Longzhi Yang

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Investigation on distributed scheduling with lot-streaming considering setup time based on NSGA-II in a furniture intelligent manufacturing

Journal of Intelligent & Fuzzy Systems (2024)

Jinxin Wang Zhanwen Wu Longzhi Yang Wei Hu Chaojun Song

Distributed flexible flowshop scheduling is getting more important in the large-scale panel furniture industry. It is vital for a higher manufacturing efficiency and economic profit. The distributed scheduling problem with lot-streaming in a flexible flow shop environment is investigated in this work. Furthermore, the actual constraints of packaging collaborative and machine setup times are considered in the proposed approach. The average order waiting time for packaging and average order delay rate is used as objectives. Non-dominated sorting method is used to handle this bi-objective optimization problem. An improved encoding method was proposed to address the large-scale orders that need to be divided into sub-lots based on genetic algorithm. The proposed approach is firstly validated by benchmark with other multi-objectives evolutionary algorithms. The results found that the proposed approach had a good convergence and diversity. Besides, the influence of the proportion of large-scale orders priority level and sub-lot size was investigated in a panel furniture manufacturing scenario. The results can be concluded that the enterprise could obtain shorter order average waiting time and delay rate when the sub-lot sizes were set as two and the order priority level was allocated in the proportion of 1:2:3:4:5.

10.3233/jifs-237378

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Error Controlled Actor-Critic

arXiv (Cornell University) (2021)

Xingen Gao Fei Chao Changle Zhou Zhen Ge Chih‐Min Lin

On error of value function inevitably causes an overestimation phenomenon and has a negative impact on the convergence of the algorithms. To mitigate the negative effects of the approximation error, we propose Error Controlled Actor-critic which ensures confining the approximation error in value function. We present an analysis of how the approximation error can hinder the optimization process of actor-critic methods.Then, we derive an upper boundary of the approximation error of Q function approximator and find that the error can be lowered by restricting on the KL-divergence between every two consecutive policies when training the policy. The results of experiments on a range of continuous control tasks demonstrate that the proposed actor-critic algorithm apparently reduces the approximation error and significantly outperforms other model-free RL algorithms.

Divergence (linguistics)

Error function

Function Approximation

Value (mathematics)

10.48550/arxiv.2109.02517

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Data Collation and Curation for the COSMOS Database

Longzhi Yang Y. Lan Kirk Arvidson Mark T.D. Cronin Steven J. Enoch

The requirements for the COSMOS database have been specified, a data quality-control, curation and governance strategy has been proposed and the system has been designed. The next steps will be the implementation into products for data collection to serve the COSMOS project within the SEURAT-1 cluster. Longzhi Yang1, Yang Lan1, Kirk Arvidson2, Mark TD Cronin3, Steven J Enoch3, Johann Gasteiger4, Dimitar Hristozov2, Judith C Madden3, Daniel Neagu1, Andrea-N Richarz3, James F Rathman5, Mick J Ridley1, Oliver Sacher4, Christof H Schwab4, Aleksey Tarkhov4, Chihae Yang5 Data Collation and Curation for the COSMOS Database

Collation

Source

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Multi-layer Lattice Model for Real-Time Dynamic Character Deformation

Computer Graphics Forum (2015)

Naoya Iwamoto Hubert P. H. Shum Longzhi Yang Shigeo Morishima

Due to the recent advancement of computer graphics hardware and software algorithms, deformable characters have become more and more popular in real-time applications such as computer games. While there are mature techniques to generate primary deformation from skeletal movement, simulating realistic and stable secondary deformation such as jiggling of fats remains challenging. On one hand, traditional volumetric approaches such as the finite element method require higher computational cost and are infeasible for limited hardware such as game consoles. On the other hand, while shape matching based simulations can produce plausible deformation in real-time, they suffer from a stiffness problem in which particles either show unrealistic deformation due to high gains, or cannot catch up with the body movement. In this paper, we propose a unified multi-layer lattice model to simulate the primary and secondary deformation of skeleton-driven characters. The core idea is to voxelize the input character mesh into multiple anatomical layers including the bone, muscle, fat and skin. Primary deformation is applied on the bone voxels with lattice-based skinning. The movement of these voxels is propagated to other voxel layers using lattice shape matching simulation, creating a natural secondary deformation. Our multi-layer lattice framework can produce simulation quality comparable to those from other volumetric approaches with a significantly smaller computational cost. It is best to be applied in real-time applications such as console games or interactive animation creation.

Skinning

Character Animation

Lattice (music)

10.1111/cgf.12749

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Citations (17)

A Secure and Privacy-Preserving E-Government Framework Using Blockchain and Artificial Immunity

IEEE Access (2023)

Noe Elisa Longzhi Yang Fei Chao Nitin Naik Tossapon Boongoen

Electronic Government (e-Government) systems constantly provide greater services to people, businesses, organisations, and societies by offering more information, opportunities, and platforms with the support of advances in information and communications technologies. This usually results in increased system complexity and sensitivity, necessitating stricter security and privacy-protection measures. The majority of the existing e-Government systems are centralised, making them vulnerable to privacy and security threats, in addition to suffering from a single point of failure. This study proposes a decentralised e-Government framework with integrated threat detection features to address the aforementioned challenges. In particular, the privacy and security of the proposed e-Government system are realised by the encryption, validation, and immutable mechanisms provided by Blockchain. The insider and external threats associated with blockchain transactions are minimised by the employment of an artificial immune system, which effectively protects the integrity of the Blockchain. The proposed e-Government system was validated and evaluated by using the framework of Ethereum Visualisations of Interactive, Blockchain, Extended Simulations (i.e. eVIBES simulator) with two publicly available datasets. The experimental results show the efficacy of the proposed framework in that it can mitigate insider and external threats in e-Government systems whilst simultaneously preserving the privacy of information.

10.1109/access.2023.3239814

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Embedded YARA rules: strengthening YARA rules utilising fuzzy hashing and fuzzy rules for malware analysis

Complex & Intelligent Systems (2020)

Nitin Naik Paul Jenkins Nick Savage Longzhi Yang Tossapon Boongoen

Abstract The YARA rules technique is used in cybersecurity to scan for malware, often in its default form, where rules are created either manually or automatically. Creating YARA rules that enable analysts to label files as suspected malware is a highly technical skill, requiring expertise in cybersecurity. Therefore, in cases where rules are either created manually or automatically, it is desirable to improve both the performance and detection outcomes of the process. In this paper, two methods are proposed utilising the techniques of fuzzy hashing and fuzzy rules, to increase the effectiveness of YARA rules without escalating the complexity and overheads associated with YARA rules. The first proposed method utilises fuzzy hashing referred to as enhanced YARA rules in this paper, where if existing YARA rules fails to detect the inspected file as malware, then it is subjected to fuzzy hashing to assess whether this technique would identify it as malware. The second proposed technique called embedded YARA rules utilises fuzzy hashing and fuzzy rules to improve the outcomes further. Fuzzy rules countenance circumstances where data are imprecise or uncertain, generating a probabilistic outcome indicating the likelihood of whether a file is malware or not. The paper discusses the success of the proposed enhanced YARA rules and embedded YARA rules through several experiments on the collected malware and goodware corpus and their comparative evaluation against YARA rules.

Malware analysis

Ransomware

10.1007/s40747-020-00233-5

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Citations (23)

Interval Type-2 TSK+ Fuzzy Inference System

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2018)

Jie Li Longzhi Yang Xin Fu Fei Chao Yanpeng Qu

Type-2 fuzzy sets and systems can better handle uncertainties compared to its type-1 counterpart, and the widely applied Mamdani and TSK fuzzy inference approaches have been both extended to support interval type-2 fuzzy sets. Fuzzy interpolation enhances the conventional Mamdani and TKS fuzzy inference systems, which not only enables inferences when inputs are not covered by an incomplete or sparse rule base but also helps in system simplification for very complex problems. This paper extends the recently proposed fuzzy interpolation approach TSK+ to allow the utilization of interval type-2 TSK fuzzy rule bases. One illustrative case based on an example problem from the literature demonstrates the working of the proposed system, and the application on the cart centering problem reveals the power of the proposed system. The experimental investigation confirmed that the proposed approach is able to perform fuzzy inferences using either dense or sparse interval type-2 TSK rule bases with promising results generated.

Fuzzy rule

Defuzzification

Interpolation

10.1109/fuzz-ieee.2018.8491448

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Citations (13)

Internal Model Control Structure Inspired Robotic Calligraphy System

IEEE Transactions on Industrial Informatics (2023)

Ruiqi Wu Fei Chao Changle Zhou Xiang Chang Longzhi Yang

Learning calligraphy writing skills in robots is regarded as a sophisticated task. Current robotic researchers have proposed many methods to implement various robotic calligraphy systems. However, several limitations of these methods, such as high computational costs and few diversities of generated results constrain the development of calligraphy robots. This article proposes a robotic writing framework based on a robotic hand–eye coordination method to solve these limitations. Inspired by the internal model control (IMC) system, a vision-motor network and a motor-vision network are built to simulate the direct and reverse models, respectively, in the IMC system of a robotic manipulator. The vision-motor network works as an action generator to convert image inputs to robotic actions, and the motor-vision network assists in the training of the vision-motor network. Thus, a pretraining of the motor-vision network is established by random writing movements of a robotic manipulator. Experimental results demonstrate that the proposed method can successfully write strokes of Chinese characters by inputting target stroke images. Although the proposed method is applied to robotic calligraphy, the underpinning research is readily applicable to many other applications, such as human–robot motion mimicking.

Calligraphy

Machine Vision

10.1109/tii.2023.3295415

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Citations (3)

Optimisation of Classifier Ensemble for Predictive Toxicology Applications

Mokhairi Makhtar Longzhi Yang Daniel Neagu Mick Ridley

Ensembles of classifiers proved potential in getting higher accuracy compared to a single classifier. High diversity in an ensemble may improve the performance results significantly. We propose an ensemble approach which has diversity calculated using disagreement measure of classification output. A CRS (Classifier Ranking System) is introduced for the selection of relevant classifiers. We also propose the Optimisation of Classifiers Ensemble Method (OCEM) technique which applies to the ensemble selection. In this paper, we focus on classification models for predictive toxicology applications, for which computational models are required to replace in vivo experiments. The results show that our method performs well in selecting the relevant ensemble model to improve the prediction from a collection of classifiers.

Ensemble Learning

Cascading classifiers

Ensemble forecasting

Probabilistic classification

10.1109/uksim.2012.41

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Citations (12)

Automatic stroke generation for style-oriented robotic Chinese calligraphy

Future Generation Computer Systems (2021)

Lin Gan Zhihua Guo Fei Chao Longzhi Yang Xiang Chang

Calligraphy

Writing system