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As an inverse problem, the parameter identification of manipulators is essential to in-situ calibration. Since the ill-posed inverse kinematic model is sensitive to the measurement value, even tiny errors will make the geometric model of the manipulator wrongly identified. To overcome this problem, a Regularized Parameter Identification Method (RPIM) is proposed to calibrate the geometric model of the manipulator. The inverse kinematic model of a 6 DOF manipulator is modified by a Tikhonov regularization to overcome its ill-posed problem. The regularization parameter is optimized by the improved L curve method to adjust the initial model to a well-posed one that approximates the real situation. A calibration system is designed to evaluate the effectiveness of the suggested method. The position of the selected targets is tested by using a laser tracker. The experimental result shows that the absolute position errors of the manipulator are 2.533mm without calibration, 0.472mm by RPIM, 1.445mm by Least Square Method (LSM), 1.353mm by Gradient Descent (GD), and 0.956mm by Gauss-Newton (GN). It shows that the absolute position error of RPIM is reduced by 81.331% after calibration, which is superior to other methods.

Turbines are typical engineering-to-order products which can be highly customized. This paper considers the final assembly control under uncertainty in turbine assembly workstations. A cyber-physical framework based on Internet of Things (IoT) is proposed for the turbine assembly, which consists of physical components, cyber components and an IoT-based monitoring system. The IoT-based monitoring modules capture real-time data of the physical components, including the workers, tools, parts and the actual assembly process. These cyber modules can generate the original scheduling for the assembly tasks, re-sequence the assembly tasks, re-assign the workers and control the logistics when an unexpected event occurs. Physical activities are undertaken in a turbine assembly workstation based on assembly instructions and guidance from the cyber components. The proposed framework can be implemented in the large turbine assembly system, which can facilitate real-time information driven assembly process monitoring and collaborative control of the task sequence, the worker assignments and logistics in a closed-loop environment. The experimental results show that our method can significantly improve the quality and the efficiency of a turbine assembly system.

The chemical terrorist attack is a type of unconventional terrorism that threatens the safety of cities. This kind of terrorist attack is highly concealed and difficult to be detected. Once the attack is successful, the consequences will be severe and the scope of impact will be enormous. Therefore, public security and emergency departments need to perform risk analysis and dynamic knowledge update to reduce risk or mitigate the effects of accidents. In order to quickly and effectively analyze the risk of chemical terrorist attacks, this article proposed a hybrid approach (B-R model) to analyze the risk of chemical terrorist attacks. First, a modular and customizable Bayesian network (BN) model library was built, which can satisfy users to select multi-dimensional risk factors. Based on the personalized BN, a risk knowledge graph (RKG) is constructed with multi-source data to realize the combination of risk analysis and knowledge acquisition. Then the threat degree of terrorist organizations, the strength of defensive forces, and the risk value of targets is calculated and displayed. The BN-RKG method provides data and theoretical support for defenders' resource allocation and emergency decision-making. Finally, a case study was conducted for a hypothetical scenario analysis. The result shows that the hybrid method can help with risk control and have the potential to support practical policymaking.

This paper develops an innovative design method of a multi-coupled line (CL) topology of low-loss dual band negative group delay (NGD) circuit. The proposed is conceived using three transmission lines (TLs) with different length in which integrate two couplers. The three traveling paths are designed to generate the dual-band NGD effect, where the CLs allow lowering the signal attenuation. The NGD circuit S-parameter model as a function of the TLs physical lengths and coupling coefficient are established. To validate the NGD circuit proof-of-concept, it is fabricated and measured. The NGD values of approximately -4.06 ns and -3.83 ns are measured at center frequencies of 2.43 GHz and 3.02 GHz, respectively. The measured transmission coefficient is better than -2.9 dB and the measured reflection one is better than -12 dB into the NGD band. The measurement results of the NGD circuit are in good agreement with the simulations results.

Urbanization has been speeding up social and economic transformations in urban communities, the smallest social units in a city. However, urbanization brings challenges to urban management and security. Therefore, a system of risk prediction of crimes may be essential to crime prevention and control in urban communities and its system improvement. To tackle crime-related problems in urban communities, this paper proposes a model of daily crime prediction by combining Long Short-Term Memory Network (LSTM) and Spatial-Temporal Graph Convolutional Network (ST-GCN) to automatically and effectively detect the high-risk areas in a city. Topological maps of urban communities carry the dataset in the model, which mainly includes two modules — spatial-temporal features extraction module and temporal feature extraction module — to extract the factors of theft crimes collectively. We have performed the experimental evaluation of the existing crime data from Chicago, America. The results show that the integrated model demonstrates positive performance in predicting the number of crimes within the sliding time range.

An assembly line is an industrial arrangement of machines, equipment, and operators for continuous flow of workpieces in mass-production operations. In an assembly line balancing problem, tasks are allocated to workstations according to their processing times and precedence relationships amongst tasks. Nowadays, some research investigated the reliability of assembly production by taking account of task time uncertainties. Our research utilizes uncertainty theory to model task time uncertainties and introduces the belief reliability measure to the assembly line production for the first time. We proposed a multi-objective optimization model that aimed at maximizing the belief reliability and minimizing the cycle time. The problem is solved using a newly developed restart neighborhood search method. The numerical experiments are conducted to verify its efficiency. The methodology proposed in this paper is applicable to any industry (including the automotive industry) when the historical data on task processing times are very scarce.

In this paper, it has been shown that the data-driven reallocation of workers in a turbine valve assembly island is necessary because the occurrence of unexpected events delays the desired completion time defined in the schedule. The main characteristics of the problem are as follows: 1) the turbine valve is placed on a fixed site, and the workers and the required equipment are moved to the assembly site; 2) the sequence of assembly tasks is fixed, which reduces the number of significant disruptions to the upstream operations; and 3) the processing time of each task is dependent of the skill level and the number of the assigned workers. This paper proposes data-driven reallocation of workers in engineering-to-order assembly islands to meet the specified due dates and use human resources efficiently. The goal is to minimize the labor cost by reallocating the workers in order to meet the due dates when an unexpected event occurs. The problem was modeled as an integer program and optimally solved by means of the LINGO (Linear Interactive and General Optimizer) 11.0 optimizer. The experimental results showed that the solutions obtained were improvements to the solutions implemented by the foreman.

Against the backdrop of advancing automation and intelligence in rail transit, the accurate detection and recognition of Overhead Contact System (OCS) mast number plates are crucial for ensuring train safety. Challenges such as scale variations, motion blur, and lighting changes have intensified the difficulty of feature extraction, revealing deficiencies in current algorithms regarding precision and processing speed. To address these challenges, we developed a YOLO-M4ST algorithm tailored for railway scenarios. This algorithm enhances the detection and recognition capabilities by refining YOLOv8 and integrating the VTR framework. YOLO-M4ST adopts lightweight MobileNetV4 as its backbone network, significantly boosting the inference speed without compromising detection accuracy. The algorithm incorporates the C2f-Star module, which replaces the bottleneck module in the C2f module, thereby notably enhancing the feature extraction capability. Furthermore, it employs a single-class target approach to reduce the input parameters. In addition, a three-stage VTR framework was utilized, eliminating the need for character segmentation, thereby shortening the training time and improving the network convergence speed and recognition accuracy. Experimental validation demonstrated that YOLO-M4ST achieved an average recognition accuracy of 99.1% on our railway dataset with an inference speed of 420 Frames Per Second (FPS).

In video coding, rate distortion optimized quantization (RDOQ), a popular version of soft-decision quantization (SDQ), achieves superior coding performance, however is ill-suited for hardware implementation due to its inherent sequential processing. On the other hand, deadzone hard-decision quantization (HDQ) is friendly to hardware implementation, however suffers from non-negligible coding performance degradation. This paper proposes a content-adaptive deadzone offset model to improve the coefficient-wise deadzone HDQ by imitating the behavior patterns of RDOQ. The contributions of this paper are characterized by twofold. On one hand, this work formulates seeking optimal deadzone offset model as a problem of binary classification, and analyzes the distribution characteristics of the optimal deadzone offsets obtained from samples by fully imitating RDOQ, and then derives adaptive deadzone offset model by maximizing the right classification probability of offset-induced rounding in HDQ. On the other hand, the distribution parameters of DCT coefficients are measured in a position-wise way, and the adaptive deadzone model is built by applying Maximum a posterior estimation method according to three characteristic parameters, i.e. quantization step size, parameter of DCT coefficients, and quantization remainder, in the sense of rate distortion optimization. Simulation results verify that the proposed adaptive HDQ algorithm, in comparison with fixed-offset HDQ, achieves 0.54% and 0.52% bit rate saving on average with almost negligible complexity increment. Simultaneously, the proposed algorithm only sacrifices smaller than 0.55% and 0.54% increment in terms of BD-BR in comparison with RDOQ. The proposed HDQ is well-suited for hardwired video coding implementation.