Research

13 pages, 5837 KiB

Open AccessArticle

Use of a Software Application to Generate a Sequence for Simulation Model Creation

by Martin Ďuriška, Gabriel Fedorko, Jana Fabianová, Vieroslav Molnár, Hana Neradilová and Filip Dolák

Appl. Sci. 2023, 13(9), 5433; https://0-doi-org.brum.beds.ac.uk/10.3390/app13095433 - 27 Apr 2023

Viewed by 1039

Abstract

The use of simulation models is currently a necessity, considering the complexity of the problems solved in many areas of engineering and scientific work (including logistics). This fact places high demands on their creation, use, and possible modification. When implementing simulation models, we [...] Read more.

The use of simulation models is currently a necessity, considering the complexity of the problems solved in many areas of engineering and scientific work (including logistics). This fact places high demands on their creation, use, and possible modification. When implementing simulation models, we often encounter limitations (depending on the software used), for which an effective solution is the application of the additional programming method. However, the method’s implementation is often associated with problems, the solution to which is, for example, the use of third-party services. Ultimately, this is an effective but lengthy process. The paper presents a new approach to the method of additional programming, which is based on using an addressable software application as a tool for generating sequences according to defined input parameters. The research was carried out using the simulation software Tecnomatix Plant Simulation and the software tool Simtalk, which is part of it. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis II)

► Show Figures

Figure 1

17 pages, 3026 KiB

Open AccessArticle

Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia

by Muhammad Muhitur Rahman, Md Shafiullah, Md Shafiul Alam, Mohammad Shahedur Rahman, Mohammed Ahmed Alsanad, Mohammed Monirul Islam, Md Kamrul Islam and Syed Masiur Rahman

Appl. Sci. 2023, 13(6), 3832; https://0-doi-org.brum.beds.ac.uk/10.3390/app13063832 - 17 Mar 2023

Cited by 5 | Viewed by 2342

Abstract

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national [...] Read more.

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R²) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO₂e) and mean absolute percentage error (0.29 GtCO₂e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO₂ equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis II)

► Show Figures

Figure 1

22 pages, 3081 KiB

Open AccessArticle

Predicting Road Crash Severity Using Classifier Models and Crash Hotspots

by Md. Kamrul Islam, Imran Reza, Uneb Gazder, Rocksana Akter, Md Arifuzzaman and Muhammad Muhitur Rahman

Appl. Sci. 2022, 12(22), 11354; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211354 - 09 Nov 2022

Cited by 11 | Viewed by 2243

Abstract

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the [...] Read more.

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord

G_{i}^{*}

statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord

G_{i}^{*}

, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis II)

► Show Figures

Figure 1

13 pages, 3512 KiB

Open AccessArticle

A pH Monitoring Algorithm for Orifice Plate Culture Medium

by Yuqi Li, Anyi Huang, Tao Zhang, Luhong Wen, Zhenzhi Shi and Lulu Shi

Appl. Sci. 2022, 12(15), 7560; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157560 - 27 Jul 2022

Cited by 1 | Viewed by 1548

Abstract

Recently, there has been renewed interest in cell therapy, which plays a key role in the clinical research of genetic diseases, advanced blood disease, and other diseases. It shows considerable clinical application value and is known as “the new pillar of future medicine”. [...] Read more.

Recently, there has been renewed interest in cell therapy, which plays a key role in the clinical research of genetic diseases, advanced blood disease, and other diseases. It shows considerable clinical application value and is known as “the new pillar of future medicine”. Automatic cell culture and operation technology is the key to ensuring scale, standardization, and stability between batches of therapeutic cells. The pH of the cell culture medium is vital for cell growth. Most cells are suitable for growth at pH 7.2~7.4. A pH of cell culture medium lower than 6.8 or higher than 7.6 is harmful to cells, and cells will degenerate or even die. At present, the monitoring method of cell culture medium pH of automatic cell culture equipment is mainly a visual observation method, which can not accurately or quickly reflect changes in the cell culture medium. To address the issue of monitoring of cell culture fluid pH for automated cell culture equipment and the inability to employ invasive sensors to measure pH during well plate culture, a pH monitoring method for orifice plate culture medium algorithm based on HSV (hue, saturation, value) model is proposed by studying the changes of cell culture medium in the process of cell culture. The research presented here reveals the laws of cell culture fluid pH change and its color moment, and the intelligent monitoring of cell culture fluid pH was successfully achieved. The problem of non-destructive monitoring of the pH of cell culture fluids in well plates is also addressed. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis II)

► Show Figures

Figure 1

20 pages, 6549 KiB

Open AccessArticle

Sensor-Based Hand Gesture Detection and Recognition by Key Intervals

by Yin-Lin Chen, Wen-Jyi Hwang, Tsung-Ming Tai and Po-Sheng Cheng

Appl. Sci. 2022, 12(15), 7410; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157410 - 23 Jul 2022

Cited by 2 | Viewed by 1821

Abstract

This study aims to present a novel neural network architecture for sensor-based gesture detection and recognition. The algorithm is able to detect and classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. Each hand gesture in [...] Read more.

This study aims to present a novel neural network architecture for sensor-based gesture detection and recognition. The algorithm is able to detect and classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. Each hand gesture in the sequence is regarded as an object with a pair of key intervals. The detection and classification of each gesture are equivalent to the identification and matching of the corresponding key intervals. A simple automatic labelling is proposed for the identification of key intervals without manual inspection of sensory data. This could facilitate the collection and annotation of training data. To attain superior generalization and regularization, a multitask learning algorithm for the simultaneous training for gesture detection and classification is proposed. A prototype system based on smart phones for remote control of home appliances was implemented for the performance evaluation. Experimental results reveal that the proposed algorithm provides an effective alternative for applications where accurate detection and classification of hand gestures by simple networks are desired. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis II)

► Show Figures

Figure 1

17 pages, 6592 KiB

Open AccessArticle

Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network

by Md Arifuzzaman, Md. Alhaz Uddin, Mohammed Jameel and Mohammad Towhidur Rahman Bhuiyan

Appl. Sci. 2022, 12(12), 5954; https://0-doi-org.brum.beds.ac.uk/10.3390/app12125954 - 11 Jun 2022

Cited by 1 | Viewed by 1331

Abstract

The finite element method (FEM) is an essential method for predicting the response of the spar platform considering all nonlinear variables. Although FEM is an extremely laborious and time-consuming process for predicting platform responses using hydrodynamic loads, artificial neural networks (ANNs) can predict [...] Read more.

The finite element method (FEM) is an essential method for predicting the response of the spar platform considering all nonlinear variables. Although FEM is an extremely laborious and time-consuming process for predicting platform responses using hydrodynamic loads, artificial neural networks (ANNs) can predict the response quickly, as required for platform management to either linger or stop the production of oil and gas. The application of ANN approaches to estimate the wave height and period from the expected wind forces is investigated in this paper. The ANN model can also predict the nonlinear responses of the spar platform subjected to the structural parameter as well as the wave height and wave period. The backpropagation technique depletes feed-forward neural networks, allowing the network to be trained. Following the formation of the neural network, rapid reactions from a freshly anticipated wind force are obtained. The results are validated via a comparison with results from a conventional finite element analysis. The findings demonstrate that the artificial neural network (ANN) technique is effective and is able to significantly reduce the required time to make predictions when compared to the conventional FEM. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis II)

► Show Figures

Figure 1

15 pages, 666 KiB

Open AccessArticle

A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras

by Soheil Vosta and Kin-Choong Yow

Appl. Sci. 2022, 12(3), 1021; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031021 - 19 Jan 2022

Cited by 34 | Viewed by 4805

Abstract

Surveillance cameras have been increasingly used in many public and private spaces in recent years to increase the security of those areas. Although many companies still recruit someone to monitor the cameras, the person recruited is more likely to miss some abnormal events [...] Read more.

Surveillance cameras have been increasingly used in many public and private spaces in recent years to increase the security of those areas. Although many companies still recruit someone to monitor the cameras, the person recruited is more likely to miss some abnormal events in the camera feeds due to human error. Therefore, monitoring surveillance cameras could be a waste of time and energy. On the other hand, many researchers worked on surveillance data and proposed several methods to detect abnormal events automatically. As a result, if any anomalous happens in front of the surveillance cameras, it can be detected immediately. Therefore, we introduced a model for detecting abnormal events in the surveillance camera feed. In this work, we designed a model by implementing a well-known convolutional neural network (ResNet50) for extracting essential features of each frame of our input stream followed by a particular schema of recurrent neural networks (ConvLSTM) for detecting abnormal events in our time-series dataset. Furthermore, in contrast with previous works, which mainly focused on hand-crafted datasets, our dataset took real-time surveillance camera feeds with different subjects and environments. In addition, we classify normal and abnormal events and show the method’s ability to find the right category for each anomaly. Therefore, we categorized our data into three main and essential categories: the first groups mainly need firefighting service, while the second and third categories are about thefts and violent behaviour. We implemented the proposed method on the UCF-Crime dataset and achieved 81.71% in AUC, higher than other models like C3D on the same dataset. Our future work focuses on adding an attention layer to the existing model to detect more abnormal events. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis II)

► Show Figures

Figure 1

16 pages, 3983 KiB

Open AccessArticle

An Intelligent Error Correction Algorithm for Elderly Care Robots

by Xin Zhang, Zhiquan Feng, Xiaohui Yang, Tao Xu, Xiaoyu Qiu and Ya Hou

Appl. Sci. 2021, 11(16), 7316; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167316 - 09 Aug 2021

Viewed by 1621

Abstract

With the development of deep learning, gesture recognition systems based on the neural network have become quite advanced, but the application effect in the elderly is not ideal. Due to the change of the palm shape of the elderly, the gesture recognition rate [...] Read more.

With the development of deep learning, gesture recognition systems based on the neural network have become quite advanced, but the application effect in the elderly is not ideal. Due to the change of the palm shape of the elderly, the gesture recognition rate of most elderly people is only about 70%. Therefore, in this paper, an intelligent gesture error correction algorithm based on game rules is proposed on the basis of the AlexNet. Firstly, this paper studies the differences between the palms of the elderly and young people. It also analyzes the misread gesture by using the probability statistics method and establishes a misread-gesture database. Then, based on the misreading-gesture library, the maximum channel number of different gestures in the fifth layer is studied by using the similar curve algorithm and the Pearson algorithm. Finally, error correction is completed under the game rule. The experimental results show that the gesture recognition rate of the elderly can be improved to more than 90% by using the proposed intelligent error correction algorithm. The elderly-accompanying robot can understand people’s intentions more accurately, which is well received by users. Full article

(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis II)

► Show Figures