In this paper,we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations.In addition,we searched for evidence that technical analysis and fundamental analysis c...In this paper,we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations.In addition,we searched for evidence that technical analysis and fundamental analysis can complement each other in these markets.To implement this research,we created a comprehensive portfolio containing the assets traded in the markets of each BRICS member.We developed an automated trading system that simulated transactions in this portfolio using technical analysis techniques.Our assessment updated the findings of previous research by including more recent data and adding South Africa,the latest member included in BRICS.Our results showed that the returns obtained by the automated system,on average,exceeded the value invested.There were groups of assets from each country that performed well above the portfolio average,surpassing the returns obtained using a buy and hold strategy.The returns from the sample portfolio were very strong in Russia and India.We also found that technical analysis can help fundamental analysis identify the most dynamic companies in the stock market.展开更多
Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic efficiency of structures during their service periods.Selecting the appropriate opt...Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic efficiency of structures during their service periods.Selecting the appropriate optimization algorithm is the key to solving structural optimal design problems.In this study,a new global optimization idea is proposed and named the moving baseline strategy.A baseline is initially set and will be repeatedly moving upward or downward to approach the optimal value.The proposed strategy is a simple but effective,general,and stable algorithm that can be used to solve constrained and unconstrained structural optimization problems.Different from traditional gradient-based,stochastic and heuristic algorithms,the developed algorithm provides a completely new idea to solve global or local optimization problems.Some unconstrained and constrained numerical benchmark examples are used to test the proposed methodology.In addition,structural optimal design problems of a ten-bar planar truss structure and a hypersonic wing structure(X-37B)are utilized to verify the effectiveness of the developed strategy in addressing structural design optimization problems in engineering.展开更多
Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algor...Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.展开更多
文摘In this paper,we investigated the profitability of technical analysis as applied to the stock markets of the BRICS member nations.In addition,we searched for evidence that technical analysis and fundamental analysis can complement each other in these markets.To implement this research,we created a comprehensive portfolio containing the assets traded in the markets of each BRICS member.We developed an automated trading system that simulated transactions in this portfolio using technical analysis techniques.Our assessment updated the findings of previous research by including more recent data and adding South Africa,the latest member included in BRICS.Our results showed that the returns obtained by the automated system,on average,exceeded the value invested.There were groups of assets from each country that performed well above the portfolio average,surpassing the returns obtained using a buy and hold strategy.The returns from the sample portfolio were very strong in Russia and India.We also found that technical analysis can help fundamental analysis identify the most dynamic companies in the stock market.
基金the National Nature Science Foundation of China(Nos.11872089,11572024,11432002)the Defense Industrial Technology Development Programs(Nos.JCK Y2016204B101,JCKY2017601B001,JCKY2018601B001)。
文摘Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic efficiency of structures during their service periods.Selecting the appropriate optimization algorithm is the key to solving structural optimal design problems.In this study,a new global optimization idea is proposed and named the moving baseline strategy.A baseline is initially set and will be repeatedly moving upward or downward to approach the optimal value.The proposed strategy is a simple but effective,general,and stable algorithm that can be used to solve constrained and unconstrained structural optimization problems.Different from traditional gradient-based,stochastic and heuristic algorithms,the developed algorithm provides a completely new idea to solve global or local optimization problems.Some unconstrained and constrained numerical benchmark examples are used to test the proposed methodology.In addition,structural optimal design problems of a ten-bar planar truss structure and a hypersonic wing structure(X-37B)are utilized to verify the effectiveness of the developed strategy in addressing structural design optimization problems in engineering.
文摘Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.
