Class CO Research Articles

EXPERIMENTAL ANALYSIS OF PID-CONTROLLED HEAT RECOVERY AIR HANDLING UNIT BY MACHINE LEARNING METHODS

The need for energy continues to increase rapidly all over the world day by day. The demand for energy is rising in developing economies and industrial production areas in these economies. Energy is one of the essential inputs for business areas. Today, most energy is used in buildings for the purpose of heating, cooling, and air conditioning. The problem with air-conditioning systems is that the airflow remains constant despite the change in the number of people in the interior areas. The interest of this paper is to explore how the thermal comfort and air quality of a cloud-based proportional integral derivative (PID)-controlled plate heat exchanger recovery air handling unit were investigated in a classroom environment. Depending on the variables of the temperature and humidity of the outdoor environment, the number of students in the classroom, and the amount of fresh air sent to the indoor environment, the temperature, humidity, and air quality values of the indoor environment were controlled. In line with the data received from the cloud-based system, indoor temperature and indoor air quality values were analyzed by using the machine learning methods separately, that is, support vector machine (SVM), Gauss process regression (GPR), regression trees (RT), and ensembles of trees (ET). In the experiment set, the class's CO<sub>2</sub>, temperature, and relative humidity values were compared with the R<sup>2</sup> values by machine learning methods when the air handling unit was started. As a result of the comparison, the R<sup>2</sup> value of the amount of CO<sub>2</sub> was obtained from the GPR method at 99%, the temperature amount from the GPR method at 93%, and the relative humidity amount from the GPR method at 98%.

Bronze Alloying Practice in ancient China - Evidence from Pre-Qin Coin Analyses

We have reviewed the published chemical analyses of the bronze coinage produced in pre-Qin China (before 221 BCE), with the aim of understanding the alloying practices which took place before these coins were cast. Conventionally it has been assumed that they were made to a ‘target composition’, by separately adding lead and tin to copper. However, by characterizing the composition of the different coin classes by trend lines rather than by calculating the average composition, we show that the majority of coins approximately fit a mixing line between two starting components – one containing copper, lead and tin (similar to the composition of bronze used to cast contemporary ritual vessels), and one containing only copper and lead. There is some archaeological evidence to support the existence of the latter as a raw material, in the form of fragments of Cu/Pb alloy broken from larger cakes, previously interpreted as primitive coinage. This proposal might have implications for how we consider the perceptions and practice of alloying processes for bronze objects in pre-Qin China.

An Image-Based Class Retrieval System for Roman Republican Coins.

We propose an image-based class retrieval system for ancient Roman Republican coins that can be instrumental in various archaeological applications such as museums, Numismatics study, and even online auctions websites. For such applications, the aim is not only classification of a given coin, but also the retrieval of its information from standard reference book. Such classification and information retrieval is performed by our proposed system via a user friendly graphical user interface (GUI). The query coin image gets matched with exemplar images of each coin class stored in the database. The retrieved coin classes are then displayed in the GUI along with their descriptions from a reference book. However, it is highly impractical to match a query image with each of the class exemplar images as there are 10 exemplar images for each of the 60 coin classes. Similarly, displaying all the retrieved coin classes and their respective information in the GUI will cause user inconvenience. Consequently, to avoid such brute-force matching, we incrementally vary the number of matches per class to find the least matches attaining the maximum classification accuracy. In a similar manner, we also extend the search space for coin class to find the minimal number of retrieved classes that achieve maximum classification accuracy. On the current dataset, our system successfully attains a classification accuracy of 99% for five matches per class such that the top ten retrieved classes are considered. As a result, the computational complexity is reduced by matching the query image with only half of the exemplar images per class. In addition, displaying the top 10 retrieved classes is far more convenient than displaying all 60 classes.

Strongly trapped two-dimensional quantum walks

Discrete time quantum walks (DTQWs) are nontrivial generalizations of random walks with a broad scope of applications. In particular, they can be used as computational primitives, and they are suitable tools for simulating other quantum systems. DTQWs usually spread ballistically due to their quantumness. In some cases, however, they can remain localized at their initial state (trapping). The trapping and other fundamental properties of DTQWs are determined by the choice of the coin operator. We introduce and analyze an up to now uncharted type of walks driven by a coin class leading to strong trapping, complementing the known list of walks. This class of walks exhibit a number of exciting properties with the possible applications ranging from light pulse trapping in a medium to topological effects and quantum search.

THE ILAC-PROJECT: SUPPORTING ANCIENT COIN CLASSIFICATION BY MEANS OF IMAGE ANALYSIS

Abstract. This paper presents the ILAC project, which aims at the development of an automated image-based classification system for ancient Roman Republican coins. The benefits of such a system are manifold: operating at the suture between computer vision and numismatics, ILAC can reduce the day-to-day workload of numismatists by assisting them in classification tasks and providing a preselection of suitable coin classes. This is especially helpful for large coin hoard findings comprising several thousands of coins. Furthermore, this system could be implemented in an online platform for hobby numismatists, allowing them to access background information about their coin collection by simply uploading a photo of obverse and reverse for the coin of interest. ILAC explores different computer vision techniques and their combinations for the use of image-based coin recognition. Some of these methods, such as image matching, use the entire coin image in the classification process, while symbol or legend recognition exploit certain characteristics of the coin imagery. An overview of the methods explored so far and the respective experiments is given as well as an outlook on the next steps of the project.

A statistical pattern analysis approach for rapid coin identification based on Eddy-current sensors

We present an effective approach based on eddy-current sensors enabling rapid identification of different coin classes in this work. The employed Eddy-current sensing technique aims to detect the induced signals which are transponded back to the induction coil so that the system can promptly process the characteristic signals and recognize authenticity and classes of inspected coins. The training and identification program in this recognition system are developed by LabVIEW® interface and thus data accuracy can be secured. The experimental results have proven the proposed eddy-current sensing method an efficient way to rapidly identify authentic coins. It is found that the maximum identification rate can achieve 14 coins per second provided a coin feeding mechanism can be appropriately designed.

Sequential Monitoring of Conditional Randomization Tests: Generalized Biased Coin Designs

For the generalized biased coin class of randomization procedures, Smythe (1988) proved asymptotic normality of the conditional linear rank test. Clinical trialists often undertake interim analysis to determine whether to stop the trial early for a substantial treatment effect. In this article, we will set up one interim analysis using a conditional randomization test. The joint asymptotic distribution of the interim test statistic and the final test statistic will be explored. We also define the concept of conditional information under a randomization model.

Classification of coins using an eigenspace approach

We present a vision-based approach to coin classification which is able to discriminate between hundreds of different coin classes. The approach described is a multistage procedure. In the first stage a translationally and rotationally invariant description is computed. In a second stage an illumination-invariant eigenspace is selected and probabilities for coin classes are derived for the obverse and reverse sides of each coin. In the final stage coin class probabilities for both coin sides are combined through Bayesian fusion including a rejection mechanism. Correct decision into one of the 932 different coin classes and the rejection class, i.e., correct classification or rejection, was achieved for 93.23% of coins in a test sample containing 11,949 coins. False decisions, i.e., either false classification, false rejection or false acceptance, were obtained for 6.77% of the test coins.