International Journal of Network Dynamics and Intelligence

Reliable Cost Prediction and Control for Intelligent Manufacture: A Key Performance Indicator Perspective

Hang Geng — 2024-03-26

Article

Reliable Cost Prediction and Control for Intelligent Manufacture: A Key Performance Indicator Perspective

Hang Geng ^1,*, Alireza Mousavi ², Nikolaos Grigorios Markatos ², Kai Chen ¹, and Xuan Gou ¹

¹ School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

² Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom

^* Correspondence: Hang.Geng@uestc.edu.cn

Received: 10 July 2023

Accepted: 11 October 2023

Published: 26 March 2024

Abstract: Intelligent manufacturing is facing significant challenges in adapting to the ever-changing equipment, instrumentation, process and economics. Such a trend together with the pressure to reliably control and contain production costs means that frequent adjusting decisions are required to adapt to incessant volatility imposed on manufacturing systems. Under this circumstance, cost-effective and quality-guaranteed manufacturing strategies would be the most logical route to reducing production costs. In this paper, a novel dynamical cost prediction and control (CPC) model is proposed to support collective decision-making in intelligent manufacturing, where the model output is the real-time prediction of possible manufacturing costs, while the inputs are generic manufacturing key performance indicators covering inventory, product quality, production efficiency, resource utilisation and environmental impact. This proposed CPC model distinguishes itself from existing ones for its capability to translate manufacturing data (at both the physical level and operation management level) into financial metrics that contribute to forming a common language between engineering, financial and administrative departments of an enterprise. The case study about the assembly line of optoelectronic devices demonstrates that, although different enterprise departments have different priorities, our CPC model helps them to achieve certain consensus on intended production that finally creates satisfactory profitability for the company at controlled manufacturing costs.

Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate

Manman Yuan — 2024-03-26

Article

Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate

Manman Yuan ^1,2,*, and Wei Qian ^1,2,*

¹ School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China

² Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China

^* Correspondence: ymmamhappy@163.com; qwei@hpu.edu.cn

Received: 4 September 2023

Accepted: 7 November 2023

Published: 26 March 2024

Abstract: In this paper, the problem of adaptive output feedback tracking is considered for a class of nonlinear systems with lower-triangular structures. A novel dynamic gain is introduced to deal with the unknown growth rate. By coupling the dynamic gain with the observer and the controller, an adaptive output tracking controller is developed, which can guarantee that all signals of the closed-loop system are globally bounded. Finally, the effectiveness of the presented control scheme is illustrated by a numerical example.

Analysing the effect of a dynamic physical environment network on the travel dynamics of forcibly displaced persons in Mali

Boesjes Freek — 2024-03-26

Article

Analysing the effect of a dynamic physical environment network on the travel dynamics of forcibly displaced persons in Mali

Boesjes Freek ¹, Jahani Alireza ², Ooink Bas ³, and Derek Groen ^2,*

¹ Utrecht University, Heidelberglaan 8, Utrecht, Utrecht 3584 CS, the Netherlands

² Computer Science Department, Brunel University London, Wilfred Brown Building, Kingston Lane, Uxbridge UB8 3PH, UK

³ Blue Team Intelligence, Jan Aertshof 3, Hoevelaken 3871 WH, the Netherlands

^* Correspondence: Derek.Groen@brunel.ac.uk

Received: 7 September 2023

Accepted: 30 January 2024

Published: 26 March 2024

Abstract: As of 2023, the world has approximately 100 million refugees, many of whom have been displaced by violent conflicts. Accurately predicting where these people may go can help non-government organisations (NGOs) and other support organisations to more effectively help these refugees. In this paper, we extend the existing flee migration forecasting model which models migration using intelligent agents with a dynamic network that represents the physical environment. In doing so, we integrate time-dependent data into four different characteristics from three public data sources. We obtain data from aspects such as the slope, drainage, soil and infrastructure, and use these aspects to systematically modify the movement preferences of forcibly displaced agents in the flee model. We showcase our approach by applying it to the 2012 northern Mali conflict. We find that numerous routes previously deemed traversable are actually inaccessible for prolonged periods according to sensor data, and a range of off-road routes are instead traversable for vehicles. We also perform a validation comparison with the original modelling approach, and find that our revised representation of travel routes leads to a reduction of 4.5% in the averaged relative difference. Our approach can be reused in other flee conflict contexts, of which five are present in the EU-funded ITFLOWS project alone. Our work provides the ability to represent a dynamic physical environment and potentially improves the simulation accuracy in a range of flee conflict situations.

Fault-tolerant formation consensus control for time-varying multi-agent systems with stochastic communication protocol

Chunyu Li — 2024-03-26

Article

Fault-tolerant formation consensus control for time-varying multi-agent systems with stochastic communication protocol

Chunyu Li, Yifan Liu, Ming Gao, and Li Sheng ^*

¹ College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China

^* Correspondence: E-mail: shengli@upc.edu.cn

Received: 4 September 2023

Accepted: 8 November 2023

Published: 26 March 2024

Abstract: This paper is concerned with the problem of fault-tolerant formation consensus control for linear time-varying (LTV) multi-agent systems (MASs) with stochastic communication protocol (SCP). The SCP is introduced to schedule the signal transmission, and only one neighbouring agent is allowed to transmit data at one instant. The purpose of this work is to design a fault-tolerant controller for each agent, so that, for all probabilistic scheduling behaviors, MASs can achieve the formation consensus performance. The state and fault are augmented into a new vector, meanwhile, each agent system is written as a singular one and a state observer is designed. By utilizing the estimated information of states and faults, the designed time-varying compensation term can reduce the impacts of unknown external disturbances and faults. Then, a sufficient condition is obtained to guarantee the performance constraint over the finite horizon for closed-loop systems. The parameters of observers and controllers are derived by solving coupled backward recursive Riccati difference equations. Finally, a numerical example is given to validate the effectiveness of the proposed fault-tolerant control scheme.

Parameter Learning of Probabilistic Boolean Control Networks with Input-Output Data

Hongwei Chen — 2024-03-26

Article

Parameter Learning of Probabilistic Boolean Control Networks with Input-Output Data

Hongwei Chen ^1,*, Qi Chen ¹, Bo Shen ¹, and Yang Liu ²

¹ College of Information Science and Technology, Donghua University, Shanghai 201620, China

² School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China

^* Correspondence: hongwei@dhu.edu.cn

Received: 23 September 2023

Accepted: 27 November 2023

Published: 26 March 2024

Abstract: This paper investigates the parameter learning problem for the probabilistic Boolean control networks (PBCNs) with input-output data. Firstly, an algebraic expression of the PBCNs is obtained by taking advantage of the semi-tensor product technique, and then, the parameter learning problem is transformed into an optimal problem to reveal the parameter matrices of a linear system in a computationally efficient way. Secondly, two recursive semi-tensor product based algorithms are designed to calculate the forward and backward probabilities. Thirdly, the expectation maximization algorithm is proposed as an elaborate technique to address the parameter learning problem. In addition, a useful index is introduced to describe the performance of the proposed parameter learning algorithm. Finally, two numerical examples are employed to demonstrate the reliability of the proposed parameter learning approach.

Learning Regression Distribution: Information Diffusion from Template to Search for Visual Object Tracking

Shuo Hu — 2024-03-26

Article

Learning Regression Distribution: Information Diffusion from Template to Search for Visual Object Tracking

Shuo Hu ^*, Jinbo Lu, and Sien Zhou

¹ School of Electrical Engineering, Yanshan University, Qinhuangdao 066000, China

^* Correspondence: hus@ysu.edu.cn

Received: 5 July 2023

Accepted: 19 October 2023

Published: 26 March 2024

Abstract: The general paradigm of traditional Siamese networks involves using cross-correlations to fuse features from the backbone, and this paradigm is limited by the inductive bias of the convolution kernel, resulting in the lack of global information. In this paper, we propose the Siamese learning regression distribution (SiamLRD) to address the local limitations of traditional cross-correlation operations on feature fusion and weak self-connections between features within different branches. The SiamLRD uses the cross-attention mechanism to replace cross-correlations between the features of the target region of interest and the template so as to enhance flexibility. Firstly, the original transformer structure is improved to be suitable for convolutional Siamese networks. The improved transformer architecture is then used to replace cross-correlation operations, resulting in more comprehensive feature fusion between branches. Secondly, we introduce a new decoder structure into the novel fusion strategy to enhance the correlation between classification scores and regression accuracy during decoding. Multiple benchmarks are used to test the proposed SiamLRD approach, and it is verified that the proposed approach improves the baseline with 5.8% in terms of AO and 9.7% in terms of SR0.75 on the GOT-10K dataset.