According to the existing mainstream automatic parking system (APS), a parking path is first planned based on the parking slot detected by the sensors. Subsequently, the path tracking module guides the vehicle to track the planned parking path. Howev...
Despite receiving much attention from researchers in the field of naval architecture and marine engineering since the early stages of modern shipbuilding, the berthing phase is still one of the biggest challenges in ship maneuvering due to the potent...
Large-scale integration of renewable energy introduces significant random perturbations to the power system, disrupting the symmetry and balance of active power, which complicates the stabilization of the system’s frequency. Inter-regional ener...
Deep Reinforcement Learning is a key technology for the diagnosis-oriented medical dialogue system, determining the type of disease according to the patient’s utterances. The existing dialogue models for disease diagnosis cannot achieve good pe...
Automation of bucket-filling is of crucial significance to the fully automated systems for wheel loaders. Most previous works are based on a physical model, which cannot adapt to the changeable and complicated working environment. Thus, in this paper...
DTCO (Design–Technology Co-optimization) facilitates communication between the design and process flows, thereby expediting the cycle of the chip development pipeline. Within the DTCO framework, the development of a standard cell library, which...
We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. In our approach, an automatic guided vehicle is equipped with two LiDAR sensors and one frontal RGB camera and learns to perform a targ...
To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed. To mitigate the curse of dime...
Plate turning is an essential step in the plate rolling process. The traditional control mode relies on the manual observation of billets and mainly manual operation. Manual plate turning becomes an external disturbance of the automatic control syste...
In this paper, the multiple autonomous underwater vehicles (AUVs) task allocation (TA) problem in ocean current environment based on a novel reinforcement learning approach is studied. First, the ocean current environment including direction and inte...
The field of autonomous air combat has witnessed a surge in interest propelled by the rapid progress of artificial intelligence technology. A persistent challenge within this domain pertains to autonomous decision-making for dogfighting, especially w...
The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used con...
Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To addre...
Automatic peer-to-peer energy trading can be defined as a Markov decision process and designed using deep reinforcement learning. We consider prosumer as an entity that consumes and produces electric energy with an energy storage system, and define t...
V2X communication is susceptible to attenuation and fading caused by external interference. This interference often leads to bit error and poor quality and stability of the wireless link, and it can easily disrupt packet transmission. In order to enh...
Maneuver decision-making is essential for autonomous air combat. However, previous methods usually make decisions to aim at the target instead of hitting the target and use discrete action spaces instead of continuous action spaces. While these simpl...
The segmented mirror co-phase error identification technique based on supervised learning methods has the advantages of simple application conditions, no dependence on custom sensors, a fast calculation speed, and low computing power requirements com...
Automatic robot gripper system which involves the automated object recognition of work-in-process in production line is the key technology of the upcoming manufacturing facility achieving Industry 4.0. Automatic robot gripper enables the manufacturin...
The Index Selection Problem (ISP) refers to the task of automatically identifying the most appropriate set of indexes for a given database workload that can minimize execution costs. However, ISP is a fundamental yet complex challenge in database man...
In the task of interactive image segmentation, the Inside-Outside Guidance (IOG) algorithm has demonstrated superior segmentation performance leveraging Inside-Outside Guidance information. Nevertheless, we observe that the inconsistent input between...
We present a novel reinforcement learning architecture that learns a structured representation for use in symbolic melody harmonization. Probabilistic models are predominant in melody harmonization tasks, most of which only treat melody notes as inde...
With the continuous development of deep reinforcement learning in intelligent control, combining automatic curriculum learning and deep reinforcement learning can improve the training performance and efficiency of algorithms from easy to difficult. M...
In recent years, reinforcement learning algorithms based on automatic curriculum learning have been increasingly applied to multi-agent system problems. However, in the sparse reward environment, the reinforcement learning agents get almost no feedba...
Many industries apply traditional controllers to automate manual control. In recent years, artificial intelligence controllers applied with deep-learning techniques have been suggested as advanced controllers that can achieve goals from many industri...
Automatic voltage regulation (AVR) systems play an important role in maintaining voltage stability, ensuring efficiency, and enhancing the reliability of synchronous generators. Although conventional PID (proportional-integral-derivative) controllers...
Software-defined networking (SDN) has become one of the critical technologies for data center networks, as it can improve network performance from a global perspective using artificial intelligence algorithms. Due to the strong decision-making and ge...
In recent years, the model compression technique is very effective for deep neural network compression. However, many existing model compression methods rely heavily on human experience to explore a compression strategy between network structure, spe...
Due to the inconsistent geometric characteristics of reinforced concrete structures with composite materials, some echo signals have a lower degree of clarity, resulting in problems such as long time, small range, and low accuracy in identifying stru...
Machine learning algorithms are effective in realizing the programming of robots that behave autonomously for various tasks. For example, reinforcement learning (RL) does not require supervision or data sets; the RL agent explores solutions by itself...
Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variab...
Fabrication drawings are essential for design evaluation, lean manufacturing, and quality detection of precast concrete (PC) components. Due to the complicated shape of PC components, the fabrication drawing needs to be customized to determine manufa...
Human-like automatic deductive reasoning has always been one of the most challenging open problems in the interdisciplinary field of mathematics and artificial intelligence. This paper is the third in a series of our works. We built a neural-symbolic...
This case study explores the use of multiple pre-treatment assessments (functional analysis, reinforcer assessment, alternative self-restraint assessment, modified augmented competing stimulus assessment) to inform a comprehensive treatment package f...
This study proposes an unmanned combat aerial vehicle (UCAV)-oriented hierarchical reinforcement learning framework to address the temporal abstraction challenge in autonomous within-visual-range air combat (WVRAC) for UCAVs. The incorporation of max...
Stereotyped vocal behavior exhibited by a seven-year-old child diagnosed with autism spectrum disorder and maintained by automatic reinforcement was placed under stimulus control through discrimination training. The training consisted of matching a g...
Automatic corrections offer direct policy supervision during assisted robotic reinforcement learning. We evaluate whether regressing on these actions improves learning beyond their use in replay. Uniform corrective distillation (UCD) adds actor regre...
External beam radiotherapy cancer treatment aims to deliver dose fractions to slowly destroy a tumor while avoiding severe side effects in surrounding healthy tissues. To automate the dose fraction schedules, this paper investigates how deep reinforc...
The primary goal of the construction industries worldwide is to improve material durability and achieve sustainability. In recent years of sustainable cement industry innovation, alkali-activated cement has emerged as one of the most promising altern...
Automatic collision avoidance decision making for vessels is a critical challenge in the development of autonomous ships and has become a central point of research in the maritime safety domain. Effective and systematic collision avoidance strategies...
Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle t...
Software maintenance and evolution account for approximately 90% of the software development process (e.g., implementation, testing, and maintenance). Bug triaging refers to an activity where developers diagnose, fix, test, and document bug reports d...
Considering the ever-growing volume of electronic documents made available in our daily lives, the need for an efficient tool to capture their gist increases as well. Automatic text summarization, which is a process of shortening long text and extrac...
Background/Aim: Motor skill acquisition is the foundation of athletic performance, from the novice learning a new technique to the elite athlete executing complex movements automatically under pressure. Although classical models have defined the neur...
Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism...
Coordinated ramp metering (CRM) is an effective strategy for mitigating congestion on urban expressways. It utilizes traffic flow measurements from the expressway network to regulate multi-ramp signals for optimal system performance. Many CRM approac...
The prevalent utilization of deterministic strategy algorithms in Multi-Agent Deep Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge in achieving stable and high-performance cooperative behavior. Addressing the...
Displacements or deformations of materials or structures are measured with linear variable differential transducers (LVDT), fibre optic sensors, laser sensors, and confocal sensor systems, while strains are measured with electro-resistant tensometers...
While Low Emission Zone (LEZ) enforcement cameras provide a constant stream of traffic data, such resources remain significantly underexploited for urban mobility planning, as their current application is restricted to enforcing vehicle access regula...
Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. M...
Lack of reinforcements is an existing drawback of 3D printed cementitious components, which is an urgent concern. A staple-inserting apparatus was developed and installed on a 3D printer and automatically fabricated 3D printed and staple-reinforced c...
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