Kwok-Leung Tsui

City University of Hong Kong | CityU · Department of Systems Engineering and Engineering Management

Background Falls pose a severe threat to the health of older adults worldwide. Determining gait and kinematic parameters that are related to an increased risk of falls is essential for developing effective intervention and fall prevention strategies. This study aimed to investigate the discriminatory parameter, which lay an important basis for deve...

Rail surface inspection is crucial for ensuring the safety and longevity of rail transport systems, grapples with the challenges posed by the scarcity of defective samples. Additionally, contemporary techniques in this domain typically fail to concurrently identify and localize defects at both image level and pixel levels. Addressing these intricac...

Predicting vehicle crashes is critical to improve urban transportation safety. It is however challenging to alarm impending crashes accurately as the volume of non-crash data dominates that of crash data. Most existing studies formulate the crash prediction as retrospective study and a binary classification problem, which offers the limited capacit...

Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was...

Sensor devices have been increasingly used in engineering and health studies recently, and the captured multi-dimensional activity and vital sign signals can be studied in association with health outcomes to inform public health. The common approach is the scalar-on-function regression model, in which health outcomes are the scalar responses while...

Background Falls pose a severe threat to the health of older adults worldwide. Determining gait and kinematic parameters that are related to an increased risk of falls is essential for developing effective intervention and fall prevention strategies. This study aimed to investigate the discriminatory parameter, which lay an important basis for deve...

The first section of this chapter introduces statistical process control (SPC) and robust design (RD), two important statistical methodologies for quality and productivity improvement. Section 11.1 describes in-depth SPC theory and tools for monitoring independent and autocorrelated data with a single quality characteristic. The relationship betwee...

In this chapter, we provide a review of the knowledge discovery process, including data handling, data mining methods and software, and current research activities. The introduction defines and provides a general background to data mining knowledge discovery in databases, following by an outline of the entire process in the second part. The third p...

Highlight: This paper proposed a new indicator, which is based optimized weights spectrum and owns brilliant properties for subsignals selection (The subsignals can be obtained by signal decomposition method such as VMD, WPT). Unlike other approaches that need meta-heuristic/heuristic algorithms (e.g., PSO, GA) to optimize parameters of signal proc...

Highlight: This paper proposed a new fault chracteristic frequency (FCF) identification method based optimized weights spectrum, and promising results are obtained when compared with Hilbert-transform-based and spectral-correlation-based FCF identification approaches. ///Abstract: Since fault characteristic frequencies (FCFs) and their harmonics ar...

Highlight: Based on optimized weights spectrum, we proposed a new powerful signal decomposition algorithm named difference mode decomposition (DMD). Excellent performance is demonstrated in the experimental cases, and the DMD is very suitable for machine condition monitoring./// Abstract: Adaptive extraction of concerned components (CC) from mixed...

Online accurate battery state-of-health (SOH) estimation is crucial for ensuring safe and reliable operations of electric vehicles (EVs). Yet, such estimation problem remains a challenge in reality due to complex battery degradation behaviors and dynamic EV operations. This paper proposes a novel deep learning-based framework, a bilateral branched...

Accurate forecasting of traffic conditions is critical for improving urban transportation safety, stability, and efficiency. It is challenging to produce explicit traffic forecasts due to complex and dynamic spatiotemporal contexts. Most existing works only capture partial characteristics and features of traffic data, and there still exists a great...

Machine learning has been widely applied to study AI-informed machinery fault diagnosis. This work proposes a sparsity constrained invariant risk minimization (SCIRM) framework, which develops machine learning models with better generalization capacities for environmental disturbances in machinery fault diagnosis. The SCIRM is built by innovating t...

Background Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks a...

The fault informative frequency band searching is crucial to envelope analysis-based machine fault diagnosis. Its success often depends on effective filters. However, existing filters encounter three problems: 1) fixed filters are not adaptive; 2) the adaptive decomposition filters are affected by key parameters; and 3) popular swarm-intelligent fi...

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate,...

The accelerated growth of elderly populations in many countries and regions worldwide is creating a major burden to the healthcare system. Intelligent approaches for continuous health monitoring have the potential to promote the transition to more proactive and affordable healthcare. Electrocardiograms (ECGs), collected from portable devices, with...

Predicting the battery lifetime at its early stage is a promising technology for accelerating the battery development, production, and design optimization. However, it is a challenging task for most existing prediction methods because information is too limited in early life cycles, and the early-cycle capacity data exhibits a weak correlation with...

Machine condition monitoring is an emerging research domain to use monitoring data to monitor machine conditions and prevent unexpected machine failures. In our previous study, the sum of weighted normalized square envelope was proposed as a generalized framework of some well-known sparsity measures including kurtosis, negative entropy, smoothness...

With the dynamic air traffic demand and the constrained capacity resources, accurately predicting airport throughput is essential to ensure the efficiency and resilience of air traffic operations. Many research efforts have been made to predict traffic throughputs or flight delays at an airport or over a network. However, it is still a challenging...

Spatiotemporal modeling and forecasting is an essential task for many real-world problems, especially in the field of transportation and public health. The complex and dynamic patterns with dual attributes of time and space create unique challenges for effective modeling and forecasting. With the advancement of data collection, storage, and sharing...

Sparsity measures that can quantify the sparsity of signals are often used as objective functions of signal processing and machine learning algorithms (e.g., sparse filtering, compressive sensing, blind deconvolution, and the fast Kurtogram, etc.). Classic sparsity measures include kurtosis, Gini index, negative entropy, the ratio of Lp norm to Lq...

Short-term travel demand forecasting is the critical first step to support transportation system management. Complex relevance among Origin-Destination (OD) pairs, temporal dependencies, and external factors bring challenges to it. An innovative deep learning approach, Multi-Fused Residual Network (MF-ResNet) is proposed to forecast travel demand....

Fall is a major threat to stroke survivors with the problems of gait and balance disorders in the rehabilitation phase following severe consequences on quality of life and a heavy burden to their families. Many solutions have been proposed to assess fall risk for elders based on inertial sensor‐based signals, however, there still exists a great cha...

Bearings are key components of rotating machines, and their condition monitoring and fault diagnosis have received much attention from academia and industry in recent years. Existing fault diagnosis methods can be generally classified into signal processing-based fault characteristic frequency (FCF) identification methods and machine learning-based...

The automated inspection and detection of foreign objects help prevent potential accidents and train derailments. Most existing approaches focus on the detection with prior labels, such as categories and locations of objects, and do not directly address detecting foreign objects of unknown categories, which can appear anytime on the rail track site...

Since bearings are key components of rotatory machines and they are prone to have faults, bearing fault diagnosis has received much attention. Because vibration signals collected from the casing of a machine contain sufficient fault information, bandpass filtering-based envelope demodulation on vibration signals has been a standard approach for bea...

Influenza outbreaks have brought increasing challenges to public health systems globally. The effective and efficient tracking of influenza can help authorities make informed and proactive decisions. In this study, we focus on nowcasting influenza epidemics at regional level. To alleviate the information lag between the release of the Centers for D...

Background Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based...

The automatic detection of major rail components using railway images is beneficial to ensure the rail transport safety. In this paper, we propose an attention-powered deep convolutional network (AttnConv-net) to detect multiple rail components including the rail, clips, and bolts. The proposed method consists of a deep convolutional neural network...

Forecasting nationwide passenger flows at city-level is an important but challenging task for passenger flow management and effective allocation of national transportation resources, as it can be affected by multiple complex factors. This study develops a forecasting framework to simultaneously forecast inbound and outbound passenger flows in irreg...

A promising method is proposed systematically to select an accurate resonance frequency band and separate refined resonance response from periodic excitation in this study. This work expanded the short-time Fourier transform (STFT)- and wavelet transform (WT)-based Kurtograms and developed a hybrid signal separation operator (SSO)-spectral kurtosis...

One dimensional convolutional neural network (1-D CNN) can be directly applied to process temporal signals in the machinery fault diagnosis. However, it requires a large amount of data to train high quality kernels and extract meaningful features. This paper develops a novel method integrating learnable variational kernels into a 1-D CNN to pay a m...

Automatic detection of rail track and its fasteners via using continuously collected railway images is important to maintenance as it can significantly improve maintenance efficiency and better ensure system safety. Dominant computer vision-based detection models typically rely on convolutional neural networks that utilize local image features and...

Accurate forecasting of traffic conditions is critical for improving safety, stability, and efficiency of a city transportation system. In reality, it is challenging to produce accurate traffic forecasts due to the complex and dynamic spatiotemporal correlations. Most existing works only consider partial characteristics and features of traffic data...

Automated inspection and detection of foreign objects on railways is important for rail transportation safety as it helps prevent potential accidents and trains derailment. Most existing vision-based approaches focus on the detection of frontal intrusion objects with prior labels, such as categories and locations of the objects. In reality, foreign...

Highlight: This paper provided 2 new sparsity measures for many signal processing and machine learning algorithms (e.g. fast Kurtogram, blind deconvolution, and sparse filtering, etc) as optimized objective functions. Abstract: Machine condition monitoring (MCM) uses signal processing and machine learning methods to analyze monitoring data and perf...

BACKGROUND Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to the mobility and balance assessment to provide quantitative information and cost-effective solution in the community environment. Nonetheless, the current sensor-base...

A multivariate control chart is proposed to detect changes in the process dispersion of multiple correlated quality characteristics. We focus on individual observations, where we monitor the data vector-by-vector rather than in (rational) subgroups. The proposed control chart is developed by applying the logarithm to the diagonal elements of the es...

High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the...

Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Bal...

Accurately predicting the lifetime of lithium-ion batteries in early cycles is crucial for ensuring the safety and reliability, and accelerating the battery development cycle. However, most of existing studies presented poor prediction results for early prediction, due to the nonlinear battery capacity fade with negligible variation in early cycles...

The Supplementary material contains the following data supporting the article entitled "Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China". Supplementary file A: The online reviews related to PDs in the first round; S...

Accurate life prediction of lithium-ion batteries is important to help assess battery quality in advance, improve long-term battery planning, and subsequently guarantee the safety and reliability of battery operations. In this study, a deep learning-based stacked denoising autoencoder (SDAE) method is proposed to directly predict battery life by ex...

A precisely parameterized battery model is the prerequisite of the model-based management of lithium-ion battery (LIB). However, the unexpected sensing of noises may discount the identification of model parameters in practical applications. This paper focuses on the noise effect compensation and online parameter identification for the widely-used e...

Highlight: This work built an important bridge between sparsity measures and quasi-arithmetic means. Abstract: Machine condition monitoring aims to use on-line sensor data to evaluate machine health conditions. One of the most crucial steps is construction of a health index for incipient fault detection and monotonic degradation assessment. Moreove...

Accurate battery lifespan prediction is critical for the quality evaluation and long-term planning of battery management systems. As battery degradation process is typically nonlinear, accurate early prediction of cycle life with significantly less degradation is extremely challenging. Approaches using machine learning techniques, which are mechani...

Highlight: This work made an improvement on sparsity measures. Though many statistics were used for machine condition monitoring, there are no statistics that can simultaneously achieve a clear incipient fault detection ( or anomaly detection) and monotonic degradation assessment (i.e., predictability of remaining useful life). The AWSPT based SMs...

While most existing degradation modeling methods for rechargeable batteries consider a deterministic degradation model such as exponential model, this paper presents a time series model for battery degradation paths resembling experimental data on cycle aging. This model is based on breaking down the degradation path into segments by fitting a mult...

Factors affecting customer comfort are crucial for the success of many services such as public transportation, health facilities, and so on. Therefore, the identification and prioritization of such factors is an important demand from stakeholders. This research aims to identify and prioritize the factors that affect in-cabin passenger comfort on hi...

Background: Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. Objective: Our study objective was two-fold: to design an...

Operating rooms (ORs) account for a significant proportion of the costs of maintaining a hospital. Surgery scheduling is optimised to reduce the costs in ORs. However, an important issue that directly affects surgery scheduling is often neglected, i.e. surgeon collaboration. In reality, a surgery requires two surgeons: the main surgeon and an assis...

Simulation optimization (SO) techniques show a strong ability to solve large-scale problems. In this article, we concentrate on stochastically constrained SO. There are some challenges to tackle the problem: 1) the objective and constraints have no analytical forms and need to be evaluated via simulation; 2) we should make a tradeoff between exploi...

Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep learning approaches, which are fast and accurate at the sam...

As a comparatively complicated and compact system with fast response, accurate control precision and high load-bearing capacity, electrohydraulic actuator (EHA) is generally composed of electronic control, hydraulic power, and mechanical drive systems, and has been widely used in aircrafts, mining machines, and transportation vehicles. Although a l...

The heterogeneity of cells in a battery pack is inevitable but brings high risks of premature failure and even safety hazards. Accordingly, for safe and long-life operation, it is necessary to adjust the state of charge (SOC) of all in-pack cells to the same level. To address this problem, this paper first proposes a battery SOC observer and analyz...

Post-stroke patients usually suffer from a higher fall risk. Identifying potential fallers and giving them proper attention could reduce their chance of a fall that results in severe injuries and decreased quality of life. In this study, we introduced a novel approach for fall risk prediction that evaluates Short-form Berg Balance Scale scores via...

BACKGROUND Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. OBJECTIVE Our study objective was two-fold: to design and im...

Rechargeable batteries become one of the most popular energy storage devices. For battery state of health prediction, discharge rate and temperature are two crucial factors that significantly affect battery discharge capacity fade. Battery discharge capacity fade modeling at different operating conditions is still an ongoing research direction. In...

This paper presents a modified batch mean charts for network intrusion detection. Also 3 variants of the modified batch mean chart are provided. Simulation based on the standard control limits and robust control limits are performed with 4 factors: cycle, noise, batch size and signal type. A regular batch mean chart was used to remove the sample da...

Background: Due to the high maintenance costs, it is critical to make full use of operating rooms (ORs). Operative duration is an important factor that guides research on surgery scheduling. Clinical effects, for example, surgery type, rationally influences operative duration. In this study, we also investigate whether the planning and scheduling...

Long-term blood pressure (BP) monitoring is a widely used approach in a homecare intelligent system. However, BP is usually measured using cuff-based devices with tedious operations in practice, which may not be cost-effective for continuous BP tracking. In this study, we propose a novel attention-based multitask network with weighting scheme for B...

State of health prediction of rechargeable batteries is an important topic in battery health management systems to infer remaining charge-discharge cycles (RCDC) and ensure high reliability of rechargeable batteries. To investigate various prognostic algorithms, battery life-cycle fade experiments were conducted in the NASA Ames Prognostic Center o...

Lithium-ion battery (LIB) is one of the mainstream of rechargeable batteries and has been widely used in electronics and electric vehicles due to its promising properties. Although the endurance and reliability are the everlasting pursuits, the performance fade of battery is inevitable. What's more, the performance diversity on capacity and fading...

Degradation dynamics modeling and health prognosis play extremely important roles in system prognostics and health management. Wiener process-based degradation models and remaining useful life (RUL) prediction methods have the advantage of high flexibility and efficiency, with features such as Brownian motion with drift and scale parameters. They c...

Interval type-2 fuzzy sets (IT2 FS) have played a prominent role in the development of type-2 (T2) fuzzy logic and fuzzy systems for application to linguistic approximation transformations. Although there have been a number of studies of individual linguistic perception understanding based on T2 fuzzy logic, few of these have paid attention to the...

The concept of system is generally defined as ‘an organized set of detailed methods, procedures and routines that are created to carry out a specific activity or solve a specific problem’, and that has been successfully applied to many domains, ranging from mechanical systems to public health systems. System health monitoring and management (SHMM)...

Whether triage targets can be achieved has been an imperative assessment of service qualities for an emergency department in healthcare management. In this research, we focus on triage targets and try to fully meet the target of fast emergency response for critical patients subject to triage requirements for other category patients by optimising th...

Retrospective analysing of fall incident reports can uncover hidden information, identify potential risk factors, and improve healthcare quality. This study explores potential fall incident clusters using word embeddings and hierarchical clustering. Fall incident reports from 7 local hospitals in Hong Kong were catalogued into 5 potential clusters...

In many industries, statistical process monitoring techniques play a key role in improving processes through variation reduction and defect prevention. Modern large-scale industrial processes require appropriate monitoring techniques that can efficiently address high-dimensional nonlinear processes. Such processes have been successfully monitored w...

For most deep learning practitioners, recurrent networks are often used for sequence modelling. However, recent researches indicate that convolutional architectures may be used to optimize recurrent networks on some machine translation tasks. Problems here are which architecture we should use for a new sequence modelling. By integrating and systema...

State-of-charge (SOC), which indicates the remaining capacity at current cycle, is key to driving range prediction of electric vehicles and optimal charge control of rechargeable batteries. In this paper, we propose a combined convolutional neural network (CNN) – long short-term memory (LSTM) network to infer battery SOC from measurable data, such...

Rolling element bearings are widely used in machines to support rotating shafts and their health conditions degrade over time due to harsh working conditions. Once a fault occurs on the surface of either an inner race or an outer race, impacts caused by rollers striking the fault surface excite resonant frequencies of a machine and then repetitive...

As key components in a rotating machinery system, bearings affect the safety of the entire mechanical system. Hence early-stage monitor of bearing degradation is critical to avoid abrupt mechanical system failure. In this paper, a novel bearing performance assessment model is constructed based on ensemble empirical mode decomposition (EEMD) and aff...

Accurate state-of-charge (SOC) estimation is critical for driving range prediction of electric vehicles and optimal charge control of batteries. In this paper, a stacked long short-term memory network is proposed to model the complex dynamics of lithium iron phosphate batteries and infer battery SOC from current, voltage, and temperature measuremen...

Empirical wavelet transform is a wavelet filter bank to decompose a bearing fault signal into several sub-signals for extracting bearing fault features and it attracts lots of attention recently because it looks like a hybrid of wavelet transform and empirical mode decomposition. However, an assumption for use of empirical wavelet transform is that...

A demo for bearing remaining useful life prediction

Many applications involve dynamic networks for which a sequence of snapshots of network structure is available over time. Studying the evolution of node propensity over time can be important in exploring and analyzing these networks. In this paper, we propose a multivariate surveillance plan to monitor node propensity in the dynamic degree correcte...

Rapid advances in information and sensor technology have led to the development of tools and methods for personalized health monitoring. These techniques support timely and efficient healthcare services by tracking the vital signs, detecting physiological changes and predicting health risks. In this paper, we propose an integrated system to monitor...

Rolling element bearings are widely used in various industrial machines, such as electric motors, generators, pumps, gearboxes, railway axles, turbines, and helicopter transmissions. Fault diagnosis of rolling element bearings is beneficial to preventing any unexpected accident and reducing economic loss. In the past years, many bearing fault detec...

High coulombic efficiency (CE) usually indicates a long battery cycle life. However, the relationship between long-term CE evolution and battery degradation is not fully understood yet. This paper explores the behavior of long-term CE and clarifies its relationship with capacity degradation. Cycle life tests are conducted on two types of mainstream...

Linear Brownian motion with constant drift is widely used in remaining useful life predictions because its first hitting time follows the inverse Gaussian distribution. State space modelling of linear Brownian motion was proposed to make the drift coefficient adaptive and incorporate on-line measurements into the first hitting time distribution. He...

[This corrects the article DOI: 10.1371/journal.pone.0184211.].

Rolling element bearings are widely used in various machines to support rotating shafts. Due to harsh working environments, the health condition of a bearing degrades over time. A typical bearing degradation process includes two phases. In Phase I, the health condition of the bearing is in normal and it exhibits a stable trend. In Phase II, the hea...

Operating rooms (ORs) account for high costs in hospitals. A well-designed surgery scheduling system can help improve facility utilization, thus reduce the cost. This paper is concerned with a surgery scheduling problem in the context where the number of surgeries in waiting list is beyond the capacity of OR. A surgeon may perform more than one sur...

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are res...

Surgery duration is usually used as an input to the operation room (OR) allocation and surgery scheduling problems. A good estimation of surgery duration benefits the operation planning in ORs. In contrast, we would like to investigate whether the allocation decisions in turn influence surgery duration. Using almost two years of data from a large h...