Knowledge Rules Research Articles

Smart Train Operation Algorithms Based on Expert Knowledge and Reinforcement Learning

During decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems for its low-cost and intelligence. This article proposes two smart train operation (STO) algorithms by integrating the expert knowledge with reinforcement learning algorithms. Compared with previous works, the proposed algorithms can realize the control of continuous action for the subway system and optimize multiple critical objectives without using an offline speed profile. First, through learning historical data of experienced subway drivers, we extract the expert knowledge rules and build inference methods to guarantee the riding comfort, the punctuality, and the safety of the subway system. Then we develop two algorithms for optimizing the energy efficiency of train operation. One is the STO algorithm based on deep deterministic policy gradient named (STOD) and the other is the STO algorithm based on normalized advantage function (STON). Finally, we verify the performance of proposed algorithms via some numerical simulations with the real field data from the Yizhuang Line of the Beijing Subway and illustrate that the developed STO algorithm are better than expert manual driving and existing ATO algorithms in terms of energy efficiency. Moreover, STOD and STON can adapt to different trip times and different resistance conditions.

Application and Research of Folk Music Composition Based on Artificial Intelligence Technology

Music is one of the important forms of human emotion expression. Using artificial methods and technologies to allow the machine to quickly identify optical scores and real-time music sounds, through the inference and optimization of music emotion models, obtain human music emotion expression patterns, and actively complete human-machine collaboration with intelligent composition, and other related services. The development of multi-source perception of affective artificial intelligence has important research value and practical significance. This article mainly integrates multiple models of existing artificial intelligence models (typed music models, Markov chain models, genetic algorithm models, neural network models, etc.) to improve the rule knowledge to form a practical and effective hybrid model. Its innovation lies in combining formulas and matrix data combinations to intelligently compose non-note units (reproducing structures) in automatic composition, so as to achieve harmony, audibility and intelligence of music.

LEGAL ENSURING OF FORENSIC ACTIVITY: MODERN ASPECT

Forensic activity at the current stage of its development is a set of specialized knowledge and procedural rules that govern legal relations related to the appointment and conduct of forensic examinations. The effectiveness of forensic activity depends on a variety of factors: regulatory and legal support, technical equipment and the level of specialized knowledge of each forensic expert.
 In our opinion, the mechanism of legal regulation of forensic activity is a process of correspondence with the norms of substantive and procedural law in regularized activity of an expert who conducts research on objects in accordance with certain standards.
 Legal ensuring of forensic activity should be considered through the prism of the terms “examination” and “expert”, which today give a clear picture of the forensic activity in general. After all, forensic activitiy ensuring is implemented through the work of an expert, by his performing an independent, objective, qualified examination.
 Based on the above, we believe that, firstly, there is an urgent need for accelerated changes in legal ensuring of forensic science, as it will contribute to the creation of optimal conditions for successful professional activity and increase the level of expert ensuring of justice in the country.
 Secondly, it is essential to introduce into legislation the provision on the status of a private forensic expert. In our opinion, this innovation may stipulate the following: “Forensic examination may be entrusted to: employees of state specialized institutions of forensic examinations; private experts who are engaged in forensic activities on the basis of a license and who have obtained the relevant qualification”. Thirdly, based on international experience, to develop an effective mechanism of relations between the subjects who implement forensic activities in order to avoid (minimize) contradictions and conflicts between representatives of state specialized institutions of forensic

Normativity of Prescriptions in Adolf Reinach’s Aprioristic Theory of Right

In the Logical Investigations, Edmund Husserl defines that which is normative as the objectively regular with its rules of regularity, which can be recognised rationally – normativity concerns the being itself and the rational cognition of the being (logic as a normative discipline establishing the rules of scientific knowledge, as the science of science). Instead, Adolf Reinach in The Apriori Foundations of the Civil Law defines the notion of norm as polysemantic and distinguishes the legal provisions (the prescriptive sentences), formulated within a given community, from the basic norms which are grounded in the objective (including moral) justness of the states of affairs. The obligation of the being and the obligation of acting exist in themselves, independently from cognition. In turn, “enactments and the propositions which express enactments” as a kind of normative sentences have the character of normalisation, but they require a person to pronounce them. The prescriptions realise and refer to what is objectively being and to the objectivity of what is being and obligatory. In my text, I present Reinach’s position on the relations between norms and provisions (as prescriptive propositions “which express enactments”) referring his theories to the Husserlian concept of normativity.

Constructive truth and creativity in contemporary Iraqi architecture

Truth and purity are an important concept in modern architectural design, as it is considered one of the significant concepts in architectural discourse. In many literatures, the significance of creative meanings can be added by the structure to the general formation through honesty and truth that resulted from the use of construction in an expressive way to achieve visual tracking of the correct paths of force transmission in the components of the construction formation, and deepen the aesthetic meanings that can be added by the construction. Thus, linking the character of creative construction truth to the constructive integrity The research problem lies in the lack of knowledge in studying the aspects of constructive truth and its range of effect on the architectural creativity sides in general and for the local architecture in particular, therefore, the research aims at clarifying the relationship between the concept of structural truth in all its intellectual and formal dimensions which affect the achieving of the construction creativity and thus the creativity of the apparent architectural production. It also discusses the creativity in architectural productions through studying the creativity sides that can be reached through the constructive truth vocabulary and items in the contemporary Iraqi architecture applications. Also and identifying the possibilities of interdependence between structural truth and structural creativity. The other aim is to analyse constructional applications that integrated with the design of the building in order to establish the fundamentals of creative architectural decision-making. In its methodology, the research methodology depends on comparison analysis style through the creating of a theoretical framework that starts from the mentioned above knowledge rule and applying it to a group of local examples to get some indicators that explain the role of structural honesty in the local architectural results.

Course of Artificial Intelligence + Composition for Young Backbone Teachers

Based on the analysis of the needs in the education field, the basic application framework scenarios of the current artificial intelligence technology in the field of music education are constructed. The characteristics of various models (typed music model, Markov chain model, genetic algorithm model, neural network model, etc.) are used to improve the rule knowledge to form a practical and effective hybrid model. The innovation lies in combining non-note units (reproducing structures) in music composition to intelligently compose tracks by combining formulas and matrix data combinations. The project conducts in-depth cooperation on the training of composition teachers and students from the fields of curriculum teaching, practical teaching, and innovation competition, and strives to combine theoretical teaching and engineering cases, practical teaching and industrial products, subject competition and intelligent technology. Combination to achieve collaborative education of industry-academia cooperation.

Security of Cloud Computing Using Adaptive Neural Fuzzy Inference System

Cloud computing can enable organizations to do more by breaking the physical bonds between an IT foundation. The raised security dangers in cloud computing must be overpowered to profit the new processing perspective that offers an imaginative arrangement of activity for relationship to IT. The purpose of the study was to reduce security’s obstacles and risks by using protection methods and approaches to ensure maximum data protection, which allows for the user to select the original security level. An adaptive neural control fuzzy system is used to resolve the unsecure and risky tasks of cloud computing. Sugeno control methods have been applied for these data protection issues in which the uncertainty because of randomness can be resolved. ANFIS identified the input parameters according to the current scenario, fuzzified the data, and integrated them into knowledge rule base. Different membership functions were used for training the data. In this article, we present a point-by-point examination of the cloud security issue. We assessed the issue from the cloud building point of view. In context of this examination, we deduce an unmistakable detail of the cloud security issue and key highlights that ought to be confirmed by any proposed security strategy. The examination and results show that the parameters dependent on ANFIS are very much intended to distinguish the oddities in cloud condition with least bogus negative rate and high discovery precision. The performance of Sugeno membership function usually gives better results and ensures the computational efficiency and accuracy of data.

Research on location fusion of spatial geological disaster based on fuzzy SVM

In order to effectively improve geological disaster response capacity, disaster tolerance capacity, reduce human and financial losses and other aspects of spatial data fusion plays a key role. Whether the location information can be effectively fused is able to monitor the occurrence of geological hazards and effectively identify the existing risks. In machine learning, the similarity and complementarity between support vector machine and fuzzy inference is the basis of their fusion. Support vector machine can achieve knowledge acquisition and learning, while fuzzy inference has the ability to infer knowledge rules. Aiming at the different attributes and dimensions of information spatial location data, a fuzzy fusion method based on support vector machine is proposed to describe Support vector machine related theories and models. Comparing the efficiency of support vector machine-fusion algorithm on GPLUS, OKLAHOMA and UNC with the other three algorithms, there is a great advantage in RMSE and time. The algorithm in this paper also has good performance in the three data sets on F1, which shows that the algorithm has a good effect.

Applying Dempster–Shafer theory for developing a flexible, accurate and interpretable classifier

Two approaches have traditionally been identified for developing artificial intelligence systems supporting decision-making: Machine Learning, which applies general techniques based on statistical analysis and optimization methods to extract information from a large amount of data looking for possible relations among them, and Expert Systems, which codify experts knowledge in rules, which are then applied to a specific situation. One of the main advantages of the first approach is its greater accuracy and wider generality for the application of the methods developed which can be used in various scenarios. By contrast, expert systems are usually more restricted and often applicable only to the domain for which they were originally developed. However, the machine learning approach requires the availability of large chunks of data, and it is much more complicated to interpret the results of the statistical methods to obtain some explanation of why the system decides, classifies, or evaluates a situation in a certain way. This issue may become very important in areas such as medicine, where it is relevant to know why the system recommends a certain treatment or diagnoses a certain illness. Likewise, in the financial sector, it might be legally required to explain that a decision to reject the granting of a mortgage loan to a person is not due to discriminatory causes such as gender or race. In order to be able to have interpretability and extract knowledge of available data we developed a classification method based on Dempster-Shafer’s Plausibility Theory. Mass assignment functions (MAF) must be established to apply this theory and they assign a weight or probability to all subsets of the possible outcomes, given the presence of a certain fact on a decision scenario. Thus MAF assignments encode expert knowledge. The method learns optimal values for the weights of each MAF using the Gradient Descent method. The presented method allows combination of MAF which have been generated by the method itself or defined by an expert with those that are derived from a set of available data. The developed method was first applied to controlled scenarios and traditional data sets to ensure that classifications and explanations are correct. Results show that the model can classify with an accuracy which is comparable to other statistical classification methods, being also able to extract the most important decision rules from the data.

Construction of knowledge rule sets for the classification of land cover information for the coastal zone of Peninsular Malaysia

ABSTRACT Landsat images have large advantages for studying land cover changes over long time periods and have become the main data source for the extraction of large-scale land cover. However, due to the image resolution limitations, it is difficult to classify second-class land cover types with high accuracy through only traditional automatic classification methods. This study selected the coastal zone of Peninsular Malaysia in Southeast Asia as the experimental area for studying the knowledge rule sets classification method. First, a system for the classification of coastal land cover suitable for Southeast Asia was established based on regional characteristics, which included 8 first-class and 18 second-class types. Then, through the combination of multiple geoscience knowledge rules, a set of knowledge rules for extracting the second-class types was established by integrating multiple sources of information, such as the temporal features, topographic features, texture features, shape features, topological features and spectral features. The experimental results showed that the knowledge rule sets were effective in the extraction of the secondary land types in the coastal zones. The overall classification accuracy was over 85%, and the Kappa coefficient was higher than 0.8. Compared with the traditional supervised classification method, the classification accuracy was obviously improved.

Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Heuristic search is a search process that uses domain knowledge in heuristic rules or procedures to direct the progress of a search algorithm. Hill climbing is a heuristic search technique for solving certain mathematical optimization problems in the field of artificial intelligence. In this technique, starting with a suboptimal solution is compared to starting from the base of the hill, and improving the solution is compared to walking up the hill. The optimal solution of the hill climbing technique can be achieved in polynomial time and is an NP-complete problem in which the numbers of local maxima can lead to an exponential increase in computational time. To address these problems, the proposed hill climbing algorithm based on the local optimal solution is applied to the message passing interface, which is a library of routines that can be used to create parallel programs by using commonly available operating system services to create parallel processes and exchange information among these processes. Experimental results show that parallel hill climbing outperforms sequential methods.

Realization of A Knowledge-based Intelligent System for Power Dispatching Plan Management

With the expanding of power grid scale in Chinese metropolis, the task intensity of power dispatchers increases rapidly in regulation of the power system operation structure and states to deal with everyday scheduled maintenance. In this paper, we propose a knowledge-based intelligent system developed to deal with daily management of the power dispatching plans. The system will analyse all the operation state changing tasks arranged for the next day and group the plans according to their association. It will automatically check the security of each power dispatching plan and generate the corresponding dispatching-order tickets. The proposed system builds up power grid ontology knowledge and first-order logic rules and integrates techniques of knowledge reasoning, natural language understanding and network topology analysis. Application shows that it can effectively realize the day-ahead power dispatching plan management (PDPM) instead of the human dispatchers.

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

Detecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi-autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi-class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context.

Application of fuzzy intelligent reasoning method in art color measurement

Under the influence of the COVID-19 epidemic situation, many countries have taken many measures to control the flow of people. The inability of people to gather makes art color measurement a problem. Color matching and coordination and color space conversion have always been the research focus of art color measurement. This paper studies a method of fuzzy intelligent reasoning in art color measurement. Based on case-based reasoning and rule-based reasoning, this method is an important self-learning and self-maintenance method in the field of artificial intelligence and expert system. On the basis of expounding the basic characteristics of color and color space, this paper designs the process principle of case-based reasoning and the process of rule-based reasoning. In this paper, case and rule knowledge representation method, case retrieval technology and rule conflict resolution strategy are established. Based on the above strategy, the color case base is established. In addition, the rule table is established by combining color-brewer with rule reasoning and referring to color-brewer. The rule table has a certain reference value for the application of intelligent reasoning method in the field of art color measurement under the influence of COVID-19 epidemic.

ABILITY TO MANAGE THE KNOWLEDGE AND COMPETITIVE ADVANTAGE: Applied to ICT Companies in the Gaza Strip affiliated with the Palestinian Information Systems Association (PITA): قدرة إدارة المعرفة والميزة التنافسية: بالتطبيق على شركات تكنولوجيا المعلومات والاتصالات في قطاع غزة المنضمة لاتحاد نظم المعلومات الفلسطينية (PITA)

The study aims to determine the impact of knowledge management on the competitive advantage of the sector companies by clarifying the impact of different components of ability to manage the knowledge and competitive advantage by applying to the ICT companies in the Gaza Strip of the Palestinian Information Technology Association ( PITA). The research problem was the main question: What is the impact of knowledge management on the competitive advantage of sector companies?. The main objective of the research was to identify the impact of knowledge management on the competitive advantage of sector companies. The researcher used the analytical descriptive approach. To achieve the objectives of the study, an electronic questionnaire made up of two parts: The first part includes the data of the study sample. The second part includes 9 main themes. The data were processed through the application of the Statistical Package for Social Sciences(SPSS). The researcher used the analytical descriptive approach, which tries to describe, assess and interpret the reality of "the impact of knowledge management on the competitive advantage of the sector companies. The study community is composed of officials at various administrative levels (top, middle and low) in ICT companies in the Gaza Strip(PITA), The researcher used an electronic questionnaire was sent to the companies through the e-mail of these companies made up of two parts: The first part includes the data of the study. One of the most important findings of the researcher There is a statistically significant impact of the variable capacity of the social infrastructure to knowledge management (organizational structure, organizational culture, individuals) on the competitive advantage. There is a significant impact of the variable capacity of knowledge management processes (acquisition, conversion, application, protection) on the competitive advantage of the companies subject of study. There is a statistically significant impact of the ability of the IT infrastructure to knowledge management on competitive advantage the most important recommendations recommended by the study. The focus was on the top management of all companies wishing to maintain their competitive advantage adopting the method of knowledge management building Administrations or departments specializing in this field have an active player position in the organizational structure of the company, The need to pay attention in the knowledge; held by the employees and motivate them to retain knowledge. The need for the company to pay attention to knowledge in its environment. Moreover, the need to pay attention to the rules of knowledge and apply these rules to achieve a competitive advantage.

Automatic Tajweed Rules Recognition using K-Nearest Neighbour (K-NN)

Tajweed refers to a pronunciation rule for Al-Quran recitation in Islam. It acts as guidance for Muslims in reciting the Al-Quran in a correct manner. Yet, Tajweed rules could be complicated as it consists of various types of laws. It could also be confusing, and difficult to remember particularly for the people who have less knowledge in Tajweed rules. Thus, a study on automatic tajweed rules recognition using image processing technique is proposed. The scope of this study is limited to Idgham laws only. Initially, the input image went through the pre-processing process which includes four sub-processes which are greyscale conversion, binary conversion, thinning and flip, and word segmentation. Next, six attributes of shape descriptor which are major axis length, minor axis length, eccentricity, filled area, solidity, and perimeter were extracted from each input image. A technique of k-Nearest Neighbour (k-NN) is employed to recognize the two types of Idgham Laws which are Idgham Maal Ghunnah and Idgham Bila Ghunnah. The performance of the proposed study is evaluated to 180 testing images which returned 84.44% of classification accuracy. The outcome of this study is expected to recognize the Tajweed rules automatically and may assist the user on a proper recitation of Al-Quran.

More aware, more protected: a cross-sectional study on road safety skills predicting the use of passive safety elements among Spanish teenagers

ObjectiveThis study had two objectives: first, to test the effects of sociodemographic variables, and the effects of three key road safety skills (knowledge–risk perception–attitudes) on the use of passive safety...

Bayesian Network-based GF-City Metropolitan Zone Modelling: an Approach Based on Location Quotient Resource Impulse Response Principle

Based on the evolution phenomenon and law of “location quotient”, this paper has studied the mapping relationship between image recognition of GF-City metropolitan area and the impulse response of local industrial resources. Based on the “location quotient” method, the level of local industry specialization in Foshan city was analysed, with the aid of VAR model. With the help of economic logic reasoning method, the VAR regression equation model improved design pattern analysis method was extracted in terms of the typical local industry semantics; a Bayesian network-based VAR model GF-City metropolitan area local industrial resources was constructed together with the impulse response network model. Based on the image recognition evaluation experiment of GF-City metropolitan area, the network combined Bayesian scoring function and search algorithm to carry out learning reasoning, and thus divided into local industrial grammar sub-network, image vocabulary sub-network and mapping relationship sub-network by modularization. According to the trend of node and conditional probability distribution, the image lexical structure and morphological organization characteristics were analysed, and the impulsive local industry resource impulse response knowledge rules were subsequently mined and characterized. Consequently, an example analysis proved the validity and applicability of the modelling method for the knowledge acquisition of the “location quotient” resource impulse response, so as to further analyse and perfect the rapid development of Foshan City with the drive of “GF-City metropolitan area” in the background of GHM Greater Bay Area.

Negligence during subject examination as the premise of responsibility for diagnostic error

Nierozpoznanie choroby pacjenta bądź mylne stwierdzenie nieistniejącego schorzenia jest niejednokrotnie wynikiem nieprawidlowo prowadzonego procesu diagnostycznego, w tym zaniedbania w realizacji jednego z zasadniczych elementow, jakim jest badanie podmiotowe. Zaniechanie przeprowadzenia wywiadu lub niedokladne zebranie informacji medycznych od pacjenta stanowi naruszenie regul wiedzy i praktyki medycznej, jak tez jest sprzeczne z wymaganą nalezytą starannością. Warunkiem przyjecia, ze doszlo do bledu diagnostycznego z powodu zaniedbania w przeprowadzeniu badania podmiotowego, jest ustalenie, ze postepowanie pracownika medycznego bylo obiektywnie niezgodne ze wskazaniami wspolczesnej wiedzy medycznej oraz mialo charakter zawiniony, tj. ze postepowanie to w danych okolicznościach sprawy moze byc przedmiotem zarzutu oraz ze bylo nieprawidlowe, przy uwzglednieniu surowych wymagan stawianych osobowym wykonującym zawody medyczne w zakresie profesjonalizmu postepowania.

Bounded Interval Fuzzy Control for Half Vehicle’s Active Suspension System

A novel efficient control scheme for an active vehicle suspension system is to be designed and simulated in this paper. A half car model has been designed and controlled using two different schemes of standard fuzzy control and bounded interval fuzzy control using MATLAB/SIMULINK. The bounded interval fuzzy control is designed to reduce the uncertainties in the fuzzy sets system and solve the non-linear control problem that the standard fuzzy control cannot handle it. It should be noted that fuzzy logic system is capable of dealing with imprecise concepts and numerous vague but the design of membership functions is nontrivial task. This is because of uncertainty degree that is caused due to road inputs profiles, fuzzy knowledge rules and immeasurable disturbance. The proposed method is expected to be able to mimic the heuristic knowledge of design the membership functions which depends on degree of uncertainty. The membership functions will be generated online during the process in order to deal with uncertainties. The simulation results have demonstrated that the proposed control exhibits better performance and stability as compared to standard fuzzy logic. In addition, the proposed scheme presents a preferable solution and balancing achievement between ride comfort and handling performance. These results demonstrated that the body accelerations and tire dynamic loads will be reduced for the vehicle suspension system in either automobiles or robotics suspension systems.