Experimental Setup. All runtime experiments were conducted on a standard desktop computer with an Intel(R) Core(TM) i7-4790K CPU running at 4.00 GHz (4 cores; 8 hard- ware threads), 32 GB RAM, and two Nvidia GeForce Titan Z GPUs (each consisting of two devices with 2880 shader units and 6 GB main memory) using single precision. The operating system was Ubuntu 14.4.3 LTS (64 Bit) with kernel 3.13.0-52, CUDA 7.0.65 (graphics driver 340.76), and OpenCL 1.2. All algorithms were implemented in C and OpenCL, where Swig was used to obtain appropriate Python interfaces.5 The code was compiled using gcc-4.8.4 at opti- mization level -O3. For the experimental evaluation, we report runtimes for both the construction and the query phase (referred to as “train” and “test” phases), where the focus is on the latter one (that makes use of the GPUs). We consider the following three implementations:
Experimental Setup. A ··· A The effectiveness of SR2NP is shown through an exper- imental setup involving a scale model representing the urban area W nearby Pisa’s Leaning Tower (see the sce- narios presented in Fagiolini et al. (2008b, 2010)). In the model, 14 agents 1, , 14 have been deployed to detect a possible intruder, represented by one or more radio controlled mini car and some agents may fail. Agents are represented by Sentilla Tmote–Sky nodes (MOTEIV (2006)), which includes a low-power MSP430 micro-controller and a ZigBee radio chip. Every xxxx run a Contiki OS version optimized for embedded systems with limited hardware resource and wireless connectivity, and use the μIP communication protocol developed by Dunkels et al. (2004a,b). Agents are equipped with 3 color LEDs and 2 light sensors, I2C–bus connectors and an A/D converter both allowing additional sensors to be installed. Additional sensors include proximity sensors such as ul- trasonic sensors, IR range finder, and PIR–based motion detectors, which are used in the experiments to monitor shaped and limited safety areas Wj, j = 1, ..., 6 (Fig. 2a). W W A ∈
Experimental Setup. In this section we describe the setup for an experimental evaluation of our prototype based on a testbed cloud using the RUBiS Web application and a synthetic workload generator.
Experimental Setup. This sub-topic is the detailed description for the experiment facility and instrumentation. This is the key part for the construction of computational domain. Sub-topics are shown in Table 2.4. Table 2.4 Sub-topics in experimental setup Components The main components of the experimental facilities. Some experimental facilities may have several components, number and size of the components should be given in this part. Boundary geometry Geometrical information for special boundary such as the fan, the release source and ignition point. - The type of the boundary (source, velocity, pressure) - The size of such special boundary (can be given in the latter facility drawing) - The location of the special boundary (can be given in the latter facility drawing) Instrumentations The instrumentations used in this experiment, detailed information should cover: - The types of the instrumentations - The numbers of the instrumentations - The position of the instrumentations (can be given in the latter facility drawing) The mutable variables in the facility Sometimes, geometry may also be a mutable factor in experiment, including - The destructible boundary and parameter of the boundary - The mutable geometry in the facility (such as the size of the obstacles is mutable when the influences of the different geometry is studied by the experiment) Drawing or detailed written The detailed description of the experiment facility. All description of facility information mentioned above should be included in the drawing.
Experimental Setup. In this section we present the configurations and hyper- parameters of the XXXx and ladder networks, as well the dataset used in our experiments.
Experimental Setup. To validate the ThoR concept, a wireless transmission experiment has been realized in a laboratory environment. Fig. 3 shows the setup used in this transmission and the spectrum of the transmitted radio frequency (RF) signals. All the components will be described in the following sections. The LO signal, generated at 8.33 GHz, can be provided by a stable frequency synthesizer or by an optical frequency comb (photonic LO). The setup can be coherent, like in Fig. 3 or incoherent, using two different LO sources for the transmitter side and receiver side.
Experimental Setup. To evaluate the proposed Fuzzy Inference method for updating the candidate points, we tested the 3D-ASM on cardiac CT data from 9 patients comparing both the simple convolution-based edge detection and the newly implemented FI-based method. For this, a statistical shape model was generated using expert drawn contours of a group of 53 patients and normals, from 3D MR data [60]. The shape parameterization pre- sented in Section 3.2.1 was applied, where each sample was divided in 16 slices, each containing 32 points for the epicardial contour and 32 points for the endocardial con- tour. To reduce model dimensionality, the model was restricted to represent 99% of the shape variation present in the training data, resulting in 33 modes of variation. The 3D ASM was applied to 9 short axis CT acquisitions of cardiac LVs. Scans were acquired with CT scanners from two different vendors, and had an axial slice thick- ness of approximately 1 mm and an in-plane resolution of 0.5 mm/pixel. All data sets were reformatted to yield short-axis image slices. − Prior to matching, the model pose was initialized manually. The initial model scale was equal to the average model scale of the training set. The model shape was initial- ized to the mean training shape, whereas position was manually initialized inside the cardiac LV. The class centers of the three tissue classes used by FCM were initialized identically for each iteration and for each patient. During model matching, deforma- tion was limited by constraining each component of the model deformation parameter vector between 3σ and +3σ. The model search ran for a fixed number of iterations, the same for both the FI-based model and the convolution-based model. For the FI-based model, a two-stage matching was employed: initially, the convolution method was used until the update step size between iterations substantially decreased. From there, the final adjustments, small scale and pose changes and deformation of the model were realized using the FI-based point generation. The model states from the last iteration for both models were used for comparing the two candidate point generation methods. The method was visually evaluated to assess whether the new candidate point gener- ation method is an improvement with respect to the convolution-based technique, by comparing results from the same iteration in the matching process. In case of match- ing failure, the match was reported as failure and excluded from further quantitative...
Experimental Setup. The ORION® open path analyser was deployed on the roof of an electrical substation within the facility, approximately six metres above ground. After installation and initial tests, it was noted that the turret which allows the multi-path distribution in the azimuth plane could not reliably rotate to the left of its home position. Nevertheless, to ensure reliable and consistent operation, the retroreflectors were deployed in an arc to the right of the ORION®’s forward position. This led to a reduction in spatial coverage of the monitoring but did not compromise the trial. The issue has been investigated and traced back to a design problem from a supplied part. This has now been fully resolved. Five retroreflectors (instead of 9 originally planned) were deployed in a 90° arc over a 90 x 80m area to the north-west and north-east of the instrument. The five retroreflectors were therefore designated 5-9, as shown in Figure 8. Retroreflector ID UTM Eastings (m) UTM Northings (m) UTM zone 5 482959.29 5030962.75 32 T 6 482946.67 5030983.82 32 T 7 482936.67 5031010.87 32 T 8 482976.23 5031002.96 32 T 9 483012.50 5031011.77 32 T
Experimental Setup. In this section we describe our testing and still under development experimental setup for human-robot cooperation in flexible manufacturing. In particular, we describe our implementation for addressing the operator’s motion tracking problem. This work is built on top of a collaborative assembly workstation (see Fig. 2 and 3) developed at the Smart Mini Factory Laboratory (SMF) of the Free University of Bozen-Bolzano. The assembly tasks consist of the assembly of different variants of pneumatic cylinders. The workstation is equipped with a mobile workbench, a block-and-tackle for lightweight applications, an integrated Kanban rack, a working procedures panel, a double lighting system, an industrial screwer and a knee lever press. Further the operator is supported by a Universal Robot UR3 cobot. The collaborative robot takes over non-value adding tasks – from a lean management stand point (Xxxx, 2009; Xxxxx et al., 2017) – like pick-and-give tasks to eliminate handling time of the operator. The sensing system is composed by a ZED-mini stereo camera and a PSENscan 2D lidar scanner with an opening angle of 275 degrees and a measurement range of up to 5.5 meters. The laser scan is aligned with the ground plane and at a fixed height of 45 cm above the ground.
Experimental Setup. A complete description of the instrumentation used for these experiments is provided in reference,29 and a summary of details specific to BeS has been provided here. Beryllium sulfide anions were formed via pulsed laser ablation30 of a Be rod in the presence of He (25 psia) seeded with CS2 (room temperature vapor pressure). Ablation was accomplished using the second harmonic of a Nd:YAG laser (532 nm), operating with a pulse energy of ~8 mJ. The ablation products were supersonically expanded into a differentially pumped vacuum chamber that housed a Wiley-McLaren time-of-flight mass spectrometer (WM – TOFMS).31 The axis of the mass spectrometer was perpendicular to the direction of the supersonic expansion. Within the mass spectrometer the anions were accelerated into a drift region where they were directed by an Einzel lens and four sets of deflector plates. A fifth set of deflector plates could be used as a mass gate for selection of the anions of interest. The anions were directed through the center of a velocity map imaging lens. This three – electrode component was modeled after the design of Eppink and Xxxxxx.32 Photodetachment of BeS- was induced by the focused beam from a tunable dye laser (both Nd/YAG and excimer pumped dye lasers were used in these measurements). The laser beam was propagated along an axis that was perpendicular to the direction of the anion beam. The photon energies were chosen to be above the detachment threshold of BeS- with an energy of 0.5 – 1 mJ per pulse, and beam diameter <2 mm. The photodetachment lasers were frequency calibrated using the B – X absorption spectrum of room temperature I2 vapor, with line positions provided by the PGOPHER software package.33 Following photodetachment, the VMI optics focused the electrons onto a set of imaging quality microchannel plates (MCPs) paired with a phosphor screen. A CCD camera recorded the images, which were averaged over several hundred thousand laser pulses using the imaging collection software designed by Li et al.34 The images were transformed using the MEVELER program.35 The MCPs were pulsed so that only the detached photoelectrons were detected. Mu – metal shielding surrounding the photodetachment and electron drift regions minimized image distortions due to external electric and magnetic fields. A photomultiplier tube (PMT) was positioned off-axis of the phosphor screen to monitor phosphor screen emission. This detection method allowed for the optimization of the anion and...
