Adaptation to Unknown Leader Velocity in Vector-field UAV Formation

This paper presents a new adaptive method forformation control of unmanned aerial vehicles (UAVs) withlimited leader information and communication. We study aformation control protocol in the framework of vector-fieldguidance where the leader can communicate its position andorientation but not its velocity. A practical motivation for thisscenario is the so-called congestion-aware control, in whichtrade-offs between the density of unmanned vehicles andcommunication interference caused by many communicatingvehicles arise: these trade-offs may require to reduce thecommunication load to avoid interference. To compensate forthe lack of knowledge of the leader velocity, each UAV makesuse of a local estimation mechanism. The resulting method isan adaptive control method, whose stability can be establishedusing Lyapunov stability. We show that the method can beextended to a distributed communication setting with a fewneighboring UAVs in place of the leader. Extensive simulationswith different formation shapes (Y, V and T formation) showthat the proposed adaptation mechanism effectively achieves theformation despite the unknown leader velocity. The proposedmechanism has a very similar performance to the ideal casewhen the leader velocity is perfectly known, and outperforms allthe non-adaptive cases in which the followers have an incorrectknowledge of the leader velocity.Keywords: Vector field, formation control, local estimation,unknown leader velocity, adaptive control

Relationship between driver gene mutations and clinical pathological characteristics in older lung adenocarcinoma

ObjectivesLung adenocarcinoma (LUAD) is the most common newly diagnosed malignant tumor in older people. As older patients age, organ function decreases, leading to increased adverse reactions to treatment. The epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase tyrosine (ALK) tyrosine kinase inhibitors (TKIs) therapy are more effective and well-tolerated than chemotherapy, while the rate of genetic testing and subsequent targeted treatment among older patients remains relatively low, the clinical benefit limitation for those patients. This study aims to investigate the mutation characteristics of LUAD diver gene and its relationship with clinicopathological features in older LUAD.Materials and methodsA total of 275 patients were diagnosed as LUAD and were over sixty years old. We utilized next-generation sequencing technology to detect and analyze gene mutations in postoperative tissue specimens, including EGFR, KRAS, ALK, ROS1, RET, MET, BRAF, HER2, PIK3CA and NRAS.ResultsA total of 90.18% (248/275) of older LUAD patients experienced genetic mutations. The EGFR (192, 69.82%) had the highest mutation rate among ten genes, followed by KRAS (21, 7.64%), MET (21, 7.64%), ERBB2 (15, 5.45%), RET (9, 3.27%), ALK (8, 2.91%), ROS1 (8, 2.91%), PIK3CA (6, 2.18%), BRAF (5, 1.82%) and NRAS (1, 0.36%). We also found thirty patients (15.63%) with EGFR mutations also having other gene mutations. The L858R mutation and exon19 deletion were the predominant EGFR mutations, accounting for 84.90% of EGFR-mutated patients. In addition, fifty-one kinds of EGFR mutations were detected, distributed in the protein tyrosine kinase catalytic domain (43, 84.31%), cysteine enriched domain (4, 7.84%), receptor binding domain (3, 5.88%), and EGFR transmembrane domain (1,1.96%). Ten cases of gene fusion mutation were detected. Two rare partner genes, PKHD1 (P60:R34) and STK39 (R33:S11), were detected by ROS1 gene fusion. RET gene fusion revealed a rare companion gene KCND2 (R11:K2). The EGFR mutations were more prevalent in female, non-smoking patients (p < 0.05), and the KRAS mutations were more common in male and smoking patients (p < 0.01). In addition, the BRAF mutations were more likely to occur in the right lung (p < 0.05).ConclusionOlder LUAD populations exhibit diverse genetic mutations, which may also exist simultaneously. Simultaneous detection of multiple genes by NGS can accelerate and enhance targeted treatment benefits for older LUAD patients, ultimately improving their quality of life

Space advanced technology demonstration satellite

The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance

Task Offloading with Power Control for Mobile Edge Computing Using Reinforcement Learning-Based Markov Decision Process

This paper proposes an efficient computation task offloading mechanism for mobile edge computing (MEC) systems. The studied MEC system consists of multiple user equipment (UEs) and multiple radio interfaces. In order to maximize the number of UEs benefitting from the MEC, the task offloading and power control strategy for a UE is optimized in a joint manner. However, the problem of finding the optimal solution is NP-hard. We then reformulate the problem as a Markov decision process (MDP) and develop a reinforcement learning- (RL-) based algorithm to solve the MDP. Simulation results show that the proposed RL-based algorithm achieves a near-optimal performance compared to the exhaustive search algorithm, and it also outperforms the received signal strength- (RSS-) based method no matter from the standpoint of the system (as it leads to a larger number of beneficial UEs) or an individual (as it generates a lower computation overhead for a UE)

Monocular Vision Obstacle Detection Method Based on Radial Optical Flow for Rotor UAV

To solve the problem of traditional Pyramid LK optical flow algorithm's poor accuracy and adaptability for rotor UAV to detect obstacle in complex outdoor environment, a monocular autonomous real-time obstacle detection method based on radial optical flow is proposed. In the optical flow, the radial optical flow is computed by fusing Pyramid LK optical flow with tangential optical flow, and a new obstacles decision strategy to detect obstacles based on the radial optical flow is put forward. Experimental results show that without increasing the complexity of algorithm, the proposed method can get a higher accuracy and better adaptability than traditional Pyramid LK algorithm, which can meet the requirements of UAV autonomous obstacle avoidance

Coordinated Control of Multi-Type Energy Storage for Wind Power Fluctuation Suppression

The fluctuations of wind power impact the stable operation of a power system as its penetration grows high. Energy storage may be a potential solution to suppress these fluctuations and has drawn much attention in recent years. As the time scale of wind power fluctuations is in a range of seconds to hours, multi-type energy storage with complementary characteristics, such as the combination of energy-type storage devices (ESD) and power-type storage device (PSD), may be technically and economically feasible to suppress multi-time-scale wind power fluctuations. Therefore, system control is very important when the power allocation among each of the energy storage units is considered. In this paper, a novel coordinated control strategy based on model predictive control (MPC) was proposed for wind power fluctuation suppression, which employs MPC for the total power required for the whole energy storage system and then allocates it between ESD and PSD with the low-pass filter algorithm (LFA) method. Due to the predictive feature of MPC, the power requirement of the energy storage system can be obtained with little time delay, which means less energy is needed. The effectiveness of the proposed control strategy was verified in a time-domain simulation system. The influence of wind speed conditions and LFA time constant on the wind/storage system were further discussed

FAILURE ANALYSIS OF THE COATING OF TRANSMISSION MECHANISM BASED ON COHESIVE ZONE MODEL

Hard coatings has the advantages of good wear resistance and high contact fatigue life which has been widely used in aerospace and aviation fields. However, the coating/subtrate interference will occur delamination failure under the heavy load, and the reliability of machine parts has been affected. Recently, study on the coatings failure mechanisms and the bonding ability of coatings is greatly focused on. The article adopting cohesive zone model combined with FEM analysis the delamination failure of the coating/subtrate interference. The results show that the simulation results based on the cohesive zone model coincides with the experimen results. On this foundation, the effects of different factors on the adhesion strength of coatings are studied. The bonding strength of coatings has been greatly increased by increasing the thickness and elastic modulus of coatings, and the carrying capacity has greatly improved

Prediction of Operating Abnormality Rate of Charging Pile Based on Generalized AR(q) Combined Regression

The stable operation of charging pile is related to the entire operation efficiency of the charging network of electric vehicles so the prediction of charging pile operation abnormality rate can help the operational department to make operational decisions in advance. This paper uses the electric vehicle charging network operating date in the north of Hebei province, based on the feature of the anomalies records of charging pile, to combine the generalized AR(q) model and the regression model and to predict the abnormality rate of electric vehicle charging network in the north of Hebei province. It is predicted that the average absolute error is 0.0044 and the acceptable prediction effect can be obtained