Evaluation of Driver-Induced Human Errors in Smart Construction Tower Crane Operations Based on DEMATEL-ISM-MICMAC

With the advent of Industry 4.0, smart construction sites have seen significant development in China. However, accidents involving digitized tower cranes continue to be a persistent issue. Among the contributing factors, human unsafe behavior stands out as a primary cause for these incidents. This study aims to assess the human reliability of tower crane operations on smart construction sites. To proactively enhance safety measures, the research employs text mining techniques (TF-IDF-Truncated SVD-Complement NB) to identify patterns of human errors among tower crane operators. Building upon the SHEL model, the study categorizes behavioral factors affecting human reliability in the man-machine interface, leading to the establishment of the Performance Shaping Factors (PSFs) system. Furthermore, the research constructs an error impact indicator system for the intelligent construction site tower crane operator interface. Using the DEMATEL method, it analyzes the significance of various factors influencing human errors in tower crane operations. Additionally, the ISM-MICMAC method is applied to unveil the hierarchical relationships and driving-dependent connections among these influencing factors. The findings indicate that personal state, operating procedures, and physical environment directly impact human errors, while personal capability, technological environment, and one fundamental organizational management factor contribute indirectly. .

Reliability evaluation of future photovoltaic systems with smart operation strategy

This paper investigates a new operation strategy for photovoltaic （PV） systems, which improves the overall reliability of the system as a result of the improvement in the reliability of the critical components. First, a mathematical model is proposed using the fault tree analysis （FTA） to estimate the reliability of the PV systems in order to find the suitable maintenance strategies. The implementations demonstrate that it is essential to employ smart maintenance plans and monitor the identified most critical components of PV systems. Then, an innovative analytical method based on the Markov process is presented to model smart operation plans in PV systems. The impact of smart operation strategy on the PV systems is then evaluated. The objective of this paper is to develop plans for improving the reliability of PV systems. A series of case studies have been conducted to demonstrate the importance of smart operation well as the applicability and method. strategies for PV systems as feasibility of the proposed

A Smart Phone-oriented Embedded Real-time Operating System

This article presents an embedded Smart Phone Operating System (SPOS) independently designed by ZTE Corporation. The SPOS is based on single kernel architecture with its multi-task real-time kernel supporting hardware platforms and resources of mainstream mobile phones. It has remarkable advantages such as highly efficient and dynamic power management, priority - based preemptive scheduling, fast startup, a variety of drivers, and excellent system stability and operability. For the development of upper layer communication protocols and application software, the SPOS provides wireless communication interfaces and the application program framework.

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.