Network value and optimum analysis on the mode of networked marketing in TV media

Purpose: With the development of the networked marketing in TV media, it is important to do the research on network value and optimum analysis in this field. Design/methodology/approach: According to the research on the mode of networked marketing in TV media and Correlation theory, the essence of media marketing is creating, spreading and transferring values. The Participants of marketing value activities are in network, and value activities proceed in networked form. Network capability is important to TV media marketing activities. Findings: This article raises the direction of research of analysis and optimization about network based on the mode of networked marketing in TV media by studying TV media marketing Development Mechanism , network analysis and network value structure.Peer Reviewe

Identifying Left Behind Passengers at Subway Stations from Auto Fare Collection Data

With the rapid growth in transport demand, it has become a frequent occurrence that passengers are left behind especially during peak hours in subway, which has led to a significant reduction in the level of service. In this paper, we propose a left behind passengers identifying method based on Automatic Fare Collection (AFC) and Automated Vehicle Location (AVL) data. Firstly, we choose the passengers with the limited deterministic information as the research objects; secondly, we propose a classification-based method for identifying left behind passengers by the probabilistic model; next, the accuracy and effectiveness of the proposed method is verified by the simulation experiment and the case of Beijing Subway. Ultimately, the proposed method will support research related to the operation, management and future development of subways

A Deep Choice Model for Hiring Outcome Prediction in Online Labor Markets

A key challenge faced by online labor market researchers and practitioners is to understand how employers make hiring decisions from many job bidders with distinct attributes. This study investigates employer hiring behavior in one of the largest online labor markets by building a datadriven hiring decision prediction model. With the limitation of traditional discrete choice model (conditional logit model), we develop a novel deep choice model to simulate the hiring behavior from 722,339 job posts. The deep choice model extends the classical conditional logit model by learning a non-linear utility function identically for each bidder within of the job posts via a pointwise convolutional neural network. This non-linear mapping can be straightforwardly optimized using stochastic gradient approach. We test the model on 12 categories of job posts in the dataset. Results show that our deep choice model outperforms the linear-utility conditional logit model in predicting hiring preferences. By analyzing the model using dimensionality reduction and sensitivity analysis, we highlight the nonlinear combination of bidders’ features in impacting employers’ hiring decisions

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

With the continuous improvement of product quality, production efficiency, and complexity, higher requirements are put forward for the reliability and stability of equipment, and the difficulty of real-time diagnosis of faults and functional failures is also increasing. The traditional fault diagnosis methods based on signal processing and Convolutional neural network cannot meet the requirements of on-site online real-time fault diagnosis of equipment. One is that the vibration signals on the industrial site are superimposed on each other, nonlinear and unstable and traditional feature extraction methods take a long time, resulting in unstable extraction results. Second, massive data and fault diagnosis algorithms need rich computing and storage resources. The traditional Convolutional neural network method conflicts with the real-time response requirements of fault diagnosis. At the same time, different models of fault diagnosis models have poor generalization ability, and the diagnostic accuracy is not high or even impossible to diagnose. To solve the above problems, this paper proposes a fault diagnosis method based on industrial Internet platform, which is equipment cloud edge collaboration + adaptive countermeasure network Transfer learning. On the edge side, the vibration signals collected from key components of the model are processed using empirical mode decomposition (EEMD) to solve the problem of signal nonlinearity and stationarity. In the cloud, EEMD signals of different models are decomposed into source domain and target domain for confrontation training, which is used as the input of the improved domain adversarial network model DANN (Domain Adversarial Neural Networks), so as to improve the accuracy of fault diagnosis of different models by using cloud computing power and the improved adversarial network Transfer learning algorithm. Through the analysis of experimental data, this paper verifies that the model after the confrontation network Transfer learning is more accurate than the traditional fault diagnosis method. Through the coordination of computing resources and real-time requirements, real-time diagnosis of cloud side collaborative bearing fault is realized

Bearing Fault Diagnosis Method Based on EEMD and LSTM

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis

Operation Mechanism of the Driving Force System of Ecosystem of Cyber-society Based on the System Dynamics

Operation of the driving force system of Ecosystem of Cyber-society needs a scientific mechanism of intervention and regulation to solve the integration problem of a variety of organizations and forces within the Ecosystem of Cyber-society, shorten the process from uncoordination to coordination, promote the orderly operation of the driving force system of Ecosystem of Cyber-society, make the system play a strong force, in order to promote the formation and rapid development of Ecosystem of Cyber-society. We analyze the driving force system of Ecosystem of Cyber-society using the theory of System Dynamics and propose a theoretical framework, and then present its operation mechanism systematically

Allocation of Electric Taxi Charging: Assessing the Layout of Charging Stations Based on Charging Frequency

Recent decades have witnessed the growth of the electric vehicles (EVs) industry due to technological developments. To overcome emerging environmental issues, some metropolises, i.e., Beijing, have employed electric taxi systems, which require tremendous investments in charging stations. However, the supporting charging facilities for EVs are not complete, and in terms of layout, there is also a situation where some charging stations have long charging queues, but some are unvisited. To overcome these difficulties, this paper aims to establish a set of charging stations layout assessment models for the electric taxi based on charging frequency and put forward targeted policy suggestions to make the charging frequency of each station more balanced, to avoid resource waste and undersupply. In this paper, a mathematical model based on integer programming is established in conjunction with the workflow of the electric taxi; in the case study, simulations are performed using the Anylogic platform and the results are statistically analyzed; moreover, we use real-time data to assess the layout of charging stations near and within the Fourth Ring Road in Beijing. The modeling and simulation results show that there is an imbalance in the current charging stations layout in Beijing. More specifically, there is a problem with charging frequency of some stations, which is being too low and some too high. Also, the charging frequency of stations will vary with passenger distribution factors. We classify the studied charging stations into four categories according to their actual usage characteristics and provide specific analysis and optimization suggestions for the different categories. Based on the assessment system in this paper, we also carried out some policy suggestions for further layout optimization. The optimized layout has a more balanced charging frequency, and the variance of charging frequency is reduced largely

Estimating Warehouse Rental Price using Machine Learning Techniques

Boosted by the growing logistics industry and digital transformation, the sharing warehouse market is undergoing a rapid development. Both supply and demand sides in the warehouse rental business are faced with market perturbations brought by unprecedented peer competitions and information transparency. A key question faced by the participants is how to price warehouses in the open market. To understand the pricing mechanism, we built a real world warehouse dataset using data collected from the classified advertisements websites. Based on the dataset, we applied machine learning techniques to relate warehouse price with its relevant features, such as warehouse size, location and nearby real estate price. Four candidate models are used here: Linear Regression, Regression Tree, Random Forest Regression and Gradient Boosting Regression Trees. The case study in the Beijing area shows that warehouse rent is closely related to its location and land price. Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation. Additionally, tree models have better performance than the linear model, with the best model (Random Forest) achieving correlation coefficient of 0.57 in the test set. Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size

Gains and losses from collusion: an empirical study on market behaviors of China’s power enterprises

Purpose: Collusion is a common behavior of oligarch enterprises aiming to get an advantage in market competition. The purpose of the research is to explore positive or negative effects from the electricity generation manufacturers’ collusion through statistical analysis approach. To be exact, these effects are discovered both in market economy at a macro-economic level and in enterprise behaviors at a micro-economic level. Design/methodology/approach: This research designs a model as an extension of Porter’s model (Green & Porter, 1984). In this model FIML is applied. Taking price bidding project launched in China’s power industry as an example, this paper conducts an empirical research on its relevant price data collected from subordinate power plants of China’s five power generation groups in the pilots. Findings: It is found in this paper that power generation enterprises are facing collusion issues in the market. To be exact, it is such a situation in which non-cooperative competition and collusion alternate. Under the competition, market is relatively steady, thus forming a lower network price. It is helpful to the development of the whole industry. However, once Cartel is formed, the price will rise and clash with power enterprises and transmission-distribution companies concerning the interests conflicts. At the same time, a higher power price will form in the market, making consumers suffer losses. All of these are bad for industry development. Not only the collusion of power enterprises affects power price but also the market power that caused by long-time Cartel will reduce the market entrant in electricity generation. Market resources are centralized in the hands of Cartel, causing a low effective competition in the market, which has passive effects on users. Implications: The empirical research also indicates that collusion undoubtedly benefits the power enterprises that involved. As a cooperation pattern, collusion can lead to the synergy between relevant companies. However, collusion harms the benefits of other market entities. During the process of enterprises creating common interests cooperatively, collusion may bring harm to the outside industry. Originality/value: Using empirical research method, the paper takes China’s power industry as an example to show the gains and losses of collusion from two aspects, namely market economy and strategic management.Peer Reviewe