Air pollution is a global issue with profound implications to human health and environmental sustainability. Especially, PM2.5, which refers to particulate matter with a size of 2.5 microns or smaller, poses significa...
详细信息
Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal subst...
详细信息
ISBN:
(纸本)9789819783441
Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. Automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging Dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for clas
There is a possibility that deepfakes will increase a great deal of false information in a very realistic way, but it seems that today such technology appears to be extremely dangerous. While generative Artificial Int...
详细信息
In this paper, we propose a Hybrid Routing Protocol based on Firefly and Simulated Annealing algorithms (HRP-FASA) as an effective protocol that optimizes the energy consumption of nodes, thus prolonging the overall n...
详细信息
Quantum Computing is a fastly growing area with many applications, including quantum machine learning (QML). Due to the rapid increase of computational power, machine learning models based on artificial neural network...
详细信息
This study addresses the fixed-time-synchronized control problem of perturbed multi-input multioutput(MIMO) systems. In the task of fixed-time-synchronized control, different dimensions of the output signal in MIMO sy...
详细信息
This study addresses the fixed-time-synchronized control problem of perturbed multi-input multioutput(MIMO) systems. In the task of fixed-time-synchronized control, different dimensions of the output signal in MIMO systems are required to reach the desired value simultaneously within a fixed time *** MIMO system is categorized into two cases: the input-dimension-dominant and the state-dimensiondominant cases. The classification is defined according to the dimension of system signals and, more importantly, the capability of converging at the same time. For each kind of MIMO system, sufficient Lyapunov conditions for fixed-time-synchronized convergence are explored, and the corresponding robust sliding mode controllers are designed. Moreover, perturbations are compensated using the super-twisting technique. The brake control of the vertical takeoff and landing aircraft is considered to verify the proposed method for the input-dimension-dominant case, which shows the essential advantages of decreasing the energy consumption and the output trajectory length. Furthermore, comparative numerical simulations are performed to show the semi-time-synchronized property for the state-dimension-dominant case.
As more and more contemporary items are linked to the internet, the Internet of Things (IoT) has grown widely. The development of the IoT has been facilitated by the confluence of numerous technologies, including ubiq...
详细信息
We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly ge...
详细信息
The COVID-19 pandemic has led to significant outbreaks in more than 220 countries worldwide, profoundly impacting the public health and lives. As of February 2024, over 774 million cases have been reported, with more ...
详细信息
Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)***,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficien...
详细信息
Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)***,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficient in practical *** few‐shot learning(FSL)methods generally ignore the potential relationships between non‐local spatial samples that would better represent the underlying features of *** solve the above issues,a novel deep transformer and few‐shot learning(DTFSL)classification framework is proposed,attempting to realize fine‐grained classification of HSI with only a few‐shot ***,the spatial attention and spectral query modules are introduced to overcome the constraint of the convolution kernel and consider the information between long‐distance location(non‐local)samples to reduce the uncertainty of ***,the network is trained with episodes and task‐based learning strategies to learn a metric space,which can continuously enhance its modelling ***,the developed approach combines the advantages of domain adaptation to reduce the variation in inter‐domain distribution and realize distribution *** three publicly available HSI data,extensive experiments have indicated that the proposed DT‐FSL yields better results concerning state‐of‐the‐art algorithms.
暂无评论