Automatically Generating a MEP Logic Chain from Building Information Models with Identification Rules

In mechanical, electrical, and plumbing (MEP) systems, logic chains refer to the upstream and downstream connections between MEP components. Generating the logic chains of MEP systems can improve the efficiency of facility management (FM) activities, such as locating components and retrieving relevant maintenance information for prompt failure detection or for emergency responses. However, due to the amount of equipment and components in commercial MEP systems, manually creating such logic chains is tedious and fallible work. This paper proposes an approach to generate the logic chains of MEP systems using building information models (BIMs) semi-automatically. The approach consists of three steps: (1) the parametric and nonparametric spatial topological analysis within MEP models to generate a connection table, (2) the transformation of MEP systems and custom information requirements to generate the pre-defined and user-defined identification rules, and (3) the logic chain completion of MEP model based on the graph data structure. The approach was applied to a real-world project, which substantiated that the approach was able to generate logic chains of 15 MEP systems with an average accuracy of over 80%.

Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism

<p>Recognizing substructures and their relations embedded in a molecular structure representation is a key process for <a></a><a>structure-activity</a> or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-the-art methods. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.</p>

Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism

<p>Recognizing substructures and their relations embedded in a molecular structure representation is a key process for <a></a><a>structure-activity</a> or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-the-art methods. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.</p>

Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism

<p>Recognizing substructures and their relations embedded in a molecular structure representation is a key process for <a></a><a>structure-activity</a> or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-art methods. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.</p>

An Analysis System for Skype Network Traffic Based on Multiple-Layer Detection

Skype is a P2P overlay network for VoIP and other applications, which is widely used all over the world. Based on analysis of the operation mechanisms, the communication processes of Skype are discussed detailedly, and then a multiple-layer based system for Skype network traffic identification is designed. The System mainly consists of three entities, which include suspicious connection table, payload detection module and traffic detection module. The functional characteristics of all entities are introduced, and the attribute set for network behavior is depicted. Finally, experiment results show the usability of the system.

A Novel Contour Generation Algorithm for Surface Reconstruction From Dexel Data

This paper presents a method of reconstructing a triangular surface patch from dexel data generated by ray casting to represent solid models for applications, such as virtual sculpting and numerically controlled (NC) machining simulation. A novel contour generation algorithm is developed to convert dexel data into a series of planar contours on parallel slices. The algorithm categorizes the dexels on two adjacent rays into different groups by using a “grouping” criterion. The dexel points in the same group are connected using a set of rules to form subboundaries. After checking the connections among all the dexel points on one slice, a connection table is created and used to obtain the points of connection in a counterclockwise sequence for every contour. Finally, the contours on all the parallel slices are tiled to obtain triangular facets of the boundary surface of the 3D object. Computational costs and memory requirements are analyzed, and the computational complexity analysis is verified by numerical experiments. Example applications are given to demonstrate the described method.

An introduction of chemical structure coding in the X-ray Powder Diffraction File

A special computer storage method for chemical structures is illustrated in this article. With an improved method of two-dimensional connection table and topology, we can fit almost all chemical structures and display them very conveniently. We have used this method in the PDF (X-Ray Powder Diffraction File) database, which contains many special structures.Key words: database, computer application, chemical structure