New human-computer interaction regulations for ship safe driving based on multi-mode data interconnection

Based on the analysis of the multi-mode data of ship mechatronics and the new human-computer interaction regulations for safety driving, a new safety driving regulation based on multi-mode data is put forward. The new regulations for ship safe driving use mechanical and electrical data to form small-world data interconnection. Artificial intelligence and human-computer interaction operation information are used to integrate and communicate, and human-computer interaction data are incorporated to standardize driving behavior to integrate historical driving data, and finally, the standardized automatic self-driving is formed. The new human-computer interaction regulations formed by the safe driving system make it possible to solve and optimize the ship safe driving mode.

Pendampingan Pembuatan Dan Pengelolaan Data Elektronik Jemaat GKJ Wates

Church parishioners data management is an important part of the administration of a church. In fact, many churches do it manually where parishionersal data is stored in physical forms or in church record books. This condition happened at GKJ Wates. The initial condition at GKJ Wates is the data from of a physical form . The data is the result of the census about 2 (two) years ago. It is realized that the data has changed a lot, so it is necessary to update before implementing electronic data management. In an effort to assist the administration of the church's data, assistance steps have been developed that include the compilation of attributes relevant to the church's data management. Furthermore, the attributes are compiled into a form that is ready to be filled out by each parishioners. This agreed form is then distributed to the parishioners through their respective district administrators. The church team, assisted by the UKDW PkM team, recorded the parishioners's data electronically, complete with a spiritual journey scheme including baptism, sidi, and marriage. By utilizing Excel Macro, the electrical data of the GKJ Wates parishioners was successfully visualized in the form of a dashboard. The visualized parishioners data includes the percentage of the parishioners by blood type and gender, the church's spiritual journey scheme based on membership category, membership records, age category, and gender. The information on this dashboard can help the church in making decisions related to the development of the parishioners at GKJ Wates.

Computer Prediction Model for Equipment Maintenance Using Cloud Computing and Secure Data-sharing

A With the popularization of automation in the industrial field, productivity has been greatly improved. However, manufacturing corporations are facing a data tsunami which brings new challenges to predictive maintenance (PdM). In recent years, many approaches and architecture for predictive maintenance have been proposed to solve some of these problems to varying degrees. This paper introduces a general framework based on the Internet of Things, cloud computing and big data analytics for PdM of industrial equipment. In this framework, smart sensors are installed on the device to obtain electrical data, which is then encrypted and uploaded to the cloud platform to predict the health condition by deep learning methods. Several working instances including feature selection, feature fusion, and Remaining Useful Life (RUL) prediction are provided. The effectiveness of the proposed methods is demonstrated by real-world cases.

Analysis and Identification of Nonlinear Acoustic Damping in Miniature Loudspeakers

Nonlinear acoustic damping is a key nonlinearity in miniature loudspeakers when the air velocity is at a high amplitude. Measurement of nonlinear acoustic damping is beneficial for predicting and analyzing the performance of miniature loudspeakers. However, the general measuring methods for acoustic impedance, such as the standing-wave tube method or the impedance tube method, are not applicable in this scenario because the nonlinear acoustic damping in miniature loudspeakers is coupled with other system nonlinearities. In this study, a measurement method based on nonlinear system identification was constructed to address this issue. The nonlinear acoustic damping was first theoretically analyzed and then coupled in an equivalent circuit model (ECM) to describe the full dynamics of miniature loudspeakers. Based on the ECM model, the nonlinear acoustic damping was identified using measured electrical data and compared with theoretical calculations. The satisfactory agreement between the identification and theoretical calculations confirms the validity of the proposed identification method.

Probabilistic cracking prediction via deep learned electrical tomography

In recent years, electrical tomography, namely, electrical resistance tomography (ERT), has emerged as a viable approach to detecting, localizing and reconstructing structural cracking patterns in concrete structures. High-fidelity ERT reconstructions, however, often require computationally expensive optimization regimes and complex constraining and regularization schemes, which impedes pragmatic implementation in Structural Health Monitoring frameworks. To address this challenge, this article proposes the use of predictive deep neural networks to directly and rapidly solve an analogous ERT inverse problem. Specifically, the use of cross-entropy loss is used in optimizing networks forming a nonlinear mapping from ERT voltage measurements to binary probabilistic spatial crack distributions (cracked/not cracked). In this effort, artificial neural networks and convolutional neural networks are first trained using simulated electrical data. Following, the feasibility of the predictive networks is tested and affirmed using experimental and simulated data considering flexural and shear cracking patterns observed from reinforced concrete elements.

An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

Perkiraan Pemakaian dan Kebutuhan Beban Energi Listrik Rayon Juwana Menggunakan Jaringan Saraf Tiruan (Radial Basis Function)

<p class="IndexTerms">Along with the development of the times the need for electrical energy is increasing. PT. PLN (Persero) as a company that supplies electrical energy must be able to meet the needs of electrical energy to run a business to supply electric power in an even amount to meet the needs of the household, industrial, social and business sectors.. The purpose of this research to determine how much the growth of electrical loads in 2020 to 2025 at PT. PLN (Persero) Rayon Juwana. To find out the amount of increase in electrical energy required. Electrical data used data for 8 years, from 2012 to 2019. The results of this study are in the form of an estimate of the use and need for load electrical energy load for the next 5 years, from 2020 to 2025, the data used are the number of subscribers, connected power, and the amount of energy using a simulated neural network with the method (radial basis function). the results of this study an increase per year an average of 1% per year. in 2019 the value is 1.07%, in 2020 it is 1.10%, 2021 is 1.21%, for 2022 it is 1.27%, 2023 is 1.28%, 2024 is 1.17% and 2025 is 1.31 %.</p>