Search

Vibration signal, as an important means for diesel engine condition detection and fault diagnosis, has attracted attention for many years. In traditional vibration signal analysis, most processing methods are for single-channel data. However, single-channel vibration signal cannot reflect the operating information of the diesel engine comprehensively because diesel engine vibration is coupled by multiple source signals. This paper proposes the MVMD band energy method for fault diagnosis by four channels of vibration signals. First, the original multivariate signals are decomposed adaptively by MVMD, which obtains a series of components with modal alignment. Then, the band energy values of each measuring point are calculated as the fault characteristics. Finally, SVM is used to realize the diagnosis and identification of diesel engine misfire. The working conditions have a great influence on the vibration signal of the cylinder. In order to obtain the best diagnostic working conditions, six working conditions are set for testing. The result shows that the fault identification rate is highest under the 1500 rpm and 50% load working condition. The fault recognition rate of this method reaches more than 99%, which is superior to the other four common methods.

In order to study the driving state of heavy vehicles, two approaches are employed hereby to establish digital twin models for analyzing the applicable scopes of the models and conducting a predictive study. To begin with, the operating parameters and the state of the vehicles are measured using instruments and apparatuses. Then, relying on a GP and deep convolutional neural network (DCNN), two digital twin models of vehicles driving state are established, respectively, which set the transmission system and power system parameters as well as weather conditions as input parameters; and vehicle running speed and torque value as output ones. Both digital twin models consider the physical rule of the vehicle to avoid overfitting in the training. The analytical results indicate that the GP-based digital twin model appears more accurate in predicting the driving parameters of the vehicles, whereas the model based on the DCNN has better convergence precision within a short span of time. The vehicle-specific digital twin models set up in this paper lay a foundation for subsequent optimization of vehicle driving state and realization of digital twin-physical entity interaction.

With the rapid development of communication and transportation technologies, the urban area is increasingly becoming an ever more dynamic, comprehensive, and complex system. Meanwhile, functional polycentricity as a distinctive feature has been characterizing urban areas around the world. However, the spatial structure of the urban area has yet to be fully comprehended from a dynamic perspective, and understanding the spatial organization of polycentric urban regions (PUR) is crucial for issues related to urban planning, traffic control, and urban risk management. The analysis of polycentricity strongly depends on the spatial scale. In order to identify functional polycentricity at the intra-unban scale, this paper presents a traffic flow-embedded and topic modeling-based methodology framework. This framework was evaluated on real-world datasets from the Wujiang district, Suzhou, China, which contains 151,419 records of taxi trajectory data and 86,036 records of points of interest (POI) data. This paper provides a novel approach to examining urban functional polycentricity via combining urban function distribution and spatial interactions. This proposed methodology can help urban authorities better understand urban dynamics in terms of function distribution and internal connectedness and facilitate urban development in terms of urban planning and traffic control.

With the aim of enhancing both reliability and fatigue life of gasket, this study combines finite element analysis, orthogonal experimental design, dynamically-guided multi-objective optimization, and the non-dominated sorting genetic algorithm with elitist strategy to optimize the geometric parameters of the cylinder gasket. The finite element method was used to analyze the temperature field, thermal-mechanical coupling stress field, and deformation of cylinder gasket. The calculation results were experimentally validated by measured temperature data, and comparison results show that the maximum error between calculated value and experiment value is 7.1%, which is acceptable in engineering problems. Based on above results and orthogonal experiment design method, the effects of five factors, including diameter of combustion chamber circle, diameter of coolant flow hole, length of the insulation zone between third and fourth cylinders, thickness of gasket, and bolt preload, on three indexes: temperature, stress, and deformation of gasket, were examined in depth. Through the variance analysis of the results, three important factors were identified to proceed later calculation. The dynamically guided multi-objective optimization strategy and the non-dominated sorting genetic algorithm were effectively used and combined to determine the optimal geometric parameters of cylinder gasket. Furthermore, calculation results suggest that temperature, stress, and deformation of the optimized cylinder gasket have been improved by 27.88 K, 16.84 MPa, and 0.0542 mm, respectively when compared with the origin object, which shows the excellent performance of gasket optimization and effectiveness of the proposed optimization strategy.

This paper studies the conditions of the successful takeoff and climbing of the aerospace vehicle. By analyzing the longitudinal kinematics model and dynamic model of the vehicle, the take-off constraints determined by integrating with the lift-drag ratio, thrust-to-weight ratio, flight speed and take-off weight. Moreover, the algebraic form of the maximum take-off weight is also derived to ensure that the aircraft can successfully take off under a fixed configuration. Finally, the simulation results verify the effectiveness of the proposed methods.

In order to reduce the maintenance cost, the design of complex electronic devices need to consider Failure Modes and Effects Analysis (FMEA). Traditional FMEA method in face of complex devices is usually lack of a formal specification in analysis process of failure modes and causes, which is easy to cause confusion and chaos; the results are prone to duplication or omission; the support for design phase is also weak. Above all, this paper presents a FMEA method for electronic devices based on annotating functional roles. Firstly, the functional roles and their corresponding failure modes are introduced; secondly, possible failures, that each functional role may experience, are analyzed and a formalized analysis method based on stress event is proposed; finally, analysis process of the FMEA method is illustrated with avionics system battery as an example. The method presented in this paper can effectively improve the efficiency and accuracy of failure analysis for electronic devices, and improve the ability of FMEA to support the design phase.

The study of digital Chinese calligraphy has become more valuable nowadays. While existing researches on Chinese calligraphy analysis are primarily focused on stroke-based character recognition and simulation, we propose a global feature descriptor to deal with style recognition problem in this paper. The proposed method extracts three categories of character features: position features, proportion features and projection features. These features are then used to train an SVM classifier of calligraphy style. We test the global feature based classifier on five-style Chinese calligraphy character set. The experimental results show that the proposed method can achieve good classification accuracy, proving the effectiveness of the global feature descriptor in calligraphy style recognition.

Market information management has always been an essential component of good marketing management practice, but only possession of market information is not sufficient. Recently, processing market information has been of increasing interest to organization researchers, they suggested that improving the market information processing capabilities is important for marketing managers to understand and track changes in users’ needs, wants, preferences and consumption patterns. However, although the literature pertaining to the study of organizational information processing is growing, there lacks a specific analysis as to using online users’ information behavior to process market information, research is still needed to guide firms on how to integrate and manage market information based on consumer behavior. In an attempt to fill in the gap, this study reviews the relevant literatures and proposes that using users’ three-dimensional information behavior to capture the user’s interest in information and filter incoming information will be a very efficient way to process market information. We will use the Mobile Internet click stream data from some WAP website to characterize the users’ online information behavior. In addition, we will examine the relationships between the three dimensions of consumer online information behavior and the performance of the website. This study attempts to contribute to a better understanding of market information processing, our understanding of how organizations process market information can be advanced substantially on the basis of principles information processing.

For complicated Software-Intensive System, it is always hard to guarantee the reliability and safety of software. Effective methods for detecting faults and isolating software fault from hardware fault are desiderated especially. In order to detect and isolate fault in SIS, a method called runtime software monitoring is studied, and a new kind of runtime software monitoring system(RSMS) is constructed in this paper. The RSMS can not only detect software fault by observing software behavior to determine whether it complies with its intended behavior, but also can assist to isolate software fault from hardware fault and to locate software fault based on fault symptoms acquired by our method. The software architecture of RSMS is presented from different views by using “4+1” view model and layer architectural style. The RSMS prototype is implemented through architecture-based software development method. By applying the prototype in practice, it proved that the RSMS prototype is feasible and effective for detecting and diagnosing faults in SIS.

Reputation systems are designed for reducing the risk entailed in interactions among total strangers in electronic marketplaces. Such systems can be used to collect and aggregate feedback on the past behavior of participants in electronic transactions, so as to derive reputation scores assumed to predict likely future behavior.The security of reputation system is vital for the system to run correctly, if reputation system is not secure, it may not be able to produce a correct result, and consequently affects the process of decision making. In this paper, a secure reputation system based on secure multiparty computation is proposed. Each of feedback provider generates an anonymous identity using smart card, and provides a rating about feedback target by a secure multiparty computation to a querying user. The system contains the following desirable features: privacy-preserving of feedback provider, tamper-preventing of feedback, masquerade-proofing of identity, shilling attack-preventing.

ERA is the acronym of environment, reactive rules and agent; the model is a new effective multi-agent cooperation framework, which has been successfully applied in a wide range of areas. Multi-port Container Stowage (MPCS) is a more practical problem. However, multi-port makes the container stowage problem more difficult. This paper presents a MPCS model based on time series and a multi-agent ERA model based on belief to solve the multi-port container stowage problem. The algorithm introduces the belief concept into agents and creates a multi-agent ERA model. The searching direction and strength of the agents are determined by the learning machine using belief parameter. Agent has the ability to evaluate its searching path. At last, the feasibility and efficiency of the model are validated by the experimental result.

Multi-Agent model based on belief interaction is used to solve the function optimization problems in this paper. “Belief” is led into the Agent, being the parameter of learning machine to decide the searching direction and intensity of the Agent in the environment. It is also the interaction information between Agents. Agent has the ability to evaluate its path in the past. In this way, Agent can find optimization object rapidly and avoid partial extremum at the same time. Finally, several benchmark problems are considered to evaluate the performance of this model. The experimental results prove the efficiency of this model when solving optimization problems.