Multi-output regression aims to utilize the correlation between outputs to achieve information transfer between dependent outputs, thus improving the accuracy of predictive models. Although the Bayesian support vector machine (BSVR) can provide both...
Due to its wide applications, multi-output learning that predicts multiple output values for a single input at the same time is becoming more and more attractive. As one of the most popular frameworks for dealing with multi-output learning, the perfo...
In regions with limited meteorological monitoring systems, spatial drought modeling is of importance for efficient water resource management. This study recommends an alternative drought modeling strategy for Standardized Precipitation Evapotranspira...
Multi-output power converters using different architectures can have significant efficiency advantages. This paper proposes a multi-output welding power supply that is based on the middle DC converter distributed architecture. This machine includes t...
The stock market index future price forecasting is one of the imperative financial time series problems. Accurately estimated future closing prices can play important role in making trading decisions and investment plannings. This work proposes a new...
Multi-load wireless power transfer systems generally require the configuration of multiple transmitting coils. Using traditional single-output inverters will increase the number of inverters, leading to increased system costs and complex structures....
Regression modelling has always been a key process in unlocking the relationships between independent and dependent variables that are held within data. In recent years, machine learning has uncovered new insights in many fields, providing prediction...
Online purchasing has developed rapidly in recent years due to its efficiency, convenience, low cost, and product variety. This has increased the number of online multi-category e-commerce retailers that sell a variety of product categories. Due to t...
Machine learning techniques are widely applied in the field of building energy analysis to provide accurate energy models. The majority of previous studies, however, apply single-output machine learning algorithms to predict building energy use. Sing...
Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy con...
Background/Objectives: Postoperative complications following adult cardiac surgery are biologically interrelated, yet most machine learning models predict single outcomes. We developed an explainable multi-output model integrating routinely collected...
Accurate aboveground vegetation biomass forecasting is essential for livestock management, climate impact assessments, and ecosystem health. While artificial intelligence (AI) techniques have advanced time series forecasting, a research gap in predic...
This study presents a single-inductor multi-output dual-path hybrid buck converter by applying a load-dependent sequencing technique to obtain output voltages (VOUTs) over a wide range. When the conventional hybrid dual-path topology is applied to mu...
In higher education, student learning relies increasingly on autonomy. With the rise in blended learning, both online and offline, students need to further improve their online learning effectiveness. Therefore, predicting students’ performance...
For the porthole hot extrusion of a thin-walled tube based on metal flow, the role of the die’s structure should be focused on to achieve precision formation, especially for multi-output extrusion and/or complex cross-sectional profiles. In ord...
Dirty paper coding (DPC) is applied to multi-input multi-output (MIMO) broadcast channels with additive Gaussian noise and one message per receiver. The method decomposes each receiver MIMO channel into parallel scalar channels and applies modulo ope...
We propose two multi-layer controlled multi-output remote state preparation (RSP) schemes for arbitrary single-qubit and single-qudit states. Twelve-particle entangled channels are constructed in two-dimensional and d-dimensional Hilbert spaces, and...
Many studies have been conducted on multi-output systems that transfer power to multiple receivers in conventional planar-type wireless power transfer (WPT) systems; however, few studies and analyses have taken into account the mutual inductance betw...
Complex systems often face significant challenges in both efficiency and performance when making long-term degradation predictions. To address these issues, this paper proposes a predictive architecture based on multi-output sparse probabilistic mode...
Hybrid Energy Storage Systems (HESSs) have gathered considerable interest due to their potential to achieve high energy and power density by integrating different storage technologies, such as batteries and capacitors, to name a few. Among the variou...
The concentrations of nitrogen (N), phosphorus (P), potassium (K), organic matter (OM), and pH in soil are critical markers of fertility that influence crop growth and yield. Traditional wet-chemical analyses are labor-intensive, time-consuming, and...
Volatile Organic Compounds (VOCs) are important contributors to indoor and occupational air pollution, such as environments involving the extensive use of paints and solvents. The routine measurement of VOCs is often limited by resource constraints,...
First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortun...
Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, metho...
Conventional validation methods for multi-output models generally assume probabilistic descriptions of all input variables. In engineering applications, however, sparse data and limited knowledge may permit some inputs to be specified only by interva...
Consolidated bioprocessing (CBP) has been widely studied as an integrated route for converting biomass into biofuels and bioproducts, yet most quantitative modeling work has focused on ethanol as a single response. Because CBP systems can generate mu...
The incorporation of slag and calcium carbonate (CaCO3) as clinker-reducing constituents offers significant potential for lowering CO2 emissions in cement production; however, their combined influence on age-dependent compressive strength remains com...
Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network contr...
Borehole breakouts significantly influence drilling operations’ efficiency and economics. Accurate evaluation of breakout size (angle and depth) can enhance drilling strategies and hold potential for in situ stress magnitude inversion. In this...
The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon em...
For an underwater Strapdown Inertial Navigation System/Doppler velocity log (SINS/DVL) integrated navigation system, the short-term failure of DVL may lead to the loss of reliable external velocity information from DVL, which will cause the SINS erro...
In practical quantum communication, quantum channels are inevitably affected by noise and decoherence, leading to their degradation into non-maximally entangled or even mixed states. As a result, conventional quantum teleportation schemes based on no...
Manual arc welding is highly dependent on operator skill, leading to variability in weld quality and an increased risk of defects; therefore, reliable monitoring methods for Shielded Metal Arc Welding (SMAW) are required, particularly in manual envir...
Fast and Relaxed Vector Fitting (FRVF) is a frequency domain system identification method widely used in electrical network modelling, but its application to mechanical systems remains limited. This study adapts FRVF to identify modal parameters of a...
Fault diagnosis is important in rotor systems because severe damage can occur during the operation of systems under harsh conditions. The advancements in machine learning and deep learning have led to enhanced performance of classification. Two impor...
Multi-reservoir joint operation is a high-dimensional, nonlinear, multi-input multi-output (MIMO) water-allocation problem. This study aimed to map the state variables of multiple water sources directly to the water allocated to each user with a sing...
Accurate prediction of the mechanical performance of polymer components fabricated by fused deposition modelling (FDM) remains challenging owing to the complex nonlinear relationships between process parameters and material properties, limiting relia...
In this study, an optimization method using various multi-output regression models as model proxies within the NSGA-II framework was applied to determine the geometric parameters (P, W, D) of L-shaped twisted tape inserts for achieving the optimal ov...
This paper presents a tightly regulated multi-output isolated converter that employs only an independently regulated synchronous Single-Switched Post-Regulator (SSPR). The proposed converter is a highly accurate single-ended secondary side post-regul...
Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. T...
This study introduces an innovative filter topology capable of providing simultaneous positive and negative gain outputs for one-fractional order LP, with high-pass, all-pass, and fractional-order shelving filter responses. The circuit, utilizing mul...
Geotechnical site characterizations aim to determine site-specific subsurface profiles and provide a comprehensive understanding of associated soil properties, which are important for geotechnical engineering design. Traditional methods often neglect...
This study aims to accurately predict abrasion resistance, measured through the Los Angeles (LA) abrasion test, and modulus of elasticity, assessed using the Micro-Deval Abrasion (MDA) test, to support structural integrity and efficient material use...
In order to ensure the production quality of high-speed laser welding, it is necessary to simultaneously monitor multiple state properties. Monitoring methods combining vision sensing and deep learning models are popular but most models used can only...
The standard 12-lead ECG is the gold-standard tool for cardiac diagnosis and monitoring; however, its multi-electrode configuration limits its use in prolonged monitoring and wearable devices. Existing portable ECG devices typically record only a sin...
Scene classification is an active research area in the remote sensing (RS) domain. Some categories of RS scenes, such as medium residential and dense residential scenes, would contain the same type of geographical objects but have various spatial dis...
Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techn...
In the field of macro-level safety studies, road traffic safety is significantly related to socioeconomic factors, such as population, number of vehicles, and Gross Domestic Product (GDP). Due to different levels of economic and urbanization, the inf...
Dry reforming of methane (DRM) offers a sustainable route to convert two major greenhouse gases—CH4 and CO2—into synthesis gas (syngas), enabling low-carbon hydrogen production and carbon utilization. This study applies fifteen machine le...
Lithium-ion battery state of health (SOH) accurate prediction is of great significance to ensure the safe reliable operation of electric vehicles and energy storage systems. However, safety issues arising from the inaccurate estimation and prediction...
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