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28 May 2024, Volume 29 Issue 3 Previous Issue   
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Automation & Computer Technologies


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Automation & Computer Technologies

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Multi-Robot Task Allocation Using Multimodal Multi-Objective Evolutionary Algorithm Based on Deep Reinforcement Learning MIAO Zhenhua(苗镇华), HUANG Wentao(黄文焘), ZHANG Yilian(张依恋), FAN Qinqin(范勤勤) 2024, 29 (3):  377-387.  doi: 10.1007/s12204-023-2679-7 Abstract ( 409 )   PDF (975KB) ( 207 )   The overall performance of multi-robot collaborative systems is significantly affected by the multirobot task allocation. To improve the effectiveness, robustness, and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper. The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allocation problems. Moreover, a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner. Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm. The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multirobot collaborative systems in uncertain environments, and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems. References | Related Articles | Metrics

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Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network LIU Zengmin (刘增敏), WANG Shentao(王申涛), YAO Lixiu(姚莉秀), CAI Yunze(蔡云泽) 2024, 29 (3):  388-399.  doi: 10.1007/s12204-022-2540-4 Abstract ( 195 )   PDF (1105KB) ( 78 )   In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle (UAV) platform, the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied. Furthermore, a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm. For the problem of object association failure caused by UAV movement, image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm. The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform, and effectively solve the problem of association failure caused by UAV movement. References | Related Articles | Metrics

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Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction CHEN Kun(陈坤), ZHAO Xu(赵旭), DONG Chunyu(董春玉), DI Zichao(邸子超), CHEN Zongzhi(陈宗枝) 2024, 29 (3):  400-413.  doi: 10.1007/s12204-022-2484-8 Abstract ( 210 )   PDF (5510KB) ( 74 )   Visual object tracking is an important issue that has received long-term attention in computer vision.The ability to effectively handle occlusion, especially severe occlusion, is an important aspect of evaluating theperformance of object tracking algorithms in long-term tracking, and is of great significance to improving therobustness of object tracking algorithms. However, most object tracking algorithms lack a processing mechanism specifically for occlusion. In the case of occlusion, due to the lack of target information, it is necessary to predict the target position based on the motion trajectory. Kalman filtering and particle filtering can effectively predict the target motion state based on the historical motion information. A single object tracking method, called probabilistic discriminative model prediction (PrDiMP), is based on the spatial attention mechanism in complex scenes and occlusions. In order to improve the performance of PrDiMP, Kalman filtering, particle filtering and linear filtering are introduced. First, for the occlusion situation, Kalman filtering and particle filtering are respectively introduced to predict the object position, thereby replacing the detection result of the original tracking algorithm and stopping recursion of target model. Second, for detection-jump problem of similar objects in complex scenes, a linear filtering window is added. The evaluation results on the three datasets, including GOT-10k, UAV123 and LaSOT, and the visualization results on several videos, show that our algorithms have improved tracking performance under occlusion and the detection-jump is effectively suppressed. References | Related Articles | Metrics

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Multi-Channel Based on Attention Network for Infrared Small Target Detection ZHANG Yanjun(张彦军), WANG Biyun(王碧云)，CAI Yunze (蔡云泽) 2024, 29 (3):  414-427.  doi: 10.1007/s12204-023-2616-9 Abstract ( 128 )   PDF (1697KB) ( 48 )   Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking systems. However, the complex background,the strong noise, and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets. A multi-channel based on attention network is proposed in this paper, aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model, high complexity and poor detection performance of deep learning algorithms. First, given the difficulty in extracting the features of infrared multiscale and small dim targets, the multiple channels are designed based on dilated convolution to capture multiscale target features. Second, the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target features. In addition, the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion block. Finally, it is verified that, compared with other state-of-the-art methods based on the datasets SIRST and MDFA, the proposed algorithm further improves the detection effect, and the model size and computational complexity are smaller. References | Related Articles | Metrics

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Fast Four-Stage Local Motion Planning Method for Mobile Robot HUANG Shan(黄山), HUANG Hongzhong(黄洪钟), ZENG Qi(曾奇) 2024, 29 (3):  428-435.  doi: 10.1007/s12204-022-2423-8 Abstract ( 92 )   PDF (1810KB) ( 32 )   Mobile robot local motion planning is responsible for the fast and smooth obstacle avoidance, which is one of the main indicators for evaluating mobile robots’ navigation capabilities. Current methods formulate local motion planning as a unified problem; therefore it cannot satisfy the real-time requirement on the platform with limited computing ability. In order to solve this problem, this paper proposes a fast local motion planning method that can reach a planning frequency of 500 Hz on a low-cost CPU. The proposed method decouples the local motion planning as the front-end path searching and the back-end optimization. The front-end is composed of the environment topology analysis and graph searching. The back-end includes dynamically feasible trajectory generation and optimal trajectory selection. Different from the popular methods, the proposed method decomposes the local motion planning into four sub-modules, each of which aims to solve one problem. Combining four submodules, the proposed method can obtain the complete local motion planning algorithm which can fast generate a smooth and collision-free trajectory. The experimental results demonstrate that the proposed method has the ability to obtain the smooth, dynamically feasible and collision-free trajectory and the speed of the planning is fast. References | Related Articles | Metrics

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Receding Horizon Control-Based Stabilization of Singular Stochastic Systems with State Delay WANG Xiaojing(王晓静),LIU Xiaohua(刘晓华), GAO Rong（高荣） 2024, 29 (3):  436-449.  doi: 10.1007/s12204-022-2550-2 Abstract ( 65 )   PDF (435KB) ( 37 )   For a class of discrete-time singular stochastic systems with multi-state delay, the stabilization problem of receding horizon control (RHC) is concerned. Due to the difficulty in solving the proposed optimization problem, the RHC stabilization for such systems has not been solved. By adopting the forward and backward equation technique, the optimization problem is solved completely. A sufficient and necessary condition for the optimization controller to have a unique solution is given when the regularization and pulse-free conditions are satisfied. Based on this controller, an RHC stabilization condition is derived, which is in the form of linear matrix inequality. It is proved that the singular stochastic system with multi-state delay is stable in the mean-square sense under appropriate assumptions when the terminal weighting matrix satisfies the given inequality. Numerical examples show that the proposed RHC method is effective in stabilizing singular stochastic systems with multi-state delay. References | Related Articles | Metrics

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Establishment of Constraint Relation of Robot Dynamics Equation Based on Kinematic Influence Coefficients Method XU Yaru(徐亚茹), LI Kehong(李克鸿), SHANG Xinna（商新娜）, JIN Xiaoming(金晓明), LIU Rong(刘荣), ZHANG Jiancheng（张建成） 2024, 29 (3):  450-456.  doi: 10.1007/s12204-023-2661-4 Abstract ( 59 )   PDF (656KB) ( 32 )   Due to the diversity of work requirements and environment, the number of degrees of freedom (DOFs) and the complexity of structure of industrial robots are constantly increasing. It is difficult to establish the accurate dynamical model of industrial robots, which greatly hinders the realization of a stable, fast and accurate trajectory tracking control. Therefore, the general expression of the constraint relation in the explicit dynamic equation of the multi-DOF industrial robot is derived on the basis of solving the Jacobian matrix and Hessian matrix by using the kinematic influence coefficients method. Moreover, an explicit dynamic equation with general constraint relation expression is established based on the Udwadia-Kalaba theory. The problem of increasing the time of establishing constraint relationship when the multi-DOF industrial robots complete complex task constraints is solved. With the SCARA robot as the research object, the simulation results show that the proposed method can provide a new idea for industrial robot system modeling with complex constraints. References | Related Articles | Metrics

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Diagnostic Method for Beam Position Monitor Based on Clustering by Fast Search and Find of Density Peaks JIANG Ruitao(姜瑞涛), YANG Xing(杨星), DENG Youming(邓又铭)，LENG Yongbin(冷用斌) 2024, 29 (3):  457-462.  doi: 10.1007/s12204-022-2546-y Abstract ( 59 )   PDF (2196KB) ( 27 )   Beam position monitors (BPMs) are important to monitor the beam moving steadily. Keeping the beam’s normal motion is an important mission for Shanghai Synchrotron Radiation Facility. Effective diagnostic analysis is an important way to accomplish this task. This paper develops a new method based on clustering analysis to diagnose the healthy of BPMs with basic running data, i.e., the β oscillation of X and Y directions and noise data. The analysis results showed that all beam position monitors (140 BPMs) can be classified into three groups: normal group, worse performance group, and fault group, respectively. In addition, the abnormal BPMs (including worse performance) could be marked. The new method showed its ability to handle faulty BPMs and it could instruct daily maintenance. On the other hand, it will be a useful supplement for data analysis in accelerator physics. References | Related Articles | Metrics

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Algorithm for Solving Traveling Salesman Problem Based on Self-Organizing Mapping Network ZHU Jianghui(朱江辉),YE Hanghang(叶航航), YAO Lixiu1(姚莉秀), CAI Yunze(蔡云泽) 2024, 29 (3):  463-470.  doi: 10.1007/s12204-022-2517-3 Abstract ( 73 )   PDF (1343KB) ( 25 )   Traveling salesman problem (TSP) is a classic non-deterministic polynomial-hard optimization problem. Based on the characteristics of self-organizing mapping (SOM) network, this paper proposes an improved SOM network from the perspectives of network update strategy, initialization method, and parameter selection. This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms. Simulations show that compared with existing SOM networks, the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy. Compared with iterated local search and heuristic algorithms, the improved SOM network algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP. References | Related Articles | Metrics

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Hierarchical Reinforcement Learning Adversarial Algorithm Against Opponent with Fixed Offensive Strategy ZHAO Yingce(赵英策), ZHANG Guanghao(张广浩), XING Zhengyu(邢正宇), LI Jianxun(李建勋) 2024, 29 (3):  471-479.  doi: 10.1007/s12204-023-2586-y Abstract ( 50 )   PDF (881KB) ( 18 )   Based on option-critic algorithm, a new adversarial algorithm named deterministic policy network with option architecture is proposed to improve agent’s performance against opponent with fixed offensive algorithm. An option network is introduced in upper level design, which can generate activated signal from defensive and offensive strategies according to temporary situation. Then the lower level executive layer can figure out interactive action with guidance of activated signal, and the value of both activated signal and interactive action is evaluated by critic structure together. This method could release requirement of semi Markov decision process effectively and eventually simplified network structure by eliminating termination possibility layer. According to the result of experiment, it is proved that new algorithm switches strategy style between offensive and defensive ones neatly and acquires more reward from environment than classical deep deterministic policy gradient algorithm does. References | Related Articles | Metrics

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Dynamic Self-Similar kc-Center Network Based on Information Dissemination WANG Li1 (李树勋), ZHANG Xuyi2 (沈珩云), YAO Yabing3 (刘斌才),HU Yinggang4*(胡迎港) 2024, 29 (3):  480-491.  doi: 10.1007/s12204-022-2559-6 Abstract ( 52 )   PDF (2996KB) ( 20 )   This study mainly focused on the dynamic self-similar kc-center network as a result of information distribution through social networks. Individual attraction with various preferences was characterized in the model as a result of reciprocal attraction among individuals and human multi-attribute. Additionally, the model incorporated the community network structure and network evolution mechanism, and a dynamic self-similar kc-center network generation model was presented. Compared with the classical scale-free network generation algorithm, the generated network embodied not only the characteristics of the small-world and scale-free, but also the characteristics of dynamic self-similar kc-center network. The experimental results were verified by comparing the real data with the experimental data. The results showed that there are dynamic self-similar kc-center networks and their internal network relationship dynamics in the micro scale, meso scale and global perspective based on information dissemination. References | Related Articles | Metrics

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Multi-AGVs Scheduling with Vehicle Conflict Consideration in Ship Outfitting Items Warehouse CHEN Yini(陈旖旎), JIANG Zuhua* (蒋祖华) 2024, 29 (3):  492-508.  doi: 10.1007/s12204-022-2561-z Abstract ( 45 )   PDF (1356KB) ( 20 )   The inbound and outbound tasks for valuable imported ship outfitting items are operated by multiple automated guided vehicles (AGVs) simultaneously in the outfitting warehouse. Given the efficiency mismatch between transportation equipment and the lack of effective scheduling of AGVs, the objective of the studied scheduling problem is to minimize the total travel time cost of vehicles. A multi-AGV task scheduling model based on time window is established considering the loading constraints of AGVs and cooperation time window constraints of stackers. According to the transportation characteristics in the outfitting warehouse, this study proposes a conflict detection method for heavy forklift AGVs, and correspondingly defines a conflict penalty function. Furthermore, to comprehensively optimize travel time cost and conflict penalty, a hybrid genetic neighborhood search algorithm (GA-ANS) is proposed. Five neighborhood structures are designed, and adaptive selection operators are introduced to enhance the ability of global search and local chemotaxis. Numerical experiments show that the proposed GA-ANS algorithm can effectively solve the problem even when the scale of the problem increases and the effectiveness of the vehicle conflict penalty strategy is analyzed. References | Related Articles | Metrics

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Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network LONARE Savita1，2* , BHRAMARAMBA Ravi2 2024, 29 (3):  509-517.  doi: 10.1007/s12204-021-2382-5 Abstract ( 108 )   PDF (525KB) ( 38 )   Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models. However, data privacy and security are always a challenge in every field where data need to be uploaded to the cloud. Federated learning (FL) is an emerging trend for distributed training of data. The primary goal of FL is to train an efficient communication model without compromising data privacy. The traffic data have a robust spatio-temporal correlation, but various approaches proposed earlier have not considered spatial correlation of the traffic data. This paper presents FL-based traffic flow prediction with spatio-temporal correlation. This work uses a differential privacy (DP) scheme for privacy preservation of participant’s data. To the best of our knowledge, this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation. The proposed framework trains the data locally at the client-side with DP. It then uses the model aggregation mechanism federated graph convolutional network (FedGCN) at the server-side to find the average of locally trained models. The results of the proposed work show that the FedGCN model accurately predicts the traffic. DP scheme at client-side helps clients to set a budget for privacy loss. References | Related Articles | Metrics

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Tree Detection Algorithm Based on Embedded YOLO Lightweight Network LV Feng(吕峰), WANG Xinyan* (王新彦), LI Lei(李磊), JIANG Quan(江泉), YI Zhengyang(易政洋) 2024, 29 (3):  518-527.  doi: 10.1007/s12204-022-2451-4 Abstract ( 107 )   PDF (2443KB) ( 36 )   To avoid colliding with trees during its operation, a lawn mower robot must detect the trees. Existing tree detection methods suffer from low detection accuracy (missed detection) and the lack of a lightweight model. In this study, a dataset of trees was constructed on the basis of a real lawn environment. According to the theory of channel incremental depthwise convolution and residual suppression, the Embedded-A module is proposed, which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model. According to residual fusion theory, the Embedded-B module is proposed, which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion. The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions. Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17% and 69.91% average precision values respectively for trunk and spherical tree, and 77.04% mean average precision value. The number of convolution parameters is 1.78 × 106, and the calculation amount is 3.85 billion float operations per second. The size of weight file is 7.11 MB, and the detection speed can reach 179 frame/s. This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots. References | Related Articles | Metrics

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New Lite YOLOv4-Tiny Algorithm and Application on Crack Intelligent Detection SONG Liboa (宋立博), FEI Yanqiongb (费燕琼) 2024, 29 (3):  528-536.  doi: 10.1007/s12204-022-2504-8 Abstract ( 89 )   PDF (1358KB) ( 27 )   Conforming to the rapidly increasing market demand of crack detection for tall buildings, the idea of integrating deep network technology into wall climbing robot for crack detection is put forward in this paper. Taking the dependence and hardware requirements when deployed on such edge devices as Raspberry Pi into consideration, the Darknet neural network is selected as the basic framework for detection. In order to improve the inference efficiency on edge devices and avoid the possible premature over-fitting of deep networks, the lite YOLOv4-tiny algorithm is then improved from the original YOLOv4-tiny algorithm and its structure is illustrated using Netron accordingly. The images downloaded from Internet and taken from the buildings in campus are processed to form crack detection data sets, which are trained on personal computer with the AlexeyAB version of Darknet to generate weight files. Meanwhile, the AlexeyAB version of Darknet accelerated by NNpack package is deployed on Raspberry Pi 4B, and the crack detection experiments are carried out. Some characteristics, e.g., fast speed and lower false detection rate of the lite YOLOv4-tiny algorithm, are confirmed by comparison with those of original YOLOv4-tiny algorithm. The innovations of this paper focus on the simple network structure, fewer network layers, and earlier forward transmission of features to prevent over-fitting, showing the new lite neural network exceeds the original YOLOv4-tiny network significantly. References | Related Articles | Metrics

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Semantic Entity Recognition and Relation Construction Method for Assembly Process Document GU Xinghai顾星海）,HUA Bao（花 豹），LIU Yahui（刘亚辉）,SUN Xuemin（孙学民）,BAO Jinsong∗（鲍劲松） 2024, 29 (3):  537-556.  doi: 10.1007/s12204-022-2474-x Abstract ( 56 )   PDF (4638KB) ( 22 )   Assembly process documents record the designers’ intention or knowledge. However, common knowledge extraction methods are not well suitable fo assembly process documents, because of its tabular form and unstructured natural language texts. In this paper, an assembly semantic entity recognition and relation construction method oriented to assembly process documents is proposed. First, the assembly process sentences are extracted from the table through concerned region recognition and cell division, and they will be stored as a key-value object file. Then, the semantic entities in the sentence are identified through the sequence tagging model based on the specific attention mechanism for assembly operation type. The syntactic rules are designed for realizing automatic construction of relation between entities. Finally, by using the self-constructed corpus, it is proved that the sequence tagging model in the proposed method performs better than the mainstream named entity recognition model when handling assembly process design language. The effectiveness of the proposed method is also analyzed through the simulation experiment in the small-scale real scene, compared with manual method. The results show that the proposed method can help designers accumulate knowledge automatically and efficiently. References | Related Articles | Metrics

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Unbalanced Graph Multi-Scale Fusion Node Classification Method ZHANG Jingke1 (张静克), HE Xinlin2 (何新林), QI Zongfeng1 (戚宗锋), MA Chao2 (马 超), LI Jianxun2 (李建勋) 2024, 29 (3):  557-565.  doi: 10.1007/s12204-022-2543-1 Abstract ( 57 )   PDF (626KB) ( 17 )   Graphs are used as a data structure to describe complex relationships between things. The node classification method based on graph network plays an important role in practical applications. None of the existing graph node classification methods consider the uneven distribution of node labels. In this paper, a graph convolution algorithm on a directed graph is designed for the distribution of unbalanced graph nodes to realize node classification based on multi-scale fusion graph convolution network. This method designs different propagation depths for each class according to the unbalance ratio on the data set, and different aggregation functions are designed at each layer of the graph convolutional network based on the class propagation depth and the graph adjacency matrix. The scope of information dissemination of positive samples is expanded relatively, thereby improving the accuracy of classification of unbalanced graph nodes. Finally, the effectiveness of the algorithm is verified through experiments on the public text classification datasets. References | Related Articles | Metrics

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Automated Time Based Multi-Criteria Bug Triage Approach: Developer Working Efficiency and Social Network Based Developer Recommendation YADAV Asmita1*, SINGH Kumar Sandeep1，2 2024, 29 (3):  566-578.  doi: 10.1007/s12204-022-2448-z Abstract ( 50 )   PDF (1488KB) ( 24 )   In software development projects, bugs are common phenomena. Developers report bugs in open source repositories. There is a need to develop high quality developer prediction model that considers developer work satisfaction, keep within limited development cost, and improve bug resolution time. To address and resolve bug report as soon as possible is the main focus of triager when a new bug is reported. Thus, developer work efficiency is an important factor in bug-fixing. To address these issues, a proposed approach recommends a set of developers that could potentially share their knowledge with each other to fix new bug reports. The proposed approach is called developer working efficiency and social network based developer recommendation (DweSn). It is a composite model that builds developers’ profile by using developer average bug fixing time, work efficiency to fix variety of bugs, as well as the developer’s social interactions with other developers. A similarity measure is applied between new bug and bugs in corpus to extract the list of capable developers from the corpus. The proposed approach only selects those developers who are active and less loaded with work. The developer with the highest profile score is assigned the bugs. We evaluated our approach on the subset of five large open-source projects including Mozilla, Netbeans, Eclipse, Firefox and OpenOffice, and compared it with the state-of-the-art. The results demonstrate that combination of developers’ efficiency with their average bug fixing time and interactions in their social network gives good accuracy and efficiently reduces bug tossing length. This approach shows an improvement in prediction accuracy, precision, recall, F-score and reduced bug tossing length up to 93.89%, 93.12%, 93.46%, 93.27% and 93.25%, respectively. The proposed approach achieved a 93% hit ratio and 93.34% mean reciprocal rank, indicating that our proposed triager is able to efficiently assign bugs to correct developers. References | Related Articles | Metrics

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Reasoning about Software Trustworthiness with Derivation Trees DENG Yuxin1* (邓玉欣),CHEN Zezhong1 (陈泽众),WANG Yang1(汪洋), DU Wenjie2(杜文杰),MAO Bifei3(毛碧飞), LIANG Zhizhang 3(梁智章), LIN Qiushi3(林秋诗),LI Jinghui3(李静辉) 2024, 29 (3):  579-587.  doi: 10.1007/s12204-022-2515-5 Abstract ( 53 )   PDF (1486KB) ( 22 )   In order to analyze the trustworthiness of complex software systems, we propose a model of evidencebased software trustworthiness called trustworthiness derivation tree (TDT). The basic idea of constructing a TDT is to refine main properties into key ingredients and continue the refinement until basic facts such as evidences are reached. The skeleton of a TDT can be specified by a set of rules, which are convenient for automated reasoning in Prolog. We develop a visualization tool that can construct the skeleton of a TDT by taking the rules as input, and allow a user to edit the TDT in a graphical user interface. In a software development life cycle, TDTs can serve as a communication means for different stakeholders to agree on the properties about a system in the requirement analysis phase, and they can be used for deductive reasoning so as to verify whether the system achieves trustworthiness in the product validation phase. We have piloted the approach of using TDTs in more than a dozen real scenarios of software development. Indeed, using TDTs helped us to discover and then resolve some subtle problems. References | Related Articles | Metrics

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Analysis of Software Trustworthiness Based on FAHP-CRITIC Method GAO Xiaotong11 (高晓彤), MA Yanfang1，2* (马艳芳), ZHOU Wei1 周伟) 2024, 29 (3):  588-600.  doi: 10.1007/s12204-022-2496-4 Abstract ( 51 )   PDF (740KB) ( 29 )   Software trustworthiness includes many attributes. Reasonable weight allocation of trustworthy attributes plays a key role in the software trustworthiness measurement. In practical application, attribute weight usually comes from experts’ evaluation to attributes and hidden information derived from attributes. Therefore, when the weight of attributes is researched, it is necessary to consider weight from subjective and objective aspects. First, a novel weight allocation method is proposed by combining the fuzzy analytical hierarchy process (FAHP) method and the criteria importance though intercrieria correlation (CRITIC) method. Second, based on the weight allocation method, the trustworthiness measurement models of component-based software are established according to the seven combination structures of components. Third, the model reasonability is verified via proving some metric criteria. Finally, a case is carried out. According to the comparison with other models, the result shows that the model has the advantage of utilizing hidden information fully and analyzing the combination of components effectively. It is an important guide for measuring the trustworthiness measurement of component-based software. References | Related Articles | Metrics