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With the rapid growth of social Internet technology, social recommender has emerged as a major research hotspot in the recommendation systems. However, traditional graph neural networks does not consider the impact of noise generated by long-distance social relations on recommendation performance. In this work, a content-only multi-relational attention network (CMAN) is proposed for social recommendation. The proposed model owns the following advantages: (i) the comprehensive trust based on the historical interaction records of users and items are integrated into the recursive social dynamic modeling to obtain the comprehensive trust of different users; (ii) social trust information is captured based on the attention network mechanism, so as to solve the problem of weight distribution in the same level domain; (iii) two levels of attention mechanisms are merged into a unified framework to enhance each other. Experiments conducted on two representative datasets demonstrate that the proposed algorithm outperforms previous methods substantially.

Under the imbalanced dataset, the performance of the base-classifier, the computing method of weight of base-classifier and the selection method of the base-classifier have a great impact on the performance of the ensemble classifier. In order to solve above problem to improve the generalization performance of ensemble classifier, a selective ensemble learning algorithm based on under-sampling for imbalanced dataset is proposed. First, the proposed algorithm calculates the number K of under-sampling samples according to the relationship between class sample density. Then, we use the improved K-means clustering algorithm to under-sample the majority class samples and obtain K cluster centers. Then, all cluster centers (or the sample of the nearest cluster center) are regarded as new majority samples to construct a new balanced training subset combine with the minority class?s samples. Repeat those processes to generate multiple training subsets and get multiple base-classifiers. However, with the increasing of iterations, the number of base-classifiers increase, and the similarity among the base-classifiers will also increase. Therefore, it is necessary to select some base-classifier with good classification performance and large difference for ensemble. In the stage of selecting base-classifiers, according to the difference and performance of base-classifiers, we use the idea of maximum correlation and minimum redundancy to select base-classifiers. In the ensemble stage, G-mean or F-mean is selected to evaluate the classification performance of base-classifier for imbalanced dataset. That is to say, it is selected to compute the weight of each base-classifier. And then the weighted voting method is used for ensemble. Finally, the simulation results on the artificial dataset, UCI dataset and KDDCUP dataset show that the algorithm has good generalization performance on imbalanced dataset, especially on the dataset with high imbalance degree.

Hierarchical key assignment scheme is an efficient cryptographic method for hierarchical access control, in which the encryption keys of lower classes can be derived by the higher classes. Such a property is an effective way to ensure the access control security of Internet of Things data markets. However, many researchers on this field cannot avoid potential single point of failure in key distribution, and some key assignment schemes are insecure against collusive attack or sibling attack or collaborative attack. In this paper, we propose a hierarchical key assignment scheme based on multilinear map to solve the multigroup access control in Internet of Things data markets. Compared with previous hierarchical key assignment schemes, our scheme can avoid potential single point of failure in key distribution. Also the central authority of our scheme (corresponding to the data owner in IoT data markets) does not need to assign the corresponding encryption keys to each user directly, and users in each class can obtain the encryption key via only a one-round key agreement protocol. We then show that our scheme satisfies the security of key indistinguishability under decisional multilinear Diffie-Hellman assumption. Finally, comparisons show the efficiency of our scheme and indicates that our proposed scheme can not only resist the potential attacks, but also guarantee the forward and backward security.

Data stream mining is one of the hot topics in data mining. Most existing algorithms assume that data stream with concept drift is balanced. However, in real-world, the data streams are imbalanced with concept drift. The learning algorithm will be more complex for the imbalanced data stream with concept drift. In online learning algorithm, the oversampling method is used to select a small number of samples from the previous data block through a certain strategy and add them into the current data block to amplify the current minority class. However, in this method, the number of stored samples, the method of oversampling and the weight calculation of base-classifier all affect the classification performance of ensemble classifier. This paper proposes a dynamic weighted selective ensemble (DWSE) learning algorithm for imbalanced data stream with concept drift. On the one hand, through resampling the minority samples in previous data block, the minority samples of the current data block can be amplified, and the information in the previous data block can be absorbed into building a classifier to reduce the impact of concept drift. The calculation method of information content of every sample is defined, and the resampling method and updating method of the minority samples are given in this paper. On the other hand, because of concept drift, the performance of the base-classifier will be degraded, and the decay factor is usually used to describe the performance degradation of base-classifier. However, the static decay factor cannot accurately describe the performance degradation of the base-classifier with the concept drift. The calculation method of dynamic decay factor of the base-classifier is defined in DWSE algorithm to select sub-classifiers to eliminate according to the attenuation situation, which makes the algorithm better deal with concept drift. Compared with other algorithms, the results show that the DWSE algorithm has better classification performance for majority class samples and minority samples.

With the rapid development of the Internet, Internet of Things (IoT) technology is widely used in people’s daily lives. As the number of IoT devices increases, the amount of data to be processed also increases. The emergence of cloud computing can process the data of IoT devices in a timely manner, and it provides robust storage and computing capabilities to facilitate data resource sharing. Since wireless communication networks are unstable and open, it is easy for attackers to eavesdrop, intercept, and tamper with the messages sent. In addition, authentication protocols designed for IoT-enabled cloud computing environments still face many security challenges. Therefore, to address these security issues, we propose an Intel software-guard-extensions (SGX)-based authentication key agreement protocol in an IoT-enabled cloud computing environment. The goal is to ensure data privacy and sustainable communication between the entities. Moreover, SGX can resist several well-known attacks. Finally, we show the security using the real-or-random model, ProVerif, and informal analysis. We also compare the security and performance of the proposed protocol with existing protocols. The comparison results show that our proposed protocol reduces the communication cost by 7.07% compared to the best one among the current protocols and ensures sufficient security.

Multilevel thresholding for image segmentation has always been a popular issue and has attracted much attention. Traditional exhaustive search methods take considerable time to solve multilevel thresholding problems. However, heuristic search algorithms have potential advantages in terms of solving such multilevel thresholding problems. Based on this idea, in this paper, a novel adaptive gravitational search algorithm (AGSA) is proposed to solve the optimal multilevel image thresholding problem; this algorithm is more efficient than the traditional exhaustive search method for grayscale image segmentation. In the AGSA, an adaptive parameter optimization strategy is used to tune the gravitational constant and the inertia weight. To verify the performance of the proposed algorithm, a series of classic test images are used to perform several experiments. In addition, the standard GSA and some optimization algorithms are compared with the proposed algorithm. The experimental results show that the proposed algorithm is obviously better than the other six algorithms. These promising results suggest that the AGSA is more suitable than existing methods for multilevel image thresholding.

If the product supply shortage during the sales period, customers will turn to other company, so the enterprise will shortage the sales opportunity. The enterprise can predict product demands, and manages the product sales. To use the supply chain management prediction system to improve the bottleneck of the inventory; thus, the hot sales product has more critical time utilization and the inventory status reflected quickly in Internet of Things (IoT). Because replenishment model cannot show the actual quantity of products on the store shelves. Therefore, this article proposed the intelligent agent-based predict system, that agent-based serves as framework to construct an integrated predict system, use radio frequency identification (RFID) technology to design the intelligent product predict shelf to extract products message, and the service oriented architecture to develop an predict information to provide the product as recommend customer services. The result of the paper, to propose an agent-based cloud computing service platform in IoT, and intelligent agents with SOA as backend cloud services. To build a prototype predict system with performance analysis. The results proved that the predict system architecture for intelligent agent-based predict system could increase operation performance and enhance effectively for customer service quality for hot sale products.

To solve the problems of poor real-time collision accuracy and low efficiency of modeling in the virtual reality environment, we propounded a depth image-based 3D modeling system and a hybrid intelligent collision detection algorithm. With the development of the 3D animation interactive system as an example, this paper uses 3D modeling technology based on depth images to build single role models, then reorganizes and merges the models to form 3D scenes. The hybrid intelligent collision detection algorithm, which combines the quantum behavior particle swarm optimization algorithm and the differential algorithm, improves the collision detection efficiency and accuracy and realizes behavior control of the characters in the interactive system. The experimental result shows that the 3D modeling technology based on depth images has greatly improved the accuracy and quantity of model texture and motion rate. By comparing the hybrid intelligent collision detection algorithm, the QPSO algorithm, and the FDH bounding box for collision detection, we conclude that the algorithm used in this paper has a shorter average collision time, more stable role behavior control, and better robustness.

Aiming at solving the problems of complex image background and difficulties in the later image segmentation, an image segmentation algorithm based on the chaotic particle swarm algorithm and fuzzy clustering is proposed. First, the color space is converted from the RGB color space into the HIS color space. Then, a hybrid algorithm consisted of chaotic particle swarm optimization and fuzzy clustering is introduced. Each color component is processed by the algorithm, and the corresponding partition graph is obtained. Finally, the color space is converted into the RGB color space to achieve the segmentation effect. Experimental results show that the new algorithm has higher accuracy to segment the image and has good robustness to noises.

Different from the conventional point source localization, rigid body localization (RBL) not only aims to estimate the position of the target but also to acquire the attitude information, which is also essential information in many Internet of Things (IoT) applications, such as the virtual reality systems, smart parking systems. This paper develops three maximum likelihood estimators (MLEs) for the RBL purpose in 3 dimensional space via a single base station. The MLEs are designed for the RBL framework, which adopts the direction of arrival (DoA) of the signal from a small scale wireless sensor network (SSWSN) mounted on the surface of the rigid target as measurement and can be realized by a single base station. The three MLEs respectively exploit the SSWSN topology information, the DoA measurement information only, as well as the equality constraint of the rotation matrix and the DoA measurement information. In addition, we implement the modified Guass-newton algorithm for the MLEs of the rotation matrix and the translation vector. Simulations show that the proposed MLE fusing the equality constraint of the rotation matrix and the DoA measurement information most approaches the Cramer-Rao Lower Bound and also outperforms the other two MLEs in terms of convergence success rate and the computational cost.

Information fusion is an important part of numerous neural network systems and other machine learning models. However, there exist some problems about fusion in scene understanding and recognition of complex environment, such as difficulty in feature extraction, small sample size and interpretability of the model. Deep reinforcement learning can combine the perception ability of deep learning with the decision-making ability of reinforcement learning to learn control strategies directly from high-dimensional original data. However, It faces these challenges, such as low optimization efficiency, poor generality of network model, small labeled samples, explainable decisions for users without a strong background on Artificial Intelligence (AI). Therefore, at the level of application and theoretical research, this paper aims to solve the above problems,the main contributions include: (1)optimize the feature representation methods based on spatial-temporal feature of the behavior characteristics in the scene, deep metric learning between adjacent layers and cross-layer learning theory, and then propose a lightweight reinforcement learning network model to solve these problems of the complexity of the model to be explained, the difficulty of extracting feature and the difficulty of tuning parameter; (2)construct the self-paced learning strategy of the deep reinforcement learning model, introduce transfer learning mechanism in the optimization process, and solve the problem of low optimization efficiency and small labeled samples; (3)design the behavior recognition framework of the multi-perspective deep knowledge transfer learning model, construct a explainable behavior descriptor, and solve the problems of poor network generality and weak 1explanation of network. Our research is of great theoretical and practical significance in the fields of artificial intelligence and public security.

With the maturity and popularization of the Internet of Things, we saw the emergence of the Internet of Vehicles. This collects and processes real-time traffic information, alleviates traffic congestion, and realizes intelligent transportation. However, sensitive information, such as real-time driving data of vehicles, are transmitted on public channels, which are easily to steal and manipulate for attackers. In addition, vehicle communications are vulnerable to malicious attacks. Therefore, it is essential to design secure and efficient protocols. Many studies have adopted asymmetric cryptosystems and fog computing to in this environment, but most of them do not reflect the advantages of fog nodes, which share the computational burden of cloud servers. Therefore, it is challenging to design a protocol that effectively uses fog nodes. In this paper, we design an authentication protocol based on a symmetric encryption algorithm and fog computing in the Internet of Vehicles. In this protocol, we first propose a four-layer architecture that significantly reduces the computational burden of cloud servers. To resist several well-known attacks, we also apply Intel software guard extensions to our protocol. This is because it can resist privileged insider attacks. We prove the security of the proposed protocol through the Real-Or-Random model and informal analysis. We also compare the performance of the proposed protocol with recent protocols. The results show better security and a lower computational cost.

The wireless sensor networks is a new technology for information acquisition and processing, which includes the techniques of sensor, computer, Internet of Things and network. Due to the fragility of wireless sensor networks, the security issue has become a prevalent concern in the wireless communication for Internet of Things. This paper offers a deep insight to the secrecy capacity of wireless sensor network and a calculable threshold of capacity based on the amplify-and-forward (AF) compressed sensing scheme. Moreover, we provide a feasible algorithm based on augmented Lagrange method for source reconstruction for the legitimate nodes and un-authorized nodes. Furthermore, we also discuss the impact of the numbers of active sensor nodes, relay nodes and eavesdropper nodes to the secrecy capacity. Simulation results demonstrate the correctness of the derived secrecy capacity.

The construction of the financial module is very important in the intelligent management of the company. The collection, transmission, and processing of relevant item information in the financial module are of great significance to the construction of the intelligent financial module. The intelligent financial processing module can improve financial office efficiency and increase the security of the financial processing process. Section 404 of the Sarbanes-Oxley Act (SOX 404) has always played a decisive role in the quality and transparency of the financial modules of the Enterprise Resource Planning (ERP) of multinational corporations, due to the mandatory nature of the Securities and Exchange Commission (SEC). The purpose of this research is to design a set of intelligent ERP financial modules for multinational corporations that could comply with SOX 404, strengthen internal controls, and improve the security and convenience of the financial module. Thereby achieving intelligent management of financial modules and intelligent prevention of risks. Our solution could fortify the preventive controls over inappropriate payments to products with quality issues, reduce manual efforts for credit memo issuance and receipt. It could also fortify the preventive controls over financial risks potentially threatened by a sudden contingent liability concerning E&O issues. It could prevent potential and unexpected frauds arisen by a malicious user to meet the requirements of SOX over preventive and detective controls over the financial system. It could also ensure the consistency of journals between the preparer’s entry and review’s approval as well as improve the completeness and timeliness of financial statement assertions.

The overwhelming increase of population will lead to an increase network traffic usage. In most products, local cache mechanism is implemented for the purpose of reducing the network traffics. However, Due to storage space limitations, lot of times, cache will get purged when there are lots of queries sent and replaced by other newest queries. It is not an efficient by using traditional methodology to build or forming cache. In order to reduce the network traffic usage, the paper propose a solution, which utilizes data mining technique with clustering concept, by gathering the current feedback data that have from our SPN (Smart Protection Network). we are able to form these data in groups with similarity, and by deploying these data to client side, to achieve the reduction of traffic usage. In prototype, this design can really reduce network traffic more than 8%.

In most antivirus products, the local cache mechanism is implemented for the purpose of reducing the time of the file scan. However, the database designed to store cache is limited, a lot of times, that most of the cache data have been purged during the file scanning since it cannot store too many CRC files in the local cache. Traditional methods of building and forming cache cannot effectively solve the problem of low cache utilization. Whereas a client with a traditional cache mechanism needs to send out multiple queries in order to accumulate a usable cache one-by-one, which causes the waste of database and time. In this paper, we first gather the current feedback data and analyze the data, and then, we explore an effective Two-Step clustering scheme to implement for the optimization of the prefetch cache strategy. Our scheme can avoid the cache being cleared prematurely and increase the probability of cache hits. Extensive experimental results demonstrate that our proposed scheme could save 11.76% time during file scanning.

Gaussian process regression (GPR) is frequently used for uncertain measurement and prediction of nonstationary time series in the Internet of Things data, nevertheless, the generalization and regression efficacy of GPR are directly impacted by its selection of hyper-parameters. In the study, a non-inertial particle swarm optimization with elite mutation-Gaussian process regression (NIPSO-GPR) is proposed to optimize the hyper-parameters of GRP. NIPSO-GPR can adaptively obtain hyper-parameters of GPR via uniform non-inertial velocity update formula and adaptive elite mutation strategy. When compared with several frequently used algorithms of hyper-parameters optimization on linear and nonlinear time series sample data, experimental results indicate that GPR after hyper-parameters optimized by NIPSO-GPR has better fitting precision and generalization ability.

A good set of the financial system could take care of all accounting entries and their impacts on the whole intelligent financial environment. The total flow of money and total expenditures will be reflected here. The intelligent financial system can improve the utilization of data, increase the work efficiency of financial personnel, and increase the security of financial processing services. This system could help the managers make their important financial decisions, financial budgeting, and so on. They can know about the financial conditions at any time. Based on the collection and processing of financial data, we design and analyze the related financial system to carry out the intelligent property of the financial environments. The system extends the quality of financial reporting, SOX compliance, and internal controls in a financial system, including credit management, revenue recognition, bad debts allowance. Once there is an early warning in the financial system, our proposed enhanced system could give a fast early response, and bridge a positive linkage about the planning department, finance department, and top managers, which could avoid potential exposure to bad debts. This system guarantees that the credible customers are selected, and avoid the potential risk of bad debts are incurred by a loose credit limit. It strengthens the preventive control over aging receivables management and detects the potential risk of bad debts in the earlier system alert.

As the Internet of Things (IoT) becomes more and more intelligent, a new computing paradigm, predictive intelligence is incorporated into many IoT applications. The devices of predictive intelligence in IoT applications must consider the power and delay consumption. As the Power-Driven X-Routing (PDXR) problem model under the advanced semiconductor design, the length-restricted condition in the multi-dynamic voltage model is introduced to save power consumption and the X-architecture is introduced to better reduce the wirelength to optimize the chip delay. As a result, the PDXR model of semiconductor design can well solve the power and delay consumption problems faced by predictive intelligence in IoT applications. To this end, an effective particle swarm optimization-based power-driven length-restricted X-routing algorithm is proposed for predictive intelligence in IoT applications. Firstly, a pre-calculated lookup table is designed to provide fast information query for the subsequent algorithm flow. Secondly, an improved particle swarm optimization algorithm is presented for the discrete PDXR problem. Thirdly, in the adjustment phase, the choice of intermediate nodes is expanded, and is no longer limited to the corner points of obstacles. Fourthly, in the post-processing phase, a removal strategy of redundant points is proposed to optimize the routing path. Finally, the wirelength is further reduced by a local topology optimization strategy, and a high-quality routing solution is obtained. The experimental results show that the proposed algorithm can achieve the best wirelength cost at a very fast speed under the constraint of restricted wirelength, so as to better satisfy the demand of the power and delay performance of semiconductor design for predictive intelligence in IoT applications.

Prediction problems are difficult to be carried out in a dynamic environment, for two key questions: one is how to monitor environmental changes, the other is how to make respond timely after the environment has changed. Multi-population strategy is often adopted to address both two key problems. However, there are two stubborn questions that limit and affect the effectiveness of the strategy, i.e., (1) Search overlaps are easy to occur which lead to lose the ability of local exploit, (2) In the course of evolution, the search range of subpopulations tends to assimilate, so the subpopulation will gradually lose the ability to explore whole search space. Therefore, this paper adopts multi-population strategy, and presents a novel intelligence algorithm based on particle swarm optimization to solve the above problem, called adaptively reversed diffusion dual-drive evolutionary algorithm (ARDDEA). (1) Firstly, ARDDEA monitors the environmental changes by setting the global dynamic sentry in each subgroup. (2) Secondly, in order to avoid searching overlapping of the sub-population, a new exclusion strategy is proposed in this paper. A new distance determination method, i.e., between-swarms average Mahalanobis​ distance, is devised in the exclusion strategy to decide the inter-population distance. If the distance is too small between two sub-populations, furtherly, a Hill–valley decision function is used to determine whether they tracked the same peak or not. If so, the inferior subpopulations will be reinitialized by a reverse diffusion operation (RD) proposed in this paper. Besides, (3) a new dual-drive kinetic updating equation is proposed to enhance the search capability of the population. The new algorithm compared with several state-of-art dynamic optimization algorithms on the moving peak problem. The results show that the ARDDEA algorithm can track the optimal solution more effectively in the dynamic environment, and shows strong robustness and adaptability. It is a hope algorithm applied to prediction problems.

In many practical applications, due to the inability to collect complete training data sets at one time, the adaptability of the classifier is poor. Online ensemble learning can better solve this problem. However, most of the data streams are imbalanced. Imbalanced data stream will greatly affect the performance of online ensemble learning algorithm. To reduce the impact of imbalanced data stream, this paper proposes a cost sensitive online ensemble learning algorithm for imbalanced data stream. The algorithm uses a variety of equalization methods, mainly including the construction of initial base-classifier, dynamic calculation of misclassification cost, sampling method of samples in data stream and calculation of weight of base-classifier. Those methods can reduce the influence of imbalanced data stream and improve the classification performance under imbalanced data stream. The experimental results show that the performance of the proposed algorithm has the better classification performance for imbalanced data stream. Finally, the algorithm is applied to the network intrusion detection, and the simulation experiment on NSL-KDD data set can reduce the missing alarm rate and the false alarm rate. The experimental results show that the algorithm can improve the detection accuracy, especially the recognition rate of unknown intrusion behavior.

Human needs motivate the improvement of computing paradigms, the emergence of soft computing is more effective in dealing with daily problems, while reducing costs, it also solves the problem of robustness and becomes easier to handle. Examples of this include collecting data on video data from smart sensors and can use that information to predict and monitor the behavior of crowd. Nowadays, with the development of cyber–physical systems and artificial intelligence, the traditional data collection and analysis system faces the risks of low transparency and high data security, making it difficult to obtain an accurate prediction result. Therefore, in this paper a novel prediction machine via self-learning generative adversarial network for soft computing application is proposed, which collects data through a series of high-precision IoT sensor devices and makes preliminary preprocessing, and further solves the crowd prediction problem based on deep learning algorithms and obtains a reliable and accurate prediction result by continuously optimizing internal parameters. The focus of this work is on the accuracy and preprocessing of data collection and crowd prediction algorithms. The prediction algorithm can be used to estimate and monitor the crowd flow in public places, and can prevent crowding, trampling and other traffic jams, such as stations, airports, large exhibitions, tourist attractions and other places. Therefore, the new prediction machine includes video capture, upload and display, data analysis and early warning operations in embedded devices, and automatically predicts crowd density. In terms of constructing the network, first, in order to obtain a clearer generated density map, the feature self-learning module is merged in the generator feature extraction stage. Secondly, in order to avoid the blur of the generated image, an adversarial loss is constructed between the generator and the discriminator, and finally to deal with multiple scales, two generator networks are constructed to adapt to large scale and small scale respectively in order to extract semantic information of different scales and use cross-scale consistency loss constraints to generate density maps.

Aiming at the problems of cold start, gray sheep and sparsity of the traditional collaborative filtering recommendation system, this paper proposes a cross-platform dynamic goods recommendation system based on reinforcement learning and edge computing. First of all, this system models the current friendship relationship networks and potential friendship relationship networks, it also constructs two layers preference prediction models. Then, we consider the frequent change characteristic of social networks and shopping platforms, we design a dynamic reinforcement learning method and edge computing to learn the minimized entropy loss error. Finally, we finish the validation experiments based on the real datasets, the results show the proposed system realizes better link prediction accuracy, and using our proposed system can obtain an obvious increase in the accuracy compared to the existing of collaborative filtering recommendation systems.

One of the core features of Intelligent Edge Computing (IEC) is real-time decision making, therefore low delay is more important for IC design under IEC architecture. And in very large scale integration routing, wirelength is one of the most important indexes affecting the final delay of the IC design. Therefore, this paper introduces the X-routing with more potential for wirelength optimization and the Steiner Minimum Tree (SMT), which is the best routing model in multi-terminal nets. Then, based on Particle Swarm Optimization (PSO) technique which has the strong global optimization ability in Soft Computing, an effective Two-Stage X-routing Steiner minimum tree construction algorithm is proposed. The proposed algorithm is divided into two stages: social learning discrete PSO searching and wirelength reduction. In the first stage, two excellent strategies are proposed to maintain a good balance between exploration and exploitation capabilities of the PSO technique: (1) Chaotic decreasing inertia weight combined with mutation operator is set to enhance the exploration capability. (2) A new social learning approach combined with crossover operator is designed to ensure the diverse evolution of the swarm while maintaining the exploitation capability. In the second stage, a strategy based on local topology optimization is proposed to further reduce the length of X-routing Steiner tree. Experiments show that the proposed algorithm can achieve the best wirelength optimization and has a strong stability, especially for large-scale SMT problem, so as to better satisfy the demand of low delay of IC design under IEC architecture.

This paper carries out the mechanism analysis of non-inertial particle swarm optimization. It is a modified algorithm based on a new kinetic equation which is applied to the prediction of non-stationary time series for the internet of things in the edge computing. In order to avoid premature convergence and accelerate convergence rate, different from standard particle swarm optimization, the modified algorithm uses a new kinetic equation to lead particles motion direction, besides generalized opposition-based learning (GOBL) and adaptive elite mutation (AEM) strategies. The work presents the second order standardized recurrence equation for the new kinetic equation whose corresponding characteristic equations can be analyzed via the difference functions to obtain the boundaries of coefficients and its convergence region. Besides, GOBL and AEM strategies are also analyzed to boost global and local convergence of the algorithm as an interpretation. It is illustrated that performance analysis of the algorithm with two well-known test functions. The good performance is further validated from application in edge computing.

Particle Swarm Optimization (PSO) and its variants are often used to train and optimize the structure and parameters of deep learning models to improve accuracy of learning results in the reasonable time-consuming. The performance of PSO is completed determined by its kinetic equation of particles. To accelerate convergence rate, a novel kinetic equation without inertial term is devised and applied to PSO, and then a non-inertial opposition-based particle swarm optimization (NOPSO) is generated combined with a adaptive elite mutation strategy and generalized opposition-based learning strategy. Simulation Experimental results show that the new kinetic equation has effectively accelerated convergence rate of PSO. Meanwhile, Theoretical analysis of the new kinetic equation is carried out by order-2 difference recurrence equation, the inference conclusions of which are consistent with the results of simulation experiments. NOPSO algorithm with a new kinetic equation is a highly competitive algorithm compared with some state-of-art PSOs and is suitable for deep learning applications.

Traditional information system and managerial mechanism cannot meet the new environment protection regulations; therefore, a new solution must be set up, else it may run the risk of losing the market and the value of product brand.This research focuses on the solutions provided by the network and communication industry, manufacture of the green products, and the establishment of a Webbased green collaboration management system, which incorporates the environment protection requests into the product development and green supply chain activities. There are three goals: (1) For the design of green products, green components and product management are set up for the purpose of de-toxin and ease of disassemble; (2) For the green supply chain, the management of hazardous substances, product recycle and disassemble are set up; (3) For the product recycling management, the recycling information is feedback for the product update and collaboration operation. The result of research can provide information for the integration of workflow and information system currently in use, provide RoHS and WEEE management solutions, and set up green collaboration information system, so that green products, brand leadership, and environment protection endeavor can be assured.

The equipment lock has been an important tool for the power company to protect the electricity metering equipment. However, the conventional equipment lock has two potential problems: vandalism and counterfeiting. To fulfill the control and track the potential illegal behavior, the human labor and paper are required to proceed with related operations, resulting in the consumption of a large amount of human resources and maintenance costs. This study focused on the design of RFID technology applied to the traditional equipment lock, which, through the mobile and electronic technology, strengthens the management/operating convenience of the lock and provides the solutions for anti-counterfeiting and spoilage detection so that the national energy can be properly protected and fairly distributed.

The equipment lock has acted as an important tool for power companies to protect the electricity metering equipment. However, the conventional equipment lock has two potential problems: vandalism and counterfeiting. To fulfil the control and track the potential illegal behaviour, the human labour and paper are required to proceed with related operations, resulting in the consumption of a large amount of human resources and maintenance costs. This research focused on the design of RFID technology applied to the traditional equipment lock, with the mobile and electronic technology, we are able to strengthen the management/operating convenience of the lock and provide the solutions for anti-counterfeiting and spoilage detection, so that the national energy can be properly protected and fairly distributed. The integration of RFID data interface and mobile sensing devices will enhance the control of electricity meters and other metering devices for the power company, it will also provide accurate and mobile support with interactive mode, real-time display by the system, and real-time information services. It will serve as the 'last-mile' management tool for the electrical equipment and improve development of the intelligent electric grids.