on the technology of digital quantity input electric energy meter in the existing digital substation, this paper proposes a real load digital input watt-hour meter detection scheme that operates online. And a scheme of digital input watt-hour meter testing equipment compatible with various protocols and synchronous traceability online real load verification technology is proposed.

This paper is concerned with the construction of high order mass-lumping finite elements on simplexes and a program for computing mass-lumping finite elements on triangles and tetrahedra. The polynomial spaces for mass-lumping finite elements, as proposed in the literature, are presented and discussed. In particular, the unisolvence problem of symmetric point-sets for the polynomial spaces used in mass-lumping elements is addressed, and an interesting property of the unisolvent symmetric point-sets is observed and discussed. Though its theoretical proof is still lacking, this property seems to be true in general, and it can greatly reduce the number of cases to consider in the computations of mass-lumping elements. A program for computing mass-lumping finite elements on triangles and tetrahedra, derived from the code for computing numerical quadrature rules presented in [7], is introduced. New mass-lumping finite elements on triangles found using this program with higher orders, namely 7, 8 and 9, than those available in the literature are reported.

PHG (parallel hierarchical grid) is an open source toolbox, developed by the State Key Laboratory of Scientific and Engineering Computing at the Chinese Academy of Sciences, for writing parallel adaptive finite element programs. Among the many adaptive finite element toolboxes and software packages, PHG is characterized by the following features: 1)\\ The ability to provide the most recent vertex bisection-based parallel local mesh refinement of unstructured conforming tetrahedral meshes. 2)\\ It supports $h$-$p$ adaptive finite element computations. 3)\\ It incorporates massively parallel with transparent dynamical load balancing. 4)\\ It provides flexible preconditioning and solutions of large sparse linear systems of equations. In this paper, the main modules and some core algorithms of PHG are described, and some parallel applications based on PHG, including the computation of iron-loss for large power transformers, simulation of ionic transport in ion channels, parasitic extraction of interconnects in integrated circuits, ice sheet simulation, and spectral element simulation of elastic waves with PML, are introduced.

This paper presents a dynamic transition trajectory planning method for fully-actuated three-dof point-mass cable-suspended parallel robots. This trajectory is designed to connect two periodic trajectories beyond the static workspace, while ensuring continuity up to the acceleration level. A lower extrema transition time is investigated in an analytical way. An example is provided to demonstrate the proposed technique. In this example, the robot is requested to transition from its source trajectory to its goal trajectory.

Cable-driven parallel robots can provide interesting advantages over conventional robots with rigid links, in particular, robots with a cable-suspended architecture can have very large workspaces. Recent research has shown that dynamic trajectories allow the robot to further increase its workspace by taking advantage of inertial effects. In our work, we consider a three-degrees-of-freedom parallel robot suspended by three cables, with a point-mass end-effector. This model was considered in previous works to analyze the conditions for dynamical feasibility of a trajectory. Here, we enhance the robot&rsquo, s capabilities by using it as a sling, that is, by throwing a mass at a suitable time. The mass is carried at the end-effector by a gripper, which releases the mass so that it can reach a given target point. Mathematical models are presented that provide guidelines for planning the trajectory. Moreover, results are shown from simulations that include the effect of cable elasticity. Finally, suggestions are offered regarding how such a trajectory can be optimized.

Regarding urban flooding issues, applying Artificial Intelligence (AI) methodologies can provide a timely prediction of imminent incidences of flash floods. The study aims to develop and deploy an effective real-time pluvial flood forecasting AI platform. The platform integrates rainfall hyetographs embedded with uncertainty analyses as well as hydrological and hydraulic modeling. It establishes a large number synthetic of torrential rainfall events and their simulated flooding datasets. The obtained data contain 6000 sets of color-classified rainfall hyetograph maps and 300,000 simulated flooding maps (water depth) in an urban district. The generated datasets are utilized for AI image processing. Through the AI deep learning classifications, the rainfall hyetograph map feature parameters are detected and extracted. The trained features are applied to predict potential rainfall events, recognize their potential inundated water depths as well as display flooding maps in real-time. The performance assessments of the platform are evaluated by Root Means Square Error (RMSE), Nash Sutcliffe Efficiency Coefficient (NSCE) and Mean Absolute Percentage Error (MAPE). The results of RMSE and NSCE indicators illustrate that the methodologies and approaches of the AI platform are reliable and acceptable. However, the values of MAPE show inconsistency. Ultimately, the platform can perform and be utilized promptly in real-time and ensure sufficient lead time in order to prevent possible flooding hazards.

This paper presents a method for predicting the remining useful life (RUL) of equipment by using multi-sensor monitoring data of equipment. Firstly, the performance degradation of equipment is evaluated based on the maximum information coefficient. Then the performance degradation model is built by using Wiener process with non-linear drift. The maximum likelihood is used to estimate the parameters of the model, and the probability density distribution of RUL considering random failure threshold is deduced. Finally, the parameters of the model are updated by improved particle filter, and the RUL of the remaining equipment is predicted.

The torsional vibration of crankshaft is an important factor in the vibration caused by the engine and causing fracture. An inline 4-cylinder engine crankshaft system will be the object to research. Finite element model of crankshaft is built by ANSYS. And analysis torsional vibration characteristics of shafting are based on the model, which include natural vibration and forced vibration. It gets main harmonic times, minor main harmonic times and corresponding engine speed in commonly used speed range. Designs parameters of torsional vibration damper (TVD) based on classical optimal approach. And tests the damping effect under different conditions based on the finite element model. The results show that the design meets the requirements of the damper, and has good damping effect. Research methods and conclusions can be applied to actual research. It can be used in similar crankshaft torsional vibration system analysis, design and analysis of torsional damper.

This paper proposes a novel method for evaluating the reliability of a virtual metrology system (VMS). The proposed method calculates a reliance index (RI) value between zero and one by analyzing the process data of production equipment to determine the reliability of the virtual metrology results. This method also defines an RI threshold. If an RI value exceeds the threshold, the conjecture result is reliable; otherwise, the conjecture result needs to be further examined. Besides the RI, the method also proposes process data similarity indexes (SIs). The SIs are defined to assess the degree of similarity between the input set of process data and those historical sets of process data used to establish the conjecture model. The proposed method includes two types of SIs: global similarity index (GSI) and individual similarity index (ISI). Both GSI and ISI are applied to assist the RI in gauging the reliance level and locating the key parameter(s) that cause major deviation, thus resolving the VMS manufacturability problem. An illustrative example involving 300-mm semiconductor foundry etching equipment is presented. Experimental results demonstrate that the proposed method is applicable to the VMS of production equipment (such as that for semiconductor and TFT-LCD).

Pedestrian detection technology which is based on vision is one of the core technologies in the intelligent transportation system. This paper focuses on the image and video in traffic system. The image and video are normalized and are extracted of histograms of oriented gradients (HOG) by using the linear support vector machine (SVM) to construct efficient classifier. It can realize accurate detection of pedestrians in traffic image. Moreover, the pedestrian detection technology is proved in disclosed INRIA database and experiments of actual traffic video image, and the method can rapidly achieve accurate detection results.

Online transaction processing (OLTP) is an important commercial application with intensive memory access. With increasing gap between processor and memory speeds, cache performance associated with data locality is the critical performance factor for OLTP applications. While OLTP applications have been shown to have significant data locality, data locality characterization and its effects on cache performance is still an open question. This paper presents a detailed study of typical OLTP queries from TPC-C. We identify a set of working sets by analysis of reuse in query processing. Based on performance experiments on typical OLTP queries with simulated system, we find cache performance is mainly impacted by critical working set that are caused by fundamental structures required to access tuples in relations. We also find that the critical working set is slightly affected by application parameters.

Advanced Studies of selection schemes between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS) are presented in this paper. Both NN and MR are applicable algorithms for implementing VM conjecture models. But a MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may has superior accuracy when equipment property drift or shift occurs. To take advantage of the merits of both MR and NN algorithms, the simple-selection scheme (SS-scheme) was proposed in CASE 2008 to enhance virtual-metrology (VM) conjecture accuracy. This SS-scheme simply selects either NN or MR output. Recently, with advanced studies, a weighted-selection scheme (WS-scheme), which computes the VM output with a weighted sum of NN and MR results, has been developed. Besides the example with the CVD process of fifth generation TFT-LCD used in the CASE 2008 paper, a new example with the photo process is also adopted in this paper to test and compare the conjecture accuracy among solo NN, solo MR, SS-scheme, and WS-scheme. One-hidden-layered back-propagation neural network (BPNN-I) is adopted for establishing the NN conjecture model. Test results show that the conjecture accuracy of the WS-scheme is the best among those of solo NN, solo MR, SS-scheme, and WS-scheme algorithms.

This simple system, which builds on the principle of isolating the nutritive solution in which water roots grow from the evaporative effects of the environment, can be invaluable in situations where space is limited and/or soil quality is poor.

This paper proposes a selection scheme (S-scheme) between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS). Both NN and MR are applicable algorithms for implementing VM conjecture models. But a MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may has superior accuracy when equipment property drift or shift occurs. To take advantage of the merits of both MR and NN algorithms, the S-scheme is proposed to enhance virtual-metrology (VM) conjecture accuracy. Two illustrative examples in the CVD process of fifth generation TFT-LCD are used to test and compare the conjecture accuracy among solo NN, solo MR, and S-scheme. One-hidden-layered back-propagation neural network (BPNN-I) is adopted for establishing the NN conjecture model. Test results show that the conjecture accuracy of S-scheme can achieve superior accuracy than solo NN and solo MR algorithms.

In this paper, we consider the resource-constrained multi-project scheduling problem (RCMPSP) and makespan minimization as objective. We present a new particle swarm optimization (PSO) approach to solve this problem. The particle representation is based on priority rules. The potential solution to the RCMPSP is, i.e., a sequence of priority rules deciding the order of scheduling the non-dummy activities, is represented by the multidimensional particle position. Hence PSO is applied to search the optimal schedule for the RCMPSP, in which a serial schedule generate scheme is adopted to transform the particle-represented priority rule to a schedule. Computational analyses are represented to verify the effective of the proposed methodology.

This paper studies a new type of dependent channel errors and examine its effect on the performance of the three basic ARQ protocols. The errors occur in burst and affect a random number of consecutive bits. Previously the error analysis was done on packet basis. Most prior works concentrate only on the throughput performance analysis, while in our work delay performance is also analysed. >

In this paper, we propose an improved CSR model for rumor spreading in mobile social networks. The dynamic equation of rumor spreading is modified to be suitable for user's habit in mobile social networks. In the acceptant probability model, negative and positive social reinforcements are considered. Furthermore, the people's accepting threshold for rumor accepting is taken into account. Analytically, a mean field theory is worked out by considering the influence of network topological structure as homogeneous. Under certain conditions, rumor spreads faster and wider in the new model than CSR rumor spreading model in homogeneous networks. Meanwhile, the multi-agent simulation results indicate that the information spreading process is sensitively dependent on initial conditions.

We present a model-based remotely-sensed image interpretation expert system embeded in a knowledge-based geographic information system (K. BIS). The KBIS consists of four sub-systems: a pictorial data base system, an image interpretation expert system, a computer-aided planning system and a computer-aided cartographic system. The image interpretation expert system represents ecological knowledge and other expert knowledge by frames. Its reasoning process consists of a forward reasoning based on the Bayes classification of Landsat imagery, a backward reasoning using frame knowledge and reasoning using a spatial consistency model. A forest inventory study was conducted in Shaxian county, in the southern part of China, using this expert system. The results have shown a significant improvement. Building image interpretation expert systems within knowledge-based pictorial systems is very convenient and efficient because there are well-organized data, knowledge and procedures available.