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Airports worldwide are actively promoting the transition of ground service vehicles from traditional fuel-powered vehicles to electric vehicles. The key to the successful implementation of this transition lies in the development of efficient electric vehicle dispatching models that comprehensively consider the charge-discharge processes of electric vehicles. However, due to the nonlinear characteristics of charge-discharge processes, finding precise solutions poses a significant challenge. Previous researchers have often used traditional energy consumption models and constant charging rates to simplify calculations, but this has resulted in inaccurate estimates of the remaining battery charge level. Furthermore, the lack of diverse pacing and charging strategies for airport ground service vehicles necessitates more adaptable solutions to enhance operational efficiency. To address these challenges, this paper uses airport electric tractors as a case study, develops an accurate model that takes into account the start-stop process and a piecewise linear charging function, designs an improved genetic algorithm that incorporates a greedy algorithm and an adaptive strategy, and develops charge-discharge coupling strategies for different configuration scenarios at Nanjing Lukou Airport to meet current and future needs. The research results indicate that compared to traditional genetic algorithms, the proposed improved genetic algorithm significantly enhances solution accuracy and convergence speed. Additionally, with the increase in flight scale, airports can appropriately enhance their charging strategies; airports with dispersed aircraft stands should devise higher pacing strategies compared to those with dense aircraft stands.

Skin cancer, the primary type of cancer that can be identified by visual recognition, requires an automatic identification system that can accurately classify different types of lesions. This paper presents GoogLe-Dense Network (GDN), which is an image-classification model to identify two types of skin cancer, Basal Cell Carcinoma, and Melanoma. GDN uses stacking of different networks to enhance the model performance. Specifically, GDN consists of two sequential levels in its structure. The first level performs basic classification tasks accomplished by GoogLeNet and DenseNet, which are trained in parallel to enhance efficiency. To avoid low accuracy and long training time, the second level takes the output of the GoogLeNet and DenseNet as the input for a logistic regression model. We compare our method with four baseline networks including ResNet, VGGNet, DenseNet, and GoogLeNet on the dataset, in which GoogLeNet and DenseNet significantly outperform ResNet and VGGNet. In the second level, different stacking methods such as perceptron, logistic regression, SVM, decision trees and K-neighbor are studied in which Logistic Regression shows the best prediction result among all. The results prove that GDN, compared to a single network structure, has higher accuracy in optimizing skin cancer detection., Comment: Published at ICSPS 2021

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and has made progress in various fields. Specifically, cooperative MARL focuses on training a team of agents to cooperatively achieve tasks that are difficult for a single agent to handle. It has shown great potential in applications such as path planning, autonomous driving, active voltage control, and dynamic algorithm configuration. One of the research focuses in the field of cooperative MARL is how to improve the coordination efficiency of the system, while research work has mainly been conducted in simple, static, and closed environment settings. To promote the application of artificial intelligence in real-world, some research has begun to explore multi-agent coordination in open environments. These works have made progress in exploring and researching the environments where important factors might change. However, the mainstream work still lacks a comprehensive review of the research direction. In this paper, starting from the concept of reinforcement learning, we subsequently introduce multi-agent systems (MAS), cooperative MARL, typical methods, and test environments. Then, we summarize the research work of cooperative MARL from closed to open environments, extract multiple research directions, and introduce typical works. Finally, we summarize the strengths and weaknesses of the current research, and look forward to the future development direction and research problems in cooperative MARL in open environments.

In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates. Recent studies attempted to generate diverse teammates to enhance the generalizable coordination ability, but were restricted by pre-defined teammates. In this work, our aim is to train agents with strong coordination ability by generating teammates that fully cover the teammate policy space, so that agents can coordinate with any teammates. Since the teammate policy space is too huge to be enumerated, we find only dissimilar teammates that are incompatible with controllable agents, which highly reduces the number of teammates that need to be trained with. However, it is hard to determine the number of such incompatible teammates beforehand. We therefore introduce a continual multi-agent learning process, in which the agent learns to coordinate with different teammates until no more incompatible teammates can be found. The above idea is implemented in the proposed Macop (Multi-agent compatible policy learning) algorithm. We conduct experiments in 8 scenarios from 4 environments that have distinct coordination patterns. Experiments show that Macop generates training teammates with much lower compatibility than previous methods. As a result, in all scenarios Macop achieves the best overall coordination ability while never significantly worse than the baselines, showing strong generalization ability.

Various time variant non-stationary signals need to be pre-processed properly in hydrological time series forecasting in real world, for example, predictions of water level. Decomposition method is a good candidate and widely used in such a pre-processing problem. However, decomposition methods with an inappropriate sampling technique may introduce future data which is not available in practical applications, and result in incorrect decomposition-based forecasting models. In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. This sampling technique with decomposition methods, such as Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is applied to predict water level time series in three different stations of Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique. In the meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1% and 1.1% respectively. We conclude that the newly developed FSDB sampling technique can be used to enhance the performance of decomposition-based hybrid model in water level time series forecasting in real world.

In cooperative multi-agent reinforcement learning (MARL), where an agent coordinates with teammate(s) for a shared goal, it may sustain non-stationary caused by the policy change of teammates. Prior works mainly concentrate on the policy change during the training phase or teammates altering cross episodes, ignoring the fact that teammates may suffer from policy change suddenly within an episode, which might lead to miscoordination and poor performance as a result. We formulate the problem as an open Dec-POMDP, where we control some agents to coordinate with uncontrolled teammates, whose policies could be changed within one episode. Then we develop a new framework, fast teammates adaptation (Fastap), to address the problem. Concretely, we first train versatile teammates' policies and assign them to different clusters via the Chinese Restaurant Process (CRP). Then, we train the controlled agent(s) to coordinate with the sampled uncontrolled teammates by capturing their identifications as context for fast adaptation. Finally, each agent applies its local information to anticipate the teammates' context for decision-making accordingly. This process proceeds alternately, leading to a robust policy that can adapt to any teammates during the decentralized execution phase. We show in multiple multi-agent benchmarks that Fastap can achieve superior performance than multiple baselines in stationary and non-stationary scenarios., Comment: In: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI'23), Pittsburgh, PA, 2023

Cooperative Multi-agent Reinforcement Learning (MARL) has attracted significant attention and played the potential for many real-world applications. Previous arts mainly focus on facilitating the coordination ability from different aspects (e.g., non-stationarity, credit assignment) in single-task or multi-task scenarios, ignoring the stream of tasks that appear in a continual manner. This ignorance makes the continual coordination an unexplored territory, neither in problem formulation nor efficient algorithms designed. Towards tackling the mentioned issue, this paper proposes an approach Multi-Agent Continual Coordination via Progressive Task Contextualization, dubbed MACPro. The key point lies in obtaining a factorized policy, using shared feature extraction layers but separated independent task heads, each specializing in a specific class of tasks. The task heads can be progressively expanded based on the learned task contextualization. Moreover, to cater to the popular CTDE paradigm in MARL, each agent learns to predict and adopt the most relevant policy head based on local information in a decentralized manner. We show in multiple multi-agent benchmarks that existing continual learning methods fail, while MACPro is able to achieve close-to-optimal performance. More results also disclose the effectiveness of MACPro from multiple aspects like high generalization ability.

Vital sign detection is used across ubiquitous scenarios in medical and health settings. Contact and wearable sensors have been widely deployed. However, they are unsuitable for patients with burn wounds or infants with insufficient attaching areas. Contactless detection can be achieved using camera imaging, but it is susceptible to ambient light conditions and creates privacy concerns. Here, we report the first demonstration of a photonic radar for non-contact vital signal detection to overcome these challenges. This photonic radar can achieve millimeter range resolution based on synthesized radar signals with a bandwidth of up to 30 GHz. The high resolution of the radar system enables accurate respiratory detection from breathing simulators and a cane toad as a human proxy. Moreover, we demonstrated that the optical signals generated from the proposed system can enable vital sign detection based on light detection and ranging (LiDAR). This demonstration reveals the potential of a sensor-fusion architecture that can combine the complementary features of radar and LiDAR for improved sensing accuracy and system resilience. The work provides a novel technical basis for contactless, high-resolution, and high-privacy vital sign detection to meet the increasing demands in future medical and healthcare applications.

Photonic generation of radio-frequency signals has shown significant advantages over the electronic counterparts, allowing the high precision generation of radio-frequency carriers up to the terahertz-wave region with flexible bandwidth for radar applications. Great progress has been made in photonics-based radio-frequency waveform generation. However, the approaches that rely on sophisticated benchtop digital microwave components, such as synthesizers and digital-to-analog converters have limited achievable bandwidth and thus resolution for radar detections. Methods based on voltage-controlled analog oscillators exhibit high time-frequency non-linearity, causing degraded sensing precision. Here, we demonstrate, for the first time, a photonic stepped-frequency (SF) waveform generation scheme enabled by MHz electronics with a tunable bandwidth exceeding 30 GHz and intrinsic time-frequency linearity. The ultra-wideband radio-frequency signal generation is enabled by using a polarization-stabilized optical cavity to suppress intra-cavity polarization-dependent instability; meanwhile, the signal's high-linearity is achieved via consecutive MHz acousto-optic frequency-shifting modulation without the necessity of using electro-optic modulators that have bias-drifting issues. We systematically evaluate the system's signal quality and imaging performance in comparison with conventional photonic radar schemes that use high-speed digital electronics, confirming its feasibility and excellent performance for high-resolution radar applications.