Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
With the explosive growth of digital music data\nbeing stored and easily reachable on the cloud, as well as\nthe increased interest in affective and cognitive computing,\nidentifying composers based on their musical work is an\ninteresting challenge for machine learning and artificial intelligence\nto explore. Capturing style and recognizing music\ncomposers have always been perceived reserved for trained\nmusical ears. While there have been many researchers targeting\nmusic genre classification for improved recommendation\nsystems and listener experience, few works have addressed\nautomatic recognition of classical piano composers as proposed\nin this paper. This paper discusses the applicability of\nn-grams on MIDI music scores coupled with rhythmic features\nfor feature extraction specifically of multi-voice scores.\nIn addition, cortical algorithms (CA) are adapted to reduce\nthe large feature set obtained as well as to efficiently identify\ncomposers in a supervised manner. When used to classify\nunknown composers and capture different styles, our proposed\napproach achieved a recognition rate of 94.4% on\na home grown database of 1197 pieces with only 0.1% of\nthe 231,542 generated featuresââ?¬â?which motivates follow-on\nresearch. The retained most significant features, indeed, provided\ninteresting conclusions on capturing music style of\npiano composers....
An acrobot is a planar robot with a passive actuator in its first joint. The main purpose of\nthis system is to make it rise from the rest position to the inverted pendulum position. This control\nproblem can be divided in the swing-up issue, when the robot has to rise itself by swinging up as a\nhuman acrobat does, and the balancing issue, when the robot has to maintain itself in the inverted\npendulum position. We have developed three controllers for the swing-up problem applied to two\ntypes of motors: small and large. For small motors, we used the State-Action-Reward-State-Action\n(SARSA) controller and the proportionalââ?¬â??derivative (PD) controller with a trajectory generator.\nFor large motors, we propose a new controller to control the acrobotââ?¬â?a pulse-width modulation\n(PWM) controller. All controllers except SARSA are tuned using a genetic algorithm....
This article describes some technical issues regarding the adaptation of a production car to a platform for the development and\ntesting of autonomous driving technologies. A universal approach to performing the reverse engineering of electric power steering\n(EPS) for the purpose of external control is also presented. The primary objective of the related study was to solve the problem\nassociated with the precise prediction of the dynamic trajectory of an autonomous vehicle. This was accomplished by deriving a\nnew equation for determining the lateral tire forces and adjusting some of the vehicle parameters under road test conductions.\nA Mivar expert system was also integrated into the control system of the experimental autonomous vehicle. The expert system\nwas made more flexible and effective for the present application by the introduction of hybrid artificial intelligence with logical\nreasoning. The innovation offers a solution to the major problem of liability in the event of an autonomous transport vehicle being\ninvolved in a collision....
The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle\nimbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In\nthis paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating\nCharacteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To\nvalidate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB),\nProbabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal\nTransformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes\nalgorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life\ncase study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance\nwith Mahalanobis Genetic Algorithm (MGA)....
Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model\nfor grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM)\noptimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date,\nthe time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output\npower are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component\nRes, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend\ncomponent, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results\nof each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained.\nThe prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABCSVM\nhas a faster calculation speed and high...
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