Open Access

Article

Article ID: 1871

Comparison of the elevator traffic flow prediction between the neural networks of CNN and LSTM

by Mo Shi, Yeol Choi

Intelligent Control and System Engineering, Vol.2, No.1, 2024; 12 Views, 5 PDF Downloads

With urbanization rapidly increasing, the demand for efficient elevator systems is becoming ever more pressing, particularly in crowded urban centers where high-rise buildings are prevalent. To solve this issue, elevator traffic analysis and prediction have emerged as critical components for optimizing elevator control systems. The elevator traffic flow prediction not only ensures smoother operations during peak usage times but also significantly reduces waiting periods for passengers, thereby enhancing overall convenience. By leveraging neural networks, the performance of elevator control systems is expected to be improved, leading to more efficient and convenient elevator utilization in both residential and commercial environments. Over the past few decades, the rapid advancements in neural networks have provided valuable tools for predicting traffic flows. In this research, a total of 655 actual ETF (Elevator Traffic Flow) data points from a typical office building on a weekday are utilized to analyze and predict traffic patterns using CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory). The objective is not only to demonstrate the applicability of the neural networks in predicting elevator traffic flow but also to conduct a comparative analysis to identify which offers greater accuracy and suitability for the elevator traffic flow prediction. By enhancing the capabilities of elevator control systems through CNN or LSTM, this research seeks to improve not only the efficiency of elevator operations but also the overall living and working environment in urban cities. The findings from this research can inform subsequent research efforts, encouraging a deeper exploration of how synthetic predictions can further optimize elevator control systems, while the synthetic elevator control system is expected to lead to significant improvements in passenger experience, reducing wait times and increasing overall satisfaction in both residential and commercial buildings. Therefore, the insights gained from this research are expected to play a crucial role in shaping the future of smart buildings, aligning with the demands of modern urban living.

Open Access

Article

Article ID: 239

A case study of Windows 11 operating system for inexperienced users

by Nicko A. Magnaye

Intelligent Control and System Engineering, Vol.1, No.1, 2023; 540 Views, 461 PDF Downloads

The researcher wants to know the effect of using windows 11 to the selected student of Mindoro State University in Bongabong Campus. The use of the Windows 11 Operating System (OS) in the students is well known, but there has been little research to address the usability and performance of this System due to differences between Windows 10 and Windows 11. Furthermore, because the Windows open-source system studies student students’ ability to navigate and learn the different features of systems, the students will be aware of the effectiveness of the Windows 11 Operating System. To know the effect of using windows 11 to the students we asked for observers to observe them. We gather that the student ever to use Windows 11 because of its design. They like Windows 11 because it offers an interface that is more like Mac with p color, or like an aesthetic and with a cleaner interface than its predecessor. Students agreed updating from Windows 10 into Windows 11 is worth it. Window 11 helps the students to have a better control, unleash their creativity, and to improve their reading and writing. Students can also enjoy using windows 11 there will be no cost with the Windows Student Use Benefit program. It also provides education-specific default settings. It has been concluded that Windows 11 has been effective and continues development.

Open Access

Article

Article ID: 427

End-to-end NILM model of industrial power data based on autoencoder transformer

by Ce Li, Fanglin Guo, Rong Yang, Hua Wang , Bo Yao

Intelligent Control and System Engineering, Vol.2, No.1, 2024; 206 Views, 151 PDF Downloads

Energy detection is an important part of intelligent power consumption, and its key technology is non-intrusive load monitoring (NILM). In this study, an end-to-end model is proposed to realize the NILM of commercial power data using the autoencoder-based transformer method. Firstly, we measured the operating power of different electrical appliances across different modes and combined the operating modes of electrical appliances. Considering the relatively large number of industrial electrical appliances, to ensure accuracy, we used Autoencoder to recode and reduce the dimension of the combined coding. Secondly, the transformer model was used to train the translation of the total power consumption information sequence and the state sequence of electrical appliances. Through our model, the electrical signals to be decomposed can be translated into different electrical state codes so as to realize load energy decomposition. Finally, when our model was applied to the gas station field data, the accuracy was as high as 90.17%.

Open Access

Article

Article ID: 315

Full vector intelligent detection of cigarette appearance based on machine vision

by Shifei Jiang, Zhaoguo Zhang, Faan Wang, Zhi Li, Kaiting Xie, Chenglin Wang, Jinhao Liang

Intelligent Control and System Engineering, Vol.1, No.1, 2023; 66 Views, 47 PDF Downloads

As the final output product of tobacco agriculture, the appearance quality of cigarettes is the key link to control. However, there is no special detection equipment for the whole appearance defect detection of tobacco, while it mainly depends on manual detection, leading to the test standard is not unique and the test data cannot be stored effectively. In this research, the shape characteristics and appearance inspection requirements of cigarettes were analyzed with the black-box method. Then, a kind of cigarette appearance quality inspection equipment was designed, and the experimental data of the equipment was analyzed with Design-Expert11. The results show that the device can image the appearance of a cigarette completely. The optimum parameters of the equipment are: the lifting speed of slide plate is 0.3 m/s, the angle of transition plate is 40°, the displacement speed of roller is 0.045 m/s, the movement speed of the slide plate is 0.4 m/s, the expansion speed of the cylinder is 20 mm/s, the spring coefficient is 3 n/s, the angle of the light source is 10°, and the height difference between the light source and the cigarette is 30 mm. The equipment can meet the needs of cigarette appearance detection and provide a reference for cylindrical object appearance detection.

Open Access

Article

Article ID: 367

Improved generalized self-consistent model in predicting the applicability of the refractory material mechanics behavior research

by Zhixing Huang, Zhigang Wang, Xianjun Li, Jiawen Li, Tianyang Zhou, Dongshuo Wang

Intelligent Control and System Engineering, Vol.1, No.1, 2023; 62 Views, 41 PDF Downloads

The improved generalized self-consistent model (GSCM) has shown good performance in predicting the mechanical properties of multiphase refractory materials. In this study, three representative refractory materials were selected to investigate the applicability of this model. Under ambient conditions, the mechanical properties of aluminum-magnesium-carbon material with multiple inclusions, magnesium-carbon material with low matrix and high aggregate content, and aluminum matrix material were predicted. The damage behavior of the materials under compression was simulated using an iterative method. The results showed that the GSCM still exhibited good predictive performance for the elastic modulus and Poisson’s ratio of multiphase inclusion materials and aluminum matrix materials, with errors of approximately 5%. When simulating the compressed damage behavior, the maximum error for AMC-type materials was around 10%, while for aluminum matrix materials, it was around 25%. The maximum errors occurred near the maximum strain, which was attributed to the excessive pore conversion rate in the GSCM when simulating material damage. At non-maximum strains, the fitting error was within an acceptable range, achieving the purpose of estimating the mechanical properties of the materials using this model. However, the predictive performance for materials with low matrix and high aggregate content was poor due to the inherent characteristics of these materials, where the matrix cannot effectively encapsulate the aggregates, resulting in heterogeneous mechanical properties at the macroscopic level. The limitations of the GSCM mechanism prevented it from achieving accurate predictions in such cases. In conclusion, the generalized self-consistent model can be applied to estimate the mechanical properties of various composite materials. However, for materials with heterogeneous mechanical properties, such as those where the matrix cannot effectively encapsulate the particle phase, the GSCM is not suitable.

Open Access

Article

Article ID: 259

Enhancing the cyclist’s journey: Augmented reality cycling glasses redefining the riding experience

by Bochi Meng, Xiaowen Tao

Intelligent Control and System Engineering, Vol.1, No.1, 2023; 118 Views, 101 PDF Downloads

In response to the growing emphasis on environmentally friendly transportation and the challenges posed by factors like the epidemic and increasing fuel costs, an escalating number of individuals are turning to cycling as a sustainable and secure means of travel. However, in this era of technological advancement, characterized by the pervasive integration of digital tools, navigating through the information-rich landscape has become a commonality for car users yet remains relatively untapped for cyclists. This paper delves into an intricate exploration of the iterative process involved in conceiving and executing an augmented reality (AR) cycling glasses application. This innovative program stands to revolutionize the cycling experience by offering a seamless conduit for accessing pertinent information even while engaged in cycling. The research method incorporated a well-structured questionnaire designed to elicit the preferences of cyclists for AR glasses in a myriad of biking scenarios. The intent was to comprehensively ascertain the divergent needs that cyclists harbor for AR glasses across varying conditions. In summation, the paper unfurls a comprehensive narrative encapsulating the design, development, and real-world testing of an AR cycling glasses program. By melding technology with the art of cycling, the program stands as a testament to the potential of human-machine collaboration in augmenting the realms of physical activities and everyday experiences.