scholarly journals Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Romero-Mujalli ◽  
Tjard Bergmann ◽  
Axel Zimmermann ◽  
Marina Scheumann
Keyword(s):  
High Frequency ◽  
Ultrasonic Vocalizations ◽  
Mouse Lemur ◽  
Primate Species ◽  
Clustering Model ◽  
Clustering And Classification ◽  
Animal Vocalizations ◽  
Validation Procedure ◽  
User Friendly

AbstractBioacoustic analyses of animal vocalizations are predominantly accomplished through manual scanning, a highly subjective and time-consuming process. Thus, validated automated analyses are needed that are usable for a variety of animal species and easy to handle by non-programing specialists. This study tested and validated whether DeepSqueak, a user-friendly software, developed for rodent ultrasonic vocalizations, can be generalized to automate the detection/segmentation, clustering and classification of high-frequency/ultrasonic vocalizations of a primate species. Our validation procedure showed that the trained detectors for vocalizations of the gray mouse lemur (Microcebus murinus) can deal with different call types, individual variation and different recording quality. Implementing additional filters drastically reduced noise signals (4225 events) and call fragments (637 events), resulting in 91% correct detections (Ntotal = 3040). Additionally, the detectors could be used to detect the vocalizations of an evolutionary closely related species, the Goodman’s mouse lemur (M. lehilahytsara). An integrated supervised classifier classified 93% of the 2683 calls correctly to the respective call type, and the unsupervised clustering model grouped the calls into clusters matching the published human-made categories. This study shows that DeepSqueak can be successfully utilized to detect, cluster and classify high-frequency/ultrasonic vocalizations of other taxa than rodents, and suggests a validation procedure usable to evaluate further bioacoustics software.

Dynamics of the processes in metal machining

1998 ◽  
Vol 2 ◽  
pp. 115-122
Author(s):  
Donatas Švitra ◽  
Jolanta Janutėnienė
Keyword(s):  
High Frequency ◽  
Machine Tools ◽  
Metal Cutting ◽  
Cutting Tool ◽  
Low Frequency ◽  
Metal Cutting Machine ◽  
Cutting Machine ◽  
Metal Machining

In the practice of processing of metals by cutting it is necessary to overcome the vibration of the cutting tool, the processed detail and units of the machine tool. These vibrations in many cases are an obstacle to increase the productivity and quality of treatment of details on metal-cutting machine tools. Vibration at cutting of metals is a very diverse phenomenon due to both it’s nature and the form of oscillatory motion. The most general classification of vibrations at cutting is a division them into forced vibration and autovibrations. The most difficult to remove and poorly investigated are the autovibrations, i.e. vibrations arising at the absence of external periodic forces. The autovibrations, stipulated by the process of cutting on metalcutting machine are of two types: the low-frequency autovibrations and high-frequency autovibrations. When the low-frequency autovibration there appear, the cutting process ought to be terminated and the cause of the vibrations eliminated. Otherwise, there is a danger of a break of both machine and tool. In the case of high-frequency vibration the machine operates apparently quiently, but the processed surface feature small-sized roughness. The frequency of autovibrations can reach 5000 Hz and more.

Molecular liver fingerprint reflects the seasonal physiology of the grey mouse lemur (Microcebus murinus) during winter

2021 ◽  
Author(s):  
Blandine Chazarin ◽  
Margaux Benhaim-Delarbre ◽  
Charlotte Brun ◽  
Aude Anzeraey ◽  
Fabrice Bertile ◽  
...  
Keyword(s):  
Glucose Intolerance ◽  
Molecular Mechanisms ◽  
Mouse Lemur ◽  
Primate Species ◽  
Whole Body ◽  
Microcebus Murinus ◽  
Late Winter ◽  
Pathological State ◽  
Fat Reserves ◽  
Mouse Lemurs

Grey mouse lemurs (Microcebus murinus) are a primate species exhibiting strong physiological seasonality in response to environmental energetic constraint. They notably store large amounts of lipids during early winter (EW), which are thereafter mobilized during late winter (LW), when food availability is low. In addition, they develop glucose intolerance in LW only. To decipher how the hepatic mechanisms may support such metabolic flexibility, we analyzed the liver proteome of adult captive male mouse lemurs, which seasonal regulations of metabolism and reproduction are comparable to their wild counterparts, during the phases of either constitution or use of fat reserves. We highlight profound changes that reflect fat accretion in EW at the whole-body level, however, without triggering an ectopic storage of fat in the liver. Moreover, molecular regulations would be in line with the lowering of liver glucose utilization in LW, and thus with reduced tolerance to glucose. However, no major regulation was seen in insulin signaling/resistance pathways, which suggests that glucose intolerance does not reach a pathological stage. Finally, fat mobilization in LW appeared possibly linked to reactivation of the reproductive system and enhanced liver detoxification may reflect an anticipation to return to summer levels of food intake. Altogether, these results show that the physiology of mouse lemurs during winter relies on solid molecular foundations in liver processes to adapt fuel partitioning while avoiding reaching a pathological state despite large lipid fluxes. This work emphasizes how the mouse lemur is of primary interest for identifying molecular mechanisms relevant to biomedical field.

scholarly journals Calculation improvement of no-load stray losses in induction motors with experimental validation

2015 ◽  
Vol 12 (3) ◽  
pp. 303-320
Author(s):  
Miloje Kostic
Keyword(s):  
High Frequency ◽  
Experimental Validation ◽  
Air Gap ◽  
New Classification ◽  
Iron Losses ◽  
Surface Iron ◽  
Space Harmonics ◽  
Rotor Cage

On the basis of the known fact that all air gap main flux density variations are enclosed by permeance slot harmonics, only one component of stray losses in rotor (stator) iron is considered in the new classification, instead of 2 components: rotor (stator) pulsation iron losses, and rotor (stator) surface iron losses. No-load rotor cage (high-frequency) stray losses are usually calculated. No-load stray losses are caused by the existence of space harmonics: the air-gap slot permeance harmonics and the harmonics produced by no-load MMF harmonics. The second result is the proof that the corresponding components of stray losses can be calculated separately for the mentioned kind of harmonics. Determination of the depth of flux penetration and calculations of high frequency iron losses are improved. On the basis of experimental validation, it is proved that the new classification of no-load stray losses and the proposed method for the calculation of the total value is sufficiently accurate.

scholarly journals A Data Mining Technique for Tourist Destination Brand Image Building

2020 ◽  
Vol 8 (6) ◽  
pp. 4617-4622
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Tourism Industry ◽  
Destination Image ◽  
Data Mining Technique ◽  
Tourist Destination ◽  
Text Data ◽  
Clustering And Classification ◽  
Google Search

The destination image branding is the domain of tourism industry where the facts and information is collected and evaluated for finding the credibility of a target tourist destination. Manual collection and processing of collected information accurately is a complicated and time consuming task therefore a data mining model is suggested ,in this presented work that collect and evaluate the destination image accurately and based on evaluation can make the recommendations about visits of tourist. In order to perform this task data mining techniques are applied on text data source. In first the data is extracted from the Google search engine and it is preprocessed for make it impure. In further the data is labeled based on the positive and negative words available in the collected facts. Finally the clustering and classification of text is performed. For clustering of data FCM (fuzzy c means) clustering algorithm and for classification the Bayesian classifier is used. Based on final classification of text data the decision is made for the destination visits.

Case-Based Reasoning and Some Typical Applications

2014 ◽  
pp. 229-267 ◽  
Author(s):  
Durga Prasad Roy ◽  
Baisakhi Chakraborty
Keyword(s):  
Problem Solving ◽  
Machine Intelligence ◽  
Case Based Reasoning ◽  
Engineering System ◽  
Yale University ◽  
Control Pattern ◽  
Clustering And Classification ◽  
How People Learn ◽  
Case Based

Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit.

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