To further reduce the carbon emission of the microgrid system under the background of"dual carbon,"a low-carbon operation strategy of multi-microgrid integrated energy system is proposed,which accounts for the coordinated charging of electric vehicles(EVs)and the"reward and punishment"-type stepped carbon-trading mechanism.First,a dynamic urgency index(DUI)is proposed to reflect the charging demand of the user and a coordinated EV-charging model is established to minimize the peak-to-valley difference of the total electric load in the microgrid.Subsequently,a two-stage power-to-gas(P2G),distributed generation(DG)and combined heat and power unit(CHP)cooperative operation model is constructed.Finally,considering the electrothermal interactions in the multi-microgrid integrated energy system,a ladder-type carbon-price mechanism with reward and punishment(LCPMRP)is implemented to constrain the carbon emission of the system,thus further improving the low-carbon economy of the system.Simulation results show that the LCPMRP can improve the low-carbon economic operation of the system when considering the interaction between electric energy and heat energy in a multi-microgrid.The proposed DUI is used to schedule EVs,which satisfies the charging demand of EV owners and reduces the peak-valley difference of the total electric load in the microgrid.The introduction of the two-stage P2G,DG,and CHP cooperative operation model reduces carbon emission in the microgrid and weakens the thermoelectric coupling relationship of the CHP system.

Recent deblurring networks have effectively restored clear images from the blurred ones. However, they often struggle with generalization to unknown domains. Moreover, these models typically focus on distortion metrics such as PSNR and SSIM, neglecting the critical aspect of metrics aligned with human perception. To address these limitations, we propose DeblurDiNAT, a deblurring Transformer based on Dilated Neighborhood Attention. First, DeblurDiNAT employs an alternating dilation factor paradigm to capture both local and global blurred patterns, enhancing generalization and perceptual clarity. Second, a local cross-channel learner aids the Transformer block to understand the short-range relationships between adjacent channels. Additionally, we present a linear feed-forward network with a simple while effective design. Finally, a dual-stage feature fusion module is introduced as an alternative to the existing approach, which efficiently process multi-scale visual information across network levels. Compared to state-of-the-art models, our compact DeblurDiNAT demonstrates superior generalization capabilities and achieves remarkable performance in perceptual metrics, while maintaining a favorable model size.

The integration of renewable energy into the grid significantly influences the spatial and temporal distribution characteristics of system frequency response. Existing fixed partitioning methods struggle to maintain the similarity of frequency responses across different time periods， while real-time partitioning methods often encounter challenges such as partitioning errors and excessive computational demands. To address these issues， this paper introduces a trend and value approximation method to measure the distance between frequency curves. This approach is compared against dynamic time warping， Euclidean distance， and symbolic aggregate approximation distance. Hierarchical clustering and k-means clustering algorithms are then employed to achieve real-time system partitioning. The number of partitions is evaluated using three indices： the silhouette-coefficient （S-C） index， Calinski-Harabas （C-H） index， and Davies-Bouldin （D-B） index. These four distance metrics， two clustering methods， and three evaluation indices are combined to form 24 real-time partitioning models. The optimal model is identified through comparative analysis， which involves calculating trend and value approximation distances， clustering via the hierarchical clustering algorithm， and determining the number of partitions based on the peak S-C index. Finally， simulations are performed in the IEEE 39-bus system， IEEE 118-bus system， and actual power system. The results demonstrate that the proposed real-time partitioning method dynamically adjusts partitioning results in response to different disturbances， locations， and time periods. It ensures similarity in regional frequency responses， enhances the accuracy and speed of system partitioning， and performs effectively in evaluating regional inertia.

Restoring 3D scenes captured under low-light con- ditions remains a fundamental yet challenging problem. Most existing approaches depend on precomputed camera poses and scene-specific optimization, which greatly restricts their scala- bility to dynamic real-world environments. To overcome these limitations, we introduce Lumos3D, a generalizable pose-free framework for 3D low-light scene restoration. Trained once on a single dataset, Lumos3D performs inference in a purely feed- forward manner, directly restoring illumination and structure from unposed, low-light multi-view images without any per- scene training or optimization. Built upon a geometry-grounded backbone, Lumos3D reconstructs a normal-light 3D Gaussian representation that restores illumination while faithfully pre- serving structural details. During training, a cross-illumination distillation scheme is employed, where the teacher network is distilled on normal-light ground truth to transfer accurate geometric information, such as depth, to the student model. A dedicated Lumos loss is further introduced to promote photomet- ric consistency within the reconstructed 3D space. Experiments on real-world datasets demonstrate that Lumos3D achieves high- fidelity low-light 3D scene restoration with accurate geometry and strong generalization to unseen cases. Furthermore, the framework naturally extends to handle over-exposure correction, highlighting its versatility for diverse lighting restoration tasks.

Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods process visual data by leveraging a flatten-and-scan strategy that converts image patches into a 1D sequence before scanning. However, this scanning paradigm ignores local pixel dependencies and introduces spatial misalignment by positioning distant pixels incorrectly adjacent, which reduces local noise-awareness and degrades image sharpness in low-level vision tasks. To overcome these issues, we propose a novel slice-and-scan strategy that alternates scanning along intra- and inter-slices. We further design a new Vision State Space Module (VSSM) for image deblurring, and tackle the inefficiency challenges of the current Mamba-based vision module. Building upon this, we develop XYScanNet, an SSM architecture integrated with a lightweight feature fusion module for enhanced image deblurring. XYScanNet, maintains competitive distortion metrics and significantly improves perceptual performance. Experimental results show that XYScanNet enhances KID by 17% compared to the nearest competitor.

Transformers, with their self-attention mechanisms for modeling long-range dependencies, have become a dominant paradigm in image restoration tasks. However, the high computational cost of self-attention limits scalability to high-resolution images, making efficiency-quality trade-offs a key research focus. To address this, Restormer employs channel-wise self-attention, which computes attention across channels instead of spatial dimensions. While effective, this approach may overlook localized artifacts that are crucial for high-quality image restoration. To bridge this gap, we explore Dilated Neighborhood Attention (DiNA) as a promising alternative, inspired by its success in high-level vision tasks. DiNA balances global context and local precision by integrating sliding-window attention with mixed dilation factors, effectively expanding the receptive field without excessive overhead. However, our preliminary experiments indicate that directly applying this global-local design to the classic deblurring task hinders accurate visual restoration, primarily due to the constrained global context understanding within local attention. To address this, we introduce a channel-aware module that complements local attention, effectively integrating global context without sacrificing pixel-level precision. The proposed DiNAT-IR, a Transformer-based architecture specifically designed for image restoration, achieves competitive results across multiple benchmarks, offering a high-quality solution for diverse low-level computer vision problems.

Blurry images may contain local and global non-uniform artifacts, which complicate the deblurring process and make it more challenging to achieve satisfactory results. Recently, Transformers generate improved deblurring outcomes than existing CNN architectures. However, the large model size and long inference time are still two bothersome issues which have not been fully explored. To this end, we propose DeblurDiNAT, a compact encoder-decoder Transformer which efficiently restores clean images from real-world blurry ones. We adopt an alternating dilation factor structure with the aim of global-local feature learning. Also, we observe that simply using self-attention layers in networks does not always produce good deblurred results. To solve this problem, we propose a channel modulation self-attention (CMSA) block, where a cross-channel learner (CCL) is utilized to capture channel relationships. In addition, we present a divide and multiply feed-forward network (DMFN) allowing fast feature propagation. Moreover, we design a lightweight gated feature fusion (LGFF) module, which performs controlled feature merging. Comprehensive experimental results show that the proposed model, named DeblurDiNAT, provides a favorable performance boost without introducing noticeable computational costs over the baseline, and achieves state-of-the-art (SOTA) performance on several image deblurring datasets. Compared to nearest competitors, our space-efficient and time-saving method demonstrates a stronger generalization ability with 3%-68% fewer parameters and produces deblurred images that are visually closer to the ground truth.

Uncertainty and volatility are the features of distributed generation (DG), which can impact greatly on the operational status of distribution network. Thus, an improved three-point estimation method and maximum entropy theory (ITPEM&ME) for calculating the probabilistic harmonic power flow in distribution network is proposed in this study. Firstly, the probabilistic model is constructed according to the uncertainty of input variables and the improved three-point estimate method is utilized to convert a probabilistic problem into a deterministic problem. Secondly, in order to obtain the distribution of the random harmonics in distribution network, the maximum entropy theory is adopted to reconstruct the statistical characteristics after the deterministic calculation is realized for each harmonic voltages. The case of an IEEE-33 system under various conditions is conducted. The results by the different algorithms indicate that the proposed method ITPEM&ME has a smaller error than the traditional method, as well as the fitting effect of the probability density function (PDF) is much closer to the practical engineering. It is feasible and accurate in dealing with probabilistic harmonic power flow problems.

在多微网系统中考虑用户的需求响应行为和加入共享储能装置均会对系统内的能量流动及设备出力情况造成影响.在此背景下,为促进储能装置的高效利用和可再生能源的就地消纳,提出一种同时计及耗能用户需求响应和共享储能的多微网系统双层优化调度策略.对共享储能运行模式、多微网系统和耗能用户自主响应行为进行建模;分别以多微网系统净收益最大、耗能用户总购能成本最低为上、下层目标,形成合作型Stackelberg互动均衡模型;将下层模型转换成KKT最优条件,随后用Big-M法和对偶定理对非线性项进行处理,将Stackelberg博弈模型转换为混合整数线性规划问题,对多微网系统的能源定价策略、共享储能动态容量划分和各微网内设备的运行状态进行求解.为促进微网间的功率交互,提出基于交互贡献度的利润分配方案.最后,采用5个方案作为算例验证了所提策略的有效性.

由于风力机组和光伏电池等分布式电源具有间歇性和不确定性等特点,提出一种改进三点估计结合最大熵理论(ITPEM&ME)的方法,用于计算配电网中的概率谐波潮流.首先,根据输入变量的不确定性构造改进三点估计的概率模型.其次,结合最大熵理论对其统计特征进行重构,得到配电网的谐波分布情况.最后,在IEEE 33节点系统上进行仿真验证,结果表明改进后三点估计法的误差低于传统的点估计法和蒙特卡洛法,且概率密度函数的拟合效果更接近工程实际,验证了所提方法在处理概率谐波潮流问题时的可行性和精确性.

Mobility in wireless sensor networks may interrupt a link established between two nodes. Since most medium access control protocols do not accommodate mobility, a node has two options to tackle a deteriorating link: (option A) to maintain data transmission until the connection breaks and then establish a new link with a new relay node; and (option B) to seamlessly transfer the communication to a more stable link in parallel to the data transmission over the existing link. To realize option B, this paper proposes a particle swarm optimization -enabled seamless handoff mechanism, which allows a sensor node to handle the transmission according to the decay in the quality of a link. To prove the optimization effect of the handoff, this paper designs a static receiver-triggered protocol, investigates the effect of mobility on the latency of the protocol, defines a criterion to initiate the handoff, estimates the real-time position of nodes by designing the particle swarm optimization method, and develops a mobility-oriented seamless handoff module on the static protocol. The latency for the transmission of a burst of data for both options is compared using NS2. The simulation result shows that the time demands to set up a new connection is larger than the time demands to handoff. The latency for a signal -hop communication when the handoff is used can be reduced by 42.42% at the lowest compared with the case when it is not used. This number can even be greater as the duty cycle, the number of hops between nodes, and the network density increase.

由于风电出力的波动及送出线路发生故障时故障电流的频偏特性，风电场送出线路纵联保护灵敏度下降甚至拒动，因此提出了基于边缘检测的风电场送出线路纵联保护算法。通过将风电场送出线路两侧采集到的电流构造为矩阵形式，并使用Sobel算子进行边缘检测，从而确定电流采样值变化大的部分。然后通过所识别到的线路两侧电流采样值变化大的部分计算平均梯度幅值并与整定值相比较，实现区内故障和区外故障的快速识别。最后通过PSCAD/EMTDC搭建了大规模风电场送出系统模型，验证了所提算法的适用性、速动性及抗过渡电阻能力。与现有送出线路的纵联保护相比，所提方法在风电场弱出力的情况下仍适用，且动作速度更快。

Most deep learning models for image deblurring are trained on pairs of clean images and their blurry counterparts, where the blurry inputs are artificially generated. However, it is impossible for these synthesized blurry images to cover all the real-world blur. Even in two synthetic datasets, the blur type, illumination, and other important image parameters could be different. Consequently, the performance of most existing deblurring models decreases when applied to real-world images and artificial blurry images from a different synthetic dataset. Very few previous deblurring works consider the gap among blurry images from different domains. Inspired by the current success of unsupervised domain adaptation (UDA) on image classification tasks, we develop, UDA-Deblur, a novel deblurring framework that utilizes domain alignment to attenuate effects of the aforementioned gap. In our work, channel attention modules are adopted to exploit the inter-channel relationship for features; multi-scale feature classifiers are designed to discriminate domain difference. UDA-Deblur is trained adversarially to align the feature distributions of the source domain and the target domain. We provide adequate quantitative and qualitative analysis to show the state-of-the-art performance of UDA-Deblur. Firstly, we evaluate the proposed UDA-Deblur on synthesized datasets related to real-life scenarios, which achieves satisfying deblurring results. We further demonstrate that our approach also outperforms prior models on real-world blurry images. For a persuasive comparison, we carefully design experiments on GoPro, HIDE and ReaBlur datasets. More importantly, this is the first work considering real-world image deblurring from a feature-level domain adaptation perspective.

The strong mobility characteristic of wireless sensor nodes has attracted extensive attention because it can lead to real-time changes in node locations , the attenuation, instability , even disruption of link quality. Focusing on these issues, this paper studies the reliability of adopting received signal strength indication (RSSI) technology to locate mobile nodes in a human-centric interior environment. To this end, this paper conducts dynamic and static experiments, analyzes the spatiotemporal performance of mobile RSSI, investigates the influence of a human body on link quality, draws the standard curve to show the one-to-one correspondence between distance and RSSI in a static scene, and proposes six denoising methods to alleviate severe RSSI fluctuation in a dynamic scene. The denoising effect is verified by comparing the consistency between the denoised mobile RSSI and static standard RSSI. The comparison is achieved by computing the root mean square error (RMSE) of RSSI in the time domain and transforming the result into the noise fast Fourier transform (FFT) spectrum in the frequency domain. Though the RMSE is reduced by 39.7% using the overall optimized filtering method, while the amplitude of noise FFT is reduced to an average of 0.02 dBm, due to the non-monotonic decrease of RSSI when the distance increases, one RSSI may correspond to multiple distances, with these distances even differing by 12.8 m. Because this number is too large for most applications, the human -centric sensor positioning using only RSSI technology is unreliable in mobile interior wireless sensor networks.

为实现更高的能量密度,探究利用干法工艺制备厚电极的电化学性能.通过构建点-线-面三维导电网络,采用干法电极制备工艺,制备 138 μm、217 μm和 303 μm厚(面密度分别为 26.7 mg/cm2、35.0 mg/cm2 和 47.9 mg/cm2)的磷酸铁锂(LiFePO4)厚电极.以 0.10 C在 2.50～4.25 V充放电,电极的可逆比容量分别为 157.5 mAh/g、158.7 mAh/g和 153.2 mAh/g,接近 30 μm厚(面密度为1.0 mg/cm2)对比电池的158.1 mAh/g.在不同电流下进行50次循环,仅0.50 C和1.00 C倍率下循环的容量受厚度影响.以 0.50 C循环时,循环曲线出现"跳水"现象,且发生时间随着厚度的增加而提前,主要是因为厚电极在较大电流下充电时,在负极表面沉积大量高比表面金属锂,导致电池内的电解液干涸.

Due to the widespread popularity of data-driven applications such as big data analytics, machine learning, and computer vision, modern systems need extremely high computational performance to meet users’ requirements. However, the conventional design of arithmetic units is not able to satisfy users with high performance and high energy efficiency simultaneously. Approximate computing is the new paradigm to design arithmetic units for high performance and high energy efficiency. For guarantees on a certain range of error rate from the approximate computing, approximate arithmetic units need an appropriate error estimator. An error estimator based on machine learning (ML) classifiers is a good option since it can foresee the detailed feature of upcoming input data with high accuracy. On the other hand, an error estimator based on ML classifiers requires a training phase, which consumes extra computational power and energy. Besides, for different applications, the error estimator is often required to have another training phase to increase its accuracy. To overcome this shortcoming, we propose Runtime Accuracy Tunable Approximate Floating-point Multipliers in this paper. Our proposed design does not require data profiling, training, and re-training phases. When the error tolerance margin of target applications is 7

热滥用是导致锂离子电池热失控的原因之一,因此加热实验是锂离子电池质量抽检或第三方测试必检项目之一.基于国家标准GB 38031—2020《电动汽车用动力蓄电池安全要求》的加热实验方法,对比研究随机选用的循环老化电芯与新电芯的热稳定性.2只循环老化电芯在2.50～3.65 V充放电,分别以1.00 C循环1622次和以2.00 C充电、1.00 C放电循环782次.循环老化电芯的热稳定性比新电芯差,但最终呈现的结果有随机性.选区电化学性能测试表明,高温下电解液与高活性嵌锂负极的反应,是负极脱锂存在空间上的不均匀特性、电芯气胀甚至出现泄压的主要原因.

This paper is an extension of the work originally presented in the 26th International Conference on Automation and Computing.This study regarding hardware prefetching aims at concealing cache misses, leading to maximizing the performance of modern processors.This paper leverages prefetch coverage improvement as a way to achieve the goal.Original work proposes two different storage buffers to enhance prefetch coverage; block offset buffer and block address buffer.The block offset buffer updates its contents with the offsets of a cache block accessed, while the block address buffer contains the address of a cache block prefetchissued.The offset buffer is utilized to speculate a local optimum offset per page.The offset buffer is proposed to adopt multiple lengths of delta history in observing offset patterns from completely trained table.This paper advances to employ incompletely trained table as well, while in other prefetching methods including original work, only completely trained candidates are utilized.Furthermore, we construct the table on the fly.Rather than using only completely built tables, we offer utilizing and updating table concurrently.This paper also proposes a refined metric from existing prefetch accuracy metric, to measure net contribution of a prefetcher.Compared to the original work, we have 2.5% and 3.8% IPC speedup increment with singleand 4-core configuration, respectively, in SPEC CPU 2006.In SPEC CPU 2017, our work achieves 4.5% and 5.5% IPC speedup improvement with single-and 4-core configuration, respectively, over the original work.Our work outperforms the 2nd best prefetcher, PPF, by 2.9% and 2.7% IPC speedup with single-and 4-core configuration, respectively, in SPEC CPU 2006.In SPEC CPU 2017, our work surpasses both Berti by 1% and SPP by 2.1% IPC speedup with 4-core configuration in SPEC CPU 2017.

针对配电网状态估计中不同量测数据的数据成分、 数据精度以及刷新频率等方面存在异同,在保证传统状态估计器结构前提下,提出了一种新的估计融合体系,同时将改进广义极大似然(GM)估计和估计融合体系相结合,用以估计系统节点电压幅值和相角.首先,采用GM估计增强估计模型的鲁棒性,通过使用自适应映射统计以及对GM估计中目标函数的权函数进行分析,将改进GM估计法用于状态估计.其次,考虑到传统量测系统与相量量测系统在测量通道以及仪表采样速率方面技术不同,在传统状态估计器的基础上充分利用相量量测数据对不同的估计模块进行状态估计.同时,利用多传感器数据融合理论(MDF)对估计结果进行融合处理,从而得到最优估计值.最后,改进的IEEE 14与IEEE 33节点配电网算例的仿真分析,验证了所提改进GM估计和估计融合体系的有效性和可靠性.

随着电网中风电、光伏渗透率的逐渐提高,电网暂态电压稳定问题愈加严重,提出一种考虑暂态电压稳定的含高渗透率风光的电网动态无功规划方法.构造基于电压二元表的区域暂态电压安全裕度指标和无功规划基准场景;基于所提指标给出动态无功补偿装置的布点方法,建立差异化动态无功补偿优化模型,利用多目标灰狼优化算法求解配置容量,并采用改进的熵权逼近理想解距离法筛选出动态无功补偿装置的待配置方案;利用摄动法确定最终配置方案,确保所得规划方案适用于所有场景.改进的IEEE 39节点系统和实际电网的仿真结果验证了所提规划方法的普适性和合理性.