Safe operation is a key concern for industrial robots. However, due to hardware failures and unstable data transmission issues, the multivariate time-series data generated by these axes often contain missing or corrupted signals, which severely hinders downstream tasks such as fault diagnosis. Additionally, the substantial volume of industrial data demands considerable time for training time-series imputation models and subsequent classification models. To address these challenges, this study proposes a multitask approach that serves both the data imputation and fault diagnosis tasks for industrial robots. Specifically, the parameters trained in the imputation model can be transferred to the fault diagnosis model, enhancing its performance and efficiency. A multitask method named Bidirectional-Graph Attention Networks Parallel Encoder (Bi-GATPE) is proposed, which employs a bidirectional graph attention network to capture the spatial dependencies among the various variables of industrial robots. Subsequently, a parallel encoder with Diagonal-Filter Attention is designed to model temporal correlations. This dual approach improves the accuracy and training speed for both the imputation and fault diagnosis tasks. Experimental studies based on real industrial robot datasets demonstrate that by modifying the feature fusion layer of the imputation task and sharing the trained parameters with the fault diagnosis task, the proposed method significantly accelerates the convergence of the fault diagnosis model while also improving diagnostic accuracy. The experiments also indicate that our method shows merits in the imputation and fault diagnosis tasks. The source code of Bi-GATPE is available at:https://github.com/miten073/Bi-GATPE.

5G networks and Subband Full Duplex (SBFD) technology aim to enhance uplink transmission efficiency in IoT environments. However, large-scale random access users in SBFD systems exacerbates cross-link interference (CLI), which could undermine its potential performance advantages. To address this challenge, we propose a priority-aware random access (PA-RA) strategy, which comprises two key components. First, access users are selected through a dynamic priority evaluation method that considers Quality of Service (QoS), real-time resources availability, and historical access attempts. Second, a centralized subband allocation method preferentially assigns resources to central regions of the uplink subband to minimize CLI, while adaptively utilizing edge resources under low-interference conditions. The simulation results demonstrate that PA-RA strategy could significantly reduce CLI compared to conventional methods, achieving high success rates of access and low latency. Furthermore, it provides a robust framework for interference management in SBFD networks with high-density IoT devices.

Time-sensitive networking (TSN), essential in industrial networks for its promise of reliable and deterministic data transmission, faces deployment challenges due to the limitations of existing time-aware shaper (TAS)-based scheduling algorithms. Specifically, the size of the generated gate control lists (GCLs) is usually too large to be deployed in actual devices. To bridge the gap between theory and practice, we propose FlexTAS, a flexible and practical solution for TSN. The key insight behind FlexTAS is that relaxing gating does not introduce uncertainty, as long as nonoverlap reserved time slots are guaranteed. FlexTAS is comprised of two main components: first, a novel gating model deviates from the conventional TAS model by incorporating selective relaxation of gating at certain nodes; and second, a deep reinforcement learning-based engine to rapidly generate valid schedules. We build a real testbed and validate the effectiveness of our proposed solution. Our evaluation demonstrates that FlexTAS effectively controls the number of gate entries within the GCL capacity of devices, while simultaneously meeting the Quality of Service(QoS) requirements of time-triggered streams. It significantly reduces the number of GCL entries by 60% to 80%, and facilitates deployment in heterogeneous networks, thus offering a practical solution for TSN.

Massive GPU acceleration processors have been used in high-performance computing systems. The Dennard scaling has led to power and thermal constraints limiting the performance of such systems. The demand for both increased performance and energy efficiency is highly desired. This article presents a multilayer low-power optimization method for warps and tasks parallelisms. We present a dynamic frequency regulation scheme for performance parameters in terms of load balance and load imbalance. The method monitors the energy parameters in runtime and adjusts adaptively the voltage level to ensure performance efficiency with energy reduction. The experimental results show that the multilayer low-power optimization with dynamic frequency regulation can achieve 40% energy consumption reduction with only 1.6% performance degradation, thus reducing 59% maximum energy consumption. It can further save about 30% energy consumption in comparison with the single-layer energy optimization.

Named entity recognition (NER) is one of the basic tasks of knowledge extraction. In view of corpus in some specific fields have sparse semantics and limited text standardization, the existing entity recognition methods have the problems of wrong potential word interference, matching potential words in some specific fields difficultly, and the key information of entities with different lengths in sentences may have interference in the representation of attention mechanism. This paper proposes a NER model integrating word and improved entity key information attention mechanism, which is called binocular attention-based BiLSTM with CNN network (BACBN). Firstly, the character feature embedding is generated through bidirectional encoder representation of transformers (BERT), and a word-level character feature attention mechanism is proposed to highlight the character features constituting words. Secondly, based on the bidirectional long short-term memory network (BiLSTM), an n-gram pooling feature attention mechanism is proposed. The prominent features corresponding to different convolution kernel sizes are obtained through convolution neural network (CNN), and the context features are weighted according to the prominent features, so as to obtain the information that contributes more to entity recognition with different lengths. The experimental results on four specific field Chinese corpus NER datasets show that BACBN model can improve the result of entity recognition.

Taking fault diagnosis corpus as the research object, an event logic knowledge graph construction method is proposed in this paper. Firstly, we propose a data labeling strategy based on a constructed event logic ontology model, then collect large-scale robot transmission system fault diagnosis corpus, and label part of the data according to the strategy. Secondly, we propose a transfer learning model called Trans-SBLGCN for event argument entity and event argument relation joint extraction. A language model is trained based on large-scale unlabeled fault diagnosis corpus and transferred to a model based on stacked bidirectional long short term memory (BiLSTM) and bidirectional graph convolutional network (BiGCN). Experimental results show that the method is superior to other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed to provide decision support for autonomous robot transmission system fault diagnosis.

Time-Sensitive Networking (TSN) is an emerging network architecture that provides bounded latency and reliable network services for time-sensitive applications. Since time-triggered flows in TSN are typically periodic, a concept of network cycle is widely used in both standards and academic researches. However, although network cycle has gained popularity, its rationale has not yet been analyzed systematically.In this paper, we mathematically evaluate the performance of several flow scheduling algorithms in terms of flow schedulability with and without employing network cycle. We observe that only when the network cycle is set to a proper value can the performance of flow scheduling be significantly improved. To better evaluate the scheduling effect, a novel assessment metric and a goal-based optimization algorithm are introduced. Our experiment results show that the network cycle-based algorithm can achieve a considerable improvement (40% - 170% improvement in the number of scheduled flows) compared to the ones with network cycle disabled.

Summary The Internet of Things is becoming widely popular in the past decade, which comes with huge amount of data. These magnanimous data, stored in data centers, put forward the new demand for the efficient management of the network. In this article, we propose Software‐Defined Congestion Control Plane (SDCCP), a hybrid network control architecture that aims to fully utilize the network while avoiding congestion. SDCCP is based on Software‐Defined Networking and CCP, in which the controller collects the network statistics and specifies the behavior of the end‐to‐end hosts by sending feedback or modifying their transport layer parameters directly. It can also be used to mitigate Distributed Denial of Service attacks and other security problems. In addition, we propose FCA, a Feedback‐based Congestion Avoidance algorithm running on SDCCP, which adapts the congestion window based on the feedback from the remote controller. We evaluate SDCCP and FCA in Mininet and the result shows that FCA can achieve high network utilization while keeping the queue length of the routers in a low level. Also, FCA is robust to noncongestion loss, and outperforms other algorithms at high loss rate.

Broadcasting is a fundamental function for disseminating messages in multihop wireless networks. Minimum-Transmission Broadcasting (MTB) problem aims to find a broadcast schedule with minimum number of transmissions. Previous works on MTB in duty-cycled networks exploit a rigid assumption that nodes have only active time slot per working cycle. In this paper, we investigated the MTB problem in duty-cycled networks where nodes are allowed arbitrary active time slots per working cycle (MTBDCA problem). Firstly, it is proved to be NP-hard and o(ln⁡Δ) -inapproximable, where Δ is the maximum degree in the network. Secondly, an auxiliary graph is proposed to integrate nodes’ active time slots into the network and a novel covering problem is proposed to exploit nodes’ multiple active time slots for scheduling. Then, a ln⁡(Δ+1) -approximation algorithm is proposed for MTBDCA and a (ln⁡(Δ+1)+Δ) -approximation algorithm is proposed for all-to-all MTBDCA. Finally, extensive experimental results demonstrate the efficiency of the proposed algorithm.