With the deep integration of the Internet of Vehicles and air-ground collaborative communication networks, urban spectrum management faces multidimensional challenges, including an increasingly prominent supply-demand imbalance of spectrum resources. Conventional spectrum coordination methods suffer from low utilization efficiency in the presence of heterogeneous network environments and dynamic service demands. To address this issue, this paper proposes a dynamic spectrum prediction method based on temporal knowledge graphs. By integrating multidimensional knowledge including electromagnetic spectrum data, equipment parameter features, and environmental data, we construct a knowledge graph for communication spectrum coordination with fusion of static and dynamic knowledge (SDKG). By employing a knowledge graph embedding (KGE) model based on the recurrent evolution network via graph convolution network (RE-GCN) combined with graph neural networks (GCNs) and a long short-term memory (LSTM) reasoning algorithm, a GCN-LSTM with RE-GCN dynamic spectrum prediction algorithm is proposed. Simulation results demonstrate that the GCN-LSTM with RE-GCN algorithm can effectively enhance frequency prediction accuracy.

In environments where vision-based depth estimation systems, such as those utilizing infrared or imaging technologies, encounter limitations—particularly in low-light conditions—alternative approaches become essential. Echo depth estimation emerges as a compelling solution by leveraging the time delay of echoes to map the geometric structure of the surrounding environment. This method offers distinct advantages in specific scenarios, providing reliable data for accurate scene understanding and 3D reconstruction. Traditional echo depth estimation techniques primarily depend on spatial information captured by the encoder and depth predictions made by the decoder. However, these methods often fail to fully exploit the rich depth features present at different simultaneous frequencies. To address this challenge, we propose an echo depth estimation method via Attention-based Hierarchical Multi-scale Feature Fusion Network (AHMF-Net). This network is designed to extract spatial depth information from echo spectrograms across multiple scales and hierarchical levels, while fusing the most relevant information using an attention mechanism. AHMF-Net introduces two key modules in hierarchical levels: the Intra-layer Multi-scale Attention Feature Fusion (IMAF) module, which functions as the encoder to capture multi-scale features across varying granularities, and the Inter-layer Multi-Scale Detail Feature Fusion (IMDF) module, which integrates features from all encoding layers into the decoder to enable effective inter-layer multi-scale fusion. Additionally, the encoder incorporates an attention mechanism that enhances depth-related features by capturing channel dependencies at multiple scales. We evaluated AHMF-Net on the Replica, Matterport3D, and BatVision datasets, where it consistently outperformed state-of-the-art models in echo-based depth estimation, demonstrating superior accuracy and robustness. The source code is publicly available at https://github.com/wjzhang-ai/AHMF-Net .

In modern operational environments, all work plans are susceptible to the influence of emergencies, such as equipment failures and resource shortages, underscoring the criticality of emergency management. Current solutions primarily rely on experienced human or employ machine scheduling models. Human-machine collaboration can leverage their respective strengths, thereby enhancing decision-making efficiency and safety. Based on this, a framework for human-machine collaborative emergency management is proposed in this paper. The emergency management process is divided into two stages: task selection and task scheduling. In the task selection phase, the Human-Machine Collaborative Decision-Making Algorithm for Dynamic Tasks (HMC-DMADT) is introduced to identify key nodes and generate task lists, and humans can correct and confirm lists they do not endorse. In the task scheduling phase, the Dynamic Task Scheduling Algorithm with Human Experience-Based Constraints (HEC-ETSA) is proposed, which integrates the Action-Mask mechanism with the Double Deep Q-Network (DDQN) algorithm to optimize action selection, ensuring decision safety and feasibility. Finally, a simulation platform is established to conduct numerous experiments within an unmanned warehousing scenario. The results demonstrate that the proposed human-machine collaborative emergency management framework effectively addresses emergencies in unmanned warehousing operations.

In many safety-critical applications, the response to emergencies is one of the most important components in maintaining the stability of workshop production and transportation. However, a robust emergency response relies on a high degree of collaboration between human and machine intelligence. In this paper, we propose a visual analysis system that can improve the collaboration of failure mitigation during the emergency response period. Our system implements 10 different scenarios with distinct types of maintenance agents, failures, and possible hazards. We illustrate our design methodology and the framework of the system in the paper. The proposed system is able to be utilized on conducting detailed failure or hazard analysis for the current scenario and intervene in the dispatch of maintenance agents in real time.

Scale-out datacenter network fabrics enable network operators to translate improved link and switch speeds directly into end-host throughput. Unfortunately, limits in the underlying CMOS packet switch chip manufacturing roadmap mean that NICs, links, and switches are not getting faster fast enough to meet demand. As a result, operators have introduced alternative, parallel fabric designs in the core of the network that deliver N-times the bandwidth by simply forwarding traffic over any of N parallel network fabrics.

As an important issue in the modern industrial chain, the safety and stability of unmanned storage is widely concerned. The preparedness of emergent incidents are required in the unmanned warehouse in order to prevent the damage of goods and the delay of the delivery tasks. However, the traditional methods of emergency damage control are costly and time-consuming, and cannot be applied to such complicated scenarios. In this paper, we propose an improved DQN algorithm that schedules the rescue agents preparedness tasks based on the prediction of the impact of the emergent incidents with Bi-LSTM. Experiments show that the method can effectively improve delivery task completion and reduce damage caused by emergent incidents.

Baggage transportation system with unmanned electric vehicles is one of the emerging research topic of the civil aviation airport. However, the battery capacity of electric delivery vehicles limits the distance and load capacity during driving, which has an influence on the decision of vehicles scheduling. To address the above problems, this paper propose a dynamic scheduling model of unmanned electric baggage transportation vehicles in airport containing both load capacity and battery capacity constraints. We also design a dynamic scheduling algorithm to process updating of flights massages in real time using the GCN-CNN-GRU neural network framework to determine the real-time driving condition of vehicles. The experiments under simulated scenarios have proved the performance of our method.

Due to hardware limitations, point clouds collected by Li-DAR devices are sparse, making it challenging to locate faraway objects accurately. In this paper, we propose an RGB-guided local point cloud completion network, which aims to improve off-the-shelf 3D object detectors by selectively densifying the collected point clouds. Rather than predicting per-pixel depth in 2D images and projecting them back to pseudo-3D point clouds, our proposed method directly predicts the existence of points in 3D space around input points. Towards this goal, we create a semi-dense labeled local points completion dataset and design a new loss for training the network in a semi-supervised manner. Extensive experiments show that the proposed method can produce reasonable and accurate dense 3D point clouds from sparse inputs, improving off-the-shelf 3D object detectors on the KITTI 3D detection benchmark. The source code of our method will be available at https://github.com/emdata-ailab/LPCC-Net.

The vastness of gaming plots and variety of environments in computer games require a large amount of labors in soundtrack compositions. Since human composers are expensive, artificial intelligence composing techniques have been proposed in several open-source projects. Current technologies have good performances at improvisations in short melody compositions, but face great challenges in industrial level automatic compositions of highly structured tracks for games. In this article, the overall structure specifying transitions and repetitions of melodies is given by human, and detailed contents like notes and rhythms are completed with a Gaussian mixture model (GMM) and generative adversarial nets (GAN). Different from recurrent neural networks, which are the mainstream automated melody generators, the GMM can be controlled to form structures because its latent space is often similar to the data space. A layered framework is devised where the basic layer composes melodies and high-level layers organize melodies according to long-term structures. In each layer, a Gaussian mixture generative model with constraints is constructed to compose candidate tracks, whereas another GMM network is trained in competition with the generator, such that optimal tracks from the generator are identified. Experiments show that the proposed framework has a high rate of composing acceptable soundtracks. Entropy curves calculated show that the composed tracks are more similar to game soundtracks than existing methods. In a user study, 11 out of 16 human criticizers favor the proposed compositions over the original GAN and recurrent neural networks.

People in crowd scenes often exhibit the characteristic of imbalanced distribution. On the one hand, people size varies largely due to the camera perspective. People far away from the camera look smaller and are likely to occlude each other, whereas people near to the camera look larger and are relatively sparse. On the other hand, the number of people also varies greatly in the same or different scenes. This article aims to develop a novel model that can accurately estimate the crowd count from a given scene with imbalanced people distribution. To this end, we have proposed an effective multi-level convolutional neural network (MLCNN) architecture that first adaptively learns multi-level density maps and then fuses them to predict the final output. Density map of each level focuses on dealing with people of certain sizes. As a result, the fusion of multi-level density maps is able to tackle the large variation in people size. In addition, we introduce a new loss function named balanced loss (BL) to impose relatively BL feedback during training, which helps further improve the performance of the proposed network. Furthermore, we introduce a new data set including 1111 images with a total of 49 061 head annotations. MLCNN is easy to train with only one end-to-end training stage. Experimental results demonstrate that our MLCNN achieves state-of-the-art performance. In particular, our MLCNN reaches a mean absolute error (MAE) of 242.4 on the UCF_CC_50 data set, which is 37.2 lower than the second-best result.

Abstract Dynamic mesh sequence (DMS) is a simple and accurate representation for precisely recording a 3D animation sequence. Despite its simplicity, this representation is typically large in data size, making storage and transmission expensive. This paper presents a novel framework that allows effective DMS compression and progressive streaming by eliminating spatial and temporal redundancy. To explore temporal redundancy, we propose a temporal frame‐clustering algorithm to organize DMS frames by their motion trajectory changes, eliminating intracluster redundancy by principal component analysis dimensionality reduction. To eliminate spatial redundancy, we propose an algorithm to transform the coordinates of mesh vertex trajectory into a decorrelated trajectory space, generating a new spatially nonredundant trajectory representation. We finally apply a spectral graph wavelet transform with color set partitioning embedded block encoding to turn the resultant DMS into a multiresolution representation to support progressive streaming. Experiment results show that our method outperforms several existing methods in terms of storage requirement and reconstruction quality.

In this paper, we present a novel crowd behavior evolution method with emotional contagion in political rallies. We first analyze the most representative political rally scenes in detail and model them into two kinds of abstract scenario. Furthermore, the “extroversion” and “empathy” factors from the OCEAN model are chosen to describe the most important individual personalities in such scenarios. Based on this, an improved emotional contagion model is proposed by combining the Susceptible-Infected-Recovered model and individual personality under different political viewpoints. Finally, the crowd in a political rally is driven to move according to the new potential moving direction generated by emotional contagion and the original direction of the individual together. The experiments show that our method can intuitively demonstrate the emotional changes of those individuals with different political perspectives and reasonably simulate the crowd movement under the political rally scenes.

Scratch pad memories (SPM) are attractive alternatives for caches on multicore systems since caches are relatively expensive in terms of area and energy consumption. The key to effectively utilizing SPMs on multicore systems is the data placement algorithm. In this paper, two polynomial time algorithms, regional data placement for multicore (RDPM) and regional data placement for multicore with duplication (RDPM-DUP), have been proposed to generate near-optimal data placement with minimum total cost. There is only one copy for each data in RDPM, while RDPM-DUP allows data duplication. Experimental results show that the proposed RDPM algorithm alone can reduce the time cost of memory accesses by 32.68% on average compared with existing algorithms. With data duplication, the RDPM-DUP algorithm further reduces the time cost by 40.87%. In terms of energy consumption, the proposed RDPM algorithm with exclusive copy can reduce the total cost by 33.47% on average. When RDPM-DUP is applied, the improvement increases up to 38.15% on average.

Multi-core systems have been a popular design for high-performance embedded systems. Scratch Pad Memory (SPM), a software-controlled on-chip memory, has been widely adopted in many embedded systems due to its small area and low energy consumption. Existing data allocation algorithms either cannot achieve optimal results or take exponential time to complete. In this paper, we propose one polynomial-time algorithms to solve the data allocation problem on multi-core system with exclusive data copy. According to the experimental results, the proposed optimal data allocation method alone reduces time cost of memory accesses by 16.45% on average compared with greedy algorithm. The proposed data allocation algorithm also can reduce the energy cost significantly.

Scratch-Pad Memory (SPM) has been widely adopted in many embedded systems as well as digital signal processor systems. This paper proposes a polynomial time optimal data placement algorithm to minimize the memory access cost of one program region for memory architectures with multiple types of memory units including SPM in order to achieve high performance with low cost. The experimental results show our algorithms can reduce time cost of memory access by 18.19% and the energy cost by 16.97% compared with random data placement, which is better than the existing greedy algorithms.