Extracting motion pattern implied in the history trajectory is important for the pedestrian trajectory prediction task. The motion pattern determines how a pedestrian moves, including but not limited to reaction of interaction, tendency of speed and direction change. Although the motion pattern is a comprehensive concept and can’t be described concretely, it is clear that it contains both long-term and short-term factors. Inspired by this, we introduce SpectrumNet which enables more effective encoding of historical motion patterns for trajectory prediction. Different from existing methods, which consider the history trajectory as a time sequence of position, SpectrumNet represents it in the frequency space by applying Fourier Transform (FT) to decompose the historical information on different time scales. SpectrumNet consists of two sub-networks, the Multi-Frequency Combination (MFC) encoder, which models the historical information by combining multiple frequency feature in the spectrum; and the Frequency Interaction (FI) encoder, which captures the interaction between pedestrians in the frequency domain. To validate the effect of SpectrumNet, we build a CVAE-based prediction system to predict stochastic future trajectory. Experiments conducted on ETH-UCY dataset show that our prediction system with SpectrumNet out-performs the previous state-of-the-art model and achieves a new record on ADE metric.

In the manufacturing sector, establishing a system for fault diagnosis and analysis on production lines is of paramount importance. This research presents a novel hierarchical anomaly management method, addressing issues such as data category imbalance, challenges in detecting abnormal signs. The study utilizes the LSTM-Rola (rolling accumulation) approach, specifically designed for time series forecasting, to effectively identify anomalies. This method employs a stacked LSTM structure in an encoder-decoder framework, combining single-step and multi-step predictions to enhance both short-term and long-term forecasting capabilities. The anomaly detection aspect of the method incorporates an accumulation of abnormal occurrences and categorizes anomalies into different levels. This strategy not only improves detection accuracy but also resonates with traditional fault mechanism theories, facilitating easier interpretation of the model. Additionally, the paper includes comparative studies on various normalization methods and early warning accumulation tactics, demonstrating the model's effectiveness. The model shows remarkable performance in time series anomaly detection, achieving an F_0.5 score of 0.8259, a high precision of 91.8%, and a recall rate of approximately 60%.

YOLO v5s is one of the commonly used one-stage object detection algorithms currently, known for its small size and fast speed. However, its main limitation is its lower accuracy. To address this issue, this paper proposes an improved YOLO v5s model, DMAC-YOLO, which utilizes the AdamPlus optimizer, an enhancement of the Adam optimizer, for higher accuracy and faster convergence compared to traditional optimizers. The model adopts a Decoupled Head approach to improve gradient propagation during training and introduces the SIoU Loss function to reduce false positives and missed detections. Additionally, by improving the network structure of the original YOLO v5s and incorporating the CBAM attention mechanism, the model's feature extraction capabilities are enhanced. Experiments show that compared to the YOLO v5s model, the DMAC-YOLO model increases [email protected] by 6.0% to 84.3% and [email protected] by 13.2% to 64.2% on the PASCAL VOC dataset. On the COCO dataset, the DMAC-YOLO model's [email protected] is improved by 4.0% to 56.7%, and [email protected] by 4.2% to 37.4%. Ablation experiments also demonstrate that the proposed improvements enable the model to converge quickly while maintaining a balance between accuracy and speed.

Abstract In this paper, the authors apply the feature pyramid network (FPN) to the single‐stage anchor‐free object detection algorithm CenterNet, and the effectiveness of the multi‐level feature fusion of FPN for the object detection algorithm is proved by experiments. However, multi‐level feature fusion leads to an increase in computational cost. In this regard, this paper proposes an object detection algorithm, called DDE‐Net, that does not use multi‐level feature fusion and only uses single‐level feature for optimization. The key component in it: the dense dilated encoder, which encourages dense information exchange of features between different spatial scales. This paper presents extensive experiments, and DDE‐Net shows strong performance compared to that of other popular models on the PASCAL VOC and on the COCO2017 dataset. On the COCO2017 dataset, the authors’ DDE‐Net achieves comparable results with its feature pyramids counterpart RetinaNet, while applying the same backbone with smaller params and GFLOPs than RetinaNet. With an image size of 512 × 512, DDE‐Net achieves 37.3 AP running at 81 fps on 2080 Ti.

Abstract Bicycle motion simulation is fundamental to urban transportation planning, virtual reality and other areas. This article proposes a data‐driven based double‐layer bicycle simulation model to consider the cyclist's decision‐making process and the bicycle's kinematic structure. This proposed model consists of two layers, the decision‐making layer and the motion layer. First, the decision‐making layer using machine learning algorithms models the decision‐making process as a regression problem to output the cyclist's decision. Then, the motion layer applies a bicycle kinematics model to output bicycle motion under physical constraints. In addition, a solution to calculate bicycles' dynamic information is proposed for the data‐driven method. Quantitative and qualitative experiments have been conducted, and results show that the double‐layer model and the parameter calculation solution can generate realistic bicycle motion simulations.

Abstract This paper proposes a novel crowd simulation method which integrates not only modelling ideas but also advantages from both data‐driven methods and crowd dynamics methods. To seamlessly integrate these two different modelling ideas, first, a fusion crowd motion model is developed. In this model the motion of crowd are driven dynamically by different forces. Part of the forces are modeled under a universal interaction mechanism, which describe the common parts of crowd dynamics. Others are modeled by examples from real data, which describe the personality parts of the agent motion. Second, a construction method for example dataset is proposed to support the fusion model. In the dataset, crowd trajectories captured in the real world are decomposed and re‐described under the structure of the fusion model. Thus, personality parts hidden in the real data could be locked and extracted, making the data understandable and migratable for our fusion model. A comprehensive crowd motion generation workflow using the fusion model and example dataset is also proposed. Quantitative and qualitative experiments and user studies are conducted. Results show that the proposed fusion crowd simulation method can generate crowd motion with the great motion fidelity, which not only match the macro characteristics of real data, but also has lots of micro personality showing the diversity of crowd motion.

In this paper, we introduced a behavioral decision and execution method to simulate crowded passengers in a waiting hall. The method, as well as its simulation framework, is designed under the special purpose of passenger safety investigation. It supports the simulation of both regular crowded passenger behaviors and emergency passenger behavior. Situations under different time tables and density control measure could easily be conducted and simulated for safety purposes.

End-to-end reliability is an important issue in building large-scale distributed enterprise applications based on multi-tier architecture, but the support of reliability as adopted in conventional replication or transactions mechanisms is not enough due to their distinct objectives - replication guarantees the liveness of computational operations by using forward error recovery, while transactions guarantee the safety of application data by using backward error recovery. Combining the two mechanisms for stronger reliability is a challenging task Current solutions, however, are typically on the assumption of simple transaction pattern where a request from a single client executes in the context of exactly one transaction at the middle-tier application server, and seldom think about some complex patterns, such as client transaction enclosing multiple client requests or nested transactions. In this paper, we first identify four transaction pattern classes, and then propose a fault-tolerant scheme that can uniformly provide exactly-once semantic reliability support for these patterns. In this scheme, application servers are passively replicated to endow business logics with high reliability and high availability. In addition, by replicating transaction coordinator, the blocking problem of 2PC protocol during distributed transactions processing is eliminated. We have implemented this approach and integrated it into our own J2EE application server, OnceAS. Also, its effectiveness is discussed in different transaction patterns and the corresponding performance is evaluated

The multi-tier architecture of J2EE provides good modularity and scalability by partitioning an application into several tiers, and becomes the mainstream for distributed applications development on Internet/Intranet. Current reliability solutions in this architecture are typically dependent on either replication, which provides at-least-once guarantee, or transaction processing, which guarantees at-most-once semantics. In practice, the end-to-end reliability guarantee of exactly-once semantics is necessary, especially for some complex transaction scenarios, such as client transaction or nested transaction. In this paper, we describe a fault-tolerant algorithm that can provide this enhanced reliability support through combining replication and transaction processing. We use passive replication to protect business processing at middle-tier application server. A client stub transparently intercepts client request and automatically resubmits it in the case of failure. In addition, transaction coordinator is passively replicated to prevent the blocking problem of distributed transaction. Also, different application scenarios are discussed to illustrate the effectiveness of this algorithm, and a performance study based on our implementation in J2EE application server, OnceAS, shows the overhead of it is acceptable

As we have been gaining more experiences in services provision, online services are becoming increasingly complex. They have moved from simple service provision and invocation to very sophisticated service interaction and cooperation. As presented in this paper, service cooperation will be a promising computation model to achieve overall goals beyond individual capabilities. Based on the supreme wide spread of service-oriented development (SODA), the process-oriented platform (POP), with favourable flexibility derived from late binding, will be the optimum approach to this end. The PI production developed by us is such a system implementation, in which architecture, components, and functionalities are also introduced in detail. We believe that the service cooperation paradigm is a hopeful solution to future service evolution, and the PI system will be an instructive explorer to reach this new horizon

As the Internet environment becomes more and more dynamic, open and mutable, future software have to be more autonomic, reactive, adaptive, cooperative, and evolvable. To meet the need, we introduce emerging service cooperation middleware providing such infrastructure support. Derived from Chinese ancient five-elements ideology, a similar service cooperation philosophy is developed. Complying the idea, we develop a workflow system, PI, supporting Process Intelligence. We believe that the service cooperation will become a feasible solution to the future complex environment.

Advancing information technologies are increasing the evolution and variation of software resources. Applications that cannot adapt to dynamic environments will decrease their usefulness, particularly to business process systems that face requirements changed frequently. After surveying related work, we propose a new formalism for modeling dynamic process in which the state space and transition function are enriched so that one model can transit to another model at runtime by the computational reflection that aims to represent and modify its own design. Such goal necessitates an appropriate runtime environment. We introduce the service cooperation middleware (SCM) because it is service-oriented, into which the reflection mechanism is easily attached to support structural and behavioural changes on processes at runtime.