手机钱包，你在用吗？

如果有相应的识别设备，“手机钱包”可以在餐饮、超市、乘火车、行政收费、农贸市场等众多领域发挥作用。     

基于ASP.NET技术物业管理系统设计与实现

物业管理系统采用结构化的开发方法,以MS Visual Studio.NET 2010作为开发工具,以MS SQL Server 2008作为后天数据库。系统基于B/S模式进行开发,系统功能共分为房产管理,住户管理,收费管理,综合管理及系统维护等模块,其功能基本涵盖了小区物业管理的各个方面。     

基于Android的物业收费管理系统设计与实现

近年来随着Android系统广泛的被用于手机当中,为了方便物业管理人员上门办理物业收费相关业务,在Eclipse平台上,采用Java语言,设计实现基于Android物业管理软件。描述该软件设计与实现,重点阐述了软件功能模块的设计、应用程序设计以及实现过程中所使用的关键技术．     

基于MVC与J2EE的小区物业管理系统开发

为提高物业管理工作的效率和质量,降低物业管理成本．结合小区物业管理的工作特点以及物业管理的实际需要,采用面向对象方法与结构化相结合的设计方法．结合目前主流的基于12EE平台和MVC设计模式的Web应用体系．采用SSH框架技术设计一个基于web的小区物业管理系统。系统可以管理房产、住户、住户收费、住户投诉、住户报修、物业设备、停车场位、小区公告等基本信息,系统还提供对应的数据统计报表生成让用户打印．从时间上、空间上给物业公司及住户带来方便．     

无源自动抄表系统及其关键技术研究

随着生活条件的不断改善，人民群众对住宅的智能化要求日益提高，同时电力、自来水、煤气、物业管理等部门出于对数据统计及收费管理的需要，也要求对传统的人工上门抄表方式进行变革。传统的脉冲式远传表，都存在致命的缺陷，提出了一种新型的无源自动抄表系统设计思路，着重讨论了无源自动抄表系统的关键技术一一维绝对式编码。     

激光雷达和图像检测的高速公路自动收费系统

为解决高速公路收费系统在实际应用过程中车辆检测精度与实时性差等问题，设计激光雷达和图像检测的高速公路自动收费系统。清分中心利用激光雷达数据和图像融合检测算法检测高速公路上车辆，通过N-Message时钟同步周期算法为系统提供统一的时间规范，实现系统内时间一致。收费中心与收费分中心管理所属区域内的收费业务，并将清分中心设定的系统配置参数发送至下级单元。收费站单元在收费分中心管理下实现车道收费，并监控车道收费情况。该系统在实际应用过程中车辆检测平均精度值最高达到85%以上，检测时间为0.066 s。     

组合式公路电子不停车收费系统

分析了高速公路不停车收费系统的优点，提出了适合我国国情的组合式电子不停车收费系统，并介绍该系统的车道系统组成和车道设备功能，阐述了组合式收费技术方案的适用条件及特点。     

电水气"一卡通"后勤综合收费管理系统的设计与实现

本文介绍了“以电控水”、“以电控气”、“以电控费”的全新理念,探讨了小区物业管理的新思路,并以IC卡预付费电能表为基础,实现了电、水、气“一卡通”后勤物业管理的综合收费管理。     

智能收费系统关键技术研究及实现

不停车电子自动收费系统是提高高速公路车辆通过率的关键技术设备之一。文章论述了智能收费系统的构成原理和关键技术，着重讨论了射频技术、网络通信、计算机视觉和系统管理等技术方法的应用概况，提出了一套具体的解决不停车自动收费系统的方案，对发展智能收费技术有一定的参考意义。     

一种新型的远程抄表系统

该系统是由结算计算机、公共电话网和智能终端3部分组成。 该系统有4种功能:①电业局、自来水公司、煤气公司、物业管理公司、代收费银行等收费部门可以通过自己的结算计算机全自动地抄取所属用户的水、电、煤气表读数;②系统有防窃装置,可及时发现窃水、窃电、窃气不法行为;③系统有时钟电路,可实现峰谷分时计费;④系统有切断设备,对欠费者和盗窃者实行断水、断电、断气等制裁。     

A Multiagent Approach to Q-Learning for Daily Stock Trading

The portfolio management for trading in the stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.     

EMP — A Database-Driven Electronic Market Place for Business-to-Business Commerce on the Internet

Electronic commerce systems for business-to-business commerce on the Internet are still in their infancy. The realization of Internet electronic markets for business-to-business following a n-suppliers: m-customers scenario is still unattainable with todays solutions. Comprehensive Internet electronic commerce systems should provide for easy access to and handling of the system, help to overcome differences in time of business, location, language between suppliers and customers, and at the same time should support the entire process of trading for business-to-business commerce. In this paper, we present a DBMS-based electronic commerce architecture and its prototypical implementation for business-to-business commerce according to a n-suppliers: m-customers scenario. Business transactions within the electronic market are realized by a set of modular market services. Multiple physically distributed markets can be interconnected transparently to the users and form one virtually central market place. The modeling and management of all market data in a DBMS gives the system a solid basis for reliable, consistent, and secure trading on the market. The generic and modular system architecture can be applied to arbitrary application domains. The system is scalable and can cope with an increasing number of single markets, participants, and market data due to the possibility to replicate and distribute services and data and herewith to distribute data, system, and network load.     

我国证券交易系统发展现状及展望

随着证券市场的飞速发展,证券业正面临经营模式的改变,越来越多的客户通过电子化的交易和资讯渠道实现证券买卖、存取资金和获取信息,本文对我国目前的证券交易系统软件进行了分类,总结了发展现状,并就此提出了今后的发展趋势。     

“电力市场设计学”的基本概念及方法论

当前电力体制改革已进入深水区,电力市场设计和规则制订已成为电力市场改革主导部门所面临的关键问题,但是电力市场设计的科学方法论尚未建立起来。本文提出“电力市场设计学”的概念,涉及电力系统工程、运筹学、微观经济学、博弈论、拍卖理论、金融学、计算经济学、实验经济学等多学科的知识。电力市场设计学以中国特色社会主义政治经济学作为总体指导思想,树立系统观并掌握系统工程方法,具体设计过程则包括基于经典微观经济学和金融学的电力市场体系初步设计、基于拍卖理论和博弈论的电力市场交易机制设计、基于计算经济学和实验经济学的电力市场仿真和电力市场交易的实地研究等步骤。电力市场设计学的基本方法已应用于南方区域电力市场体系和交易品种设计等工作,其所包含的深刻丰富的内容还有待进一步深入研究和应用。     

基于探针的动态期货交易监控系统设计与实现

随着分布式系统的发展,系统复杂性大大的增加,为了分析和了解分布式系统的运行情况以及分析系统的瓶颈,对于分布式系统的监控变得重要起来。期货交易系统是一种典型的分布式系统,它在期货经济发展中具有重要的意义。为了保证期货交易系统能够安全、稳定的运行,分析系统的瓶颈,对于其运行状态进行监控,是十分必要的。本文把表达式解析应用到监控的数据分析中,从灵活性、可配置性和扩展性等角度出发,设计并实现了基于探针的动态的期货交易监控系统。并且对于系统的特点进行了深入的分析。     

An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms

Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

证券公司集中监控系统设计

证券市场是完整的市场体系的重要组成部分，对整个经济的运行具有重要影响.证券交易系统的安全稳定运行是证券业界非常关注的课题.本文设计一个可以快速实时响应、灵活监控证券公司各个交易系统的网络连通和业务功能运行情况的监控系统（RSCMS），以提高部门的实时监控效率，并为系统的运行情况提供数据评估的依据.本文研究了监控系统关键技术的解决方法，并提供了在证券公司运行的成功案例     

Developing a rule change trading system for the futures market using rough set analysis

Many technical indicators have been selected as input variables in order to develop an automated trading system that determines buying and selling trading decision using optimal trading rules within the futures market. However, optimal technical trading rules alone may not be sufficient for real-world application given the endlessly changing futures market. In this study, a rule change trading system (RCTS) that consists of numerous trading rules generated using rough set analysis is developed in order to cover diverse market conditions. To change the trading rules, a rule change mechanism based on previous trading results is proposed. Simultaneously, a genetic algorithm is employed with the objective function of maximizing the payoff ratio to determine the thresholds of market timing for both buying and selling in the futures market. An empirical study of the proposed system was conducted in the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. The proposed trading system yields profitable results as compared to both the buy-and-hold strategy, and a system not utilizing a genetic algorithm for maximizing the payoff ratio.     

The defensive use of IT in a newly vulnerable market: The New York Stock Exchange, 1980–2007

New technologies enable entrants to create business models that threaten incumbents in a range of industries. This paper extends the framework of newly vulnerable markets to explore the dynamics of competition between entrants and an incumbent through a study of the New York Stock Exchange. For over 200 years, the NYSE has operated a physical market for trading securities. Beginning in the 1960s, it employed information technology to process increasing trading volumes and to disseminate data on stock prices and volumes. The Exchange invested heavily in IT for its trading floor, and defended it against electronic markets enabled by new technologies. In 2005 various pressures forced the NYSE into a merger with Archipelago, a leading electronic exchange. The NYSE’s latest market system, Hybrid, launched in 2006, is the first at the NYSE that enables investors to bypass the trading floor completely. Despite the new technology, its share of trading volumes fell. This paper documents four eras of IT management at the exchange, presenting a detailed 27-year history. The evidence shows that while IT investments helped the Exchange defend floor trading and its market share for a number of years, it finally had to adopt the technology of all-electronic exchanges.