Identifying failure modes is an important task to improve the design and reliability of a product and can also serve as a key input in sensor selection for predictive maintenance. Failure mode acquisition typically relies on experts or simulations wh...
6 August 2018
Compared to information technology (IT) revolutions, which are characterized by disruptive innovations, the innovations required for the 4th Industrial Revolution will be characterized by the cumulativeness of the innovations. Therefore, we will need...
The brain often switches freely between focused attention and divergent thinking, and the Default Mode Network (DMN) is activated during brain rest. Since its discovery, the DMN, together with its function and characteristics, indicates that learning...
Gradient descent method is an essential algorithm for learning of neural networks. Among diverse variations of gradient descent method that have been developed for accelerating learning speed, the natural gradient learning is based on the theory of i...
Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. Th...
The forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new m...
This paper presents a novel iterative learning sliding mode controller (ILSMC) that can be applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) subject to model uncertainties and external disturbances. Here, the proposed IL...
This paper presents the development and implementation of a reinforcement learning agent as the mode selector for a multi-chamber actuator in a load-sensing architecture. The agent selects the mode of the actuator to minimise system energy losses. Th...
Deep learning for fine-grained radar mode recognition faces a major bottleneck—its heavy reliance on expensively labeled data. To address this, we propose a novel semi-supervised framework that effectively leverages unlabeled data. Through an e...
Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial...
Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diese...
Due to its orbital angular momentum (OAM), optical vortex has been widely used in communications and LIDAR target detection. The OAM mode recognition based on deep learning is mostly based on the basic convolutional neural network. To ensure high-pre...
A major challenge in device to device (D2D) communications is determining the appropriate communication modes for each potential D2D pair. In dynamic networks, the continuous movement of devices increases the complexity of channel state modeling, whi...
This paper addresses a second-order sliding mode control method for the formation problem of multirobot systems. The formation patterns are usually symmetrical. This sliding mode control is based on the super-twisting law. In many real-world applicat...
Transportation mode selection is pivotal for navigating through cities plagued by heavy traffic congestion. This plays a crucial role in ensuring the efficient utilization of time and resources to achieve the desired objectives. Given the complex dyn...
To address the tracking control problem of the periodic motion fast tool servo system (FTS), we propose a control method that combines adaptive sliding mode control with closed-loop iterative learning control. Adaptive sliding mode control enhances t...
In this study, we aimed to evaluate the performance of various machine learning (ML) classifiers to predict mode choice of movement-challenged persons (MCPs) based on data collected through a questionnaire survey of 384 respondents in Dhaka, Banglade...
We experimentally demonstrate the application of a double deep Q-learning network algorithm (DDQN) for design of a self-starting fiber mode-locked laser. In contrast to the static optimization of a system design, the DDQN reinforcement algorithm is c...
In many laser application scenarios, concentrated optical energy, high coherence, and narrow spectral linewidth are critical optical characteristics that ensure the excellent performance of lasers. These characteristics can be achieved when a laser o...
Building a multimode transportation system could effectively reduce traffic congestion and improve travel quality. In many cities, use of public transport and green travel modes is encouraged in order to reduce the emission of greenhouse gas. With th...
With the continuous expansion of power grids and the gradual increase in operational uncertainty, it is progressively challenging to meet the capacity requirements for power grid development based on manual experience. In order to further improve the...
Electric micro-mobility modes, such as e-scooters and e-bikes, are increasingly used in urban areas, posing challenges for accurate travel mode detection in mobility studies. Traditional supervised learning approaches require large labeled datasets,...
In shearography-based tire testing, so-called “Mode Hops”, abrupt phase changes caused by laser mode changes, can lead to significant disturbances in the interference image analysis. These artifacts distort defect assessment, lead to rete...
The seismically deficient column details in existing reinforced concrete buildings affect the overall behavior of the building depending on the failure type of the column. The purpose of this study is to develop and validate a machine-learning-based...
Sliding mode control, an algorithm known for its stability and robustness, has been widely used in designing robot controllers. Such controllers inevitably exhibit chattering; numerous methods have been proposed to deal with this problem in the past...
Background/Objectives: Epilepsy is a neurological disorder that severely impacts patients’ quality of life. In clinical practice, specific pharmacological and surgical interventions are tailored to distinct seizure types. The identification of...
The Laguerre-Gaussian (LG) beam demonstrates great potential for optical communication due to its orthogonality between different eigenstates, and has gained increased research interest in recent years. Here, we propose a dual-output mode analysis me...
Due to increasing traffic congestion, travel modeling has gained importance in the development of transportion mode detection (TMD) strategies over the past decade. Nowadays, recent smartphones, equipped with integrated inertial measurement units (IM...
To address the limitations of the traditional methods that are used to extract features from non-stationary signals and capture temporal dependency relationships, a rolling bearing fault diagnosis method combining variational mode decomposition (VMD)...
A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design,...
In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM v...
Operating mode identification is an important prerequisite for precise deceptive jamming technology against synthetic aperture radar (SAR). In order to solve the problems of traditional spaceborne SAR operating mode identification, such as low identi...
The freight transportation system faces complex operations globally to meet customer demands. Intense competition prompts companies to enhance performance. Transportation modes (road, sea, air) impact service levels, each with distinct features, bene...
A hybrid control architecture is proposed for enhancing the stability and energy management of DC microgrids (DCMGs) integrating photovoltaic generation, batteries, and supercapacitors. The approach combines nonlinear Sliding Mode Control (SMC) for f...
Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicia...
Predicting how people choose their travel modes accurately is important in the transportation field. Machine learning (ML) and neural networks (NNs) have gradually become popular in recent years. However, which is better is seldom discussed in previo...
Based on a set of deep learning and mode decomposition methods, a short-term prediction model for PM2.5 concentration for Beijing city is established in this paper. An ensemble empirical mode decomposition (EEMD) algorithm is first used to decompose...
In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same ope...
Trajectory tracking control of autonomous underwater vehicles (AUVs) faces challenges in complex nearshore environments due to model uncertainties and external environmental disturbances. Traditional control methods often rely on expert knowledge and...
In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning....
Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development...
One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the be...
Extracting features manually and employing preeminent knowledge is overly utilized in methods to conduct fault diagnosis. A diagnosis approach utilizing intelligent methods of the optimized variational mode decomposition and deep transfer learning is...
Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-p...
To reduce the impact of periodic pulsating torque and non-periodic disturbances on the speed control performance of permanent magnet synchronous motors (PMSMs), a sliding mode variable structure control method incorporating iterative learning compens...
Reinforced concrete (RC) slabs are the main load-bearing member of engineering structures, which may be threatened by blast loading. Predicting and analyzing the damage condition and failure mode of RC slab is a necessary means to ensure structural s...
Grating couplers are essential components in silicon photonics that facilitate the coupling of light between waveguides and fibers. Optimization of the grating couplers to reach <1 dB loss when coupling to single-mode fibers (SMFs) has been report...
Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine...
During the process of satellite capture by a flexible base–link–joint space robot, the base, joints, and links vibrate easily and also rotate in a disorderly manner owing to the impact torque. To address this problem, a repetitive learning sliding mo...
Iterative learning based digital peak current mode control (PCMC) is proposed in this paper. The proposed control method provides excellent current reference tracking against variations in input voltage, load, and circuit parameters. Compared to othe...
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