A software tool for constructing traditional Chinese medical expert systems

The traditional fault diagnosis expert system is dependent on knowledge acquisition of the experts. Knowledge acquisition is recognized as the problem of expert system. In addition, there are also some limitations of adaptive capacity, learning ability and real-time. And artificial neural network with good fault-tolerance and associative memory function, as well as very strong self-adaptive, self-learning ability, just can make up for the limitations of traditional expert system. This paper will construct a new expert system with the artificial neural network into and fault tree. Besides fault tree and neural network, this article mainly introduces the system model of fault diagnosis of the fire control computer and sensor subsystem, the method and process of fault diagnosis. In this expert system, we use the object-oriented production rule to represent the knowledge, which solves the bottleneck problem of the diagnostic knowledge acquisition effectively. The inferential process begins with the abnormal event and finally finds all of the possible faults and the faulty component. For some possible faulty components, which have large number of fault samples, the neural network model can be used to diagnose. The training network of fault samples employs the BP neural network. Finally, simulation training results show that the fault diagnosis expert system based on the combination of fault tree and neural network is rational and effective in fault diagnosis of the fire control system, realizes perfectly the combination of new knowledge and old one, and can grasp the state of systems dynamically.

For a complicated system based on high technology, once a part breaks down, the entire system can not work normally. Moreover, due to the complexity of its structure and fault causes, the fault diagnosis of the system is also complex and indeterminate, a single test equipment can hardly finish a difficult diagnose task, and fault diagnosis expert system can resolve these problems effectively. The traditional diagnose expert systems have many problems such as the bottleneck of knowledge acquisition, the fragility of knowledge, the pool ability of self-study, the inefficient reasoning, and the monotonicity of reasoning, so there are certain limitations. But the artifical neural networks technology is a new system, it is an mathematical model that applies the structure like the joint of synapses in hypothalamic neurons, which has the strong ability to study, and can learn from samples, obtain knowledge, store it in the network in the form of weight and threshold, and it is easy to implement the parallel processing, has the character of association memory, own the better robust. it ability of adaptive self-study is manifested mainly in adjusting the weight of network according to the change of enviroment by learning algorithms, so as to adapt to the environmental change. But the neural network can not explain its own reasoning. Therefore we will apply the neural network to the expert knowledge system, which can make them learn each other's good points mutually for common progress, constructing the new neural network expert system. The system is applied to the power fault diagnosis, achieving good results.

Aim: This study investigated the causation between Sjögren’s syndrome (SS) and rheumatoid arthritis (RA) based on Mendelian randomization (MR), with the purpose of promoting research on the academic thought of distinguished veteran doctors of traditional Chinese medicine (TCM) via bioinformatics with information technology and providing new basis for better inheriting and carrying forward the valuable experience of a National Master of TCM, Zhizheng Lu. Methods: The causation between RA and SS in TCM was inferred from the perspective of coordination between dryness and dampness. For further assessment, MR was performed based on the summary data of genome-wide association studies (GWAS), with genetic loci closely related to SS(instrumental variables (IVs)), RA(research outcome), inverse-variance weighted (IVW) and MR-Egger regression and weighted median (WME) ( main MR methods). Heterogeneity was evaluated by Cochran Q test, pleiotropy by MR-Egger regression, and robustness by the Leave-One-Out method. Results: Except for MR-Egger, all other methods showed a clear statistical significance in the association between SS and RA (P<0.05). Conclusion: This study, based on the academic thought of distinguished TCM veteran doctor and the thought “moistening dryness” of National Master of TCM, Zhizheng Lu, speculated that there may be a potential association between SS and RA. MR results demonstrated the clear genetic causation between SS and RA, and the guiding role of the academic thought of distinguished TCM veteran doctor in clinical practice genetically.

This work focuses on the construction of an expert system shell for airborne equipment design. Traditional expert systems are constructed using a single monolithic software program for a specific application. Since the airborne equipment design is critical & complex, it demands the use of expert system technology. Since present aircrafts take in different equipments for different purposes, it is not feasible to think in terms of independently developed monolithic expert system program for each equipment. To overcome these problems, we have designed and developed a complex competent system i.e., a generic component based expert system for airborne equipment design. The main advantage of the component-based approach is that, the knowledge base is not hard coded and these components can be coupled with any domain-specific knowledge bases. The expert system with its expertise knowledge is made to guide the designer by providing design guidelines & testing procedures for the desired equipment. The expert system also audits the design and provides guidelines to modify & to improve the design.

This paper investigates a fault diagnosis expert system base on artificial neural network (ANN-FD-ES) for mixed-signal circuits. We built this system by given a faulty circuit. Based on the transient response testing (TRT) on the mixed-signal circuits, both analog and digital signal characteristics are unified. The system includes knowledge base, reasoning machine, explanatory, and interface. The diagnostic portion of the system is based on neural-calculation approach, which is used to establish the knowledge base composed of the weigh values and threshold values. With this approach, conclusions are developed directly from the outputs of the net, rather than the domain knowledge in traditional expert system. Compared with the traditional expert system, this system is good at processing data. The experimental results show that the system not only improves the shortcoming of the traditional expert system such as the deficiency on knowledge acquisition and self-learning capability, but also achieves a satisfied fault diagnosis efficiency.

Due to the complex structure and massive volume of large equipment in petrochemical enterprises, it was very difficult to carry out inspection and maintenance work. The traditional expert system used a single knowledge base and reasoning machine, so the processing efficiency and prediction accuracy were low. In this paper, the dimensionality reduction which is used to process a large amount of physical information collected by the sensors, remove redundant components and extract main characteristic parameters. Meanwhile, combined with the powerful pattern recognition and judgment ability of fuzzy neural network, the forward reasoning mechanism and error back propagation keeping continuous training correction until meeting the accuracy requirements, the expert system gave the equipment fault prediction conclusion. Finally, taking main fan oil station circulating oil pump and motor of heavy oil catalytic cracking unit and nine stages of bearing damage as monitoring objects, the fault predictions was implemented by the following steps: data collection, feature extraction, dimension reduction of big data and expert system with the fusion of fuzzy neural network, and it is proved that the expert system with fuzzy neural network based on dimension reduction can greatly improve the convergence speed.

In recent years there has been a shift from traditional expert systems to cooperative systems for workgroups. This shift stems from the realization that designers must consider the workplace's contextual information if they are to create successful applications, rather than developing applications to perform a task in isolation. The traditional expert systems development methods are insufficient to create effective cooperative systems. This special issue focuses on some of the central problems that arise when building cooperative expert systems.

The secondary system of the intelligent substation has a complex logic structure and a variety of failure modes, making it difficult and time-consuming to locate the fault. Aiming at the shortcomings of traditional fault location methods, a fault tree-based positioning method is proposed. Fault tree analysis method can effectively react to fault alarms and the causal relationship between fault locations that cause them. The fault tree analysis method is applied to the expert system, which solves the problems of low efficiency, lack of knowledge and inaccurate reasoning results of the traditional rule-based expert system, and the logical relationship between knowledge is not reflected. Aiming at the difficulty of obtaining the probability of the event at the bottom of the fault tree, the fuzzy number method is proposed to improve the traditional fault tree method, and the triangular fuzzy number is used to express the fuzzy probability of the bottom events, thereby improving the efficiency of the fault maintenance and positioning of the maintenance personnel.

There are only a limited number of computer-based systems designed to support anesthesiologists in the operating room. This is evident from the very small number of publications on this topic. These systems may be classified by the functions they perform and include: intelligent anesthesia workstations (with current data acquisition, conditioning and analysis subsystems), systems to detect critical conditions in patients (including expert systems in smart alarm capacity), anesthesia management systems (for planning and management) and drug administration systems. Drug administration systems may be subdivided into open-loop and closed-loop systems. The techniques applied for design of such systems vary extensively. There are traditional rule-based expert systems and probability-based systems, and more recently developed artificial intelligence methods, such as neural networks and fuzzy logic. Computers are valuable tools that have the potential to assist anesthesiologists in carrying out cumbersome and monotonous processes. Future efforts may result in the development of sophisticated systems capable of assuming more responsibilities and of reducing human workload and stress.

Iterative learning belief rule-base inference methodology using evidential reasoning for delayed coking unit

Diabetes mellitus (DM) is one of the most serious diseases threatening human health and because of that, it is imperative to look for drugs to tackle it. The Tibetan medicine, a traditional medical system used in China, is currently being the focus of research towards the discovery of new effective drugs against several diseases. Based on the literature survey of Tibetan medicine monographs and drug standards, the Tibetan medicine, and Tibetan prescription used in the traditional Tibetan medical system, here, we summarise the methods indicated for DM treatment. In the Tibetan medical system, 56 types of Tibetan medicine and 25 Tibetan prescriptions were found for the treatment of DM. The most commonly used are Curcuma, Berberidis Cortex, and Carthami Flos. Their names, families, medicinal parts, phytochemical components, and pharmacological activities were described in detail in our research. These Tibetan medicines and prescriptions are valuable gifts from the Tibetan medicine to the world and may be the source of potential drugs for the treatment of DM. With the help of modern phytochemistry, pharmacology, metabonomics, and/or clinical trial methods, further research is needed to prove its medicinal value, identify bioactive components, elucidate potential mechanisms of action, and assess potential side effects or toxicity. This study provides the first available data compilation for the ethnic medical knowledge of Tibetan medicine for the treatment of DM, providing new ideas and sources for drugs against DM.

ObjectivesThis study identifies inequities in the provincial-level geographical distribution of traditional Chinese Medicine (TCM) hospital beds and doctors in China from 2004 to 2014. This provides policy implications of the optimal allocation of TCM health care resources.MethodsOur study used province level data on TCM hospital beds and doctors from 2004 to 2014. These data were obtained from the China TCM Yearbook 2004–2014 and the China Statistical Yearbook 2004–2014.Global and local spatial autocorrelation was performed by using Moran’s index and the local Moran’s index to describe the spatial distribution of TCM hospital beds (doctors) as well as their density. A Gini coefficient was used to estimate inequalities in the geographic distribution of TCM hospital beds (doctors) based on their density. Correlations of the Gini coefficients between TCM hospital beds and doctors were calculated by Pearson correlation analysis.ResultsAll indicators of TCM hospital beds and doctor density have increased over the past 11 years. The number of TCM hospital beds per 10,000 populations increased the fastest. Geographical clustering was not obvious in the density distribution of TCM hospital beds or doctors, as no significant spatial autocorrelation was found. Gini coefficients showed that from 2004 to 2014 the distribution of TCM hospital beds per 10,000 population and doctors per 10,000 populations were equitable between different regions. A large gap existed in the distribution inequality of TCM hospital beds (doctors) per square kilometer among different regions.ConclusionTargeted health policy with equitable distribution of TCM hospital beds (doctors) per square kilometer and the balance and coordination of related resources should be a priority in shaping China’s healthcare system reform.

The expert neural network system the author has developed for use in DNA sequence analysis combines a traditional symbolic expert system with an artificial neural network. The resulting hybrid system can accurately model underlying domain knowledge to improve accuracy, generalization performance, and information-processing speed.

The pre-built knowledge of traditional expert systems is only capable of limited responses to changes in the operating environment. If the data input is imperfect, a traditional system may fail to reach any rational conclusions. In this paper, we introduce the concept of self-adaptability to the inference process of an expert system, and propose a model that is capable of handling unexpected user input effectively and efficiently. Such a system can formulate operational knowledge on the move for inference. With this self-adaptive capability, an expert system can reach useful conclusions, even when the input data is insufficient. The architecture of the proposed system encodes domain knowledge with semantic networks. It also defines four types of adaptation, namely, condition knowledge adaptation, operational knowledge adaptation, conclusion knowledge adaptation, and presentation adaptation, and focuses on how the first three contribute to the adaptive capability of the system. In addition, to enable a self-adaptive expert system to effectively produce better conclusions, two entropy-based measuring mechanisms are proposed: one minimizes the information loss during knowledge adaptation, while the other selects the best attribute relation during the generation of operational knowledge. We have proved that a self-adaptive expert system based on this architecture can always reach a regular conclusion or an abstract conclusion, which is a more meaningful conclusion by automatically modifying its operational knowledge in response to user feedback during the inference process, even in unexpected situations.

Stable operation of the power system is inseparable from the normal operation of a variety of electrical equipment; partial discharge signal power processing system to determine the time difference is the basis of the PD source localization. Typical methods of feature points difference estimate the time difference, the difference based on generalized correlation time, seek time difference based on the frequency domain, the parametric model law. In this paper, starting from two aspects, one to study the application of the theory of Hilbert - Huang transform transformer partial discharge signal spectrum and marginal spectrum analysis, and has done a simulation experiment results show that the application of the transformer partial discharge fault type the recognition is very effective; on the other hand, a partial discharge of the design expert system based on fuzzy reasoning transformer, this system is built in the traditional expert systems and fuzzy inference and fuzzy reasoning is based on the an intelligent decision-making method based on fuzzy mathematics, has been widely used in various fields, intelligent fault diagnosis of this article will be applied to the transformer partial discharge. Finally, a simulation experiment, this paper for transformer fault diagnosis, especially transformer partial discharge pattern recognition has a very important reference value.