Faculty Opinions recommendation of Using a neural network and spatial clustering to predict the location of active sites in enzymes.

2003 ◽  
Author(s):  
Deyou Zheng
Keyword(s):  
Neural Network ◽  
Active Sites ◽  
Spatial Clustering

Using A Neural Network and Spatial Clustering to Predict the Location of Active Sites in Enzymes

2003 ◽  
Vol 330 (4) ◽  
pp. 719-734 ◽  
Author(s):  
Alex Gutteridge ◽  
Gail J Bartlett ◽  
Janet M Thornton
Keyword(s):  
Neural Network ◽  
Active Sites ◽  
Spatial Clustering

scholarly journals Developing an On-Road Object Detection System Using Monovision and Radar Fusion

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Ya-Wen Hsu ◽  
Yi-Horng Lai ◽  
Kai-Quan Zhong ◽  
Tang-Kai Yin ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Spatial Clustering ◽  
Detection System ◽  
Warning System ◽  
Recognition System ◽  
Target Object ◽  
Fusion Algorithm ◽  
Detection Rates ◽  
Distance Information

In this study, a millimeter-wave (MMW) radar and an onboard camera are used to develop a sensor fusion algorithm for a forward collision warning system. This study proposed integrating an MMW radar and camera to compensate for the deficiencies caused by relying on a single sensor and to improve frontal object detection rates. Density-based spatial clustering of applications with noise and particle filter algorithms are used in the radar-based object detection system to remove non-object noise and track the target object. Meanwhile, the two-stage vision recognition system can detect and recognize the objects in front of a vehicle. The detected objects include pedestrians, motorcycles, and cars. The spatial alignment uses a radial basis function neural network to learn the conversion relationship between the distance information of the MMW radar and the coordinate information in the image. Then a neural network is utilized for object matching. The sensor with a higher confidence index is selected as the system output. Finally, three kinds of scenario conditions (daytime, nighttime, and rainy-day) were designed to test the performance of the proposed method. The detection rates and the false alarm rates of proposed system were approximately 90.5% and 0.6%, respectively.

scholarly journals A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2641 ◽  
Author(s):  
Aydin Jadidi ◽  
Raimundo Menezes ◽  
Nilmar de Souza ◽  
Antonio de Castro Lima
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
North Carolina ◽  
Feature Selection ◽  
Spatial Clustering ◽  
Noise Detection ◽  
Nsga Ii ◽  
Data Set ◽  
The Neural Network ◽  
Hourly Data

The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safety to supply–demand balance and economic load dispatching. Given a data set, a multi-layer perceptron neural network (MLPNN) is a strong tool for solving the forecasting problems. Furthermore, noise detection and feature selection in a data set with numerous variables including meteorological parameters and previous values of GHI are of crucial importance to obtain the desired results. This paper employs density-based spatial clustering of applications with noise (DBSCAN) and non-dominated sorting genetic algorithm II (NSGA II) algorithms for noise detection and feature selection, respectively. Tuning the neural network is another important issue that includes choosing the hidden layer size and activation functions between the layers of the network. Previous studies have utilized a combination of different parameters based on trial and error, which seems to be inefficient in terms of accurate selection of the desired features and also tuning of the neural network. In this research, two different methods—namely, particle swarm optimization (PSO) algorithm and genetic algorithm (GA)—are utilized in order to tune the MLPNN, and the results of one-hour-ahead forecasting of the GHI are subsequently compared. The methodology is validated using the hourly data for Elizabeth City located in North Carolina, USA, and the results demonstrated a better performance of GA in comparison with PSO. The GA-tuned MLPNN reported a normalized root mean square error (nRMSE) of 0.0458 and a normalized mean absolute error (nMAE) of 0.0238.

Maritime Anomaly Detection using Density-based Clustering and Recurrent Neural Network

2019 ◽  
Vol 72 (04) ◽  
pp. 894-916 ◽  
Author(s):  
Liangbin Zhao ◽  
Guoyou Shi
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Situational Awareness ◽  
Short Term Memory ◽  
Spatial Clustering ◽  
Dbscan Algorithm ◽  
The Neural Network ◽  
Density Based Clustering

Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. Based on data from the Chinese Zhoushan Islands, experiments verified the applicability of the proposed method. The results show that the proposed method can detect anomalous behaviours of a vessel regarding speed, course and route quickly.

scholarly journals LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090783
Author(s):  
Jingqiu Ren ◽  
Ke Bao ◽  
Guanghua Zhang ◽  
Li Chu ◽  
Weidang Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Spatial Clustering ◽  
Indoor Positioning ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Radial Basis ◽  
Positioning Algorithm

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

What catalysis needs from the electron microscopist

1988 ◽  
Vol 46 ◽  
pp. 694-695
Author(s):  
Alexis T. Bell
Keyword(s):  
Active Sites ◽  
Heterogeneous Catalysts ◽  
Catalyst Deactivation ◽  
Surface Composition ◽  
Internal Surface ◽  
Active Phase ◽  
Atomic Scale ◽  
Metal Catalyst ◽  
Internal Surface Area ◽  
Porous Support

Heterogeneous catalysts, used in industry for the production of fuels and chemicals, are microporous solids characterized by a high internal surface area. The catalyticly active sites may occur at the surface of the bulk solid or of small crystallites deposited on a porous support. An example of the former case would be a zeolite, and of the latter, a supported metal catalyst. Since the activity and selectivity of a catalyst are known to be a function of surface composition and structure, it is highly desirable to characterize catalyst surfaces with atomic scale resolution. Where the active phase is dispersed on a support, it is also important to know the dispersion of the deposited phase, as well as its structural and compositional uniformity, the latter characteristics being particularly important in the case of multicomponent catalysts. Knowledge of the pore size and shape is also important, since these can influence the transport of reactants and products through a catalyst and the dynamics of catalyst deactivation.

Scanning luminescence x-ray microscopy: Progress toward selective staining using microspheres

1994 ◽  
Vol 52 ◽  
pp. 74-75
Author(s):  
C. Jacobsen ◽  
J. Fu ◽  
S. Mayer ◽  
Y. Wang ◽  
S. Williams
Keyword(s):  
Visible Light ◽  
Active Sites ◽  
Light Emission ◽  
Chinese Hamster ◽  
Polystyrene Spheres ◽  
Good Resistance ◽  
X Ray ◽  
Selective Staining ◽  
Visible Light Emission ◽  
Specific Labelling

In scanning luminescence x-ray microscopy (SLXM), a high resolution x-ray probe is used to excite visible light emission (see Figs. 1 and 2). The technique has been developed with a goal of localizing dye-tagged biochemically active sites and structures at 50 nm resolution in thick, hydrated biological specimens. Following our initial efforts, Moronne et al. have begun to develop probes based on biotinylated terbium; we report here our progress towards using microspheres for tagging.Our initial experiments with microspheres were based on commercially-available carboxyl latex spheres which emitted ~ 5 visible light photons per x-ray absorbed, and which showed good resistance to bleaching under x-ray irradiation. Other work (such as that by Guo et al.) has shown that such spheres can be used for a variety of specific labelling applications. Our first efforts have been aimed at labelling ƒ actin in Chinese hamster ovarian (CHO) cells. By using a detergent/fixative protocol to load spheres into cells with permeabilized membranes and preserved morphology, we have succeeded in using commercial dye-loaded, spreptavidin-coated 0.03μm polystyrene spheres linked to biotin phalloidon to label f actin (see Fig. 3).

Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

1996 ◽  
Vol 54 ◽  
pp. 282-283
Author(s):  
Badrinath Roysam ◽  
Hakan Ancin ◽  
Douglas E. Becker ◽  
Robert W. Mackin ◽  
Matthew M. Chestnut ◽  
...  
Keyword(s):  
Image Analysis ◽  
Structural Changes ◽  
Sampling Methods ◽  
Spatial Clustering ◽  
Three Dimensional ◽  
Field Of View ◽  
Cell Counting ◽  
Depth Of Field ◽  
Tissue Cell ◽  
Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

The design and catalytic performance of molybdenum active sites on an MCM-41 framework for the aerobic oxidation of 5-hydroxymethylfurfural to 2,5-diformylfuran

2019 ◽  
Vol 9 (3) ◽  
pp. 811-821 ◽  
Author(s):  
Zhao-Meng Wang ◽  
Li-Juan Liu ◽  
Bo Xiang ◽  
Yue Wang ◽  
Ya-Jing Lyu ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Active Sites ◽  
Aerobic Oxidation ◽  
Catalytic Performance ◽  
Mcm 41

The catalytic activity decreases as –(SiO)3Mo(OH)(O) > –(SiO)2Mo(O)2 > –(O)4–MoO.

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