Aims and Study Design: Lifts are presently inevitable requirements in modern day building complexes especially multiple floor buildings to keep up with human transportation. Due to this fact, efficient, cost-effective and reliable lift systems are required in these buildings. In this work, the design of a robust lift group control system based on programmable logic controllers (PLC) is introduced. Methodology: To achieve this goal, good choices of a hardware platform comprising a PLC complex and a software platform to feed instructions (ladder logic) to the PLC are needed. These platforms are linked via a network connection over an Ethernet. The development of the control network based on fuzzy control is illustrated, which the lift controller uses to recognize traffic and respond to the detected traffic patterns. A simplified form of the algorithm from the moment of a hall call to the point of execution of this call command is presented with simulations demonstrating lift prioritization and scheduling based on dependence on interplay between the input fuzzy variables. Results: The presented design method shows advantages in accounting for numerous factors such as priority fitting, space availability and lift proximity when calculating and implementing quicker hall call responses, which helps in minimizing waiting time of passengers as demonstrated with the 5 floor double lift system. . Conclusion: The work is believed to set a basis for future PLC-based lift group control systems (PLC-LGCS).
Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science
Lifts play an important role in human transportation in multi-storage buildings, which experience continuous improvements to their architecture and structure. As a result of these improvements, the development of efficient lift systems with more programs is required to meet these changes. In this work, a lift control system based on a programmable logic controller (PLC) is introduced, elucidating the development of the lift control algorithm and network based on a dispatching algorithm that utilizes a fuzzy system and exploits the traffic situation and condition. The PLC language ladder logic is implemented to facilitate a reduction in the average waiting time of passengers and the power consumption. Ladder diagrams for different scenarios are compared. The analysis of personnel-machine-environment (P-M-E) system conditions was conducted, examining numerous physical factors that could pose health and safety threats to workers. The present study opens doors for future lift systems studies based on PLC and the estimation of a safe workplace for machines and operators.
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