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This study aims to develop professional competence indicators for underwater welding technicians for offshore wind power generation in Taiwan. A literature analysis methodology was employed to gather and investigate research studies related to competence indicators in the underwater welding domain of offshore wind power generation. Subsequently, the Delphi method was utilized to conduct a three-round questionnaire survey, aiming to seek expert opinions regarding the appropriateness and differentiation of these competency indicators. To examine the consistency and significance of expert opinions, the data were subjected to K–S single-sample analysis and K–W one-way analysis of variance. The study identified three main dimensions of professional competency indicators for underwater welding technicians in offshore wind power generation: professional skills, professional knowledge, and workplace attitudes. These dimensions further led to the identification of 10 sub-dimensions, including equipment operation, welding practice, welding inspection, metal materials, welding graphics, occupational safety, quality standards, process improvement, self-management, and teamwork. These sub-dimensions further informed the identification of 75 specific behavioral components as criteria. This study provides findings to enhance future staff training and talent recruitment, benefiting relevant units and managers. These results contribute to enhancing the competence and performance of personnel in underwater welding for offshore wind power generation.

Under the plan of net-zero carbon emissions in 2050, the high penetration of distributed renewable energies in distribution networks will cause the operation of more complicated distribution networks. The development of edge computing platforms will help the operator to monitor and compute the system status timely and locally, and it can ensure the security operation of the system. In this paper, a novel EDGE computing platform that is implemented by a graphics processing unit in the existing feeder terminal unit (FTU) is proposed for smart applications in distribution networks with distributed renewable energies and loads. This platform makes timely forecasts of the feeder status for the next seven days in accordance with historical weather, sun, and loading data. The forecast solver uses the machine learning long short-term memory (LSTM) method. Thereafter, the power calculation analyzers transform feeder topology into the circuit model for transient-state, steady-state, and symmetrical component analyses. An important-factor explainer parses the LSTM model into the concise value of each historical datum. All information transports to remote devices via the internet for the real-time monitor feature. The software stack of the EDGE platform consists of the database archive file system, time-series forecast solver, power flow analyzers, important-factor explainer, and message queuing telemetry transport (MQTT) protocol communication. All open-source software packages, such as SQLite, LSTM, ngspyce, Shapley Additive Explanations, and Paho-MQTT, form the aforementioned function. The developed EDGE forecast and power flow computing platform are helpful for achieving FTU becoming an Internet of Things component for smart operation in active distribution networks.

In this study, the authors implemented an intelligent long-term care system based on deep learning techniques, using an AI model that can be integrated with the Lab’s Virtual Instrumentation Engineering Workbench (LabVIEW) application for sentiment analysis. The input data collected is a database of numerous facial features and environmental variables that have been processed and analyzed; the output decisions are the corresponding controls for sentiment analysis and prediction. Convolutional neural network (CNN) is used to deal with the complex process of deep learning. After the convolutional layer simplifies the processing of the image matrix, the results are computed by the fully connected layer. Furthermore, the Multilayer Perceptron (MLP) model embedded in LabVIEW is constructed for numerical transformation, analysis, and predictive control; it predicts the corresponding control of emotional and environmental variables. Moreover, LabVIEW is used to design sensor components, data displays, and control interfaces. Remote sensing and control is achieved by using LabVIEW’s built-in web publishing tools.

Air pollution prediction is an important issue for regulators and practitioners in a sustainable era. Air pollution, especially PM2.5 resulting from industrialization, has fostered a wave of global weather migration and jeopardized human health in the past three decades. Taiwan has evolved as a highly developed economy and has a severe PM2.5 pollution problem. Thus, the control of PM2.5 is a critical issue for regulators, practitioners and academics. More recently, GA-SVM, an artificial-intelligence-based approach, has become a preferred prediction model, attributed to the advances in computer technology. However, hourly observation of PM2.5 concentration tends to present the GARCH effect. The objective of this study is to explore whether the integration of GA-SVM with the GARCH model can build a more accurate air pollution prediction model. The study adopts central Taiwan, the region with the worst level of PM2.5, as the source of observations. The empirical implementation of this study took a two-step approach; first, we examined the potential existence of the GARCH effect on the observed PM2.5 data. Second, we built a GA-SVM model integrated with the GARCH framework to predict the 8 h PM2.5 concentration of the sample region. The empirical results indicate that the prediction performance of our proposed alternative model outperformed the traditional SVM and GA-SVM models in terms of both MAPE and RMSE. The findings in this study provide evidence to support our expectation that adopting the SVM-based approach model for PM2.5 prediction is appropriate, and that prediction performance can be improved by integrating the GARCH model. Moreover, consistent with our prior expectation, the evidence further supports that taking the GARCH effect into account in the GA-SVM model significantly improves the accuracy of prediction. To the knowledge of the authors, this study is the first to attempt to integrate the GARCH effect into the GA-SVM model in the prediction of PM2.5. In summary, with regard to the development of sustainability for both regulators and practitioners, our results strongly encourage them to take the GARCH effect into consideration in air pollution prediction if a regression-based model is to be adopted. Furthermore, this study may shed light on the application of the GARCH model and SVM models in the air pollution prediction literature.

This design activity is an innovative method of rapid assembly system for folding chairs. The research methods used in this study included literature analysis, TRIZ the method, human factors engineering (HFE), universal design, and IPA. First, the design of the folding chair and the layout of the literature related to the chair are analyzed. The contradiction matrix was used in the TRIZ method to find the principle of invention and analyze it, to provide a reference for the innovative design of folding chairs, and then use the relevant literature on materials to help design and select materials quickly. Next, the chair is assembled and unfolded and universal design is applied to design it into an acceptable product, causing it to be more popular. The human–machine interaction with HFE makes the design of rapid assembly and the design of folding chairs faster and safer. Finally, the IPA method is used to understand the subjective feelings of customers on the products of this research and the satisfaction and importance are used as the basis for evaluation; a two-dimensional matrix graph is drawn. After drawing a simulation using drawing software, it is compared with the traditional folding chair and the research results are as follows: (1) Quick unfolding function: use the pull ring upwards and it naturally unfolds downwards by gravity. (2) Quick folding and storage function: use the foot pedal, single-foot operation, and the folding chair can be quickly stored. (3) Rapid transportation and unfolding function: The chair back can be connected with other chair backs and has the functions of quick unfolding and quick storage, so as to realize the quick placement of the folding chair. This design activity realizes the preliminary design concept and design simulation of the product design through a systematic and innovative method. Through this design activity, we can understand the problems existing in product improvement before product development, so as to conduct market research and production cost assessment in the actual mass production later, and further reduce the cost of product design improvement.

This paper proposes an adaptive Kalman filter (AKF) control algorithm for capacitive touch panels under the radiation effect of a commercial fluorescent lamp. The predicted covariance matrix of the proposed algorithm is timely updated by two-dimensional gesture compensation. It adaptively adjusts the filter correction manner between fast tracking and smoothing modes. The trajectory area and velocity concept builds up the compensation method and proves in real-time practice by a microprocessor unit. The electromagnetic radiation effect is investigated to obtain the lamp emission noise level. A comprehensive system structure provides the flexibility and efficiency in competing with different algorithms for comparison. A microprocessor unit completed the AKF method calculation in 8.23 ms under fluorescent lamp lightening condition in real time. All Python environments are implemented in the merit of hardware peripheral support, rich programming environments, and open source modules.

In this study, the authors aimed to realize a smart home using an AI model that can be integrated with the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) application to realize environment control. The collected input data were outdoor temperature, indoor temperature, humidity, illumination, and indoor person count. The output control decisions included control of air conditioners, dehumidifiers, power curtains, and lights. An artificial neural network was utilized to process the input data for machine learning for the objective of achieving a comfortable environment. In addition, the control decision predictions made by this AI model were analyzed for model loss and model accuracy. This study implemented the model. Specifically, LabVIEW was used to design the sensing component, data display, and control interface, and Python was used to establish the intelligent model. Moreover, by using the web publishing tool built into LabVIEW, remote sensing and control were fulfilled in this implementation.

The following topics are dealt with: learning (artificial intelligence); neural nets; feature extraction; pattern classification; object detection; time series; sport; stock markets; optimisation; medical computing.

Artificial intelligence (AI) technology–based intelligent robots are constructed using different technologies, such as Internet of Things (IoT), big data, deep learning, machine learning, neural network, and expert system. This particular type of robots can increase the work efficiency of humans and improve their quality of life. From the industry perspective, AI robots possess unlimited potential for development, and they are projected to be a 10-trillion-dollar industry. In this study, the critical technology of IoT is applied to develop a teaching module for an IoT smart home robot. Teaching and evaluation are performed through an embedded thematic-approach teaching strategy in the course named Automatic Measurement and Monitoring. This research aims to teach students how to integrate IoT technology into robot design and construction to build IoT smart home robots. This cross-disciplinary research incorporates emerging technology—integration of smart home, robot construction, and IoT technologies—into industrial education, teaching material and equipment development, and experimental teaching and evaluation. The participating students were juniors or seniors from the Department of Electrical Engineering or Electromechanical Engineering at the University of Technology.

This work aims to develop an integrated fault location and restoration approach for microgrids (MGs). This work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations is utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve the fault location and restoration problem in MGs.

It is to use the Simulation Program with Integrated Circuit Emphasis(SPICE) Model in railway traction system simulations. There are no complex mathematical equations nor software programing activity for derive the multiphase transformer performance waveforms. There are three railway operation cases in the linear loading, non-linear loading and operational accident short circuit fault for validate its application. The contribution is to propose the new multiphase power system analysis technique in the low entry effort.

With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

The expression and calculation of transmission loss (TL) play key roles for solving the power system economic dispatch (ED) problem. ED including TL must compute the total TL and incremental transmission loss (ITL) by executing power flow equations. However, solving the power flow equations is time-consuming and may result in divergence by the iteration procedure. This approach is unsuitable for real-time ED in practical power systems. To avoid solving nonlinear power flow equations, most power companies continue to adopt the TL formula in ED. Traditional loss formulas are composed of network parameters and in terms of the generator’s real power outputs. These formulas are derived by several assumptions, but these basic assumptions sacrifice accuracy. In this study, a new expression for the loss formula is proposed to improve the shortcomings of traditional loss formulas. The coefficients in the new loss formula can be obtained by recording the power losses according to varying real and reactive power outputs without any assumptions. The simultaneous equations of the second-order expansion of the Taylor series are then established. Finally, the corresponding coefficients can be calculated by solving the simultaneous equations. These new coefficients can be used in optimal real and reactive power dispatch problems. The proposed approach is tested by IEEE 14-bus and 30-bus systems, and the results are compared with those obtained from the traditional B coefficient method and the load flow method. The numerical results show that the proposed new loss formula for ED can hold high accuracy for different loading conditions and is very suitable for real-time applications.

The calculation of the magnitudes and phase angles of the bus voltage is a challenging task in real-time applications for power systems. Voltage profile, which denotes the present conditions of a power system, is determined by executing the traditional AC power flow program or by searching the supervisory control and data acquisition system. The AC power flow program is not suitable for several real-time applications, such as contingency analysis and security control calculations, because of its complexity and convergence problems. Fast computation is the major concern in such applications. In this paper, a new method based on sensitivity factors, referred to as Jacobian-based distribution factors (JBDFs), is proposed for calculating the magnitudes and phase angles of bus voltages. This method requires setting up JBDFs and deriving optimal solution paths of bus voltage for non-swing buses through dynamic programming under base-case loading conditions. Under real-time conditions, the proposed method initially calculates real and reactive power line flows via JBDFs, and then computes the voltage magnitudes and phase angles of non-swing buses through the derived optimal solution paths. The excellence of the proposed hybrid calculation method is verified by IEEE test systems. Simulation results demonstrate that the proposed method exhibits fast computation and high accuracy. Thus, the method is suitable for real-time applications. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper proposes a simple and efficient approach for the optimal dispatch in a medium-voltage microgrid (MG) with various types of distributed generation (DG). The fuel costs generated by these DGs are determined using quadratic and linear functions dependent on the types of DGs. Instead of using the traditional Lagrange multiplier method for power system economic dispatch, the proposed direct search method (DSM) approach is able to handle several inequality constraints without introducing any multipliers and furthermore it can solve the non-derivative problems or the fuel cost functions being much more complicated. Accordingly, the DSM is proposed for determining the optimal dispatch of MGs with various types of DG to minimize generation costs under grid-tied and autonomous operations. Results demonstrate that the proposed DSM is a highly suitable and simple approach to determining the optimal dispatch in medium-voltage MGs with various types of DG.

In this study, we examined the development of industry-oriented safety degree curricula at a college level. Based on a review of literature on the practices and study of the development of safety curricula, we classified occupational safety and health curricula into the following three domains: safety engineering, health engineering, and safety and health management. We invited 44 safety professionals to complete a four-round survey that was designed using a modified Delphi technique. We used Chi-square statistics to test the panel experts' consensus on the significance of the items in the three domains and employed descriptive statistics to rank the participants' rating of each item. The results showed that the top three items for each of the three domains were Risk Assessment, Dangerous Machinery and Equipment, and Fire and Explosion Prevention for safety engineering; Ergonomics, Industrial Toxicology, and Health Risk Assessment for health engineering; and Industrial Safety and Health Regulations, Accident Investigation and Analysis, and Emergency Response for safety and health management. Only graduates from safety programmes who possess practical industry-oriented abilities can satisfy industry demands and provide value to the existence of college safety programmes.

A spiral-ADDIEE&R model is introduced and utilized to develop, implement and evaluate an industry oriented technology course, automatic measurement technology. A process is built to develop teaching material and training device. The practical competence indicators for automatic measurement technology will be found using the Delphi questionnaire answered by industrial supervisors, engineers and university scholars. The teaching material contains 6 chapters and 27 sections for developing and a multi-functional automatic measurement platform that includes over 20 kinds of sensors and measurement devices, designed and constructed for student training use. An experimental class is given with assessment performed for examining the flaws in this course development process.

This paper aims to evaluate the maximum interconnected capacity of the distributed generation(DG), which been interconnected into the distribution feeders. The ±2.5% voltage derivation limit of the Taipower interconnected guides are adopted for evaluating the maximum interconnected capacity of DG, and the unbalanced power flow program is used to solve for the bus voltage profiles before and after the DG interconnected into a primary feeder. The IEEE 13-Bus feeder is modified as a sample system. The simulation scenariosinclude the individual phase and three-phase connections between the DG and the primary feeder. The outcomes demonstrate that the maximum interconnected capacity of DG is dependent on the short-circuit capacity, connection location, load demand, and voltage variation limit. Keywords-distributed generation; maximum interconnected capacity; power flow; voltage variation