Intelligent Air Research Articles

Basic Theoretical Provisions of Entropy Approach for Intelligent Air Transportation Management

Basic Theoretical Provisions of Entropy Approach for Intelligent Air Transportation Management

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

For situation assessment in air combat, there may be incomplete information because of new technologies and unknown or uncertain targets and threats. In this paper, an improved method of situation assessment for air combat environment considering incomplete frame of discernment in the evidence theory is proposed to get a more accurate fusion result for decision making in the battlefield environment. First, the situation in air combat is assessed with knowledge. Then, the incomplete frame of discernment in the generalized evidence theory, which is an extension of Dempster–Shafer evidence theory, is adopted to model the incomplete and unknown situation assessment. After that, the generalized combination rule in the generalized evidence theory is adopted for fusion of situations in intelligent air combat. Finally, real-time decision-making in situation assessment can be reached for actions to take. Experiments in situation assessment of air combat with incomplete and uncertain situations show the rationality and effectiveness of the proposed method.

A multi-view attention-based spatial–temporal network for airport arrival flow prediction

Accurate airport arrival flow prediction is a precondition for intelligent air traffic flow management. However, most existing studies focus on the dynamic traffic flow in a single-airport scenario, which usually ignores the spatial interactions among airports. Modelling network-wide spatial dependencies among airports is difficult because it requires models to consider multiple underlying factors jointly. We propose a multi-view fusion approach to automatically learn an adjacency matrix from flight duration and flight schedule factors. The learned adjacency matrix is then fed into a specially designed graph convolutional block, which governs the message passing process among airports. Finally, the graph convolutional block with the learned adjacency matrix is embedded into the gated recurrent units to capture temporal dependencies. Experimental results on a real-world dataset for the multistep prediction task show the effectiveness and efficacy of the proposed model. In addition, visualisation and analysis of the learned adjacency matrix verify that the proposed multi-view fusion approach is capable of learning informative spatial interaction patterns.

Intelligent air defense task assignment based on hierarchical reinforcement learning.

Modern air defense battlefield situations are complex and varied, requiring high-speed computing capabilities and real-time situational processing for task assignment. Current methods struggle to balance the quality and speed of assignment strategies. This paper proposes a hierarchical reinforcement learning architecture for ground-to-air confrontation (HRL-GC) and an algorithm combining model predictive control with proximal policy optimization (MPC-PPO), which effectively combines the advantages of centralized and distributed approaches. To improve training efficiency while ensuring the quality of the final decision. In a large-scale area air defense scenario, this paper validates the effectiveness and superiority of the HRL-GC architecture and MPC-PPO algorithm, proving that the method can meet the needs of large-scale air defense task assignment in terms of quality and speed.

A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc networks

Air pollution has changed ecosystem and atmosphere. It is dangerous for environment, human health, and other living creatures. This contamination is due to various industrial and chemical pollutants, which reduce air, water, and soil quality. Therefore, air quality monitoring is essential. Flying ad hoc networks (FANETs) are an effective solution for intelligent air quality monitoring and evaluation. A FANET-based air quality monitoring system uses unmanned aerial vehicles (UAVs) to measure air pollutants. Therefore, these systems have particular features, such as the movement of UAVs in three-dimensional area, high dynamism, quick topological changes, constrained resources, and low density of UAVs in the network. Therefore, the routing issue is a fundamental challenge in these systems. In this paper, we introduce a Q-learning-based routing method called QFAN for intelligent air quality monitoring systems. The proposed method consists of two parts: route discovery and route maintenance. In the part one, a Q-learning-based route discovery mechanism is designed. Also, we propose a filtering parameter to filter some UAVs in the network and restrict the search space. In the route maintenance phase, QFAN seeks to detect and correct the paths near to breakdown. Moreover, QFAN can quickly identify and replace the failed paths. Finally, QFAN is simulated using NS2 to assess its performance. The simulation results show that QFAN surpasses other routing approaches with regard to end-to-end delay, packet delivery ratio, energy consumption, and network lifetime. However, communication overhead has been increased slightly in QFAN.

The push strategy of product design knowledge in cloud environment with the multidimensional hierarchical context and SSA-BPNN model

To assist designers in completing design tasks and improve the efficiency of product design, more and more design knowledge push approaches are coming about. With developments of information technology and cloud computing, knowledge service goes with the whole design process and it is represented by the knowledge push forming. The knowledge push is complex engineering, however, existing knowledge push approaches in traditional design patterns cannot exactly meet the personalized knowledge requirements of designers in cloud environment. To address this issue, we attempt to explore the push strategy of product design knowledge in cloud environment. Firstly, knowledge demands are analyzed with the multidimensional hierarchical context model and the design knowledge modeling is represented following characteristics of the cloud-based design pattern. Then, the knowledge push architecture contains the matching of knowledge to obtain alternative knowledge and the knowledge importance degree prediction with SSA-BPNN model to overcome the inaccuracy of judgments, which is put forward. Moreover, the push coefficient is calculated as the knowledge push reference. Eventually, an illustrative case of intelligent air conditioner product design in the cloud platform is taken to verify the application and performance of the proposed approach. We expect that this work could provide useful insights into the product design knowledge push in cloud environment.

A Game-Learning-Based Smooth Path Planning Strategy for Intelligent Air–Ground Vehicle Considering Mode Switching

Numerous missions in both civil and military fields involve the pursuit-evasion problem of vehicles. With vertical take-off and landing capability, the intelligent air–ground vehicle expands its feasible path to 3-D space, which has great advantages in the pursuit. This vehicle requires adequate path planning to obtain an optimal 3-D path and further improve the pursuit efficiency. The planning process of the air–ground vehicle currently faces the technical difficulties of acquiring the proper takeoff timing and position while optimizing the planning trajectory, especially in a complex environment with dense obstacles. To solve the above issues, a game-learning-based smooth path planning strategy for the intelligent air–ground vehicle considering mode switching is proposed in this article. First, a new reward function of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm, considering the influence of flight obstacle crossing parameters, is presented to explore the short forward track distance. Second, in the update rule, the pursuit-evasion game acts in the mode switching decisions. During interactive learning between the vehicle and environment, this game constantly updates the Nash equilibrium solutions for mode switching and gets a series of switching decisions of the pursuer vehicle (ego vehicle). Third, a double-yaw correction for path smoothing modification is proposed to reduce turning points and avoid local path deviations. This modification provides heuristic information for the exploration of the environment, which significantly speeds up the convergence speed of the algorithm. Finally, the proposed strategy is verified on a 1000 m*1000 m map with 0–200 m obstacle height. Results show that this strategy is effective to decrease the 253-m distance compared with the traditional reinforcement learning algorithm and has a faster convergence speed. The number of trajectory direction changes is 36% less than that of the game-learning algorithm only considering mode switching. The unreasonable large angle turns are eliminated.

Short-Term Trajectory Prediction Based on Hyperparametric Optimisation and a Dual Attention Mechanism

Highly accurate trajectory prediction models can achieve route optimisation and save airspace resources, which is a crucial technology and research focus for the new generation of intelligent air traffic control. Aiming at the problems of inadequate extraction of trajectory features and difficulty in overcoming the short-term memory of time series in existing trajectory prediction, a trajectory prediction model based on a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) network combined with dual attention and genetic algorithm (GA) optimisation is proposed. First, to autonomously mine the data association between input features and trajectory features as well as highlight the influence of important features, an attention mechanism was added to a conventional CNN architecture to develop a feature attention module. An attention mechanism was introduced at the output of the BiLSTM network to form a temporal attention module to enhance the influence of important historical information, and GA was used to optimise the hyperparameters of the model to achieve the best performance. Finally, a multifaceted comparison with other typical time-series prediction models based on real flight data verifies that the prediction model based on hyperparameter optimisation and a dual attention mechanism has significant advantages in terms of prediction accuracy and applicability.

Multi-UAV cooperative swarm algorithm in air combat based on predictive game tree

<p indent="0mm">An unmanned aerial vehicle (UAV) broadly refers to all kinds of remotely controlled aerial vehicles that do not require pilots to board and drive. Due to its small size, low cost, large quantity, and safety, a UAV is widely used in modern air combat. However, current studies on UAV air combat algorithms are mostly conducted in highly simplified scenarios with low precision. In addition, most of the methods used in these studies are limited by existing expert knowledge and cannot fully exploit the advantages of intelligent air combat algorithms. Therefore, this study investigates a multi-UAV cooperative swarm algorithm in air combat based on the predictive game tree. First, Unity3D is used to build a simulation environment close to a real air combat scene. In addition, a human-machine interaction environment, including UI, VR, weather system, and multifunctional screen, is realized. Then, a set of tactical maneuvers is encapsulated on the basis of existing air combat knowledge. Scripted aircraft formations are realized, and a set of air combat evaluation functions is designed. On this basis, an air combat artificial intelligence framework based on the predictive game tree is proposed. This algorithm completes the task of role assignment and maneuver decision-making, and XGBoost is used to transform it into an online real-time algorithm. Using the state machine algorithm as the baseline, the effectiveness of our air combat algorithm is verified through air combat confrontation experiments on a high-precision simulation platform.

Data-driven thermal preference prediction model with embodied air-conditioning sensors and historical usage behaviors

Predicting building occupants' thermal comfort, especially distinguishing individual thermal preference, is challenging but helpful for developing intelligent air conditioning (AC) control technology. Many studies have tried to solve this issue but heavily relied on extra sensing technology. This study sought to understand how occupants interact with AC devices and to predict occupants' thermal preference with limited sensors. Data from embodied sensors in 251 AC devices and occupants' interactions with these devices were analyzed. Five machine learning (ML) algorithms were applied to predict AC device's setting temperature changing actions. The results show that different occupants' AC usage behavior varied greatly in setting temperature preference and adjusting time. Users can be categorized as “prefer cool,” “prefer warm”, and “prefer neutral” according to the room temperature and setting temperature distributions; or as “regular type” and “irregular type” according to the time when the setting temperature was adjusted. More than 60% of users tended to set AC temperature in the range of 25°C–28°C. By applying random forest algorithm and proper data preprocesses, models can predict “increase setting temperature” and “decrease setting temperature” actions with 72.1%–87.3% accuracy. The model performance increases with larger samples and by adding Month and Hour as input features. With 30–50 times of training, the thermal preference learning curves for individual AC devices can reach relatively stable state. Lastly, a setting temperature control logic for air conditioners was discussed. Hopefully, this work can help to develop intelligent AC control methods that maximize occupants' thermal comfort while reducing energy consumption.

Intelligent Air Cutting Monitoring System for Milling Process

This research proposes a method for auto-monitoring the air cutting condition of a machining process, so the air cutting time can be further improved to enhance the machining efficiency. A two-way data communication module with a CNC controller was established to achieve real-time monitoring and control function via TCP/IP protocol. The module enables the identification of the executing NC block and the cutting time during machining. The spindle load current and cutting vibration signals were used to on-line diagnose the cutting state (effective cutting or air cutting), and their associated NC codes were identified and recorded at the same time, based on the executing NC block in the CNC controller. An algorithm adopting this state change information to determine effective cutting time and air cutting time was developed and used to build an intelligent air cutting monitoring system, with a friendly human–machine interface. The system can detect air cutting time, effective cutting time, machine idle time, as well as the total machining time to improve the air cutting time to enhance the machining efficiency. Verification experiments were conducted, and the results showed the proposed method can accurately detect the air cutting occurrence and its associated NC blocks in the NC program.

Internet-of-Things object model

Background: With the advancement of communication technology and advanced sensors, there are massive demands for Internet-of-Things (IoT) applications in buildings, communities, factories, parks, etc. Accessing IoT devices provides convenience for scene management and monitoring, ameliorating production and life intelligently. However, due to the lack of a unified model for IoT devices, data is often skipped over IoT platforms and transmitted to applications directly. This leads to the fact that each manufacturer needs to produce its devices and develop its customized software, which hugely increases the development cycle. On the other hand, it is difficult to convey information between different systems, limiting cross- system control. Moreover, digital twin relies on large amounts of heterogeneous data, and it is impracticable to provide enough data without a unified model for device description. Methods: First, we illustrate the motivation, design goals, and design principles for creating the Internet-of-Things Object Model (IoT-OM). Then we propose a unified description to define IoT devices. The proposed concept has been accepted by several companies, and we analyse one platform that adopts the model. To demonstrate the effectiveness of the model, we introduce two projects based on the platform. One project is an intelligent fire protection system, and another project is an intelligent air quality monitoring system. Results: We measured the time taken by five companies when developing IoT devices and their applications, including the development cycle duration without utilizing the proposed model and the duration using the model at China Mobile’s OneNET platform. The results prove that the proposed model can significantly shorten the development cycle. Conclusions: This paper proposes a model for IoT devices, which helps to unify heterogeneous data among different manufacturers and helps to shorten the development cycles for developers.

A study on development of intelligent ground-based air defense radar under the background of modern war

Abstract The new characteristics of modern war put forward new requirements for intelligent ground-based air defense radar. This paper refines the new characteristics of modern war from the aspects of combat object, combat environment and combat style; Based on this, from the perspective of adapting to modern war, this paper analyzes and summarizes the intelligent development of ground-based air defense radar in intelligent perception, resource allocation, man-radar integration and so on, which plays a guiding role in the theoretical research and engineering realization of ground-based air defense radar technology. In addition, the combat effectiveness of intelligent and traditional ground air defense radar is evaluated by analytic hierarchy process, which can be used as a reference for subsequent research.

Research on electric vehicle range under cold condition

The effect of cold temperature on Electric Vehicle (EV) was investigated on an A class sedan through theoretical and experimental tests. Under cold weather, the drive range was reduced by 42.8% due to reasons such as the energy consumption of cabin heating, the reduction of electric energy capacity of battery and a reduction of efficiency of Electric Drive System (EDS). The cabin heating system is the biggest contributor for the reduction of drive range under cold temperature. Unlike the conventional vehicle where the engine waste heat is recycled, a Positive Temperature Coefficient (PTC) heater which directly withdraws electric energy from battery is used as the heat source for EV. This results in a reduction of EV range. Two approaches that can reduce the energy consumption of cabin heating system were investigated, namely the adoption of Intelligent Air Recirculation (IAR) and Heat Pump (HP). IAR and HP brought an increase of EV range under −7°C NEDC by 7.1%–7.9%, respectively. Another factor that causes range reduction is the deterioration of electric energy capacity of battery. To mitigate this adverse influence, research was conducted on two approaches: Motor Heat Recycle System (MHRS) and Active Motor Heat Control (AMHC). The adoption of MHRS system could improve the EV range by 7.9% while AMHC yielded an improvement of range by 2.1%. When the temperature decreases, the system efficiency of EDS decreases. One reason is the increase of oil splash loss as the oil viscosity increases under cold temperature. Hence, one type of oil with lower viscosity was tested and the results showed that the EDS efficiency could be improved by 1.5% which accounted for a 2.2% increase in driving range. With these measures implemented onto the test vehicle, the EV range under cold temperature −7°C NEDC could be improved by 26.5%.

Aircraft Rotation Detection in Remote Sensing Image Based on Multi-Feature Fusion and Rotation-Aware Anchor

Due to the variations of aircraft types, sizes, orientations, and complexity of remote sensing images, it is still difficult to effectively obtain accurate position and type by aircraft detection, which plays an important role in intelligent air transportation and digital battlefield. Current aircraft detection methods often use horizontal detectors, which produce significant redundancy, nesting, and overlap of detection areas and negatively affect the detection performance. To address these difficulties, a framework based on RetinaNet that combines a multi-feature fusion module and a rotating anchors generation mechanism is proposed. Firstly, the multi-feature fusion module mainly realizes feature fusion in two ways. One is to extract multi-scale features by the feature pyramid, and the other is to obtain corner features for each layer of feature map, thereby enriching the feature expression of aircraft. Then, we add a rotating anchor generation mechanism in the middle of the framework to realize the arbitrary orientation detection of aircraft. In the last, the framework connects two sub-networks, one for classifying anchor boxes and the other for regressing anchor boxes to ground-truth aircraft boxes. Compared with state-of-the-art methods by conducting comprehensive experiments on a publicly available dataset to validate the proposed method performance of aircraft detection. The detection precision (P) of proposed method achieves 97.06% on the public dataset, which demonstrates the effectiveness of the proposed method.

Research on Intelligent Control System of Air Conditioning based on Internet of Things Intelligent Control System of Air Conditioning

The current air conditioning intelligent control system cannot achieve the ideal energy-saving effect. The indoor temperature and humidity control is not good enough either. Therefore, an intelligent air-conditioning control system based on Internet of Things technology is designed. The hardware part of the system includes system control motherboard, sensor module, execution control structure, wireless communication module and access layer. The software includes the design of communication layer, the design of monitoring management, and the design of intelligent indoor air-conditioning temperature remote control algorithm. The experimental results show that the control effect of the intelligent air conditioner is more accurate and energy-saving, the opening degree of the air conditioning valve is larger, and the comfort is improved. The indoor temperature and humidity of the proposed system are both more ideal.

DEVELOPMENT OF METHODS OF POSITIONING, LOCALIZATION AND DATA COLLECTION FROM NODES OF A FREE MOBILE SENSOR NETWORK USING INTELLIGENT ADAPTIVE TELECOMMUNICATION AEROPLATFORMS

Background. The article presents the results of a study of methods of positioning, localization and data collection from nodes of a mobile wireless sensor network using intelligent adaptive telecommunication air platforms. To implement the study of this research topic, an analysis of literary sources on this topic was carried out. Based on a fairly rich bibliographic material, this work has the main task of examining, analyzing and systematizing already known approaches to positioning objects in wireless sensor networks using intelligent adaptive telecommunication air platforms and suggesting options for their development.&#x0D; Objective. The aim of the work is to improve the methods of direct data collection of TA from the nodes of BSM, the general directions of synthesis of which are defined in the work.&#x0D; Methods. Methods of cluster analysis (network clustering), graph theory (research of analytical models of BSM with TA functioning, construction of cluster topology), theory of telecommunication networks (when calculating bandwidth in BSM with TA radio channels) and theory were used to solve the formulated problem. (when developing a positioning model for telecommunications air platforms)&#x0D; Results. A technique for evaluating the effectiveness of methods for collecting data from wireless sensor networks using intelligent adaptive telecommunication air platforms is proposed.&#x0D; Conclusions. The method of collecting TA monitoring data from the main nodes of clustered BSM has been improved. The method of estimation of efficiency of methods of data collection with BSM by telecommunication air platforms is offered.

Evaluation of bio-aerosols type, density, and modeling of dispersion in inside and outside of different wards of educational hospital.

Exposure to bioaerosols in the air of hospitals is associated with a wide range of adverse health effects due to the presence of airborne microorganisms. Intensity and type of health effects depend on many factors such as the type, density, and diversity of bioaerosols in hospital environments. Therefore, identifying and determining their distribution in hospital environment contribute to reduce their adverse effects and maintain the physical health of patients and staff, as well as find the source of infections and possible allergies due to the presence of bioaerosols. Therefore, the present study was conducted to determine the type and concentration of the bacterial and fungal bioaerosols, and their distribution in the indoor and outdoor air of a teaching hospital to establish a reference for future studies or measures. The air samples were collected with a one-stage Anderson sampler and particle mass counter for a period of four months in the fall and winter of 2019. In total, 262 bacterial and fungal samples were collected from the air of the wards of Tohid Hospital, Sanandaj, Iran. Antibiotic resistance test, bacterial identification by PCR method, and modeling the dispersion of concentrations of bio-aerosols were also conducted. In order to identify bacteria and fungi, some biochemical and molecular tests and microscopic and macroscopic characteristic methods were applied, respectively. The results showed that the highest and lowest densities of the bioaerosols were observed in lung and operating wards (336.67 and 15.25 CFU/m3). Moreover, the highest and least concentrations of particles were seen in the emergency and operating wards, respectively. The most common fungi isolated from the hospital air were Penicillium (24.7%), Cladosporium (23. 4%), Aspergillus niger (13.3%), and Aspergillus Flavus (11.4%). Furthermore, the highest concentration of the isolated bacterium was Staphylococcus hemolyticus (31.84%). Most bacteria showed the highest resistance to gentamicin. The overall average hospital air pollution to bioaerosols was slightly higher than the standards proposed by international organizations. Due to the high concentration of bioaerosols and particles in the studied hospital, providing suitable conditions such as temperature, humidity, proper ventilation, and intelligent air conditioning system using efficient ventilation systems, and restricting the entrance of wards can reduce airborne particles in hospital environment.

Federated conditional generative adversarial nets imputation method for air quality missing data

The air quality is a topic of extreme concern that attracts a lot of attention in the world. Many intelligent air quality monitoring networks have been deployed in various places, especially in big cities. These monitoring networks collect air quality data with some missing data for some reasons which pose an obstacle for air quality publishing and studies. Generative adversarial nets (GAN) methods have achieved state-of-the-art performance in missing data imputation. GAN-based imputation method needs enough training data while one monitoring network has just a few and poor quality monitoring data and these data sets do not meet the independent identical distribution (IID) condition. Therefore, one monitoring network side needs to utilize more monitoring data from other sides as far as possible. However, in the real world, these air quality monitoring networks are owned by different organizations — companies, the government even some secret units. Many of them cannot share detailed monitoring data due to security, privacy, and industrial competition. In this paper, it is the first time to propose a conditional GAN imputation method under a federated learning framework to solve the data sets that come from diverse data-owners without sharing. Furthermore, we improve the vanilla conditional GAN performance with Wasserstein distance and “Hint mask” trick. The experimental results show that our GAN-based imputation methods can achieve the best performance. And our federated GAN imputation method outperforms the GAN imputation method trained locally for each participant which means our imputation model can work. Our proposed federated GAN method can benefit model quality by increasing access to air quality data through private multi-institutional collaborations. We further investigate the effects of data geographical distribution across collaborating participants on model quality and, interestingly, we find that the GAN training process with a federated learning framework performs more stable.

Development of Intelligent Smart Air Conditioning Service Using Non-Contact Bio-Signal and Emotional Lighting

Recently, in the mature life market of Home appliances, technical differentiation of products using convergence technology has been receiving a lot of response to satisfy consumer demand. In this paper, the convergence technology (bio-signal, emotional lighting, ASMR) service for air conditioners has been developed. Intelligent smart air conditioner service is equipped with IR-UWB radar sensor to collect bio-signal (respiration, movement) by non-contact method and operate air conditioner according to user’s biorhythms, and air conditioner is operated to make a pleasant sleeping environment through sleep monitoring. Warm/Cool white and RGB LED combination works according to the user’s emotions. In addition, Eight ASMR stored on the server are serviced through air conditioning speaker. The IR-UWB radar sensor for bio-signal collection designed with antenna frequency 6.0–8.5 GHz and vertical/horizontal 65° to enable object location tracking at distance of 8 m. The respiration rate can be measured 0–40 times and the average error rate of the respiration rate measurement is 4.22%. Emotional lighting consisting of Warm/Cool White and RGB LEDs can control color (RED: x-0.68 y-0.30, GREEN: x-0.15 y-0.71, BLUE: x-0.13 y-0.06) and color temperature (2700–6500 K). Maximum CRI was measured at 92. The air conditioner operation information and sensor information are transmitted by JSON method using HTTP protocol and stored, processed, analyzed, and serviced on the big data platform. Our smart air conditioning service platform proved reliability with a data transmission error rate of 0.01%.