Reduced order field-circuit modeling of squirrel cage induction machines for automotive applications

Purpose The aim of the paper is to investigate the impacts of simplifications of a reduced-order simulation model of squirrel cage induction machines (SCIMs) by numerical experiments. Design/methodology/approach Design of setups to isolate the main influences on the results of the reduced-order model of SCIMs. Results of time-stepping finite element calculations are used as benchmark. Findings Whereas neglecting eddy current effects and the assumption of a sinusoidal rotor current distribution leads to acceptable deviations in regular inverter operation, the sampling and interpolation of the machine parameters in a two-axis coordinate system considerably deteriorate the model accuracy. Using a polar coordinate system for this purpose is expected to significantly improve the model quality. Originality/value Preparing the ground for a successful, both fast and accurate simulation model of SCIMs as parts of electrified drivetrains.

De novo protein structure prediction by incremental inter-residue geometries prediction and model quality assessment using deep learning

Motivation: The successful application of deep learning has promoted progress in protein model quality assessment. How to use model quality assessment to further improve the accuracy of protein structure prediction, especially not reliant on the existing templates, is helpful for unraveling the folding mechanism. Here, we investigate whether model quality assessment can be introduced into structure prediction to form a closed-loop feedback, and iteratively improve the accuracy of de novo protein structure prediction. Results: In this study, we propose a de novo protein structure prediction method called RocketX. In RocketX, a feedback mechanism is constructed through the geometric constraint prediction network GeomNet, the structural simulation module, and the model quality evaluation network EmaNet. In GeomNet, the co-evolutionary features extracted from MSA that search from the sequence databases are sent to an improved residual neural network to predict the inter-residue geometric constraints. The structure model is folded based on the predicted geometric constraints. In EmaNet, the 1D and 2D features are extracted from the folded model and sent to the deep residual neural network to estimate the inter-residue distance deviation and per-residue lDDT of the model, which will be fed back to GeomNet as dynamic features to correct the geometries prediction and progressively improve model accuracy. RocketX is tested on 483 benchmark proteins and 20 FM targets of CASP14. Experimental results show that the closed-loop feedback mechanism significantly contributes to the performance of RocketX, and the prediction accuracy of RocketX outperforms that of the state-of-the-art methods trRosetta (without templates) and RaptorX. In addition, the blind test results on CAMEO show that although no template is used, the prediction accuracy of RocketX on medium and hard targets is comparable to the advanced methods that integrate templates.

Application of a Thermal Performance-Based Model to Prediction Energy Consumption for Heating of Single-Family Residential Buildings

Energy consumption for heating of single-family residential buildings is a basic item in energy balance and significantly affects their operating costs. Accuracy of heat consumption assessment in existing buildings to a large extent determines the decision on taking actions aimed at heat consumption rationalization, both at the level of a single building and at regional or national level. In the case of energy calculations for the existing buildings, a problem often arises in the form of lack of complete architectural and construction documentation of the analyzed objects. Therefore, there is a need to search for methods that will be suitable for rapid energy analysis in existing buildings. These methods should give satisfactory results in predicting energy consumption when there is limited access to data characterizing the building. Therefore, the aim of this study was to check the usefulness of a model based on thermal characteristics for estimating energy consumption for heating in single-family residential buildings. The research was conducted on a group of 84 buildings, for which the energy characteristics were determined based on the actual energy consumption. In addition, information was collected on variables describing these buildings in terms of construction technology and building geometry, from which the following were extracted for further calculations: cubic capacity, heated area, and year of construction. This made it possible to build a prediction model, which enables the application of a fast, relatively simple procedure of estimating the final energy demand index for heating buildings. The resulting calculations were compared with actual values (calculated from energy bills) and then evaluated according to the standards for evaluating model quality proposed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). In this way, it was possible to determine whether, in the absence of building documents, the indicative method gives good results when estimating the energy demand for heating single-family residential buildings.

Penggunaan Technology Acceptance Model Dalam Mengukur Kualitas Layanan Platform E-Commerce

Seiring dengan meningkatnya pertumbuhan ecommerce di Indonesia, ternyata jumlah pengaduan akan ketidakpuasan konsumen terhadap layanan ecommerce pun meningkat setiap tahunnya. Penelitian ini bertujuan menganalisis kualitas layanan ecommerce dalam meningkatkan kepuasan konsumen dengan technology acceptance model. Platform yang dianalisis dalam penelitian ini adalah Tokopedia dengan menggunakan bagan quality function deployment. Hasil analisis menunjukkan bahwa tingkat kepuasan konsumen masih berada di bawah tingkat harapan konsumen. Sistem yang user friendly, user interface yang simpel dan jelas, otomatisasi fitur saran, sistem pembayaran yang terintegrasi, dan membuat kolom pencarian terlihat jelas merupakan lima prioritas karakteristik teknis yang harus menjadi perhatian perusahaan karena sesuai dengan kebutuhan konsumen. Penelitian ini menyarankan kepada Tokopedia untuk melakukan evaluasi dan perbaikan secara berkala serta melakukan testing aplikasi secara rutin sehingga dapat meminimalisir terjadinya error dan bug. Tokopedia juga perlu lebih tanggap dengan keluhan pelanggan dan pihak pengembang bisa lebih update dalam menangani keluhan yang berkaitan dengan error dan ketidakstabilan aplikasi. Kata kunci: E-commerce, Kualitas Pelayanan, Technology Acceptance Model, Quality Function Deployment.

Equine-Assisted Activities and Therapies: State-of-the-Art Review

The purpose of this paper is to explore what is known about equine-assisted activities and therapies based on Roy’s adaptation model. Quality of life for vulnerable populations who engage with equine-assisted activities and therapies is considered the main concept here. This state-of-the-art review was conducted from four databases ranging from January 2019 to February 2020. Limited studies examined the effect of equine-assisted activities and therapies on cancer survivors, although preliminary data were promising. The rural context was not extensively examined. Thus, equine-assisted activities and therapies may enhance the quality of life through four adaptation modes.

Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

Biomarker identification is very important to differentiate the grade groups in the histopathological sections of prostate cancer (PCa). Assessing the cluster of cell nuclei is essential for pathological investigation. In this study, we present a computer-based method for cluster analyses of cell nuclei and performed traditional (i.e., unsupervised method) and modern (i.e., supervised method) artificial intelligence (AI) techniques for distinguishing the grade groups of PCa. Two datasets on PCa were collected to carry out this research. Histopathology samples were obtained from whole slides stained with hematoxylin and eosin (H&E). In this research, state-of-the-art approaches were proposed for color normalization, cell nuclei segmentation, feature selection, and classification. A traditional minimum spanning tree (MST) algorithm was employed to identify the clusters and better capture the proliferation and community structure of cell nuclei. K-medoids clustering and stacked ensemble machine learning (ML) approaches were used to perform traditional and modern AI-based classification. The binary and multiclass classification was derived to compare the model quality and results between the grades of PCa. Furthermore, a comparative analysis was carried out between traditional and modern AI techniques using different performance metrics (i.e., statistical parameters). Cluster features of the cell nuclei can be useful information for cancer grading. However, further validation of cluster analysis is required to accomplish astounding classification results.

Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique

Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 38% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses.

A system dynamic model for assessing the level of BIM implementation in construction phase: a China case study

PurposeBuilding information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase.Design/methodology/approachAn SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation.FindingsThe simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM.Originality/valueThis study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.