Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction

Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases. Medical experts, however, still need to understand the reasoning behind the predictions before trusting them. In their day-to-day practice, physicians prefer using gene expression profiles, consisting of a discretized subset of all data from gene expressions: in these profiles, genes are typically reported as either over-expressed or under-expressed, using discretization thresholds computed on data from a healthy control group. A discretized profile allows medical experts to quickly categorize patients at a glance. Building on previous works related to the automatic discretization of patient profiles, we present a novel approach that frames the problem as a multi-objective optimization task: on the one hand, after discretization, the medical expert would prefer to have as few different profiles as possible, to be able to classify patients in an intuitive way;on the other hand, the loss of information has to be minimized. Loss of information can be estimated using the performance of a classifier trained on the discretized gene expression levels. We apply one common state-of-the-art evolutionary multi-objective algorithm, NSGA-II, to the discretization of a dataset of COVID-19 patients that developed either mild or severe symptoms. The results show not only that the solutions found by the approach dominate traditional discretization based on statistical analysis and are more generally valid than those obtained through single-objective optimization, but that the candidate Pareto-optimal solutions preserve the sense-making that practitioners find necessary to trust the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms.

At present, the COVID-19 epidemic is still spreading at home and abroad, and the foreign exchange market is highly volatile. From financial institutions to individual investors, foreign exchange asset allocation has become important contents worthy of attention. However, most intelligent optimization algorithms (hereinafter IOAS) adopt the existing data and ignore the forecasted one in the foreign exchange portfolio allocation, which will result in a huge difference between portfolio allocation and actual demand; at the same time, many IOAS are less adaptable and have lower optimization ability in portfolio problems. To solve the aforementioned problems, this paper first proposed a DETS based on hybrid tabu search and differential evolution algorithms (DEAs), which has excellent optimization ability. Subsequently, the DETS algorithm was applied to support vector machine (SVM) model. Experiments show that, compared with other algorithms, the MAE and RMSE obtained by using DETS optimization parameters are reduced by at least 3.79 and 1.47%, while the CTR is improved by at least 2.19%. Then combined with the DETS algorithm and Pareto sorting theory, an algorithm suitable for multi-objective optimization was further proposed, named NSDE-TS. Finally, by applying NSDE-TS algorithm, the optimal foreign exchange portfolio is acquired. The empirical analysis shows that the Pareto front obtained by this algorithm is better than that of NSGA-II. Since the lower the uniformity index and convergence index, the stronger the optimization performance of the corresponding algorithm, compared with NSGA-II, its uniformity and convergence index decreased by 15.7 and 39.6%.

Research on supply chain sourcing decision under the dual disturbance of demand uncertainty and forecast inaccuracy

Motivated by the trend of overseas/cross-regional firms returning to domestic/local and the background of COVID-19 normalization, a sourcing decision model is proposed based on the cost-signal game under the demand uncertainty and forecast inaccuracy. We explore the trade-off between the efficient cross-regional sourcing and responsive local sourcing. The results show that the sourcing decision depends on the linkage performances of cost and information. Cross-regional sourcing always brings mighty cost performance, while the local sourcing can not always give full play to information performance advantage. Because if both firms choose local sourcing, the correlation effect between forecast information will hedge signal accuracy effect. Greater demand uncertainty and more accurate cross-regional sourcing forecast are driving films to return. Interestingly, this return may benefit all the firms, and break the Prisoner's Dilemma of symmetric cross-regional sourcing. The reason is that the returning can alleviate competition by inducing a new equilibrium sourcing structure. In response, this mixed equilibrium endows firms with "follower advantage” to realize local-Pareto improvement. With the increase of demand uncertainty and forecast inaccuracy, this mixed equilibrium will turn to the symmetric local sourcing, which temporarily reaches the overall Pareto-optimum. However, it will eventually fall into the Prisoner's Dilemma of lose-lose situation. In addition, it is also found that the poor local sourcing forecast will endow firms with "mover advantage”, which will lead to the mover becoming better while the follower becoming worse. © 2022 Systems Engineering Society of China. All rights reserved.

Implementing the Practice of Birth Companion in Labor During the COVID-19 Pandemic: A Quality Improvement Endeavor in India.

Objective We aim to implement the practice of birth companions (BC) (from 0% to 90%) during labor to provide respectful maternity care (RMC) during the coronavirus disease 2019 (COVID-19) pandemic. Methods This was a prospective quality improvement (QI) study conducted in the Department of Obstetrics and Gynecology at All India Institute of Medical Sciences (AIIMS), Rishikesh, India. The methodology given by the World Health Organization (WHO)'s Point of Care Continuous Quality Improvement (POCQI) manual was followed, and standard tools of quality improvement were used to attain the objective. Results The QI team conducted a cause and effect analysis to understand the reasons why birth companions were not allowed during childbirth. The Pareto principle derived at three most important causes of the problem: absence of a defined policy, ignorance of guidelines promoting BC even during the pandemic, and relatives could enter wards only after a negative reverse transcriptase polymerase chain reaction (RTPCR) report, which could take up to 48 hours. Multiple change ideas were tested by means of Plan-Do-Study-Act (PDSA) cycles that were successful in bringing about desired change and improvement in the delivery of quality healthcare. Conclusion QI methodology was effective in promoting and achieving more than 90% birth companionship in labor and thus helpful in providing respectful maternity care even during the COVID-19 pandemic.

Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge (Extended Abstract)

We describe the deep learning-based COVID-19 cases predictor and the Pareto-optimal Non-Pharmaceutical Intervention (NPI) prescriptor developed by the winning team of the 500k XPRIZE Pandemic Response Challenge. The competition aimed at developing data-driven AI models to predict COVID-19 infection rates and to prescribe NPI Plans that governments, business leaders and organizations could implement to minimize harm when reopening their economies. In addition to the validation performed by XPRIZE with real data, our models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. Our experience contributes to a necessary transition to more evidence-driven policy-making during a pandemic. © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

Segmentized quarantine policy for managing a tradeoff between containment of infectious disease and social cost of quarantine

By the end of 2021, COVID-19 had spread to over 230 countries, with more than 5.4 million deaths. To contain the disease spread, many countries have deployed non-pharmaceutical intervention strategies, most notably contact tracing and self-quarantine policy. We have observed that containment of disease spread by such social distancing policy come at a large social cost, and prolonged pandemic raised the necessity of more sustainable policy with the least disruption to economic and societal activities. This research aims to investigate a segmentized quarantine policy where we apply different quarantine policies for different population segments with a goal of better managing the tradeoff between the benefit and cost of a quarantine strategy. Motivation for a segmentized policy is that different population groups, e.g., school students vs adults with jobs, exhibit different patterns of societal activities, thereby imposing different risks to disease spread. We define a segmentized quarantine policy in two dimensions - range of contact tracing and quarantine period, and determine the two parameters for each population segment to achieve two objectives: to minimize the total number of infected cases and to minimize the total days of self-quarantine. We use Agent-based Epidemics Simulation to evaluate the quarantine policies, and Evolutionary Algorithm is used to obtain the Pareto front of our problem. Our results demonstrate the effectiveness of the segmentized quarantine policies, and we identify the conditions where they outperform the uniform policy. We also find in the Pareto optimal solutions that only some population segments are recommended special policy features while other segments are subject to the conventional policy. The results suggest that segmentized quarantine policy is valid in terms of efficiency and sustainability, and the suggestions and framework presented are expected to be of great help in establishing public health decisions to prepare for an upcoming pandemic like COVID-19. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Dominant Resource Fairness with Meta-Types

Inspired by the recent COVID-19 pandemic, we study a generalization of the multi-resource allocation problem with heterogeneous demands and Leontief utilities. Unlike existing settings, we allow each agent to specify requirements to only accept allocations from a subset of the total supply for each resource. These requirements can take form in location constraints (e.g. A hospital can only accept volunteers who live nearby due to commute limitations). This can also model a type of substitution effect where some agents need 1 unit of resource A or B, both belonging to the same meta-type. But some agents specifically want A, and others specifically want B. We propose a new mechanism called Dominant Resource Fairness with Meta Types which determines the allocations by solving a small number of linear programs. The proposed method satisfies Pareto optimality, envy-freeness, strategy-proofness, and a notion of sharing incentive for our setting. To the best of our knowledge, we are the first to study this problem formulation, which improved upon existing work by capturing more constraints that often arise in real life situations. Finally, we show numerically that our method scales better to large problems than alternative approaches. © 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.

COVID-19 confrontation system: An look based on organizational technical requirements

Introduction: The lack of references to crises similar to COVID-19 in the past makes it difficult to predict what may happen in the immediate future. Logically, the present effects are easily documented, but those that will leave their mark on the different actors in the mid and long terms are more subjected to debate. Objective: To diagnose the performance of the COVID-19 confrontation system in Cuba, showing the best direction to follow when making decisions. Methods: The COVID-19 confrontation system was designed, with a process approach. Five organizational technical requirements were proposed to assess its performance and a set of strategy was proposed under the Pareto principle. Results: The process map of the system against COVID-19 was obtained, with high results of reliability, stability, recovery dynamics, flexibility and adequate reaction time;apart from the segmentation into three conglomerate groups to establish strategies. Conclusions: The COVID-19 confrontation system is diagnosed, obtaining high values, through the design of the process map, which allows directing the strategies by regions in the country. © 2021, Editorial Ciencias Medicas. All rights reserved.