Research on the Supply Chain Partnership Relations with Association Rule Analysis

Due to that many fuzzy and uncertain association relations represented by some phenomenon in the process of enterprises cooperation in supply chain, data mining method was try out in this paper. At first, concept and deducing procedure was introduced, and then 300 samples from a survey was pretreatment, in succession cooperation relations of enterprises in supply chain was studied combined with association analysis method. Some acceptable conclusions were obtained, which can explain the relations between modality and measures of cooperation of enterprises reasonably

Influence of rivet to sheet edge distance on fatigue strength of self-piercing riveted aluminium joints

Self-piercing riveting (SPR) is one of the main joining methods for lightweight aluminium automotive body structures due to its advantages. In order to further optimise the structure design and reduce the weight but without compromising strength, reduction of redundant materials in the joint flange area can be considered. For this reason, the influence of rivet to sheet edge distance on the fatigue strengths of self-piercing riveted joints was studied. Five edge distances, 5 mm, 6 mm, 8 mm, 11.5 mm and 14.5 mm, were considered. The results showed that the SPR joints studied in this research had high fatigue resistance and all specimens failed in sheet material along joint buttons or next to rivet heads. For lap shear fatigue tests, specimens failed in the bottom sheet at low load amplitudes and in the top sheet at high load amplitudes except for specimens with very short edge distance of 5 and 6 mm; whereas, for coach-peel fatigue tests, all specimens failed in the top sheet. For both lap shear and coach-peel fatigue tests, specimens with an edge distance of 11.5 mm had the best fatigue resistance. It was found that for coach-peel fatigue, length of crack developing path before specimens lost their strengths was the main factor that determined the fatigue life of different specimens; for lap shear fatigue, the level of stress concentration and subsequent crack initiation time was the main factor that determined the fatigue life

Routing Algorithm with Uneven Clustering for Energy Heterogeneous Wireless Sensor Networks

Aiming at the “hotspots” problem in energy heterogeneous wireless sensor networks, a routing algorithm of heterogeneous sensor network with multilevel energies based on uneven clustering is proposed. In this algorithm, the energy heterogeneity of the nodes is fully reflected in the mechanism of cluster-heads’ election. It optimizes the competition radius of the cluster-heads according to the residual energy of the nodes. This kind of uneven clustering prolongs the lifetime of the cluster-heads with lower residual energies or near the sink nodes. In data transmission stage, the hybrid multihop transmission mode is adopted, and the next-hop routing election fully takes account of the factors of residual energies and the distances among the nodes. The simulation results show that the introduction of an uneven clustering mechanism and the optimization of competition radius of the cluster-heads significantly prolonged the lifetime of the network and improved the efficiency of data transmission

Instance Segmentation in the Dark

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. In this work, we take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. The proposed method is motivated by the observation that noise in low-light images introduces high-frequency disturbances to the feature maps of neural networks, thereby significantly degrading performance. To suppress this ``feature noise", we propose a novel learning method that relies on an adaptive weighted downsampling layer, a smooth-oriented convolutional block, and disturbance suppression learning. These components effectively reduce feature noise during downsampling and convolution operations, enabling the model to learn disturbance-invariant features. Furthermore, we discover that high-bit-depth RAW images can better preserve richer scene information in low-light conditions compared to typical camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our analysis indicates that high bit-depth can be critical for low-light instance segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a low-light RAW synthetic pipeline to generate realistic low-light data. In addition, to facilitate further research in this direction, we capture a real-world low-light instance segmentation dataset comprising over two thousand paired low/normal-light images with instance-level pixel-wise annotations. Remarkably, without any image preprocessing, we achieve satisfactory performance on instance segmentation in very low light (4~\% AP higher than state-of-the-art competitors), meanwhile opening new opportunities for future research.Comment: Accepted by International Journal of Computer Vision (IJCV) 202

A 3D parallel Particle-In-Cell solver for extreme wave interaction with floating bodies

Floating structures are widely used for vessels, offshore platforms, and recently considered for deep water floating offshore wind system and wave energy devices. However, modelling complex wave interactions with floating structures, particularly under extreme conditions, remains an important challenge. Following the three-dimensional (3D) parallel particle-in-cell (PIC) model developed for simulating wave interaction with fixed bodies, this paper further extends the methodology and develops a new 3D parallel PIC model for applications to floating bodies. The PIC model uses both Lagrangian particles and Eulerian grid to solve the incompressible Navier-Stokes equations, attempting to combine both the Lagrangian flexibility for handling large free-surface deformations and Eulerian efficiency in terms of CPU cost. The wave-structure interaction is resolved via inclusion of a Cartesian cut cell method based two-way strong fluid-solid coupling algorithm that is both stable and efficient. The numerical model is validated against 3D experiments of focused wave interaction with a floating moored buoy. Good agreement between the numerical and experimental results has been achieved for the motion of the buoy and the mooring force. Additionally, the PIC model achieves a CPU efficiency of the same magnitude as that of the state-of-the-art OpenFOAM ® model for an extreme wave-structure interaction scenario

Thermoelectric property studies on thallium-doped lead telluride prepared by ball milling and hot pressing

Thallium doping into lead telluride has been demonstrated to increase the dimensionless thermoelectric figure-of-merit (ZT) by enhancing Seebeck coefficient due to the creation of resonant states close to Fermi level without affecting the thermal conductivity. However, the process is tedious, energy consuming, and small in quantities since it involves melting, slow cooling for crystal growth, long time annealing, post-crushing and hot pressing. Here we show that a similar ZT value about 1.3 at 400 °C is achieved on bulk samples with grain sizes of 3–7 μm by ball milling a mixture of elemental thallium, lead, and tellurium and then hot pressing the ball milled nanopowders

Event-Triggered Multi-Lane Fusion Control for 2-D Vehicle Platoon Systems with Distance Constraints

This paper investigates the event-triggered fixedtime multi-lane fusion control for vehicle platoon systems with distance keeping constraints where the vehicles are spread in multiple lanes. To realize the fusion of vehicles in different lanes, the vehicle platoon systems are firstly constructed with respect to a two-dimensional (2-D) plane. In case of the collision and loss of effective communication, the distance constraints for each vehicle are guaranteed by a barrier function-based control strategy. In contrast to the existing results regarding the command filter techniques, the proposed distance keeping controller can constrain the distance tracking error directly and the error generated by the command filter is coped with by adaptive fuzzy control technique. Moreover, to offset the impacts of the unknown system dynamics and the external disturbances, an unknown input reconstruction method with asymptotic convergence is developed by utilizing the interval observer technique. Finally, two relative threshold triggering mechanisms are utilized in the proposed fixed-time multi-lane fusion controller design so as to reduce the communication burden. The corresponding simulation results also verify the effectiveness of the proposed strategy

Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication

It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, in particular deep learning (DL), is expected to be one of the key technological enablers of 6G by offering a new paradigm for the design and optimization of networks with a high level of intelligence. In this article, we introduce an emerging DL architecture, known as the transformer, and discuss its potential impact on 6G network design. We first discuss the differences between the transformer and classical DL architectures, and emphasize the transformer's self-attention mechanism and strong representation capabilities, which make it particularly appealing for tackling various challenges in wireless network design. Specifically, we propose transformer-based solutions for various massive multiple-input multiple-output (MIMO) and semantic communication problems, and show their superiority compared to other architectures. Finally, we discuss key challenges and open issues in transformer-based solutions, and identify future research directions for their deployment in intelligent 6G networks.Comment: 9 pages, 6 figures. The current version has been accepted by IEEE Wireless Communications Magzin