Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors

Establishing an effective local feature descriptor and using an accurate key point matching algorithm are two crucial tasks in recognizing and registering on the 3D point cloud. Because the descriptors need to keep enough descriptive ability against the effect of noise, occlusion, and incomplete regions in the point cloud, a suitable key point matching algorithm can get more precise matched pairs. To obtain an effective descriptor, this paper proposes a Multi-Statistics Histogram Descriptor (MSHD) that combines spatial distribution and geometric attributes features. Furthermore, based on deep learning, we developed a new key point matching algorithm that could identify more corresponding point pairs than the existing methods. Our method is evaluated based on Stanford 3D dataset and four real component point cloud dataset from the train bottom. The experimental results demonstrate the superiority of MSHD because its descriptive ability and robustness to noise and mesh resolution are greater than those of carefully selected baselines (e.g., FPFH, SHOT, RoPS, and SpinImage descriptors). Importantly, it has been confirmed that the error of rotation and translation matrix is much smaller based on our key point matching algorithm, and the precise corresponding point pairs can be captured, resulting in enhanced recognition and registration for three-dimensional surface matching.

Fault detection and diagnosis in building energy systems: A tool chain for the automated generation of training data

The application of fault detection and diagnosis (FDD) algorithms in building energy management systems (BEMS) has great potential to increase the efficiency of building energy systems (BES). The usage of supervised learning algorithms requires time series depicting both nominal and component faulty behaviour for their training. In this paper, we introduce a method that automates Modelica code extension of BES models in Python with fault models to approximate real component faults. The application shows two orders of magnitude faster implementation compared to manual modelling, while no errors occur in the connections between fault and component models.

Obtaining and Preliminary Characterization of Some Polyethylene Composites with Nickel-Silver Ferrite Filler

Samples of LDPE (low-density polyethylene) and LDPE-PANSA (low-density polyethylene -4-Amino-3-hydroxy- 1-naphthalenesulfonic acid) copolymer with Ag0.5�Ni0.5�Fe2O4 powder (as a filler) composites were developed. Following the preliminary characterizations on the thermooxidability (by thermal analysis techniques), the dielectric behavior (by dielectric spectroscopy technique), the mechanical behavior, etc. it was found that the developed materials do not show significant changes after 240 h exposure to 150 mW / m2 UV. The addition of 3wt% PANSA in LDPE has the effect of increasing the mechanical performance of polymer composites with Ag0.5�Ni0.5�Fe2O4 filler. The addition of 15 wt% ferritic powder leads to significant increases in dielectric losses (by about 100% in the case of pure LDPE and about 185% of the LDPE copolymer with 3 wt% PANSA) and to the increase of the real component of the relative permittivity (by about 34.4 % in LPDE, respectively about 36.4% in LPDE copolymer / 3% wt PANSA). Dielectric behavior of the investigated materials indicates that the effect of Ag0.5�Ni0.5�Fe2O4 powder in LDPE and of copolimer LDPE with 3 wt% PANSA consists in the increasing of the shielding efficiency of electromagnetic waves - the maximum effect being recorded in the case of the composite material with the content: LDPE 84.5 wt%, 2.5 wt% / PANSA and 13% wt% Ag0.5�Ni0.5�Fe2O4.

Improving the method of determining the mass fraction of magnesium carbonate and the study of the chemical composition of carbonate rocks for the effective conduct of the technological process of sugar production

The article considers an improved method for determining the content of magnesium carbonate in the carbonate rock. An adjusted method for determining the content of magnesium carbonate was included in the complete establishment of chemical analysis of limestone, which includes the determination of moisture, impurities insoluble in hydrochloric acid, the amount of one and a half oxides of aluminum and iron, calcium carbonate and magnesium carbonate (advanced method), calcium sulfate, alkali metal oxides of potassium and sodium. The obtained experimental data were entered into a single table and summed up the material balance of all components of the carbonate rock. As a result, it was found that this technique includes the following criteria: it is the most accurate, most accessible, and cheap. The use of adjusted methods for determining the content of calcium and magnesium carbonate in limestone will make it possible to establish the objective chemical composition of the carbonate rock and avoid several technological problems. Namely, the excess of uncontrolled magnesium carbonate contributes to the formation of the liquid phase, which in turn reduces the concentration of chemically active lime and promotes the formation of melts in the lime kiln, deteriorating filtration rates, clogging the evaporating station, and so on. Therefore, having information about the real component composition of limestone, the technologist will be able to adjust the technological process in advance, which will lead to the preservation of natural resources while the quality of finished products will not decrease.

Deep Learning Based on Fourier Convolutional Neural Network Incorporating Random Kernels

In recent years, convolutional neural networks have been studied in the Fourier domain for a limited environment, where competitive results can be expected for conventional image classification tasks in the spatial domain. We present a novel efficient Fourier convolutional neural network, where a new activation function is used, the additional shift Fourier transformation process is eliminated, and the number of learnable parameters is reduced. First, the Phase Rectified Linear Unit (PhaseReLU) is proposed, which is equivalent to the Rectified Linear Unit (ReLU) in the spatial domain. Second, in the proposed Fourier network, the shift Fourier transform is removed since the process is inessential for training. Lastly, we introduce two ways of reducing the number of weight parameters in the Fourier network. The basic method is to use a three-by-three sized kernel instead of five-by-five in our proposed Fourier convolutional neural network. We use the random kernel in our efficient Fourier convolutional neural network, whose standard deviation of the Gaussian distribution is used as a weight parameter. In other words, since only two scalars for each imaginary and real component per channel are required, a very small number of parameters is applied compressively. Therefore, as a result of experimenting in shallow networks, such as LeNet-3 and LeNet-5, our method achieves competitive accuracy with conventional convolutional neural networks while dramatically reducing the number of parameters. Furthermore, our proposed Fourier network, using a basic three-by-three kernel, mostly performs with higher accuracy than traditional convolutional neural networks in shallow and deep neural networks. Our experiments represent that presented kernel methods have the potential to be applied in all architecture based on convolutional neural networks.

Performance of a microjet using component map scaling

Purpose This paper aims to investigate the cycle performance of a small size turbojet engine used in unmanned aerial vehicles at 0–5,000 m altitude and 0–0.8 Mach flight speeds with real component maps. Design/methodology/approach The engine performance calculations were performed for both on-design and off-design conditions through an in-house code generated for simulating the performance of turbojet engines at different flight regimes. These calculations rely on input parameters in which fuel composition are obtained through laboratory elemental analysis. Findings Exemplarily, according to comparative results between in-house developed performance code and commercially available software, there is 0.25% of the difference in thrust value at on-design conditions. Practical implications Once the on-design performance parameters and fluid properties were determined, the off-design operation calculations were performed based on the compressor and turbine maps and scaling methodology. This method enables predicting component maps and fitting them to real conditions. Originality/value A method to be used easily by researchers on turbojet engine performance calculations which best fits to real conditions.

Advances in the Study of Gas Hydrates by Dielectric Spectroscopy

The influence of kinetic hydrate inhibitors on the process of natural gas hydrate nucleation was studied using the method of dielectric spectroscopy. The processes of gas hydrate formation and decomposition were monitored using the temperature dependence of the real component of the dielectric constant ε′(T). Analysis of the relaxation times τ and activation energy ΔE of the dielectric relaxation process revealed the inhibitor was involved in hydrogen bonding and the disruption of the local structures of water molecules.

Riemann zeros from Floquet engineering a trapped-ion qubit

The non-trivial zeros of the Riemann zeta function are central objects in number theory. In particular, they enable one to reproduce the prime numbers. They have also attracted the attention of physicists working in random matrix theory and quantum chaos for decades. Here we present an experimental observation of the lowest non-trivial Riemann zeros by using a trapped-ion qubit in a Paul trap, periodically driven with microwave fields. The waveform of the driving is engineered such that the dynamics of the ion is frozen when the driving parameters coincide with a zero of the real component of the zeta function. Scanning over the driving amplitude thus enables the locations of the Riemann zeros to be measured experimentally to a high degree of accuracy, providing a physical embodiment of these fascinating mathematical objects in the quantum realm.

General Metal to Metal Static Seals Leak Tightness Overview - R&D Processes, FEA and Tests and Further Code Implementation

Latest development in oil and gas industry is focused on higher pressure and temperature. In addition, standard oil and gas components are more frequent adapted to the other applications like aerospace, food processing, renewable energy. For those conditions, tightness is even more critical than before. In the existing codes the requirements for different tightness classes can be find as well as detailed fugitive emission test descriptions. However, the complete design methodology for HPHT seal solution is still missing. There is no engineering procedure/methodology in the code which will link the design concept, R&D process to the prototype and real tests results. The method which will predict tightness for each size and each configuration/condition is not there. Therefore, there is an urgent need to understand the leakage phenomena and describe it by simplified and safe engineering method. Method, which will connect the design concept with real behavior and be in agreement (or conservative) with test results. By having such method, it will be possible to understand better the sealing mechanism in the existing solutions as well as designing new robust, simple, and cheap (optimized) solutions. There are some more or less accurate methods/rules which are already existing in the supplier companies. There is also more and more research about the continuum flow between two surfaces in contact. Based on those sources in this paper we will present the concept for metal to metal sealing solution designing methodology which can be implemented for future revision of the designing codes. The method which can be used simply and effectively and help to understand the current designs and further R&Ds. Presented here design concept, will be described based on example. The example will go through the process from design concept to the real component tightness test.

A new type of quantum walks based on decomposing quantum states

In this paper, the 2-state decomposed-type quantum walk (DQW) on a line is introduced as an extension of the 2-state quantum walk (QW). The time evolution of the DQW is defined with two different matrices, one is assigned to a real component, and the other is assigned to an imaginary component of the quantum state. Unlike the ordinary 2-state QWs, localization and the spreading phenomenon can coincide in DQWs. Additionally, a DQW can always be converted to the corresponding 4-state QW with identical probability measures. In other words, a class of 4-state QWs can be realized by DQWs with 2 states. In this work, we reveal that there is a 2-state DQW corresponding to the 4-state Grover walk. Then, we derive the weak limit theorem of the class of DQWs corresponding to 4-state QWs which can be regarded as the generalized Grover walks.