Illumination-Invariant Flotation Froth Color Measuring via Wasserstein Distance-Based CycleGAN With Structure-Preserving Constraint

Liu, Jin Ping; He, Jiezhou; Xie, Yongfang; Gui, Weihua; Tang, Zhaohui; Ma, Tianyu; He, Junbin; Niyoyita, Jean Paul

doi:10.1109/tcyb.2020.2977537

Cited by 54 publications

(10 citation statements)

References 40 publications

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“…Scholars also proposed various methods to cope with the effects of environmental variables such as different illumination conditions in real-life productions. For example, Liu et al proposed an illumination-invariant froth color measuring method (WDSPCGAN) [40] by solving a structure-preserved image-to-image color translation task. is would help keep the performance of the classification algorithms stable regardless of various lighting conditions.…”

Section: Related Workmentioning

confidence: 99%

Cost‐Sensitive Siamese Network for PCB Defect Classification

Miao

Liu

et al. 2021

Computational Intelligence and Neuroscience

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After the production of printed circuit boards (PCB), PCB manufacturers need to remove defected boards by conducting rigorous testing, while manual inspection is time-consuming and laborious. Many PCB factories employ automatic optical inspection (AOI), but this pixel-based comparison method has a high false alarm rate, thus requiring intensive human inspection to determine whether alarms raised from it resemble true or pseudo defects. In this paper, we propose a new cost-sensitive deep learning model: cost-sensitive siamese network (CSS-Net) based on siamese network, transfer learning and threshold moving methods to distinguish between true and pseudo PCB defects as a cost-sensitive classification problem. We use optimization algorithms such as NSGA-II to determine the optimal cost-sensitive threshold. Results show that our model improves true defects prediction accuracy to 97.60%, and it maintains relatively high pseudo defect prediction accuracy, 61.24% in real-production scenario. Furthermore, our model also outperforms its state-of-the-art competitor models in other comprehensive cost-sensitive metrics, with an average of 33.32% shorter training time.

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Section: Related Workmentioning

confidence: 99%

Cost‐Sensitive Siamese Network for PCB Defect Classification

Miao

Liu

et al. 2021

Computational Intelligence and Neuroscience

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“…is the consequent parameter set. Layer 5 (Total output layer): The output of an ANFIS is obtained by summing the outputs obtained by each rule in the defuzzification layer, i.e., 5 i…”

Section: A Anfismentioning

confidence: 99%

“…Hence, many CIPs are often running in a non-ideal operating condition. One of the key reasons is that it is intractable or unavailable to detect some key production indicators (KPIs) of CIPs timely by hardware sensors [3,4], especially the chemical composition-relevant KPIs, which are mainly obtained by infrequent on-site manual sampling with laboratory assays [5].…”

Section: Introductionmentioning

confidence: 99%

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

Liu

Jiang

et al. 2020

IEEE Access

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In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

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“…So far, various schemes have been introduced to control nonlinear systems [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. As well, for HIV–immune systems, as nonlinear systems, there are a wide variety of controllers in the literature, including a fuzzy discrete event system approach [ 59 , 60 ], feedback control [ 61 , 62 ], sliding mode control [ 14 ], and optimal control [ 63 , 64 , 65 , 66 , 67 ].…”

Section: Introductionmentioning

confidence: 99%

A Caputo–Fabrizio Fractional-Order Model of HIV/AIDS with a Treatment Compartment: Sensitivity Analysis and Optimal Control Strategies

Wang

Jahanshahi

Wang

et al. 2021

Entropy

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Although most of the early research studies on fractional-order systems were based on the Caputo or Riemann–Liouville fractional-order derivatives, it has recently been proven that these methods have some drawbacks. For instance, kernels of these methods have a singularity that occurs at the endpoint of an interval of definition. Thus, to overcome this issue, several new definitions of fractional derivatives have been introduced. The Caputo–Fabrizio fractional order is one of these nonsingular definitions. This paper is concerned with the analyses and design of an optimal control strategy for a Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic. The Caputo–Fabrizio fractional-order model of HIV/AIDS is considered to prevent the singularity problem, which is a real concern in the modeling of real-world systems and phenomena. Firstly, in order to find out how the population of each compartment can be controlled, sensitivity analyses were conducted. Based on the sensitivity analyses, the most effective agents in disease transmission and prevalence were selected as control inputs. In this way, a modified Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic is proposed. By changing the contact rate of susceptible and infectious people, the atraumatic restorative treatment rate of the treated compartment individuals, and the sexual habits of susceptible people, optimal control was designed. Lastly, simulation results that demonstrate the appropriate performance of the Caputo–Fabrizio fractional-order model and proposed control scheme are illustrated.

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Illumination-Invariant Flotation Froth Color Measuring via Wasserstein Distance-Based CycleGAN With Structure-Preserving Constraint

Cited by 54 publications

References 40 publications

Cost‐Sensitive Siamese Network for PCB Defect Classification

Cost‐Sensitive Siamese Network for PCB Defect Classification

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

A Caputo–Fabrizio Fractional-Order Model of HIV/AIDS with a Treatment Compartment: Sensitivity Analysis and Optimal Control Strategies

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