bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh perbedaan level pupuk urin terfermentasi pada media tanam hidroponik terhadap komposisi dan kecernaan nutrien fodder sorgum
Authors: Harwanto Harwanto , Hendarto Eko , Bahrun Bahrun , Putra Joni Johanda , Hidayat Nur
Published: December 2021

Objective: This study aims to determine the nutritional value and nutrient digestibility by in vitro of fodder sorghum (Sorghum bicolor (L.) Moench) from the effect of the addition of fermented urine fertilizer to the hydroponic media.

Methods: The research used sorghum Numbu varieties grown hydroponically in the fodder phase, which was carried out in June - September 2020. The research treatments consist of water medium (T1), fermented urine 12.5 mL/L (T2) and 25 mL/L media (T3) with 4 replications. The urine fertilizer used was derived from fermented Ongole Crossbreed cow urine for 21 days. Fodder sorghum planted for 15 days. The results of the fodder harvest were analyzed by proximate and in vitro nutrient digestibility. Observation nutritional value parameters consists of dry matter (DM), organic matter (OM), crude protein (CP), ether extract (EE), crude fiber (CF), total nutrient digestible (TDN), dry matter digestibility (DMD) and organic matter digestibility (OMD). Data were analyzed using one way ANOVA design, the significance by Duncan's Multiple Range Test.

Results: The results showed that the addition of fermented urine had no effect on the DM, OM, and EE, but had a significant effect (P <0.05) on CP, CF, TDN and nutrient digestibility. The T2 and T3 treatments increased crude protein by 6.31% and 11.23% compared to T1. T3 treatment increased organic matter digestibility by 3.06% compared to T1.
Conclusions: It can be concluded that the crude protein and organic matter digestibility of sorghum fodder at 15 days harvest stage, increases with the addition of fermented cattle urine up to 25.0 ml/L hydroponic media.
Efek hipoglikemik dan hipolipidemik dendeng daging itik curing dengan ekstrak kurkumin kunyit pada Tikus Wistar
Authors: Dewi Sri Hartati Candra , Wariyah Chatarina , Astuti Niken
Published: December 2021

Objective: This research aimed to determine hypoglycemic and hypolipidemic effects of cured duck meat jerky with curcumin using male Wistar rats as experimental animals.

Methods: This research involved a completely randomized design in oneway pattern with 4 treatments on diabetic (TD) and hyperlipidemic (HD) rats : zero jerky (standard feed, PS), low jerky 0.96 g/head/day (DR), high jerky 1.91g/head/day (DT) and vitamin E 28.80 mg/head/day as control antioxidants (equivalent to 400 IU tocopherol). There were 24 rats divided into 4 treatments for hypoglycemic and hypolipidemia each. The treatment spanned 4 weeks, with hypoglycemic parameters (body weight and blood glucose) observed every week, and hypolipidemic (total cholesterol, triglycerides, HDL, and LDL). Furthermore, an analysis of variance was conducted to determine body weight. When a significant difference was observed, it was continued with Duncan's New Multiples Range Test, while other parameters were analyzed descriptively.

Results: The hypoglycemic effect showed a significant difference in body weight where the PS treatment were relatively stable, while DT and vitamin E treatment experienced the highest weight loss. Blood glucose also decreased with the increasing addition of curcumin and vitamin E. Meanwhile, the hyperlipidemic showed a decrease in cholesterol, triglycerides, and LDL, while HDL increased as the concentration of curcumin from jerky increased, the amount of which was equivalent to antioxidant control of vitamin E.
Conclusions: The diet of cured duck meat jerky as much as 1.91g/head/day (equivalent to 106.26 g/person/day or 400 IU tocopherol/vitamin E) can lower diabetic blood glucose and improve blood lipid profile.
Effects of mammae hand massages on oxytocin release, milk yield, and milk quality in dairy cows
Authors: Kentjonowaty Inggit , Mardhotillah Achmad Bagus Adhiluhung , Susilawati Trinil , Surjowardojo Puguh
Published: December 2021

Objective: The objective of this study was conducted to evaluate the effects of Mammae Hand Massages (MHM) on oxytocin release, milk yield, and milk quality in dairy cows.

Methods: Twelve dairy cows with the following criterion: 5-yr-old, 1st to 9th month of lactation, and average body weight of 390 ± 5.55 kg were used. Cows were assigned in a randomized block design with 4 treatments, i.e T0 (without massage), T1 (MHM for 20 s), T2 (MHM for 50 s), and T3 (MHM for 80 s). Oxytocin release, milk yield, and milk quality were measured accordingly.

Results: The data obtained were analyzed using analysis of variance (ANOVA). The results showed that MHM had a very significant effect on milk production (P <0.01) as evidenced by the MHM 50 s treatment obtained the highest average milk production (13.08 ± 3.38 liters/head/day) compared to the MHM 20 s, MHM 80 s and control. MHM (1-1.6 volts) for 50 s resulted in the highest release of oxytocin (0.22955 pcg/0.1mL) at 240 seconds compared to control, MHM 20 s and 80 s. Likewise, the percentage of milk protein content (2.96 ± 0.03) and milk fat content (4.27 ± 0.70) was highest at MHM 50 s.
Conclusions: It can be concluded that MHM (1-1.6 volts) for 50 seconds increases the release of oxytocin, milk production, and milk quality in terms of the percentage of milk protein and fat content.
Inventarisasi potensi emisi metana (CH4) pada peternakan sapi perah di Kecamatan Pujon, Kabupaten Malang
Authors: Mahmud Ali , Prima Ari
Published: December 2021

Objective: The methane emissions in ruminants such as dairy cows was one of the causes of climate change. The aimed of this study was to make an inventory of methane emissions from dairy farms in Pujon District. The methane emission data inventory was expected to assist the government in making policies as an effort to mitigation of methane emissions.

Methods: The secondary data used in this study were obtained from journals, books, literature related to research, and data from the Central Statistics Agency (BPS). Methane emissions was calculated using the Tier 1 method according to IPCC reference. The reason for the inventory using the tier 1 method was caused that the specific data related to emissions on dairy cows in Malang Regency were not available. The data obtained were processed descriptively.

Results: The data obtained shown that the population of dairy cows in Pujon District from 2013-2015 has increased, in 2016 it experienced a significant decline, namely 14.2%, then from 2017-2019 it continued to decline. Methane emissions from enteric fermentation from 2013-2019 averaged 23.13 Gg CO2-eq / year. Methane emissions from manure management in dairy cows in Pujon District from 2013-2019 were an average of 11.75 Gg CO2-eq / year. The highest methane emissions were in 2015, and the lowest was in 2019.
Conclusions: Based on the results of the study, it can be concluded that methane emissions from dairy cows in Pujon District increased from 2013-2015, there was a significant reduction in emissions in 2016 – 2019. Feeding with balanced nutrients, using ingredients of concentrated and forage containing good nutrients quality is an effort to mitigate methane that can applied by farmers.
The effect of using fermented lime (Citrus aurantifolia) waste powder (FLWP) on performance, lymphoid organs, and heterophil/lymphocyte ratio of broiler chickens
Authors: Shihah Hanna Dzawish , Sunarti Dwi , Sumarsih Sri
Published: December 2021

Objective: Fermented lime waste had potential value as a feed additive for poultry. A Bioactive compound such as citric acid in the lime waste can improve the broiler's performance and body resistance. The study purposed to evaluate the effect of using fermented lime waste powder (FLWP) on the broiler performance and body resistance.

Methods: This study used 200 female broiler strain Lohman MB 202 chickens. The research design used a completely randomized design with four treatment levels of fermented lime waste powder (FLWP) by 0%, 1%, 2%, and 3% in every rations with each treatment replicated five times. The parameters observed are feed intake, feed conversion ratio, final body weight, carcass percentage, abdominal fat percentage, lymphoid organ’s relative weight, and heterophil/lymphocyte ratio (H/L ratio). Data were calculated using the analysis of variance and difference test with Duncan's Multiple Range Test. Data analysis were calculated using the SPSS 19.0 program.

Results: The result indicates that using FLWP improved (p<0.05) the relative weight of the Bursa of fabricius at T0 0.11%; T1 0.19%; T2 0.19%; and T3 0.17% respectively. The FLWP at 3% level improved (p<0.05) broiler chicken’s H/L ratio. Performance, carcass percentage, and abdominal fat percentage among treatments were not influenced (p>0.05) by FLWP addition.

Conclusions: It concludes that adding FLWP at a 1% level increases the relative weight of Bursa of fabricius (0.17-0.19%) without depressed performance of broiler.
Kecernaan, fermentabilitas dan produksi protein ruminal pelepah sawit yang difermentasi dengan isolat mikrobia rumen kerbau secara in vitro
Authors: Nuswanatara Limbang Kustiawan , Pangestu Eko , Sunarso Sunarso , Christiyanto Marry
Published: December 2021

Objective: The study aimed investigated effect of microbial isolate levels and fermentation time on fermentability regarding ammonia (NH3) production, volatile fatty acids (VFA) and microbial protein production, dry matter digestibility and organic matter digestibility in vitro.

Methods: The experiment was conducted at the Laboratory of Nutrition and Feed Science, Faculty of Animal and Agricultural Sciences, Universitas Diponegoro. In vitro experiment was performed using a completely randomized design with a factorial pattern with 2 factors and 4 replications. The treatments were microbial isolate levels (1 and 3%) and fermentation time (14 and 28 days). The parameters observed included production of NH3, VFA, microbial protein and total protein as well as the digestibility of dry matter and organic matter. The data were analyzed based on analysis of variance and if there was a significant effect the data were further analyzed with Duncan’s Multiple Range Test.

Results: The amount of microbial isolate and fermentation time affected rumen ammonia production. On rumen microbial protein content, the amounts of microbial isolate and fermentation time had substantial impact (P<0.05). The isolate level and fermentation time, however, had no interaction effect on VFA production, dry matter digestibility, or organic matter digestibility. The fermentation time influenced (P<0.05) the production of VFA and the digestibility of dry matter, but the isolate level and fermentation time had no effect on total protein production or organic matter digestibility.

Conclusions: Processing of palm fronds through fermentation using buffalo rumen cellulolytic microbial isolates increased nutrient values of palm fronds.
Morphometrics characterization in White Fulani, Muturu and Pasundan cows
Authors: Ahmed Ridwan Olawale , Putra Widya Pintaka Bayu , Arifin Johar , Bello Semiu Folaniyi
Published: December 2021

Objective: This study was aimed to characterize three different cattle breeds based on the morphometrics.

Methods: Five (5) morphometrics which include body length (BL), chest girth (CG), withers height (WH), rump length (RL) and chest depth (CD) were measured in each animal. A total of 119 cows belonging to White Fulani (40 heads), Muturu (40 heads) and Pasundan (39 heads) were used in this study. The age of animal study was 20.93±1.62 months (White Fulani), 21.63±1.75 months (Muturu) and 20.46±2.99 months (Pasundan). Hence, two statistical methods of canonical discriminant analysis (CDA) and hierarchical cluster analysis (HCA) methods were computed in this study to characterize of animals based on their morphometrics using SPSS 16.0 package.

Results: Mostly the morphometrics of White Fulani and Pasundan cows are similar while those of Muturu are significantly lower than the two other breeds. Four morphometric measurements of CG, RL, BL and WH were identified as a discriminator variables in animal study based on the CDA method. The CDA of morphometrics able to classify 85% (White Fulani), 100% (Muturu) and 74.4% (Pasundan) of the animal into their original breed group.

Conclusions: The closeness between observations from White Fulani and Pasundan might be due to them being both Zebu (Bos indicus) unlike the Muturu with a type breed of Bos bracycheros.
Performance of Kampung Super chicken and Bangkok chicken first generation (G1) until the age of 12 weeks
Authors: Rahayu Fetty Febriana , Depison Depison , Gushairiyanto Gushairiyanto
Published: December 2021

Objective: The purpose of this research was to determine the performance of the first generation (G1) Kampung Super and Bangkok chicken aged 12 weeks.

Methods: The research material were G1 Kampung Super and Bangkok chicken with 174 samples for each strain. This research was using the experimental method. The data collected included body weight, body weight gain, body measurements, and selected responses. The body weight, body weight gain, and body measurements data were analyzed using t-test independent, while the average value vector of body measured using T2-Hotelling test. Principal Component Analysis ware used to identify the characteristics of body shape and size.

Results: The results of this study showed that body weight, body weight gain of DOC-4 weeks, 4-8 weeks, 8-12 weeks, and body measurements of G1 Kampung Super chickens were real different higher (P<0.05) than G1 Bangkok. The identifier of the size of the G1 Kampung Super and Bangkok chicken is the chest circumference. The characteristics of the shape G1 Kampung Super chicken is the length of the wings and the G1 Bangkok chicken is the lower body lenght. The selection response of G1 Kampung Super is higher than G1 Bangkok.

Conclusions: The performance of G1 Kampung Super chicken was higher than G1 Bangkok chicken but the selection response value of G1 Bangkok chicken was higher than G1 Kampung Super chicken. The highest body weight gain of G1 Kampung Super and Bangkok chickens was achieved at the age of 8-12 weks.
Keragaman genotipik entok (Cairina moschata) berdasarkan polimorfisme protein darah
Authors: Sutopo Sutopo , Lestari Dela Ayu , Setiaji Asep , Kurnianto Edy
Published: December 2021

Objective: The aim of this study was to identified genetic diversity of muscovy duck in Central Java through blood protein polymorphisms by using electrophoresis method.

Methods: Blood sample was collected from a total of 60 muscovy ducks from the districts of Demak, Magelang and Pekalongan, 20 samples each. The Electrophoresis Polyacrylamide Gel of Electrophoresis–Thin Layer Electrophoresis (PAGE-TLE) was used in this study. Parameters observed were protein of albumin (Alb), ceruloplasmin (Cp), transferrin (Tf) and amylase-I (Am-I). Gene frequency, heterozygosity (Ho) and genetic distance were analyzed by using DISPAN program.

Results: The results showed that the Alb, Cp, Tf and Am-I of muscovy duck showing polymorphic characters. Gene frequency of AlbB (0.61) was higher compared to AlbA (0.39). Gene frequency of CpA (0.62) was higher than the CpB (0.38). Furthermore, gene frequency of TfA and TfB were 0.32 and 0.68, respectively as well as gene frequency of Am-IA (0.82) was higher than Am-IB (0.18). Muscovy duck population of Demak showed higher Ho value for Alb and Am-I, whereas Magelang showed higher value for Cp and Tf. Population of muscovy duck of Demak showed closer genetic distance to Pekalongan than Magelang.
Conclusions: The protein blood locus of three population of muscovy duck in Central Jawa showed polymorphic characters.
Field Robotics: Applications and Fundamentals
Authors: Fukao Takanori ,   University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Published: December 2021

Field robotics is an area that is impelled by an application-driven approach by its nature. In this paper, I first review certain actual application areas of field robotics. Then, I discuss the current status of the application of field robotics in three common technologies: (1) mapping and path planning; (2) self-localization, recognition, and decision-making; and (3) dynamics and control. I then conclude by presenting future perspectives.
Development of AUV MONACA - Hover-Capable Platform for Detailed Observation Under Ice –
Authors: Yamagata Hirokazu , Kochii Shuma , Yoshida Hiroshi , Nogi Yoshifumi , Maki Toshihiro ,   Institute of Industrial Science, The University of Tokyo 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan,   Japan Agency for Marine-Earth Science and Technology (JAMSTEC) 2-15 Natsushima-cho, Yokosuka, Kanagawa 237-0061, Japan,   National Institute of Polar Research (NIPR) 10-3 Midori-cho, Tachikawa, Tokyo 190-8518, Japan,   Department of Polar Science, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies (SOKENDAI) 10-3 Midori-cho, Tachikawa, Tokyo 190-8518, Japan
Published: December 2021

The melting of ice and changes in ocean currents in Antarctica must be investigated to understand global climate change. In this regard, the volume changes of sea ice and ice shelves, bathymetry, and ocean currents in the Antarctic Ocean must be measured in three dimensions. Therefore, the use of autonomous underwater vehicles (AUVs), which can directly observe under ice, is being considered. The authors developed an AUV named Mobility Oriented Nadir AntarctiC Adventurer (MONACA) to observe sea ice and the lower region of the ice shelf in the Antarctic Ocean. Herein, we describe MONACA and its basic autonomous navigation methods (altitude control, depth control, and waypoint tracking), as well as report the results of a sea experiment conducted in Shimoda Bay, Japan. During the 5-day sea trial, the MONACA successfully measured bathymetry by tracking 15 waypoints in sequence, switching the control criteria in the-axis direction between 3 m depth and 3 m altitude.
Numerical and Experimental Analysis of Portable Underwater Robots with a Movable Float Device
Authors: Sakagami Norimitsu , Shibata Mizuho , Ueda Tomohiro , Ishizu Kensei , Yokoi Kenshiro , Kawamura Sadao ,   Tokai University 3-20-1 Orido, Shimizu-ku, Shizuoka, Shizuoka 424-0902, Japan,   Kindai University 1-1 Takaya Umenobe, Higashi-hiroshima, Hiroshima 739-2116, Japan,   Ritsumeikan University 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan
Published: December 2021

This report describes a numerical and experimental study of a posture control device based on a movable float for portable underwater robots. We numerically analyzed the static stability using a stability curve and allowable spatial range of a center-of-gravity shift caused by a payload shift or manipulator configuration. Further, we proposed a feedback controller based on direct pitch and roll signals to change and maintain robot posture. We tested the feedback control using a numerical simulator and conducted experiments in a water tank using two portable underwater robots to demonstrate the effectiveness of the movable float device and proposed controller. The results of the field experiments showed that the device and proposed controller can be employed for effective underwater operations of portable underwater robots.
An Optimal Design Methodology for the Trajectory of Hydraulic Excavators Based on Genetic Algorithm
Authors: Yuasa Takamichi ,   Osaka University 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan, Ishikawa Masato , Ogawa Satoshi
Published: December 2021

Hydraulic excavators are one type of construction equipment used in various construction sites worldwide, and their usage and scale are diverse. Generally, the work efficiency of a hydraulic excavator largely depends on human operation skills. If we can comprehend the experienced operation skills and utilize them for manual control assist, semi-automatic or automatic remote control, it would improve its work efficiency and suppress personnel costs, reduce the operator’s workload, and improve his/her safety. In this study, we propose a methodology to design efficient machine trajectories based on mathematical models and numerical optimization, focusing on ground-level excavation as a dominant task. First, we express its excavation trajectory using four parameters and assume the models for the amount of excavated soil and the reaction force based on our previous experiments. Next, we combine these models with a geometrical model for the hydraulic excavating machine. We then assign the amount of soil to a performance index preferably to be maximized and the amount of work to a cost index preferably to be minimized, both in the form of functions of the trajectory parameters, resulting in an optimization problem that trades them off. In particular, we formulate (1) a multi-objective optimization problem maximizing a weighted linear combination of the amount of soil and the amount of work as an objective function, and (2) a single-objective optimization problem maximizing the amount of soil under a given upper bound on the amount of work, so that we can solve these optimization problems using the genetic algorithm (GA). Finally, we conclude this paper by suggesting our notice on design methodology and discussing how we should provide the optimization method as mentioned above to the users who operate hydraulic excavators.
On the Geometric Featureless Visual Velocity of UGV in an Agriculture Scale
Authors: Sakai Satoru ,   Shinshu University 4-17-1 Wakasato, Nagano 380-0928, Japan, Nakabayashi Daiki
Published: December 2021

The paper discusses a camera-based velocity estimation for unmanned ground vehicles in an agriculture scale. The proposed concept-based method does not require any geometric feature and focuses on a mapping between the captured images only. The paper provides three pilot experiments. First, we check an assumption of the proposed concept by a field experiment. Second, we check the verification by a set of numerical and laboratory experiments. Third, we check the verification by the field experiment. In the sense that the existence and sensitivity of a representation of the mapping are verified experimentally, the feasibility of the proposed concept is confirmed.
Robotic Forklift for Stacking Multiple Pallets with RGB-D Cameras
Authors: Iinuma Ryosuke , Hori Yusuke , Onoyama Hiroyuki , Kubo Yukihiro , Fukao Takanori ,   Ritsumeikan University 1-1-1 Nojihigashi, Kusatsu-shi, Shiga 525-8577, Japan,   University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Published: December 2021

We propose a robotic forklift system for stacking multiple mesh pallets. The stacking of mesh pallets is an essential task for the shipping and storage of loads. However, stacking, the placement of pallet feet on pallet edges, is a complex problem owing to the small sizes of the feet and edges, leading to a complexity in the detection and the need for high accuracy in adjusting the pallets. To detect the pallets accurately, we utilize multiple RGB-D (RGB Depth) cameras that produce dense depth data under the limitations of the sensor position. However, the depth data contain noise. Hence, we implement a region growing-based algorithm to extract the pallet feet and edges without removing them. In addition, we design the control law based on path following control for the forklift to adjust the position and orientation of two pallets. To evaluate the performance of the proposed system, we conducted an experiment assuming a real task. The experimental results demonstrated that the proposed system can achieve a stacking operation with a real forklift and mesh pallets.
Tomato Recognition for Harvesting Robots Considering Overlapping Leaves and Stems
Authors: Ikeda Takeshi , Fukuzaki Ryo , Sato Masanori , Furuno Seiji , Nagata Fusaomi ,   Sanyo-Onoda City University  1-1-1 Daigaku-dori, Sanyoonoda, Yamaguchi 756-0884, Japan,   Yanagiya Machinery Co., Ltd.  189-18 Yoshiwa, Ube, Yamaguchi 759-0134, Japan,   Nagasaki Institute of Applied Science  536 Aba-machi, Nagasaki, Nagasaki 851-0193, Japan,   National Institute of Technology, Kitakyushu College  5-20-1 Shii, Kokuraminami, Kitakyushu, Fukuoka 802-0985, Japan
Published: December 2021

In recent years, the declining and aging population of farmers has become a serious problem. Smart agriculture has been promoted to solve these problems. It is a type of agriculture that utilizes robotics, and information and communication technology to promote labor saving, precision, and realization of high-quality production. In this research, we focused on robots that can harvest tomatoes. Tomatoes are delicate vegetables with a thin skin and a relatively large yield. During automatic harvesting of tomatoes, to ensure the operation of the harvesting arm, an input by image processing is crucial to determine the color of the tomatoes at the time of harvesting. Research on robot image processing technology is indispensable for accurate operation of the arm. In an environment where tomatoes are harvested, obstacles such as leaves, stems, and unripe tomatoes should be taken into consideration. Therefore, in this research, we propose a method of image processing to provide an appropriate route for the arm to ensure easy harvesting, considering the surrounding obstacles.
Spread Spectrum Sound with TDMA and INS Hybrid Navigation System for Indoor Environment
Authors: Tientadakul Romprakhun , Nakanishi Hiroaki , Shiigi Tomoo , Huang Zichen , Tsay Lok Wai Jacky , Kondo Naoshi ,   Graduate School of Engineering, Kyoto University Kyoto daigaku-katsura, Nishikyo-ku, Kyoto 615-8540, Japan,   Department of Ocean Mechanical Engineering, National Fisheries University 2-7-1 Nagata-Honmachi, Shimonoseki, Yamaguchi 759-6595, Japan,   Graduate School of Agriculture, Kyoto University Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan
Published: December 2021

Indoor navigation plays an essential role in agricultural robots that operate in greenhouses. One of the most effective methods for indoor navigation is the spread spectrum sound (SS-sound) system. In this system, the time of arrival (ToA) of the spread spectrum modulated sound is used for localization. However, there is a near-far problem. Transmitting the SS-sound from multiple anchors using time division multiple access (TDMA) is adequate to solve the near-far problem. However, localization is impossible because the ToA from multiple anchors cannot be simultaneously acquired. To solve this problem, a method for combining the SS-sound system with TDMA and an inertial navigation system is proposed in this study. The effectiveness of the proposed method was demonstrated through numerical simulations of a ground robot and experimentally using a crawler robot.
LPWAN-Based Real-Time 2D SLAM and Object Localization for Teleoperation Robot Control
Authors: Junaedy Alfin ,   Department of Intelligent Robotics, Toyama Prefectural University 5180 Kurokawa, Imizu, Toyama 939-0398, Japan, Masuta Hiroyuki , Sawai Kei , Motoyoshi Tatsuo , Takagi Noboru
Published: December 2021

In this study, the teleoperation robot control on a mobile robot with 2D SLAM and object localization using LPWAN is proposed. The mobile robot is a technology gaining popularity due to flexibility and robustness in a variety of terrains. In search and rescue activities, the mobile robots can be used to perform some missions, assist and preserve human life. However, teleoperation control becomes a challenging problem for this implementation. The robust wireless communication not only allows the operator to stay away from dangerous area, but also increases the mobility of the mobile robot itself. Most of teleoperation mobile robots use Wi-Fi having high-bandwidth, yet short communication range. LoRa as LPWAN, on the other hand, has much longer range but low-bandwidth communication speed. Therefore, the combination of them complements each other’s weaknesses. The use of a two-LoRa configuration also enhances the teleoperation capabilities. All information from the mobile robot can be sent to the PC controller in relatively fast enough for real-time SLAM implementation. Furthermore, the mobile robot is also capable of real-time object detection, localization, and transmitting images. Another problem of LoRa communication is a timeout. We apply timeout recovery algorithms to handle this issue, resulting in more stable data. All data have been confirmed by real-time trials and the proposed method can approach the Wi-Fi performance with a low waiting time or delay.
Human Tracking of a Crawler Robot in Climbing Stairs
Authors: Orita Yasuaki , Takaba Kiyotsugu , Fukao Takanori ,   Ritsumeikan University 1-1-1 Noji-higashi, Kusatsu-shi, Shiga 525-8577, Japan,   University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Published: December 2021

There are many reports of secondary damage to crews during firefighting operations. One way to support and enhance their activities is to get robots to track them and carry supplies. In this paper, we propose a localization method for stairs that includes scene detection. The proposed method allows a robot to track a person across stairs. First, the scene detection autonomously detects that the person is climbing the stairs. Then, the linear model representing the first step of the staircase is combined with the person’s trajectory for localization. The method uses omnidirectional imaging and point clouds, and the localization and scene detection are available from any posture around the stairs. Finally, using the localization result, the robot automatically navigates to a posture where it can climb the stairs. Verification confirmed the accuracy and real-time capability of the method and demonstrated that the actual crawler robot autonomously chooses a posture that is ready for climbing.
Attractive Force Estimation of a Magnetic Adsorption Unit for Inspection UAVs
Authors: Higashi Yoshiyuki ,   Kyoto Institute of Technology Goshokaido-cho, Matsugasaki, Sakyo-ku, Kyoto 606-8585, Japan, Yamazaki Kenta , Masuda Arata , Miura Nanako , Sawada Yuichi
Published: December 2021

This paper presents an attractive force estimation system and an automatic activation system for an electropermanent magnet (EPM) for an inspection UAV. Adsorption to infrastructures for inspection at a distance is extremely difficult to perform safely because the operator cannot detect the state of adsorption of the drone equipped with a magnetic adsorption device. Therefore, in this paper, we clarify the relationship between the magnetic flux density and attractive force of the EPM through experiments, and develop an estimation algorithm for the attractive force based on the results. An automatic activation system, using the induced voltage in the coil when the EPM approaches the magnetic substance, is developed and mounted on a quadrotor for a flight experiment along with the estimation system for the attractive force. The developed system is verified using flight and adsorption experiments on the quadrotor.
A Novel Method for Goal Recognition from 10 m Distance Using Deep Learning in CanSat
Authors: Akiyama Miho , Saito Takuya ,   Graduate School of Electrical and Information Engineering, Shonan Institute of Technology  1-1-25 Tsujido-nishikaigan, Fujisawa, Kanagawa 251-8511, Japan,   Department of Information Science, Faculty of Engineering, Shonan Institute of Technology  1-1-25 Tsujido-nishikaigan, Fujisawa, Kanagawa 251-8511, Japan
Published: December 2021

In this study, we propose a method for CanSat to recognize and guide a goal using deep learning image classification even 10 m away from the goal, and describe the results of demonstrative evaluation to confirm the effectiveness of the method. We applied deep learning image classification to goal recognition in CanSat for the first time at ARLISS 2019, and succeeded in guiding it almost all the way to the goal in all three races, winning the first place as overall winner. However, the conventional method has a drawback in that the goal recognition rate drops significantly when the CanSat is more than 6–7 m away from the goal, making it difficult to guide the CanSat to the goal when it moves away from the goal because of various factors. To enable goal recognition from a distance of 10 m from the goal, we investigated the number of horizontal regions of interest divisions and the method of vertical shifts during image recognition, and clarified the effective number of divisions and recognition rate using experiments. Although object detection is commonly used to detect the position of an object from an image by deep learning, we confirmed that the proposed method has a higher recognition rate at long distances and a shorter computation time than SSD MobileNet V1. In addition, we participated in the CanSat contest ACTS 2020 to evaluate the effectiveness of the proposed method and achieved the zero-distance goal in all three competitions, demonstrating its effectiveness by winning first place in the comeback category.
Image Mosaicking and Localization Using a Camera Mounted on a Hanging-Type Wall Climbing Robot
Authors: Sano Shigenori , Takaki Daisuke , Ishida Atsunori , Ishida Teruhiro ,   Toyohashi University of Technology 1-1 Hibarigaoka, Tempaku, Toyohashi, Aichi 441-8580, Japan,   Sanshin Construction Materials Co., Ltd. 35-1 Ninowari, Jinno-shinden, Toyohashi, Aichi 441-8077, Japan
Published: December 2021

Owing to the revision of Japanese building law in 2008, the demand for wall inspections has been increasing. Currently, wall inspections are performed by workers using hammering devices; this involves dangerous work at high elevations. Therefore, we developed an inspection system using NOBORIN®, a hanging-type wall climbing robot. In this paper, we introduce the robot and its hammering inspection system, and propose a method for image mosaicking and localization using images captured from an equipped camera. The estimated values are used to correct the elevation motion(s) of the robot.
Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments
Authors: Sun Leyuan , Singh Rohan P. , Kanehiro Fumio ,   Department of Intelligent and Mechanical Interaction Systems, Graduate School of Science and Technology, University of Tsukuba 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577 Japan,   CNRS-AIST JRL (Joint Robotics Laboratory), International Research Laboratory (IRL) 1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan,   National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan
Published: December 2021

Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework’s performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks.
Development of Tele-Operated Underfloor Mobile Manipulator
Authors: Sato Shunsuke ,   University of Tsukuba 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Song Tianlin , Aiyama Yasumichi
Published: December 2021

In this study, we propose a tele-operated mobile manipulator for conducting underfloor work, including both inspection and repair. We focus on a caulking operation, and develop a mobile manipulator for finding and repairing cracks in a foundation. When designing the mobile manipulator, we consider a typical narrow underfloor environment, and perform detailed calculations. As a user interface, we furnish an image-pointing system for enabling us to operate the manipulator simply by clicking on a hand camera image. Then, we evaluate its performance based on navigation experiments, trajectory tracking experiments, and overall evaluation experiments.
Improved 3D Human Motion Capture Using Kinect Skeleton and Depth Sensor
Authors: Bilesan Alireza , Komizunai Shunsuke , Tsujita Teppei , Konno Atsushi ,   Graduate School of information Science and Technology, Hokkaido University Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan,   Department of Mechanical Engineering, National Defense Academy of Japan 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan
Published: December 2021

Kinect has been utilized as a cost-effective, easy-to-use motion capture sensor using the Kinect skeleton algorithm. However, a limited number of landmarks and inaccuracies in tracking the landmarks’ positions restrict Kinect’s capability. In order to increase the accuracy of motion capturing using Kinect, joint use of the Kinect skeleton algorithm and Kinect-based marker tracking was applied to track the 3D coordinates of multiple landmarks on human. The motion’s kinematic parameters were calculated using the landmarks’ positions by applying the joint constraints and inverse kinematics techniques. The accuracy of the proposed method and OptiTrack (NaturalPoint, Inc., USA) was evaluated in capturing the joint angles of a humanoid (as ground truth) in a walking test. In order to evaluate the accuracy of the proposed method in capturing the kinematic parameters of a human, lower body joint angles of five healthy subjects were extracted using a Kinect, and the results were compared to Perception Neuron (Noitom Ltd., China) and OptiTrack data during ten gait trials. The absolute agreement and consistency between each optical system and the robot data in the robot test and between each motion capture system and OptiTrack data in the human gait test were determined using intraclass correlations coefficients (ICC3). The reproducibility between systems was evaluated using Lin’s concordance correlation coefficient (CCC). The correlation coefficients with 95% confidence intervals (95%CI) were interpreted substantial for both OptiTrack and proposed method (ICC > 0.75 and CCC > 0.95) in humanoid test. The results of the human gait experiments demonstrated the advantage of the proposed method (ICC > 0.75 and RMSE = 1.1460°) over the Kinect skeleton model (ICC < 0.4 and RMSE = 6.5843°).
Intelligent Path Planning Approach for Autonomous Mobile Robot
Authors: Al-Adwan Ibrahim M. ,   Al-Balqa Applied University (BAU) P.O. Box: 15008, Amman 11134, Jordan
Published: December 2021

This paper presents a new path planning algorithm for an autonomous mobile robot. It is desired that the robot reaches its goal in a known or partially known environment (e.g., a warehouse or an urban environment) and avoids collisions with walls and other obstacles. To this end, a new, efficient, simple, and flexible path finder strategy for the robot is proposed in this paper. With the proposed strategy, the optimal path from the robot’s current position to the goal position is guaranteed. The environment is represented as a grid-based map, which is then divided into a predefined number of subfields to reduce the number of required computations. This leads to a reduction in the load on the controller and allows a real-time response. To evaluate the flexibility and efficiency of the proposed strategy, several tests were simulated with environments of different sizes and obstacle distributions. The experimental results demonstrate the reliability and efficiency of the proposed algorithm.
Generative Adversarial Network for Imitation Learning from Single Demonstration
Authors: Duc Tho Nguyen , Tran Chanh Minh , Tan Phan Xuan , Kamioka Eiji
Published: December 2021

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing considered tasks despite the limitation in the number of expert demonstrations, which clearly indicate the potential of our model.
Performance Analysis of Propagation in VHF Military Tactical Communication System
Authors: Yusof Azita Laily , Halim Hafizi , Ya'acob Norsuzila , Mohd Hanapiah Nur Haidah
Published: December 2021

The main challenge of military tactical communication systems is the accessibility of relevant information on the particular operating environment required for the determination of the waveform's ideal use. The existing propagation model focuses mainly on broadcasting and commercial wireless communication with a highs transceiver antenna that is not suitable for numerous military tactical communication systems. This paper presents a study of the path loss model related to radio propagation profile within the suburban in Kuala Lumpur. The experimental path loss modeling for VHF propagation was collected from various suburban settings for the 30-88 MHz frequency range. This experiment was highly affected by ecological factors and existing wave propagation effects such as reflection, diffraction, scattering, and Doppler effect. Radio propagation performance is evaluated by collecting received power at the allocated substation and comparing it against existing propagation models. The existing propagation model also will be tuned close to the measurement value by identifying the best path loss exponent to perform a suitable model for a suburban area. Theoretical assessments and analysis of the initial measurement stage for radio propagation show the extensive contribution of radio field from potential obstacles at lower VHF frequencies for both short and medium ranges around there. The explanation indicates the standard radio propagation prediction models that are generally reasonable for the suburban area. From the general error analysis, it is seen that, the performance of the LDPL with adjusting path loss exponent is the suitable model since it has least value of error metrics.
Evaluation Method of Mesh Protocol over ESP32 and ESP8266
Authors: Afifie Nur Azzurin , Khang Adam Wong Yoon , Bin Ja'afar Abd Shukur , Amin Ahmad Fairuz Bin Muhammad , Alsayaydehahmad Jamil Abedalrahim Jamil , Indra Win Adiyansyah , Herawan Safarudin Gazali , Ramli Arnidza Binti
Published: December 2021

Internet of Things (IoT) is one of the newest matters in both industry and academia of the communication engineering world. On the other hand, wireless mesh networks, a network topology that has been debate for decades that haven’t been put into use in great scale, can make a transformation when it arises to the network in the IoT world nowadays. A Mesh IoT network is a local network architecture in which linked devices cooperate and route data using a specified protocol. Typically, IoT devices exchange sensor data by connecting to an IoT gateway. However, there are certain limitations if it involves to large number of sensors and the data that should be received is difficult to analyze. The aim of the work here is to implement a self-configuring mesh network in IoT sensor devices for better independent data collection quality. The research conducted in this paper is to build a mesh network using NodeMCU ESP 8266 and NodeMCU ESP 32 with two types of sensor, DHT 11 and DHT 22. Hence, the work here has evaluated on the delay performance metric in Line-of-Sight (LoS) and Non-Line-of-Sight (nLos) situation based on different network connectivity. The results give shorter delay time in LoS condition for all connected nodes as well as when any node fail to function in the mesh network compared to nLoS condition. The paper demonstrates that the IoT sensor devices composing the mesh network is a must to leverage the link communication performance for data collection in order to be used in IoT-based application such as fertigation system. It will certainly make a difference in the industry once being deployed on large scale in the IoT world and make the IoT more accessible to a wider audience.
Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
Authors: Hassan Fadratul Hafinaz , Omar Mohd Adib
Published: December 2021

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%.
Fast Processing RNA-Seq on Multicore Processor
Authors: Bin Lee Jia , Abdul Hamid Nor Asilah Wati , Ismail Zurita , Laham Mohamed Faris
Published: December 2021

RNA Sequencing (RNA-Seq) is the sequencing and analysis of transcriptomes. The main purpose of RNA-Seq analysis is to find out the presence and quantity of RNA in an experimental sample under a specific condition. Essentially, RNA raw sequence data was massive. It can be as big as hundreds of Gigabytes (GB). This massive data always makes the processing time become longer and take several days. A multicore processor can speed up a program by separating the tasks and running the tasks’ errands concurrently. Hence, a multicore processor will be a suitable choice to overcome this problem. Therefore, this study aims to use an Intel multicore processor to improve the RNA-Seq speed and analyze RNA-Seq analysis's performance with a multiprocessor. This study only processed RNA-Seq from quality control analysis until sorted the BAM (Binary Alignment/Map) file content. Three different sizes of RNA paired end has been used to make the comparison. The final experiment results showed that the implementation of RNA-Seq on an Intel multicore processor could achieve a higher speedup. The total processing time of RNA-Seq with the largest size of RNA raw sequence data (66.3 Megabytes) decreased from 317.638 seconds to 211.916 seconds. The reduced processing time was 105 seconds and near to 2 minutes. Furthermore, for the smallest RNA raw sequence data size, the total processing time decreased from 212.380 seconds to 163.961 seconds which reduced 48 seconds.