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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 18248

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Prof. Dr. Zhixiong Li

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Yonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
Interests: dynamic system modeling and optimization; sensor data analysis; machine learning and applications
Special Issues, Collections and Topics in MDPI journals

Dr. Yongjun Pan

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Guest Editor

College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400030, China
Interests: vehicle system dynamics; lightweight design of automotive components
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, sustainable designs for autonomous systems are required. This Special Issue aims to emphasize the role of sustainable development for autonomous systems.

Artificial intelligence and machine learning enable modelling, dynamics analysis, and control of complex autonomous systems, for example, unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and intelligent vehicles (IVs). In order to perform effective analyses of new concepts and evaluate different design alternatives, while avoiding the risks and costs associated with extensive field experimentation, artificial intelligence and machine learning are crucial. Setting aside the epistemological significance of artificial intelligence and machine learning, the following question remains open: what is the exact contribution of artificial intelligence and machine learning, relative to sustainability development, for autonomous systems?

This Special Issue aims to provide a comprehensive overview of current ideas and findings in the modelling, dynamics analysis, and control of autonomous systems, including those in land, ocean, and aerial transportations. Specifically, the issue aims to: (i) present the current state-of-the-art about autonomous systems with regard to the design of experiments, field observations, and mathematical modeling; and (ii) identify potential research directions and technologies that will drive innovations in the field of autonomous systems.

Additionally, this Special Issue welcomes submissions employing new and emerging technologies and approaches, such as virtual and augmented reality, artificial intelligence, and digital twin modeling.

This Special Issue invites original and innovative research papers with critical perspectives for current and new approaches applied, and we encourage the submission of works investigating new statistical methods to manually or automatically assess the success of models. The papers collected in this Special Issue will cover these topics from diverse multi- and cross-disciplinary perspectives, including theoretical and numerical methods and experimental studies.

We invite contributions from physics, civil engineering, and energy and computer sciences, addressing sustainability development for autonomous systems.

Prof. Dr. Zhixiong Li
Dr. Yongjun Pan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

autonomous systems
physics-based modelling
deep learning
artificial intelligence
digital twin

Published Papers (9 papers)

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Research

14 pages, 5284 KiB

Open AccessArticle

Analysis of Human Factors in Typical Accident Tests of Certain Type Flight Simulator

by Guanghua Xing, Yingjun Sun, Fajiang He, Pengcheng Wei, Shicheng Wu, Haojie Ren and Zhixiong Chen

Sustainability 2023, 15(3), 2791; https://doi.org/10.3390/su15032791 - 3 Feb 2023

Cited by 3 | Viewed by 1867

Abstract

With the improvement of modern aviation equipment manufacturing technology, there are relatively few failures due to the unreliability of the aircraft. However, human factors which resulted in air crashes and unsafe events are raised. In this paper, for many typical accident scenarios of a particular plane, the flight simulation verifications of the pilot’s workload and behavior are carried out on the certain transport category airplane, namely the six-degrees-of-freedom full-motion flight simulator. The subjective and physiological evaluation methods combine to analyze the human factors of pilots in the sudden typical accident scene during a flight mission. The study uses eye trackers, professional heart rate monitors, cameras, and other equipment to collect the pilot’s physiological information during the flight mission, and allows the pilots to fill in the subjective evaluation scale, establishing a subjective and objective evaluation index system. Thus, the human factors of pilots in typical fault situations are analyzed. The analysis shows the combined personal and accurate evaluation method, with the test equipment and environment proposed by this paper being feasible for the human factor evaluation in the accident or incident of transport category airplanes. It will benefit aviation stakeholders in determining the proper action to decrease the workload to an acceptable level. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

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17 pages, 2357 KiB

Open AccessArticle

Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots

by Rongshen Lai, Zhiyong Wu, Xiangui Liu and Nianyin Zeng

Sustainability 2023, 15(3), 2483; https://doi.org/10.3390/su15032483 - 30 Jan 2023

Cited by 10 | Viewed by 1947

Abstract

In terms of mobile robot path planning, the traditional A* algorithm has the following problems: a long searching time, an excessive number of redundant nodes, and too many path-turning points. As a result, the shortest path obtained from planning may not be the optimal movement route of actual robots, and it will accelerate the hardware loss of robots. To address the aforementioned problems, a fusion algorithm for path planning, combining the improved A* algorithm with segmented second-order Bézier curves, is proposed in this paper. On the one hand, the improved A* algorithm is presented to reduce unnecessary expansion nodes and shorten the search time, which was achieved from three aspects: (1) the traditional 8-neighborhood search strategy was adjusted to 5-neighborhood according to the orientation of the target point relative to the current node; (2) the dynamic weighting factor of the heuristic function was introduced into the evaluation function of the traditional A* algorithm; and (3) the key node extraction strategy was designed to reduce the redundant nodes of the optimal path. On the other hand, the optimal path planned by the improved A* algorithm was smoothed using segmented second-order Bézier curves. The simulation results show that the improved A* algorithm can effectively reduce the search time and redundant nodes and the fusion algorithm can reduce the path curvature and path length to a certain extent, improving path safety. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

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15 pages, 3745 KiB

Open AccessArticle

Influence of Exhaust Temperature and Flow Velocity of Marine Diesel Engines on Exhaust Gas Boiler Heat Transfer Performance

by Dezhi Jiang, Haoxian Yu, Zhihan Wang, Adam Glowacz, Grzegorz Królczyk and Zhixiong Li

Sustainability 2023, 15(1), 753; https://doi.org/10.3390/su15010753 - 31 Dec 2022

Cited by 5 | Viewed by 3569

Abstract

Due to the relatively cheap price of diesel, most Marine engines use diesel as Marine fuel, but its emissions contain a lot of carbon. To reduce carbon emissions, International Maritime Organization (IMO) has established an Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing-Ship Index (EEXI). Currently, a popular way is to reduce EEDI by optimizing the heat transfer performance of exhaust gas boilers on new ships, but there is little research on the EEXI index of existing ships. For operating ships, the thermal conductivity of exhaust gas boiler materials and other parameters has been fixed, so the main factors affecting the heat transfer coefficient of the exhaust gas boiler are exhaust gas temperature and flow velocity. Therefore, this paper studies the influence of engine exhaust temperature and flow rate on boiler heat transfer coefficients and optimizes it to achieve the EEXI value required by IMO. Firstly, based on the conservation of mass and energy as the basic equation, a heat exchange model of the exhaust gas boiler is established by using the hybrid modeling method and lumped parameter method. Secondly, for the given boiler, since other parameters are basically unchanged, the input temperature and flow rate of the model are changed by the control variable method, and the temperature of the boiler outlet is simulated by the test algorithm. Through the simulation operation of an Aalborg OC-type boiler, the results show that when the exhaust gas flow velocity is 15 m/s, 17.2 m/s, 22.4 m/s and 25 m/s, respectively, the heat transfer coefficient at each flow velocity increases first and then slowly decreases with the increase of temperature, and there is an optimal temperature at each velocity, which is 230 °C, 227 °C, 225 °C and 224 °C, respectively. The innovation of this study lies in the research on the inlet temperature and flow rate of the exhaust gas boiler of the operating ship based on the EEXI, and the relevant results are obtained, which provides theoretical guidance for the operation management of the exhaust gas boiler of the operating ship. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

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24 pages, 4797 KiB

Open AccessArticle

A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability

by Biao Li, Tao Wang, Chunxiao Li, Zhen Dong, Hua Yang, Yi Sun and Pengfei Wang

Sustainability 2022, 14(24), 16378; https://doi.org/10.3390/su142416378 - 7 Dec 2022

Cited by 2 | Viewed by 1162

Abstract

LCC and EL models have been widely used in recent years to determine the decommissioning life of equipment in energy companies, with LCC (life-cycle cost) being the total “lifetime” cost of the equipment from the time it is put into operation until the end of its decommissioning and disposal; the average annual cost of the equipment can be calculated based on the LCC. The overall LCC can be calculated as the average annual LCC, while the EL is the age of the equipment at which its average annual LCC is the lowest. It is believed that the decommissioning of the equipment in the EL year will result in the lowest annual average equipment turnover, thus maximizing the economic benefits of the equipment. Recently, LCC and EL research has been gradually introduced to the energy field, but there remains a lack of research depth. In current practice, energy equipment LCCs are mainly determined by selecting a portion of inventoried equipment to serve as a sample record for all costs incurred. The intent is to derive the economic life of the equipment-year by directly seeking its average annual cost, but this method tends to downplay maintenance, overhaul, and other cost events as “random small probability events”. This method is also incomplete for evaluating the decommissioning life of equipment whose average annual cost strictly decreases year-by-year. In this study, we analyzed the use of 75,220 KV transformers that were put into service by an energy company in 1986 as a case study (costs for this type of equipment were first recorded strictly in terms of LCC in 1986), used Isolated Forest (IF) to screen the outliers of various types of data costs, and then probability-corrected the corrected dataset with a Welbull distribution (Welbull). Then, we employed a stochastic simulation (MC) to calculate the LCC of the equipment and determined its economic lifetime (EL) and compared the results of the stochastic simulation method with those of the traditional method to provide a more reasonable explanation for the “small probability” of cost occurrences. Next, we predicted the average cost of the equipment given a use-period of 38-41-years using AHA, Bi-LSTM, and other comparative algorithms, compared the MAE, MAPE, and RMES indexes, selected the most suitable prediction model, and produced a predicted cost under the chosen method to obtain the economic life of the equipment. Finally, we compared our results with the design life of the equipment (design life being the technical life expectancy of a product based on the expectations of the manufacturer), and determined its best retirement age by comprehensively studying and judging the economic and technical benefits. The retirement age analysis was guided by by a comprehensive study of economic and technical benefits. We refer to our decommissioning life determination model as Monte Carlo -artificial hummingbird algorithm–BiLSTM–lifecycle cost model (MC-AHABi-LCC). We found that the decommissioning life obtained by MC-AHABi-LCC is closer to the actual equipment decommissioning life than that given by standard LCC and EL analysis and that our model is more accurate and scientific. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

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19 pages, 4970 KiB

Open AccessArticle

An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints

by Shoujing Zhang, Tiantian Hou, Qing Qu, Adam Glowacz, Samar M. Alqhtani, Muhammad Irfan, Grzegorz Królczyk and Zhixiong Li

Sustainability 2022, 14(19), 12120; https://doi.org/10.3390/su141912120 - 25 Sep 2022

Cited by 5 | Viewed by 1636

Abstract

Aiming at the distributed flexible job shop scheduling problem under dual resource constraints considering the influence of workpiece transportation time between factories and machines, a distributed flexible job shop scheduling problem (DFJSP) model with the optimization goal of minimizing completion time is established, and an improved mayfly algorithm (IMA) is proposed to solve it. Firstly, the mayfly position vector is discrete mapped to make it applicable to the scheduling problem. Secondly, three-layer coding rules of process, worker, and machine is adopted, in which the factory selection is reflected by machine number according to the characteristics of the model, and a hybrid initialization strategy is designed to improve the population quality and diversity. Thirdly, an active time window decoding strategy considering transportation time is designed for the worker–machine idle time window to improve the local optimization performance of the algorithm. In addition, the improved crossover and mutation operators is designed to expand the global search range of the algorithm. Finally, through simulation experiments, the results of various algorithms are compared to verify the effectiveness of the proposed algorithm for isomorphism and isomerism factories instances. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

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15 pages, 3630 KiB

Open AccessArticle

A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm

by Lili Dai, He Lu, Dezheng Hua, Xinhua Liu, Hongming Chen, Adam Glowacz, Grzegorz Królczyk and Zhixiong Li

Sustainability 2022, 14(18), 11747; https://doi.org/10.3390/su141811747 - 19 Sep 2022

Cited by 5 | Viewed by 1568

Abstract

Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

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14 pages, 266 KiB

Open AccessArticle

Legal Challenges in Protecting the Rights of Cruise Ship Crew at the Post COVID-19 Pandemic Era

by Yanan Yu, Marcin Lorenc and Yude Shao

Sustainability 2022, 14(16), 9875; https://doi.org/10.3390/su14169875 - 10 Aug 2022

Cited by 1 | Viewed by 1864

Abstract

The unexpected outbreak of the COVID-19 pandemic has harmed the shipping industry, especially the cruise sector. During this period, the cruise crew, as a neglected subject, experienced great work, life and psychological pressures. However, many states, including China, do not pay enough attention to the legal protection of their rights. The legal literature on this issue is insufficient, and this paper attempts to fill the gap. This paper aims to give a legal suggestion for how to protect the legal rights of cruise crews in ways that are both responsible and effective in the post-COVID-19 pandemic era. To achieve the goal, this paper adopts legal research methods to analyze the application of international conventions and Chinese laws and regulations. The paper discusses the legal limitations on the rights’ protection of cruise crews in the context of the COVID-19 pandemic, and the research results are legal considerations and suggestions for the protection of the cruise crew. In addition to taking reasonable measures to reduce the impact of the epidemic on cruise crews, the legitimate rights and interests of all cruise crew individuals should be realized as much as possible under existing international conventions and domestic laws. It is important for states to further improve crew and labour legislation and strengthen international cooperation to deal with the impact of the global pandemics on the cruise. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

26 pages, 5994 KiB

Open AccessArticle

Real-Time Human Fault Detection in Assembly Tasks, Based on Human Action Prediction Using a Spatio-Temporal Learning Model

by Zhujun Zhang, Gaoliang Peng, Weitian Wang and Yi Chen

Sustainability 2022, 14(15), 9027; https://doi.org/10.3390/su14159027 - 23 Jul 2022

Viewed by 1742

Abstract

Human fault detection plays an important role in the industrial assembly process. In the current unstructured industrial workspace, the definition of human faults may vary over a long sequence, and this vagueness introduces multiple issues when using traditional detection methods. A method which could learn the correct action sequence from humans, as well as detect the fault actions based on prior knowledge, would be more appropriate and effective. To this end, we propose an end-to-end learning model to predict future human actions and extend it to detect human faults. We combined the auto-encoder framework and recurrent neural network (RNN) method to predict and generate intuitive future human motions. The convolutional long short-term memory (ConvLSTM) layer was applied to extract spatio-temporal features from video sequences. A score function was implemented to indicate the difference between the correct human action sequence and the fault actions. The proposed model was evaluated on a model vehicle seat assembly task. The experimental results showed that the model could effectively capture the necessary historical details to predict future human actions. The results of several fault scenarios demonstrated that the model could detect the faults in human actions based on corresponding future behaviors through prediction features. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

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16 pages, 1081 KiB

Open AccessArticle

Comparison of Total Factor Productivity of Rice in China and Japan

by Liting Gao, Qianhui Gao and Marcin Lorenc

Sustainability 2022, 14(12), 7407; https://doi.org/10.3390/su14127407 - 17 Jun 2022

Cited by 7 | Viewed by 2003

Abstract

Because rice is one of China’s staple foods, studying the total factor productivity (TFP) of rice is of great importance for China’s food security. There are many similarities between rice production in China and Japan. Japan has achieved an effective supply of high-quality rice under the constraints of insufficient production resources and limited environmental capacity. In this paper, we use the DEA Malmquist index method to comparatively analyze the production efficiency of the rice industry in China and Japan, as well as its trends and changes. The contribution of each decomposition index is analyzed by using grey correlation, and kernel density estimation is used to analyze the dynamic evolution of rice productivity in both countries. The empirical results show that rice TFP in Japan is higher than that in China. Technological progress is an important driver of TFP and is the main reason for the difference in rice TFP between the two countries. The concentration of rice TFP distribution in China is decreasing, and regional differences are increasing, whereas in Japan, the opposite trend is observed, with the proportion of areas of high TFP increasing in both countries. Full article

(This article belongs to the Special Issue Recent Advances in Sustainability Development for Autonomous Systems)

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