Sports injuries can be a major problem for athletes. Therefore, sports injury protection has become a key focus of attention in sports and medical circles. With widening participation in sport, ...related injuries can have an impact at national level. However, many areas around the world lack adequate medical resources. It takes more time and money for local people to get to the nearest rehabilitation department or physical therapy studio. Artificial intelligence (AI) has undergone vigorous development, leading to increased computing speed and accuracy. Nowadays, two-dimensional image signals can be used for body-posture recognition. This research is based on the Openpose limb-detection AI model, which has corrective exercise training elements and uses functional motion-detection technology as the diagnostic basis (combined with physical therapists’ clinical knowledge of rehabilitation interventions). We propose a 2-D imaging physical health detection system. The system is divided into four main parts: a mobile app user interface, the computing server, professional interface and database. A user video is recorded by the app. The computing server then calculates keypoints of the human body through Openpose and converts these data into clinical test indicators. Finally, health indicators are displayed, and the app registers action scores. The professional interface presents users with health feedback and recommends suitable rehabilitation videos. The computing server is divided into two parts: limb detection and the detection system. The limb-detection aspect is divided into four parts: sample collection, video processing, keypoint processing and results comparison. The detection system is subdivided into system architecture and a health evaluation (forming the basis for video recommendations). The key contributions of this paper are that the proposed system can calculate body posture and automatically detect the physical condition and health of the body. In addition to reducing dependence on professional human resources, the system can also save the trouble of manual angle measurement in traditional physical therapy.
As the basic model for very large scale integration routing, the Steiner minimal tree (SMT) can be used in various practical problems, such as wire length optimization, congestion, and time delay ...estimation. In this paper, an effective algorithm based on particle swarm optimization is presented to construct a multilayer obstacle-avoiding X-architecture SMT (ML-OAXSMT). First, a pretreatment strategy is presented to reduce the total number of judgments for the routing conditions around obstacles and vias. Second, an edge transformation strategy is employed to make the particles have the ability to bypass the obstacles while the union-find partition is used to prevent invalid solutions. Third, according to the feature of ML-OAXSMT problem, we design an edge-vertex encoding strategy, which has the advantage of simple and effective. Moreover, a penalty mechanism is proposed to help the particle bypass the obstacles, and reduce the generation of via at the same time. Experimental results show that our algorithm from a global perspective of multilayer structure can achieve the best solution quality among the existing algorithms. Finally, to our best knowledge, we redefine the edge cost and then construct the obstacle-avoiding preferred direction X-architecture Steiner tree, which is the first work to address this problem and can offer the theory supports for chip design based on non-Manhattan architecture.
Constructing a timing-driven Steiner tree is very important in VLSI performance-driven routing stage. Meanwhile, non-Manhattan architecture is supported by several manufacturing technologies and now ...well appreciated in the chip manufacturing circle. However, limited progress has been reported on the non-Manhattan performance-driven routing problem. In this paper, an efficient algorithm, namely, TOST_BR_MOPSO, is presented to construct the minimum-cost spanning tree with a minimum radius for performance-driven routing in Octilinear architecture (one type of the non-Manhattan architecture) based on multi-objective particle swarm optimization (MOPSO) and Elmore delay model. Edge transformation is employed in our algorithm to make the particles have the ability to achieve the optimal solution while Union-Find partition is used to prevent the generation of invalid solution. For the purpose of reducing the number of bends which is one of the key factors of chip manufacturability, we also present an edge-vertex encoding strategy combined with edge transformation. To our best knowledge, no approach has been proposed to optimize the number of bends in the process of constructing the non-Manhattan timing-driven Steiner tree. Moreover, the theorem of Markov chain is used to prove the global convergence of our proposed algorithm. Experimental results indicate that the proposed MOPSO is worthy of being studied in the field of multi-objective optimization problems, and our algorithm has a better tradeoff between the wire length and radius of the routing tree and has achieved a better delay value. Meanwhile, combining edge transformation with the encoding strategy, the proposed algorithm can significantly reduce nearly 20 % in the number of bends.
Global Routing (GR) is a crucial and complex stage in the Very Large-Scale Integration (VLSI) design, which minimizes interconnect wirelength and delay to optimize the overall chip performance. ...Steiner tree construction is one of the basic models of VLSI physical design, which is usually used in the initial topology creation for noncritical nets in physical design. In a GR process, a Steiner Minimum Tree (SMT) algorithm can be invoked millions of times, which means that SMT algorithm has great significance for the final quality of GR. Some of the research works are surveyed in this paper to understand GR and SMT problems and to learn the available solutions. Firstly, we systematically dissect three types of subproblems in Steiner tree construction and three types of GR methods. Then, we investigate the recent progress under two new technology models. Finally, the survey concludes with a summary of possible future research directions.
Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive ...text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively.
Dear editor, Rectilinear Steiner minimal tree （RSMT） has been widely used in several modern very large scale integration （VLSI） circuit design phases. Because of its importance, it has been fully ...studied in the past decades. In addition, the density of modern VLSI chips has increased significantly. In today＇s VLSI designs, there are increasingly more obsta- cles, such as IP blocks and pre-routed nets, these obstacles cannot be run through during the rout- ing process, and thus the obstacle-avoiding RSMT （OARSMT） construction problem has received in- creasing attention in recent years.
In the era of the knowledge economy, one of the key issues is how to integrate and use intelligent information systems to collect data and make valuable predictions to support business decisions. ...Intelligent information systems use artificial intelligence to enhance system performance, giving the enterprise a competitive advantage. This paper uses ontology technology for user requirements analysis—defining the class, slot, and instance of the ontology—and then designs the system architecture based on that ontology. In accordance with the firm’s requirements, we build a data warehouse system that is integrated with different data sources within the enterprise and that supports a web-based interface in the Internet of Things (IoT). The system also supports the standard queries, reports, summary tables, and datasets required for data mining. The proposed data mining method for manufacturing industries that use Bayesian network incorporates Bayesian theory and graphical models and can predict causal and probabilistic relationships among a set of variables. Our results bring information system functionality closer to satisfying the real-world needs of business. The proposed system can reduce production cycle times, increase the speed and accuracy with which production information is analyzed, and offer predictions that can be used for better business decisions. Data mining technology can improve the efficiency of manufacturing processes by using feedback data to tune the manufacturing parameters and improve the accuracy of yield rate predictions, giving the firm a greater competitive advantage.
Obstacle-avoiding Steiner minimal tree (OASMT) construction has become a focus problem in the physical design of modern very large-scale integration (VLSI) chips. In this article, an effective ...algorithm is presented to construct an OASMT based on X-architecturex for a given set of pins and obstacles. First, a kind of special particle swarm optimization (PSO) algorithm is proposed that successfully combines the classic genetic algorithm (GA), and greatly improves its own search capability. Second, a pretreatment strategy is put forward to deal with obstacles and pins, which can provide a fast information inquiry for the whole algorithm by generating a precomputed lookup table. Third, we present an efficient adjustment method, which enables particles to avoid all the obstacles by introducing some corner points of obstacles. Finally, an excellent refinement method is discussed to further enhance the quality of the final routing tree, which can improve the quality of the solution by 7.93% on average. To our best knowledge, this is the first time to specially solve the single-layer obstacle-avoiding problem in X-architecture. Experimental results show that the proposed algorithm can further shorten wirelength in the presence of obstacles. And it achieves the best solution quality in a reasonable runtime among the existing algorithms.
The belief-rule-base (BRB) inference methodology using the evidential reasoning (ER) approach is widely used in different fields, such as fault diagnosis, system identification, and decision ...analysis. However, the calculation characteristic of the conventional rule activation weight makes the inference system have the rule zero activation problem. The difficulty of constructing partial derivatives restricts the optimization of parameters using the gradient method. Hence, this paper proposes a new belief rule structure and its gradient training method to solve the rule zero activation problem during the inference process and improve inference accuracy. The Gaussian function is applied to calculate the activation weight of the rule with the new structure. Its characteristics avoid the zero activation problem caused by the attribute reference value set in the original method. Based on the newly proposed method, the corresponding distance-sensitive parameter is set for each attribute, and the weight parameter of each rule is discarded. It simplifies the calculation of rule activation weights in the inference process and enables the partial derivatives of the parameters of the inference system to be easily constructed. In the parameter optimization, the momentum optimization gradient stochastic descent method is used to train the new BRB system, which improves the training speed and accuracy compared with the conventional methods. Experiments with nonlinear function fitting, oil pipeline leak detection, and classification of several public datasets are carried out to verify whether the new BRB system trained with momentum stochastic gradient descent (SGDM-BRB) has better performance than other conventional methods. The experimental results show that in the case of complete data, SGDM-BRB has higher accuracy and faster training speed than the conventional methods.
Routing is a complex and critical stage in the physical design of Very Large Scale Integration (VLSI), minimizing interconnect length and delay to optimize overall chip performance. With the rapid ...development of modern technology, VLSI routing faces enormous challenges such as large delay, high congestion, and high-power consumption. As a rising optimization method, Swarm Intelligence (SI) inspired from collective intelligence behaviors through cooperation or interaction with the environment provides effectiveness and robustness for solving NP-hard problems. Many researchers have consequently used SI techniques to solve routing-related problems in VLSI. This paper reviews the application of several SI techniques to the VLSI routing filed. Firstly, five commonly used SI techniques and related models, and three classic routing problems are described: Steiner tree construction, global routing and detailed routing. Then an overview of the current state of this field is given according to the above categories, and the survey offers informative discussions from five aspects: 1) Steiner minimum tree construction; 2) wirelength-driven routing; 3) obstacle-avoiding routing; 4) timing-driven routing; 5) power-driven routing. Finally, under three new technology models: X-architecture, multiple dynamic supply voltage and via-pillar, the future development trends are pointed as follows: 1) suggesting suitable SI techniques to specific routing problems for advanced technology models; 2) exploring new and available SI techniques that have not yet been applied to VLSI routing.
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