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All results (except beam and column rotation) were collected using a data logger.
The data logger recorede all measurements at every 5 second intervals.
For beam and column rotation, the data were measured seperately on column and beam web by two independent inclinometers and recorded manually along the course of loading.
All data were recorded throughout the duration of the test until failure.
The data collection software in the computer was checked to ensure all channels connecting to the instruments on the specimens functioned properly.

But the reduction of metal or iron parts of landmines and the disturbance of metal or iron chips in minefield have increased the false alarm of electromagnetic induction detection or magnetic detection.
Data acquisition and control system could complete the amplification and the reading of the output signal from detector, the delay system control, voltage source control and so on.
Data processing and analysis system could accomplish software processing and data analysis[9-11].
Results and discussion By using the transmission testing system, the data sampling interval is 0.004ps, the record number is 1805.
The absorption spectrum have confirmed the correctness and reliability of the testing data, which compared with those of other organizations are basically identical[7][12].

Its main task, is to control a robot drive through the built-in accelerometer, to control a robotic arm and display important user data received from the robot.
Fig. 2 #next Mars rover, as a six-wheeled robot While transmitting the data frame, two parameters are distributed from 0-200 on the basis of which is determined speed on the left and right side of the vehicle.
Accelerometer Equations Transmitting control data have been processed in an unconventional way.
Fig. 5 List of paired devices After specific device selection, a control tab [fig. 6] appears in which the user can steer the robot and preview important data coming from the robot.
In the case of high temperatures (more than 35 ℃), sweating hands make the capacitive screen no longer detect touch and smartphone do not transmit control signals or transmit data less frequently.

Introduction With the rapid development of modern railway construction in our country, the pros and cons of locomotive diesel engine performance, people's environmental awareness, the requirement of energy conservation and emissions reduction, the challenge of comprehensive performance of locomotive diesel engine is becoming more and more big.
In this article, taking 16V265H diesel engine as the research object, after using UG software to build entity model, put the entity model of the nozzle imported into GAMBIT, meshing and set the import and export of the fluid, then, using FLUENT, considering the characteristics of nozzle and the test data to determine the reasonable boundary conditions, doing a data simulation of porous nozzle fuel three-dimensional flow field and proving the validity of the results.
The entity model According to the entity drawing of 16V265H locomotive diesel engine nozzle and related data, using UG to build the entity model of nozzle.
The following is generating the assembly drawing of nozzle and the distance between the top of the needle valve and the top of the needle valve body is 6 mm, then according to the related data of nozzle, establishing the physical model of nozzle flow channel, as shown in Fig.3.
If we have the actual test data, we could directly calculate the average mass flow of fuel.

The pre-investment analysis of «pure» construction projects risks can be carried out with the help of the information analysis system, which, according to the input data describing the project, can identify potential risk events and propose methods for managing the identified risks [1].
The information system algorithm in the pre-investment analysis The first stage of «Data Input» includes the process of identifying the individual characteristics of a construction project, which is due to the filling of the decision-maker, questionnaire data on the developer, contractors, project general information.
At this stage, the increasing or decreasing probability coefficients are assigned to the risks, which are characterised by extremely high or low values of the corresponding parameters when entering the input data; the risks not inherent in the object are excluded.
The distribution chain for materials in construction industry To minimise the «pure» risks associated with the negative impact of substandard products on the construction the distribution of responsibility for the quality of building materials in each of the stages (fig.2) and, transferring data to the digital field can be applied.
These risks can be minimised by automating processes, minimising the «human» factor, which will lead to a significant reduction in potential negative factors, which means that the investor will have greater confidence that the building or structure will be built on time and within the allotted project budget.

Thus, thermal conductivity reductions provide the only mechanism for increasing the figure of merit.
The variation of power factor with boron content calculated from the electrical conductivity and seebeck coefficient data of Fig. 1 and Fig. 2 at elevated temperature is shown Fig. 3.
Electronic thermal conductivity increase with boron content, but it exhibits lower data values than that of others over 1.0wt% boron content.
Z value with Boron content calculated from the power factor and thermal conductivity data is shown in Fig. 7.
The electrical conductivity increases with boron content.Because the excessive dopant concentration affected the lattice structure and the scattering center, analyzed data containing over 1.0wt% boron shows lower electrical conductivity. 2 The power factor has the largest calculated data value at 0.5wt% boron content. 3.

Cutting force data comparison: (a) Simulation results (b) Experimental results.
Chip measurement data comparison: (a) Simulation results (b) Experimental results.
However, the decreasing proportion predicted by the model is slightly larger than the experimental data.
Chip morphology was predicted and compared with the experimental data as well.
Cook, A constitutive model and data for metals, in: 7th International Symposium on Ballistics. 1983

The important guarantee of control performance are the choice of bearing calibration,the realization of soft measurement data processing in a DCS(Distributed Control System),the calculation of the model and the correction of module,and soft measurement model generalization ability of ascension.
Fig.2 A typical calculation process of genetic algorithm K - means algorithm combining with literature[8] research proposed an anomaly detection algorithm based on the nearest neighbor clustering algorithm and genetic algorithm.It has carried on the clustering analysis of historical data for sewage treatment and successfully found out the abnormal data.It has established the fault rules according to the clustering results.It also has a certain practical reference value for the establishment of the fault diagnosis system in sewage treatment technology.
Stetp2:The soft measurement technology based on ANN in wastewater treatment ANN take a simple nonlinear neuron as a processing unit.And it is a nonlinear dynamic system with large-scale distributed parallel processing ability through extensive connection form; The characteristics of self-organizing, self-learning and distributed associative memory, and the nonlinear approximation have attracted wide attention in control group[9].Under the condition of object does not have prior knowledge,the soft measurement method based on ANN can directly establish model and has strong ability of on-line correction according to the I/O data of objects[15].It take primary variables as network input, BOD as network output, to solve the problem of the soft measurement in the wastewater quality through the training of all kinds of learning algorithm.A kind of typical BOD neural network soft measurement hierarchy is shown in fig.3.
Fig.3 A kind of typical BOD neural network soft measurement hierarchy From the optimization of network structure and the ascension of real-time data processing ability,in recent years, there is a based on PCA(Principal Component Analysis) of the artificial neural network soft measurement method.And it is used in sewage treatment system.
General expression is: (1) The basic idea of SVM is to limited training samples from the input space nonlinear mapped to a high-dimensional feature space, and obtained by solving the quadratic convex programming problem globally unique optimal solution.The method solves the general method of study that is difficult to solve the problem,such as easily trapped in local minimum problem, the structure learning method, type selection to rely too much on experience and so on,to improve the generalization ability of the model.Combined with parameter characteristic analysis, punish the optimization of parameters and kernel function or methods of knowledge reduction,it ensure the accuracy and real-time water quality soft measurement in the process of sewage treatment.

Our data shows that, at high polarity, the current flow charge rate is a smooth shift with propanol solvent compared with methanol, whereas at a higher transition energy for a system with methanol solvent.
The data of absorption Spectrum (UV-vis) and Electrochemical Measurement of Zn-tri-PcNc-8 is given in table (1)[26-28].
Data of UV-vis Absorption Spectrum and Electrochemical Measurement of Zn-tri-PcNc-8[27].
Data results of the transition energy ΛTE(eV) at Fe/ ZnPc interface.
The Fe/ Zn-tri-PcNc-8 shows main charge transfer around potential 1.965 eV and 2.038 eV at Methanol and n- Propanol solvents followed by a less charge transfer rate at around 2.648 eV and 2.726 eV, while the charge transfer rate data for Fe/ Zn-tri-PcNc-8 molecule system at n-propanol solvent was larger than Fe/ Zn-tri-PcNc-8 molecule system at Methanol solvent.

To objectively evaluate the challenge, data from selected local foundries were analysed together with and compared to international data from selected countries from industry and specific foundry macro level.
The data from these questionnaires were collated.
In numerous cases the data needed to be verified and exact definitions of the metrics used obtained from the custodians of the international data, this was done normally through direct correspondence with the reputable custodian and/or data collection agency allowing for higher confidence and certainty of the metrics and their calculation.
[6] CAEF, The European Foundry Association – Statistics Data – 2008,09&10 - www.caef.eu [7] NADCA, data from Questionnaires sent to American, Canadian and Japanese die casting companies
[12] National Foundry Technology Network (NFTN), data from NFTN Booklet 1, “Energy Usage Reduction and Efficiency Increase in Foundries version 1 - 2011”, C.