The Working Group II contribution to the IPCC’s Sixth Assessment Report states that effective adaptation to the changing climate will require transformational changes in how people live. This article explores the potential for climate services to cat...
Countries across the world aspire towards climate resilient sustainable development. The interacting processes of climate change, land change, and unprecedented social and technological change pose significant obstacles to these aspirations. The pace...
Did the novel planning arrangements in the National Adaptation Programmes of Action (NAPAs) enable stakeholders to substantively influence adaptation planning? If so, does the observed influence have potential for more transformational adaptation? We...
Adaptation provides a new perspective for the study of farmers’ livelihood transformation and sustainability. This research aimed to explore the influencing factors of different types and adaptation of farmers’ livelihood transformation d...
Community-based adaptation (CBA) has become a credible remedy to climate change adaptation, emphasizing local participation and community-defined priorities. However, its transformative potential remains limited when structural root causes of vulnera...
The integration of renewable energy generation and nonlinear power electronic equipment into the grid brings about complex harmonics and interharmonics problems. The amplitude and frequency of harmonics and interharmonics should be detected by high t...
In conventional radar signal processing, the cascade of pulse compression (i.e., matched filter) and Radon-Fourier transform (RFT) can extract the estimated scattering coefficient of the target in the range-velocity dimension through long-time cohere...
Tone mapping (TM) aims to display high dynamic range scenes on media with limited visual information reproduction. Logarithmic transformation is a widely used preprocessing method in TM algorithms. However, the conventional logarithmic transformation...
The quality of remote sensing images has been greatly improved by the rapid improvement of unmanned aerial vehicles (UAVs), which has made it possible to detect small objects in the most complex scenes. Recently, learning-based object detection has b...
In recent years, the heritage preservation debate has seen a growing interest in emerging theories in which the concept of potential plays an essential role. Starting from the assumption that memory is an evolving mental construct, the present paper...
Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve accuracy and robustness. Howev...
Climate change has seriously affected agriculture and many aspects of the life of local people in the Vietnam Mekong Delta (VMD). Learning to shift towards sustainable development to successfully adapt to climate change is essential. The VACB (V&mdas...
The COVID-19 pandemic and quarantine have forced students to use distance learning. Modern information technologies have enabled global e-learning usage but also revealed a lack of personalization and adaptation in the learning process when compared...
In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) and transformer architectures have each contributed to considerable advancements. CNNs possess potent local feature representation skills, whereas transformers excel in...
Worldwide only about four percent of the estimated $500 billion-plus in public and private climate finance in 2017 was destined for adaptation. However, institutions like the World Bank are positioning themselves for a transformation in adaptation fi...
The discrete cosine transform (DCT) is widely used for image and video compression. Lossy algorithms such as JPEG, WebP, BPG and many others are based on it. Multiple modifications of DCT have been developed to improve its performance. One of them is...
20 July 2019
Recognition of projected increases in exposure to large-scale hazard events over the coming decades has identified a need to develop how disaster risk reduction and recovery are conceptualized and enacted. This paper discusses some strategies for pur...
This paper proposes a transformer temperature early warning method based on an adaptive sliding window and stacking ensemble learning algorithm, aiming to improve the accuracy and robustness of temperature prediction. The transformer temperature earl...
The changes in operating conditions of a power transformer can cause a shift in the distribution of partial discharge data, leading to the gradual generation of unlabeled new data, which results in the degradation of the original partial discharge de...
Disasters result where hazards and vulnerabilities intersect. The concept of vulnerability itself is mainly a social construct and the extent to which this can be overcome while transforming disaster-prone systems has often been emphasised in the cri...
Vehicle mass is crucial to autonomous vehicles control. Affected by the nonlinearity of vehicle dynamics between vehicle states, it is still a tough issue to estimate vehicle mass precisely and stably. The transformer aided adaptive extended Kalman f...
Local strategies and policies are key in climate adaptation, although research shows significant barriers to progress. Sweden, often seen as progressive in climate change issues, has struggled in adopting a sufficient local climate adaptation organiz...
A conduction transformation-based coordination method for product structure optimization design was proposed for the conflict between satisfying product functional requirements and realizing adaptive modification. Customer functional requirements wer...
Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous mac...
Non-Intrusive Load Monitoring (NILM) is an effective energy consumption analysis technology, which just requires voltage and current signals on the user bus. This non-invasive monitoring approach can clarify the working state of multiple loads in the...
Accurate short-term photovoltaic (PV) power generation forecasting is critical for the stable integration of renewable energy into the grid. This study proposes a Transformer model enhanced with an adaptive sparse self-attention (ASSA) mechanism for...
At present, generative AI has problems of insufficient content generation accuracy, weak personalized response, and low reasoning efficiency in adaptive learning scenarios, which limit its in-depth application in intelligent teaching. To solve this p...
The commitment to understanding the implications of a 1.5 °C global temperature warming limit has contributed to a growing realisation that transformative adaptation is necessary to avoid catastrophic environmental and social consequences. This is pa...
Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human–computer interaction. Through recent advancements in deep learning technologies, monocular depth...
Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware S...
Batch normalization (BN) has become a foundational component in modern deep neural networks. However, one of its disadvantages is its reliance on batch statistics that may be unreliable or unavailable during inference, particularly under test-time do...
Nowadays, video is a common social media in our lives. Video summarisation has become an interesting task for information extraction, where the challenge of high redundancy of key scenes leads to difficulties in retrieving important messages. To addr...
Accurate hour-ahead photovoltaic (PV) forecasts are essential for grid balancing, intraday trading, and renewable integration. While Transformer architectures have recently reshaped time series forecasting, their application to short-term PV predicti...
To address the performance degradation of cross-domain object detection under various illumination conditions and adverse weather scenarios, this paper introduces a novel method a called Step-wise Domain Adaptation DEtection TRansformer (SDA-DETR). O...
Person re-identification (ReID) is the problem of cross-camera target retrieval. The extraction of robust and discriminant features is the key factor in realizing the correct correlation of targets. A model based on convolutional neural networks (CNN...
Unsupervised domain adaptation (UDA) aims to reapply the classifier to be ever-trained on a labeled source domain to a related unlabeled target domain. Recent progress in this line has evolved with the advance of network architectures from convolutio...
In order to incorporate climate adaptation in spatial planning change is required, because climate change impacts the way we live. This implies that spatial planning, as the arranger of the spatial organisation and layout needs to be able to support...
The survival of an organization in the contemporary competitive and dynamic global business environment is associated with its creative workforce. This study explores the impact of transformational leadership (TL) and employee adaptiveness on workfor...
Magnetic resonance imaging (MRI) plays an important role in disease diagnosis. The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements. Thus, the...
High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image cont...
This paper presents a comprehensive approach to detect violent events in videos by combining CrimeNet, a Vision Transformer (ViT) model with structured neural learning and adversarial regularization, with an adaptive threshold sliding window model ba...
In real-world scenarios, deviations in the frequency response of instrumentation transformers can lead to distorted harmonic measurements, highlighting the critical role harmonic measurement plays in assessing power quality. The blind channel equaliz...
Time-frequency analysis (TFA) technology is an important tool for analyzing non-Gaussian mechanical fault vibration signals. In the complex background of infinite variance process noise and Gaussian colored noise, it is difficult for traditional meth...
State monitoring is very important for the safe operation of high-voltage transformers. A non-contact vibro-acoustic detection method based on the Blind Source Separation (BSS) was proposed in this paper to promote the development of transformer on-l...
With the increasing threat of Unmanned Aerial Vehicle (UAV) intrusions, enhancing the perceptual capabilities of counter-UAV systems has become essential. Although various advanced algorithms have been developed in the domain of object tracking, reso...
Multivariate time series forecasting requires modeling complex and evolving spatio-temporal dependencies as well as frequency-domain patterns; however, the existing Transformer-based approaches often struggle to effectively capture dynamic inter-seri...
Active noise control (ANC) technology can be able to reduce the low frequency noise effectively, and has been widely applied in limited enclosed equipment, such as cars, aircraft cockpits, and headphones, etc. Compared with the traditional noise redu...
In order to identify the horizontal seismic motion owning the largest pulse energy, and represent the dominant pulse-like component embedded in this seismic motion, we used the adaptive wavelet transform algorithm in this paper. Fifteen candidate mot...
Polynomial phase signals (PPSs) have numerous applications in many fields including radar, sonar, geophysics, and radio communication systems. Therefore, estimation of PPS coefficients is very important. In this paper, a novel approach for PPS parame...
Most adaptive learning systems personalize around content sequencing and difficulty adjustment rather than transforming instructional material within the lesson itself. This paper presents the STREAM (Semantic Transformation and Real-Time Educational...
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