Interactive Modules Research Articles

Simultaneous segmentation and classification of colon cancer polyp images using a dual branch multi-task learning network.

Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods.

CTNet: Contrastive Transformer Network for Polyp Segmentation.

Segmenting polyps from colonoscopy images is very important in clinical practice since it provides valuable information for colorectal cancer. However, polyp segmentation remains a challenging task as polyps have camouflage properties and vary greatly in size. Although many polyp segmentation methods have been recently proposed and produced remarkable results, most of them cannot yield stable results due to the lack of features with distinguishing properties and those with high-level semantic details. Therefore, we proposed a novel polyp segmentation framework called contrastive Transformer network (CTNet), with three key components of contrastive Transformer backbone, self-multiscale interaction module (SMIM), and collection information module (CIM), which has excellent learning and generalization abilities. The long-range dependence and highly structured feature map space obtained by CTNet through contrastive Transformer can effectively localize polyps with camouflage properties. CTNet benefits from the multiscale information and high-resolution feature maps with high-level semantic obtained by SMIM and CIM, respectively, and thus can obtain accurate segmentation results for polyps of different sizes. Without bells and whistles, CTNet yields significant gains of 2.3%, 3.7%, 3.7%, 18.2%, and 10.1% over classical method PraNet on Kvasir-SEG, CVC-ClinicDB, Endoscene, ETIS-LaribPolypDB, and CVC-ColonDB respectively. In addition, CTNet has advantages in camouflaged object detection and defect detection. The code is available at https://github.com/Fhujinwu/CTNet.

RGBT Tracking via Challenge-Based Appearance Disentanglement and Interaction.

RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role in representing the target appearance in RGBT tracking. In this paper, we propose a novel approach, which performs target appearance representation disentanglement and interaction via both modality-shared and modality-specific challenge attributes, for robust RGBT tracking. In particular, we disentangle the target appearance representations via five challenge-based branches with different structures according to their properties, including three parameter-shared branches to model modality-shared challenges and two parameter-independent branches to model modality-specific challenges. Considering the complementary advantages between modality-specific cues, we propose a guidance interaction module to transfer discriminative features from one modality to another one to enhance the discriminative ability of weak modality. Moreover, we design an aggregation interaction module to combine all challenge-based target representations, which could form more discriminative target representations and fit the challenge-agnostic tracking process. These challenge-based branches are able to model the target appearance under certain challenges so that the target representations can be learned by a few parameters even in the situation of insufficient training data. In addition, to relieve labor costs and avoid label ambiguity, we design a generation strategy to generate training data with different challenge attributes. Comprehensive experiments demonstrate the superiority of the proposed tracker against the state-of-the-art methods on four benchmark datasets.

Development and Validation of a Teaching Module based on the Traditional Approach of the Japanese Bansho Plan Towards the Mastery of Quadratic Equations

This study is an effort to develop and evaluate the validity of the Bansho Plan-Based Teaching Module to improve students’ Higher Order Thinking Skills (HOTS) mastery of the topic of Quadratic Equations. The interactive module comprises six lesson units to provide students with the opportunity to explore the application of the Quadratic Equations concept in real-life situations. The design, development, and validity evaluation of the module was based on the design and development research approach. The discussion focused on two phases, namely the requirements analysis and the development design. The Morrison, Ross, and Kemp (2007) model consisting of nine components was used to support the module development in the first stage of the current study. Meanwhile, the second stage involved an evaluation of the module’s validity and the implementation of a pilot study. Responses from five Mathematics education experts were analyzed using the Percentage Calculation Method (PCM). The highest mean score for content validity coefficient was obtained by the knowledge level aspect with 0.96. This confirms that the module is in line with the national curriculum, as well as the complexity level of all problems presented in the module is not confusing and the module is capable of applying HOTS to the topic of Quadratic Equations. Furthermore, a reliability test was done on the learning objectives through a pilot study involving thirty students. The results showed that the module obtained a Cronbach’s Alpha coefficient value of 0.915, indicating its good consistency. One major contribution of this study is that it highlights the uniqueness of combining multimedia modules with the traditional approach of the Bansho Plan. It creates an awareness that traditional teaching approaches, such as the Bansho Plan, should not be labelled as outdated, but instead its wisdom needs to be explored and adapted to the demands and needs of 21st century teaching.

Beyond Appearance: Multi-Frame Spatio-Temporal Context Memory Networks for Efficient and Robust Video Object Segmentation.

Current video object segmentation approaches primarily rely on frame-wise appearance information to perform matching. Despite significant progress, reliable matching becomes challenging due to rapid changes of the object's appearance over time. Moreover, previous matching mechanisms suffer from redundant computation and noise interference as the number of accumulated frames increases. In this paper, we introduce a multi-frame spatio-temporal context memory (STCM) network to exploit discriminative spatio-temporal cues in multiple adjacent frames by utilizing a multi-frame context interaction module (MCI) for memory construction. Based on the proposed MCI module, a sparse group memory reader is developed to enable efficient sparse matching during memory reading. Our proposed method is generic and achieves state-of-the-art performance with real-time speed on benchmark datasets such as DAVIS and YouTube-VOS. In addition, our model exhibits robustness to sparse videos with low frame rates.

Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT.

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.

Research on Key Technologies of Remote Operation and Maintenance System for Substation Automation Equipment

Abstract With the help of automation technology, the efficiency and reliability of substation operations are synchronized. However, the equipment will operate under abnormal problems for a long time. If the problem is not resolved promptly, the consequences will be unimaginable. For timely detection of problems, this paper proposes a method for the construction of remote operation and maintenance systems. The remote operation and maintenance system is mainly composed of two-factor authentication, responsibility area authentication, and permission authentication. The security of remote operations and maintenance operations is guaranteed with the support of these key technologies. This paper enhances the safety and stability of automation equipment by offering remote wake-up, remote reset, historical information retrieval, and forwarding table monitoring services in the operation and maintenance service module. Adopting IoT technology, the core management module of remote management of master stations is constructed to ensure the safety of remote operations and maintenance data interaction between master stations and sub-stations. Simulation experiments are being conducted to evaluate the performance of IoT technology in remote operations and maintenance interactions at the main station. The energy consumption of the data interaction module of substation automation equipment is 0.19 and 0.012 mA for 1 hour and 1 day, respectively, for the equipment to operate for 5-10 days, where the advantages of IoT technology are reflected. The processing time of the operation and maintenance personnel under the traditional operation and maintenance and remote operation and maintenance modes were analyzed through comparative experiments, and the processing time of the device dead time was reduced from 175.46 minutes to 4.356 minutes after using the remote operation and maintenance system, and the efficiency of the operation and maintenance personnel was improved.

Multimodal Composition Example Mining for Composed Query Image Retrieval.

Composed query image retrieval task aims to retrieve the target image in the database by a query that composes two different modalities: a reference image and a sentence declaring that some details of the reference image need to be modified and replaced by new elements. Tackling this task needs to learn a multimodal embedding space, which can make semantically similar targets and queries close but dissimilar targets and queries as far away as possible. Most of the existing methods start from the perspective of model structure and design some clever interactive modules to promote the better fusion and embedding of different modalities. However, their learning objectives use conventional query-level examples as negatives while neglecting the composed query's multimodal characteristics, leading to the inadequate utilization of the training data and suboptimal construction of metric space. To this end, in this paper, we propose to improve the learning objective by constructing and mining hard negative examples from the perspective of multimodal fusion. Specifically, we compose the reference image and its logically unpaired sentences rather than paired ones to create component-level negative examples to better use data and enhance the optimization of metric space. In addition, we further propose a new sentence augmentation method to generate more indistinguishable multimodal negative examples from the element level and help the model learn a better metric space. Massive comparison experiments on four real-world datasets confirm the effectiveness of the proposed method.

CLEAR: Cluster-Enhanced Contrast for Self-Supervised Graph Representation Learning.

This article studies self-supervised graph representation learning, which is critical to various tasks, such as protein property prediction. Existing methods typically aggregate representations of each individual node as graph representations, but fail to comprehensively explore local substructures (i.e., motifs and subgraphs), which also play important roles in many graph mining tasks. In this article, we propose a self-supervised graph representation learning framework named cluster-enhanced Contrast (CLEAR) that models the structural semantics of a graph from graph-level and substructure-level granularities, i.e., global semantics and local semantics, respectively. Specifically, we use graph-level augmentation strategies followed by a graph neural network-based encoder to explore global semantics. As for local semantics, we first use graph clustering techniques to partition each whole graph into several subgraphs while preserving as much semantic information as possible. We further employ a self-attention interaction module to aggregate the semantics of all subgraphs into a local-view graph representation. Moreover, we integrate both global semantics and local semantics into a multiview graph contrastive learning framework, enhancing the semantic-discriminative ability of graph representations. Extensive experiments on various real-world benchmarks demonstrate the efficacy of the proposed over current graph self-supervised representation learning approaches on both graph classification and transfer learning tasks.

Enhancing Low-Light Light Field Images With a Deep Compensation Unfolding Network.

This paper presents a novel and interpretable end-to-end learning framework, called the deep compensation unfolding network (DCUNet), for restoring light field (LF) images captured under low-light conditions. DCUNet is designed with a multi-stage architecture that mimics the optimization process of solving an inverse imaging problem in a data-driven fashion. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. Additionally, DCUNet includes a content-associated deep compensation module at each optimization stage to suppress noise and illumination map estimation errors. To properly mine and leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module that comprehensively exploits redundant information in LF images. The experimental results on both simulated and real datasets demonstrate the superiority of our DCUNet over state-of-the-art methods, both qualitatively and quantitatively. Moreover, DCUNet preserves the essential geometric structure of enhanced LF images much better. The code is publicly available at https://github.com/lyuxianqiang/LFLL-DCU.

Real‐Time Gait Intention Recognition for Active Control of Unilateral Knee Exoskeleton

Real‐time gait estimation is important for the synchrony control of robotic exoskeleton to provide walking assistance. However, for stroke patients with hemiplegic paralysis, the gait pattern is very complex. Accurate and timely gait intention recognition is therefore difficult. To achieve human–robot synchrony control for an unilateral knee exoskeleton, a gait intention recognizer coupling the adaptive frequency oscillator (AFO) and back propagation neural networks (BPNN) is proposed in this paper. The BPNN is trained with gait data of healthy subjects and stroke patients to improve the accuracy of recognized gait pattern, which is then imported into flexible interaction module to provide appropriate assistance. To evaluate the performance of gait intention recognition, three stroke patients were recruited to conduct level ground walking tests. The kinematic and biomechanical data were captured in each test and processed for the evaluation. Experimental results demonstrate the effectiveness of gait intention recognition and movement assistance.

Contextual Dependency Vision Transformer for spectrogram-based multivariate time series analysis

Multivariate time series (MTS) analysis plays an important role in various real-world applications. Existing Transformer-based methods address this problem based on hierarchical semantic representations across different scales. However, most of them ignore exploiting the helpful multiple temporal and variable relationships within the hierarchical semantic representations. To this end, this paper proposes a novel method named Contextual Dependency Vision Transformer (CD-ViT), which generates multi-grained semantic information based on spectrogram and explores mutual dependencies between multi-variable and multi-temporal representations. CD-ViT contains two key modules, i.e., the Hierarchical Variable-dependency Transformer (HVT) module and the Bidirectional Temporal-dependency Interaction (BTI) module. Specifically, the HVT module progressively establishes mutual dependencies between multiple variables, from fine to coarse scales, with shared parameters. The BTI module employs two bidirectional flows to fuse multi-temporal tokens through zoom-in and zoom-out operations. Comprehensive experiments on widely used datasets, including UEA, Olszewski, UCI, MIMIC III, and ETT, demonstrate that the proposed approach achieves significant improvement on three popular tasks, i.e., classification, regression, and forecasting. The code is available at https://github.com/Kali-github/CD-ViT.

Pengembangan Modul Interaktif Berbasis Augmented Reality Pada Materi Bangun Ruang Kelas V Sekolah Dasar

This research aims to develop interactive modules based on augmented reality on grade 5 building materials as a teaching tool in delivering lesson materials and supporting the learning process carried out at SDN 2 Kelayu Selatan in the 2022/2023 school year. This research is a research and development (Research and Development) using the ADDIE development model which consists of 5 steps, namely (1) analysis, (2) design, (3) development, (4) implementation, (5) evaluation. This study was conducted on grade 5 students with a total of 11 students. This research and development instrument uses validation sheets and student response questionnaires. The results of the media expert validation test with a total score of 94 are in the range of X > 79.68 scores in the "very good" category. The results of the material expert validation test with a total score of 92 are in the range of X > 79.68 scores in the "very good" category. The results of the questionnaire of student responses to the validity and effectiveness of using the module developed by comparing "yes" and "no" answers, obtained a "very good" response with a percentage of "yes" answers as much as 96.6% overall.

Hybrid CNN-transformer based meta-learning approach for personalized image aesthetics assessment

Personalized Image Aesthetics Assessment (PIAA) is highly subjective, as people's aesthetic preferences vary greatly. Traditional generic models struggle to capture the unique preferences of each individual, and PIAA often deals with limited samples from individual users. Furthermore, it requires a holistic consideration of diverse visual features in images, including both local and global features. To address these challenges, we propose an innovative network that combines the power of transformer and Convolutional Neural Networks (CNNs) with Meta-Learning for PIAA (TCML-PIAA). Firstly, we leverage both Vision Transformer blocks and CNNs to extract long-term and short-term dependencies, mining richer and heterogeneous aesthetic attributes from these two branches. Secondly, to effectively fuse these distinct features, we introduce an Aesthetic Feature Interaction Module (AFIM), designed to seamlessly integrate the aesthetic features extracted from CNNs and ViT, enabling the interaction and fusion of aesthetic information across different modalities. We also incorporate a Channel-Spatial Attention Module (CSAM), embedding it within both the CNNs and the AFIM to enhance the perception of different regions in images, further exploring the aesthetic cues in images. Experimental results demonstrate that our TCML-PIAA outperforms existing state-of-the-art methods on benchmark databases.

Upaya Meningkatkan Kompetensi Profesional Guru SDN Andongsili Kecamatan Mojotengah Kabupaten Wonosobo melalui Coaching Model GROW Tahun Ajaran 2023/2024

Professional competence is one of the 4 important competencies that teachers must master. However, in reality, the professional competence of teachers is still lacking. This is the case for teachers at Andongsili primary school, Mojotengah sub-district, Wonosobo district. Teachers have not been able to improvise their own syllabus. Teachers still use the syllabus taken from the KKG results. Teachers who have implemented classroom action research usually only do it for their final assignment, while problems that occur in the classroom are usually only solved through discussions with the principal or colleagues. Teachers have not optimally utilized the development of technology and communication. For example, in making learning media and interactive modules, teachers have also not done reflection actions optimally after each lesson. This study aims to improve teachers' professional competence through GROW model coaching for teachers at SD Negeri Andongsili, Mojotengah District, Wonosobo Regency. This type of research is school action research (PTS), while the approach uses a quantitative approach. This research was conducted at Andongsili State Elementary School, Mojotengah District, Wonosobo Regency. The research subjects were teachers at SD Negeri Andongsili, Mojotengah District, Wonosobo Regency consisting of 7 teachers. The object of this research is teachers' professional competence. The research was conducted in two cycles consisting of three competencies each cycle, namely planning, implementation, and reflection. This research was conducted collaboratively between researchers and teachers at SD Negeri Andongsili, Mojotengah District, Wonosobo Regency. The data collection instruments used were test results and documentation. The results of this study indicate that the application of the GROW Coaching model can improve the professional competence of teachers at Andongsili State Elementary School, Mojotengah District, Wonosobo Regency The increase is shown from the average score of the pre tes and posttest to cycle II. Pre tes cycle I, the average score obtained was 74.28. The score increased in cycle II, which was 84.29. There was an increase of 10.01 points. In addition to the increase in pre tes, the pos tes score also increased. The pos tes score in cycle I obtained an average of 87.86, while cycle II increased to 92.86. In other words, there was an increase of 5 points. Because 100% of Andongsili teachers, Mojotengah District, Wonosobo Regency have managed to meet the KKM, this research is said to be successful. Keywords: Coaching, school action research, competence

GIVA: Interaction-aware trajectory prediction based on GRU-Improved VGG-Attention Mechanism model for autonomous vehicles

Predicting future trajectories is crucial for autonomous vehicles, as accurate predictions enhance safety and inform subsequent decision-making and planning modules. This is however a challenging task due to the complex interactions between surrounding vehicles. Existing methods struggled to extract deep representations and often overlook spatial dependence. To address this problem, this paper introduces GIVA, an interaction-aware trajectory prediction method based on the Gated Recurrent Unit (GRU)-Improved Visual Geometry Group (VGG)-Attention Mechanism model. GIVA first encodes the historical trajectories of the target vehicle and its surrounding vehicles using a GRU Encoder. Next, an Interaction Module, which combines the Improved VGG Pooling Module and the Attention Mechanism Pooling Module, effectively captures spatial interaction features between vehicles. The Improved VGG Pooling Module extracts more detailed and effective interaction information, while the Attention Mechanism Pooling Module emphasizes the importance of surrounding vehicles for the target vehicle’s future trajectory. Lastly, the dynamic encoding feature of the target vehicle and the fused interaction feature are concatenated and input into a GRU Decoder to generate the future trajectory. Experiments on the public Next Generation Simulation (NGSIM) dataset showcase the effectiveness of GIVA compared to existing prediction approaches, demonstrating its potential for improving autonomous vehicle performance.

Multi-Level Attention Interactive Network for Cloud and Snow Detection Segmentation

The ground is typically hidden by cloud and snow in satellite images, which have a similar visible spectrum and complex spatial distribution characteristics. The detection of cloud and snow is important for increasing image availability and studying climate change. To address the issues of the low classification accuracy and poor generalization effect by the traditional threshold method, as well as the problems of the misdetection of overlapping regions, rough segmentation results, and a loss of boundary details in existing algorithms, this paper designed a Multi-level Attention Interaction Network (MAINet). The MAINet uses a modified ResNet50 to extract features and introduces a Detail Feature Extraction module to extract multi-level information and reduce the loss of details. In the last down-sampling, the Deep Multi-head Information Enhancement module combines a CNN and a Transformer structure to make deep semantic features more distinct and reduce redundant information. Then, the Feature Interactive and Fusion Up-sampling module enhances the information extraction of deep and shallow information and, then, guides and aggregates each to make the learned semantic features more comprehensive, which can better recover remote sensing images and increase the prediction accuracy. The MAINet model we propose performed satisfactorily in handling cloud and snow detection and segmentation tasks in multiple scenarios. Experiments on related data sets also showed that the MAINet algorithm exhibited the best performance.

A gated cross-domain collaborative network for underwater object detection

Underwater object detection (UOD) plays a significant role in aquaculture and marine environmental protection. Considering the challenges posed by low contrast and low-light conditions in underwater environments, several underwater image enhancement (UIE) methods have been proposed to improve the quality of underwater images. However, only using the enhanced images does not improve the performance of UOD, since it may unavoidably remove or alter critical patterns and details of underwater objects. In contrast, we believe that exploring the complementary information from the two domains is beneficial for UOD. The raw image preserves the natural characteristics of the scene and texture information of the objects, while the enhanced image improves the visibility of underwater objects. Based on this perspective, we propose a Gated Cross-domain Collaborative Network (GCC-Net) to address the challenges of poor visibility and low contrast in underwater environments, which comprises three dedicated components. Firstly, a real-time UIE method is employed to generate enhanced images, which can improve the visibility of objects in low-contrast areas. Secondly, a cross-domain feature interaction module is introduced to facilitate the interaction and mine complementary information between raw and enhanced image features. Thirdly, to prevent the contamination of unreliable generated results, a gated feature fusion module is proposed to adaptively control the fusion ratio of cross-domain information. Our method presents a new UOD paradigm from the perspective of cross-domain information interaction and fusion. Experimental results demonstrate that the proposed GCC-Net achieves state-of-the-art performance on four underwater datasets.

Interactive Digital Teaching Module Based on Differentiated Instruction and Its Impact on Students' Problem-Solving Skills

Differentiated Instruction (DI) is a solution offered in the Merdeka Belajar curriculum, where students study chemistry material according to their abilities, what they like, and their individual needs so that they are not frustrated and feel like they have failed in their learning experience. This study aims to develop interactive digital teaching modules based on Differentiated Instruction (DI) that meet valid, practical, and effective criteria for problem-solving abilities. This research is development research based on the ADDIE model with stages: analysis, design, development, implementation, and evaluation. The trial sample was class X students of SMAN 1 Latambaga. The results showed validity with a value of 2.59, Practical with a value of 3.31 on teacher activity and a weight of 3.41 on student activity. Effective with good student response 74%. Problem-solving ability increases with a test score gain of 0.32 in the medium category. It is concluded that the interactive digital teaching module based on Differentiated Instruction (DI) can be used in the learning process because it begins with a diagnostic assessment so that learning is by students' abilities and learning styles

A Novel Approach to Evaluate GNSS-RO Signal Receiver Performance in Terms of Ground-Based Atmospheric Occultation Simulation System

The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers.