An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

This paper proposes a method to study unmanned aerial vehicles (UAV) maneuvering decision in air combat based on deterministic policy gradient. Aiming at the problem of decision space continuity, based on reinforcement learning theory, the Deep Deterministic Policy Gradient (DDPG) algorithm architecture is used to overcome the dimensional catastrophe caused by the discretization of decision variables and achieve air combat decisions in a continuous decision space for the problem of continuous decision space. In the design of the reward function, based on the traditional distance and angle evaluation factors, the energy function is added to improve the accuracy of the reward function for air combat situation description. Through autonomous reinforcement learning training, UAV gradually learns to acquire strategies for air combat decision making without the priori knowledge, which enables UAV to gain maneuver dominance advantage in air combat. Simulation experiments show that based on the algorithm model proposed in this paper, UAV can conduct the autonomous learning process, complete the air combat maneuver decision and obtain the dominance advantage according to the air combat maneuver decision-making environment.

In a typical game scenario, the attention in unmanned aerial vehicle (UAV) air combat should be focused on both sides' maneuvering decision strategies. However, most existing studies focus only on improving their own maneuvering decisions, ignoring the importance of the opponent's strategy in the two-player game. This paper proposes a reinforcement learning (RL)-based air combat decision method considering the opponent's maneuvering strategy. Through the proposed limited imitation offline RL (LIORL) method, the opponent's air combat decision method is refined using existing air combat data. Based on the enemy's superior strategy, an air combat simulation environment is created, and the RL method is applied to train the agent for UAV air combat maneuvering decision strategies. No current research utilizes static datasets for the acquisition of adversary strategies. This study, in contrast, offers a more reliable approach in comparison to methodologies predicated on assuming adversary strategies. Ablation experiments have been meticulously executed to showcase the capability of the LIORL algorithm in identifying the optimal enemy strategy while exerting a lesser impact on the dataset than incumbent algorithms. The employment of the LIORL algorithm enables the agent to refine the adversary's strategy, resulting in an augmented win rate against our UAV. Through air combat simulations, we empirically validate the agent's decision-making process based on the adversary's strategy, thereby affirming the efficacy of the proposed methodology.

Similarity and cluster analysis are important aspects for analyzing microarray data. Based on our perspective of viewing microarrays as time series data, both similarity analysis and cluster analysis are carried out through indexing on time series data using R*-Trees. We have developed algorithms for similarity and cluster analysis on microarray data, and conducted experimental studies and comparative studies. First, our study shows that principle components analysis (PCA) has superiority over several other methods (such as DFT and PAA) as far as distance conservation is concerned. A similarity analysis tool based on PCA has been developed, which is able to explore less R*-Tree nodes before finding its similar counterparts and returns less false positives than other methods. In addition, we also extend R*-Tree's application to cluster analysis. With the aid of R*-Tree indexing, two clustering algorithms. KMeans-R and Hierarchy-R, are proposed as an improved version of K-Means and hierarchical clustering, respectively. Experiments for similarity search and cluster analysis based on proposed algorithms have been carried out and have shown favorable results. Experiments related to yeast cell cycle dataset are reported in this paper.

BackgroundHyperlactatemia is common in the intensive care unit (ICU) and relevant to prognosis, while the process of lactate normalization requires a relatively long period. We hypothesized that the dynamic change in base excess (BE) would be associated with ICU mortality and lactate clearance.MethodsWe performed a retrospective cohort study of adult patients with hyperlactatemia admitted to the ICU from 2016 to 2021. The patients were divided into two groups according to whether the peak BE in 12 h was reached in the first 6 h. We compared ICU mortality and lactate clearance at 6 and 12 h after ICU admission.ResultsDuring the study period, 1,608 patients were admitted to the ICU with a lactate concentration of >2.0 mmol/L and stayed in the ICU for >24 h. The mortality rate was 11.2%. The patients were divided into two groups according to whether the peak BE was reached in the first 6 h following ICU admission: Peak BE12h ≤ 6h and Peak BE12h > 6h. The patients were also recorded as whether bicarbonate treatment was received (bicarbonate group, CRRT included) or not (non-bicarbonate group). Furthermore, lactic acid clearance patterns were identified by time-series clustering (TSC) using various algorithms and distance measures. We compared ICU mortality and lactate clearance at 6 and 12 h after ICU admission with logistic regression. After adjustment for other confounding factors, we found that Peak BE12h > 6h was independently associated with ICU mortality with an odds ratio of 2.231 (p = 0.036) in the bicarbonate group and 2.359 (p < 0.005) in the non-bicarbonate group. In addition, based on the definition of >10% lactate clearance at 6 h or >30% at 12 h, we found that Peak BE12h ≤ 6h had 85.2% sensitivity and 38.1% specificity for effective lactate clearance. In time-series clustering analysis, four categories were discriminated, and pattern of lactic acid clearance reveals the early prognostic value of BE in clearance of lactic acid.ConclusionA prolonged time to reaching the peak BE was independently associated with ICU mortality. In patients with hyperlactatemia, Peak BE12h ≤ 6h could be used as an indicator to predict effective lactate clearance.

Target threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indicators, and accurate and objective assessment results cannot be obtained. A target threat assessment algorithm based on linear discriminant analysis (LDA) and improved glowworm swarm optimization (IGSO) algorithm to optimize extreme learning machine (ELM) is proposed in this paper. Firstly, the linear discriminant analysis method is used to classify the threat assessment indicators, eliminate the correlation between the assessment indicators, and achieve dimensionality reduction of the assessment indicators. Secondly, a prediction model with multiple parallel extreme learning machines as the core is constructed, and the input weights and thresholds of extreme learning machines are optimized by the improved glowworm swarm optimization algorithm, and the weighted integration is carried out according to the training level of the kernel. Then, the threat assessment index functions of angle, speed, distance, altitude, and air combat capability are constructed, respectively, and the sample data of air combat target threat assessment are obtained by combining the structure entropy weight method. Finally, the air combat data is selected from the air combat maneuvering instrument (ACMI), and the accuracy and real-time performance of the LDA-IGSO-ELM algorithm are verified through simulation. The results show that the algorithm can quickly and accurately assess target threats.

Unmanned aircraft systems can perform some more dangerous and difficult missions which manned aircraft systems cannot perform. For tasks with high complexity, such as air combat, maneuvering decision mechanism is required to sense the combat environment and make the optimal strategy in real time. This paper formulates one-to-one air combat maneuvering problem in 3D environment, and proposes an approximate dynamic programming approach to make optimal maneuvering decisions automatically. The aircraft searches for combat strategies based-on Reinforcement Leaning, while sensing the environment, taking available maneuvering actions and receiving feedback reward signals. To solve the problem of dimensional explosion in the air combat, the proposed method is implemented through feature selection, trajectory sampling, function approximation and Bellman backup operation in the air combat simulation environment. This approximate dynamic programming approach provides a fast response to rapid changing tactical situations, and learns effective strategies to fight against the opponent aircraft.

The recent development of technology helps in the revolutionary war and it controls the war which is influenced by brilliant planning. The maneuver aircraft of intelligent algorithm aids the pilot to decide the particular position on the battlefield. Nowadays the hardware components of radar and missiles are widely used and the beyond visual range is the most popular method applied in air combat. The introduction of close-range air combat maneuver decisions generates the attention of researchers in artificial intelligence. Most of the existing methods are based on autonomous aircraft focused in air combat scenario but manual air combats are widely applied in dual aircraft. Based on the factors mentioned above, a novel hierarchical maneuver decision architecture is applied to a dual-aircraft close-range air combat scenario. Subsequently, the soft actor-critic algorithm is merged with competitive self-play which integrates the knowledge of sub-strategies. Further, the reinforcement learning technique is employed to achieve an approximate Nash equilibrium master strategy. The experimental results show that the hierarchical architecture exhibits good performance, symmetry, and robustness. The research generates a solution for intelligent formation of air combat in the future and guidance for manned or unmanned aircraft cooperative combat.

Water scarcity in Northeast Brazil has caused latent perturbations in hydropower generation, which is undesirable for Energy Planning. Thus, this study aims to identify the influence of climate on hydropower generation by Sobradinho Dam in Bahia by: (i) assessing the streamflow climatology (1964-2017) and rainfall (1964-2015) through time series analysis, hypothesis testing and cluster analysis; (ii) assessing hydropower generation (2000-2017) using climate and energy data, through principal component analysis and dynamic regression models. The results indicated reductions of up to 30% in the mean climatological streamflow patterns; and reductions in rainfall amounts of 22.9%, 13.3% and 12.4% at latitudes 9°, 12°and 13°South, respectively. Decreasing trends were found in simulations of hydropower generation under the influence of different climate variables. Thus, the hydroelectric system operates in contingency, due to the growing energy load demand resulting in more energy imports in Northeast Brazil.

Received: 29 October 2012 Abstract Accepted: 28 November 2012 Traditional statistical process control approaches are less effective in dealing with multivariate and autocorrelated processes. With the continual increase in process complexity, this inefficiency is becoming more apparent. A special type of multivariate and autocorrelated process is a process occurring within a heterogeneous production environment (a variety of types of machines, pots, etc. used for the same task). This makes the quality control of such processes more difficult. The approach presented in the paper utilizes time series fitting, cluster analysis and association mining in relation to a single data mining model for the analysis of complex multivariate autocorrelated processes. The aim is to divide the production cells (machines, pots, etc.) into groups exhibiting similar behaviors. This can then be used for more effective quality control of the entire process and afterwards to analyze the reasons for this behavior. This paper includes someof the results obtained from applying the model to an actual multivariate high autocorrelated process, the production of primary aluminum using the Hall-Heroult electrolysis process. The Hall-Heroult electrolysis process is a continual process that is ongoing in several pots simultaneously. The average plant operates 300 pots. Therefore, the quality control of such a complex process faces many issues concerning monitoring and problem diagnosis. The paper describes a method for dividing the pots into control groups exhibiting similar behaviors, which can then be used in the planning phase of the quality control analysis and to make improvements within these groups and thereby within the whole process.

The rising incidence of myocardial infarction (MI) in individuals under 50 years of age underscores an urgent need for innovative rehabilitation strategies that extend beyond hospital care, empowering young patients to reclaim active lives through sustained physical activity and remote monitoring. Wearable health technologies hold transformative potential here, as studies demonstrate their ability to boost exercise capacity daily steps while reducing rehospitalizations in post-MI recovery. This study thus assesses the clinical value of wearable devices in remotely tracking motor activity among young adults during early MI rehabilitation. Using the SiDLY Care Pro wristband, continuous non-invasive measurements of heart rate, oxygen saturation, and physical activity were collected from 62 of 80 post-MI patients (&lt;50 years) over seven days, alongside validated questionnaires (IPAQ, SF-36, DASS-21). Time-series clustering and principal component analysis characterized heart rate dynamics and activity patterns. Most participants showed sedentary behavior (2000–4000 steps/day), though self-reported health and psychological well-being were satisfactory. The device provided reliable, clinically meaningful data, particularly when linked to clinician feedback. Participants expressed an interest in using such technologies, especially if supported through reimbursement and professional guidance. Despite limitations—short monitoring timelines, small heterogeneous samples, and accuracy constraints—the findings suggest that wearable systems can enhance remote monitoring, patient engagement, and early intervention in post-MI care. Broader studies and supportive policies are recommended. Overall, integrating wearable technologies with professional oversight and patient participation may substantially improve recovery and outcomes for young MI survivors.

The unmanned aerial vehicle (UAV) has been applied in unmanned air combat because of its flexibility and practicality. The short-range air combat situation is rapidly changing, and the UAV has to make the autonomous maneuver decision as quickly as possible. In this paper, a type of short-range air combat maneuver decision method based on deep reinforcement learning is proposed. Firstly, the combat environment, including UAV motion model and the position and velocity relationships, is described. On this basic, the combat process is established. Secondly, some improved points based on proximal policy optimization (PPO) are proposed to enhance the maneuver decision-making ability. The gate recurrent unit (GRU) can help PPO make decisions with continuous timestep data. The actor network's input is the observation of UAV, however, the input of the critic network, named state, includes the blood values which cannot be observed directly. In addition, the action space with 15 basic actions and well-designed reward function are proposed to combine the air combat environment and PPO. In particular, the reward function is divided into dense reward, event reward and end-game reward to ensure the training feasibility. The training process is composed of three phases to shorten the training time. Finally, the designed maneuver decision method is verified through the ablation study and confrontment tests. The results show that the UAV with the proposed maneuver decision method can obtain an effective action policy to make a more flexible decision in air combat.

Cluster analysis is to be included among the favorite data mining techniques. Cluster analysis of time series has received great attention only recently mainly because of the several difficult issues involved. Among several available methods, genetic algorithms proved to be able to handle efficiently this topic. Several partitions are considered and iteratively selected according to some adequacy criterion. In this artificial “struggle for survival” partitions are allowed to interact and mutate to improve and produce a “high quality” solution. Given a set of time series two genetic algorithms are considered for clustering (the number of clusters is assumed unknown). Both algorithms require a model to be fitted to each time series to obtain model parameters and residuals. These methods are applied to a real data set concerned with the visitors flow recorded, in state owned museums with paid admission, in the Lazio region of Italy.

Dual-granularity weighted ensemble clustering

Network intrusion behaviour data is the imbalanced data. It includes a large amount of normal behavior data and a small amount of intrusion behavior data. The traditional selective ensemble learning algorithm will lead to high false negative rate. This paper proposes a selective ensemble learning algorithm for imbalanced data based on under sampling (SELAUS). First of all, the algorithm uses Bootstrap method to extract samples equal to the number of samples of a few classes from majority class samples to construct multiple balanced training subsets. Then, in order to ensure that the obtained base classifiers have great differences, several features are randomly selected on the training subset and a decision tree is constructed as the base classifier using CART algorithm. This method can also make some base classifiers have poor performance, so it can select and integrate base classifiers instead of all base classifiers. In order to accurately evaluate the generalization error of the classifier for imbalanced dataset, this paper defines the performance evaluation method in the imbalanced dataset and the difference evaluation method between the base classifiers. Then the generalization error of each base classifier is calculated, and the base classifier is selected according to the generalization error. In the integration of weighted voting, the weight of each base classifier is calculated by the weight calculation method for imbalanced data. Finally, the validity of the algorithm is verified by UCI dataset and applied to network intrusion detection. The simulation results show that the algorithm can improve the detection rate of minority class samples, that is to say, reduce the false negative rate.

Rheumatoid arthritis causes joint inflammation due to immune abnormalities, resulting in joint pain and swelling. In recent years, there have been considerable advancements in the treatment of this disease. However, only approximately 60% of patients achieve remission. Patients with multifactorial diseases shift between states from day to day. Patients may remain in a good or poor state with few or no transitions, or they may switch between states frequently. The visualization of time-dependent state transitions, based on the evaluation axis of stable/unstable states, may provide useful information for achieving rheumatoid arthritis treatment goals. Energy landscape analysis can be used to quantitatively determine the stability/instability of each state in terms of energy. Time-series clustering is another method used to classify transitions into different groups to identify potential patterns within a time-series dataset. The objective of this study was to utilize energy landscape analysis and time-series clustering to evaluate multidimensional time-series data in terms of multistability. We profiled each patient's state transitions during treatment using energy landscape analysis and time-series clustering. Energy landscape analysis divided state transitions into two patterns: "good stability leading to remission" and "poor stability leading to treatment dead-end." The number of patients whose disease status improved increased markedly until approximately 6 months after treatment initiation and then plateaued after 1 year. Time-series clustering grouped patients into three clusters: "toward good stability," "toward poor stability," and "unstable." Patients in the "unstable" cluster are considered to have clinical courses that are difficult to predict; therefore, these patients should be treated with more care. Early disease detection and treatment initiation are important. The evaluation of state multistability enables us to understand a patient's current state in the context of overall state transitions related to rheumatoid arthritis drug treatment and to predict future state transitions.