Efficient Modulation Research Articles

Simulation and experimental study of grid-connected photovoltaic power plants in a hot desert climate with fixed and dual-axis tracking

ABSTRACT This paper presents a comprehensive investigation into the performance of grid-connected photovoltaic (PV) power plants situated in a hot desert climate. The study employs a combination of simulation models and experimental data to assess the impact of two tracking systems – fixed and dual-axis – on the overall efficiency and energy yield of the PV installations. Through detailed simulations, we analyze the dynamic behavior of the PV systems under varying solar irradiance and the atmospheric conditions typical of hot desert environments. The dual-axis tracking system is compared against the conventional fixed-mount system to evaluate its effectiveness in maximizing energy production and improving the overall efficiency of the PV power plants. Experimental data collected from an actual grid-connected PV facility in a hot desert region is incorporated to validate the simulation results and enhance the accuracy of the study. The observed performance metrics include energy output, system reliability, and the impact of environmental factors on long-term sustainability. In general, in all weather conditions, solar tracking provides better yields than a fixed solar module, generating about 32% and 12% more energy in sunny and overcast weather conditions, respectively. Under sunny weather, the two-axis sun tracking system and a fixed tilt angle produce 333.228 kWh and 247.56 kWh of energy, respectively. The energy yield generated on a partly cloudy day by a two-axis sun tracking system and a fixed tilt angle PV system is 281.496 kWh and 257.856 kWh, respectively. In addition, the highest electricity gain for two-axis solar trackers versus fixed PV plants was found to be 34.6%, and the lowest value was found to be 9.16%. The experimental analysis shows that the efficiency of the polycrystalline PV module is 12% and 14% on sunny days and cloudy days, respectively. The comparison between fixed and dual-axis tracking systems contributes to the optimization of PV installations, enabling more informed decisions for sustainable energy production in regions characterized by high temperatures and intense solar radiation. To support the expanding applications of grid-tied PV systems with a sun tracker in the same or comparable location and climate conditions (Saharan environment), these studies have the practical utility of advising and offering helpful feedback to engineers, system designers, contractors, and investors.

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method’s effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

3D Conjugated Hole Transporting Materials for Efficient and Stable Perovskite Solar Cells and Modules.

The orthogonal structure of the widely used hole transporting material (HTM) Spiro-OMeTAD imparts isotropic conductivity and excellent film-forming capability. However, inherently weak intra- and inter-molecular π-π interactions result in low intrinsic hole mobility. Herein, a novel arylamine derivative, termed FTPE-ST, with a twist conjugated dibenzo[g,p]chrysene core and coplanar 3,4-ethylenedioxythiophene (EDOT) as extended donor units, was designed to enhance intra- and inter-molecular π-π interactions, without compromising on solubility. The 3D configuration provides the material multi-direction charge transport as well as excellent solubility even in 2-methylanisole (2-MA), and its large conjugated delocalization backbone endows the HTM with a high hole mobility (7.2 × 10-4 cm2V-1s-1). Moreover, the sulfur donors in the EDOT units coordinate to lead ions on the perovskite surface, leading to stronger interfacial interactions and the suppression of defects at the perovskite/HTM interface. As a result, perovskite solar cells (PSCs) employing FTPE-ST as the HTM achieve a champion power conversion efficiency (PCE) of 25.21% with excellent long-time stability, which is one of the highest PCEs for non-spiro HTMs in n-i-p PSCs. In addition, the excellent film-forming capacity of the HTM enables the fabrication of FTPE-ST-based large-scale PSCs (1.0 cm2) and modules (29.0 cm2), which achieve PCEs of 24.21% (certificated 24.17%) and 21.27%, respectively. This article is protected by copyright. All rights reserved.

A More Efficient Algorithm for Small Target Detection in Unmanned Aerial Vehicles

Due to the relatively high shooting altitude of unmanned aerial vehicles (UAV), the captured images often contain a multitude of small‐scale targets. To solve the problems of small target scale, lack of semantic information, and high miss detection in drone target detection, in this paper we proposed a more effective unmanned aerial vehicle small target detection algorithm(MEU‐YOLOv5) based on YOLOv5s. Firstly, an efficient global contextual module is proposed to enhance the algorithm's performance in feature extraction while reducing the excessive loss of shallow features. Secondly, a small‐scale target detector is added to enhance the algorithm's detection capability for smaller targets. Lastly, a recursive multi‐level feature fusion path is introduced to better fuse the shallow and deep features of the images, reducing overfitting and improving the algorithm's generalizability and robustness. Experimental results demonstrated that compared to YOLOv5s, MEU‐YOLOv5 achieves a 7.4% improvement in mAP@0.5 and a 4.9% improvement in mAP@0.5:0.95. Additionally, the overall performance of this algorithm surpassed various algorithms in the YOLO series, including YOLOv3, YOLOv5l, YOLOv5m, and YOLOv8s. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

MUNet++: Multilevel wavelet nested UNet++ demoiréing residual network

When mobile phones and digital cameras are used to capture information on a screen or store scenes with rich textures, an unpleasant moiré phenomenon occurs, which seriously degrades the quality of the image and affects subsequent image processing tasks. Therefore, to remove the moiré patterns, we propose a multilevel wavelet nested UNet++ demoiréing residual network called MUNet++. Unlike these methods based on the RGB domain, our method is performed in the frequency domain. The multilevel discrete wavelet transform (DWT) and the inverse discrete wavelet transform (IDWT) are elegantly embedded into the UNet++ encoder-decoder structure and transformed to the multilevel frequency domain, which captures more moiré features and preserves details of the image. In addition, we design a dual branch efficient pixel fusion attention module (DBEPFAM) that combines multiscale information, channel attention and the proposed pixel attention fusion module (PAFM) to improve the representation of features. In the network layer, a simple nonlinear unit (NU) is designed and embedded in the middle of the network layer to improve the representation capability of the network. Experimental results on two public datasets show that MUNet++ can effectively remove the moiré patterns and outperform existing state-of-the-art techniques.

Excitation of symmetry-protected vertical modes through arrays of coaxial sub-λ apertures for THz applications

Abstract We numerically study the excitation of a Symmetry-Protected Vertical Mode (SPVM) within a specific periodic structure of coaxial apertures made in gold. The geometrical parameters are chosen to operate in the THz domain. Contrarily to classical SP modes that correspond to a mode propagating in the direction of periodicity of the structure, here the SP mode consists on the Transverse Electro Magnetic (TEM) guided mode that propagates vertically inside the coaxial aperture along the metal thickness. This feature makes the spectral properties of the excited mode less-sensitive to the spatial extension of the grating allowing thus the design of less-cumbersome devices. We demonstrate that the spectrally localized resonance corresponding to this mode is particularly sensitive to mechanical perturbation (acoustic wave for instance) of the geometry, enabling the design of highly efficient THz modulators operating at λ ≈ 300 μm with a sensitivity as great as 12.4 μm/◦ of the tilt angle of its metal core.

Cycle-Star Motifs: Network Response to Link Modifications

Understanding efficient modifications to improve network functionality is a fundamental problem of scientific and industrial interest. We study the response of network dynamics against link modifications on a weakly connected directed graph consisting of two strongly connected components: an undirected star and an undirected cycle. We assume that there are directed edges starting from the cycle and ending at the star (master–slave formalism). We modify the graph by adding directed edges of arbitrarily large weights starting from the star and ending at the cycle (opposite direction of the cutset). We provide criteria (based on the sizes of the star and cycle, the coupling structure, and the weights of cutset and modification edges) that determine how the modification affects the spectral gap of the Laplacian matrix. We apply our approach to understand the modifications that either enhance or hinder synchronization in networks of chaotic Lorenz systems as well as Rössler. Our results show that the hindrance of collective dynamics due to link additions is not atypical as previously anticipated by modification analysis and thus allows for better control of collective properties.

Interfacial competing interaction eliminates voids in the buried interface for efficient and stable perovskite solar modules on bare NiOX

Perovskite solar cells (PSCs) with impressive efficiency is promising for commercialization. However, when scaling up PSCs to large-area perovskite solar modules (PSMs), the rapid degradation of performance and stability still remains a grand challenge. One of the main causes of the degradation is the presence of local voids in the buried interface for large-area films, which we find could be attributed to the unbalanced interfacial interaction between precursor solvent and substrate. Here we improve a scalable and low-temperature sequential two-step chemical bath deposition process aiming for high quality NiOX. Further with the modulation of A-site cations to enhance the interaction between DMSO and perovskite precursor, the potential for DMSO absorbing on the NiOX could be eliminated. This strategy can inhibit the delay movement of DMSO on NiOX and improve the buried interfacial quality, with compact morphology and absent of voids. The inverted structured PSCs and PSMs based on the bare NiOX and modified perovskite precursor reach a champion efficiency of 22.40% and 19.09%, respectively, which are both among the highest reported values. The highly efficient PSMs also exhibit a long-term operational stability, which retain 84% of the initial performance after 1056 h operating under one-sun illumination.

An Assessment of a Photovoltaic System’s Performance Based on the Measurements of Electric Parameters under Changing External Conditions

This article presents an analysis of the performance of a 14.04 kWp grid-connected photovoltaic (PV) installation consisting of monocrystalline silicon, polycrystalline silicon and bifacial glass–glass monocrystalline silicon modules. The photovoltaic system was mounted in Poland, a location characterized by temperate climate conditions. On the basis of the obtained results, the photovoltaic parameters were determined in accordance with the international standard. The annual energy yield of the entire system was 1033 kWh/kWp, and the performance ratio achieved was 83%. The highest average daily final yield was in the range of 4.0–4.5 kWh/kWp for each photovoltaic technology under investigation. In the cold part of the year, the efficiency of the photovoltaic modules was estimated to be 15%, and it was estimated to be 7% during the warm part of the year. Array capture losses accounted for around 0.75 kWh/kWp of energy loss per day, whereas the inverter efficiency was over 95% during the months that are beneficial for energy production.

High-speed mid-infrared silicon-based electro-optic modulator at 2 μm

We demonstrate the Mach-Zehnder interferometer (MZI) silicon-based electro-optic modulators in the short-wave mid-infrared 2 μm band. The waveguide structure and doping position of the devices are optimized by simulation. The devices are manufactured on a 220 nm silicon-on-insulator (SOI) platform, and are measured through self-built short-wave mid-infrared measurement platform. The device with the length of 3.5 mm has a measured modulation efficiency of 1.853 V cm at 3 V reverse bias. The highspeed characterization is also performed, and the device with the length of 3.5 mm has a data rate of over 20 Gb/s at the voltage swing of 2.8 Vpp with on-off keying (OOK) modulation formats. The modulation speed of the 2 mm device can reach over 40 Gb/s with 4-level pulse amplitude modulation (PAM-4) formats at a voltage swing of 2.5 Vpp.

Tin Oxide Bilayer as Effective Electron Transport Layers for Efficient and Stable Perovskite Solar Modules

Tin oxide (SnO2) is one of the most promising electron transport layers (ETL) for the commercialization of perovskite solar cells (PSCs) due to its excellent electron mobility and high transparency, along with its low processing temperature. However, the inherent defects, nonuniform coating, and poor surface morphology of SnO2 may be detrimental to the physicochemical properties, such as conductivity and electron mobility at the interface, lead to a decrease in the open‐circuit voltage (VOC) and a reduction in device stability. In this study, a method that combines atomic layer deposition and chemical bath deposition techniques to solve these issues is presented. The presence of bilayer ETLs enhances the coverage of the SnO2 film and optimizes the morphology of the buried surface of perovskite, which not only facilitates the interfacial charge transfer but also suppresses recombination reactions. As a result, a significant increase in VOC and efficiency has been achieved compared to devices with only a single layer. Additionally, the large‐area perovskite solar module (active area: 48.0 cm2) achieves a champion efficiency of 17.9%. The PSCs retain more than 93% of their initial efficiency after 700 h of continuous operation under 1‐sun illumination and 25 °C.

SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion

The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightweight and efficient feature extraction module (LEM) for multi-scale feature extraction, enhancing the capability to extract blurry features. Simultaneously, we adopt spatial and channel reconstruction convolution (ScConv) to reconstruct the spatial and channel features of the feature maps, enhancing the spatial localization and semantic representation of defects. Additionally, we adopt the Weighted Bidirectional Feature Pyramid Network (BiFPN) for defect feature fusion, thereby enhancing the capability of the model in detecting low-contrast defects. Finally, we discuss the impact of different data augmentation methods on the model accuracy. Extensive experiments are conducted on the NEU-DET dataset, resulting in a final model achieving an mAP of 81.2%. Remarkably, this model only required 2.01 M parameters and 5.9 GFLOPs of computation. Compared to state-of-the-art object detection algorithms, our approach achieves a higher detection accuracy while requiring fewer computational resources, effectively balancing the model size and detection accuracy.

Mechanics Manipulation in Large-area Organic Solar Modules Achieving Over 16.5% Efficiency

High-efficiency organic solar cells (OSCs) are typically produced through spin-coating, restricting their application to small areas. Blade-coating, however, emerging as a promising method for large-scale production, yet faces challenges in film morphology optimization, which often leads to reduced power conversion efficiency (PCE). This study delves into the influence of both liquid and solid additives on the morphology of active layer in blade-coated OSCs, comparing them with spin-coated counterparts, using the high-efficiency PM6:D18:BTP-eC9 active layer. For the first time, we discovered the distinct impacts of solid versus liquid additives on the film uniformity, phase separation and crystalline regulation in blade-coating technique. Our findings reveal that liquid additives in blade-coating trigger outward Marangoni flow, causing undesirable material aggregation and phase separation, thereby impairing device performances. Conversely, switching to solid additives, like 1,4-Diiodobenzene (DIB), prevents these detrimental changes in fluid mechanics and preserves the desired additive effects. We demonstrate that solid additives can significantly change these inferior behaviors introduced by liquid additives in blade-coating, regulate phase separation, enhance π-π accumulation and delay crystallization, and ultimately boost OSC efficiency. Using DIB solid additive, we achieved a PCE of 18.81% in blade-coated devices. Scaling up by 252 times, the PCE of large-area OSC module (15.64 cm²) sustained at 16.70% (certified 16.66%), ranking among the highest efficiency for OSC modules reported so far. These modules also exhibited exceptional storage stability, retaining 98% efficiency after 5880 hours in a nitrogen atmosphere. This research also provides a comprehensive understanding from various film characterizations and the perspective of fluid mechanics normally lack in the research. This research not only establishes a new framework for high-performance and large-area OSC modules but also extends its findings to other OSC systems with different additives, demonstrating a roll-to-roll compatible technique.

Design and development of efficient and compact 20 kW 325MHz solid-state amplifier for superconducting accelerator applications.

An efficient and compact, 20 kW solid-state power amplifier (SSA) at 325MHz has been designed and developed in-house, using single stage combining. It comprises of 24 nos. of 1 kW power amplifier (PA) modules, a 24-way Wilkinson power combiner and divider, and other peripheral systems. The typical gain and conversion efficiency of the PA modules at 1.0 kW output is 21.7 dB and 66.6%, respectively. It is demonstrated that overall power gain and AC to RF efficiency of this SSA at 20 kW is 88.5 dB and 54.8%, respectively, which matches closely with the design estimates. The harmonic content in the RF output is < -40 dBc for all the harmonics. The results of the Monte Carlo simulation are also presented, showing lower bound on combining efficiency with a degree of confidence if magnitude and phase data for 24 inputs are randomly chosen from a normal distribution's pre-defined interval. The salient features of this SSA include power density of 12.7 kW/m3, AC to RF efficiency of 54.8% at 20 kW, and guaranteed output of 20 kW with one failed PA module and 18.1 kW under two failed PA modules condition.

A Cascaded GRU-Based Stereoscopic Matching Network for Precise Plank Measurement

Abstract Wooden plank images in industrial measurements often contain numerous textureless areas. Furthermore, due to the thin plate structure, the three-dimensional disparity of these planks is predominantly confined to a narrow range. Consequently, achieving accurate three-dimensional matching of wooden plank images has consistently presented a challenging task within the industry. In recent years, deep learning has progressively supplanted traditional stereo matching methods due to its inherent advantages, including rapid inference and end-to-end processing. Nonetheless, the acquisition of datasets for stereo matching networks poses an additional challenge, primarily attributable to the difficulty in obtaining accurate disparity data. Thus, this paper presents a novel stereo matching method incorporating three key innovations. Firstly, an enhanced Gated Recurrent Unit(GRU) network is introduced, accompanied by a redesigned structure to achieve higher matching accuracy. Secondly, an efficient preprocessing module is proposed, aimed at improving the algorithm's efficiency. Lastly, in response to the challenges posed by datasets acquisition, we innovatively employed image simulation software to obtain a high-quality simulated dataset of wooden planks. To assess the feasibility of our approach, we conducted both simulated and real experiments. The experiments results clearly exhibit the superiority of our method when compared to existing approaches in terms of both stability and accuracy. In the simulation experiment, our method attained a bad1.0 score of 2.1% (compared to the baseline method's 9.76%); In the real experiment, our method achieved an average error of 0.104mm (compared to the baseline method's 0.268mm). It is worth noting that our study aims to address the challenge of acquiring datasets for deep learning and bridging the gap between simulated and real data, resulting in increased applicability of deep learning in more industrial measurement domains.

Ultrafast modulable 2DEG Huygens metasurface

Huygens metasurfaces have demonstrated remarkable potential in perfect transmission and precise wavefront modulation through the synergistic integration of electric resonance and magnetic resonance. However, prevailing active or reconfigurable Huygens metasurfaces, based on all-optical systems, encounter formidable challenges associated with the intricate control of bulk dielectric using laser equipment and the presence of residual thermal effects, leading to limitations in continuous modulation speeds. Here, we present an ultrafast electrically driven terahertz Huygens metasurface that comprises an artificial microstructure layer featuring a two-dimensional electron gas (2DEG) provided by an AlGaN/GaN heterojunction, as well as a passive microstructure layer. Through precise manipulation of the carrier concentration within the 2DEG layer, we effectively govern the current distribution on the metasurfaces, inducing variations in electromagnetic resonance modes to modulate terahertz waves. This modulation mechanism achieves high efficiency and contrast for terahertz wave manipulation. Experimental investigations demonstrate continuous modulation capabilities of up to 6 GHz, a modulation efficiency of 90%, a transmission of 91%, and a remarkable relative operating bandwidth of 55.5%. These significant advancements substantially enhance the performance of terahertz metasurface modulators. Importantly, our work not only enables efficient amplitude modulation but also introduces an approach for the development of high-speed and efficient intelligent transmissive metasurfaces.

GD-YOLO: An improved convolutional neural network architecture for real-time detection of smoking and phone use behaviors

Chemical safety accidents cause significant socio-economic and environmental hazards, and safety accidents caused by unsafe worker behaviors are preventable. Detection of workers' smoking and phone use behaviors in chemical parks can avoid such safety accidents from the root. Aiming at the issues of reduced accuracy in detecting small objects and low arithmetic capability within industrial equipment. This paper proposes a GD-YOLO network based on YOLOv7 for detecting smoking and phone use behaviors. First, an efficient feature extraction module, D-LAN, is designed to solve the problem of difficult extraction of small object features. Second, a lightweight G-LAN module is proposed to reduce the complexity of the model. Third, WIoU and Mish are introduced to improve the model's performance further, creating a more efficient model. The experimental results show that the mean average precision (mAP) and the number of computational parameters increased by 16.80% and decreased by 21.54%, respectively, compared with YOLOv7. GD-YOLO performs better than other existing mainstream methods in precision and complexity.

A novel non-pretrained deep supervision network for polyp segmentation

In this paper, we propose a non-pretrained deep supervision network (NPD-Net) for polyp segmentation. Unlike previous deep supervision networks that rely on ground truth (GT) or pre-training with GT to supervise deep features(the prediction maps from decoder), we propose a novel deep supervision strategy that directly utilizes the GT encoder (that encodes GT to get its maps) after initialization to mitigate overfitting and enhance generalization ability without pre-training, in other words, a non-pretrained. This strategy makes up the gap of directly using GT for deep supervision while mitigates the risk of overfitting due to leverage the well-train pre-trained weights on a small polyp datasets. In addition, we introduce a simple and efficient parallel dual attention module (PDA) to enhance the global modeling ability. PDA executes spatial and channel attention in parallel, and adopts implicit positional encoding and transpose operation to reduce computational complexity. Finally, NPD-Net is able to effectively supervise deep features, expand the range of context information acquisition and improve segmentation performance, particularly in terms of generalization ability. Our experimental results on five benchmark datasets demonstrate that NPD-Net outperforms other state-of-the-art methods.

LIT-Former: Linking In-Plane and Through-Plane Transformers for Simultaneous CT Image Denoising and Deblurring.

This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perform denoising due to low-dose and deblurring for super-resolution separately. Up to date, little work was done for simultaneous in-plane denoising and through-plane deblurring, which is important to obtain high-quality 3D CT images with lower radiation and faster imaging speed. For this task, a straightforward method is to directly train an end-to-end 3D network. However, it demands much more training data and expensive computational costs. Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks. LIT-Former has two novel designs: efficient multi-head self-attention modules (eMSM) and efficient convolutional feed-forward networks (eCFN). First, eMSM integrates in-plane 2D self-attention and through-plane 1D self-attention to efficiently capture global interactions of 3D self-attention, the core unit of transformer networks. Second, eCFN integrates 2D convolution and 1D convolution to extract local information of 3D convolution in the same fashion. As a result, the proposed LIT-Former synergizes these two sub-tasks, significantly reducing the computational complexity as compared to 3D counterparts and enabling rapid convergence. Extensive experimental results on simulated and clinical datasets demonstrate superior performance over state-of-the-art models. The source code is made available at https://github.com/hao1635/LIT-Former.

Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks.

With the rapid advances in autonomous driving, it becomes critical to equip its sensing system with more holistic 3D perception. However, widely explored tasks like 3D detection or point cloud semantic segmentation focus on parsing either the objects or scenes. In this work, we propose to address the challenging task of LiDAR-based Panoptic Segmentation, which aims to parse both objects and scenes in a unified manner. In particular, we propose Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm. DS-Net features a dynamic shifting module for complex LiDAR point cloud distributions. We present an efficient learnable clustering module, dynamic shifting, which adapts kernel functions for different instances. To further explore the temporal information, we extend the single-scan processing framework to its temporal version, 4D-DS-Net, for the task of 4D Panoptic Segmentation, where the same instance across multiple frames should be given the same ID prediction. Instead of naively appending a tracking module to DS-Net, we propose to solve the 4D panoptic segmentation in a more unified way. Specifically, 4D-DS-Net first constructs 4D data volume by aligning consecutive LiDAR scans, upon which the temporally unified instance clustering is performed to obtain the final results. Extensive experiments on two large-scale autonomous driving LiDAR datasets, SemanticKITTI and Panoptic nuScenes, are conducted to demonstrate the effectiveness and superior performance of the proposed solution.