Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.

An Integrated Analysis on the Synergistic Reduction of Carbon and Pollution Emissions from China’s Iron and Steel Industry

Decarbonization and decontamination of the iron and steel industry(ISI),which contributes up to 15%to anthropogenic CO_(2) emissions(or carbon emissions)and significant proportions of air and water pollutant emissions in China,are challenged by the huge demand for steel.Carbon and pollutants often share common emission sources,indicating that emission reduction could be achieved synergistically.Here,we explored the inherent potential of measures to adjust feedstock composition and technological structure and to control the size of the ISI to achieve carbon emission reduction(CER)and pollution emission reduction(PER).We investigated five typical pollutants in this study,namely,petroleum hydrocarbon pollutants and chemical oxygen demand in wastewater,particulate matter,SO_(2),and NO_(x) in off gases,and examined synergies between CER and PER by employing cross elasticity for the period between 2022 and 2035.The results suggest that a reduction of 8.7%-11.7%in carbon emissions and 20%-31%in pollution emissions(except for particulate matter emissions)could be achieved by 2025 under a high steel scrap ratio(SSR)scenario.Here,the SSR and electric arc furnace(EAF)ratio serve critical roles in enhancing synergies between CER and PER(which vary with the type of pollutant).However,subject to a limited volume of steel scrap,a focused increase in the EAF ratio with neglection of the available supply of steel scrap to EAF facilities would lead to an increase carbon and pollution emissions.Although CER can be achieved through SSR and EAF ratio optimization,only when the crude steel production growth rate remains below 2.2%can these optimization measures maintain the emissions in 2030 at a similar level to that in 2021.Therefore,the synergistic effects between PER and CER should be considered when formulating a development route for the ISI in the future.

Research Progress and Prospects of Total Factor Productivity in the Construction Industry

The high-quality development of the construction industry fundamentally stems from the significant improvement of total factor productivity.Therefore,it is of crucial significance for promoting the development of the construction industry to a higher level by scientifically and accurately measuring the total factor productivity of the construction industry and deeply analyzing the influencing factors behind it.Based on a comprehensive consideration of research methods and influencing factors,this paper systematically reviews the existing relevant literature on total factor productivity in the construction industry,aiming to reveal the current research development trend in this field and point out potential problems.This effort aims to provide a solid theoretical foundation and valuable reference for further in-depth research,and jointly promote the continuous progress and development of total factor productivity research in the construction industry.

Smart Parking Management System “MOTAH”

With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.

基于障碍物和车位检测的单阶段多任务YOLO-Parking算法研究

文章提出一种基于YOLOv4的端到端多任务网络模型用于自动泊车系统中的感知任务,以环视图像(around view monitor, AVM)作为网络输入,基于卷积网络提取图像特征信息,通过YOLO和DMPR-PS(directional marking-point regression-parking slot)检测头实现停车位与障碍物并行检测。在PS 2.0公开数据集上进行验证的结果表明,所提出的多任务检测方法能够同时检测停车位和障碍物,障碍物识别平均精度均值达到89.72%,车位识别查准率达到93.53%,网络检测速率为34.0帧/s,在满足自动泊车感知任务需求的同时提升了系统的检测效率。该文研究成果对自动泊车感知技术的发展具有一定的意义。

FROM THE EDITOR-IN-CHIEF Innovative technologies significantly propel the development of casting industry

At this memorable moment,we are excited to celebrate the 20th anniversary of CHINA FOUNDRY journal.Since its inception in August 2004,this academic platform dedicated to disseminating Chinese casting technology has traversed two full decades.Over this period,we have received strong support from numerous domestic and international industry experts and scholars.

Land-population-industry based village evolution and its influencing factors in the upper Tuojiang River

With economic development and urbanization in China,the rural settlements have experienced great change.To explore the evolution process of rural settlements in terms of land,population and industry can reveal the development law of rural spatial distribution,population structure and industrial economy in different stages and regions.Studying the development status and evolution characteristics of villages in the upper Tuojiang River basin in Southwest China in the past 20 years are of significant value.The upper Tuojiang River basin includes the main types of terrain found in the Southwest region:mountainous,plains,and hills,exhibiting a certain typicality of geographical characteristics.This study took towns and townships at the town-level scale as the basic unit of research,and constructed an evaluation system for village evolution based on'land,population,and industry'.It employed Criteria Importance Through Inter-Criteria Correlation(CRITIC)analysis to examine the characteristics of village evolution in the area from 2000 to 2020,and used geographic detector analysis to identify the leading factors affecting village evolution.The results show that:(1)From 2000 to 2010,villages in the upper Tuojiang River basin experienced significant changes,and the pace of these transformations slowed from 2010 to 2020.(2)From a comprehensive perspective,from 2000 to 2020,villages in hilly areas show a decline,while villages in plain areas near the city center show a positive urbanization development.(3)Road accessibility and distance from the city center are the main factors that explain the spatial differentiation of village evolution degree in the study area.This study elucidates the spatiotemporal evolution characteristics of villages in the upper Tuojiang River basin and identifies the primary factors contributing to their changes,which will provide a reference for investigating the development of rural areas in different terrains of Southwest China.

The Effects of Disruptive Thinking on the Food Industry*

This study aimed to understand disruptive thinking and how its ideas can change the food industry. This was achieved by identifying, studying, and understanding the impacts, current trends, and different disruptive ideas and innovations emerging in the food industry. The study was conducted through interpretive research philosophy by carrying out secondary data collection processes, where both qualitative and quantitative information was presented. Deductive approaches were also selected to apply existing theories and models, which were used to construct research hypotheses and present detailed findings. The study finds that, with disruptive thinking, enhancements in the product life cycle, new flavors, and improvements in food packaging have been possible. The supply chain, which is always considered a complex part of the food industry, has been streamlined, offering greater transparency and real-time tracking and improving quality control across distribution systems.

A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

Ingredient optimization plays a pivotal role in the copper industry,for which it is closely related to the concentrate utilization rate,stability of furnace conditions,and the quality of copper production.To acquire a practical ingredient plan,which should exhibit long duration time with sufficient utilization and feeding stability for real applications,an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace conditions.To address the complex challenges posed by this integer programming model,including multiple coupling feeding stages,intricate constraints,and significant non-linearity,a multi-stage differential-multifactorial evolution algorithm is developed.In the proposed algorithm,the differential evolutionary(DE)algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed model.First,unlike traditional time-consuming serial approaches,the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm caused by the feeding stability in a parallel manner.Second,a repair algorithm is employed to adjust infeasible ingredient lists in a timely manner.In addition,a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary algorithm.Finally,the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted,which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed approaches.It is practically helpful for reducing material cost as well as increasing production profit for the copper industry.

Application of Connected Truck Data to Evaluate Spatiotemporal Impact of Rest Area Closures on Ramp Parking

Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.

Path Tracking Controller Design of Automated Parking Systems via NMPC with an Instructible Solution

Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents.The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method(RF-LNMPC)in order to improve the overall path tracking accuracy in parking conditions.Firstly,A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively.Secondly,the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization,of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution.Thirdly,the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented.The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically.Finally,the proposed RF-LNMPC is implemented on a selfdriving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions.The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

Determinants of COVID-19 Face Mask Litter in Coastal Urban Parking Lots: Implications for Source Modelling of Potential Microplastic Pollution

Despite cities being recognized as being potential sources of microplastic pollution to the wider environment, most surveys of COVID-19 plastic-based litter have been undertaken through linear transects of marine beaches. For the far fewer number of studies conducted on inland and urban locations, the site-specific focus has primarily been surveys along the length of streets. The present study is the first to specifically assess the standing stock (i.e., moment-in-time) of littered face masks for the entire surface area of urban parking lots. The density of face masks in 50 parking lots in a Canadian coastal town (0.00054 m2 ± 0.00051 m2) was found to be significantly greater than the background level of littering of town streets. Face mask density was significantly related to visitation “usage” of parking lots as gauged by the areal size of the lots and of their onsite buildings, as well as the number of vehicles present. Neither parking lot typology nor estimates of inferred export (various measures of wind exposure) and entrapment (various metrics of obstruction) of face masks had a significant influence on the extent of whole-lot littering. In consequence, modelling of the potential input of mask-derived microplastics to the marine environment from coastal communities can use the areal density of face masks found here in association with the total surface area of lots for individual municipalities as determined through GIS analysis.

Collision-free parking recommendation based on multi-agent reinforcement learning in vehicular crowdsensing

The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle participation.However,instead of being an isolated module,the incentive mechanism usually interacts with other modules.Based on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real time.Specifically,we utilize an LSTM method to predict parking areas roughly for recommendations accurately.Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects.In order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making.Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is Pareto-optimal.Finally,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.

Investigation into the Methodology and Implementation of Life Cycle Engineering under China’s Carbon Reduction Target in the Process Industry

The industrial sector is the primary source of carbon emissions in China.In pursuit of meeting its carbon reduction targets,China aims to promote resource consumption sustainability,reduce energy consumption,and achieve carbon neutrality within its processing industries.An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology.This strategy aims to attain an optimal solution for material performance,resource consumption,and environmental impact.In this study,five types of technologies were considered:raw material replacement,process reengineering,fuel replacement,energy recycling and reutilization,and material recycling and reutilization.The meaning,methodology,and development status of life cycle engineering technology abroad and domestically are discussed in detail.A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption,carbon emissions,product performance,and recycling of secondary resources in a manufacturing process.This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry,as an example.The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

Impacts of agri-food e-commerce on traditional wholesale industry:Evidence from China

Rapidly expanding studies investigate the effects of e-commerce on company operations in the retail market.However,the interaction between agri-food e-commerce(AEC)and the traditional agri-food wholesale industry(AWI)has not received enough attention in the existing literature.Based on the provincial panel data from 2013 to 2020 in China,this paper examines the effect of AEC on AWI,comprising three dimensions:digitalization(DIGITAL),agrifood e-commerce infrastructure and supporting services(AECI),and agri-food e-commerce economy(AECE).First,AWI and AEC are measured using an entropy-based combination of indicators.The results indicate that for China as a whole,AWI has remained practically unchanged,whereas AEC exhibits a significant rising trend.Second,the findings of the fixed-effect regression reveal that DIGITAL and AECE tend to raise AWI,whereas AECI negatively affects AWI.Third,threshold regression results indicate that AECI tends to diminish AWI with three-stage inhibitory intensity,which manifests as a first increase and then a drop in the inhibition degree.These results suggest that with the introduction of e-commerce for agricultural product circulation,digital development will have catfish effects that tend to stimulate the vitality of the conventional wholesale industry and promote technical progress.Furthermore,the traditional wholesale industry benefits financially from e-commerce even while it diverts part of the traditional wholesale circulation for agricultural products.

Keeping planes safe:the role of IECQ in the aerospace industry

The avionics industry relies on the quality,reliability,and safety of electronic components.This is critically important given avionics failures can quickly become disastrous.Therefore,assurance of meeting stringent criteria for safety and quality becomes paramount.IECQ,the IEC Quality Assessment System,provides the necessary certification of compliance for electronic components that apply to critical industry sectors,including aerospace.

A Review on Security and Privacy Issues Pertaining to Cyber-Physical Systems in the Industry 5.0 Era

The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems(CPSs)seamlessly integrate physical processes with advanced digital technologies.However,as industries become increasingly interconnected and reliant on smart digital technologies,the intersection of physical and cyber domains introduces novel security considerations,endangering the entire industrial ecosystem.The transition towards a more cooperative setting,including humans and machines in Industry 5.0,together with the growing intricacy and interconnection of CPSs,presents distinct and diverse security and privacy challenges.In this regard,this study provides a comprehensive review of security and privacy concerns pertaining to CPSs in the context of Industry 5.0.The review commences by providing an outline of the role of CPSs in Industry 5.0 and then proceeds to conduct a thorough review of the different security risks associated with CPSs in the context of Industry 5.0.Afterward,the study also presents the privacy implications inherent in these systems,particularly in light of the massive data collection and processing required.In addition,the paper delineates potential avenues for future research and provides countermeasures to surmount these challenges.Overall,the study underscores the imperative of adopting comprehensive security and privacy strategies within the context of Industry 5.0.

Production and achievements of Sesamum indicum industry in the world:Past and current state

Sesame production is important in agriculture,food industry,and the crop diversity due to its rich nutritional profile and health benefits.Despite its significant value,sesame is still an orphan crop that has received little scientific attention,resulting in low yield compared to other major oilseed crops.This review offers a comprehensive overview of the present state of production,knowledge,and research advancements concerning Sesamum indicum on a global scale.The FAOSTAT database was extensively used to examine the global trends from 1961 to 2021.In the past 60 years,global sesame production has substantially increased,with Asia and Africa being the primary producers.The integration of omics technologies and biotechnological interventions has revolutionized our understanding of the genetic basis of sesame,enhanced productivity,invigorated stress resilience,and improved seed quality.High-throughput sequencing methods such as RNA-seq,RAD-seq,SLAF-seq,and GBS technology are used in various studies,linkage mapping,and identification of trait-associated markers.Fine linkage maps,and multi-omics studies such as genomics,proteomics,transcriptomics,and metabolomics have been employed in sesame research for gene and QTL mapping.Proteins and metabolic pathways related to oil content,yield,and stress tolerance were reported.Genes and QTLs related to yield and its components,drought,salt,and osmotic stress tolerance were discovered.Candidate genes associated with capsule shattering and seed shattering were recently revealed.For more achievement in sesame,it is important to enhance sesame production efficiency through mechanization,advanced agricultural practices,and knowledge dissemination to farmers.MAS and multi-omics integration should be particularly reinforced.The advancements in sesame production present a significant and promising opportunity for farmers,governments,and stakeholders in the agricultural sector.

Game theory in network security for digital twins in industry

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.

Exploring Paths for the High-Quality Development of Forest-Based Wel ness Industry:A Case Study of Sichuan Province

The forest-based wellness industry,as a rapidly growing sector that integrates various business forms with extensive coverage and an extended industrial chain,is undergoing rapid development due to the increasing aging population and people's suboptimal health conditions.As a forerunner in developing the forest-based wellness industry,Sichuan province is known for its early development,proactive efforts,diverse models,and significant impact in this industry.It has achieved certain milestones in terms of top-level design,pilot demonstration,standardized guidance,and public awareness campaigns to promote the development of this industry.Therefore,this paper utilizes Sichuan as a case study to systematically summarize and analyze the key practices made by the province in promoting the rapid development of the industry by investigating the development trajectory of the forest-based wellness industry.Additionally,it examines the development trends of this industry from the perspectives of supply,demand,and consumption.Finally,this paper proposes several measures to facilitate the high-quality development of the forest-based wellness industry.These measures encompass nurturing specialized talent in forest-based wellness,enhancing market players'capabilities in this domain,conducting extensive research on technologies that promote this industry,actively seeking support from relevant policies,and promoting integrated development across diverse sectors.