Experimental Setup Sample Clauses
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:
(1) bufferkdtree(i): The adapted buffer k-d tree implementation with both FindLeafBatch and ProcessAllBuffers being conducted on i GPUs.
(2) kdtree(i): A multi-core implementation of a k-d tree-based search, which runs i threads in parallel on the CPU (each handling a single query).
(3) brute(i): A brute-force implementation that makes use of i GPUs to pro- cess the queries in a massively-parallel manner. The parameters for the buffer k-d tree implementation were fixed to appro- priate values.6 Note that both competitors of bufferkdtree have been evaluated extensively in the literature; the reported runtimes and speed-ups can thus be put in a broad context. For simplicity, we fix the number k of nearest neighbors to k = 10 for all experiments. We focus on several data-intensive tasks from the field of astronomy. Note that a similar runtime behavior can be observed on data sets from other domains as well as long as the dimensionality of the search space is moderate (e.g., from d = 5 to d = 30). We follow our previous work and consider the psf mag, psf model mag, and all mag data sets of dimensionality d = 5, d = 10, and d = 15, respectively; for a description, we refer to ▇▇▇▇▇▇▇ et al. [8]. In addition, we consider a new dataset derived from the Catalina Realtime Transient Survey 5 The code is publicly available under ▇▇▇▇▇://▇▇▇▇▇▇.▇▇▇/▇▇▇▇▇▇▇/bufferkdtree. 6 For a tree of height h, we fixed B = 224−h and the number M of indices fetched from input and reinsert in each iteration of Algorithm 1 to M = 10 · B.
Experimental Setup. In order to evaluate the performance of Jam-X, we carry out experiments in two small-scale indoor testbeds deployed in office environments with USB-powered ▇▇▇▇▇. In the first testbed, we use JamLab, a tool for controlled interference generation [5] to evaluate the impact of interference in a real- istic and repeatable fashion. In JamLab, interference is either replayed from trace files that contain RSSI values recorded under interference, or from models of specific devices [5]. In particular, we use JamLab to emulate the interference ▇▇▇- terns produced by microwave ovens, by Bluetooth, and by Wi-Fi devices. In the latter case, the interference emulates a continuous file transfer. To avoid additional interference as much as possible, we carry out the experiments in this testbed during the night, when Wi-Fi activity in the office building is lowest. In the second testbed, we do not use JamLab, but we deliberately choose an 802.15.4 channel af- fected by interference, namely channel 18. On channel 18 there is Wi-Fi traffic and sometimes also interference from microwave ovens in a nearby kitchen. For the experiments, we use two ▇▇▇▇▇ S and R. Node S -25 dBm and 0 dBm. R replies to the message using the transmission power contained in the packet, i.e., the same one used by S. By using different transmission powers, we create different types of links for each handshake. Each packet is sent after a random interval in the order of tens of milliseconds, and nodes remain on the same channel for the whole duration of the experiment. Each experiment consists of several hundred thousand handshakes.
Experimental Setup. The purpose of the experimental study is to find out how the different evaluation techniques for finding robust optima compare when used within the same algorithmic basis, namely the (5/2DI, 35)-σSA-ES and the CMA-ES. The general experimental settings, shown in Table 8.1, restrict to one particular search space dimension size, n = 10, and an evaluation budget of 10, 000 function evaluations, which is taken as a standard setup throughout this chapter. For the assessment of the quality of each scheme, we record the final solution quality over multiple runs. Here, the final solution quality refers to a highly accurate ▇▇▇▇▇-▇▇▇▇▇ approximation (using m = 1000 samples) of the expected objective function value of the solution returned after each optimization run). Search space dimension size n = 10 integration using 1000 samples, (mean, std, median) over all runs, and rank sum for ranking of the algorithmic schemes
8.1: The general experimental setup. Table 8.2: The test problems used for empirical comparison.
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 e...
Experimental Setup. To determine the performance and scalability of FUEGO on the Xeon Phi, we have performed a set of self-play experiments. The program with N threads plays as the first player against another instance of the same program but now with N/2 threads. It is a type of experiment that has been widely adopted for performance and scalability studies of MCTS [CWvdH08a, BG11]. We carry out the experiments on both the Xeon Phi co-processor and the Xeon CPU. Our results will allow a comparison between the two. In our experiments, the performance of FUEGO is reported by (A) playout speedup (see Eq. 2.6) and (B) playing strength (see Eq. 2.7). We defined both metrics in Section 2.5. Here we operationalize the definitions. The scalability is the trend that we observe for these metrics when the number of resources (threads) is increasing.
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. 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. A · A 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.
4.1. Testbed cloud
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 (▇▇▇▇, 2009; ▇▇▇▇▇ 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. 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.