Second-level Nodes Research Articles

Coupling analysis of crane accident risks based on Bayesian network and the N-K model

Crane usage is pervasive on construction sites, however, it is associated with a notably high accident rate. The analyzing of crane accident risks is essential for accident prevention, control, and ensuring the safety of lifting operations. Hence, significant emphasis should be placed on understanding the interaction among various risk factors. This paper proposes a quantitative coupling method for human, machine, management, and environmental risk factors in crane accidents, leveraging Bayesian networks (BN) and the N-K model. Firstly, text mining technology and fault tree analysis are employed to analyze the causes of crane accidents and categorize the associate risk factors. Secondly, the types of risk coupling resulting from human, machine, management, and environmental risk factors are defined. Thirdly, the BN model is developed based on the analysis of crane accident risksand its N-K model. Fourthly, the parameters of the risk coupling nodes in the developed BN are determined based on the calculation results of the N-K model. Finally, for the risk coupling types with high coupling values and the first-level node and second-level node, the failure probability is analyzed through posterior probability and sensitivity analysis. The results indicate that factors related to man and management significantly impact crane accidents and warrant enhanced attention. The interplay among multiple risk factors significantly influences the probability of crane accidents, necessitating careful attention.

Topology identification method of low voltage distribution network based on signal injection

Aiming at the difficulty of distance identification between nodes in the distribution network, a topology identification method for a low-voltage distribution network based on the signal injection method was proposed. The distance matrix between nodes is obtained through the injection and extraction of the pulse current signal. According to the distance matrix, the information matrix is calculated to identify the busbar, first-level node, second-level node, first-level branch, and second-level branch. The low-voltage distribution network model is built in PSCAD simulation software, and the proposed identification method is verified and tested. Simulation results show that the proposed method has high accuracy in connection relation identification and distance identification and provides a new path for low-voltage distribution network topology identification.

ETM-IoT: Energy-Aware Threshold Model for Heterogeneous Communication in the Internet of Things

The internet of things (IoT) has a wide variety of applications, which in turn raises many challenging issues. IoT technology enables devices to closely monitor their environment, providing context-aware intelligence based on the real-time data collected by their sensor nodes. The IoT not only controls these devices but also monitors their user's behaviour. One of the major issues related to IoT is the need for an energy-efficient communication protocol which uses the heterogeneity and diversity of the objects connected through the internet. Minimizing energy consumption is a requirement for energy-constrained nodes and outsourced nodes. The IoT nodes deployed in different geographical regions typically have different energy levels. This paper focuses on creating an energy-efficient protocol for IoT which can deal with the clustering of nodes and the cluster head selection process. An energy threshold model is developed to select the appropriate cluster heads and also to ensure uniform distribution of energy to those heads and member nodes. The proposed model envisages an IoT network with three different types of nodes, described here as advanced, intermediate and normal nodes. Normal nodes are first-level nodes, which have the lowest energy use; intermediate nodes are second-level nodes, which have a medium energy requirement; and the advanced class are third-level nodes with the highest energy use. The simulation results demonstrate that the proposed algorithm outperforms other existing algorithms. In tests, it shows a 26% improvement in network lifetime compared with existing algorithms.

Research on Optimization and Design of Sustainable Urban Underground Logistics Network Framework

As a new mode of transportation, the underground logistics system (ULS) has become one of the solutions to the problems of environmental pollution and traffic congestion. Considering the environmental and economic factors in urban logistics, this paper conducts comprehensive design and optimization research on the network nodes and passages of urban underground logistics and proposes a relatively complete framework for a sustainable underground logistics network. A hybrid method is proposed, which includes the set cover model used to perform the first location of urban underground logistics nodes, the fuzzy clustering method applied to classify the located logistics nodes into the first-level and second-level nodes considering the congestion in different urban areas of the city and a mixed integer programming model proposed to optimize and design the underground logistics passage to find optimal passage parameters at every underground logistics node. Based on the above hybrid method, a sustainable underground logistics network framework including all-levels logistics nodes and passages is formed, with a subdistrict of Nanjing as a case study. The discussion of results shows that this underground logistics network framework proposal is very effective in reducing logistics time cost, exhaust emission and congestion cost. It provides support for decisions in the design and development of urban sustainable underground logistics networks.

Model of Two-Years Forecasting of the Anti-Existent Patients With Acute Decompensation of Heart Failure on the Background of the Intermediate Fraction of Left Ventricle

Build a prognostic model using clinical, laboratory, and instrumental data to predict mortality in patients with midrange left ventricular ejection fraction (mrLVEF) within two years after hospitalization for acute decompensated heart failure (ADHF). The study included 121 patients hospitalized for ADHF with mrLVEF ranging from 40% to 49.9% (91 males and 30 females, mean age 64.6±14.8 years). The independent sample used to validate thestatistical model included 71 patients with ADHF and mrLVEF with a mean age of 65.59±12.12years. Sex distribution of the independent sample was 51 males (70.8% of the independent sample), 20females (27.8% of the total independent sample). In-hospital mortality of patients included in the study was 4.2%, and long-term mortality was 36.8%. We developed a tool to assess the risk of two-year mortality using classification trees. The root node is the red blood cell distribution width-coefficient of variation (RDW-CV); its diagnostic value in this model was 13.3%. The second-level nodes are glomerular filtration rate (GFR), with a cutoff level of 35 mL/min/1.73 m2, and chronic kidney disease (CKD). The third-level nodes are sex, the anterior-posterior dimension of the left atrium, with the cutoff level >47 mm, and low red blood cell count <4.22x1012/L. The estimated sensitivity of the model is 71.4%; estimated specificity is 85.7%. This model can be used to assess long-term mortality risk and identify groups of patients with mrLVEF who require closer monitoring.

Research and Optimization of D-Start Lite Algorithm in Track Planning

With the widespread application of the aircraft, how to achieve the safety and efficiency of the aircraft during flight has attracted widespread attention. The track planned by the traditional D-Start Lite algorithm is very close to the obstacle. Due to the large size of the aircraft itself, it is very likely that the aircraft will collide with the obstacle when operating in a real environment. However, the existing D-Start Lite algorithm is only a single solution to the safety problem of trajectory planning or to improve the efficiency of the algorithm's trajectory search. Therefore, this article optimizes the existing D-Start Lite algorithm. First, this article adopts the strategy of selecting priority level by child nodes. The child nodes in the vertical direction are selected as the first-level nodes. The state of the first-level node determines whether the second-level node on the diagonal needs to be expanded, so that a safe distance is generated between the planned path and the obstacle, thus improving the safety of the path. However, a certain space is reserved between the track and the obstacle, which results in the increase of the number of nodes in the path and the low efficiency of search. This article improves and optimizes the valuation function and heuristic function in the algorithm. Therefore, the number of expansion nodes and the number of inflection points are reduced, and the search efficiency of the algorithm is improved. Therefore, the improved algorithm improves the efficiency of trajectory search while ensuring the safety of trajectory planning. And the effectiveness and feasibility of the algorithm are proved by the MATLAB platform.

FORECASTING THE GROUNDWATER LEVEL OF CEMENT RAW MATERIALS DEPOSIT BASED ON DYNAMIC NEIGHBORHOOD MODELS

Introduction. The development of a mathematical model for the groundwater level of a deposit of cement raw materials located in the Zadonian-Yelets aquifer, which is the principal domestic water supply source for the city of Lipetsk, is considered. Therefore, it is necessary to provide ongoing monitoring and to have the possibility to predict the water level under the field development. The work objectives are the identification and study of a dynamic neighborhood model with variable hierarchical neighborhoods of the groundwater level that enables to adequately predict value of the water level in the examined wells.Materials and Methods. The definition of a dynamic neighborhood model with variable hierarchical neighborhoods is given, differing by time-varying double-level neighborhood communications between the first- and second-level nodes. At each next discrete instant of time, the neighborhood model nodes change their state under the influence of the online parameters and node states included in their neighborhood. As a subcase, we consider a model with line state recalculation functions. Parametric identification of the dynamic neighborhood model consists in finding the system parameters for each second-level node, and is based on the ordinary least squares.Research Results. A linear dynamic neighborhood model with variable hierarchical neighborhoods for predicting the groundwater level in a cement raw material deposit located in the Zadonian-Yelets aquifer is developed. The software using C++ is developed for the parametric identification and simulation of the functioning of the dynamic neighborhood model under consideration. It enables to determine parameters of the node state recalculation functions for a given structure, and also to predict the model behavior in the operation process. A hierarchical structure is given, and a parametric identification of the linear dynamic neighborhood model of the groundwater level is carried out. After the parametric identification on the teaching data selection, the mathematical model is checked on the test sample.Discussion and Conclusions. The obtained average ratio errors of the identification and forecast suggest the developed model validity and enable to recommend it for predicting the underground water level of a cement raw materials deposit.

A multi-objective location and channel model for ULS network

In this paper, we construct a multi-objective location and channel model for ULS network to alleviate the urban traffic congestion problem. First, we construct a multi-objective node selection model and obtain the position of the first-level nodes in the ULS network by using agglomerative hierarchical clustering method. Then, we obtain the position of the second-level nodes in the ULS network by using the greedy algorithm. We also calculate the service scope, actual traffic volume and transport rate of first-level node for each node based on the determined node group. After that, we select the optimal channel scheme to make nodes at different levels of the ULS and its load more balanced by using the plant growth simulation algorithm. We extract the key variables of the problem, quantify some indicators with reasonable quantification, construct a multi-objective location and channel model based on the actual logistics situation for the specific region and achieve reasonable results to meet multiple objectives. Therefore, the model could be used as a reference for the construction of urban ULS network.

Media handling aspects of multimedia conferencing in broadband wireless ad hoc networks

The emergence of broadband wireless technologies has made broadband wireless ad hoc networks a reality. Broadband wireless ad hoc networks are infrastructure-less and can be used in a wide range of scenarios in private, public, commercial, and military settings. Multimedia conferencing is an important application area. Its two main components are signaling and media handling. This article focuses on the media handling aspects or multimedia conferencing in broadband wireless ad hoc networks, with a critical review of the state of the art and a proposed novel architecture. The main components of this architecture are a distributed mixing system and a self-organizing system. The distributed mixing system is a two-level structure. The first level is a full mesh network of active nodes that act as media mixers. The second level is made up of passive nodes that rely on the mixers in the first level. Each second-level node is connected to a first-level node. The self-organizing system is based on a resource efficient scheme that provides for the automatic allocation (or deallocation) of mixers when the network grows (or shrinks). Furthermore, it enables the automatic assignment of new nodes to mixers when they join the conference. We have built a proof-of-concept prototype of the architecture and have evaluated the self-organizing system through simulations. We have simulated conferences and observed how mixers are allocated and deallocated. The simulations show that our self-organizing system utilizes resources efficiently in most cases

Towards wireless sensor networks with enhanced vision capabilities

Wireless sensor networks are expected to become an important tool for various security, surveillance and/or monitoring applications. The paper discusses selected practical aspects of development of such networks. First, design and implementation of an exemplary wireless sensor network for intrusion detection and classification are briefly presented. The network consists of two levels of nodes. At the first level, relatively simple microcontroller-based nodes with basic sensing devices and wireless transmission capabilities are used. These nodes are used as preliminary detectors of prospective intrusions. The second-level sensor node is built around a high performance FPGA controlling an array of cameras. The second-level nodes can be dynamically reconfigured to perform various types of visual data processing and analysis algorithms used to confirm the presence of intruders in the scene and to classify approximately the intrusion, if any. The paper briefly presents algorithms and overviews hardware of the network. In the last part of the paper, prospective directions for wireless sensor networks are analyzed and certain recommendations are included.

A computerized scoring system for use with content analysis scales

1. 1. This is a report of the development of a computerized scoring system for use with content analysis scales of speech. The goal has been to surpass the content analysis of single, isolated words in natural language and to focus the analysis on the quality and quantity of meaning carried in clauses or sentences and, eventually, in a broader contextual framework. 2. 2. Using the Gottschalk-Gleser content analysis Hostility scales, the first step taken was the use of an efficient parser that contained a sufficiently powerful grammar to characterize the subset of English that is often encountered in psychiatric interviews. Wood's Augmented Transition Network Parser was modified to run on a PDP-10 computer, and its grammar was enlarged to cover certain linguistic constructions that frequently occur in spoken discourse. In addition, a small dictionary of several hundred entries was created, permitting the maintenance of this dictionary in core. 3. 3. Since the Gottschalk-Gleser content analysis method derives a score on the basis of the action verb in a clause in conjunction with the noun-phrases that function as actors and recipients of this action, a technique was developed for assigning “meaning” to each of these constituents. Accordingly, verbs were assigned semantic features called verb-types based on the thematic categories and weights of the Gottschalk-Gleser Hostility Outward scale. 4. 4. The infinitive form of the verb in each spoken clause was retrieved from the parse and looked up in the semantic-features dictionary. The parser performed the required morphological analysis and stripped the inflectional ending of the verb, yielding its infinitive or root form. 5. 5. The computer next assigned semantic features to the noun-phrases that function as actors and recipients of the action verbs. In the simplest instances, the noun-phrase received the semantic features of its head noun. In more complex instances, for example in prepositional-phrases, noun-phrases were located as semantic markers for the initial noun-phrase. 6. 6. The scoring algorithm for arriving at the score for each clause was obtained after examining the set of semantic features assigned to the above-mentioned noun-phrases and head verb. An information structure (scoretree) was used for advice on how to interpret the combinations of noun-phrases and head verbs, and it was indexed by the verb-type. At present, each scoretree has only two levels; the first level is made up of tests for the features of the subject of the clause in question and the second level is composed of tests for the object. Which scoretree to access is determined by locating the verb-type of the head verb in the clause; the first-level node is obtained by overlapping the features of the subject noun-phrase with the features of successive first-level nodes until some partial match is achieved. The second-level node is located from the first-level node overlapping the features of the object noun-phrase with successive nodes until another partial match is achieved. Associated with the second-level node is the score that the total clause is to receive. 7. 7. In testing this automated method on 100 sentences taken at random from the manual of instructions for using the Gottschalk-Gleser content analysis scales, 60% of them were correctly parsed and scored. As another preliminary test of the information retrieved by this computerized method, six 5-min speech samples were scored by human content analysis technicians, and their scores were compared to the computerized scoring system at its present level of development. A Spearman rank difference correlation of 0.80 was obtained between the ranked scores using the two methods. 8. 8. The shortcomings and further steps to take in the development of this content analysis method are outlined and discussed.