Online mapping and end-to-end (E2E) planning in autonomous driving are still largely sensor-centric, leaving rich map priors—HD/SD vector maps, rasterized SD maps, and satellite imagery—underused due to heterogeneity, pose drift, and inconsistent availability at test time. We present emph{UMPE}, a Unified Map Prior Encoder that can ingest any subset of four priors and fuse them with BEV features for both mapping and planning. emph{UMPE} has two branches. The vector encoder pre-aligns HD/SD polylines with a frame-wise SE(2) correction, encodes points via multi-frequency sinusoidal features, and produces polyline tokens with confidence scores. BEV queries then apply cross-attention with confidence bias, followed by normalized channel-wise gating to avoid length imbalance and to softly down-weight uncertain sources. The raster encoder shares a ResNet-18 backbone conditioned by FiLM (scaling/shift at every stage), performs SE(2) micro-alignment, and injects priors through zero-initialized residual fusion so the network starts from a do-no-harm baseline and learns to add only useful prior evidence. A vector-then-raster fusion order reflects the inductive bias of “geometry first, appearance second.” On nuScenes mapping, emph{UMPE} lifts MapTRv2 from 61.5 ?67.4 mAP (+5.9) and MapQR from 66.4 ?71.7 mAP (+5.3). On Argoverse2, emph{UMPE} adds +4.1 mAP over strong baselines. emph{UMPE} is compositional: when trained with all priors, it outperforms single-prior models even when only one prior is available at test time, demonstrating powerset robustness. For E2E planning (VAD backbone, nuScenes), emph{UMPE} reduces trajectory error from 0.72 ?0.42 m L2 (avg. ?.30 m) and collision rate from 0.22% ?0.12% (?.10%), surpassing recent prior-injection methods. These results show that a unified, alignment-aware treatment of heterogeneous map priors yields better mapping and better planning.

End-to-end (E2E) driving has become a cornerstone of both industry deployment and academic research, offering a single learnable pipeline that maps multi-sensor inputs to actions while avoiding hand-engineered modules. However, the reliability of such pipelines strongly depends on how well they handle uncertainty: sensors are noisy, semantics can be ambiguous, and interaction with other road users is inherently stochastic. Uncertainty also appears in multiple forms: classification vs. localization, and, crucially, in both static map elements and dynamic agents. Existing E2E approaches model only static-map uncertainty, leaving planning vulnerable to overconfident and unreliable inputs. We present UniUncer, the first lightweight, unified uncertainty framework that jointly estimates and uses uncertainty for both static and dynamic scene elements inside an E2E planner. Concretely: (1) we convert deterministic heads to probabilistic Laplace regressors that output per-vertex location and scale for vectorized static and dynamic entities; (2) we introduce an uncertainty-fusion module that encodes these parameters and injects them into object/map queries to form uncertainty-aware queries; and (3) we design an uncertainty-aware gate that adaptively modulates reliance on historical inputs (ego status or temporal perception queries) based on current uncertainty levels. The design adds minimal overhead and drops throughput by only ~0.5 FPS while remaining plug-and-play for common E2E backbones. On nuScenes (open-loop), UniUncer reduces average L2 trajectory error by 7%. On NavsimV2 (pseudo closed-loop), it improves overall EPDMS by 10.8% and achieve notable Stage-2 gains in challenging, interaction-heavy scenes. Ablations confirm that dynamic-agent uncertainty and the uncertainty-aware gate are both necessary.

Objective In recent years, the continuous development of uncooled infrared detectors has promoted the research of high-resolution athermal infrared zoom systems. However, most of the existing long-wave infrared zoom systems are only compatible with small-format detectors and cannot capture more image details. At the same time, due to the limitations of large relative aperture and materials in the long-wave infrared band, there are still difficulties in miniaturization and athermal design. Therefore, most scholars have introduced high-order even aspheric surfaces and diffractive elements in the design. Although the use of aspheric and diffractive surfaces theoretically improves the image quality of the system, the diffraction efficiency is affected by wavelength and the environment, and it requires high processing accuracy and is costly. Moreover, the traditional power-series aspheric surface has low optimization efficiency and is prone to introduce sudden changes in surface slope, which is not conducive to detection and processing. Thanks to its orthogonal characteristics, the Q-type aspheric surface shows great advantages in design and processing. It can not only improve the design efficiency but also reduce the difficulty of processing and detection. Therefore, in order to improve the detection efficiency of the uncooled infrared zoom optical system and avoid the problems such as efficiency loss and high processing difficulty caused by the use of diffractive surfaces and high-order even aspheric surfaces, it is of great significance to design a large-format infrared continuous zoom optical system based on the Q-type aspheric surface. Methods This study designs a long-wave infrared zoom optical system with a large target surface based on the orthogonality and slope-controllable characteristics of Q-type aspheric surfaces. The system consists of six lenses and adopts mechanical positive-group compensation zoom. By reasonably distributing the optical power and matching infrared materials, the achromatism and athermalization of the optical system are achieved within the full focal length range. For high-optical-power components, germanium material with a relatively high refractive index is selected, which is beneficial to the compact design of the system. For low-optical-power components, chalcogenide glass with a relatively low refractive index is used. Meanwhile, chalcogenide glass has a low temperature-refractive index, and when paired with germanium material, it can compensate for the defocus phenomenon of germanium material caused by temperature sensitivity. Active athermalization technology is used to achieve defocus compensation under wide-temperature changes by axially moving a lens in the rear fixed group. A method of multi-point fitting of the cam curve is adopted. Multiple focal-length configurations are inserted within the focal-length range of 30-150 mm. The movement of the zoom group and the compensation group is controlled by the cam curve to ensure the stability of the image plane. The Q-type aspheric surface on the front surface of the rear fixed group is converted into an even-order aspheric surface, and the two are compared to verify the advantages of the Q-type aspheric surface. Finally, tolerance analysis and Monte Carlo simulation are carried out to evaluate the manufacturability and stability of the system and improve the engineering feasibility of the system. Results and Discussions This study designed a long-wave infrared continuous zoom optical system based on Q-type aspheric surfaces. The system consists of six lenses with a total length of only 210 mm. The results show that in the 8 -12 & micro;m wavelength range, the system achieves good imaging performance within the focal length range of 30-150 mm. The modulation transfer function (MTF) is greater than 0.3 at 40 lp/mm. The distortion of the full field of view is less than 2.3 degrees o. All indicators meet the design requirements. In the temperature range from-40 degrees C to +60 degrees C , the maximum thermal defocus of the system is less than 15 & micro;m, which is better than the system's depth of focus (28 & micro;m), indicating good thermal stability. Through the optimization of the cam curve, smooth movement of the zoom group and the compensation group is achieved. The cam curve has no inflection points, ensuring the stability of the zoom process. Compared with traditional even-order aspheric surfaces, the coefficient values of Q-type aspheric surfaces are 8-19 times higher, effectively reducing the rounding errors in processing. The maximum sag deviation is only 25.7 & micro;m, making it easy to process. Tolerance analysis shows that under a 90 degrees o yield rate, the MTF values at each focal length are better than 0.28, indicating good process feasibility. This study verifies the advantages of Q-type aspheric surfaces in large-target infrared zoom systems and provides an effective solution for the development of high-performance and low-cost infrared optical systems. Conclusions Most traditional long-wave infrared zoom systems are only compatible with small-format detectors. Considering the development trend of infrared detectors, this design aims at a large-format uncooled long-wave infrared detector with an array specification of 1280 pixel & times; 1024 pixel. A long-wave infrared continuous zoom optical system with a zoom range of 30 mm to 150 mm is designed using Q-type aspheric surfaces. The system has an F-number of 1.2 and a maximum field of view of 36.9 degrees . It adopts mechanical positive-group compensation zoom, featuring a simple structure and a smooth zoom curve. An active temperature compensation method is used to make the MTF curve of the system approach the diffraction limit in the temperature range of-40 degrees C to +60 degrees C. The thermal defocus amount is less than 15, meeting the requirements for use in high and low temperatures. In addition, when converting the Q-type aspheric surface into an even-order aspheric surface with the same number of terms, it is found through comparison that the coefficients of the Q-type aspheric surface are 8 to 19 times those of the even-order aspheric surface, which can retain more significant figures and achieve higher fitting accuracy. By introducing the Q-type aspheric surfaces in the design, the optimization efficiency is improved while the processing difficulty is reduced. The system avoids the use of diffractive surfaces, reduces costs, and has high application value in fields such as tracking, reconnaissance, and monitoring.

Objective Solar-blind ultraviolet zoom optical systems are of significant value in applications such as corona detection of power equipment, ultraviolet communication, and environmental monitoring. However, existing systems often suffer from issues like complex structures, large volumes, and difficulties in aberration correction. These problems are particularly pronounced in harsh industrial environments characterized by high temperature and humidity, where they can lead to a substantial decline in detection accuracy. The challenges primarily stem from the severe material dispersion in the ultraviolet band and the requirement for dynamic compensation using multiple lenses during the zoom process, which increases design complexity and manufacturing costs. In fact, the manufacturing costs of some high-precision systems can even exceed the affordability of small and medium-sized enterprises. Traditional solutions often rely on diffractive optical elements or complex mechanical structures, which not only reduce optical efficiency but also impair environmental adaptability; meanwhile, the complex structures further increase the frequency and cost of equipment maintenance. Therefore, developing a compact, high-resolution, and stably zooming solar-blind ultraviolet optical system is of great importance for both scientific research and engineering applications. Based on the INOCTURN-SUV image-intensified ultraviolet detector, this study aims to design a high-performance continuous ultraviolet zoom optical system. The focus is on addressing key technical challenges such as aberration correction in the ultraviolet band, miniaturization, high imaging quality requirements, and stable zoom performance, thereby providing a reliable solution for the practical application of solar-blind ultraviolet detection technology. Methods In this study, a material combination of fused silica and calcium fluoride is selected. By leveraging their complementary dispersion characteristics in the ultraviolet band, effective correction of both axial and lateral chromatic aberration is achieved. Using optical design software, the curvature, thickness, and spacing of the lenses are meticulously optimized to minimize aberrations within the 240-280 nm wavelength range as much as possible. Additionally, in designing the zoom structure, an opto-mechanical co-simulation approach is employed to optimize the zoom curve, which helps reduce the number of moving lens groups. As a result, the system achieves a continuous zoom range of 65-130 mm while successfully controlling the total system length within 200 mm, striking a balance between performance and compactness. Furthermore, to address the issues related to the machining accuracy of traditional aspheric surfaces, a Q-type aspheric design is introduced. The surface shape of this aspheric structure is described based on orthogonal polynomials-a technique that helps reduce the correlation between coefficients and significantly improves the accuracy of surface shape fitting. Finally, the imaging quality is evaluated using metrics such as the modulation transfer function (MTF) and distortion. In conjunction with tolerance analysis, the assembly and adjustment processes are further optimized, ensuring that the overall performance of the system fully meets the demands of practical applications and laying a solid foundation for its successful implementation in real-world scenarios. Results and Discussions This study has successfully designed a high-performance ultraviolet zoom optical system with a zoom range of 65-130 mm and an overall system length of less than 200 mm, whose structure is shown in Fig. 2. The system exhibits excellent imaging quality: the full-field MTF exceeds 0.7 at the Nyquist frequency of 42 lp/mm, as presented in Fig. 4, and the full-field distortion is less than 2.7%, as shown in Fig. 5. Through the optimized material combination of fused silica and calcium fluoride, the system not only effectively corrects chromatic aberration in the ultraviolet band but also avoids the use of expensive materials. In terms of structural design, while simplifying the zoom mechanism, the number of moving lens groups is reduced, which significantly improves system stability. The application of Q-type aspheric surfaces solves the coefficient coupling problem inherent in traditional even-order aspheric surfaces and enhances machining accuracy, with the specific deviation from the best-fit sphere shown in Fig. 3. Furthermore, by avoiding the use of diffractive optical elements, the system's environmental adaptability is enhanced. The smooth cam curve, depicted in Fig. 6, ensures the reliability and extended service life of the actual system. Moreover, opto-mechanical co-simulation and rigorous tolerance analysis guarantee the engineering feasibility of the system, providing reliable support for practical production and application. Conclusions This study focuses on the key technical challenges of solar-blind ultraviolet zoom optical systems and innovatively proposes a set of high-performance solutions based on material optimization and Q-type aspheric surface design. Adhering to the principle of miniaturization, the system designed through this scheme, relying on a scientific structural layout and parameter configuration, has successfully achieved excellent imaging quality and stable zoom performance, with all indicators meeting practical standards. These research results provide solid and crucial technical support for the application of solar-blind ultraviolet detection technology in practical scenarios, such as the accurate identification of equipment faults in power inspection and the clear capture of targets in military reconnaissance. Looking ahead, we can further explore the integrated application of freeform surfaces and metasurface optical technologies in such systems, which may further improve the comprehensive performance and significantly reduce the volume of the system, thereby injecting new impetus into the innovative development of ultraviolet optical systems.

The surface quality of optical components plays a decisive role in advanced imaging, precision manufacturing, and high-power laser systems, where even defects can induce abnormal scattering and degrade system performance. Addressing the limitations of conventional single-view inspection methods, this study presents a panoramic multi-angle scattered light field acquisition approach integrated with deep learning-based recognition. A hemispherical synchronous imaging system is designed to capture complete scattered distributions from surface defects in a single exposure, ensuring both structural consistency and angular completeness of the measured data. To enhance the interpretation of complex scattering patterns, we develop a tailored lightweight network, SFD-YOLO, which incorporates the PSimam attention module for improved salient feature extraction and the Efficient_Mamba_CSP module for robust global semantic modeling. Using a simulated dataset of multi-width scratch defects, the proposed method achieves high classification accuracy with strong generalization and computational efficiency. Compared to the baseline YOLOv11-cls, SFD-YOLO improves Top-1 accuracy from 92.5% to 95.6%, while reducing the parameter count from 1.54 M to 1.25 M and maintaining low computational cost (Flops 4.0G). These results confirm that panoramic multi-angle scattered imaging, coupled with advanced neural architectures, provides a powerful and practical framework for optical surface defect detection, offering valuable prospects for high-precision quality evaluation and intelligent defect inversion in optical inspection.

Modern OS kernels, such as Linux, employ the eBPF subsystem to enable user space to extend kernel functionality. To ensure safety, an in-kernel verifier statically analyzes these extensions; however, its imprecise analysis frequently results in the erroneous rejection of safe extensions, exposing a critical tension between the precision and computational complexity of the verifier that limits kernel extensibility. We propose a proof-guided abstraction refinement technique that significantly enhances the verifier's precision while preserving low kernel space complexity. Rather than incorporating sophisticated analysis (e.g., via new abstract domains) directly into the verifier, our key insight is to decouple the complex reasoning to user space while bridging the gap through formal proofs. Upon encountering uncertainties, the verifier initiates an abstraction refinement procedure rather than rejecting the extension. As the refinement involves nontrivial reasoning, the verifier simply delineates the task and delegates it to user space. A formal proof is produced externally, which the verifier subsequently checks in linear time before adopting the refined abstraction. Consequently, our approach achieves high precision via user space reasoning while confining kernel space operations to an efficient proof check. Evaluation results show that our technique enables the verifier to accept 403 out of 512 real-world eBPF programs that were previously rejected erroneously, paving the way for more reliable and flexible kernel extensions.

Fusing the camera and LiDAR information in the unified BEV representation serves as the elegant paradigm for the 3D detection tasks. Current multi-modal fusion methods in BEV can be categorized into LSS-based and Transformer-based in terms of their view transformation. The former leverages inaccurate depth prediction and massive pseudo points for perspective-to-BEV transformation while the latter only fetches sparse image features to the BEV representation. To overcome their shortcomings, an optimized view transformation is proposed, which can be easily modulated into the LSS-based methods. The proposed module capitalizes on the LSS mechanism to establish dense associations between perspective pixels and BEV grids. It utilizes the attention mechanism to compute similarity scores for each associated pair during feature aggregation. Starting from the BEVFusion baseline, we further introduce (1) cross-attention within the associated subsets to transfer image features into the BEV, and (2) a multi-scale feature fusion mechanism for LSS-based view transformation. Extensive experiments on nuScenes validate the effectiveness and efficiency of our proposed module, which achieves an increase of 1.3% in mAP compared to the baseline model.

In this paper, we present LiDAR-Net, a new real-scanned indoor point cloud dataset, containing nearly 3.6 billion precisely point-level annotated points, covering an expansive area of 30,000m(2). It encompasses three prevalent daily environments, including learning scenes, working scenes, and living scenes. LiDAR-Net is characterized by its non-uniform point distribution, e.g., scanning holes and scanning lines. Additionally, it meticulously records and annotates scanning anomalies, including reflection noise and ghost. These anomalies stem from specular reflections on glass or metal, as well as distortions due to moving persons. LiDAR-Net's realistic representation of non-uniform distribution and anomalies significantly enhances the training of deep learning models, leading to improved generalization in practical applications. We thoroughly evaluate the performance of state-of-the-art algorithms on LiDAR-Net and provide a detailed analysis of the results. Crucially, our research identifies several fundamental challenges in understanding indoor point clouds, contributing essential insights to future explorations in this field. Our dataset can be found online: http://lidar-net.njumeta.com.

This paper introduces state embedding, a novel and highly effective technique for validating the correctness of the eBPF verifier, a critical component for Linux kernel security. To check whether a program is safe to execute, the verifier must track over-approximated program states along each potential control-flow path; any concrete state not contained in the tracked approximation may invalidate the verifier's conclusion. Our key insight is that one can effectively detect logic bugs in the verifier by embedding a program with certain approximation-correctness checks expected to be validated by the verifier. Indeed, for a program deemed safe by the verifier, our approach embeds concrete states via eBPF program constructs as correctness checks. By construction, the resulting state-embedded program allows the verifier to validate whether the embedded concrete states are correctly approximated by itself; any validation failure therefore reveals a logic bug in the verifier. We realize state embedding as a practical tool and apply it to test the eBPF verifier. Our evaluation results highlight its effectiveness. Despite the extensive scrutiny and testing undertaken on the eBPF verifier, our approach, within one month, uncovered 15 previously unknown logic bugs, 10 of which have already been fixed. Many of the detected bugs are severe, e.g., two are exploitable and can lead to local privilege escalation.

LiDAR and camera are two essential sensors for 3D object detection in autonomous driving. LiDAR provides accurate and reliable 3D geometry information while the camera provides rich texture with color. Despite the increasing popularity of fusing these two complementary sensors, the challenge remains in how to effectively fuse 3D LiDAR point cloud with 2D camera images. Recent methods focus on point-level fusion which paints the LiDAR point cloud with camera features in the perspective view or bird's-eye view (BEV)-level fusion which unifies multi-modality features in the BEV representation. In this paper, we rethink these previous fusion strategies and analyze their information loss and influences on geometric and semantic features. We present SemanticBEVFusion to deeply fuse camera features with LiDAR features in a unified BEV representation while maintaining per-modality strengths for 3D object detection. Our method achieves state-of-the-art performance on the large-scale nuScenes dataset, especially for challenging distant objects. The code will be made publicly available.

Aiming to solve the problem of color distortion and loss of detail information in most dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature enhancement is proposed. Firstly, the feature extraction enhancement module is used to capture the detailed information of hazy images and expand the receptive field. Secondly, the channel attention mechanism and pixel attention mechanism of the feature fusion enhancement module are used to dynamically adjust the weights of different channels and pixels. Thirdly, the context enhancement module is used to enhance the context semantic information, suppress redundant information, and obtain the haze density image with higher detail. Finally, our method removes haze, preserves image color, and ensures image details. The proposed method achieved a PSNR score of 33.74, SSIM scores of 0.9843 and LPIPS distance of 0.0040 on the SOTS-outdoor dataset. Compared with representative dehazing methods, it demonstrates better dehazing performance and proves the advantages of the proposed method on synthetic hazy images. Combined with dehazing experiments on real hazy images, the results show that our method can effectively improve dehazing performance while preserving more image details and achieving color fidelity.

In order to eliminate the beam jitter caused by the air disturbance and the vibrating devices, the error of light spotted jitter must be detected firstly. The beam jitter error can be corrected by a compensator to realize the beam stability control system. In this paper, an Improved Centroid Algorithm (ICA) combining with the Gaussian Fitting Algorithm (GFA), the Bilinear Interpolation and the Weighted Center of Mass (WCOM) was proposed. A Gaussian template is used to roughly locate the spot image in order to improve the efficiency and stability of the algorithm. The light spot image is interpolated by the Bilinear Interpolation to improve the accuracy of centroid identification. The identification accuracy and efficiency of the algorithm were verified by 100 sets of light spot experiments. The experimental results showed that the identification accuracy and efficiency of the Improved Centroid Algorithm were higher than those of the other algorithms. Our algorithm was of great significance for improving the performance of the beam stability control system.

Voice disorders are one of the incipient symptoms of Parkinson's disease (PD). Recent studies have shown that approximately 90% of PD patients suffer from vocal disorders. Therefore, it is significant to extract pathological information on the voice signals to detect PD. In this paper, a feature, named energy direction features based on empirical mode decomposition (EDF-EMD), is proposed to show the different characteristics of voice signals between PD patients and healthy subjects. Firstly, the intrinsic mode functions (IMFs) were obtained through the decomposition of voice signals by EMD. Then, the EDF is obtained by calculating the directional derivatives of the energy spectrum of each IMFs. Finally, the performance of the proposed feature is verified on two different datasets: dataset-Sakar and dataset-CPPDD. The proposed approach shows the best average resulting accuracy of 96.54% on dataset-Sakar and 92.59% on dataset-CPPDD. The results demonstrate that the method proposed in this paper is promising in the field of PD detection.

Irrigated agriculture is the dominant user of world’s fresh water which feeds the world’s growing population. Conflicts between stakeholders; incompatibility of economic, social, and environmental development; and uncertainties in water supply and demand restrict the sustainable development of irrigated agriculture. This study developed a multi-scale multi-objective programming model for simultaneous optimal allocation of irrigation water and cropland to balance conflicts between farmers’ income and sustainable development of irrigation districts (reflected in economic, social, and environmental aspects). Consideration of the joint uncertainties of water supply and demand helps provide practical and indicative schemes for agricultural water and land allocation. The developed model was applied to a real case study in an irrigation district in northeast China. Farmers’ income, net economic benefit, resources allocation equity, and global warming potential were coordinated by optimally allocating limited water and cropland resources to different crops in different subareas under different combinational scenarios of water supply and demand. The performance of the model was evaluated, based on the concept of “adaptability” which can help realize the degree of ability of the irrigated agricultural system to adapt to changing environment. The developed model can help plan irrigation water and cropland resources in a sustainable way, and can be a reference for similar irrigation systems worldwide.

Graphene is a kind of two-dimensional lamellar nano-material with excellent barrier property, and it has important application in the field of anticorrosive coatings. The effect of graphene addition on the corrosion resistance of zinc-rich epoxy coating and glass flake epoxy coating was investigated by opencircuit potential(Eocp), AC impedance and corrosion morphology. The results showed that the radius of arc resistance of epoxy zinc-rich coating with graphene was smaller than that of epoxy zincrich coating without graphene. The arc resistance with epoxy glass flake coating decreased gradually in 528h. It showed from the corrosion morphology observation that the addition of graphene improved the corrosion resistance of the zinc-rich coating, but reduced the corrosion resistance of the epoxy glass flake coating.

In this paper, we present an automatic inspection system based on micro motion sensors for detecting the “looseness” of rail fasteners. The system is composed of a low-power MEMS accelerometer and a Global System for Mobile Communications (GSM) unit, and can detect loose fasteners and upload the results to a cloud server in real-time. Finite Element Method (FEM) is used to analyze rail vibration characteristics in the vertical direction as the rail is excited by mechanical pulse inputs. On this basis, the Chao-Shen Entropy theory was applied to identify fastener looseness reliably. In addition, field experiments were also conducted on Datong-Qinhuangdao Railway, the longest coal-transport railway in the world. The experimental results show that fastener looseness can slow down the attenuation of rail vibration in the time domain and produce a large entropy value. Using the method of amplitude entropy, loose fasteners can be identified reliably for looseness factor >60%. The proposed system has been experimental validated to enable real-time detection of railway fastener looseness and could potentially bring significant benefits for the day-to-day maintenance of railways.

冷轧轧制力预报结果直接影响板(带)材轧制精度和产品质量.冷轧工艺复杂,参数耦合性强,模型不易建立且与实际偏差较大,针对这些问题,提出一种改进在线序列极限学习机.在初始训练阶段使用量子粒子群算法优化权值和阈值;在线训练阶段根据当前训练数据中隐含层对网络输出的贡献度调节网络的拓扑结构,实现了结构和参数的自组织,并结合极限学习机变形抗力子模型在线预报轧制力.实验结果表明,该自组织在线序列极限学习机在训练速度和精度方面较之人工蜂群优化的反向传播神经网络和基于增强型增量极限学习机有较大的提高.

A high-performance single-pole single-throw (SPST) RF switch for mobile phone RF front-end modules (FEMs) was designed and characterized in a 0.13 μm partially depleted silicon-on-insulator (PD SOI) process. In this paper, the traditional series-shunt configuration design was improved by introducing a suitably large DC bias resistor and leakage-preventing PMOS, together with the floating body technique. The performance of the RF switch is greatly improved. Furthermore, a new Ron × Coff testing method is also proposed. The size of this SPST RF switch is 0.2 mm2. This switch can be widely used for present 4G and forthcoming 5G mobile phone FEMs.

为研究轧机垂直振动系统过程中辊缝间摩擦及张力对冷轧机非线性振动特性的影响,在考虑辊缝摩擦及张力作用的基础上建立了四自由度非线性垂直振动模型.采用时滞反馈控制与多尺度法结合求解系统主共振幅频特性方程,运用奇点稳定性理论对系统稳定性进行分析.仿真分析了辊缝摩擦及张力对振动幅值的影响,结果表明辊缝摩擦和张力对轧机垂直振动有很大影响,并且通过适当调节时滞参数可以消除振动系统的跳跃现象.由稳定性分析得到了辊缝摩擦和张力对系统稳定性影响关系及出现各种不同奇点的条件.

Allocation and management of agricultural water resources is an emerging concern due to diminishing water supplies and increasing water demands. To achieve economic, social, and environmental goals in a specific irrigation district, decisions should be made subject to the changing water supply and water demandthe two critical random parameters in agricultural water resources management. This paper presents the foundations of a systematic framework for agricultural water resources management, including determination of distribution functions, joint probability of water supply and water demand, optimal allocation of agricultural water resources, and evaluation of various schemes according to agricultural water resources carrying capacity. The maximum entropy method is used to estimate parameters of probability distributions of water supply and demand, which is the basic for the other parts of the framework. The entropy-weight-based TOPSIS method is applied to evaluate agricultural water resources allocation schemes, because it avoids the subjectivity of weight determination and reflects the dynamic changing trend of agricultural water resources carrying capacity. A case study using an irrigation district in Northeast China is used to demonstrate the feasibility and applicability of the framework. It is found that the framework works effectively to balance multiple objectives and provides alternative schemes, considering the combinatorial variety of water supply and water demand, which are conducive to agricultural water resources planning.